HOW RISKY REALLY ARE SHIPPING AND AIRLINE COMMON STOCKS?

                                       Stephen X.H. Gong
       ...
co-variation or co-movement. Changes in the economic, political and sociological environment,
which affect securities mark...
are shipping, are taken from University of Chicago's Centre of Research on Security Prices
(CRSP) database. The actual num...
Since beta is known to be also sensitive to the stock market index (as proxy for the
unobservable market portfolio of all ...
Index) are used. Under both of the Dimson and the Scholes-Williams methods, however, the
airlines average beta is statisti...
In estimating beta from historical return data, it is assumed that beta is relatively stable in a
period of 5 to 10 years....
on stocks        3              33%           18%               82%          52%
                 4              37%      ...
The estimation of risk has implications for determining the required rate of return on capital
employed, which is an impor...
Sharpe, W.F. and Cooper, G.M. (1972). Risk-return classes of New York Stock Exchange
common stocks, 1931-1967. Financial A...
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How Risky Really Are Shipping And Airline Common Stocks.doc.doc

  1. 1. HOW RISKY REALLY ARE SHIPPING AND AIRLINE COMMON STOCKS? Stephen X.H. Gong Department of Shipping and Transport Logistics The Hong Kong Polytechnic University E-mail: stlxhg@polyu.edu.hk 1. Introduction In a market economy, the securities market has an extremely important role to play because it helps to direct scarce economic resources to their most productive uses. Investors decide whether or not to purchase or sell a security on the basis of their perception of the risk-return trade-off; they demand high returns for a security that has high investment risk. When deciding whether to take up an investment project, therefore, a firm accepts the project if its expected payoff is no less than the opportunity cost of capital. Rather than the cost of borrowing or the company’s weighted average cost of capital, the appropriate measure of the cost of capital is the highest return forgone on an alternative investment of comparable risk. In modern finance, a security’s risk is measured by its marginal contribution to the portfolio risk or variance of the securities the investor holds. If an investor has no superior information or abilities than other investors taken together, his best strategy is to diversify, i.e. he should allocate his funds among risky securities in the same proportions as the market portfolio of risky securities. Depending on his risk aversion, an investor can then combine his holdings of risky securities with risk-free securities (e.g. U.S. treasury bills) to arrive at his optimal point of consumption-investment. If all investors behave this way, Sharpe (2000) shows that the risk of an security is the sensitivity of its return to changes in the return to the market portfolio (of all risky securities), and its payoff or risk premium (i.e. expected return in excess of the return to the risk-free security) is proportional to the risk premium to the market portfolio, with the proportional factor being the security’s beta (this is known as the Capital Asset Pricing Model). In other words, an investor is only remunerated for bearing market or systematic risk rather than total risk (variance of return), since some of the latter can be diversified away. Because securities (and the companies that issue them) tend to be affected by the same general market or macro-economic factors, it is expected that securities will exhibit a certain degree of
  2. 2. co-variation or co-movement. Changes in the economic, political and sociological environment, which affect securities markets, therefore, are sources of systematic risk. At the level of a firm, these macro-economic factors exert their influence on security risk and return via such factors as the business cycles (and the resultant earnings variability), sales, and profits, etc. Other micro-economic factors affecting securities' risk include operating and financial leverage, dividend payout ratio, standard of management in the industry, and the like. As these macro- and micro-economic factors change over time, and as the firm's responsiveness to such changes change over time, it is predictable that beta will change over time, and along with it, expected return to the security. The ocean shipping and air transport sectors are perceived as highly risky, cyclical businesses that are susceptible to changes in the macro-economic factors. This is further aggravated by their typically high operating leverage and financial leverage. As a result of these economic and operating characteristics, it is generally expected that securities of these sectors should have relatively high risks, as industry practitioners often claim (see Stokes, 1996). Indeed, Cullinane and Gong (2002) find support for this conventional wisdom by presenting evidence that water transportation new issues (IPOs) have to incur higher underpricing costs than other transport industry sectors because of their perceived higher investment risks. It is unclear, however, whether these risk-laden industry sectors have high systematic risk, or just high total risk or variance. To the extent that they have high total risk but only low systematic risk, well- diversified investors should be satisfied with correspondingly low returns. 