The Conditional CAPM Does Not Explain Asset-Pricing Anomalies

Average stock returns for North America, Europe, and Asia Pacific increase with the book-to-market ratio (B/M) and profitability and are negatively related to investment. For Japan the relation between average returns and B/M is strong, but average returns show little relation to profitability or investment. A five-factor model that adds profitability and investment factors to the three-factor model of Fama and French (1993) largely absorbs the patterns in average returns. As in Fama and French (2015a,b), the model’s prime problem is failure to capture fully the low average returns of small stocks whose returns behave like those of low profitability firms that invest aggressively.

International Tests of a Five-Factor Asset Pricing Model

We investigate whether investor sentiment affects the cross-section of stock returns in China A-share market.The evidence shows that the sensitivity of stock returns to sentiment changes is different. When an index of investor sentiment takes high values,low tangible assets,high Debt-asset ratio,and non-dividend-paying stocks earn relatively higher returns,when sentiment is low,the aforementioned categories of stocks earn relatively lower returns.When sentiment is high,low-price,unprofitable and high book-market ratio stocks earn relatively higher returns and vice versa,but which is insignificant.The capitalization, volatility and institutional ownership appear to have no significant cross-section effect of investor sentiment on its characteristic Portfolio return.

Investor Sentiment and the Cross-Section of Stock Returns

We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature. We identify the best performing methods (trees and neural networks) and trace their predictive gains to allowance of nonlinear predictor interactions that are missed by other methods. All methods agree on the same set of dominant predictive signals which includes variations on momentum, liquidity, and volatility. Improved risk premium measurement through machine learning simplifies the investigation into economic mechanisms of asset pricing and highlights the value of machine learning in financial innovation.

/pdf/empirical-asset-pricing-via-machine-learning-vq3ztficgc.pdf

Empirical Asset Pricing via Machine Learning

https://www.nber.org/system/files/working_papers/w24540/w24540.pdf

Characteristics Are Covariances: A Unified Model of Risk and Return

https://www.nber.org/system/files/working_papers/w24070/w24070.pdf

Shrinking the cross-section

We argue that tests of reduced-form factor models and horse races between "characteristics" and "covariances'" cannot discriminate between alternative models of investor beliefs. Since asset returns have substantial commonality, absence of near-arbitrage opportunities implies that the SDF can be represented as a function of a few dominant sources of return variation. As long as some arbitrageurs are present, this conclusion applies even in an economy in which all cross-sectional variation in expected returns is caused by sentiment. Sentiment investor demand results in substantial mispricing only if arbitrageurs are exposed to factor risk when taking the other side of these trades.

Interpreting Factor Models

We argue that tests of reduced‐form factor models and horse races between “characteristics” and “covariances” cannot discriminate between alternative models of investor beliefs. Since asset returns have substantial commonality, absence of near‐arbitrage opportunities implies that the stochastic discount factor can be represented as a function of a few dominant sources of return variation. As long as some arbitrageurs are present, this conclusion applies even in an economy in which all cross‐sectional variation in expected returns is caused by sentiment. Sentiment‐investor demand results in substantial mispricing only if arbitrageurs are exposed to factor risk when taking the other side of these trades.

We construct a robust stochastic discount factor (SDF) that summarizes the joint explanatory power of a large number of cross-sectional stock return predictors. Our method achieves robust out-of-sample performance in this high-dimensional setting by imposing an economically motivated prior on SDF coefficients that shrinks the contributions of low-variance principal components of the candidate factors. While empirical asset pricing research has focused on SDFs with a small number of characteristics-based factors --- e.g., the four- or five-factor models discussed in the recent literature --- we find that such a characteristics-sparse SDF cannot adequately summarize the cross-section of expected stock returns. However, a relatively small number of principal components of the universe of potential characteristics-based factors can approximate the SDF quite well.

Shrinking the Cross Section

The optimal factor timing portfolio is equivalent to the stochastic discount factor. We propose and implement a method to characterize both empirically. Our approach imposes restrictions on the dynamics of expected returns which lead to an economically plausible SDF. Market-neutral equity factors are strongly and robustly predictable. Exploiting this predictability leads to substantial improvement in portfolio performance relative to static factor investing. The variance of the corresponding SDF is larger, more variable over time, and exhibits different cyclical behavior than estimates ignoring this fact. These results pose new challenges for theories that aim to match the cross-section of stock returns.<br><br>Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at <a href="http://www.nber.org/papers/&#119;26708" TARGET="_blank">www.nber.org</a>.<br>

/pdf/the-economics-of-factor-timing-3ufikj5m8g.pdf

Serhiy Kozak

Papers

Shrinking the cross-section

Interpreting Factor Models

Interpreting Factor Models

Shrinking the Cross Section

The Economics of Factor Timing