John Sneed is AssistantProfessor, College ofBusiness and Technology,                               Earnings Forecasting Mo...
Little theory is currently available in the accounting literature for identify-                      ing accounting inform...
ent on the accuracy of earnings forecasts (Chatfield, Moyer, and Sisneros 1989).     Chang and Most (1980) found earnings ...
t                   = year.                           Most of these independent variables have been tested previously, eit...
However, Megna and Mueller (1991) found that adjusting for the capital-     like nature of advertising and R&D expenditure...
studies consistently find a lagged relationship between the timing of R&D                           expenditures and the r...
average of capital expenditures in the model. This variable is scaled by net fixed     assets to allow cross-sectional com...
CE                  = five-year average capital expenditures/net fixed                                                 ass...
Each of the three models (Ou, Economic, Combined) is estimated on an     industry-specific basis for the ten-year period 1...
not perform as well as either model for the 1982 to 1985 time period. The                           interpretation of the ...
Endnotes     1. For surveys of this literature, see Hopwood and McKeown (1986) and Bao,     Leis, Lin and Manegold (1983)....
Bibliography                           Albrecht, W. Steve., Lookabill, Larry L., and McKeown, James C. "The Time          ...
, Frecka, Thomas J., and McKeown, James C. "Market Association Tests     and FASB StatementNo. 33 Disclosures: A Reexamina...
Hirschey, Mark H. "Intangible Capital Aspects of Advertising and R&D                           Expenditures."TheJournal of...
Shepherd, William G. "The Elements of Market Structure." The Review of     Economics and Statistics 54, No. 1 (1972): pp.2...
Table 1:                               Comparisons of Forecast Accuracy (Adjusted R-Squared) Industry-Specific Models:    ...
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Earnings forecasting

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Earnings forecasting

  1. 1. John Sneed is AssistantProfessor, College ofBusiness and Technology, Earnings Forecasting Models:Department ofAccounting/Finance, Adding a TheoreticalUniversity of Nebraska atKearney, West CenterE200,Kearney,NE Foundation for the Selection of68849-4420, USA. Explanatory Variables by John E. Sneed 1. Introduction The purpose of this study is to determine if an earnings forecasting model based on factors hypothesised to result in differential profits across firms (industries) reduces model error relative to the model developed by Ou (1990). Initial research attempting to forecast earnings found that the random walk model, where current years earnings are the prediction for next year, provides the best forecast of annual earnings (Ball and Watts 1972; Foster 1973; Beaver, Kettler, and Scholes 1970; Albrecht, Lookabill, and McKeown 1977; Brealey 1969). Ou (1990) developed an earnings forecasting model using financial statement information beyond prior years earnings as the explanatory variables that outperformed the random walk model in predicting annual earnings. While Ou (1990) identified a set of financial statement variables that improved predictive performance relative to the random walk model, other researchers question the validity of her variable selection process. Greig (1991) argues there is no theoretical support for the accounting descriptors used in Ous model. The statistical variable selection process originally used by Ou and Penman (1989) and used again by Ou (1990) in developing her earnings forecasting model indicates the potential existence of the "data snooping" bias discussed by Dimson and Marsh (1990). "Data snooping" occurs when the relationships in a data set influence the researchers choice of model specifica- tion. While "data snooping" may be useful in exploratory analysis, the forecast accuracy of models developed in this manner can be overstated. The lack of theory in selecting the variables also makes it difficult to interpret the results or to determine their significance. It is difficult to predict the effect of variables on earnings without a theoretical basis to determine the expected results. The results could be driven by a causal relationship or by misspecification in the selected models (Greig 1991). Ou and Penman (1992) state that their earlier analysis (1989) was an empirical analysis, with no contextual basis for the approach. They argue that, without guiding principles, their framework cannot be embraced as a prescrip- tion for fundamental analysis. The first objective of this article is to incorporate a theoretical framework in selecting variables to be included in an earnings forecasting model and to determine if this model outperforms Ous model.42 Management Research News