2. Empirical Estimates of Systematic Risk in Shipping and Airlines Stocks In this article, we set out to determine empirically the systematic risk levels of shipping and airlines stocks. In order to be as representative of the industries as possible, we selected our sample firms from Bloomberg’s list of shipping and airline stocks as of September 2001. All companies that have monthly returns data for at least 60 consecutive months are selected, and their returns taken from Datastream International (for 345 stocks, of which 219 are shipping). To enable a comparison with results in a previous study using only U.S. data (see Kavussanos and Marcoulis, 2001), we also estimated beta for a sub-sample of stocks over the same period as in Kavussanos and Marcoulis (2001). Returns for these 27 U.S.-listed stocks, of which 14 2
  3. 3. are shipping, are taken from University of Chicago's Centre of Research on Security Prices (CRSP) database. The actual number of stocks used varies in different research designs. The systematic risk or beta of a stock is estimated by performing a simple linear regression of the following form (known as the ‘market model’): ~ ~ ~ Rit = α i + β i Rmt + ε it (1) ~ ~ where Rit is the (raw) return to stock i at time t, Rmt the return to the market portfolio at time t, ~ α i is the intercept, and ε it is a zero mean random disturbance term. This regression is run for each security over the 60 months (5 years) closest to September 2001, or a date on which the stock was last listed. This method of beta estimation through the market model is in line with the tradition in finance and accounting research. One implicit assumption is that beta is stable over time and thus an unbiased estimate may be obtained from historical returns data. To provide a sensitivity analysis, we also used two beta estimation techniques that are designed to deal with the problem of thin trading, a problem that is believed to be particularly significant in generally small-capitalization transportation shares. The first is the Scholes-Williams (1977) method, which suggests the following adjustment to account for thin trading: ˆ ˆ ˆ +1 ( β −1i + β i + β i ) β SW i (2) (1 + 2 ρ m ) ˆ ˆ ˆ ˆ +1 where β −1i , β i and β i are obtained in an OLS regression of stock return on one-period lagged market return, on contemporaneous market return, and on one-period lead market return, respectively. ρ m is the first-order serial correlation coefficient of market returns. ˆ The second procedure is that of Dimson (1979), which is an aggregated coefficient estimate that can be estimated by the following: m β Dim i ∑ βˆ k =− m i+k (3) ˆ where the beta estimates β i+ k are obtained from a multiple OLS regression of individual stock returns against lagged, contemporaneous and leading market returns. In accordance with most existing studies, we limit the number of leads and lags to 1 only. 3
  4. 4. Since beta is known to be also sensitive to the stock market index (as proxy for the unobservable market portfolio of all risky securities) used, for U.S.-listed stocks we separately used the CRSP-equally weighted and the CRSP-value-weighted index whereas for other international stocks, we used the individual country’s stock market index as well as the MSCI All Countries’ Index (as proxy for the world portfolio of risky securities). The key results are summarized as follows. For the sub-sample of 27 U.S. listed stocks, we find that the air (water) transport industry displays a systematic risk level that is statistically significantly (insignificantly) higher than the market average1. This conclusion is robust to the use of different estimation procedures, including the estimation method (the market model versus the Scholes-Williams model or the Dimson’s model), estimation period, and market index (value- versus equally- weighted, as well as different proxies for the market) 2. The risk levels for these two sectors are both higher than those estimated in Kavussanos and Marcoulis (2001). They reported that the average beta for U.S. listed water transportation shares during the period July 1984 to June 1995 is only 0.94, which is significantly lower than the market average of unity. They also reported an average beta of 0.97 for air transportation shares in the same period, which is insignificantly different from unity. Although our new beta estimates are higher than those in the previous study, they are still much lower than what one may expect for these ostensibly risk-laden industries. One possible interpretation is that maybe the high risks usually attributed to these industries are in fact diversifiable risk, and their systematic risk (the only risk for which investors need to be remunerated) may in fact be relatively low or at least not as high as suggested by industry practitioners3. The results using the larger sample of international stocks reveal a similar picture. As reported in Table 1, using the market model, the average beta for airlines is close to (greater than) the market average of unity when the respective local stock indices (the MSCI All Countries’ 1 A stock with beta greater than one (unity) is known as an “aggressive” stock, while a stock with beta lower than one is known as “defensive”. The market average beta is one by definition. 