Asset Pricing Tests Research Articles

A Re-Examination of the Book-Tax Difference Pricing Anomaly

I examine whether book-tax differences (BTDs) are mispriced. Using time-series asset pricing tests, I provide evidence of hedge returns to trading on BTDs consistent with prior evidence from security-level return predictability tests. BTDs are a function of accruals and growth, so I consider whether BTD mispricing is accrual and growth mispricing, two economically significant asset pricing anomalies. Using operating cash flows-to-price (CFO/P), which jointly captures accrual and growth mispricing, I provide consistent evidence that the returns to trading on BTDs are subsumed by the returns to trading on CFO/P. My findings suggest that BTD mispricing is the accrual and growth anomalies in disguise, rather than a distinct anomaly. Overall, the evidence casts doubt on the ability of BTDs to provide unique, unpriced information for future earnings and their usefulness as a mispricing signal. This study provides useful evidence to investors and informs calls for additional tax-related disclosures.

Comparing Asset Pricing Models

ABSTRACTA Bayesian asset pricing test is derived that is easily computed in closed form from the standard F‐statistic. Given a set of candidate traded factors, we develop a related test procedure that permits the computation of model probabilities for the collection of all possible pricing models that are based on subsets of the given factors. We find that the recent models of Hou, Xue, and Zhang (2015a, 2015b) and Fama and French (2015, 2016) are dominated by a variety of models that include a momentum factor, along with value and profitability factors that are updated monthly.

Are the Fama-French factors really compensation for distress risk?

In this paper, we revisit the question whether the Fama-French factors are manifestation of distress risk premiums. To this end, we develop new tests specifically aimed at dissecting the Fama-French factor returns from a distress risk premium. While we find that value and small-cap exposures are typically associated with distress risk, our results also indicate that distress risk is not priced and that the small-cap and value premiums are priced beyond distress risk. Moreover, the distress risk exposures of common small-cap and value factors do not have explanatory power in asset pricing tests. Our results are robust to international out-of-sample analyses and have important implications for investors engaging in small-cap and value strategies.

Noise in Asset-Pricing Tests and Anomalies

Asset-pricing theories involve expectations whereas asset-pricing tests are almost universally performed on noisy realizations. This paper addresses this fundamental yet difficult problem to find that the noise in realized returns endogenizes the market factor, thereby causing the measured risk, abnormal return, and risk premium to be biased and inconsistent. The causal effects of noise are so detrimental to time-series tests that they can explain why firm characteristics related to size, value, financing, profitability, and investment tend to produce market anomalies. Noise is also harmful to the measured risk premium and can explain why a nonexistent risk-return tradeoff is often inferred from cross-sectional tests despite the relevance of risk. Statistically consistent estimators are proposed to address the endogenous effects of noise, and they appear to perform much better than the existing ones.

Ex-post risk premia estimation and asset pricing tests using large cross sections: The regression-calibration approach

We propose a modification of the two-pass cross-sectional regression approach for estimating ex-post risk premia in linear asset pricing models, suitable for the case of large cross sections and short time series. Employing the regression-calibration method, we provide a beta correction method, which deals with the error-in-variables problem, based on which we construct an N-consistent estimator of ex-post risk premia and develop associated novel asset pricing tests. Empirically, we reject the implications of the CAPM and the Fama–French three-factor and five-factor models but also offer new evidence on the relevance of the HML factor for pricing large cross sections of individual stocks.

Predicting Financial Crisis by Examining Risk-Return Relationship

We test the risk-return relationship in and around the global financial crisis for three market groups namely developed, emerging and frontier markets, from June 2005 to June 2016. Four risk proxies are used viz. Standard Deviation, Semi Deviation, Beta and Downside Beta. We find that the global financial crisis had a de-coupling effect as the return associations between global financial markets declined for the post crisis period. We also observe high positive correlations between alternative risk measures implying that asset pricing tests shall be robust to choice of risk measure. Next, we find that the risk-return relationship is absent in the pre-crisis period while it is significantly negative in the post crisis period using standard deviation based measures. Thus, the breaking down of risk-return relationship may be an important predictor of any forthcoming crisis. Further, global capital markets seem to behave like distressed economies in the post crisis period and, hence, exhibit option like behavior. We re-verify our hypothesis by using data in and around the Asian Crisis Period and confirm the absence of risk-return relationship prior to the crisis period. The study contributes to capital market literature with special emphasis on financial crisis.

Asset Pricing Bubbles Under Rational Expectations

The Efficiency Market Hypotheses (EMH) imply rational investors and no asset mispricing in the medium run. This paper critically evaluates on the point of whether an asset price bubble is an irrational phenomenon that cannot be detected. Thereby, I review the existing literature and reflect on asset pricing tests to detect bubbles. I show that the emergence of rational bubbles within the EMH framework is indeed possible as long as a number of assumptions is not violated. As researchers show, these assumption do not hold in practice, which means that rational bubbles can be ruled out. Thus, a bubbles is always an irrational phenomenon that can be explained with behavioural models such as feedback trading and herding. This implies that EMH does not hold. Hence, irrational bubbles can only emerge in inefficient markets. Models trying to detect bubbles fail to identify the existence of bubbles ex ante and ex interim. Ex post, they can prove that a bubble had existed.