  2. 2. Little theory is currently available in the accounting literature for identify- ing accounting information that is useful in predicting future earnings, with the exception of prior years earnings. However, the economics literature has identified factors believed to be responsible for the existence of excess profits for some firms/industries in the long-run. Excess profits are defined, in this literature, as profits above the overall market rate of return. The factors responsible for the existence of excess profits in the long-run also should be significant in forecasting future earnings. I will determine if a theoretically developed earnings forecasting model, using the factors believed to be respon- sible for the existence of excess profits in the long-run as the explanatory variables, reduces model error relative to Ous model. Bernard (1993,2) states "the reliance of capital markets research on stock prices to divine Value-relevant factors is not only unnecessary, but limited in its ability to address at least one of the key issues infinancialstatement analysis: improving on current profitability as a predictor of future profitability." Some studies have had limited success in predicting changes in return on equity or earnings (Freeman, Ohlson, and Penman 1982; Ou and Penman 1989; Ou 1990). However, much of the success is due to the mean reversion in the earnings measure. The key question is to determine what beyond current return on equity would help in predicting future earnings (Bernard 1993). Ous (1990) model incorporates prior years earnings as well as additional financial statement variables, finding that these additional variables improve the performance of the model. However, as mentioned earlier, the variables were selected using statistical techniques. The second objective of this article is to determine if the theoretically selected variables also improve the performance of the model when added with prior years earnings. I will combine the variables in the theoretically selected model with the variables capturing prior years earnings from Ous model to determine if the theoretically selected variables add explanatory power to prior years earnings. I also will compare the accuracy of this model to Ous model to determine if the theoretically selected variables perform as well as Ous statistically selected variables after controlling for prior years earnings. The results of the analysis indicate that Ous model, including prior years earnings, consistently outperforms the theoretically selected model, which does not include prior years earnings. However, when prior years earnings are added to the theoretical model, the two models performance is approximately equal. This finding supports the conclusion that the theoretically selected variables perform as well as Ous statistically selected variables after controlling for prior years earnings. 2. Importance of the Study The importance of earnings forecasts is indicated by the significance of earnings expectations in firm valuation, security selection, and cost of capital models. Accurate valuation analysis and cost of capital estimation techniques are depend-Volume 19 Number 11 1996 43
  3. 3. ent on the accuracy of earnings forecasts (Chatfield, Moyer, and Sisneros 1989). Chang and Most (1980) found earnings and dividends forecasts, and the growth rates in these forecasts, to be key inputs in investor decision models. Because evidence exists that users of accounting information include earnings forecasts in making financial decisions, improved earnings forecasts will improve the information set available to the decision maker. The importance of earnings forecasts has resulted in substantial research attempting to develop an optimal earnings forecasting model. 3. Ous Earnings Forecasting Model Ou (1990) developed an earnings forecasting model by statistically selecting variables that were associated with future earnings. Her earnings forecasting model is specified as follows: ROAi,t+1 . = BO + B1GWINVNi,t + B2GWSALEi,t + B3CHGDPSi,t + B4GWDEPi,t + B5GWCPXli,t + B6GWCPX2i,t, + B7RORi,t + B8CHGRORi,t + Ei,t where: ROA = earnings before interest and taxes/total assets; GWINVN = percentage growth in the inventory to total assets ratio; GWSALE = percentage growth in the net sales to total assets ratio; CHGDPS = change in dividends per share, relative to that of the previous year; GWDEP = percentage growth in depreciation expense; GWCPX1 = percentage growth in the capital expenditures to total assets ratio; GWCPX2 = GWCPX1, with a one-year lag; ROR = the accounting rate of return, i.e. income before extraordinary items divided by total owners equity as of the beginning of the year; CHGROR = change in ROR, relative to the previous years ROR; E = error term; i = firm;44 Management Research News
