Asset Pricing Models and Industry Sorted Portfolios

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1 Asset Pricing Models and Industry Sorted Portfolios Author: Marijn de Vries ANR: Faculty: Faculty of Economics and Business studies Programme: Bedrijfseconomie Supervisor: Jiehui Hu MSc Date: 7/6/2012 1

2 Table of Contents I. Introduction... 3 II. Theoretical background... 5 Modern portfolio theory... 5 Capital Asset Pricing Model... 6 CAPM anomalies... 7 Fama- French Three Factor Model... 7 Fama- French- Carhart Model... 8 Pricing industry portfolios... 9 III. Data IV. Methodology V. Empirical results VI. Conclusions References Appendix

3 I. Introduction Over time, many different models have been developed that help explain the return on stocks by looking at risk factors. The basis for many of these models lies in the Capital Asset Pricing Model (Sharpe, 1964/ Lintner, 1965) which was developed in the early sixties. This model was in turn based on earlier work by Markowitz (1952), in which he presented his Modern Portfolio Theory. In the following decades, many different researchers have tried to extend the basic CAPM. They have tried to further specify the different risk factors that help to explain stock returns in order to make the model even more powerful and reliable. Two of these newer asset pricing models are the Fama- French Three Factor Model (FF3) and the Fama- French- Carhart Four Factor Model (FFC). While these later models tend to explain a larger portion of the variation found in stock returns over long periods of time, they may not necessarily perform better when we look at short time periods with extraordinary economic conditions such as a financial crisis. We suspect that even though all of the before mentioned models are specified correctly for the long time periods, problems may arise due to redundancy of some of the variables used in the FF3 and FFC models as a result of a temporary shift in risk loadings, as well as multicolinearity for the Credit Crunch period. This suspicion is based on the fact that the extra variables used in the FF3 and FFC models are not true risk factors: they were added to the CAPM model in order to explain certain empirically observed anomalies and are therefore proxies for one or more true risk factors. If some of these proxies measure the aggregated risk that results from several true risk factors in different proportions then a temporary shift in weights of true risk factors could cause the proxies to become highly correlated. Another problem that has become apparent from previous research is the fact that accurately pricing assets on an industry level causes problems when we use the CAPM or FF3 asset pricing models (Fama& French, 1997, Moerman, 2005). These papers report a drop in the R 2 as well as an increase in the pricing error for some regressions when the models are applied to industry based portfolio returns. It seems that the usefulness of the variables in these models is not the same for all industries; Fama and French (1997) speculate that this is due to uncertainty about the true risk factors, as well as the shifts in risk loadings that occur over time. The degree in which industry returns are sensitive to changes in risk loadings may not be the same 3

4 for all industry portfolios. If this is indeed the case then studying the differences between industries could lead to a better understanding of the underlying true risk factors of the proxy variables used in the FF3 and FFC models, which in turn could lead to a better explanation of the variation in stock returns. While some research has been done on the application of the FF3 and CAPM models on industry sorted portfolios, we have yet to find a paper that also looks at the results for the FFC model. The research that was done on industry portfolio pricing with the use of these models is scarce and often inconclusive. So, in this paper we will try to address the following questions: 1) can all three models be used to explain the variation in stock returns both for the long term, as well as the Credit Crunch period, and 2) which of these models provides the greatest benefits when we apply them to industry based portfolios for both the 10 year period as well as the crisis period in terms of explaining power, fit and reliability? In order to answer these questions we will use a data set consisting of daily industry portfolio returns as well as the daily risk factors and risk free interest rates for the period , which we acquired from the website of Kenneth French. In our analyses we will focus on regressions for the whole 10 year period, as well as a sub period for the credit crunch crisis which consists of data from to In the first part of this paper we will take a closer look at the three models and their theoretical background. We will then proceed with a description of the dataset and methodology we used and finally we present our findings which we will try to link back to the theory discussed below. 4

