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Which Free Cash Flow Is Value Relevant? An Empirical Investigation Mostafa M. Maksy Kutztown University of Pennsylvania Gary T. Chen University of Illinois at Chicago This study attempts to identify which definition of free cash flow (FCF) is the most value relevant. The results would help retail investors make better decisions, and may encourage accounting standards setters to require companies to use a specific definition of FCF to enhance comparability. Using a sample of 115,940 observations covering the period 1988 to 2010, the study empirically shows that the FCF that has the most significant association with stock price changes is the one defined as cash flow from operations less net cash outflow for investing activities less cash outflow for preferred stock dividends. INTRODUCTION While the finance literature may have a somewhat generally accepted definition of free cash flow (FCF), as the literature review below indicates, the accounting literature has a wide variety of definitions of FCF. The objective of this study is to empirically identify which accounting definition of FCF has the highest information content, or the most value relevant. This study aims to provide two contributions to the literature. First, it attempts to identify a specific definition of FCF that is most relevant to accounting information users in terms of predicting future changes in stock prices as this would help retail investors make better decisions. The study focuses the attention on retail investors as opposed to other users of financial statements, such as institutional investors or bank lenders, because retail investors, on average, are less sophisticated users of financial statements and may be more easily confused by the different definitions of FCF used by various companies. Prior research finds that, as of 2005, 57 million U.S. households owned stock and that retail investors owned 26% of all equities (Harris 2010). Since the major objective of financial reporting is to provide information that is useful for decision-making, the first contribution of this study is to enhance the objective of accounting. Second, the results of this study may have major implications for financial accounting standard setters, such as the Financial Accounting Standards Board (FASB) and the International Accounting Standards Board (IASB). While the FASB, in Statement of Financial Accounting Standard (SFAS) No. 95, and the IASB, in International Accounting Standard (IAS) No. 7, require companies to report Cash Flow from Operations (CFO) on the Statement of Cash Flows (SCF), they have so far discouraged companies from reporting CFO per share. The FASB and the IASB are concerned that requiring, or even encouraging, companies to report CFO per share may be construed by some that they are moving away from accrual-basis accounting toward cash-basis accounting. Thus, they require companies to report Earnings Per Share (EPS), which .

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Which Free Cash Flow Is Value Relevant? An Empirical Investigation Mostafa M. Maksy Kutztown University of Pennsylvania Gary T. Chen University of Illinois at Chicago This study attempts to identify which definition of free cash flow (FCF) is the most value relevant. The results would help retail investors make better decisions, and may encourage accounting standards setters to require companies to use a specific definition of FCF to enhance comparability. Using a sample of 115,940 observations covering the period 1988 to 2010, the study empirically shows that the FCF that has the most significant association with stock price changes is the one defined as cash flow from operations less net cash outflow for investing activities less cash outflow for preferred stock dividends. INTRODUCTION While the finance literature may have a somewhat generally
accepted definition of free cash flow (FCF), as the literature review below indicates, the accounting literature has a wide variety of definitions of FCF. The objective of this study is to empirically identify which accounting definition of FCF has the highest information content, or the most value relevant. This study aims to provide two contributions to the literature. First, it attempts to identify a specific definition of FCF that is most relevant to accounting information users in terms of predicting future changes in stock prices as this would help retail investors make better decisions. The study focuses the attention on retail investors as opposed to other users of financial statements, such as institutional investors or bank lenders, because retail investors, on average, are less sophisticated users of financial statements and may be more easily confused by the different definitions of FCF used by various companies. Prior research finds that, as of 2005, 57 million U.S. households owned stock and that retail investors owned 26% of all equities (Harris 2010). Since the major objective of financial reporting is to provide information that is useful for decision-making, the first contribution of this study is to enhance the objective of accounting. Second, the results of this study may have major implications for financial accounting standard setters, such as the Financial Accounting Standards Board (FASB) and the International Accounting Standards Board (IASB). While the FASB, in Statement of Financial Accounting Standard (SFAS) No. 95, and the IASB, in International Accounting Standard (IAS) No. 7, require companies to report Cash Flow from Operations (CFO) on the Statement of Cash Flows (SCF), they have so far discouraged companies from reporting CFO per share. The FASB and the IASB are concerned that requiring, or even encouraging,
companies to report CFO per share may be construed by some that they are moving away from accrual- basis accounting toward cash-basis accounting. Thus, they require companies to report Earnings Per Share (EPS), which is based on accrual Journal of Accounting and Finance vol. 14(6) 2014 189 accounting, on the face of the Income Statement (I/S) but discourage companies from reporting CFO per share on the face of the SCF or anywhere else in the annual report. The results of this study might encourage accounting standard setters (e.g., the FASB and IASB) to require companies to report a specific definition of FCF (but not FCF per share) in the body of the SCF or in the supplementary disclosures at the bottom of the SCF. SFAS No. 95 already requires companies to disclose cash paid for income taxes and for interest expense at the bottom of the SCF. Perhaps, the FASB would require companies to disclose FCF together with these two items. This requirement would prohibit companies from voluntarily disclosing FCF of whatever definition they prefer. Adhikari and Duru (2006) report that companies that voluntarily disclose FCF information use a wide variety of definitions of FCF (apparently, each company is using the definition that shows the highest amount of FCF) and these companies, on average, are less profitable and more leveraged than other firms in their own industries. Having companies report FCF that is calculated in the same way would enhance comparability of accounting information across firms. Comparability is one of the enhancing
qualitative characteristics of useful financial information as stated in FASB’s SFAC No. 8. The remaining sections of the paper cover the literature review, the proposed model, sample, statistical results, conclusions, and limitations of the study, respectively. The final section provides some suggestions for further research. LITERATURE REVIEW In the finance literature, there is no wide variation of FCF definitions. Jensen (1986) is regarded as the seminal paper that laid out the basic definition of FCF. Jensen (1986) hypothesizes that FCF increases agency costs because the managers of companies with high FCF spend it on acquiring negative net present value (NPV) projects for the purpose of satisfying their ego (being managers of large-size companies) and possibly for increasing their own compensation. He proves his hypothesis by showing that, after acquisition, the return on investment of acquirers is lower than before the acquisition. In light of that, he defines FCF as “cash flow in excess of that required to fund all projects that have positive net present value when discounted at the relevant cost of capital.” He argues that managers should not acquire negative NPV projects and should instead distribute the FCF as dividends to the stockholders. If managers want to acquire new companies they should do so using borrowed capital rather the FCF. In this way, creditors would discipline managers (because they have the power to force the company into bankruptcy) and pressure them not to invest in negative NPV projects. The majority of papers in the finance literature tend to agree with Jensen’s hypothesis (see,
e.g., Mann and Sicherman (1991), Opler and Titman (1993), Dhumale (1998), Carroll and Griffith (2001), and Freund, Prezas, and Vasudevan (2003)). The problem with Jensen’s definition of FCF is that it is not publicly available and, thus, unobservable. Companies do not disclose the actual set of positive NPV projects that they have at any point in time or even for a given year. Thus, Lang, Stulz, and Walking (1991) used a measure of Tobin’s q (the ratio of market to book value of equity) to proxy for this. The assumption is that if average q is less than 1, the marginal investment opportunity is negative. Lang et al. (1991) note that the FCF hypothesis implies that the acquirer’s return should be negatively related to FCF in low q firms, and unrelated to FCF in high q firms. They find that high q bidders have significantly higher mean returns than low q bidders, and higher median returns. As predicted by the FCF hypothesis, their low q, high FCF firms are the worst performers of any of their sample sub-sets. One notable exception to Jensen’s FCF hypothesis is Gregory (2005) who used a dataset of UK take-overs and proxies for FCF similar to those used by Lang et al. (1991). Gregory (2005) reported that, contrary to Jensen’s FCF hypothesis, there is evidence that acquirers with high FCF perform better than acquirers with low FCF. Unlike the finance literature, the accounting literature has many definitions of FCF. FCF is defined differently from academic article to academic article, textbook to textbook, professional article to professional article, from company to company (and some companies change their definition of FCF from time to time), and from all these to the popular press. For example, Mandalay Resort (formerly known as
Circus Circus) was one of the first companies to report FCF information in its 1988 annual report. Over the years, it has changed its FCF definition. In 1988, it defined it as Operating Income (OI), but in 2000, it 190 Journal of Accounting and Finance vol. 14(6) 2014 added back pre-opening expenses, abandonment loss, depreciation and amortization (D&A), interest, dividend, and other income, as well as proceeds from disposal of equipment and other assets. Prior to 1999, Coca-Cola defined FCF as CFO less Cash Flow for Investing activities (CFI). In 1999, it changed the definition to CFO less “business investment.” An analysis of its 1999’s SCF indicates that by “business investment” Coca-Cola meant “acquisitions and investments.” That change in definition increased its FCF in 1999 by almost $2 billion. Mills, Bible, and Mason (2002) report the following different definitions of FCF by popular magazines and investment advisory service organizations: Money Magazine: OI – Capital Expenditures (CE) – Changes in Working Capital (W/C). Forbes Magazine: Net Income (NI) + D&A + or – W/C adjustments – maintenance CE. Harry Domasb’s Winning Investing: CFO – Cash paid for Property, Plant & Equipment (PPE) – Dividends. The Motley Fool: NI + D&A – changes in W/C + or – cash outlay for taxes.
Value Line: NI + Depreciation – Dividends – CE – required debt repayments – any other scheduled cash outlays. InvestorLinks: NI + D&A – CE – Dividends. Advisors Inner Circle Fund: NI + D&A – CE. Subramanyam & Wild (2009, p. 417) define FCF as CFO less Capital Expenditures required to Maintain Productive Capacity (CEMPC) less Total Dividends (TD). In the same edition, they mention another definition: FCF = Net Operating Profits After Tax (NOPAT) – Increase in Net Operating Assets (NOA). Kieso, Weygandt, and Warfield (2013, p. 234) defines FCF as CFO – CE – TD. Searches for “free cash flow definition”, on Google search engine, produced about 3.46 million entries for this title, the first of which is “Definitions of Free Cash Flow on the Web”. Table 1 presents the 15 definitions under this title, together with the web address associated with each definition. It is interesting to note that every one of the 15 definitions is different from the others. Adhikari and Duru (2006) report that of the 548 firms of their sample that voluntarily reported FCF information, 283 (51.6%) defined FCF as CFO – CE, 117 (21.4%) defined FCF as CFO – CE – Dividends, and 64 (11.7%) defined FCF as CFO – CFI. The remaining 84 firms (15.3%) defined FCF in four different other ways. Penman and Yehuda (2009), using a definition of FCF as CFO less cash investments, find that a dollar more of FCF is, on average, associated with
approximately a dollar less in the market value of the business. They also find that this definition of FCF has no association with changes in the market value of the equity. Furthermore, controlling for the cash investment component of FCF, they find that CFO also reduces the market value of the business dollar-for-dollar and is unrelated to the changes in market value of the equity. GuruFocus.com, a website that tracks market insights and news of investment gurus, published two research studies (Gurufocus 2013a and 2013b) concluding that earnings and book values are significantly correlated with stock prices but FCF, defined as CFO – CE and acquisitions, is not. On the other hand, Habib (2011) show that firms with greater growth opportunities and free cash flow, defined as the difference between CFO and CE, will have a higher value price and, additionally, FCF is positively related to stock return. Similarly, Shahmoradi (2013), using the same definition of FCF and a sample of listed companies in Tehran Stock Exchange between 2002 and 2011, reports a relationship (significant at the .05 level) between FCF and stock return of firms. The above review of the literature, especially the accounting literature, indicates that FCF is defined in many different ways. The objective of this study is to determine which one of these definitions, if any, is most correlated with (and, thus, is hypothesized to be the best predictor of) stock price changes. The following section describes the proposed model to be used to answer the research question of this study. PROPOSED MODEL
The authors argue that FCF should be defined not only as the cash flow that is cost free (i.e., that is generated internally from operating activities) but also “the cash flow that management is free to do Journal of Accounting and Finance vol. 14(6) 2014 191 whatever it wants with it as long as management actions may not lead to the firm getting out of business”. Actions that may lead to the firm getting out of business include (a) not maintaining existing operating capacity (i.e. not replacing worn out PPE) and (b) not paying the annual installment of mandatorily redeemable preferred stock or the annual dividend on preferred stock. Not maintaining the existing operating capacity will lead to the gradual liquidation of the firm until it eventually gets out of business. Not paying the annual installment of mandatorily redeemable preferred stock or the annual dividend on preferred stock will not lead to gradual liquidation of the firm but may lead to future difficulties in obtaining financing through the equity markets. Creditors and investors may deal with the company only if they are paid exuberantly high returns (which would be prohibitively high cost for the firm) or may stop dealing with the firm altogether if they determine that their downside risk is becoming too great compared to their upside reward. It can also be argued that not paying the debt that becomes currently due may lead the firm to bankruptcy because risk-averse creditors may force the firm to liquidate in order to recuperate their costs. However, most firms have lines of credit or
refinancing programs so the debt that becomes currently due is paid out from new borrowing that occurs in the current period. Thus, there is no need to pay the debt that becomes currently due this period out of internally generated cash flow from operating activities in the current period. The annual installment due and preferred stock dividend on mandatorily redeemable preferred stock are not available in the Compustat database. They can only be obtained from a review of the notes to the financial statements. Considering the large size of the study sample (about 115,940 observations) that would be cost and time prohibitive. In addition, many companies do not have mandatorily redeemable preferred stock and many of those that do usually do not disclose the information in the footnotes based on the GAAP loophole that management believes the information is not material. To substitute for that information the authors decided to subtract preferred stock dividends (PSD) from CFO in the determination of FCF. While regular preferred stock are not exactly similar to mandatory redeemable preferred stock (since dividend declaration and payment on regular preferred stock is discretionary), the nonpayment of PSD may give the same signal to creditors and investors as the nonpayment of mandatorily redeemable preferred stock dividends. Furthermore, the subtraction of total PSD from CFO in the determination of FCF may compensate to some degree for the non-subtraction of debt that becomes currently due this period. In light of the above discussion, the authors hypothesize that FCF should be defined as follows: FCF = CFO – CEMPC – PSD
Where: FCF = Free Cash Flow CFO = Cash Flow from Operating activities CEMPC = Capital Expenditure required to Maintain Productive Capacity PSD = Preferred Stock Dividends The authors decided to estimate CEMPC as the inflation- adjusted depreciation and amortization expense (D&A) for the current year. However, because of the large size of the sample and the variety of industries included there in, there is no inflation index that can be used to adjust D&A for all the companies in the sample. The authors tried to use the general consumer price index (CPI) for this purpose but found out that the mean inflation-adjusted D&A for the sample is actually greater than the mean for total CE for the current year. That indicates that the general CPI is not appropriate because its use would mean that, on average, the companies in the sample not only are not expanding, but they are not even maintaining their existing productive capacity. Consequently, the authors decided to use the current year unadjusted D&A as a proxy for CEMPC. However, since the objective of this empirical study is to determine which FCF is a better predictor of stock prices, the study model will include other definitions of FCF besides the definition hypothesized here. Since there are so many definitions of FCF as illustrated in the literature review, the authors decided to include in the statistical analyses only those definitions that are most common. The following nine definitions will be included:
192 Journal of Accounting and Finance vol. 14(6) 2014 FCF1 = CFO - CEMPC FCF2 = CFO - CE FCF3 = CFO - CFI FCF4 = CFO - CEMPC - PSD FCF5 = CFO - CE - PSD FCF6 = CFO - CFI – PSD FCF7 = CFO – CEMPC - TD FCF8 = CFO – CE – TD FCF9 = CFO – CFI - TD Where: TD = Total Dividends paid on common and preferred stock. It should be noted that FCF4 is our hypothesized definition, and FCF8 is Standard & Poors’ definition and is reported directly in its COMPUSTAT database. Since the change in the stock price per share (∆SPPS) may be affected by changes in sales per share (∆SPS), earnings per share (∆EPS), dividend per share (∆DPS), and book value per share (∆BVPS), the proposed model includes all these variables so they can be controlled for to show the effect of change in FCF per share (∆FCFPS) on ∆SPPS. Also, to control for the size of the firm, the natural logarithm of total sales (lnTS) and natural logarithm of total assets (lnTA) will be included in the model as well. Because stock price changes may vary from industry to industry, the authors include in the model dummy
variables to control for the industry fixed effects. The authors use Fama-French industry classifications. The authors also control for year-end fixed effects. Thus, the proposed model is as follows: ΔSPPS = B0 + B1ΔSPS + B2ΔEPS + B3ΔDPS + B4ΔBVPS + B5ΔFCFPS1-9 + B6lnTS + B7lnTA + B 8 IND1-44 + € (1) The definitions of the model variables are provided in Appendix A. ΔFCFPS = FCFPSt – FCFPS t – 1 where FCFPS1t = FCF1/weighted average number of common shares outstanding during year t. This weighted average number of common shares will be computed by dividing NI by EPS for year t. The same rule applies for FCFPS2 through FCFPS9. THE STUDY SAMPLE The study sample includes all companies listed in COMPUSTAT for the 23-year period 1988 to 2010. After eliminating all firm year observations that have missing variables, the final sample is composed of 115,940 observations. The study period starts from 1988 because SFAS 95, which requires companies to disclose CFO, was issued in 1987. Because the model uses the changes from year to year, observations from the year 1988 will represent the changes from 1987 to 1988 data. The study period ends in 2010 because this is the last year with available data on COMPUSTAT at the time of collection. The year 2008 was a very abnormal year as total market indexes
took a big dive because of the world’s financial crisis that started during that year. In that year, the Dow Jones Industrial average lost 31 percent of its value (but at one point, in November of that year, it was down 39 percent). The NASDAQ index lost 39 percent (but in November 2008 it was down 46 percent). Similarly, the S&P 500 Cash Index lost 36 percent (but in November 2008 it was down 43 percent). Because of that abnormality, the authors thought that the change in stock prices during 1988 was affected by psychological factors much more so than by financial factors. As a result, the authors ran the model using a sample of observations ending in 2007. The results were not significantly different from the results based on the study sample ending in 2010. Journal of Accounting and Finance vol. 14(6) 2014 193 STATISTICAL RESULTS Table 2 presents Pearson correlation coefficients for all the study and control variables. As the table indicates, all FCF definitions, except for FCF2, FCF5 and FCF8, have positive associations with changes in stock price (Δspps) at the 5% significance level. Among the control variables, Δspps is positively associated with changes in total sales per share (Δsps), changes
in earnings per share (Δeps), changes in book value per share (Δbvps), natural logarithm of total sales (lnsale), and natural logarithm of total assets (lnat) and these associations are statistically significant at the 5% level. Furthermore, Δsps, Δeps, and Δdps are statistically significantly associated with all definitions of FCF whereas lnsale and lnat are statistically significant with some of the FCF specifications suggesting that these variables would be appropriate controls. The correlations presented in Table 2 already present some interesting results which the authors validate in a multi-variate framework shown in the next table. Table 3 presents regression coefficients for nine models by including one FCF definition at a time in the model. Along with the control variables specified in Model (1), the authors also include year and industry fixed effects. Industry categories are based on the Fama-French (1997) 48-industry classification scheme. These fixed effects control for heterogeneity at the industry and year level that may not be captured by our set of controls (such as the high tech industry boom of the 1990s or the recent financial crisis of 2008). As the table shows, all FCF definitions, except for FCF2, FCF5 and FCF8, have positive associations with changes in stock price (Δspps) at the 1% significance level after controlling for other determinants of changes in stock price. Among the control variables, Δsps is negatively associated with changes in stock price and is statistically significant at the 1% level across all specifications of FCF. Δeps and Δbvps are both positively associated with Δspps and statistically significant at the 10% level or better in all models.