  4. 4. t = year. Most of these independent variables have been tested previously, either directly or indirectly, in information content studies predicting future firm attributes (Ou 1990). The future earnings amount (ROA) is defined as earnings before interest and taxes, or "regular income" (Financial Accounting Standards Board (FASB) 1979). The FASB (1979), as part of the conceptual framework project and (1979a) as justification for disclosing holding gains and losses separate from continuing operations, recognised the need for disclosure of and research on the descriptive power of "regular income" based on its decision-usefulness potential (Stewart 1989). Ou and Penman (1992) argue that the task of determining "intrinsic values" is one of predicting future earnings and assessing the rate at which they should be capitalised. They argue that value should be based on operating activities but be independent of financing and investing activities. Accurate forecasts of income from continuing operations should provide additional information to the users of financial statement information when making investment decisions. Earnings before interest and taxes is the future earnings amount used in this analysis. This variable is scaled by total assets to permit cross-sectional comparisons. This variable will be the dependent variable for both Ous model and the theoretically specified model. 4. Theoretical Development of an Earnings Forecasting Model Having found that differential profits exist acrossfirmsand across industries in the long-run, economists have attempted to find an explanation for the persist- ence of excess profits. Incorporating the factors that result in differential profits into an earnings forecasting model should improve the accuracy of the forecasts. I will include the factors identified by economists as being responsible for differential profits in an earnings forecasting model to determine if they reduce model error relative to Ous model. 4.1 Improper Measurement of Profits One explanation of differential profits is that excess profits appear to exist because accountants fail to properly measure profits, and more importantly assets (Weiss 1969; Block 1974; Ayanian 1975). Among other measurement problems, they argue that accountants fail to account for the intangible capital created by advertising and research and development (R&D) expenditures (Megna and Mueller 1991). These studies assume advertising and R&D expenditures are capital expen- ditures, where the benefits accrue over multiple periods and exceed the value of the expenditures in the period that they occur. They argue that a proper accounting of the capital-like nature of these expenditures would eliminate observed differences in profitability rates across firms, as well as observed differences in mean industry rates (Megna and Mueller 1991).Volume 19 Number 11 1996 45
  5. 5. However, Megna and Mueller (1991) found that adjusting for the capital- like nature of advertising and R&D expenditures did not eliminate profitability differentials across firms or across industries. Their results supported the assumption that R&D expenditures provide long-term benefits, finding long- term benefits in all four industries examined. However, the results on advertis- ing expenditures are mixed, as the expenditures only provided long-term benefits in two of the four industries examined. Their results suggest that additional factors are responsible, in part, for the existence of excess profits. 4.2 Differential Returns on Intangible Capital A second potential explanation for the existence of differential profits is that firms may earn different returns on investments in intangible capital, like R&D and advertising. Most prior studies assume the relationship between advertising and R&D expenditures and the benefits they provide is the same across all firms in an industry, and often assume the relationship is the same across industries (Megna and Mueller 1991). This assumption is based on treating intangible capital like physical capital. For physical capital, with constant returns to scale, each additional machine produces the same output as the previous machine of the same type. If markets are competitive, each additional machine will earn the same revenue as the one before. If all firms have equal access to product and capital goods markets, equalising costs, each firm