5 II. Theoretical background Modern portfolio theory As we have already mentioned before, the basis for the CAPM and other subsequent models can be found in the Modern Portfolio Theory as described by Markowitz (1952). In the formulation of this theory Markowitz used two important assumptions about investor behavior. The first assumption is that all investors (should) strive to maximize the expected return on their investments. The second assumption is that investors will try to keep the variance, or fluctuation, of these expected returns as low as possible. The first makes sense; an investor should always try to get the most profit from his investment. But the actual profit for a certain holding period is not fixed for most investments because of uncertainty in cash flows and discount rates. So we can only base our portfolio selection on estimated-, or expected returns. But regardless of the discount rates we use for different assets and the change of these discount rates over time, an investor will always choose to invest all his wealth in the asset with the highest discounted value. This would imply that investors have no use for diversification. But if we add the second assumption; the need for investors to keep the variance of the expected returns as low as possible for a given expected return, a new picture emerges. Why would an investor choose to invest in a portfolio that has huge variances in the returns over a portfolio that has a much more gradual return if the average returns are exactly the same? The answer is quite simple: he doesn t. He will try to maximize the expected return of his portfolio and at the same time minimize the variance of the expected returns. Markowitz combined these two important facts and shows that the covariance between different assets can be used to lower (or increase) the variance in expected returns. This means that by diversifying the portfolio, an investor can lower the risk while keeping the expected return of the total investment constant. This approach leads to the formation of well diversified efficient portfolios based on the investor s preferred combination of expected return and variance. It gives investors the opportunity to maximize profits while, at the same time, minimizing the risk associated with the investment. 5

6 Capital Asset Pricing Model The CAPM model as described by Sharpe (1964) and Lintner (1965) builds on Markowitz s Modern Portfolio Theory. The fact that diversification can be used to reduce the variance of expected returns for a portfolio of assets is applied to the market as a whole. All investors will try to attain a portfolio that maximizes the expected return and at the same time minimizes the risk associated with holding the portfolio. The only way they can do this is by buying and selling assets. In doing so, the price of the assets change and therefore the expected return changes. This process results in an efficient frontier for all investors; the assets that comprise an efficient portfolio may change over time, but the minimum amount of risk that needs to be incurred to attain a certain expected return is the same for all investors. This means that in a state of equilibrium there will be a simple linear relation between risk and return for any efficient combination of risky assets in the market. Sharp and Lintner show that, in their model, the correlation between the efficient market portfolio and all other assets is caused by their common dependence on the general state of the economy. If this is true then investors could diversify all risk with the sole exception of the risk associated with the overall level of economic productivity or systematic risk. This means that when we assess the risk of an asset only the sensitivity of the asset to systematic risk matters. Because of this, asset prices will continue to change until there is a linear relationship between the expected return of an asset and its sensitivity to systematic risk. So in the CAPM there is only one risk factor: an asset s expected return depends only on the exposure of the asset to the overall market risk. So the expected return of asset i can be found by adding the risk free interest rate to the expected excess market return multiplied by the sensitivity of asset i to this market: E[R i ]=r f + ß i (E[R m ]-r f ) From this we can construct the Security Market Line, this is the graphical representation of the linear relation between all assets ß s and expected returns. If the CAPM holds then all assets should fall on this line in the equilibrium state. This was used by Black, Jensen& Scholes (1972). By measuring α, the distance that the actual 6

7 returns lie above or below the SML prediction for the expected return, they argued that if the CAPM was right then α should never be significantly different from 0 for longer periods of time. CAPM anomalies Black, Jensen& Scholes (1972) found that there was indeed a portion of stock returns that could not be explained by the Capital Asset Pricing Model. In other words; α was significantly different from 0 for groups of stocks during certain time periods. And they were not alone in finding discrepancies between the CAPM expected returns and real world stock returns. For instance, Banz (1981) found that small firms (measured by total market capitalization) had higher risk adjusted returns than large firms. In addition, Fama& French (1988/1992) found that not only size, but also the book-tomarket ratio of a firm helps to explain its stock returns. While both size and BE/ME ratio can be used to explain a part of the expected returns of a stock, they are firm characteristics and not risk factors. Fama& French (1993,1995) argue that both of these firm characteristics are a measure of (future) profitability; they found that stocks with a low BE/ME ratio are more profitable than stocks with a high BE/ME for a period of 4 years before and 5 years after the raking was made. Low BE/ME is characteristic for firms with a high average return on equity (growth stocks) and a high BE/ME ratio signals low earnings compared to equity (often a signal of financial distress). This financial distress can be explained by a number of causes, for instance firm inefficiency, high levels of (involuntary) leverage, or cash flow problems (Chan& Chen, 1991). Furthermore, companies with a low market capitalization tend to have lower earnings on book equity than stocks with a high market capitalization (Fama& French, 1992, 1995). So while BE/ME ratios and size by themselves are not risk factors, they do seem to be good proxies for risk factors than can help explain a stock s expected return. Fama- French Three Factor Model The observed anomalies in the stock market returns from previously mentioned research led to the addition of two new risk factors to the original CAPM (Fama& French 1992, 1993, 1995, 1997). These factors were constructed by forming portfolios 7