Overall, Table 3 confirms the results of the univariate correlations in Table 2. It is interesting to note that FCF8, which is Standard & Poor’s definition of free cash flow, does not have any significant association with changes in stock prices. All three definitions of FCF that do not have any significant associations with changes in stock prices have one thing in common: they all include capital expenditures (CE) as a deduction from CFO. That is the case whether CE alone is deducted (FCF2), CE and preferred stock dividends (PSD) are deducted (FCF5), or CE and total dividends (TD) are deducted (FCF8). Apparently, PSD and TD have very negligible effect, if any, on stock price changes. This is also borne out by the fact that when CEMPC (capital expenditure required to maintain productive capacity) or CFI (cash flow from investing activities) are deducted from CFO (FCF1 and FCF3 respectively) there are significant associations with stock price changes. This is the case whether PSD is also deducted (FCF4 and FCF6) or TD is also deducted (FCF7 and FCF9). Of the six FCF definitions that have significant associations with stock price changes, the three that have CFI as a deduction from CFO (FCF3, FCF6 and FCF9) have the most significant associations. Of those latter three, FCF6 (CFO – CFI –PSD) has a little bit more significant association with stock price changes than the other two. CONCLUSIONS In light of the statistical results above, the authors conclude that FCF6 is the most value- relevant definition of free cash flow. While the authors’ hypothesized definition of free cash flow (FCF4) was significantly associated with stock price changes, it was not the
one that had the most association. This could be due to the possibility that the un-inflation-adjusted depreciation and amortization expense does not really approximate capital expenditures required to maintain productive capacity. Another reason could be that the stock market participants do not make an effort to determine capital expenditures required to maintain productive capacity when they are making their investment decisions. In any event, the authors recommend that the standards setters, particularly the FASB and IASB, should require companies to disclose that FCF in the body of the SCF or at its bottom together with the cash outflow for income taxes and interest expense. Short of that, the standard setters should at least require companies that voluntarily disclose FCF to use only the FCF definition identified by this study. Furthermore, if a 194 Journal of Accounting and Finance vol. 14(6) 2014 company departs from this definition, the independent auditor should consider this departure as a violation of GAAP. LIMITATIONS AND SUGGESTIONS FOR FURTHER RESEARCH The study is subject to some limitations. The most important limitation is the possibility that the study model did not include other variables that may have influenced stock price changes and is correlated with our definitions of free cash flow. The combined effect of those other variables is represented by the error
term ∑ in the model. Adding year and industry fixed effects help mitigate some concerns but not all regarding unobservable explanatory variables. Another limitation is that there may be other formulas for free cash flow which may be more value-relevant than the ones included in this study. While the authors tried to develop as comprehensive a list as possible, other definitions of free cash flow may possibly exist. One suggestion for further research is to replicate the study using other variables that could possibly have more effect on stock prices than the variables included in the study model. Another suggestion would be to investigate whether a trading strategy could be developed for buying (shorting) stock of firms which have the greatest positive (negative) change in one or more measures of FCF over the prior year. REFERENCES Adhikari, A. and A. Duru, 2006. Voluntary Disclosure of Free Cash Flow Information. Accounting Horizons 20 (4), December, pp. 311–332. Carroll, C and J. M. Griffith, 2001. Free Cash Flow, Leverage, and Investment Opportunities. Quarterly Journal of Business and Economics 40 (3 & 4), pp.141-153. Dhumale, R. 1998. Earnings Retention as a Specification Mechanism in Logistic Bankruptcy Models: A Test of the Free Cash Flow Theory. Journal of Business Finance & Accounting 25(7 & 8), September/October, pp. 1005-1023. Fama, F. and K. R. French, 1997. Industry Costs of Equity. Journal of Financial Economics 43, pp. 153-
193. Financial Accounting Standards Board. SFAC No.8 Conceptual Framework for Financial Reporting, Chapter 1, The objective of General Purpose Financial Reporting, and Chapter 3, Qualitative Characteristics of Useful Financial Information. FASB (September 2010). Financial Accounting Standards Board. SFAS No. 95: Statement of Cash Flows. FASB (November 1987). Freund, S., A.P. Prezas, and G. K. Vasudevan, 2003. Operating Performance and Free Cash Flow of Asset Buyers. Financial Management (winter), pp. 87-106. Gregory, A., 2005. The Long Run Abnormal Performance of UK Acquirers and the Free Cash Flow Hypothesis. Journal of Business Finance & Accounting 32 (5 & 6), June/July, pp. 777-814. GuruFocus.com. 2013a. Earnings, Free Cash Flow, and Book Value? Which Parameters are Stock Prices More Correlated To? http://www.gurufocus.com/news/225255/earnings-free-cash- flow-book- value-which-parameters-are-stock-prices-most-correlated-to-. August 2, 2013. GuruFocus.com. 2013b. Is Free Cash Flow Overrated for its Importance in Stock Valuations? http://www.gurufocus.com/news/225642/is-free-cash-flow- overrated-for-its-importance-in-stock- valuation. August 8, 2013. Habib, A., 2011. Growth Opportunities, Earnings Permanence
and the Valuation of Free Cash Flow, Australasian Accounting Business & Finance Journal 5 (4), pp. 101-122. Harris, L., 2010. Missing in Activism: Retail Investor Absence in Corporate Elections. Columbia Business Law Review 1, pp. 104-204. International Accounting Standards Board. IAS No. 7: Statement of Cash Flows. IASB (September 2007). Jensen, M.C., 1986. Agency Costs of Free Cash Flow, Corporate Finance, and Takeovers. American Economic Review 76 (2), pp. 323-29. Kieso, D., J. Weygandt, and T. Warfield, 2013. Intermediate Accounting, 15th Ed., New York, NY: John Wiley & Sons. Journal of Accounting and Finance vol. 14(6) 2014 195 Lang, L. H. P., R. M. Stulz, and R.A. Walking, 1991. A Test of the Free Cash Flow Hypothesis: The Case of Bidder Returns. Journal of Financial Economics 29, pp. 315– 35. Mann, S. V., and N. W. Sicherman, 1991. The Agency Costs of Free Cash Flow: Acquisition Activity and Equity Issues. The Journal of Business 64 (2), pp. 213-227. Mills, J., L. Bible, and R. Mason, 2002. Rough Waters for Comparability: Defining Free Cash Flow. The CPA Journal (January), pp. 37–41.
Opler, T.C. and S. Titman, 1993. The Determinants of Leveraged Buyout Activity: Free Cash Flows vs. Financial Distress Costs. Journal of Finance 48 (1), December, pp. 1985-99. Penman, S. and N. Yehuda, 2009. The Pricing of Earnings and Cash Flows and an Affirmation of Accrual Accounting." Review of Accounting Studies 14 (4), pp. 453- 479. Shahmoradi, N. , 2013. The Effect of Growth Opportunities and Stable Profitability on Market Value of Free Cash Flows of Listed Companies in Tehran Stock Exchange. Journal of Basic and Applied Scientific Research 3 (8), pp. 495-501. Subramanyam, K. R. and J. J. Wild, 2009. Financial Statement Analysis, 10th Ed., Burr Ridge, IL: McGraw-Hill/Irwin. APPENDIX A VARIABLE DEFINITIONS Δspps Change in stock price between the end of the next fiscal year and the current year. Δfcfps1 Change in the difference between cash flow from operations (CFO) and depreciation and amortization expense (DP) over the current fiscal year. Δfcfps2 Change in the difference between cash flow from
operations (CFO) and capital expenditures (CE) over the current fiscal year. Δfcfps3 Change in the difference between cash flow from operations (CFO) and cash flow from investing activities (CFI) over the current fiscal year. Δfcfps4 Change in cash flow from operations (CFO) minus depreciation and amortization expense (DP) minus preferred stock dividends (PSD) over the current fiscal year. Δfcfps5 Change in cash flow from operations (CFO) minus capital expenditures (CE) minus preferred stock dividends (PSD) over the current fiscal year. Δfcfps6 Change in cash flow from operations (CFO) minus cash flow from investing activities (CFI) minus preferred stock dividends (PSD) over the current fiscal year. Δfcfps7 Change in cash flow from operations (CFO) minus depreciation and amortization expense (DP) minus total dividends (TD) over the current fiscal year. Δfcfps8 Change in cash flow from operations (CFO) minus capital expenditures (CE) minus total dividends (TD) over the current fiscal year. Δfcfps9 Change in cash flow from operations (CFO) minus cash flow from investing activities (CFI) minus total dividends (TD) over the current fiscal year.
Δsps changes in total sales per share over the current fiscal year. Δeps change in earnings per share over the current fiscal year. Δdps change in dividends per share over the current fiscal year. Δbvps change in book value per share over the current fiscal year. lnsale natural logarithm of total sales over the current fiscal year. Lnat natural logarithm of total assets at the current fiscal year end. 196 Journal of Accounting and Finance vol. 14(6) 2014 TABLE 1 DEFINITIONS OF FREE CASH FLOW ON THE WEB 1. In corporate finance, free cash flow (FCF) is cash flow available for distribution among all the securities holders of an organization. They include equity holders, debt holders, preferred stock holders, convertible security holders, and so on. en.wikipedia.org/wiki/Free_cash_flow 2. Net income plus depreciation and amortization, less changes in working capital, less capital expenditure. en.wiktionary.org/wiki/free_cash_flow 3. Adjusted operating cash flow less interest and tax paid, prior to distributions to shareholders. This is the cash flow available for payments of dividends and share buybacks as well as repayments of capital on loans. www.reed- lsevier.com/investorcentre/glossary/Pages/Home.aspx
4. Cash flow from operating activities, investments, financial items and tax and the effect of restructuring measures on cash flow. www.investor.rezidor.com/phoenix.zhtml 5. equals EBITDA minus net interest expense, capital expenditures, change in working capital, taxes paid, and other cash items (net other expenses less proceeds from the disposal of obsolete and/or substantially depleted operating fixed assets that are no longer in operation). www.cemex.com/ic/ic_glossary.asp 6. This item on the cash flow statement represents the sum of cash flows generated by operating and investing activities. investors.benettongroup.com/phoenix.zhtml 7. How much money a company could pay shareholders out of profits without expanding, but without running down its existing operations either. moneyterms.co.uk/d/ 8. Represents a common measure of internally generated cash and is defined as cash from operations less fixed asset purchases. portal.acs.org/portal/PublicWebSite/about/aboutacs/financial/W PCP_012234 9. Cash available after financing operations and investments, available to pay down debt. www.graduates.bnpparibas.com/glossary.html 10. A stock analyst's term with a definition that varies somewhat depending on the particular analyst. It usually approximates operating cash flow minus necessary
capital expenditures. ... www.jackadamo.com/glossary.htm 11. The amount of money that a business has at its disposal at any given time after paying out operating costs, interest payments on bank loans and bonds, salaries, research and development and other fixed costs. www.premierfoods.co.uk/investors/shareholder- services/Glossary.cfm 12. Net Operating Profit After Tax minus Year-to-Year change in Net Capital. www.intrinsicvalue.com/glossary.htm 13. The increase in cash from one period to the next. www.knowledgedynamics.com/demos/BreakevenFlash/Glossary Main.htm 14. Cash flow after operating expenses; a good indicator of profit levels. healthcarefinancials.wordpress.com/2008/01/24/equity-based- securities-terms-and-definitions- for-physicians/ 15. The surplus cash generated from operating activities recognized in the profit and loss account. This expresses a company's internal financing power, which can be used for investments, the repayment of debt, dividend payments and to meet funding requirements. www.deutsche-euroshop.de/berichte/gb2004/glossar_e.php Journal of Accounting and Finance vol. 14(6) 2014 197
T A B L E 2 P E A R SO N C O R R E L A T IO N
C O E F F IC IE N T S Δ spps Δ fcfps 1 Δ fcfps 2 Δ fcfps 3 Δ
fcfps 4 Δ fcfps 5 Δ fcfps 6 Δ fcfps 7 Δ fcfps 8 Δ fcfps 9 Δ sps Δ eps Δ dps Δ
bvp s lnsale lnat Δ spps 1.00 Δ fcfps1 0.01 1.00 Δ fcfps2
0.00 0.94 1.00 Δ fcfps3 0.07 0.73 0.56 1.00 Δ fcfps4 0.01 1.00 0.94 0.73
1.00 Δ fcfps5 0.00 0.94 1.00 0.56 0.94 1.00 Δ fcfps6 0.07 0.73 0.56 1.00 0.73 0.56
1.00 Δ fcfps7 0.01 0.96 0.92 0.64 0.96 0.92 0.64 1.00 Δ fcfps8 0.00 0.88 0.95 0.46
0.88 0.95 0.46 0.94 1.00 Δ fcfps9 0.07 0.73 0.57 1.00 0.73 0.57 1.00 0.67 0.49 1.00 Δ sps
0.03 0.18 0.18 0.23 0.18 0.18 0.23 0.17 0.16 0.23 1.00 Δ eps 0.03 0.39 0.37 0.39 0.39 0.37 0.39 0.30 0.27
0.37 0.30 1.00 Δ dps 0.00 0.06 0.01 0.24 0.06 0.01 0.24 -0.21 -0.29 0.17 0.01 0.29 1.00 Δ bvps 0.06
-0.24 -0.17 -0.27 -0.24 -0.17 -0.27 -0.20 -0.13 -0.26 0.05 0.14 -0.12 1.00 lnsale 0.01 0.00 0.00 -0.01 0.00 0.00 -0.01 0.00 0.00
-0.01 0.01 0.00 0.00 0.02 1.00 lnat 0.01 -0.01 0.00 -0.01 -0.01 0.00 -0.01 -0.01 0.00 -0.01 0.01 0.00 0.00 0.02 0.90 1.00
V ariables are defined in A ppendix A . N um bers in bold indicate significance at the 5% level. 198 Journal of Accounting and Finance vol. 14(6) 2014 T A B L E 3 A SSO C IA T IO N
B E T W E E N V A R IO U S M E A SU R E S O F F R E E
-C A SH -F L O W A N D C H A N G E S IN ST O C K P R IC
E S V ariables Predicte d Sign Δ spps (1) Δ spps (2) Δ spps (3) Δ spps (4) Δ spps (5) Δ
spps (6) Δ spps (7) Δ spps (8) Δ spps (9) Δ fcfps1 + 0.066** * Δ fcfps2 + 0.006
(0.53) Δ fcfps3 + 0.16*** (47.13) Δ fcfps4 + 0.067** * Δ fcfps5
+ 0.006 (0.54) Δ fcfps6 + 0.16*** (47.19) Δ fcfps7 + 0.066** *
Δ fcfps8 + 0.006 (0.53) Δ fcfps9 + 0.16*** (47.13) Δ sps -0.041** -0.028**
-0.205** -0.041** -0.028** -0.205** -0.041** -0.028** -0.205** Δ eps 0.057*
(1.71) 0.059* (1.78) 0.572** * 0.056* (1.71) 0.059* (1.78) 0.573** * 0.057* (1.71) 0.059* (1.78) 0.572** * Δ dps -0.047 (-0.29)
0.098 (0.61) -0.088 (-0.55) -0.048 (-0.29) 0.098 (0.6) -0.087 (-0.55) 0.02 (0.12) 0.104 (0.65) 0.072 (0.45) Δ bvps 0.146** * 0.142** *
0.186** * 0.146** * 0.142** * 0.186** * 0.146** * 0.142** * 0.186** * L nsale 0.168 (0.26)
0.155 (0.24) 0.35 (0.55) 0.167 (0.26) 0.155 (0.24) 0.35 (0.55) 0.168 (0.26) 0.155 (0.24) 0.35 (0.55) Journal of Accounting and Finance vol. 14(6) 2014 199 L nat 0.279 (0.4)
0.29 (0.42) 0.134 (0.2) 0.28 (0.4) 0.29 (0.42) 0.134 (0.2) 0.279 (0.4) 0.29 (0.42) 0.134 (0.2) Intercept -3.452 (-0.17) -2.164 (-0.11) -12.129 (-0.62)
-3.46 (-0.17) -2.167 (-0.11) -12.14 (-0.62) -3.452 (-0.17) -2.164 (-0.11) -12.129 (-0.62) Industry Fixed E ffects Y es Y es Y es Y
es Y es Y es Y es Y es Y es Y ear Fixed E ffects Y es Y es Y es
Y es Y es Y es Y es Y es Y es O bservations 115,940 115,940 115,940 115,940 115,940 115,940 115,940 115,940 115,940
A djusted R 2 0.0044 0.0040 0.0228 0.0044 0.0041 0.0228 0.0044 0.0041 0.0228 T his table provides the results of regressing the change in future stock prices of a firm (Δ spps) on various m easures of changes in free cash flow (Δ fcfps1 - Δ fcfps9) and control variables. C oefficients are provided w
ith t-statistics in parentheses below . V ariables are defined in A ppendix A . ***, **, and * represent tw o-tailed p-value significance levels of 0.01, 0.05, and 0.1 respectively. 200 Journal of Accounting and Finance vol. 14(6) 2014 Copyright of Journal of Accounting & Finance (2158-3625) is the property of North American Business Press Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. 120 International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 FREE CASH FLOW AND PERFORMANCE PREDICTABILITY: AN INDUSTRY ANALYSIS