Mutual Fund Flows and Downside Risk

There is little empirical evidence regarding downside risk in asset pricing, due in part to problems inherent in estimating downside risk. We argue that Berk and van Binsbergen (2016)’s approach to testing asset pricing models using the relation between investor flows and risk-adjusted fund returns is well suited for examining the merits of downside risk. We extend the analysis of Berk and van Binsbergen (2016) and Barber et al. (2016) by showing that investors care more about downside market risk than unconditional market risk when choosing mutual funds. Our study provides novel insights regarding investors’ preferences. We find that investors’ sensitivity to downside risk increases following market crises and is more pronounced among funds with conservative investment objectives. Finally, we find that investors’ response to downside risk is not subsumed by information/influence from Morningstar ratings.

Elephants and the Cross-Section of Expected Returns

Cross-sectional asset pricing tests with GMM can generate spuriously high explanatory power for factor models when the moment conditions are specified such that they allow the estimated factor means to substantially deviate from the observed sample averages. In fact, by shifting the weights on the moment conditions, any level of cross-sectional fit can be attained. This property is a feature of the GMM estimation design and applies to strong as well as weak factors, and to all sample sizes and test assets. We reveal the origins of this bias theoretically, gauge its size using simulations, and document its relevance empirically.

Unemployment fluctuations and the predictability of currency returns

We investigate whether unemployment fluctuations generate predictability in the cross-section of currency excess returns. We find that currencies with lower growth in the unemployment rate appreciate while currencies with higher growth in the unemployment rate depreciate. As a result, an investment strategy that involves investing in the former and short selling of the latter produces positive and sizable excess returns. Asset pricing tests show that the predictability is not driven by exposure to traditional risk factors such as global equity risk, global foreign exchange volatility risk, and downside risk but is related instead to an idiosyncratic unemployment risk.

Macroeconomic Risks and Asset Pricing: Evidence from a Dynamic Stochastic General Equilibrium Model

We study the relation between macroeconomic fundamentals and asset pricing through the lens of a dynamic stochastic general equilibrium (DSGE) model. We provide full-information Bayesian estimation of the DSGE model using macroeconomic variables and extract the time series of four latent fundamental shocks of the model: neutral technology shock, investment-specific technological shock, monetary policy shock, and risk shock. Asset pricing tests show that our model-implied four-factor model can explain a number of prominent cross-sectional return spreads: size, book-to-market, investment, earnings, and long-term reversal. The investment-specific technological shock and risk shock play the most important role in explaining those return spreads. This paper was accepted by Neng Wang, finance.

Macroeconomic Risks and Asset Pricing: Evidence from a Dynamic Stochastic General Equilibrium Model

We study the relation between macroeconomic fundamentals and asset pricing through the lens of a dynamic stochastic general equilibrium (DSGE) model. We provide full-information Bayesian estimation of the DSGE model using macroeconomic variables and extract the time-series of four latent fundamental shocks of the model: neutral technology shock, investment-specific technological shock, monetary policy shock, and risk shock. Asset pricing tests show that our model-implied four-factor model can explain a number of prominent cross-sectional return spreads: size, book-to-market, investment, earnings, and long-term reversal. The investment-specific technological shock and risk shock play the most important role in explaining those return spreads.

Conditional Skewness in Asset Pricing Tests

If asset returns have systematic skewness, expected returns should include rewards for accepting this risk. We formalize this intuition with an asset pricing model which incorporates conditional skewness. Our results show that conditional skewness helps explain the cross-sectional variation of expected returns across assets and is significant even when factors based on size and book-to-market are included. Systematic skewness is economically important and commands a risk premium, on average, of 3.60 percent a year. Our results also suggest that the momentum effect is related to systematic skewness. The low expected return momentum portfolios have higher skewness than high expected return portfolios.

Expected Stock Returns

Contrary to the standard practice of using past average realized returns when testing asset pricing models, this paper analyzes the factor structure and the cross-sectional variability of expected returns. We show that the first two principal components explain 99.5% of the variability of (lower bound) expected returns. Quality, funding illiquidity, default premium and market-wide variance risk premium explain most of the time-varying behavior of the first principal component. Market-wide illiquidity significantly explains the second principal component. The cross-sectional fit of several asset pricing models using expected returns is consistently better than the one with average realized returns. The factor loadings of the two principal components explain more than 90% of the cross-sectional variability of expected returns. The most successful model is a multi-factor model with the market, and the four aggregate factors that explain the first principal component. This model is able to explain more than 95% of the cross-sectional variability of expected excess returns, with a corrected R-square of 60%, which is (asymptotically) different from zero.

Flight-to-quality, economic fundamentals, and stock returns

Abstract We find that the order flow differential (OFD), a flight-to-quality measure constructed as the difference between large- and small-cap stock order flows, strongly and negatively forecasts output growth and interest rates in the U.S. The predictive ability of OFD for future macroeconomic fundamentals is robust to the inclusion of return factors and business cycle predictors, and it is thus a state variable candidate in the spirit of Merton (1973). Consistent with this view, we document that OFD commands a statistically significant negative risk premium in cross-sectional asset pricing tests.