within an industry receives the same marginal revenues (profits) from capital goods expenditures. If R&D and advertising expenditures have the same effects as capital goods expenditures, each firm/in- dustry should receive the same marginal revenue for the expenditures. However, the assumptions of constant returns and perfect competition are inappropriate when advertising and R&D are important activities in the com- petitive process. Advertising and R&D are information creation and distribu- tion activities used to differentiate products. Information is a good whose production and distribution cannot be modeled using constant returns assump- tions, since the value of its output is subjective. The absence of constant returns will result in different relationships between firms5 sales or profits and invest- ments in advertising and R&D (Megna and Mueller 1991). Megna and Mueller (1991) found that advertising and R&D expenditures significantly influence future earnings, with the influence varying across firms and across industries. I will include advertising and R&D expenditures in the earnings forecasting model to capture differential returns on expenditures for intangible capital. When incorporating these variables in an earnings forecasting model, it is important to match the expenditures to the benefits they provide. Empirical research consistently finds a lagged relationship between the timing of R&D expenditures and when benefits occur (Ravenscraft and Scherer 1982; Branch 1974; Hirschey 1982; Hirschey and Weygandt 1985; Bublitz, Frecka, and McKeown 1985; Bublitz and Ettredge 1989; Megna and Mueller 1991). These46 Management Research News
  6. 6. studies consistently find a lagged relationship between the timing of R&D expenditures and the related increases in revenues. Ravenscraft and Scherer (1982) found a lag of four to six years. Based on the lagged relationship between R&D expenditures and the benefits they provide, I will use the average R&D expense over afive-yearperiod to capture the effects of the lag between the expenditures and the benefits they provide. The five-year period was selected based on the results from earlier studies (Ravenscraft and Scherer 1982; Megna and Mueller 1991). This lag relationship could explain why Lev and Thiagarajan (1991) did not find the anticipated relationship between market returns and R&D expenditures, using only current year expenditures. Empirical research on the relationship between advertising expenditures and the benefits they provide finds inconsistent results (Schmalensee 1972; Norris 1984). Some recent studies find a lag relationship between advertising expenditures and the benefits they provide (Hirschey 1982; Hirschey and Weygandt 1985). However, later studies extending the earlier studies do not support the longevity of benefits from advertising expenditures (Bublitz, Frecka, and McKeown 1985; Bublitz and Ettredge 1989). Megna and Mueller (1991) found that advertising expenditures did not provide long-term benefits in two of four industries examined. Since most recent studies fail to support the longevity of benefits from advertising expenditures, I will include advertising expense for the year prior to the forecast year in the model. The advertising and R&D expense variables both will be scaled by sales to allow cross-sectional comparability. Both variables should have a positive relationship with future earnings asfirmsmake these expenditures expecting to increase earnings. 4.3 Existence of Market Power A third explanation for differential profits is that firms earn excess profits because they have market power that results in less than perfect competition (Collins and Preston 1968; Comanor and Wilson 1967; Porter 1974; Weiss 1974). Prior studies find differences in profitability related to differences in market share, a measure of market power (Shepherd 1972 and 1975; Raven- scraft 1983; Mueller 1986). Researchers have found that firms with larger market shares earn higher returns on capital expenditures (Ravenscraft 1983; Caves and Pugel 1980). Some argue that this result occurs because largerfirmshave superior investment opportunities that are not available to smaller firms. Therefore, larger firms should earn excess returns on capital expenditures (Baumol 1967; Hall and Weiss 1967). If large firms earn excess profits on capital expenditures, due to superior investment opportunities, including capital expenditures in an earnings fore- casting model will help capture differences across firms. Since capital expendi- tures, like R&D expenditures, provide long-term benefits, I will use the five-yearVolume 19 Number 11 1996 47