8 based on company size and BE/ME- ratio. The first was a risk factor that corrects for the size effect (SMB), measured by the relative market capitalization. The second was a factor that accounts for the previously unexplained differences in the variance of returns of growth and value stocks (HML). This model became known as the Fama- French Three Factor Model (FF3): E[R i ]=r f + b i (E[R m ]-r f ) + s i SMB + h i HML The addition of these two new risk factors provided a significant increase in the R 2 while at the same time lowering the pricing error α. However, the method used by Fama and French to construct the SMB and HML portfolios was also subject to criticism. Kothari, Shanken and Sloan (1995) argue that the COMPUSTAT data used by Fama and French was subject to a survivor bias, especially the data on small stocks with a high BE/ME ratio could be affected because small, financially distressed companies could have been delisted from the stock market (for instance because of bankruptcy or low trading volumes) before they were added to the compustat dataset that was used to form the portfolios. Fama and French (1995) address these concerns; their first remark is that the survivor bias problem is much smaller for their research because they use value weighted returns instead of the equal weighted returns used by Kothari, Shanken and Sloan (1995). They also argue that this bias problem does not apply to the large firms: even when the bottom half of the size sorted data is dropped, the high BE/ME stocks still outperform the low BE/ME stocks. But their most important argument is that the fact that small stocks with high BE/ME ratios do not survive only reinforces their evidence that these stocks have persistent low earnings. Fama- French- Carhart Model The third and last model that we will be using in this paper is the Fama- French- Carhart model (FFC). This model builds on the FF3- model by adding a momentum risk factor. This risk factor is included in order to explain the one year momentum anomaly found by Jegadeesh and Titman (1993). They found that the returns of portfolios based on prior performance of individual assets yields abnormal returns. At first these abnormal returns were explained as market inefficiency caused by a lag in the reaction to new information on stocks (Chan, Jegadeesh& Lakonishok, 1995). But the fact that this momentum anomaly is persistent over time (Jegadeesh& Titman, 8

9 1993) and is found in various countries (Asness, Liew& Stevens, 1997), suggest that there is a common risk factor that can explain this momentum anomaly. Carhart (1997) compares this new expanded model with the CAPM and FF3 and shows that the addition of a momentum factor significantly lowers the average pricing error when these models are applied to portfolios that are sorted on their prior year s performance. Furthermore, he reports that the FFC risk factor loadings are not substantially affected by multicollinearity. This suggests that the new momentum factor explains a part of the variation in stock returns that is not captured by the risk factors in the CAPM and FF3 models. Pricing industry portfolios The CAPM, FF3 and the FFC models perform relatively well when used on the (US) stock market as a whole. When these models are used to evaluate returns of portfolio s based on industry, this changes. Research has show that accurately pricing assets on an industry level causes problems (Fama& French, 1997, Moerman, 2005). Both the CAPM and FF3 models show a reduction in R 2 as well as an increase in α when they are applied to industry based portfolio returns. In addition, the estimated cost of equity (CE) becomes imprecise. The standard errors for the CE become more than three percent for both models, so when the models are used to form a prediction interval for the cost of equity using a one-standard-error bounded interval the interval becomes quite large which makes it practically useless (Fama& Fench, 1997). We can illustrate this with an example: if we would have an annualized market return of 4 % and a standard error of 3% for this premium, then the one- standard- error bounds for the CE of an investment opportunity with a beta of 1 would be 1% for the lower bound and 7% for the upper bound. The two-standard-error bounds would become -2% and 10%. Because these intervals are so large, accurately estimating the profitability of an investment becomes almost impossible. Furthermore, the estimates of the CAPM and FF3 differ more than two percent for many industries (Fama& Fench, 1997). These differences are likely driven by uncertainty about true risk factors and imprecise estimates of period-by-period risk loadings (Fama& Fench, 1997). This uncertainty in the prediction of the CE causes problems for companies when these CE estimates are used to evaluate investment opportunities; an error in the estimated CE could cause the rejection of profitable investments or, even worse, the 9