Coskewness in Islamic, Socially Responsible and Conventional Mutual Funds: An Asset Pricing Test

Intuition suggests that constraint investment strategies will result in losses due to a limited portfolio allocation. Two types of constrained assets have been particularly growing over the last few decades: Islamic Mutual Funds and Socially Responsible Mutual Funds. Although research regarding the performance of these types of constrained investments has been performed, little attention has been given to their relative performance. In this paper we assess, and rank, the relative performance of Islamic, Socially Responsible, and conventional mutual funds from 11 Islamic markets and the United States by expanding the traditional mean-variance frontier to account for higher moments; constrained assets tend to be smaller and skewed in nature, thus violating the normality assumption under the mean-variance frontier. We find that controlling for skewness risk, by using an unconditional coskewness measure, has the power to improve asset pricing tests by expanding the mean-variance frontier specification. We find supporting evidence suggesting that Islamic mutual funds perform better than Socially Responsible Investing, which in turn outperform conventional mutual funds.

Does the Fama and French Five‐Factor Model Work Well in Japan?

AbstractIn this study, we investigate whether the five‐factor model by Fama and French (2015) explains well the pricing structure of stocks with long‐run data for Japan. We conduct standard cross‐section asset pricing tests and examine the additional explanatory power of the new Fama and French factors; robust‐minus‐weak profitability factor and conservative‐minus‐aggressive investment factor. We find that robust‐minus‐weak and the conservative‐minus‐aggressive factors are not statistically significant when we conduct generalized method of moments (GMM) tests with the Hansen–Jagannathan distance measure. Thus, we conclude that the original version of the Fama and French five‐factor model is not the best benchmark pricing model for Japanese data during our sampling period from the year 1978 to the year 2014.

Applying the Empirical Average Cost of Capital: Estimating the Cost of Funds at the Firm and Industry Levels

This paper develops a new measure of the cost of capital, the empirical average cost of capital (EACC), which is consistent with existing methods of calculating the weighted average cost of capital but primarily uses information from the firm’s financial statements and requires fewer and less subjective inputs. Our model combines the regression-based approach of asset pricing models such as the CAPM with the implied cost of capital’s reliance on accounting and market price data. The EACC model is very flexible and can identify the optimal balance between accounting- and market-based data that should be used when estimating the cost of capital for individual firms as well as for industries and larger economic sectors. In addition, the model can be estimated using either time series, cross-sectional, or panel data sets. Estimates of the EACC for 58 U.S. industries are compared to five conventional “textbook” estimates of the weighted average cost of capital currently published by Duff & Phelps. We find the EACC yields forecasts of future net operating profit after taxes that compare favorably to the five published measures of the weighted average cost of capital, as well as the average and median of these measures. We also demonstrate how the EACC method can be used within corporate finance research via a conventional event study that estimates the impact of the 2010 Deepwater Horizon oil rig disaster on BP’s and its industry’s cost of capital. In addition, we show how our EACC method can be applied within an investments context via a conventional asset pricing test that uses our industry EACC estimates to construct Carhart-type long-short portfolios and find that our approach can generate a significant alpha of +38.4 bps per month. Overall, the results suggest the EACC can be an effective complementary method for corporate managers, investors, financial analysts, accountants, regulators, and academics in deriving estimates of a company’s or industry’s cost of capital.

Modeling non-normality using multivariate t: implications for asset pricing

PurposeThe purpose of this paper is to show that multivariate t-distribution assumption provides a better description of stock return data than multivariate normality assumption.Design/methodology/approachThe EM algorithm is applied to solve the statistical estimation problem almost analytically, and the asymptotic theory is provided for inference.FindingsThe authors find that the multivariate normality assumption is almost always rejected by real stock return data, while the multivariate t-distribution assumption can often be adequate. Conclusions under normality vs under t can be drastically different for estimating expected returns and Jensen’s αs, and for testing asset pricing models.Practical implicationsThe results provide improved estimates of cost of capital and asset moment parameters that are useful for corporate project evaluation and portfolio management.Originality/valueThe authors proposed new procedures that makes it easy to use a multivariate t-distribution, which models well the data, as a simple and viable alternative in practice to examine the robustness of many existing results.

Aggregation of Information About the Cross Section of Stock Returns: A Latent Variable Approach

We propose a new approach to estimation of expected returns on individual stocks from a large number of flrm characteristics. We treat expected returns and betas as latent variables and develop a procedure that fllters them out using the characteristics as signals and imposing restrictions implied by an underlying asset pricing model. The procedure is more e‐cient than alternatives and robust to data mining. The estimates of expected returns obtained by applying our method to thirteen asset pricing anomalies generate a wide cross-sectional dispersion of realized returns. Our results provide evidence of strong commonality in asset pricing anomalies. The use of portfolios based on the estimated expected returns as test assets increases the power of asset pricing tests.