  7. 7. average of capital expenditures in the model. This variable is scaled by net fixed assets to allow cross-sectional comparability. This variable should have a positive relationship with future earnings as largerfirmswould have superior investment opportunities while having higher levels of capital expenditures. Other economic studies argue that large firms earn excess profits due to efficiency differences (Demsetz 1973; Carter 1978; Mueller 1986). These studies argue that firms maintain excess profits in the long run because they operate more efficiently than their competitors. The efficiency is usually argued to result from economies of scale, where larger firms achieve lower costs (Hall and Weiss 1967; Scherer 1973). If largerfirmsoperate more efficiently due to economies ofscale, their costs should be lower relative to sales. I will include the cost of goods sold divided by sales variable in the model to capture the effects of firm efficiency. This variable should have a negative relationship with future earnings, since higher costs imply that a firm is operating inefficiently. 4.4 Firm-Specific Risk A fourth explanation for differential profits is firm-specific risk, wherefirmsarc being rewarded with higher profits for bearing above average risk. Higher risk firms should exhibit greater earnings variation than lower risk firms. Models forecasting earnings need to incorporate a measure of risk to capture this variability. Different proxies have been used in the literature to measure risk. Based on earlier studies, Megna and Mueller (1991) used the ratio of firms equity to total assets to measure the effects of risk in firms returns on assets (Hall and Weiss 1967; Baker 1973; Bothwell, Cooley, and Hall 1984).2 They found that firm-specific risk was positively associated with profits. I expect the relationship between risk and profitability to be positive in my model, as firms will be rewarded for bearing higher levels of risk. Based upon the above discussion, the earnings forecasting model is speci- fied as follows: ROAi,t+1 = BO + BlRKi,t + B2RDi,t + B3AEi,t + B4CEi,t + B5CGSi,t + Ei,t where: ROA = earnings before interest and taxes/total assets; RK = equity/total assets; RD =five-yearaverage R&D expense/sales; AE = advertising expense/sales;48 Management Research News
  8. 8. CE = five-year average capital expenditures/net fixed assets; CGS = cost of goods sold/sales; E = error term; i = firm; t = year. This model identifies variables that capture the factors that economists believe are responsible for differential profits in the long run. If the economists are correct, these variables should be significant in predicting earnings. The RD, AE, and CE variables are expected to have a positive relationship with earnings, as firms are expected to make money on these expenditures. The RK and CGS variables are expected to have a negative relationship with earnings, as the RK variable is an inverse measure of risk. 5. Methodology for the Analysis Ou (1990) developed an earnings forecasting model, incorporating financial statement information beyond prior years earnings, that outperformed the random walk model in forecasting annual earnings. However, Ous model has been criticised in the literature because she statistically selected the variables to be included in the model. The first objective of this study is to theoretically select the variables to be included in an earnings forecasting model and to determine if this model outperforms Ous model. This issue was examined by comparing the forecast accuracy of Ous model with the forecast accuracy of the economic model. Bernard (1993) argues that a key element offinancialstatement analysis is improving on current profitability as a predictor of future profitability. Ous model included a measure of the current year earnings (ROR) as well as prior years earnings (CHGROR). The economic model does not include measures of prior years earnings, which weakens its predictive ability. The variables capturing prior years earnings (ROR and CHGROR) were added to the economic model, and the explanatory power of this model also was compared to Ous model. The data for the analysis include observations from the oil exploration (SIC 1311), electronic computers (SIC 3571), and eating places (SIC 5812) indus- tries for the years 1979 to 1988. Prior research (Sneed 1995a) indicates that industry-specific models improve forecast accuracy relative to combining firms from different industries in the same model. These three industries were selected for the analysis because there were enough firms in each industry to fit industry-specific models when classifying industry at the four-digit SIC level.Volume 19 Number 11 1996 49