2 The use of a value weighted index tend to produce a slightly higher beta than the use of an equally weighted index. The average (pair-wise) difference is statistically significant. 3 We noted that some U.S. firms in previous studies were misclassified as shipping or airlines stocks (some actually belong to utilities or paper mills), but even after correcting this problem (by replacing the misclassified firms with randomly selected industry firms that genuinely belong to the respective industries) the systematic risk levels remained relatively low and are similar in magnitude to those reported here. 4
  5. 5. Index) are used. Under both of the Dimson and the Scholes-Williams methods, however, the airlines average beta is statistically greater than the market average of unity, indicating that airlines stocks are generally more risky than the average stock. Table 1. Beta for international sample of shipping and airline stocks Panel A. Air transportation stocks Obs=70 MM beta Dimson beta Scholes-Williams beta Local index MSCI index Mean 0.95 1.12** 1.15** 1.14** Stdev 0.33 0.43 0.53 0.51 Minimum 0.40 0.46 0.14 0.15 Maximum 1.73 2.80 2.77 2.60 Range 1.33 2.34 2.63 2.45 Panel B. Water transportation stocks obs=102 OLS beta Dimson beta Scholes-Williams beta Local index MSCI index Mean 0.88*** 1.02 0.95 0.93 Stdev 0.32 0.38 0.42 0.42 Minimum 0.13 0.33 -0.07 -0.14 Maximum 1.85 1.79 1.88 1.89 Range 1.72 1.46 1.95 2.02 ** (***) Significantly different from unity at the 5% (1%) level. To reduce measurement errors and possible aggregation problems when working with individual securities, we next used the S&P 500 Airlines Index, MSCI New ACWIF Air Transportation Index, the Oslo Shipping Index, and MSCI New ACWIF Marine Transportation Index as proxies for the air transportation and water transportation industry index, respectively. These industry indexes were regressed via OLS against the MSCI World Index. The beta coefficients for each regression are taken to be an estimate of the industry beta relative the world stock market. As the results in Table 2 indicate, the industry betas are even lower than the arithmetic averages we arrived at earlier; in two cases the betas are statistically lower than the market average of unity. Table 2. Industry Beta Estimates Relative to the World Stock Market (MSCI World) R p = β 0 + β1 * Rm + ε Independent variable Period Beta R2 S&P 500 AIRLINES 1/76-9/01 0.90 0.21 MSCI New ACWIF AIR INDEX 1/83-9/01 0.62** 0.24 OSLO SHIPPING INDEX 1/99-9/01 0.97 0.22 MSCI NEW ACWIF MARINE INDEX 1/99-9/01 0.79** 0.35 ** Statistically lower than unity at the 5% level 5
  6. 6. In estimating beta from historical return data, it is assumed that beta is relatively stable in a period of 5 to 10 years. However, changes in risk-class membership are not unimportant since they give rise to transactions costs for investors who adopt a strategy of basing their prediction of future beta on measures of beta in the past (Sharpe and Cooper, 1972). To investigate whether the assumption of beta stability is tenable, the following procedure is adopted. For every year during the period January 1980 through September 2001, beta for each stock that has monthly return data available in the preceding 60 months (minimum 48 months data must be available) is estimated using the market model. The beta estimates are classified into one of ten deciles of beta values, with 10% of the highest-beta stocks being assigned into decile 10 and 10% of the lowest-beta stocks into decile 1. This procedure is repeating in each of the following years, using returns data from the immediately preceding 60 months. Then, a stock’s beta risk class in each year is compared with first the class in the succeeding year, then the class five years hence. While the first comparison uses 48 months of common data, the second involves no overlapping periods. For brevity, only summary results are reported in Table 4. Focusing first on air transportation stocks, it is obvious that the systematic (beta) risk of these industry stocks is unstable over time. For example, while 91% of stocks that belonged in risk class 1 in year t remained in the same class or within one class after one year’s time, only 59% of stocks in risk class 1 in year t still remained in the same risk class or within one risk class after five years. These results indicate that there is substantial instability (a lack of constancy) in beta values over a 5-year period. The relative stability of beta over shorter intervals (e.g. one year) than over longer intervals (e.g. five years) may imply that air transportation stocks are constantly affected by macro- or micro-factors changing over time. A similar impression is gleaned for the water transportation stocks. The beta risk of these industry stocks appears more stable over one-year intervals than over a longer interval. For example, while 85% of stocks remained in the same risk class or within risk class 1 after one year, only 44% still remained in the same risk class or within risk class 1 after five years. Table 4. Summary Transition Matrix for International Stocks Risk class Proportion in same risk Proportion within one risk in year T class class In year In year T+5 In year In year T+5 T+1 T+1 Air 1 71% 41% 91% 59% transportati 2 48% 15% 91% 58% 6