Karen Nunez Elon University ABSTRACT This study investigates the ability of Free Cash Flow to predict performance in capital intensive and non-capital intensive industries. This study provides empirical evidence on Free Cash Flow versus traditional performance indicators and indicates whether Free Cash Flow better summarizes firm performance as reflected in stock returns/prices. This study makes three contributions. First, Free Cash Flow, considered by some as a refinement of cash flow and a more contemporary measure is used. Second, the predictability of Free Cash Flow is compared to traditional measures of performance. Third, this study extends the research on industry comparisons by using industry-specific analyses to examine the predictability of Free Cash Flow. Results indicate that Free Cash Flow is significantly different from Operating Cash Flow and Net Income, but there are mixed results on differences in the relative explanatory power in capital intensive and non-capital intensive industries. Keywords: Cash Flow, Free Cash Flow, Capital intensity, Industry analysis
INTRODUCTION Little of the existing research has considered Free Cash Flow for measuring firm performance. Prior studies have focused on operating cash flows. However, some analysts claim that Free Cash Flow better captures capital intensity, and is a better measure of performance in capital intensive industries (Tole, McCord, & Pugh, 1992). Financial reporting as required by SFAC No. 1, is designed to provide information to investors, creditors and others, about an enterprise’s financial performance (Financial Accounting Standards Board (FASB), 1985). While most investors focus on Earnings, Value Line (2011) suggests that other performance measures, like Free Cash Flow should be considered because Earnings can be affected by accounting methods and managerial discretion (manipulation), whereas, Free Cash Flow is harder to manipulate. However, there is very little empirical evidence on the predictability of Free Cash Flow. The objective of this study is to examine whether Free Cash Flow or more traditional financial measures better predict performance. Some analysts (Tole, McCord, & Pugh, 1992) claim that Free Cash Flow better captures capital intensity, and hence is a better measure of performance in capital intensive industries. Operating cash flows have been the focus of the prior research, therefore, the existing research
offers little evidence on the ability of Free Cash Flow to measure performance. Free Cash Flow definitions vary widely between companies and between industries, because U.S. GAAP does not require firms to disclose Free Cash Flow, and it provides little guidance on measuring Free Cash Flow. Some guidance is provided by the International Accounting Standards Board with International Accounting Standard (IAS) 7, which recommends that Free Cash Flow should be recognized as “cash from operations less the amount of capital expenditures required to maintain the firm’s present productive capacity” (International Accounting Standards Board (IASB), 1977). International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 121 This study makes three important contributions. First, Free Cash Flow, considered by some as a refinement of cash flow and a more contemporary measure is used. Second, the predictability of Free Cash Flow is compared to the predictability of operating cash flows and earnings. Third, this study extends the research on industry comparisons by using industry- specific analyses to examine the predictability of Free Cash Flow in capital intensive versus non- capital intensive industries. BACKGROUND AND LITERATURE REVIEW
The prior literature on cash flows focuses on operating cash flows vs. earnings to explain performance, as proxied by abnormal stock returns (Dechow, 1994; Bowen, Burgstahler & Daley, 1987; Livnat & Zarowin, 1990). While the term Free Cash Flow is widely used in the press and in the business world, U. S. GAAP does not require firms to disclose Free Cash Flow, and as a result few firms voluntarily report it. Free Cash Flow definitions are not uniform and there is little theoretical or conceptual guidance on how to calculate Free Cash Flow (Adhikari & Duru, 2006). Firms reporting Free Cash Flow either use a Cash flow from operations-based method, or an income-based method to calculate Free Cash Flow (Adhikari & Duru, 2006). Adhikari and Duru (2006) determined that income-based methods are used to calculate Free Cash Flow by only a small percentage of firms, 14.2 percent. Income-based methods typically start with earnings before interest, taxes, depreciation and amortization as a proxy for Cash flow from operating activities, and then make various adjustments. Additionally, half of the Free Cash Flow reporting firms use a Cash flow from operations-based method where Free Cash Flow is calculated one of two ways: (1) A capital maintenance perspective-Cash flow from operating activities less capital expenditures necessary to maintain the productive capacity of the firm, and (2) An all- inclusive perspective- Cash flow from operating activities less capital expenditures, plus proceeds from fixed asset sales and
changes in long-term investments (Adhikari & Duru, 2006). Over 50 percent of the firms using a Cash flow from operations-based method rely on the capital maintenance perspective. The capital maintenance perspective is consistent with guidance provided by The International Accounting Standards Board (IAS 7). Firms operating in capital-intensive industries require significant investments in capital to start and maintain operations. Non-capital intensive industries generally depend on labor rather than capital, and are thus not considered capital intensive. The automobile, chemical, telecommunications, and refinery industries are often considered examples of capital-intensive industries. The household products industry, insurance companies and other service oriented industries generally depend on labor rather than capital, and are often considered examples of non-capital-intensive industries (Investing Answers, 2014). Capital investments are necessary to equip firms with essential tools and high tech machinery necessary for operations. In most capital-intensive industries, millions of dollars must be invested. For example, oil companies must spend millions of dollars setting up oil rigs, oil refineries and other infrastructure in order to bring in oil. Telecommunications companies must set up a network of phone lines, fiber-optic lines and other equipment in order to service customers. Because of significant investments in capital, companies in capital-intensive industries are often marked by high levels of depreciation and fixed assets on the balance sheet.
The Electric Utility industry is another example of a capital- intensive industry. Electric Utility 122 International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 firms often undertake large-scale construction programs to update aging infrastructures, add capacity, and to comply with environmental regulations. Substantial depreciation expense usually results from the significant capital expenditures. The depreciation expense (a non-cash expense) leads to net operating cash flows that significantly exceed net income. Given the distortional effect depreciation expense has on net operating cash flows, Tole, McCord and Pugh (1992), suggest that cash flows are a better measure of performance than net income for a capital-intensive industry like the Electric Utility industry. Generally, the Electric Utility Industry reports Free Cash Flow with a capital maintenance perspective, and Free Cash Flow is defined as operating cash flow minus capital expenditures (Tole, McCord & Pugh, 1992; Bilicic & Connor, 2004). Moreover, Tole, McCord & Pugh (1992), recommend Free Cash Flow to equity investors as a better measure of performance than net income. Seminal cash flow studies focus on operating cash flows versus earnings, to explain performance as measured by abnormal stock returns (Dechow, 1994; Bowen, Burgstahler &
Daley, 1987; Livnat & Zarowin, 1990). These studies generally demonstrate that cash flows and earnings both provide incremental information, but do not directly address the relative superiority of one measure over the other. In a more current study, Burgstahler, Jiambalvo & Pyo (1998) find that cash flow has more predictive ability than earnings, but Finger (1994) found mixed results. Further, Barth, Cram and Nelson (2001) find cash flows have more predictive ability than earnings. None of the earlier studies focus on Free Cash Flow or capital intensity. One possible explanation for the mixed results of prior research is the failure to focus on a more relevant measure like Free Cash Flow, or a failure to focus on industry-specific samples. Nunez (2013) considers Free Cash Flow and the Electric Utility Industry. Nunez (2013) found that Free Cash Flow is significantly different from Operating Cash Flow and Net Income, but could not detect significant differences in the relative explanatory power of Free Cash Flow, Operating Cash Flow and Net Income. This study attempts to build on Nunez (2013) by considering the predictability of Free Cash Flow in both capital and non-capital intensive industries. HYPOTHESES AND METHODOLOGY This study examines whether Free Cash Flow is a better measure of performance than net income and operating cash flows, for capital intensive and non- capital intensive industries. Tole, McCord & Pugh (1992) suggests that for a capital intensive
industry like the Electric Utility Industry, cash flows are a better measure of performance than net income, and Free Cash Flow is better than operating cash flow. Therefore, the authors of this study expect Free Cash Flow to have greater performance predictability than Operating Cash Flow and Net Income for firms in capital intensive industries. Further, we expect Free Cash Flow to have less performance predictability than Operating Cash Flow and Net Income for non-capital intensive industries. The first hypothesis considers the relation between capital intensity and free cash flow. Building on Tole, McCord and Pugh (1992) the authors expect capital intensive firms to have lower levels of Free Cash Flow, and non-capital intensive firms to have higher levels of Free Cash Flow. The hypothesis stated in the null: H1: The association between Free Cash Flow and capital intensity does not differ between capital intensive firms and non-capital intensive firms. International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 123 As suggested by Tole, McCord & Pugh (1992) the authors of this study expect Free Cash Flow to be a better measure of performance than operating cash flows and net income, for capital
intensive firms, and operating cash flows and net income to be better measures of performance than Free Cash Flow for non-capital intensive firms. The second hypothesis, stated in the null: H2: The predictability of Free Cash Flow does not differ between capital intensive firms and non-capital intensive firms. The authors used an independent measure of capital intensity, as defined in prior literature. The Fixed Asset Ratio (FAR) is plant, property and equipment divided by noncash total assets, based on Kang and Zhau (2010). Kang and Zhau (2010) defined capital intensive industries as having a mean industry fixed asset ratio of 0.5 or greater. Based on the approach used by Kang and Zhau (2010), the authors utilized two groups: Group A-capital intensive industries where the mean industry FAR is 0.5 or greater, and Group B-non-capital intensive industries where the mean industry FAR is less than 0.5. Also, we used Fama and French (1997) to guide our industry classifications using SIC/NAICS codes. To test Hypothesis 1 and examine the relationship between capital intensity and Free Cash Flow observed by scatterplots. The direction, magnitude and shape of the relationships is conveyed in the plots. The measurement of the relationship between capital intensity and Free Cash Flow is based on the following variables, Y = f(X) (1)
Where (Compustat descriptions are in parentheses), Y= Free Cash Flow, calculated as Operating Activities Net Cash Flow minus Capital Expenditures (OANCF- CAPX), and X = Fixed Asset Ratio, calculated as plant, property and equipment divided by noncash total assets, (PPENT/(AT- CH). The estimated correlation coefficients used to measure the direction and strength of the association, and to draw more definitive inferences. Commonly used measures of association include the Pearson and Spearman correlation coefficients, Goodman and Kruskal’s gamma (γ) and Kendall’s tau (τ). Pearson’s correlation coefficient requires normally distributed variables or it will produce unreliable results, and the Spearman rank correlation requires a monotonic underlying relationship between variables (Goktas & Isci, 2011). Goodman and Kruskal’s gamma (γ), is a non-parametric measure of rank correlation that does not rely on any assumptions on the distributions of X or Y, or the distribution of (X,Y), and it does not consider tied pairs (Blumberg, Cooper, & Schindler, 2011). A tied pair occurs when observations have the same value on the X variable, on the Y variable or on both. Kendall’s tau (τ) is recognized as a refinement of gamma (γ) that considers tied pairs (Blumberg, Cooper, & Schindler, 2011). The Kendall’s tau b (τb) used to measure the direction and strength of the association between capital intensity and Free Cash Flow. Kendall’s tau b (τb) is a non-parametric measure of rank correlation that does not rely on any assumptions on the distributions of X or Y, or the
distribution of (X,Y), it does consider tied pairs and is suitable for data tables of any size (Blumberg, Cooper, & Schindler, 2011). The following models used to test Hypothesis 2 and examine the predictability of Free Cash Flow: Rt = a0 + a1 FCFt + et, (2a) 124 International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 Rt = a0 + a1 OCFt + et, (2b) Rt = a0 + a1 NIt + et, (2c) Where (Compustat descriptions are in parentheses), R is raw annual returns; FCF is Free Cash Flow, calculated as Operating Activities Net Cash Flow minus Capital Expenditures (OANCF-CAPX), OCF is Operating Cash Flow, Operating Activities Net Cash Flow (OANCF); NI is net income after extraordinary items and discontinued operations (NI). All variables except R, are deflated by market value of common equity at the previous fiscal year-end. Models 2a, b and c are based on Dhaliwal, Subramanyam, and Trezevant (1999). Kim & Kross (2005) used a similar model to test the explanatory power of earnings and cash flows. To draw more definitive inferences, and to minimize the potential econometric and theoretical problems with returns models, the authors used price
models (Kothari & Zimmerman, 1995). Pt = a0 + a1 FCFt + et, (3a) Pt = a0 + a1 OCFt + et, (3b) Pt = a0 + a1 NIt + et, (3c) Where, P is market value of common equity (PRCC) at fiscal year-end. All variables are deflated by the number of shares of common stock outstanding (CSHO) at fiscal year-end, adjusted for stock splits and stock dividends (AJEX). METHODOLOGY Methods Some industries had a small number of firms and time periods available for study, therefore the observations were pooled across time to increase the number of observations and the power of the regression models. Pooling the data can introduce cross-sectional and time series dependencies in the sample data, which could understate the standard errors of the regression coefficients and inflate the t-statistics. To mitigate this, Huber-White (1967) standard errors are used in the regression models for the construction of the t-statistics. The Huber-White robust standard error estimator produces correct standard errors even if
the observations are correlated and heteroscedastic (Huber 1967; White 1980). Maximum- likelihood estimates are generally preferable to ANOVA and OLS estimates so the full maximum likelihood procedure for estimating the parameters of the regressions is used, (see Searle, 1988; Harville, 1988; Searle, Casella and McCulloch, 1992). Firm-specific and time- specific intercepts are also used in the models. Sample and Data Collection The Compustat Database was used to identify the initial sample of 131,861 observations from 2000-2012. Firms with insufficient data to calculate the Fixed Asset Ratio (FAR), Free Cash Flow, Operating Cash Flow, Net Income, and market value were deleted, resulting in 72,246 observations. Observations for which the test variable falls in the top and bottom percentile of the test-variable distribution were eliminated from the sample. The resulting International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 125 sample is composed of 64,566 observations, representing 11,036 firms and 48 industries. Table 1 provides a list of industries used in this study.