  9. 9. Each of the three models (Ou, Economic, Combined) is estimated on an industry-specific basis for the ten-year period 1979 to 1988. Since the error of each model is an indirect measure of the forecast error that would have occurred if the model had been used to forecast earnings for this period of time, the adjusted R2 is used to measure the accuracy of each model. The adjusted R2 s from the three models are compared for each industry to determine which model provides the best forecast of annual earnings. Prior research (Sneed 1995 b) also indicates that segmenting earnings forecasting models into shorter time periods reduces model error relative to estimating the model over longer time periods. For this analysis, I segmented each industry-specific model into three time periods: 1986-1988; 1982-1985; 1979-1981. These time periods were selected based upon economic conditions during these periods. Each of the three industries would have three models, one for each time period, resulting in a total of nine models. However, there were not enough observations to estimate an earnings forecasting model for the electronic computers industry for the 1979-1981 time period, so there are only eight models included in the analysis. The forecast accuracy (adjusted R2 ) of the three models (Ou, Economic, Combined) are compared for each industry/time-pe- riod to determine which model provides the best forecasts of annual earnings. 6. Results and Conclusions Table 1 presents the adjusted R2 for the three different forecasting models, using industry-specific models. The model for the oil exploration industry used the 511 observations over the 1979 to 1988 period. The 73 observations over the 1982 to 1988 period were used for the model of the electronic computers industry. For the eating places industry, 283 observations from the 1979 to 1988 period were used to estimate the model. For all industry-specific models analysed, Ous model substantially outper- forms the economic forecasting model. The lowest adjusted R2 for Ous model is 25.3 percent, while the highest adjusted R2 for the economic model is 20.9 percent. The combined model, adding the ROR and CHGROR variables to the economic model, is more accurate than Ous model for two of the three industry-specific models. However, the differences between these two models are fairly small. Table 2 presents the adjusted R2 of the different models for the oil exploration industry, partitioning each model into three time periods. Ous model substantially outperforms the economic model for all threetimeperiods. Ous model also outperforms the combined model for two of the three time periods. Again, the differences between the combined model and Ous model are fairly small. Table 3 presents the adjusted R2 for the models of the electronic computers industry, using the models partitioned by time. Ous model outperforms the economic and combined models for the 1986 to 1988 time period, but does50 Management Research News
  10. 10. not perform as well as either model for the 1982 to 1985 time period. The interpretation of the results from models in this industry is limited by the small sample sizes for the two time periods. Table 4 presents the adjusted R2 for the models of the eating places industry, using the models partitioned by time. Ous model outperforms the economic model in all three time periods. Ous model also outperforms the combined model in two of the three time periods, with a substantial difference for the 1982 to 1985 period. The average adjusted R2 for each model across all the estimated models is summarised as follows: Model Averaged Adjusted R2 Ou .3119 Economic .1683 Combined .3055 The primary conclusion from this analysis is that Ous model substantially outperforms the economic model in forecasting earnings, providing superior results in ten of the eleven models examined. However, the economic model does not include variables to capture prior years earnings, which provide the majority of the explanatory power in Ous model. Of the eleven models examined, Ous model outperforms the combined model in six cases, while the combined model is superior in the other five. Also, the mean adjusted R2 for the two models across all models estimated are very close. This result suggests that the theoretically selected variables, when combined with variables to capture the effects of prior years earnings, perform as well as the statistically selected variables. The improvement in the explanatory power of the economic model when the ROR and CHGROR variables are added indicates the importance of including variables to capture prior years earnings in an earnings forecasting model.Volume 19 Number 11 1996 51