10 acceptance of projects with a negative NPV. The ability to accurately estimate the CE is therefore of vital importance when making finance decisions. It is suggested in previous literature that the SMB and HML factors could be related to a firm s relative financial distress. As we have mentioned before, inefficient firms (relative inefficient use of capital, for instance inefficient production lines) with high leverage and cash flow problems, seem to drive the small firm effect according to Chan & Chen (1991). Furthermore, Fama and French (1992) speculate that the premium associated with high book equity to market equity (BE/ME) might be due to the risk of financial distress. This is supported by results from Fama and French (1994), which shows that industry sorted portfolios experience periods of growth and distress and that the HML loading is not constant over time. While there is no hard theoretical evidence that conclusively proves that size and book-to-market factors proxy for financial distress, some recent studies that focused on industry based portfolio returns for US (Fama & French, 1994), UK (Hussain& Toms, 2002) and European markets (Moerman, 2005) report similar results in different markets which suggests that the SMB and HML proxies could be related to a true risk factor that measures financial distress. The rationale behind this link is that some industries are affected more strongly by economy wide changes that take place during business cycles than others, thus causing increased industry growth or increasing the risk of financial distress for certain industries while others remain relatively unaffected. If this is indeed the case and the SMB and HML factors are able to capture (a portion of) this industry dependent systematic risk, the FF3 model should clearly outperform the CAPM both in terms of R 2 and α when these models are applied to industry sorted portfolio returns. Furthermore we would expect to see a large shift in the risk loadings for these factors for cyclical industries during the Credit Crunch period compared to the whole time period, while non-cyclical industries risk loadings should remain relatively unaltered. As industries experience growth and distress through various periods of time, industry risk may have an idiosyncratic element that is specific to the industry for a period of time causing under or over-reaction to the market (Hussain & Toms, 2002). 10

11 III. Data In this paper we will be using a long time series of daily average value weighted industry returns data based on all companies listed on the NYSE, AMEX and NASDAQ for which the relevant data was available during the period January 2001 to December These returns, as well as the industry definitions which can be found in the appendix, were retrieved from Kenneth French s website. The 10 industries were defined using the first four digits of the stocks sic codes. We also retrieved the four daily risk factors from Kenneth French s website, as well as the daily risk free interest rate for the 10 year period. The daily excess industry returns were computed by subtracting the risk-free rate from the daily return of each industry. We also created a dummy variable for the Credit Crunch period: D crisis =1 for the period to This period is based on the NBER US Business Cycle Expansions and Contractions reports and captures the period in which the first shock of the credit crunch caused a contraction in the US economy. Even though this time period is not directly based on the stock market returns, it covers the height of the effects of the crisis on the US stock market. This enables us to compare the performance of the different models during normal times and the Credit Crunch period. Furthermore, it encompasses enough observations for making statistical inferences. The methods used to construct our risk factors can be found in Fama and French (1992, 1993, 1996), Griffin (2002), Moerman (2005) and Carhart(1997). While we did not construct these risk factors for this particular paper, but instead used the risk factors provided by Kenneth French, we still feel it is important to describe how they were constructed for completeness of this paper. Firstly, all stocks are ranked on market capitalization (size). The sample median is calculated and the sample is split into two groups; one portfolio of companies with a large market capitalization (L), the other with small market capitalization (S). The sample is also sorted on the BE/ME ratio. The top 30% form the high book-to-market portfolio (H), the middle 40% the middle portfolio (M), and the bottom 30% is the low book-to-market portfolio (L). Six combinations are then formed: SL, SM, SH, 11