Table 1 List of Industries Abbreviation INDUSTRY # of firms # of Observations Aero Aircraft 37 297 Agric Agriculture 41 223 Autos Autos and Trucks 126 818 Banks Banking 972 5,162 Beer Alcoholic Beverages 31 190 BldMt Construction Materials 161 1,060 Books Printing and Publishing 59 352 Boxes Shipping Containers 21 119 BusSv Business Services 1,423 7,749 Chems Chemicals 187 1,199 Chips Electronic Equipment 570 3,845 Clths Apparel 100 683 Cnstr Construction 94 548 Coal Coal 41 195 Comps Computers 394 2,289 Drugs Pharmaceutical Prod 736 4,501 ElcEq Electrical Equipment 142 1,037 Energy Petro and Nat Gas 762 3,815 FabPr Fabricated Products 28 163 Fin Trading 316 1,462 Food Food Products 134 937 Fun Entertainment 144 856 Gold Precious Metals 308 1,484 Guns Defense 15 119 Hlth Healthcare 141 915 Hshld Consumer Goods 121 749 Insur Insurance 178 803 LabEq Meas and Contrl Equip 168 1,343 Mach Machinery 268 1,837 Meals Rest, Hotel, Motel 200 1,228 MedEq Medical Equipment 337 2,127 Mines Nonmetallic Mining 442 2,111
Misc Miscellaneous 155 662 Paper Business Supplies 89 595 PerSv Personal Services 102 629 RlEst Real Estate 122 626 Rtail Retail 358 2,434 Rubbr Rubber and Plastic Products 78 503 Ships Shipbuilding, Rail Eq 13 102 Smoke Tobacco Products 5 43 Soda Candy and Soda 24 157 Steel Steel Works Etc 126 731 Telem Telecommunications 308 1,686 Toys Recreational Products 75 415 Trans Transportation 268 1,641 Txtls Textiles 26 170 Util Utilities 273 1,933 Whlsl Wholesale 317 2,023 Total 11,036 64,566 126 International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 Descriptive Statistics Table 2 reports descriptive statistics for variables used to estimate the models, and for key firm size variables used to gain additional insight about firm characteristics. Descriptive statistics are presented for the entire sample, and to gain additional insight, the sample is further classified
based on capital intensity. Columns 1 and 2 (all observations) of Table 2 report means and standard deviations for the total sample of 64,566 observations; columns 3 and 4 (capital intensive firms) report means and standard deviations for 15,287 observations, 24% of the total observations, representing firms that have a mean FAR of 0.5 or greater; and the last two columns (non-capital intensive firms) report means and standard deviations for 49,279 observations, 76% of the total observations, representing firms that have a mean FAR of less than 0.5. Table 2 Descriptive Statistics All Observations Capital Intensive Firms Non-Capital Intensive Firms Mean Std Dev Mean Std Dev Mean Std Dev FCF 51.77 204.66 22.52 190.64 60.84 207.99 FAR 0.30 0.29 0.75 0.14 0.16 0.14 CAPX 61.59 212.78 146.41 359.75 35.26 127.39 OCF 113.20 342.54 168.64 459.80 96.00 294.79 NI 50.79 205.07 61.37 234.15 47.51 195.06 R 0.20 1.17 0.25 1.24 0.19 1.14 ROE (0.26) 99.20 (1.83) 189.69 0.22 41.61
LTD 326.63 1,177.94 546.30 1,595.65 258.37 1,003.93 TOTASS 1,508.79 5,193.28 1,765.40 4,819.36 1,429.19 5,301.44 PPE 431.73 1,780.40 1,181.53 3,255.18 199.14 798.58 TOTSALE 1,067.84 3,809.89 1,165.26 4,198.20 1,037.62 3,680.64 MVE 1,200.71 3,735.78 1,320.43 3,708.36 1,163.58 3,743.50 BVE 0.30 0.29 0.75 0.14 0.16 0.14 No. of Obs 64,566 15,287 49,279 Where*, FCF= Free Cash Flow=Operating Cash Flow minus Capital Expenditures (OANCF-CAPX) FAR= Fixed Asset Ratio=Plant, Property & Equip/Noncash Total Assets ((PPENT/(AT-CH CAPX= Capital Expenditures (CAPX) OCF= Operating Cash Flow=Operating Activities Net Cash Flow (OANCF) NI=Net Income after extraordinary items and discontinued operations (NI) R= Raw annual percentage returns ROE=Return on equity, NI (NI) divided by Book Value of Equity (CEQ) LTD=Long term debt (DLTT) TOTASS=Total assets (AT) PPE=Plant, Property and Equipment (PPENT) TOTSALE=Total sales (SALE) MVE=Market value of equity= price times common shares outstanding (PRCC x CSHO) BVE=Book value of equity (CEQ)
*Compustat item description in parentheses. International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 127 The average market value (MVE) for the entire sample is $1,200.71 million. Capital intensive firms are considerably larger with an average market value of $1,320.43, 13% larger than the average market value of non-capital intensive firms, $1,163.58. The other size-based characteristic, book value of equity (BVE), exhibits the same pattern. The earnings variable (Net Income) in Table 2 indicates capital intensive firms are more profitable, with an average Net Income of $61.37 million compared to $47.51 million for non- capital intensive firms. Return on equity (ROE) is included because it is a more relative measure of profitability and it indicates that capital intensive firms are not relatively more profitable as they have an ROE of -183% compared to an ROE of 22% for non-capital intensive firms. Capital intensive firms are characterized by having significant capital investment leading to substantial depreciation expense, hence Operating cash flow (OCF) is 275% of Net income (significantly different at the 1% level), but only 202% of Net Income for non-capital intensive firms (significantly different at the 5% level). Furthermore, Free Cash Flow is only 37% of Net income for capital intensive firms (significantly different at the
1% level) but 81% of Net income for non-capital intensive firms (significantly different at the 5% level). These results suggest that mean Operating cash flow is significantly different from mean Net income and mean Free Cash flow is significantly different from mean Operating Cash flow, for both capital intensive and non-capital intensive firms. EMPIRICAL TESTS To test Hypothesis 1 the relationship between Free Cash Flow and capital intensity is examined. The authors expected capital intensive firms to have lower levels of Free Cash Flow, and non-capital intensive firms to have higher levels of Free Cash Flow. Consistent with expectations, capital intensive firms have a mean Fixed Asset Ratio (FAR) of 0.75, which is significantly different (at the 1% level) from the mean Fixed Asset Ratio (FAR) of 0.16 for non- capital intensive firms. Further, capital intensive firms have a mean Free Cash Flow of $22.52, which is significantly different (at the 1% level) from the mean Free Cash Flow of $60.84 for non-capital intensive firms. These results lend some support to Hypothesis 1. In our next step, we prepared scatter plots of the relationship between Free Cash Flow and Capital Intensity. The scatter plots are reported in Figure 1. Panel A of Figure 1 demonstrates the relationship of Free Cash Flow and Capital Intensity for capital intensive firms, and Panel B demonstrates the relationship of Free Cash Flow and Capital Intensity for non-
capital Intensive firms. There is some indication from the scatter plots that capital intensive firms have lower levels of Free Cash Flow, and non-capital intensive firms have higher levels of Free Cash Flow. Table 3 presents correlations between Free Cash Flow and Capital Intensity for capital and non-capital intensity firms.. Pearson and Spearman correlation coefficients, as well as the non-parametric measure of rank correlation, Kendall’s tau were utilized. The correlation coefficients for capital intensive firms are all significant at the 1% level. Only the Pearson correlation coefficient for non-capital intensive firms is significant (at the 1% level). Overall, evidence from the descriptive statistics, the scatter plots and the correlation coefficients supports Hypothesis 1, and indicate that capital intensive firms have lower levels of Free Cash Flow, and non-capital intensive firms have higher levels of Free Cash Flow. To test Hypothesis 2 and to examine whether Free Cash Flow is a better measure of performance than Operating Cash Flow and Net Income, we estimate models (2a) – (2c). Summary model statistics are reported in Table 4. A coefficient significantly different from zero 128 International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 on Free Cash Flow, Operating Cash Flow and Net Income
indicates the variable provides significant explanatory power. Free Cash Flow, Operating Cash Flow and Net Income are not Figure 1 Scatter Plot of Free Cash Flow and FAR (Capital Intensity): Panel A: Capital Intensive Firms Panel B: Non-Capital Intensive Firms International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 129 Table 3 Correlations Between Free Cash Flow and Capital Intensity CAPITAL INTENSIVE FIRMS Capital Intensity (FAR) (Pearson) Capital Intensity (FAR) (Spearman) Capital Intensity (FAR) (Kendall's
tau b) Free Cash Flow (FCF) -0.144 -0.022 -0.147 (0.000) (0.000) (0.000) NON-CAPITAL INTENSIVE FIRMS Capital Intensity (FAR) (Pearson) Capital Intensity (FAR) (Spearman) Capital Intensity (FAR) (Kendall's tau b) Free Cash Flow (FCF) -0.027 -0.001 -0.002 (0.000) P-values are in parentheses. significant for capital intensive firms, however, Operating Cash Flow and Net Income are both significant at the 1% level for non-capital intensive firms. This result provides some support for hypothesis 2, in that we expect Operating Cash Flow and Net Income to be better measures of performance for non-capital intensive firms, and these variables should have more explanatory power than Free Cash Flow. The maximum likelihood procedure
does not produce a formal R2 statistic, therefore, the pseudo R2 (Cox & Snell, 1981) measures are reported. The three capital intensive models and the three non-capital intensive models have pseudo R2 measures of nearly 8%, and all are significant at the 1% level using the null model likelihood ratio test (not reported in Table 4). To estimate price models (3a) – (3c) as suggested in Kothari & Zimmerman (1995), to minimize the potential econometric and theoretical problems associated with the returns models used in (2a) – (2c). Table 5 reports summary model statistics for price models (3a) – (3c). Free Cash Flow is significant for capital intensive firms, and Free Cash Flow and Operating Cash Flow are both significant for non-capital intensive firms at the 1% level. These results provide further support for hypothesis 2. Also, consistent with the returns models, the three capital intensive models and the three non-capital intensive models have pseudo R2 measures of nearly 8%, and all are significant at the 1% level using the null model likelihood ratio test (not reported in Table 5). The likelihood ratio tests and pseudo-R2 measures are of limited use in making comparisons across measures, and cannot be used to compare non-nested models (Burnham & Anderson, 2002). The authors used an approach suggested by Biddle, Seow & Siegel (1995) to compare the three measures of performance. Their approach is based on the Wald statistic. The Wald Statistic can be used to test equality of coefficients across
regression equations. It will be used to test the null hypothesis that the parameter estimates from the Free Cash Flow Model (2a) 130 International Journal of Business, Accounting, and Finance , Volum e 8, N um ber 2, Fall 2014 T able 4 R esults of the estim ation of returns m odels that test w hether Free C ash Flow is a better m easure of perform ance than O perating C ash Flow and N
et Incom e Panel A : C apital Intensive Firm s Panel B : N on-C apital Intensive Firm s *S ignificant at the 10% level. ** S ignificant at the 5% level. *** S ignificant at the 1% level. M O
D EL a IN T b FC F b O C F b N I b Pseudo- R 2 (2a) 0.1607*** -0.00001 7.5%
(2b) 0.1607*** -0.00001*** 7.5% (2c) 0.1608*** 0.00002*** 7.5% N 49,279 49,279 49,279
M O D EL a IN T b FC F b O C F b N I b Pseudo- R 2 (2a) -0.0048
0.00008 7.7% (2b) -0.005 -0.00021 7.7% (2c) -0.0038 0.00016 7.7% N
15,287 15,287 15,287 aM odels: (2a) R t = a 0 + a 1 FCF t + e t , (2b) R t = a 0 + a 1 O CF
t + e t , (2c) R t = a 0 + a 1 N It + et , W here, R= Raw annual percentage returns FC F= Free C ash Flow O CF=O perating Cash Flow
N I N t I N otes: The sam ple consists of all 2000-2012 observations that have C om pustat data needed to calculate the Fixed A sset R atio, Free C ash Flow , O perating C ash Flow , N et Incom e, M arket value of C om m on Equity, and R
eturns. O bservations for w hich the test variable falls in the top and bottom percentile of the test-variable distribution are elim inated from the sam ple. A ll variables except R are deflated by m arket value of com m on equity at the previous fiscal year-end. International Journal of Business, Accounting, and Finance , Volum e 8, N um ber 2, Fall 2014 131 T able 5 R esults of the estim
ation of price m odels that test w hether Free C ash Flow is a better m easure of perform ance than O perating C ash Flow and N et Incom e Panel A : C apital Intensive Firm s Panel B : N on-C apital Intensive Firm s
*S ignificant at the 10% level. ** S ignificant at the 5% level. *** S ignificant at the 1% level. M O D EL a IN T b FC F b O C F
b N I b Pseudo- R 2 (3a) 0.1607*** -0.0000007 7.5% (3b) 0.1607*** -0.0000005*** 7.5%
(3c) 0.1608*** 0.00013 7.5% N 49,279 49,279 49,279 M O D EL a IN T b FC
F b O C F b N I b Pseudo- R 2 (3a) -0.0051 1E-06*** 7.7% (3b) -0.0052 2E-06
7.7% (3c) -0.005 1E-06 7.7% N 15,287 15,287 15,287 aM odels: (3a) P
t = a 0 + a 1 FCF t + e t , (3b) P t = a 0 + a 1 O CF t + e t , (3c) P t = a 0 + a 1 N It + et , W
here, P= M arket value of com m on equity at fiscal year end, Price (PRC C ) FC F= Free C ash Flow O CF=O perating Cash Flow N I N t I N otes: The sam
ple consists of all 2000-2012 observations that have C om pustat data needed to calculate the Fixed A sset R atio, Free C ash Flow , O perating C ash Flow , N et Incom e, M arket value of C om m on Equity, and R eturns. O bservations for w hich the test variable falls in the top and bottom percentile of the test-variable distribution are elim inated from the sam ple. A
ll variables except R are deflated by m arket value of com m on equity at the previous fiscal year-end. 132 International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 are equal to the Operating Cash Flow Model (2b), or equal to the Net Income Model (2c). Vectors of estimated coefficients and the variance-covariance matrices are used to form the test statistic. A necessary condition for this application of the Wald test is that the regression equations being compared must have the same size coefficient vectors, and the same size variance-covariance matrices. The Wald statistic used in this study is based on a comparison of model (2a) to model (2b), and a comparison of model (2a) to model (2c) for capital intensive and non-capital intensive firms. The statistics were also used to compare model (3a) to model (3b), and a comparison of model (3a) to model (3c) for capital intensive and non-capital intensive firms. For testing the null hypothesis, the Wald statistic (Liao, 2004) is W= (β̂g - β̂g*)΄ [var (β̂g) + var (β̂g*)]ˉ (β̂g - β̂g*) ,
Where β is the coefficient vector containing all parameter estimates for the regression equation, var (·) is the estimated variance-covariance matrix for the coefficients, the operator on the first term (·)΄ is the transpose, and the operator on the middle term [·]ˉ is the generalized inverse. The probability of this equality approaches one asymptotically. The degrees of freedom for the test equals the number of rows in the first or the third matrix. The Wald statistic is chi-square (χ2) distributed for large samples. The Wald statistics are reported in Table 6. Panel A reports the Wald statistics for capital intensive firms, and Panel B reports the Wald statistics for non-capital intensive firms. None of the statistics in Panel A are statistically significant at conventional levels, suggesting that there is no relative difference between the ability of Free Cash Flow and Operating Cash Flow, or between Free Cash Flow and Net Income to predict performance as reflected in stock returns/prices, for capital intensive firms. Also, none of the statistics in Panel B are statistically significant at conventional levels, suggesting that there is no relative difference between the ability of Free Cash Flow and Operating Cash Flow, or between Free Cash Flow and Net Income to predict performance as reflected in stock returns/prices, for non-capital intensive firms. Table 6 Summary Statistics for the Wald Test: A test of the equality of coefficients across regression equations
Panel A: CAPITAL INTENSIVE FIRMS Model 2A vs 2B Model 2A vs 2C Model 3A vs 3B Model 3A vs 3C Wald Statistic 0.8731 0.3461 0.0602 0.2621 (>0.100) (>0.100) (>0.100) (>0.100) Panel B: NON-CAPITAL INTENSIVE FIRMS Model 2A vs 2B Model 2A vs 2C Model 3A vs 3B Model 3A vs 3C Wald Statistic 0.0003 11.234 2.324 0.8516 (>0.100) (>0.100) (>0.100) (>0.100)
P-values are in parentheses. International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 133 LIMITATIONS OF THIS STUDY Because this study only focuses on capital intensive firms and non-capital intensive firms, it does not capture industry differences and the effects on capital intensity. As a result, the results may not be applicable to specific industries because of differences in levels of capital intensity. RECOMMENDATIONS FOR FUTURE RESEARCH Future research in this area should focus on obtaining a better understanding of industry differences and the effects on capital intensity. Additional research is needed on the effect of capital intensity on Free Cash Flow, and the role that capital intensity plays in the predictability of Free Cash Flow. More precise and convincing results might be obtainable with industry groups formed based on levels of capital intensity.