  11. 11. Endnotes 1. For surveys of this literature, see Hopwood and McKeown (1986) and Bao, Leis, Lin and Manegold (1983). 2. They also considered the variance of firms profitability to proxy for firm- specific risk, but this proxy had weaker explanatory power than the equity to total asset ratio. 3. Concentration ratio had been argued to be a determinant of excess profits. However, Ravenscraft (1983) and Amato and Wilder (1985) find that, when market share (size) is included in the model, the influence of the concentration ratio is weak. 4. As mentioned earlier, the 1979 to 1981 time period was omitted from the analysis for the electronic computers industry since there were only a few observations for this period. 5. There were 42 observations for the 1986 to 1988 period and 31 observations for the 1982-1985 period.52 Management Research News
  12. 12. Bibliography Albrecht, W. Steve., Lookabill, Larry L., and McKeown, James C. "The Time Series Properties of Annual Earnings." Journal of Accounting Research, 15, No. 2 (1977): pp.226-244. Amato, Louis and Wilder, Ronald. "The Effects of Firms Size on Profit Rates in U.S. Manufacturing." Southern EconomicJournal 52, No. 1 (1985): pp.181- 190. Ayanian, Robert. "Advertising and Rate of Return."Journal of Law and Econom- ics 18, No. 2 (1975): pp.479-506. Baker, Samuel H. "Risk, Leverage, and Profitability: An Industry Analysis." The Review of Economics and Statistics 55, No. 4 (1973): pp.503-507. Ball, Ray and Watts, Ross. "Some Time Series Properties of Accounting Income." Journal of Finance 27, No. 3 (1972): pp.69-99. Bao, Da-hsien, Lewis, Merrill T., Lin, W. Thomas, and Manegold, James G. "Applications of Time-Series Analysis in Accounting: A Review." Journal of Forecasting 2, No. 4 (1983): pp.405- 423. Baumol, William. Business Behavior Value and Growth. New York: Harcourt Brace Jovanovich, 1967. Beaver, William, Kettler, Paul, and Scholes, Myron. "The Association Between Market Determined and Accounting Determined Risk Measures." The Account- ing Review 45, No. 3 (1970): pp.654-681. Bernard, Victor L. "Accounting-Based Valuation Methods, Determinants of Market-to-Book Ratios, and Implications for Financial Statement Analysis." Working Paper, The University of Michigan, June 1993. Block, Harry. "Advertising and Profitability: A Reappraisal."Journalof Political Economy 82, No. 2 (1974): pp.267-286. Bothwell, James L., Cooley, ThomasF.,and Hall, Thomas E. "A New View of the Market Structure-Performance Debate."Journal ofIndustrial Economics 32, No. 4 (1984): pp.397-418. Branch, Ben. "Research and Development Activity and Profitability: A Distrib- uted Lag Analysis."Journal of Political Economy 82,No. 5 (1974):pp.999-1011. Brealey, Richard A. An Introduction to Risk and Return on Common Stocks. Cambridge: MIT Press, 1969. Bublitz, Bruce B. and Ettredge, Michael E. "The Information on Discretionary Outlays: Advertising, Research, and Development." The Accounting Review 64, No. 1 (1989): pp.108-124.Volume 19 Number 11 1996 53
  13. 13. , Frecka, Thomas J., and McKeown, James C. "Market Association Tests and FASB StatementNo. 33 Disclosures: A Reexamination "Journal of Account- ing Research Supplement 23 (1985): pp.1-23. Carter, John R. "Collusion, Efficiency, and Antitrust." Journal of Law and Economics 21, No. 2 (1978): pp.435-444. Caves, Richard E. and Pugel, Th. Differences in Conduct and Performance: Viable Strategies in U.S. Manufacturing. New York University Monograph Series in Finance and Economics: Monograph 1980-2,1980. Chang, Lucia S. and Most, Kenneth S. "Financial Statements and Investment Decisions." Working Paper, Florida International University, Miami, 1980. Chatfield, Robert E.,Moyer, Charles R., and Sisneros, Phillip M. "The Accuracy of Long-Term Earnings Forecasts for Industrial Firms." Quarterly Journal of Business and Economics 28,No. 3 (1989): pp.91-104. Collins, Norman R. and Preston, Lee E. Concentration and Price-Cost Margins in Manufacturing Industries. Berkeley: University of California Press, 1968. Comanor, William S. and Wilson, Thomas A. "Advertising, Market Structure and Performance." The Review of Economics and Statistics 49, No. 4 (1967): pp.423-440. Demsetz, Harold. "Industry Structure, Market Rivalry, and Public Policy." Journal ofLaw and Economics 16, No. 1 (1973): pp.1-9. Dimson, Elroy and Marsh, Paul. "Volatility Forecasting Without Data-Snoop- ing." Journal ofBanking and Finance 14, No. 3 (1990): pp.399-421. Financial Accounting Standards Board.FASB Discussion Memorandum. "Report- ing Earnings." Norwalk, CT: FASB, 1979. . Statement ofFinancial Accounting Standards No. 33. "Financial Reporting and Changing Prices." Norwalk, CT: FASB 1979a. Foster, George F. "Stock Market Reaction to Estimates of Earnings Per Share by Company Officials." Journal of Accounting Research 11, No. 1 (1973): pp.25-27. Freeman, Robert N., Ohlson, James A., and Penman, Stephen H. "Book Rate-of-Return and Predictions of Earnings Changes: An Empirical Investiga- tion." Journal of Accounting Research 20, No. 2 (1982): pp.639-652. Greig, Anthony C. "Fundamental Analysis and Subsequent Stock Returns." Working Paper, University of Rochester, January, 1991. Hall, Marshall and Weiss, Leonard W "Firm Size and Profitability." The Review ofEconomics and Statistics 49,No. 3 (1967): pp.319-331.54 Management Research News