12 BL, BM, BH. The SMB is the simple average of all small (S) portfolio returns minus big (B) portfolio returns: SMB= (SL+SM+SH-BL-BM-BH)/3 And the HML is constructed by taking the average of the high BE/ME portfolio returns minus the low BE/ME portfolio returns for each month: HML= (BH+SH-SL-BL)/2 To construct the momentum factor six value weighted portfolios were created on size and 11 months prior returns. These six portfolios are formed from the intersections of the 2 size sorted stocks, and 3 prior 11 months return sorted portfolios. The three prior return portfolios are defined as the top 30%, middle 40% and bottom 30%. The momentum risk factor can then be calculated by taking the average return on the two high prior return portfolios minus the average return of the two low prior return portfolios: MOM=(SH+BH-SL-BL)/2 12

13 IV. Methodology We will start by performing an OLS regression of the three different models on daily excess stock returns on the whole data set (all industries). The regression models that we will be using are as follows: R it -R ft = α i +β i [R mt -R f ]+e it R it -R ft = α i +b i [R mt -R ft ]+s i SMB t +h i HML t +e it R it -R ft = α i +b i [R mt -R ft ]+s i SMB t +h i HML t + m i MOM t +e it With: R it = return of portfolio i on month t R ft = risk-free rate in month t R mt = market return in month t β i, b i, s i, h i, m i = unconditional sensitivities of asset i for respective risk factor α i = pricing error e it = error term And SMB, HML and MOM are returns of the value-weighted, zero-investment, factor-mimicking portfolios for size, book-to-market equity and prior returns. We will use the regression results to provide a benchmark for both R 2 and α for all three models, using F- tests to determine if the regressions as a whole are statistically significant and t-tests to check if any of the variables used in the regressions are insignificant. The pricing error will be checked to determine if it is significantly different from zero using t-statistics. This is in line with previous research Fama and French (1992, 1993, 1996). The second step is to perform these same regressions for just the crisis period. We will then try to determine if any of these models are incorrectly specified for this particular period. Because if this is indeed the case then we cannot accurately explain stock returns for the crisis period using that particular model. We will check for multicolinearity using correlation matrices and variance inflation factors for the 13

14 independent variables (VIF). The regression results of the models that survive these tests can then be compared to the regression results for the whole ten year period. We make this comparison to see if a shift in the sensitivity to the different risk factors can be found. And again, the R 2 and α will be recorded and α will be tested to see if it is significantly different from zero. The last step is to zoom in on the industry based portfolio s and, using the results from the previous tests, determine which of the three different models is most suited to explain the returns. Furthermore, we want to determine if the shift in sensitivities to the different risk factors as observed from the whole dataset regression is consistent with the shifts observed on industry level. 14

15 V. Empirical results In this part of the paper we will present the results of our empirical research as described in the previous chapter. The first step in this process is to regress the three different models on the sample data for the whole 10 year period as well as the data for the crisis period and assess their relative performance. We will be using the same performance criteria as Moerman (2005). The first performance criteria is the absolute pricing error; if the model is indeed able to accurately explain stock returns, α should not be significantly greater than zero. As a second performance criterion we use the adjusted R 2, which gives us the explanatory power of the different models. The results of these regressions can be found in Table IV and Table V in the appendix. When we take a look at the results from table IV we can clearly see that the adjusted R 2 increases with the number of variables used in the regression. For the whole 10 year time period regressions the adjusted R 2 for the CAPM lies between and with an average of for the industry regressions. For the FF3 model these adjusted R 2 s were and with an industry average of For the FFC model these respective R 2 s were , and All of the reported R 2 are highly significant. This would imply that each of the variables adds to the explaining power of the model, making the more complex models better suited to explain the variation in daily industry stock returns. From these numbers we can also conclude that these three models are able to explain between 53.63% and 93.66% of the variation in stock returns for this 10 year period. Another thing that we can see from the results in table IV is that the regressions yield almost no significant pricing errors for this time period, the exceptions being the two industries Manufacturing and Other. The results of the regressions for the crisis period (table V) paint a similar picture: the models that use more factors outperform the less complex models in terms of adjusted R 2. While the F- values of the regressions have decreased, all reported R 2 s are still highly significant. The R 2 s for the crisis period regressions are much higher than those of the full time period regressions with averages for the industry regressions for the CAPM and FF3 at and respectively. The absolute pricing errors seem to have increased a bit but they are not significant in any of the regressions 15