CONCLUSIONS Overall, the results presented in this paper are mixed. Simple t- tests demonstrate that mean Free Cash Flow is statistically different from mean Operating Cash Flow, and mean Net Income for both capital intensive and non-capital intensive firms. Evidence from the descriptive statistics, the scatter plots and the correlation coefficients indicate that capital intensive firms have lower levels of Free Cash Flow, and non-capital intensive firms have higher levels of Free Cash Flow. Our results also indicate some support for Operating Cash Flow and Net Income as better measures of performance for non-capital intensive firms. Also, there is some indication that Free Cash Flow is a better measure of performance for capital intensive firms. This study makes three important contributions. First, Free Cash Flow, considered by some as a refinement of cash flow and a more contemporary measure is used. Second, the predictability of Free Cash Flow is compared to the predictability of operating cash flows and earnings. Third, this study has extended the research on industry comparisons by using industry- specific analyses to examine the predictability of Free Cash Flow in capital intensive versus non- capital intensive industries. REFERENCES
Adhikari, A., & Duru, A. (December 2006). Voluntary Disclosure of Free Cash Flow Information, Accounting Horizons, 20(4), 311-332. Barth, M., Cram, D., & Nelson, K. (January 2001). Accruals and the Prediction of Future Cash Flows. The Accounting Review:76 (1), 27-58. Biddle, G., Seow, G., & Siegel, A. (Summer 1995). Relative versus Incremental Information Content. Contemporary Accounting Research, 12(1), 1-23. Bilicic, G. W., & Connor, I. C. (October 2004). The Dividend Yield Trap, Higher Payouts aren’t enough over the long term, Public Utilities Fortnightly. 134 International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 Blumberg, B., Cooper, D., & Schindler, P. (2011). Business Research Methods. McGraw-Hill Education – Europe. Bowen, R., Burgstahler, D., & Daley, L. (1986). Evidence on the relationships between earnings and various measures of cash flow, The Accounting Review, 61, 713-25.
Burgstahler, D, Jiambalvo, J. & Pyo, Y. (1998). The informativeness of cash flows for future cash flows. Working paper, University of Washington. Burnham, K. P., & Anderson, D. R.. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretical Approach. 2d ed. New York: Springer-Verlag. Cox, D. R., & Snell, E. J. (1981). Applied statistics: principles and examples. London; New York: Chapman and Hall. Dhaliwal, D., Subramanyam, K. R., & Trezevant, R. (1999). Is comprehensive income superior to net income as a measure of firm performance? Journal of Accounting & Economics, 26 43-67. Dechow, P. (1994). Accounting earnings and cash flows as measures of firm performance: The role of accounting accruals, Journal of Accounting and Economics, 18, 3-43. Fama, E., & French, K. (1997). Industry costs of equity, Journal of Financial Economics, 43, 153-193.
Financial Accounting Standards Board. (FASB). (1985). Statement of Financial Accounting Concepts No. 1, Elements of Financial Statements. Norwalk, CT: FASB. Finger, C.. (1994). The ability of earnings to predict future earnings and cash flow, Journal of Accounting Research, 32, 210-223. Goktas, A., & O. Isci. (2011). A Comparison of the Most Commonly Used Measures of Association for Doubly Ordered Square Contingency Tables via Simulation, Metodoloski zvezki, 8 (1), 17-37. Huber, P. (1967). The behavior of maximum likelihood estimates under non-standard Conditions. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1, 221-33. Harville, D.A. (1988). Mixed-Model Methodology: Theoretical Justifications and Future Directions. Proceedings of the Statistical Computing Section, American Statistical Association, New Orleans, 41–49. International Accounting Standards Board, (IASB). (1977). Statement of Cash Flows.
International Accounting Standards No. 7. London, United Kingdom. International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 135 InvestingAnswers. Capital Intensive. Retrieved August 13, 2014 from http://www.investinganswers.com/financial- dictionary/economics/capital-intensive-350 Liao, T. (2004). Comparing Social Groups: Wald Statistics for Testing Equality Among Multiple Logit Models, International Journal of Comparative Sociology, 3-16. Kang, S., & Zhao, Y. (2010). Information Content and Value Relevance of Depreciation: A Cross-Industry Analysis. The Accounting Review, 85, 1, 227- 260. Kim, M., & Kross, W. (2005). The ability of earnings to predict future operating cash flows has been increasing – not decreasing, Journal of Accounting Research, 43, 5, 753-780. Kothari, S. P., & Zimmerman , J. L. (1995). Price and return models. Journal of Accounting and Economics 20, 155-192.
Livnat, J., & Zarowin, P. (1990). The incremental information content of cash-flow components. Journal of Accounting and Economics, 13, 25-46. Nunez, K. (March 2013), Free Cash Flow and Performance Predictability in Electric Utilities, Journal of Business and Policy Research, 19-38. Searle, S.R. (1988). Mixed Models and Unbalanced Data: Wherefrom, Whereat, and Whereto?’, Communications in Statistics—Theory and Methods, 17, 935– 968. Searle, S.R., Casella, G., & McCulloch, C.E. (1992). Variance Components, New York: John Wiley & Sons. Tole, T., McCord, S., & Pugh, W. (1992). How Cash Flow Pays Dividends, Public Utilities Fortnightly; Oct 15, 130-138. Value Line. (March 2011),.Free Cash Flow, http://www.valueline.com/Tools/Educational_Articles/Stocks_D etail.aspx?id=9959 White, H. (1980). A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity’, Econometrica, 48,4, 817-838.
About the Author: Karen Nunez is an assistant professor of accounting at Elon University in Elon, North Carolina. Dr. Nunez received her Ph.D. in Accounting and Economics from The University of Oklahoma. She also has an M.B.A. in Finance and General Management from The University of Michigan, and a B.S. in Accounting and Economics from Fairleigh Dickinson University. She is a Certified Management Accountant (CMA), and has both professional and academic expertise. Copyright of International Journal of Business, Accounting, & Finance is the property of International Academy of Business & Public Administration Disciplines (IABPAD), LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.

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  • 1. Which Free Cash Flow Is Value Relevant? An Empirical Investigation Mostafa M. Maksy Kutztown University of Pennsylvania Gary T. Chen University of Illinois at Chicago This study attempts to identify which definition of free cash flow (FCF) is the most value relevant. The results would help retail investors make better decisions, and may encourage accounting standards setters to require companies to use a specific definition of FCF to enhance comparability. Using a sample of 115,940 observations covering the period 1988 to 2010, the study empirically shows that the FCF that has the most significant association with stock price changes is the one defined as cash flow from operations less net cash outflow for investing activities less cash outflow for preferred stock dividends. INTRODUCTION While the finance literature may have a somewhat generally
  • 2. accepted definition of free cash flow (FCF), as the literature review below indicates, the accounting literature has a wide variety of definitions of FCF. The objective of this study is to empirically identify which accounting definition of FCF has the highest information content, or the most value relevant. This study aims to provide two contributions to the literature. First, it attempts to identify a specific definition of FCF that is most relevant to accounting information users in terms of predicting future changes in stock prices as this would help retail investors make better decisions. The study focuses the attention on retail investors as opposed to other users of financial statements, such as institutional investors or bank lenders, because retail investors, on average, are less sophisticated users of financial statements and may be more easily confused by the different definitions of FCF used by various companies. Prior research finds that, as of 2005, 57 million U.S. households owned stock and that retail investors owned 26% of all equities (Harris 2010). Since the major objective of financial reporting is to provide information that is useful for decision-making, the first contribution of this study is to enhance the objective of accounting. Second, the results of this study may have major implications for financial accounting standard setters, such as the Financial Accounting Standards Board (FASB) and the International Accounting Standards Board (IASB). While the FASB, in Statement of Financial Accounting Standard (SFAS) No. 95, and the IASB, in International Accounting Standard (IAS) No. 7, require companies to report Cash Flow from Operations (CFO) on the Statement of Cash Flows (SCF), they have so far discouraged companies from reporting CFO per share. The FASB and the IASB are concerned that requiring, or even encouraging,
  • 3. companies to report CFO per share may be construed by some that they are moving away from accrual- basis accounting toward cash-basis accounting. Thus, they require companies to report Earnings Per Share (EPS), which is based on accrual Journal of Accounting and Finance vol. 14(6) 2014 189 accounting, on the face of the Income Statement (I/S) but discourage companies from reporting CFO per share on the face of the SCF or anywhere else in the annual report. The results of this study might encourage accounting standard setters (e.g., the FASB and IASB) to require companies to report a specific definition of FCF (but not FCF per share) in the body of the SCF or in the supplementary disclosures at the bottom of the SCF. SFAS No. 95 already requires companies to disclose cash paid for income taxes and for interest expense at the bottom of the SCF. Perhaps, the FASB would require companies to disclose FCF together with these two items. This requirement would prohibit companies from voluntarily disclosing FCF of whatever definition they prefer. Adhikari and Duru (2006) report that companies that voluntarily disclose FCF information use a wide variety of definitions of FCF (apparently, each company is using the definition that shows the highest amount of FCF) and these companies, on average, are less profitable and more leveraged than other firms in their own industries. Having companies report FCF that is calculated in the same way would enhance comparability of accounting information across firms. Comparability is one of the enhancing
  • 4. qualitative characteristics of useful financial information as stated in FASB’s SFAC No. 8. The remaining sections of the paper cover the literature review, the proposed model, sample, statistical results, conclusions, and limitations of the study, respectively. The final section provides some suggestions for further research. LITERATURE REVIEW In the finance literature, there is no wide variation of FCF definitions. Jensen (1986) is regarded as the seminal paper that laid out the basic definition of FCF. Jensen (1986) hypothesizes that FCF increases agency costs because the managers of companies with high FCF spend it on acquiring negative net present value (NPV) projects for the purpose of satisfying their ego (being managers of large-size companies) and possibly for increasing their own compensation. He proves his hypothesis by showing that, after acquisition, the return on investment of acquirers is lower than before the acquisition. In light of that, he defines FCF as “cash flow in excess of that required to fund all projects that have positive net present value when discounted at the relevant cost of capital.” He argues that managers should not acquire negative NPV projects and should instead distribute the FCF as dividends to the stockholders. If managers want to acquire new companies they should do so using borrowed capital rather the FCF. In this way, creditors would discipline managers (because they have the power to force the company into bankruptcy) and pressure them not to invest in negative NPV projects. The majority of papers in the finance literature tend to agree with Jensen’s hypothesis (see,
  • 5. e.g., Mann and Sicherman (1991), Opler and Titman (1993), Dhumale (1998), Carroll and Griffith (2001), and Freund, Prezas, and Vasudevan (2003)). The problem with Jensen’s definition of FCF is that it is not publicly available and, thus, unobservable. Companies do not disclose the actual set of positive NPV projects that they have at any point in time or even for a given year. Thus, Lang, Stulz, and Walking (1991) used a measure of Tobin’s q (the ratio of market to book value of equity) to proxy for this. The assumption is that if average q is less than 1, the marginal investment opportunity is negative. Lang et al. (1991) note that the FCF hypothesis implies that the acquirer’s return should be negatively related to FCF in low q firms, and unrelated to FCF in high q firms. They find that high q bidders have significantly higher mean returns than low q bidders, and higher median returns. As predicted by the FCF hypothesis, their low q, high FCF firms are the worst performers of any of their sample sub-sets. One notable exception to Jensen’s FCF hypothesis is Gregory (2005) who used a dataset of UK take-overs and proxies for FCF similar to those used by Lang et al. (1991). Gregory (2005) reported that, contrary to Jensen’s FCF hypothesis, there is evidence that acquirers with high FCF perform better than acquirers with low FCF. Unlike the finance literature, the accounting literature has many definitions of FCF. FCF is defined differently from academic article to academic article, textbook to textbook, professional article to professional article, from company to company (and some companies change their definition of FCF from time to time), and from all these to the popular press. For example, Mandalay Resort (formerly known as
  • 6. Circus Circus) was one of the first companies to report FCF information in its 1988 annual report. Over the years, it has changed its FCF definition. In 1988, it defined it as Operating Income (OI), but in 2000, it 190 Journal of Accounting and Finance vol. 14(6) 2014 added back pre-opening expenses, abandonment loss, depreciation and amortization (D&A), interest, dividend, and other income, as well as proceeds from disposal of equipment and other assets. Prior to 1999, Coca-Cola defined FCF as CFO less Cash Flow for Investing activities (CFI). In 1999, it changed the definition to CFO less “business investment.” An analysis of its 1999’s SCF indicates that by “business investment” Coca-Cola meant “acquisitions and investments.” That change in definition increased its FCF in 1999 by almost $2 billion. Mills, Bible, and Mason (2002) report the following different definitions of FCF by popular magazines and investment advisory service organizations: Money Magazine: OI – Capital Expenditures (CE) – Changes in Working Capital (W/C). Forbes Magazine: Net Income (NI) + D&A + or – W/C adjustments – maintenance CE. Harry Domasb’s Winning Investing: CFO – Cash paid for Property, Plant & Equipment (PPE) – Dividends. The Motley Fool: NI + D&A – changes in W/C + or – cash outlay for taxes.