  14. 14. Hirschey, Mark H. "Intangible Capital Aspects of Advertising and R&D Expenditures."TheJournal ofIndustrial Economics 30, No. 4 (1982): pp.375- 390. and Weygandt, Jerry J. "Amortization Policy for Advertising and Research and Development Expenditures." Journal of Accounting Research 23, No. 1 (1985): pp.326-335. Hopwood, William S. and McKeown, James C. "Univariate Time-Series Analy- sis of Quarterly Earnings: Some Unresolved Issues." Studies in Accounting Research No. 25. Sarasota, Fl.: American Accounting Association, 1986. Lev, Baruch and Thiagarajan. S. Ramu. "Fundamental Information Analysis." Journal of Accounting Research 31, No. 2 (1993): pp.190-215. Megna, Pamela and Mueller, Dennis C. "Profit Rates and Intangible Capital." The Review of Economics and Statistics 73, No. 4 (1991): pp.633-642. Mueller, Dennis C. Profits in the Long Run. Cambridge: Cambridge University Press, 1986. Norris, Vincent P. "The Economic Effects of Advertising: A Review of the Literature." Current Issues and Research in Advertising 7, No. 2 (1984): pp.39- 134. Ou, Jane A."TheInformation Content of Nonearnings Accounting Numbers as Earnings Predictors." Journal of Accounting Research 28, No. 1 (1990): pp.144-163. and Penman, Stephen H. "Financial Statement Analysis and the Prediction of Stock Returns." Journal of Accounting and Economics 11, No. 4 (1989): pp.295-329. . "Financial Statement Analysis and the Evaluation of Market-to-Book Ratios." Working Paper, Santa Clara University, August, 1992. Porter, Michael E. "Consumer Behavior, Retailer Power and Market Perform- ance in Consumer Goods Industries." The Review of Economics and Statistics 56, No. 4 (1974): pp.419-436. Ravenscraft, David J. "Structure-Profit Relationships at the Line of Business and Industry Level." The Review ofEconomics and Statistics 65, No. 1 (1983): pp.22-31. and Scherer, Frederic M. "The Lag Structure of Returns to Research and Development." Applied Economics 14, No. 6 (1982): pp.603-620. Scherer, Frederic M. The Determinants of Industrial Plant Sizes in Six Nations." The Review ofEconomics and Statistics 55, No. 2 (1973): pp.135-145. Schmalensee, Richard. The Economics ofAdvertising. Amsterdam: North-Hol- land, 1972.Volume 19 Number 11 1996 55
  15. 15. Shepherd, William G. "The Elements of Market Structure." The Review of Economics and Statistics 54, No. 1 (1972): pp.25-37. . The Treatment ofMarket Power. New York: Columbia University Press, 1975. Sneed, John E. "The Predictability of Reported Earnings: Industry Effects." Working Paper, The University of Nebraska at Kearney, 1995. ."The Predictability of Reported Earnings: Time Effects." Working Paper, The University of Nebraska at Kearney, 1995. Stewart, Jenice P. "Income Disclosure, Descriptive Power and Cash Flows." Research in Accounting Regulation 3, (1989): pp.153-182. Weiss, Leonard W. "Advertising, Profits, and Corporate Taxes." The Review of Economics and Statistics 51, No. 4 (1969): pp.421-430. . "The Concentration-Profits Relationship and Antitrust." in Goldschmid, H.J., Mann, H.M., and Weston, J.H. Industrial Concentration: The New Learn- ing. Boston: Little-Brown, 1974.56 Management Research News
  16. 16. Table 1: Comparisons of Forecast Accuracy (Adjusted R-Squared) Industry-Specific Models: 1979-1988 Oil Exploration Electronic Computers Eating Places Ous Model .253 .353 .272 Economic Model .108 .209 .099 Combined Model .285 .392 .236 N 511 73 283 Table 2: Comparisons of Forecast Accuracy (Adjusted R-Squared) Oil Exploration Industry 1986-1988 1982-1985 1979-1981 Ous Model .201 .304 .251 Economic Model .144 .078 .065 Combined Model .281 .232 .240 N 216 175 120 Table 3: Comparisons of Forecast Accuracy (Adjusted R-Squared) Electronic Computers Industry 1986-1988 1982-1985 Ous Model .488 .105 Economic Model .299 .328 Combined Model .477 .342 N 42 31 Table 4: Comparisons of Forecast Accuracy (Adjusted R-Squared) Eating Places Industry 1986-1988 1982-1985 1979-1981 Ous Model .235 .546 .423 Economic Model .233 .055 .233 Combined Model .328 .159 .389 N 106 104 73Volume 19 Number 11 1996 57

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