16 performed. So the models seem to explain a larger portion of the variation in stock returns for the crisis period than for the whole 10 year period. But when we look at Table VI we can see a problem; for the whole period the correlations between the variables used in the three different models seems to lie within a reasonable range, but the correlation matrix for the crisis period subset indicates that a multicollinearity problem arises for the momentum factor used in the FFC model. This is confirmed when we look at the VIF value for the momentum factor which is The momentum factor seems to have an unacceptably high correlation with the HML factor for this time period. These two factors seem to explain, for a large part, the same variation in returns. This could indicate that the FFC model is not correctly specified for the crisis period, making it less suitable to explain the variation in returns for this period even though it performs well in terms of R 2 and Jensen s Alpha. When we look at the shift in risk loadings for the different time periods for the three models (Tables VII, IIX & IX), a striking picture emerges; for the whole time period regressions just four of the industry portfolios have a negative coefficient for the HML factor in both the FF3 and FFC models, and at the same time the complete data set regressions yield positive coefficients for the HML factor in both models. For the Crisis period regressions we see a major shift: both regressions for the whole crisis data set result in a negative relation between HML and returns, and almost all of the individual industry portfolios have a negative coefficient for the HML factor. This would mean that for the whole 10 year time period a high book- to- market value has a positive effect on returns, but during the crisis period a high HML has a negative effect on (portfolio) returns. These results seems to be in line with findings from previous empirical studies that we mentioned before, and it could be an indication that Fama and French (1992) were right when they speculated that the HML risk premium is associated with the risk of financial distress. The SMB risk loadings also change but to a much lesser degree. The FF3 and FFC regressions for the whole 10 year time period show a negative relation between SMB and returns but for the crisis period the sign changes and the effect of SMB on the returns becomes positive. However this sign change is only observed for the whole data set regressions, not for the individual industry portfolios (with the exception of the industry Other when the FF3 model is used). 16

17 If we consider the individual industries we find that the there are several industries for which the three models seem to perform above average. The industries are Manufacturing, Hightech and Other. The regressions for these industries yield high R 2 s for all three models for both the 10 year time period as well as the crisis period. However, if we look at the 10 year period we also find that the industries Manufacturing and Other are the only ones that produce significant (albeit small) pricing errors. So ironically, the industries for which the models used in this paper seem to explain the variation in daily returns best in terms of R 2 also seem to hint at the existence of pricing errors. When we look at the FF3 regression results for these industries for the crisis period we find that these are the only two industries for which the coefficient for the HML factor has a positive sign. 17

18 VI. Conclusions Based on our results we find that while all three models can be used to explain returns for the whole ten year period, a multicolinearity problem arises if we apply the FFC model to the crisis period returns. The momentum factor that is used in the FFC model becomes highly correlated with the HML factor. While the problem does not seem to be severe, the gains in terms of R 2 when including the momentum factor in the regressions are small compared to the results generated by the reduced model (FF3). This becomes especially clear when we compare the momentum risk loadings for the whole time period to the crisis period risk loadings for the whole data set regressions. The coefficient of the momentum variable drops from for the 10 year period to during the crisis period. This could be an argument to use the FF3 model instead of the FFC model for this particular period. The regressions of the industry based portfolio returns provide us with some interesting results: the industries that perform best in terms of R 2 (Manufacturing and Other) are also the only two industries for which the regressions yielded significant pricing errors. Furthermore the Manufacturing and Other industry portfolios are the only two that have a positive risk loading for the HML factor in the FF3 regressions for the crisis period. So even though the models are able to explain a large part of the variation in daily excess returns for these two industry portfolios, there is still a significant pricing error present. The fact that these two industries are the only two for which the HML risk loadings do not change from positive to negative also raises questions. It would seem that these two industries in particular are especially interesting for further research, especially concerning the underlying risk factor(s) of the HML proxy. Both the FF3 and the FFC models rely on proxies for true risk factors. Even though the use of these proxies improves the results of the regressions they also create problems, especially when we apply these models to industry based portfolios. The multicolinearity found in the FFC model for the crisis period is just one example of this. However, the shift in risk loadings observed for HML and SMB for nearly all industries during the crisis period could be an indication that these variables are 18