  • 7. Value Line: NI + Depreciation – Dividends – CE – required debt repayments – any other scheduled cash outlays. InvestorLinks: NI + D&A – CE – Dividends. Advisors Inner Circle Fund: NI + D&A – CE. Subramanyam & Wild (2009, p. 417) define FCF as CFO less Capital Expenditures required to Maintain Productive Capacity (CEMPC) less Total Dividends (TD). In the same edition, they mention another definition: FCF = Net Operating Profits After Tax (NOPAT) – Increase in Net Operating Assets (NOA). Kieso, Weygandt, and Warfield (2013, p. 234) defines FCF as CFO – CE – TD. Searches for “free cash flow definition”, on Google search engine, produced about 3.46 million entries for this title, the first of which is “Definitions of Free Cash Flow on the Web”. Table 1 presents the 15 definitions under this title, together with the web address associated with each definition. It is interesting to note that every one of the 15 definitions is different from the others. Adhikari and Duru (2006) report that of the 548 firms of their sample that voluntarily reported FCF information, 283 (51.6%) defined FCF as CFO – CE, 117 (21.4%) defined FCF as CFO – CE – Dividends, and 64 (11.7%) defined FCF as CFO – CFI. The remaining 84 firms (15.3%) defined FCF in four different other ways. Penman and Yehuda (2009), using a definition of FCF as CFO less cash investments, find that a dollar more of FCF is, on average, associated with
  • 8. approximately a dollar less in the market value of the business. They also find that this definition of FCF has no association with changes in the market value of the equity. Furthermore, controlling for the cash investment component of FCF, they find that CFO also reduces the market value of the business dollar-for-dollar and is unrelated to the changes in market value of the equity. GuruFocus.com, a website that tracks market insights and news of investment gurus, published two research studies (Gurufocus 2013a and 2013b) concluding that earnings and book values are significantly correlated with stock prices but FCF, defined as CFO – CE and acquisitions, is not. On the other hand, Habib (2011) show that firms with greater growth opportunities and free cash flow, defined as the difference between CFO and CE, will have a higher value price and, additionally, FCF is positively related to stock return. Similarly, Shahmoradi (2013), using the same definition of FCF and a sample of listed companies in Tehran Stock Exchange between 2002 and 2011, reports a relationship (significant at the .05 level) between FCF and stock return of firms. The above review of the literature, especially the accounting literature, indicates that FCF is defined in many different ways. The objective of this study is to determine which one of these definitions, if any, is most correlated with (and, thus, is hypothesized to be the best predictor of) stock price changes. The following section describes the proposed model to be used to answer the research question of this study. PROPOSED MODEL
  • 9. The authors argue that FCF should be defined not only as the cash flow that is cost free (i.e., that is generated internally from operating activities) but also “the cash flow that management is free to do Journal of Accounting and Finance vol. 14(6) 2014 191 whatever it wants with it as long as management actions may not lead to the firm getting out of business”. Actions that may lead to the firm getting out of business include (a) not maintaining existing operating capacity (i.e. not replacing worn out PPE) and (b) not paying the annual installment of mandatorily redeemable preferred stock or the annual dividend on preferred stock. Not maintaining the existing operating capacity will lead to the gradual liquidation of the firm until it eventually gets out of business. Not paying the annual installment of mandatorily redeemable preferred stock or the annual dividend on preferred stock will not lead to gradual liquidation of the firm but may lead to future difficulties in obtaining financing through the equity markets. Creditors and investors may deal with the company only if they are paid exuberantly high returns (which would be prohibitively high cost for the firm) or may stop dealing with the firm altogether if they determine that their downside risk is becoming too great compared to their upside reward. It can also be argued that not paying the debt that becomes currently due may lead the firm to bankruptcy because risk-averse creditors may force the firm to liquidate in order to recuperate their costs. However, most firms have lines of credit or
  • 10. refinancing programs so the debt that becomes currently due is paid out from new borrowing that occurs in the current period. Thus, there is no need to pay the debt that becomes currently due this period out of internally generated cash flow from operating activities in the current period. The annual installment due and preferred stock dividend on mandatorily redeemable preferred stock are not available in the Compustat database. They can only be obtained from a review of the notes to the financial statements. Considering the large size of the study sample (about 115,940 observations) that would be cost and time prohibitive. In addition, many companies do not have mandatorily redeemable preferred stock and many of those that do usually do not disclose the information in the footnotes based on the GAAP loophole that management believes the information is not material. To substitute for that information the authors decided to subtract preferred stock dividends (PSD) from CFO in the determination of FCF. While regular preferred stock are not exactly similar to mandatory redeemable preferred stock (since dividend declaration and payment on regular preferred stock is discretionary), the nonpayment of PSD may give the same signal to creditors and investors as the nonpayment of mandatorily redeemable preferred stock dividends. Furthermore, the subtraction of total PSD from CFO in the determination of FCF may compensate to some degree for the non-subtraction of debt that becomes currently due this period. In light of the above discussion, the authors hypothesize that FCF should be defined as follows: FCF = CFO – CEMPC – PSD
  • 11. Where: FCF = Free Cash Flow CFO = Cash Flow from Operating activities CEMPC = Capital Expenditure required to Maintain Productive Capacity PSD = Preferred Stock Dividends The authors decided to estimate CEMPC as the inflation- adjusted depreciation and amortization expense (D&A) for the current year. However, because of the large size of the sample and the variety of industries included there in, there is no inflation index that can be used to adjust D&A for all the companies in the sample. The authors tried to use the general consumer price index (CPI) for this purpose but found out that the mean inflation-adjusted D&A for the sample is actually greater than the mean for total CE for the current year. That indicates that the general CPI is not appropriate because its use would mean that, on average, the companies in the sample not only are not expanding, but they are not even maintaining their existing productive capacity. Consequently, the authors decided to use the current year unadjusted D&A as a proxy for CEMPC. However, since the objective of this empirical study is to determine which FCF is a better predictor of stock prices, the study model will include other definitions of FCF besides the definition hypothesized here. Since there are so many definitions of FCF as illustrated in the literature review, the authors decided to include in the statistical analyses only those definitions that are most common. The following nine definitions will be included:
  • 12. 192 Journal of Accounting and Finance vol. 14(6) 2014 FCF1 = CFO - CEMPC FCF2 = CFO - CE FCF3 = CFO - CFI FCF4 = CFO - CEMPC - PSD FCF5 = CFO - CE - PSD FCF6 = CFO - CFI – PSD FCF7 = CFO – CEMPC - TD FCF8 = CFO – CE – TD FCF9 = CFO – CFI - TD Where: TD = Total Dividends paid on common and preferred stock. It should be noted that FCF4 is our hypothesized definition, and FCF8 is Standard & Poors’ definition and is reported directly in its COMPUSTAT database. Since the change in the stock price per share (∆SPPS) may be affected by changes in sales per share (∆SPS), earnings per share (∆EPS), dividend per share (∆DPS), and book value per share (∆BVPS), the proposed model includes all these variables so they can be controlled for to show the effect of change in FCF per share (∆FCFPS) on ∆SPPS. Also, to control for the size of the firm, the natural logarithm of total sales (lnTS) and natural logarithm of total assets (lnTA) will be included in the model as well. Because stock price changes may vary from industry to industry, the authors include in the model dummy
  • 13. variables to control for the industry fixed effects. The authors use Fama-French industry classifications. The authors also control for year-end fixed effects. Thus, the proposed model is as follows: ΔSPPS = B0 + B1ΔSPS + B2ΔEPS + B3ΔDPS + B4ΔBVPS + B5ΔFCFPS1-9 + B6lnTS + B7lnTA + B 8 IND1-44 + € (1) The definitions of the model variables are provided in Appendix A. ΔFCFPS = FCFPSt – FCFPS t – 1 where FCFPS1t = FCF1/weighted average number of common shares outstanding during year t. This weighted average number of common shares will be computed by dividing NI by EPS for year t. The same rule applies for FCFPS2 through FCFPS9. THE STUDY SAMPLE The study sample includes all companies listed in COMPUSTAT for the 23-year period 1988 to 2010. After eliminating all firm year observations that have missing variables, the final sample is composed of 115,940 observations. The study period starts from 1988 because SFAS 95, which requires companies to disclose CFO, was issued in 1987. Because the model uses the changes from year to year, observations from the year 1988 will represent the changes from 1987 to 1988 data. The study period ends in 2010 because this is the last year with available data on COMPUSTAT at the time of collection. The year 2008 was a very abnormal year as total market indexes
  • 14. took a big dive because of the world’s financial crisis that started during that year. In that year, the Dow Jones Industrial average lost 31 percent of its value (but at one point, in November of that year, it was down 39 percent). The NASDAQ index lost 39 percent (but in November 2008 it was down 46 percent). Similarly, the S&P 500 Cash Index lost 36 percent (but in November 2008 it was down 43 percent). Because of that abnormality, the authors thought that the change in stock prices during 1988 was affected by psychological factors much more so than by financial factors. As a result, the authors ran the model using a sample of observations ending in 2007. The results were not significantly different from the results based on the study sample ending in 2010. Journal of Accounting and Finance vol. 14(6) 2014 193 STATISTICAL RESULTS Table 2 presents Pearson correlation coefficients for all the study and control variables. As the table indicates, all FCF definitions, except for FCF2, FCF5 and FCF8, have positive associations with changes in stock price (Δspps) at the 5% significance level. Among the control variables, Δspps is positively associated with changes in total sales per share (Δsps), changes
  • 15. in earnings per share (Δeps), changes in book value per share (Δbvps), natural logarithm of total sales (lnsale), and natural logarithm of total assets (lnat) and these associations are statistically significant at the 5% level. Furthermore, Δsps, Δeps, and Δdps are statistically significantly associated with all definitions of FCF whereas lnsale and lnat are statistically significant with some of the FCF specifications suggesting that these variables would be appropriate controls. The correlations presented in Table 2 already present some interesting results which the authors validate in a multi-variate framework shown in the next table. Table 3 presents regression coefficients for nine models by including one FCF definition at a time in the model. Along with the control variables specified in Model (1), the authors also include year and industry fixed effects. Industry categories are based on the Fama-French (1997) 48-industry classification scheme. These fixed effects control for heterogeneity at the industry and year level that may not be captured by our set of controls (such as the high tech industry boom of the 1990s or the recent financial crisis of 2008). As the table shows, all FCF definitions, except for FCF2, FCF5 and FCF8, have positive associations with changes in stock price (Δspps) at the 1% significance level after controlling for other determinants of changes in stock price. Among the control variables, Δsps is negatively associated with changes in stock price and is statistically significant at the 1% level across all specifications of FCF. Δeps and Δbvps are both positively associated with Δspps and statistically significant at the 10% level or better in all models.
  • 16. Overall, Table 3 confirms the results of the univariate correlations in Table 2. It is interesting to note that FCF8, which is Standard & Poor’s definition of free cash flow, does not have any significant association with changes in stock prices. All three definitions of FCF that do not have any significant associations with changes in stock prices have one thing in common: they all include capital expenditures (CE) as a deduction from CFO. That is the case whether CE alone is deducted (FCF2), CE and preferred stock dividends (PSD) are deducted (FCF5), or CE and total dividends (TD) are deducted (FCF8). Apparently, PSD and TD have very negligible effect, if any, on stock price changes. This is also borne out by the fact that when CEMPC (capital expenditure required to maintain productive capacity) or CFI (cash flow from investing activities) are deducted from CFO (FCF1 and FCF3 respectively) there are significant associations with stock price changes. This is the case whether PSD is also deducted (FCF4 and FCF6) or TD is also deducted (FCF7 and FCF9). Of the six FCF definitions that have significant associations with stock price changes, the three that have CFI as a deduction from CFO (FCF3, FCF6 and FCF9) have the most significant associations. Of those latter three, FCF6 (CFO – CFI –PSD) has a little bit more significant association with stock price changes than the other two. CONCLUSIONS In light of the statistical results above, the authors conclude that FCF6 is the most value- relevant definition of free cash flow. While the authors’ hypothesized definition of free cash flow (FCF4) was significantly associated with stock price changes, it was not the
  • 17. one that had the most association. This could be due to the possibility that the un-inflation-adjusted depreciation and amortization expense does not really approximate capital expenditures required to maintain productive capacity. Another reason could be that the stock market participants do not make an effort to determine capital expenditures required to maintain productive capacity when they are making their investment decisions. In any event, the authors recommend that the standards setters, particularly the FASB and IASB, should require companies to disclose that FCF in the body of the SCF or at its bottom together with the cash outflow for income taxes and interest expense. Short of that, the standard setters should at least require companies that voluntarily disclose FCF to use only the FCF definition identified by this study. Furthermore, if a 194 Journal of Accounting and Finance vol. 14(6) 2014 company departs from this definition, the independent auditor should consider this departure as a violation of GAAP. LIMITATIONS AND SUGGESTIONS FOR FURTHER RESEARCH The study is subject to some limitations. The most important limitation is the possibility that the study model did not include other variables that may have influenced stock price changes and is correlated with our definitions of free cash flow. The combined effect of those other variables is represented by the error
  • 18. term ∑ in the model. Adding year and industry fixed effects help mitigate some concerns but not all regarding unobservable explanatory variables. Another limitation is that there may be other formulas for free cash flow which may be more value-relevant than the ones included in this study. While the authors tried to develop as comprehensive a list as possible, other definitions of free cash flow may possibly exist. One suggestion for further research is to replicate the study using other variables that could possibly have more effect on stock prices than the variables included in the study model. Another suggestion would be to investigate whether a trading strategy could be developed for buying (shorting) stock of firms which have the greatest positive (negative) change in one or more measures of FCF over the prior year. REFERENCES Adhikari, A. and A. Duru, 2006. Voluntary Disclosure of Free Cash Flow Information. Accounting Horizons 20 (4), December, pp. 311–332. Carroll, C and J. M. Griffith, 2001. Free Cash Flow, Leverage, and Investment Opportunities. Quarterly Journal of Business and Economics 40 (3 & 4), pp.141-153. Dhumale, R. 1998. Earnings Retention as a Specification Mechanism in Logistic Bankruptcy Models: A Test of the Free Cash Flow Theory. Journal of Business Finance & Accounting 25(7 & 8), September/October, pp. 1005-1023. Fama, F. and K. R. French, 1997. Industry Costs of Equity. Journal of Financial Economics 43, pp. 153-
  • 19. 193. Financial Accounting Standards Board. SFAC No.8 Conceptual Framework for Financial Reporting, Chapter 1, The objective of General Purpose Financial Reporting, and Chapter 3, Qualitative Characteristics of Useful Financial Information. FASB (September 2010). Financial Accounting Standards Board. SFAS No. 95: Statement of Cash Flows. FASB (November 1987). Freund, S., A.P. Prezas, and G. K. Vasudevan, 2003. Operating Performance and Free Cash Flow of Asset Buyers. Financial Management (winter), pp. 87-106. Gregory, A., 2005. The Long Run Abnormal Performance of UK Acquirers and the Free Cash Flow Hypothesis. Journal of Business Finance & Accounting 32 (5 & 6), June/July, pp. 777-814. GuruFocus.com. 2013a. Earnings, Free Cash Flow, and Book Value? Which Parameters are Stock Prices More Correlated To? http://www.gurufocus.com/news/225255/earnings-free-cash- flow-book- value-which-parameters-are-stock-prices-most-correlated-to-. August 2, 2013. GuruFocus.com. 2013b. Is Free Cash Flow Overrated for its Importance in Stock Valuations? http://www.gurufocus.com/news/225642/is-free-cash-flow- overrated-for-its-importance-in-stock- valuation. August 8, 2013. Habib, A., 2011. Growth Opportunities, Earnings Permanence
  • 20. and the Valuation of Free Cash Flow, Australasian Accounting Business & Finance Journal 5 (4), pp. 101-122. Harris, L., 2010. Missing in Activism: Retail Investor Absence in Corporate Elections. Columbia Business Law Review 1, pp. 104-204. International Accounting Standards Board. IAS No. 7: Statement of Cash Flows. IASB (September 2007). Jensen, M.C., 1986. Agency Costs of Free Cash Flow, Corporate Finance, and Takeovers. American Economic Review 76 (2), pp. 323-29. Kieso, D., J. Weygandt, and T. Warfield, 2013. Intermediate Accounting, 15th Ed., New York, NY: John Wiley & Sons. Journal of Accounting and Finance vol. 14(6) 2014 195 Lang, L. H. P., R. M. Stulz, and R.A. Walking, 1991. A Test of the Free Cash Flow Hypothesis: The Case of Bidder Returns. Journal of Financial Economics 29, pp. 315– 35. Mann, S. V., and N. W. Sicherman, 1991. The Agency Costs of Free Cash Flow: Acquisition Activity and Equity Issues. The Journal of Business 64 (2), pp. 213-227. Mills, J., L. Bible, and R. Mason, 2002. Rough Waters for Comparability: Defining Free Cash Flow. The CPA Journal (January), pp. 37–41.
  • 21. Opler, T.C. and S. Titman, 1993. The Determinants of Leveraged Buyout Activity: Free Cash Flows vs. Financial Distress Costs. Journal of Finance 48 (1), December, pp. 1985-99. Penman, S. and N. Yehuda, 2009. The Pricing of Earnings and Cash Flows and an Affirmation of Accrual Accounting." Review of Accounting Studies 14 (4), pp. 453- 479. Shahmoradi, N. , 2013. The Effect of Growth Opportunities and Stable Profitability on Market Value of Free Cash Flows of Listed Companies in Tehran Stock Exchange. Journal of Basic and Applied Scientific Research 3 (8), pp. 495-501. Subramanyam, K. R. and J. J. Wild, 2009. Financial Statement Analysis, 10th Ed., Burr Ridge, IL: McGraw-Hill/Irwin. APPENDIX A VARIABLE DEFINITIONS Δspps Change in stock price between the end of the next fiscal year and the current year. Δfcfps1 Change in the difference between cash flow from operations (CFO) and depreciation and amortization expense (DP) over the current fiscal year. Δfcfps2 Change in the difference between cash flow from
  • 22. operations (CFO) and capital expenditures (CE) over the current fiscal year. Δfcfps3 Change in the difference between cash flow from operations (CFO) and cash flow from investing activities (CFI) over the current fiscal year. Δfcfps4 Change in cash flow from operations (CFO) minus depreciation and amortization expense (DP) minus preferred stock dividends (PSD) over the current fiscal year. Δfcfps5 Change in cash flow from operations (CFO) minus capital expenditures (CE) minus preferred stock dividends (PSD) over the current fiscal year. Δfcfps6 Change in cash flow from operations (CFO) minus cash flow from investing activities (CFI) minus preferred stock dividends (PSD) over the current fiscal year. Δfcfps7 Change in cash flow from operations (CFO) minus depreciation and amortization expense (DP) minus total dividends (TD) over the current fiscal year. Δfcfps8 Change in cash flow from operations (CFO) minus capital expenditures (CE) minus total dividends (TD) over the current fiscal year. Δfcfps9 Change in cash flow from operations (CFO) minus cash flow from investing activities (CFI) minus total dividends (TD) over the current fiscal year.
