Multifactor explanations of asset pricing anomalies

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The models are ordered along the horizontal axis in order of increasing performance based on the proportion of characteristic-sorted cross-sections matched ; characteristics are ordered along the vertical axis in order of increasing matching difficulty measured as the fraction of all three-factor models able to match the return cross-section generated by sorting stocks on a given characteristic. Both the performance measure, and the frequency with which three-factor models match each cross-section are listed in parentheses along each axis.

Each cell on the figure is shaded black if the -factor model is able to match the cross-section based on characteristic ; shaded gray if the -factor model is unable to match the cross-section based on characteristic , and shaded white if factor model includes a factor constructed using characteristic. A few patterns are apparent. A few characteristics generated particularly challenging cross-sections of test portfolios, matched only by the few highest-ranked models.

Several characteristics are virtually impossible to reconcile with empirical three-factor models constructed using our procedure. The other characteristics seem to be more or less difficult to span depending on the subsample. Such lack of stability is consistent with the spurious nature of performance of many of the randomly constructed -factor models. The low degree of correlation in relative model performance across the two sub-samples is partly due to the sampling errors, but it also suggests that performance of many models in our set may be spurious.

Another possibility for data-mining is associated with the choice of the empirical procedure for return factor construction. Thus far we have used a straightforward procedure for constructing return factors as long-short portfolios of the top and bottom deciles of stocks sorted on each characteristic. One popular alternative approach, following Fama and French , prescribes a two-dimensional sort: first on firm size and then on a characteristic in case of Fama, the characteristic is the book-to-market ratio.

We apply a conceptually similar approach in our setting. Specifically, for each characteristic, we first sort firms into big and small big firms are above the median in market capitalization, small firms are below , form long-short portfolios within each size class, and then average the returns on the two long-short portfolios to construct a return factor. In Table 9, we report cross-sectional correlations of performance between the empirical factor models formed using our univariate factor construction method and the corresponding models with factors formed via the double-sorting procedure.

In Tables 10 and 11 we report very different top-twenty and bottom-twenty factor model lists compared to Tables 5 and 6. As an example, the model using net stock issues NSI and liquidity LIQ is the top twenty performing factor models in our original full-sample analysis Table 5 , but it is one of the worst-performing models over the full sample under the double-sorting method Table We can also compare overall factor model performance using the original one-dimensional sort factor construction Figure 3 panel A and the double-sort factor construction Figure 4.

While we observed in Table 9 a low correlation in model performance across the two factor construction methods, the relative predictability of characteristics is very similar. Similarly, investment-to-capital IK also appears to be spanned only by the highest-ranked models.

Finally, we examine the improvement in model performance caused by moving from three to four factors in the pricing models. We repeat our analysis by considering the universe of 2, four-factor models, consisting of the market portfolio and three -factors based on our list of 27 firm characteristics. We present the results for four-factor models in Appendix B. The best-performing four-factor model in Table B. Many of the twenty best-performing four-factor models add factors constructed on momentum MOM , standardized unexpected earnings SUE , investment over assets IA , and asset growth AG to one of the top-performing three-factor models.

All of these additions are based on characteristics that present the most challenge to the three-factor -models, as we show in Figure 3. Adding such factors to the three-factor models produces a slight mechanical improvement in performance by excluding the corresponding cross-section from the set of test portfolios. Beyond that, the improvement is minimal: most challenging cross-sections have little correlation with each other or with other -factors, and therefore it is not possible to capture many additional cross-sections by introducing a fourth -factor.

The potential hazards of data-mining are well known. Our findings show just how difficult it is to judge the performance of empirically constructed factor pricing models when both the return factors and the target cross-sections of assets are chosen in a virtually unrestricted manner. While the impressive performance of some of the models we consider is spurious, some models must indeed capture economically meaningful sources of risk.

Distinguishing one set from the other purely based on empirical performance seems difficult - if the factors included in a theoretically grounded risk-factor model are some of the many possible -factors, such a model is likely to be defeated in a pure performance horse-race by the spuriously picked champions.