19 proxies one or more true risk factors that measure financial distress as suggested by Fama and French (1992) which is not included in the CAPM model. The results of our research clearly illustrate the need for further research into the true underlying risk factors of the SMB, HML and UMD variables that are used in the FF3 and FFC models. Looking at industry portfolios could be a good starting point for this research. Comparing the results of the application of these models to industry portfolios and the real world differences between the industries could lead to important insights into the theoretical risk factors that affect assets returns. 19

20 References Asness, C. S., Liew, J. M., & Stevens, R. L. (1997). Parallels Between the Cross-Sectional Predictability of Stock and Country Returns. The Journal of Portfolio Management, 23(3), doi: /jpm Carhart, M. M. (1997). On Persistence in Mutual Fund Performance. The Journal of Finance, 52(1), Chan, K. C., & Chen, N.-F. (1991). Structural and Return Characteristics of Small and Large Firms. The Journal of Finance, 46(4), Chan, L. K. C., Jegadeesh, N., & Lakonishok, J. (1995). Momentum Strategies. National Bureau of Economic Research Working Paper Series, No Fama, E. F., & French, K. R. (1988a). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), doi: / x(88) Fama, E. F., & French, K. R. (1988b). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), Fama, E. F., & French, K. R. (1992). The Cross-Section of Expected Stock Returns. The Journal of Finance, 47(2), Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), doi: / x(93) Fama, E. F., & French, K. R. (1994). Industry costs of equity. Working paper, Graduate School of Business, University of Chicago, Chicago, IL, revised July 1995 Fama, E. F., & French, K. R. (1995). Size and Book-to-Market Factors in Earnings and Returns. The Journal of Finance, 50(1), Fama, E. F., & FrencH, K. R. (1996). Multifactor Explanations of Asset Pricing Anomalies. The Journal of Finance, 51(1), Fama, E. F., & French, K. R. (1997). Industry costs of equity. Journal of Financial Economics, 43(2), doi: /s x(96) Fama, E. F., & French, K. R. (2004). The Capital Asset Pricing Model: Theory and Evidence. The Journal of Economic Perspectives, 18(3), doi: / Griffin, J. M. (2002). Are the Fama and French Factors Global or Country Specific? Review of Financial Studies, 15(3), doi: /rfs/ Hussain, S. I., & Toms, S. (2002). Industry Returns, Single and Multifactor Asset Pricing Tests. SSRN elibrary. doi: /ssrn Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1),

21 Jensen, M. C., Black, F., & Scholes, M. S. (1972). The Capital Asset Pricing Model: Some Empirical Tests. Michael C. Jensen, STUDIES IN THE THEORY OF CAPITAL MARKETS, Praeger Publishers Inc., Kothari, S. P., Shanken, J., & Sloan, R. G. (1995). Another Look at the Cross-Section of Expected Stock Returns. The Journal of Finance, 50(1), Lessard, D. R. (1974). World, National, and Industry Factors in Equity Returns. The Journal of Finance, 29(2), Liew, J., & Vassalou, M. (2000). Can book-to-market, size and momentum be risk factors that predict economic growth? Journal of Financial Economics, 57(2), doi: /s x(00) Lintner, J. (1965). The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets. The Review of Economics and Statistics, 47(1), Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), Moerman, G. A. (2005). How Domestic is the Fama and French Three-Factor Model? An Application to the Euro Area. SSRN elibrary. Moskowitz, T. J., & Grinblatt, M. (1999). Do Industries Explain Momentum? The Journal of Finance, 54(4), doi: / Post, T., & Van Vliet, P. (2004). Do Multiple Factors Help or Hurt? SSRN elibrary. doi: /ssrn Rolf W, B. (1981). The relationship between return and market value of common stocks. Journal of Financial Economics, 9(1), doi: / x(81) Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. The Journal of Finance, 19(3),