  • 23. Δsps changes in total sales per share over the current fiscal year. Δeps change in earnings per share over the current fiscal year. Δdps change in dividends per share over the current fiscal year. Δbvps change in book value per share over the current fiscal year. lnsale natural logarithm of total sales over the current fiscal year. Lnat natural logarithm of total assets at the current fiscal year end. 196 Journal of Accounting and Finance vol. 14(6) 2014 TABLE 1 DEFINITIONS OF FREE CASH FLOW ON THE WEB 1. In corporate finance, free cash flow (FCF) is cash flow available for distribution among all the securities holders of an organization. They include equity holders, debt holders, preferred stock holders, convertible security holders, and so on. en.wikipedia.org/wiki/Free_cash_flow 2. Net income plus depreciation and amortization, less changes in working capital, less capital expenditure. en.wiktionary.org/wiki/free_cash_flow 3. Adjusted operating cash flow less interest and tax paid, prior to distributions to shareholders. This is the cash flow available for payments of dividends and share buybacks as well as repayments of capital on loans. www.reed- lsevier.com/investorcentre/glossary/Pages/Home.aspx
  • 24. 4. Cash flow from operating activities, investments, financial items and tax and the effect of restructuring measures on cash flow. www.investor.rezidor.com/phoenix.zhtml 5. equals EBITDA minus net interest expense, capital expenditures, change in working capital, taxes paid, and other cash items (net other expenses less proceeds from the disposal of obsolete and/or substantially depleted operating fixed assets that are no longer in operation). www.cemex.com/ic/ic_glossary.asp 6. This item on the cash flow statement represents the sum of cash flows generated by operating and investing activities. investors.benettongroup.com/phoenix.zhtml 7. How much money a company could pay shareholders out of profits without expanding, but without running down its existing operations either. moneyterms.co.uk/d/ 8. Represents a common measure of internally generated cash and is defined as cash from operations less fixed asset purchases. portal.acs.org/portal/PublicWebSite/about/aboutacs/financial/W PCP_012234 9. Cash available after financing operations and investments, available to pay down debt. www.graduates.bnpparibas.com/glossary.html 10. A stock analyst's term with a definition that varies somewhat depending on the particular analyst. It usually approximates operating cash flow minus necessary
  • 25. capital expenditures. ... www.jackadamo.com/glossary.htm 11. The amount of money that a business has at its disposal at any given time after paying out operating costs, interest payments on bank loans and bonds, salaries, research and development and other fixed costs. www.premierfoods.co.uk/investors/shareholder- services/Glossary.cfm 12. Net Operating Profit After Tax minus Year-to-Year change in Net Capital. www.intrinsicvalue.com/glossary.htm 13. The increase in cash from one period to the next. www.knowledgedynamics.com/demos/BreakevenFlash/Glossary Main.htm 14. Cash flow after operating expenses; a good indicator of profit levels. healthcarefinancials.wordpress.com/2008/01/24/equity-based- securities-terms-and-definitions- for-physicians/ 15. The surplus cash generated from operating activities recognized in the profit and loss account. This expresses a company's internal financing power, which can be used for investments, the repayment of debt, dividend payments and to meet funding requirements. www.deutsche-euroshop.de/berichte/gb2004/glossar_e.php Journal of Accounting and Finance vol. 14(6) 2014 197
  • 38. V ariables are defined in A ppendix A . N um bers in bold indicate significance at the 5% level. 198 Journal of Accounting and Finance vol. 14(6) 2014 T A B L E 3 A SSO C IA T IO N
  • 55. A djusted R 2 0.0044 0.0040 0.0228 0.0044 0.0041 0.0228 0.0044 0.0041 0.0228 T his table provides the results of regressing the change in future stock prices of a firm (Δ spps) on various m easures of changes in free cash flow (Δ fcfps1 - Δ fcfps9) and control variables. C oefficients are provided w
  • 56. ith t-statistics in parentheses below . V ariables are defined in A ppendix A . ***, **, and * represent tw o-tailed p-value significance levels of 0.01, 0.05, and 0.1 respectively. 200 Journal of Accounting and Finance vol. 14(6) 2014 Copyright of Journal of Accounting & Finance (2158-3625) is the property of North American Business Press Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. 120 International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 FREE CASH FLOW AND PERFORMANCE PREDICTABILITY: AN INDUSTRY ANALYSIS
  • 57. Karen Nunez Elon University ABSTRACT This study investigates the ability of Free Cash Flow to predict performance in capital intensive and non-capital intensive industries. This study provides empirical evidence on Free Cash Flow versus traditional performance indicators and indicates whether Free Cash Flow better summarizes firm performance as reflected in stock returns/prices. This study makes three contributions. First, Free Cash Flow, considered by some as a refinement of cash flow and a more contemporary measure is used. Second, the predictability of Free Cash Flow is compared to traditional measures of performance. Third, this study extends the research on industry comparisons by using industry-specific analyses to examine the predictability of Free Cash Flow. Results indicate that Free Cash Flow is significantly different from Operating Cash Flow and Net Income, but there are mixed results on differences in the relative explanatory power in capital intensive and non-capital intensive industries. Keywords: Cash Flow, Free Cash Flow, Capital intensity, Industry analysis
  • 58. INTRODUCTION Little of the existing research has considered Free Cash Flow for measuring firm performance. Prior studies have focused on operating cash flows. However, some analysts claim that Free Cash Flow better captures capital intensity, and is a better measure of performance in capital intensive industries (Tole, McCord, & Pugh, 1992). Financial reporting as required by SFAC No. 1, is designed to provide information to investors, creditors and others, about an enterprise’s financial performance (Financial Accounting Standards Board (FASB), 1985). While most investors focus on Earnings, Value Line (2011) suggests that other performance measures, like Free Cash Flow should be considered because Earnings can be affected by accounting methods and managerial discretion (manipulation), whereas, Free Cash Flow is harder to manipulate. However, there is very little empirical evidence on the predictability of Free Cash Flow. The objective of this study is to examine whether Free Cash Flow or more traditional financial measures better predict performance. Some analysts (Tole, McCord, & Pugh, 1992) claim that Free Cash Flow better captures capital intensity, and hence is a better measure of performance in capital intensive industries. Operating cash flows have been the focus of the prior research, therefore, the existing research
  • 59. offers little evidence on the ability of Free Cash Flow to measure performance. Free Cash Flow definitions vary widely between companies and between industries, because U.S. GAAP does not require firms to disclose Free Cash Flow, and it provides little guidance on measuring Free Cash Flow. Some guidance is provided by the International Accounting Standards Board with International Accounting Standard (IAS) 7, which recommends that Free Cash Flow should be recognized as “cash from operations less the amount of capital expenditures required to maintain the firm’s present productive capacity” (International Accounting Standards Board (IASB), 1977). International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 121 This study makes three important contributions. First, Free Cash Flow, considered by some as a refinement of cash flow and a more contemporary measure is used. Second, the predictability of Free Cash Flow is compared to the predictability of operating cash flows and earnings. Third, this study extends the research on industry comparisons by using industry- specific analyses to examine the predictability of Free Cash Flow in capital intensive versus non- capital intensive industries. BACKGROUND AND LITERATURE REVIEW
  • 60. The prior literature on cash flows focuses on operating cash flows vs. earnings to explain performance, as proxied by abnormal stock returns (Dechow, 1994; Bowen, Burgstahler & Daley, 1987; Livnat & Zarowin, 1990). While the term Free Cash Flow is widely used in the press and in the business world, U. S. GAAP does not require firms to disclose Free Cash Flow, and as a result few firms voluntarily report it. Free Cash Flow definitions are not uniform and there is little theoretical or conceptual guidance on how to calculate Free Cash Flow (Adhikari & Duru, 2006). Firms reporting Free Cash Flow either use a Cash flow from operations-based method, or an income-based method to calculate Free Cash Flow (Adhikari & Duru, 2006). Adhikari and Duru (2006) determined that income-based methods are used to calculate Free Cash Flow by only a small percentage of firms, 14.2 percent. Income-based methods typically start with earnings before interest, taxes, depreciation and amortization as a proxy for Cash flow from operating activities, and then make various adjustments. Additionally, half of the Free Cash Flow reporting firms use a Cash flow from operations-based method where Free Cash Flow is calculated one of two ways: (1) A capital maintenance perspective-Cash flow from operating activities less capital expenditures necessary to maintain the productive capacity of the firm, and (2) An all- inclusive perspective- Cash flow from operating activities less capital expenditures, plus proceeds from fixed asset sales and
  • 61. changes in long-term investments (Adhikari & Duru, 2006). Over 50 percent of the firms using a Cash flow from operations-based method rely on the capital maintenance perspective. The capital maintenance perspective is consistent with guidance provided by The International Accounting Standards Board (IAS 7). Firms operating in capital-intensive industries require significant investments in capital to start and maintain operations. Non-capital intensive industries generally depend on labor rather than capital, and are thus not considered capital intensive. The automobile, chemical, telecommunications, and refinery industries are often considered examples of capital-intensive industries. The household products industry, insurance companies and other service oriented industries generally depend on labor rather than capital, and are often considered examples of non-capital-intensive industries (Investing Answers, 2014). Capital investments are necessary to equip firms with essential tools and high tech machinery necessary for operations. In most capital-intensive industries, millions of dollars must be invested. For example, oil companies must spend millions of dollars setting up oil rigs, oil refineries and other infrastructure in order to bring in oil. Telecommunications companies must set up a network of phone lines, fiber-optic lines and other equipment in order to service customers. Because of significant investments in capital, companies in capital-intensive industries are often marked by high levels of depreciation and fixed assets on the balance sheet.
  • 62. The Electric Utility industry is another example of a capital- intensive industry. Electric Utility 122 International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 firms often undertake large-scale construction programs to update aging infrastructures, add capacity, and to comply with environmental regulations. Substantial depreciation expense usually results from the significant capital expenditures. The depreciation expense (a non-cash expense) leads to net operating cash flows that significantly exceed net income. Given the distortional effect depreciation expense has on net operating cash flows, Tole, McCord and Pugh (1992), suggest that cash flows are a better measure of performance than net income for a capital-intensive industry like the Electric Utility industry. Generally, the Electric Utility Industry reports Free Cash Flow with a capital maintenance perspective, and Free Cash Flow is defined as operating cash flow minus capital expenditures (Tole, McCord & Pugh, 1992; Bilicic & Connor, 2004). Moreover, Tole, McCord & Pugh (1992), recommend Free Cash Flow to equity investors as a better measure of performance than net income. Seminal cash flow studies focus on operating cash flows versus earnings, to explain performance as measured by abnormal stock returns (Dechow, 1994; Bowen, Burgstahler &
  • 63. Daley, 1987; Livnat & Zarowin, 1990). These studies generally demonstrate that cash flows and earnings both provide incremental information, but do not directly address the relative superiority of one measure over the other. In a more current study, Burgstahler, Jiambalvo & Pyo (1998) find that cash flow has more predictive ability than earnings, but Finger (1994) found mixed results. Further, Barth, Cram and Nelson (2001) find cash flows have more predictive ability than earnings. None of the earlier studies focus on Free Cash Flow or capital intensity. One possible explanation for the mixed results of prior research is the failure to focus on a more relevant measure like Free Cash Flow, or a failure to focus on industry-specific samples. Nunez (2013) considers Free Cash Flow and the Electric Utility Industry. Nunez (2013) found that Free Cash Flow is significantly different from Operating Cash Flow and Net Income, but could not detect significant differences in the relative explanatory power of Free Cash Flow, Operating Cash Flow and Net Income. This study attempts to build on Nunez (2013) by considering the predictability of Free Cash Flow in both capital and non-capital intensive industries. HYPOTHESES AND METHODOLOGY This study examines whether Free Cash Flow is a better measure of performance than net income and operating cash flows, for capital intensive and non- capital intensive industries. Tole, McCord & Pugh (1992) suggests that for a capital intensive
  • 64. industry like the Electric Utility Industry, cash flows are a better measure of performance than net income, and Free Cash Flow is better than operating cash flow. Therefore, the authors of this study expect Free Cash Flow to have greater performance predictability than Operating Cash Flow and Net Income for firms in capital intensive industries. Further, we expect Free Cash Flow to have less performance predictability than Operating Cash Flow and Net Income for non-capital intensive industries. The first hypothesis considers the relation between capital intensity and free cash flow. Building on Tole, McCord and Pugh (1992) the authors expect capital intensive firms to have lower levels of Free Cash Flow, and non-capital intensive firms to have higher levels of Free Cash Flow. The hypothesis stated in the null: H1: The association between Free Cash Flow and capital intensity does not differ between capital intensive firms and non-capital intensive firms. International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 123 As suggested by Tole, McCord & Pugh (1992) the authors of this study expect Free Cash Flow to be a better measure of performance than operating cash flows and net income, for capital
  • 65. intensive firms, and operating cash flows and net income to be better measures of performance than Free Cash Flow for non-capital intensive firms. The second hypothesis, stated in the null: H2: The predictability of Free Cash Flow does not differ between capital intensive firms and non-capital intensive firms. The authors used an independent measure of capital intensity, as defined in prior literature. The Fixed Asset Ratio (FAR) is plant, property and equipment divided by noncash total assets, based on Kang and Zhau (2010). Kang and Zhau (2010) defined capital intensive industries as having a mean industry fixed asset ratio of 0.5 or greater. Based on the approach used by Kang and Zhau (2010), the authors utilized two groups: Group A-capital intensive industries where the mean industry FAR is 0.5 or greater, and Group B-non-capital intensive industries where the mean industry FAR is less than 0.5. Also, we used Fama and French (1997) to guide our industry classifications using SIC/NAICS codes. To test Hypothesis 1 and examine the relationship between capital intensity and Free Cash Flow observed by scatterplots. The direction, magnitude and shape of the relationships is conveyed in the plots. The measurement of the relationship between capital intensity and Free Cash Flow is based on the following variables, Y = f(X) (1)
  • 66. Where (Compustat descriptions are in parentheses), Y= Free Cash Flow, calculated as Operating Activities Net Cash Flow minus Capital Expenditures (OANCF- CAPX), and X = Fixed Asset Ratio, calculated as plant, property and equipment divided by noncash total assets, (PPENT/(AT- CH). The estimated correlation coefficients used to measure the direction and strength of the association, and to draw more definitive inferences. Commonly used measures of association include the Pearson and Spearman correlation coefficients, Goodman and Kruskal’s gamma (γ) and Kendall’s tau (τ). Pearson’s correlation coefficient requires normally distributed variables or it will produce unreliable results, and the Spearman rank correlation requires a monotonic underlying relationship between variables (Goktas & Isci, 2011). Goodman and Kruskal’s gamma (γ), is a non-parametric measure of rank correlation that does not rely on any assumptions on the distributions of X or Y, or the distribution of (X,Y), and it does not consider tied pairs (Blumberg, Cooper, & Schindler, 2011). A tied pair occurs when observations have the same value on the X variable, on the Y variable or on both. Kendall’s tau (τ) is recognized as a refinement of gamma (γ) that considers tied pairs (Blumberg, Cooper, & Schindler, 2011). The Kendall’s tau b (τb) used to measure the direction and strength of the association between capital intensity and Free Cash Flow. Kendall’s tau b (τb) is a non-parametric measure of rank correlation that does not rely on any assumptions on the distributions of X or Y, or the
  • 67. distribution of (X,Y), it does consider tied pairs and is suitable for data tables of any size (Blumberg, Cooper, & Schindler, 2011). The following models used to test Hypothesis 2 and examine the predictability of Free Cash Flow: Rt = a0 + a1 FCFt + et, (2a) 124 International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 Rt = a0 + a1 OCFt + et, (2b) Rt = a0 + a1 NIt + et, (2c) Where (Compustat descriptions are in parentheses), R is raw annual returns; FCF is Free Cash Flow, calculated as Operating Activities Net Cash Flow minus Capital Expenditures (OANCF-CAPX), OCF is Operating Cash Flow, Operating Activities Net Cash Flow (OANCF); NI is net income after extraordinary items and discontinued operations (NI). All variables except R, are deflated by market value of common equity at the previous fiscal year-end. Models 2a, b and c are based on Dhaliwal, Subramanyam, and Trezevant (1999). Kim & Kross (2005) used a similar model to test the explanatory power of earnings and cash flows. To draw more definitive inferences, and to minimize the potential econometric and theoretical problems with returns models, the authors used price
  • 68. models (Kothari & Zimmerman, 1995). Pt = a0 + a1 FCFt + et, (3a) Pt = a0 + a1 OCFt + et, (3b) Pt = a0 + a1 NIt + et, (3c) Where, P is market value of common equity (PRCC) at fiscal year-end. All variables are deflated by the number of shares of common stock outstanding (CSHO) at fiscal year-end, adjusted for stock splits and stock dividends (AJEX). METHODOLOGY Methods Some industries had a small number of firms and time periods available for study, therefore the observations were pooled across time to increase the number of observations and the power of the regression models. Pooling the data can introduce cross-sectional and time series dependencies in the sample data, which could understate the standard errors of the regression coefficients and inflate the t-statistics. To mitigate this, Huber-White (1967) standard errors are used in the regression models for the construction of the t-statistics. The Huber-White robust standard error estimator produces correct standard errors even if
  • 69. the observations are correlated and heteroscedastic (Huber 1967; White 1980). Maximum- likelihood estimates are generally preferable to ANOVA and OLS estimates so the full maximum likelihood procedure for estimating the parameters of the regressions is used, (see Searle, 1988; Harville, 1988; Searle, Casella and McCulloch, 1992). Firm-specific and time- specific intercepts are also used in the models. Sample and Data Collection The Compustat Database was used to identify the initial sample of 131,861 observations from 2000-2012. Firms with insufficient data to calculate the Fixed Asset Ratio (FAR), Free Cash Flow, Operating Cash Flow, Net Income, and market value were deleted, resulting in 72,246 observations. Observations for which the test variable falls in the top and bottom percentile of the test-variable distribution were eliminated from the sample. The resulting International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 125 sample is composed of 64,566 observations, representing 11,036 firms and 48 industries. Table 1 provides a list of industries used in this study.