Eugene F. Fama - Google Scholar Citations

The winner in such a horse-race is not necessarily a superior risk model. For example, the momentum factor MOM appears in at least one of the three best-performing three-factor models for the full sample, and each of the half-samples. Yet, without a convincing attribution of the return spread on the momentum-sorted portfolios to a well-understood source of risk, it is difficult to interpret momentum as a primitive risk factor of first-order economic importance. Other situations may be more ambiguous, and one may be able to offer at least a tentative ex-post theoretical justification for the top-performing model.

Such theory-mining can add a patina of false legitimacy to the spurious pricing models, exacerbating the effects of data-mining. For example, the top-performing model based on the standardized-unexpected-earnings SUE and the cashflow-to-price CP factors suggests some tantalizing possibilities for straddling the behavioral and neoclassical asset pricing paradigms to "motivate" a hybrid pricing model with empirical performance that is literally second to none. Needless to say, a superficial theory adds no more value than a spurious empirical result. In summary, our analysis lends further support to the notion that to distinguish meaningful pricing models from the spurious ones, we should place less weight on the number of seemingly anomalous return cross-sections the models are able to match, and instead closely scrutinize the theoretical plausibility and empirical evidence in favor or against their main economic mechanisms.

3.1 Characteristic-Sorted Portfolios

Ang, A. Hodrick, Y. Xing, and X. Zhang The cross-section of volatility and expected returns. Journal of Finance 61 1 , Baker, M. Bradley, and J. Wurgler Benchmarks as limits to arbitrage: understanding the low-volatility anomaly.


Financial Analysts Journal 67, Ball, R. Brown An empirical evaluation of accounting income numbers. Journal of Accounting Research 6 2 , Banz, R. The relationship between return and market value of common stocks. Journal of Financial Economics 9, Basu, S. Journal of Finance 32 3 , The relationship between earnings' yield, market value and return for nyse common stocks. Journal of Financial Economics 12, Bernard, V. Thomas Post-earnings-announcement drift: delayed price response or risk premium?

Journal of Accounting Research 27, Bhandari, L. Journal of Finance 43 2 , Black, F. Jensen, and M. Scholes Praeger Publishers Inc. Chan, L. Hamao, and J. Lakonishok Fundamentals and stock returns in japan. Jouranl of Finance 46 5 , Jegadeesh, and J.

Momentum strategies. Journal of Finance 51 5 , Chen, L. Novy-Marx, and L. An alternative three-factor model. Cohen, R. Gompers, and T. Vuolteenaho Who underreacts to cash- flow news? Journal of Financial Economics 66, Cooper, M. Gulen, and M. Schill Asset growth and the cross-section of stock returns.

Journal of Finance 63 4 , Daniel, K. Titman Market reactions to tangible and intangible information.

Capital Asset Pricing Model

Journal of Finance 61 4 , DeBondt, W. Thaler Does the stock market overreact? Journal of Finance 40 3 , Eisfeldt, A. Papanikolaou Organization capital and the cross-section of expected returns. Journal of Finance. Fairfield, P. Whisenant, and T. Yohn The Accounting Review 78 1 , Fama, E. French Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33 1 , The cross-section of expected stock returns. Journal of Finance 47 2 , Multifactor explanations of asset pricing anomalies. Journal of Finance 51 1 , Journal of Financial Economics 82, Dissecting anomalies.

Ferson, W. Warning: Attribute-sorted portfolios can be hazardous to your research. Saitou, K. Sawaki, and K. Kubota Eds.

Center for Academic Societies, Osaka, Japan. Frazzini, A. Pedersen , October. Betting against beta. Gibbons, M. Ross, and J.

Validity of Fama and French Three Factor Model with Capm in Kuala Lumpur Stock Exchange Market

Shanken A test of the efficiency of a given portfolio. Econometrica 57 5 , Haugen, R. Baker Commonality in the determinants of expected stock returns. Journal of Financial Economics 41, Hirshleifer, D. Hou, S. Teoh, and Y. Do investors overvalue firms with bloated balance sheets? Journal of Accounting and Economics 38, Ikenberry, D. Lakonishok, and T. Vermaelen Market underreaction to open market share repurchases.