22 Appendix TABLE I Industry definitions This table provides an overview of the industry classifications used in this paper and the sic codes used in their formation. Industry Industry name Description Sic codes number 1 Consumer nondurables 2 Consumer durables Food, Tobacco, Textiles, Apparel, Leather, Toys Cars, TV's, Furniture, Household Appliances Manufacturing Machinery, Trucks, Planes, Chemicals, Off Furn, Paper, Com Printing Energy Oil, Gas, and Coal Extraction and Products Hitech Business Equipment -- Computers, Software, and Electronic Equipment and related services Telecom Telephone and Television Transmission Shops Wholesale, Retail, and Some Services (Laundries, Repair Shops) Health Healthcare, Medical Equipment, and Drugs Utilities Utilities Other Other -- Mines, Constr, BldMt, Trans, Hotels, Bus Serv, Entertainment, Finance other 22

23 TABLE II Sample breakdown by industry and fiscal year This table provides a breakdown of the dataset to the number of observations per industry per year. Industry/ year total cons nondur cons dur manufacturing energy hightech telecom shops health utilities other Total

24 Table III Descriptive statistics This table provides descriptive statistics for all variables, returns and risk free interest rate measured in daily percentages. Variable Description T Mean Median St.dev. Min. Max. Industry level variables cons nondur Value weighted excess return for industry cons dur Value weighted excess return for industry manufacturing Value weighted excess return for industry energy Value weighted excess return for industry hightech Value weighted excess return for industry telecom Value weighted excess return for industry shops Value weighted excess return for industry health Value weighted excess return for industry utilities Value weighted excess return for industry other Value weighted excess return for industry market variables RF Risk free rate E_RETURN Value weighted Excess market return SMB Size risk factor HML BE/ME risk factor UMD Momentum risk factor Other variables CRISIS =1 if observation is between and

25 TABLE IV Regression results: full time period This table reports the absolute pricing error in the first column and the adjusted R 2 in the second column for the regressions for the three models using the whole dataset SMB, HML, MOM and market returns. The first is the regression on the whole data set, the others are regression results of the industry based subsets. All α are not significant except for those marked with *(significant at 0.05) and **(significant at 0.1). All R 2 are significant at CAPM FF3 FFC α R 2 α R 2 α R 2 Whole data set Industry portfolio cons nondur cons dur manufacturing * ** ** energy hightech telecom shops health utilities other * * average TABLE V Regression results: Crisis period This table reports the absolute pricing error in the first column and the adjusted R 2 in the second column for the regressions for the CAPM and FF3 models using the crisis dataset SMB, HML and market returns. The first is the regression on the whole crisis data set, the others are regression results of the industry based subsets. α marked with ** are significant at 0.1 level, all other α are not significant (highest significance level is 0.355), all R 2 are significant at level Whole data set CAPM FF3 FFC α R 2 α R 2 α R Industry portfolio cons nondur cons dur ** manufacturing energy ** hightech telecom shops health utilities other average

26 TABLE VI Correlations Correlations for full dataset Correlations for crisis dataset R i-r f R m-r f SMB HML MOM R i-r f R m-r f SMB HML MOM R i-r f 1 R i-r f 1 R m-r f R m-r f SMB SMB HML MOM HML MOM TABLE VII Regression results: CAPM risk loadings Whole data set Whole period Crisis period ß t ß t Industry portfolio cons nondur cons dur manufacturing energy hightech telecom shops health utilities other

27 TABLE IIX a Regression results: FF3 risk loadings Whole data set Whole period b t s t h t Industry portfolio cons nondur cons dur manufacturing energy hightech telecom shops health utilities other TABLE IIX b Regression results: FF3 risk loadings Whole data set Crisis period b t s t h t Industry portfolio cons nondur cons dur manufacturing energy hightech telecom shops health utilities other

28 TABLE IX a Regression results: FFC risk loadings Whole data set Whole period b t s t h t m t Industry portfolio cons nondur cons dur manufacturing energy hightech telecom shops health utilities other TABLE IX b Regression results: FFC risk loadings Whole data set Crisis period b t s t h t m t Industry portfolio cons nondur cons dur manufacturing energy hightech telecom shops health utilities other

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