  • 70. Table 1 List of Industries Abbreviation INDUSTRY # of firms # of Observations Aero Aircraft 37 297 Agric Agriculture 41 223 Autos Autos and Trucks 126 818 Banks Banking 972 5,162 Beer Alcoholic Beverages 31 190 BldMt Construction Materials 161 1,060 Books Printing and Publishing 59 352 Boxes Shipping Containers 21 119 BusSv Business Services 1,423 7,749 Chems Chemicals 187 1,199 Chips Electronic Equipment 570 3,845 Clths Apparel 100 683 Cnstr Construction 94 548 Coal Coal 41 195 Comps Computers 394 2,289 Drugs Pharmaceutical Prod 736 4,501 ElcEq Electrical Equipment 142 1,037 Energy Petro and Nat Gas 762 3,815 FabPr Fabricated Products 28 163 Fin Trading 316 1,462 Food Food Products 134 937 Fun Entertainment 144 856 Gold Precious Metals 308 1,484 Guns Defense 15 119 Hlth Healthcare 141 915 Hshld Consumer Goods 121 749 Insur Insurance 178 803 LabEq Meas and Contrl Equip 168 1,343 Mach Machinery 268 1,837 Meals Rest, Hotel, Motel 200 1,228 MedEq Medical Equipment 337 2,127 Mines Nonmetallic Mining 442 2,111
  • 71. Misc Miscellaneous 155 662 Paper Business Supplies 89 595 PerSv Personal Services 102 629 RlEst Real Estate 122 626 Rtail Retail 358 2,434 Rubbr Rubber and Plastic Products 78 503 Ships Shipbuilding, Rail Eq 13 102 Smoke Tobacco Products 5 43 Soda Candy and Soda 24 157 Steel Steel Works Etc 126 731 Telem Telecommunications 308 1,686 Toys Recreational Products 75 415 Trans Transportation 268 1,641 Txtls Textiles 26 170 Util Utilities 273 1,933 Whlsl Wholesale 317 2,023 Total 11,036 64,566 126 International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 Descriptive Statistics Table 2 reports descriptive statistics for variables used to estimate the models, and for key firm size variables used to gain additional insight about firm characteristics. Descriptive statistics are presented for the entire sample, and to gain additional insight, the sample is further classified
  • 72. based on capital intensity. Columns 1 and 2 (all observations) of Table 2 report means and standard deviations for the total sample of 64,566 observations; columns 3 and 4 (capital intensive firms) report means and standard deviations for 15,287 observations, 24% of the total observations, representing firms that have a mean FAR of 0.5 or greater; and the last two columns (non-capital intensive firms) report means and standard deviations for 49,279 observations, 76% of the total observations, representing firms that have a mean FAR of less than 0.5. Table 2 Descriptive Statistics All Observations Capital Intensive Firms Non-Capital Intensive Firms Mean Std Dev Mean Std Dev Mean Std Dev FCF 51.77 204.66 22.52 190.64 60.84 207.99 FAR 0.30 0.29 0.75 0.14 0.16 0.14 CAPX 61.59 212.78 146.41 359.75 35.26 127.39 OCF 113.20 342.54 168.64 459.80 96.00 294.79 NI 50.79 205.07 61.37 234.15 47.51 195.06 R 0.20 1.17 0.25 1.24 0.19 1.14 ROE (0.26) 99.20 (1.83) 189.69 0.22 41.61
  • 73. LTD 326.63 1,177.94 546.30 1,595.65 258.37 1,003.93 TOTASS 1,508.79 5,193.28 1,765.40 4,819.36 1,429.19 5,301.44 PPE 431.73 1,780.40 1,181.53 3,255.18 199.14 798.58 TOTSALE 1,067.84 3,809.89 1,165.26 4,198.20 1,037.62 3,680.64 MVE 1,200.71 3,735.78 1,320.43 3,708.36 1,163.58 3,743.50 BVE 0.30 0.29 0.75 0.14 0.16 0.14 No. of Obs 64,566 15,287 49,279 Where*, FCF= Free Cash Flow=Operating Cash Flow minus Capital Expenditures (OANCF-CAPX) FAR= Fixed Asset Ratio=Plant, Property & Equip/Noncash Total Assets ((PPENT/(AT-CH CAPX= Capital Expenditures (CAPX) OCF= Operating Cash Flow=Operating Activities Net Cash Flow (OANCF) NI=Net Income after extraordinary items and discontinued operations (NI) R= Raw annual percentage returns ROE=Return on equity, NI (NI) divided by Book Value of Equity (CEQ) LTD=Long term debt (DLTT) TOTASS=Total assets (AT) PPE=Plant, Property and Equipment (PPENT) TOTSALE=Total sales (SALE) MVE=Market value of equity= price times common shares outstanding (PRCC x CSHO) BVE=Book value of equity (CEQ)
  • 74. *Compustat item description in parentheses. International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 127 The average market value (MVE) for the entire sample is $1,200.71 million. Capital intensive firms are considerably larger with an average market value of $1,320.43, 13% larger than the average market value of non-capital intensive firms, $1,163.58. The other size-based characteristic, book value of equity (BVE), exhibits the same pattern. The earnings variable (Net Income) in Table 2 indicates capital intensive firms are more profitable, with an average Net Income of $61.37 million compared to $47.51 million for non- capital intensive firms. Return on equity (ROE) is included because it is a more relative measure of profitability and it indicates that capital intensive firms are not relatively more profitable as they have an ROE of -183% compared to an ROE of 22% for non-capital intensive firms. Capital intensive firms are characterized by having significant capital investment leading to substantial depreciation expense, hence Operating cash flow (OCF) is 275% of Net income (significantly different at the 1% level), but only 202% of Net Income for non-capital intensive firms (significantly different at the 5% level). Furthermore, Free Cash Flow is only 37% of Net income for capital intensive firms (significantly different at the
  • 75. 1% level) but 81% of Net income for non-capital intensive firms (significantly different at the 5% level). These results suggest that mean Operating cash flow is significantly different from mean Net income and mean Free Cash flow is significantly different from mean Operating Cash flow, for both capital intensive and non-capital intensive firms. EMPIRICAL TESTS To test Hypothesis 1 the relationship between Free Cash Flow and capital intensity is examined. The authors expected capital intensive firms to have lower levels of Free Cash Flow, and non-capital intensive firms to have higher levels of Free Cash Flow. Consistent with expectations, capital intensive firms have a mean Fixed Asset Ratio (FAR) of 0.75, which is significantly different (at the 1% level) from the mean Fixed Asset Ratio (FAR) of 0.16 for non- capital intensive firms. Further, capital intensive firms have a mean Free Cash Flow of $22.52, which is significantly different (at the 1% level) from the mean Free Cash Flow of $60.84 for non-capital intensive firms. These results lend some support to Hypothesis 1. In our next step, we prepared scatter plots of the relationship between Free Cash Flow and Capital Intensity. The scatter plots are reported in Figure 1. Panel A of Figure 1 demonstrates the relationship of Free Cash Flow and Capital Intensity for capital intensive firms, and Panel B demonstrates the relationship of Free Cash Flow and Capital Intensity for non-
  • 76. capital Intensive firms. There is some indication from the scatter plots that capital intensive firms have lower levels of Free Cash Flow, and non-capital intensive firms have higher levels of Free Cash Flow. Table 3 presents correlations between Free Cash Flow and Capital Intensity for capital and non-capital intensity firms.. Pearson and Spearman correlation coefficients, as well as the non-parametric measure of rank correlation, Kendall’s tau were utilized. The correlation coefficients for capital intensive firms are all significant at the 1% level. Only the Pearson correlation coefficient for non-capital intensive firms is significant (at the 1% level). Overall, evidence from the descriptive statistics, the scatter plots and the correlation coefficients supports Hypothesis 1, and indicate that capital intensive firms have lower levels of Free Cash Flow, and non-capital intensive firms have higher levels of Free Cash Flow. To test Hypothesis 2 and to examine whether Free Cash Flow is a better measure of performance than Operating Cash Flow and Net Income, we estimate models (2a) – (2c). Summary model statistics are reported in Table 4. A coefficient significantly different from zero 128 International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 on Free Cash Flow, Operating Cash Flow and Net Income
  • 77. indicates the variable provides significant explanatory power. Free Cash Flow, Operating Cash Flow and Net Income are not Figure 1 Scatter Plot of Free Cash Flow and FAR (Capital Intensity): Panel A: Capital Intensive Firms Panel B: Non-Capital Intensive Firms International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 129 Table 3 Correlations Between Free Cash Flow and Capital Intensity CAPITAL INTENSIVE FIRMS Capital Intensity (FAR) (Pearson) Capital Intensity (FAR) (Spearman) Capital Intensity (FAR) (Kendall's
  • 78. tau b) Free Cash Flow (FCF) -0.144 -0.022 -0.147 (0.000) (0.000) (0.000) NON-CAPITAL INTENSIVE FIRMS Capital Intensity (FAR) (Pearson) Capital Intensity (FAR) (Spearman) Capital Intensity (FAR) (Kendall's tau b) Free Cash Flow (FCF) -0.027 -0.001 -0.002 (0.000) P-values are in parentheses. significant for capital intensive firms, however, Operating Cash Flow and Net Income are both significant at the 1% level for non-capital intensive firms. This result provides some support for hypothesis 2, in that we expect Operating Cash Flow and Net Income to be better measures of performance for non-capital intensive firms, and these variables should have more explanatory power than Free Cash Flow. The maximum likelihood procedure
  • 79. does not produce a formal R2 statistic, therefore, the pseudo R2 (Cox & Snell, 1981) measures are reported. The three capital intensive models and the three non-capital intensive models have pseudo R2 measures of nearly 8%, and all are significant at the 1% level using the null model likelihood ratio test (not reported in Table 4). To estimate price models (3a) – (3c) as suggested in Kothari & Zimmerman (1995), to minimize the potential econometric and theoretical problems associated with the returns models used in (2a) – (2c). Table 5 reports summary model statistics for price models (3a) – (3c). Free Cash Flow is significant for capital intensive firms, and Free Cash Flow and Operating Cash Flow are both significant for non-capital intensive firms at the 1% level. These results provide further support for hypothesis 2. Also, consistent with the returns models, the three capital intensive models and the three non-capital intensive models have pseudo R2 measures of nearly 8%, and all are significant at the 1% level using the null model likelihood ratio test (not reported in Table 5). The likelihood ratio tests and pseudo-R2 measures are of limited use in making comparisons across measures, and cannot be used to compare non-nested models (Burnham & Anderson, 2002). The authors used an approach suggested by Biddle, Seow & Siegel (1995) to compare the three measures of performance. Their approach is based on the Wald statistic. The Wald Statistic can be used to test equality of coefficients across
  • 80. regression equations. It will be used to test the null hypothesis that the parameter estimates from the Free Cash Flow Model (2a) 130 International Journal of Business, Accounting, and Finance , Volum e 8, N um ber 2, Fall 2014 T able 4 R esults of the estim ation of returns m odels that test w hether Free C ash Flow is a better m easure of perform ance than O perating C ash Flow and N
  • 81. et Incom e Panel A : C apital Intensive Firm s Panel B : N on-C apital Intensive Firm s *S ignificant at the 10% level. ** S ignificant at the 5% level. *** S ignificant at the 1% level. M O
  • 86. 15,287 15,287 15,287 aM odels: (2a) R t = a 0 + a 1 FCF t + e t , (2b) R t = a 0 + a 1 O CF
  • 87. t + e t , (2c) R t = a 0 + a 1 N It + et , W here, R= Raw annual percentage returns FC F= Free C ash Flow O CF=O perating Cash Flow
  • 88. N I N t I N otes: The sam ple consists of all 2000-2012 observations that have C om pustat data needed to calculate the Fixed A sset R atio, Free C ash Flow , O perating C ash Flow , N et Incom e, M arket value of C om m on Equity, and R
  • 89. eturns. O bservations for w hich the test variable falls in the top and bottom percentile of the test-variable distribution are elim inated from the sam ple. A ll variables except R are deflated by m arket value of com m on equity at the previous fiscal year-end. International Journal of Business, Accounting, and Finance , Volum e 8, N um ber 2, Fall 2014 131 T able 5 R esults of the estim
  • 90. ation of price m odels that test w hether Free C ash Flow is a better m easure of perform ance than O perating C ash Flow and N et Incom e Panel A : C apital Intensive Firm s Panel B : N on-C apital Intensive Firm s
  • 91. *S ignificant at the 10% level. ** S ignificant at the 5% level. *** S ignificant at the 1% level. M O D EL a IN T b FC F b O C F
  • 96. t = a 0 + a 1 FCF t + e t , (3b) P t = a 0 + a 1 O CF t + e t , (3c) P t = a 0 + a 1 N It + et , W
  • 97. here, P= M arket value of com m on equity at fiscal year end, Price (PRC C ) FC F= Free C ash Flow O CF=O perating Cash Flow N I N t I N otes: The sam
  • 98. ple consists of all 2000-2012 observations that have C om pustat data needed to calculate the Fixed A sset R atio, Free C ash Flow , O perating C ash Flow , N et Incom e, M arket value of C om m on Equity, and R eturns. O bservations for w hich the test variable falls in the top and bottom percentile of the test-variable distribution are elim inated from the sam ple. A
  • 99. ll variables except R are deflated by m arket value of com m on equity at the previous fiscal year-end. 132 International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 are equal to the Operating Cash Flow Model (2b), or equal to the Net Income Model (2c). Vectors of estimated coefficients and the variance-covariance matrices are used to form the test statistic. A necessary condition for this application of the Wald test is that the regression equations being compared must have the same size coefficient vectors, and the same size variance-covariance matrices. The Wald statistic used in this study is based on a comparison of model (2a) to model (2b), and a comparison of model (2a) to model (2c) for capital intensive and non-capital intensive firms. The statistics were also used to compare model (3a) to model (3b), and a comparison of model (3a) to model (3c) for capital intensive and non-capital intensive firms. For testing the null hypothesis, the Wald statistic (Liao, 2004) is W= (β̂g - β̂g*)΄ [var (β̂g) + var (β̂g*)]ˉ (β̂g - β̂g*) ,
  • 100. Where β is the coefficient vector containing all parameter estimates for the regression equation, var (·) is the estimated variance-covariance matrix for the coefficients, the operator on the first term (·)΄ is the transpose, and the operator on the middle term [·]ˉ is the generalized inverse. The probability of this equality approaches one asymptotically. The degrees of freedom for the test equals the number of rows in the first or the third matrix. The Wald statistic is chi-square (χ2) distributed for large samples. The Wald statistics are reported in Table 6. Panel A reports the Wald statistics for capital intensive firms, and Panel B reports the Wald statistics for non-capital intensive firms. None of the statistics in Panel A are statistically significant at conventional levels, suggesting that there is no relative difference between the ability of Free Cash Flow and Operating Cash Flow, or between Free Cash Flow and Net Income to predict performance as reflected in stock returns/prices, for capital intensive firms. Also, none of the statistics in Panel B are statistically significant at conventional levels, suggesting that there is no relative difference between the ability of Free Cash Flow and Operating Cash Flow, or between Free Cash Flow and Net Income to predict performance as reflected in stock returns/prices, for non-capital intensive firms. Table 6 Summary Statistics for the Wald Test: A test of the equality of coefficients across regression equations
  • 101. Panel A: CAPITAL INTENSIVE FIRMS Model 2A vs 2B Model 2A vs 2C Model 3A vs 3B Model 3A vs 3C Wald Statistic 0.8731 0.3461 0.0602 0.2621 (>0.100) (>0.100) (>0.100) (>0.100) Panel B: NON-CAPITAL INTENSIVE FIRMS Model 2A vs 2B Model 2A vs 2C Model 3A vs 3B Model 3A vs 3C Wald Statistic 0.0003 11.234 2.324 0.8516 (>0.100) (>0.100) (>0.100) (>0.100)
  • 102. P-values are in parentheses. International Journal of Business, Accounting, and Finance , Volume 8, Number 2, Fall 2014 133 LIMITATIONS OF THIS STUDY Because this study only focuses on capital intensive firms and non-capital intensive firms, it does not capture industry differences and the effects on capital intensity. As a result, the results may not be applicable to specific industries because of differences in levels of capital intensity. RECOMMENDATIONS FOR FUTURE RESEARCH Future research in this area should focus on obtaining a better understanding of industry differences and the effects on capital intensity. Additional research is needed on the effect of capital intensity on Free Cash Flow, and the role that capital intensity plays in the predictability of Free Cash Flow. More precise and convincing results might be obtainable with industry groups formed based on levels of capital intensity.
  • 103. CONCLUSIONS Overall, the results presented in this paper are mixed. Simple t- tests demonstrate that mean Free Cash Flow is statistically different from mean Operating Cash Flow, and mean Net Income for both capital intensive and non-capital intensive firms. Evidence from the descriptive statistics, the scatter plots and the correlation coefficients indicate that capital intensive firms have lower levels of Free Cash Flow, and non-capital intensive firms have higher levels of Free Cash Flow. Our results also indicate some support for Operating Cash Flow and Net Income as better measures of performance for non-capital intensive firms. Also, there is some indication that Free Cash Flow is a better measure of performance for capital intensive firms. This study makes three important contributions. First, Free Cash Flow, considered by some as a refinement of cash flow and a more contemporary measure is used. Second, the predictability of Free Cash Flow is compared to the predictability of operating cash flows and earnings. Third, this study has extended the research on industry comparisons by using industry- specific analyses to examine the predictability of Free Cash Flow in capital intensive versus non- capital intensive industries. REFERENCES
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  • 109. About the Author: Karen Nunez is an assistant professor of accounting at Elon University in Elon, North Carolina. Dr. Nunez received her Ph.D. in Accounting and Economics from The University of Oklahoma. She also has an M.B.A. in Finance and General Management from The University of Michigan, and a B.S. in Accounting and Economics from Fairleigh Dickinson University. She is a Certified Management Accountant (CMA), and has both professional and academic expertise. Copyright of International Journal of Business, Accounting, & Finance is the property of International Academy of Business & Public Administration Disciplines (IABPAD), LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.