  7. 7. on stocks 3 33% 18% 82% 52% 4 37% 12% 77% 38% 5 33% 9% 74% 35% 6 26% 10% 70% 36% 7 33% 9% 76% 34% 8 25% 23% 71% 48% 9 37% 10% 84% 46% 10 65% 23% 86% 44% Water 1 66% 28% 85% 44% transportati 2 48% 15% 84% 47% on stocks 3 3% 15% 47% 49% 4 30% 23% 54% 55% 5 30% 14% 76% 34% 6 33% 18% 78% 39% 7 35% 10% 78% 38% 8 40% 16% 80% 55% 9 49% 25% 88% 59% 10 58% 22% 80% 38% The above results led us to believe that beta for the shipping and airline stocks may be unstable over time, and thus care must be taken in interpreting a beta estimate (either for an individual stock or for the industry as a whole) obtained in a single time period. Our last analysis examined the so-called ‘intervalling effect’, i.e. whether beta value is influenced by the interval (e.g. monthly return versus quarterly return or daily return) over which it is computed. As reported in Table 5, there is some evidence that beta estimated from monthly returns is lower than when it is estimated from quarterly returns. Table 5. Intervalling Effect: Monthly (M) versus Quarterly (Q) returns Panel A. Difference air transportation stock beta: monthly versus quarterly returns 1997-2001 (M-Q) 1991-1996 (M-Q) 1991-2001 (M-Q) Observations (n) 86 50 51 Mean difference 0.09 -0.30 -0.00 Standard dev 0.54 0.70 0.39 t-statistic# 1.63* -3.04*** -0.07 Panel B. Difference in water transportation stock beta: monthly versus quarterly returns 1997-2001 (M-Q) 1991-1996 (M-Q) 1991-2001 (M-Q) Observation 180 107 107 Mean -0.01 -0.09 -0.01 Std 0.39 0.46 0.22 t-statistic# -0.38 -2.04** -0.40 *, ** , *** Significant at the 10%, 5%, 1% level. 3. Conclusion 7
  8. 8. The estimation of risk has implications for determining the required rate of return on capital employed, which is an important input to the investment analysis. Despite being generally regarded as highly risky businesses, the airlines and shipping stocks in fact display a systematic risk level that is at best close to the market average, although slightly different results were obtained under different research designs. There is also evidence to suggest that beta on an individual stock’s level appears to be unstable over time. There is also evidence of some intervalling effect, although this is inconclusive. For a (well-diversified) company that wishes to establish its required cost of equity capital in a particular project, it can start by estimating the systematic risk of its publicly-traded equity or the average industry stock, and then compute the required rate of return on such equity through the Capital Asset Pricing Model. If the project is to be financed by a mix of debt and equity, then the average cost of capital is the weighted average of the cost of these two sources of funds. However, it needs to be stressed again that the required rate of return for the project (no matter how it is financed) depends solely on its own systematic risk, as measured by the marginal contribution of its future payoffs to the portfolio risk of the company’s investments (theoretically, this should be a proportional holding of the market portfolio). Given the various difficulties in arriving at a reliable beta estimate from historical return data, one has to be careful in using as the benchmark a beta estimate obtained for a single stock or over a single historical time period. On a more pragmatic note, future research should aim to identify more reliable ways of determining the cost of capital applicable for investment decision-making. References Cullinane, K. and Gong, X. (2002). The mispricing of transportation initial public offerings in the Chinese mainland and Hong Kong. Maritime Policy & Management 29(2), 107-118. Dimson, E. (1979). Risk measurement when shares are subject to infrequent trading. Journal of Financial Economics 7, 197-226. Kavussanos, M.G. and Marcoulis, S. (2001). Risk and return in transportation and other U.S. and global industries. Boston: Kluwer Academic Publishers. Sharpe, W.F. (2000). Portfolio theory and capital markets. New York: McGraw-Hill, Inc. 8
  9. 9. Sharpe, W.F. and Cooper, G.M. (1972). Risk-return classes of New York Stock Exchange common stocks, 1931-1967. Financial Analysts Journal, March-April, 46-54, 81. Scholes, M. and Williams, J. (1977). Estimating betas from non-synchronous data. Journal of Financial Economics 5, 309-327. Stokes, P. (1996). Problems faced by the shipping industry in raising capital in the securities markets. Maritime Policy & Management 23(4), 397-405. (This article is based on a paper by Gong, X., Firth, M., Cullinane, K. and Wang, T.F. (2002) A High Risk – Low Beta Business? presented at the International Association of Maritime Economists Annual Conference, Panama, November 11-15, 2002.) 9

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