Journal of Financial Economics 39, Jegadeesh, N. Returns to buying winners and selling losers: implications for stock market efficiency. Journal of Finance 48 1 , Lakonishok, J. Shleifer, and R. Vishny Contrarian investment, extrapolation, and risk. Journal of Finance 49 5 , Lee, C. Swaminathan Price momentum and trading volume. Journal of Finance 55 5 , Lewellen, J. Nagel, and J.

Shanken , July. A skeptical appraisal of asset-pricing tests. Litzenberger, R. Ramaswamy The effects of dividends on common stock prices: tax effects or information effects? Journal of Finance 37 2 , Lo, A. MacKinlay Data-snooping biases in tests of financial asset pricing models. Review of Financial Studies 3 3 , Loughran, T. Ritter The new issues puzzle.

Journal of Finance 50 1 , Lyandres, E. Sun, and L. The new issues puzzle: testing the investment- based explanation. Review of Financial Studies 21 6 , Miller, M. Dividends and taxes: some empirical evidence. Journal of Political Economy 90 6 , Novy-Marx, R. Pseudo-predictability in conditional asset pricing tests: explaining anomaly performance with politics, the weather, global warming, sunspots, and the stars.

Ohlson, J. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research 18 1 , Pastor, L. Stambaugh Liquidity risk and expected stock returns. Journal of Political Economy 3 , Piotroski, J. Journal of Accounting Research 38, Pontiff, J. Woodgate Share issuance and cross-sectional returns. Journal of Finance 63 2 , Rosenberg, B. Reid, and R. Lanstein Persuasive evidence of market inefficiency. Journal of Portfolio Management 11, Sloan, R. Do stock prices fully reflect information in accruals and cash flows about future earnings?

The Accounting Review 71 3 , Titman, S. Wei, and F. Xie Capital investments and stock returns. Journal of Financial and Quantitative Analysis 39 4 , Xing, Y. Interpreting the value effect through the q-theory: an empirical investigation. Review of Financial Studies 21 4 , Table 1 contains the monthly value-weighted average returns, CAPM alphas, and Fama-French alphas for the 27 characteristic-based return factors, over the whole and subsample periods. Factors are the high minus low portfolio from sorting firms into ten portfolios with respect to the underlying firm characteristic.

Table 1a. Table 1c. Table 2 presents results from a principal component analysis on the 27 characteristic-based return factors. The table shows the proportion of cumulative variation that the first principal components can capture. Results are presented over the whole sample period and subsamples and Table 3 presents factor loadings for the first three principal components extracted from the set of 27 factor returns.

Loadings are shown for the whole sample period and subsamples and Details on characteristic definitions and construction is in Appendix A. Table 4 presents results from regressing the characteristic-based return factors on the benchmark three-factor model, consisting of the market portfolio and the first two principal component vectors of the return factors.

The alpha coefficient, t-statistic, and from the regression is shown in the table for the whole sample period and subsamples and Table 5 lists the characteristic-based factors that constitute the top twenty linear factor models, in terms of the proportion of remaining characteristics they can capture, via the equal-weighted method.

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Top factor models are shown for the whole sample period and subsamples and The universe of factor models is all three-factor models consisting of the market portfolio and two characteristic return factors C1, C2 from our list of Table 6 lists the characteristic-based factors that constitute the bottom twenty linear factor models, in terms of the proportion of remaining characteristics they can capture, via the equal-weighted method. Bottom factor models are shown for the whole sample period and subsamples and Table 7. Table 7 shows the rank correlation and correlation of factor model performance for the first subsample period versus the second subsample period HML : H igh m inus l ow factor that accounts for the spread between value and growth companies based on their book-to-market ratio.

Value stocks are companies with high book to value. Construction: Double sorting. First divide stocks into two sets based on market capitalisation. Then split each set into three based on their book-to-market ratio. The SMB is the average return of the three small portfolios minus the average return of the three big portfolios, i.

The HML is the average return of two value portfolios minus the average return of the two growth portfolios, i. Step 1 Let n denote the number of assets.