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  5. Origins of Risk Factors and Anomalies
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Origins of Risk Factors and Anomalies

Paper Session

Saturday, Jan. 6, 2024 10:15 AM - 12:15 PM (CST)

Marriott Rivercenter, Grand Ballroom Salon K, & L
Hosted By: American Finance Association
  • Chair: Svetlana Bryzgalova, London Business School

Anomaly Never Disappeared: The Case of Stubborn Return Trading

Xi Dong
,
CUNY-Baruch College
Yuqing Yang
,
CUNY-Baruch College

Abstract

Defying conventional beliefs, our analysis of 260 anomalies over the last half-century reveals that they have not vanished as market efficiency improves, because their alphas predominantly materialize over the long haul—an overlooked timeframe with far-reaching financial and real implications. The enduring long-run alphas in recent years are driven by the anomalies that retail investors trade against, yielding staggering value-weighted two-year alphas of 23%. Incorporating retail trading, we develop asset pricing models that surpass existing prominent models in explaining these long-run alphas. We propose and validate a hypothesis: the stubbornness of retail investors underpins long-run alphas, inflicting long-horizon risks on arbitrageurs. Our findings imply that as society advances and other frictions fade, the unyielding nature of financially naive individuals will remain an enduring impediment to market efficiency.

Wisdom of the Institutional Crowd: Implications for Anomaly Returns

AJ Yuan Chen
,
University of Southern California
Gerard Hoberg
,
University of Southern California
Miao Zhang
,
University of Southern California

Abstract

We study the implications of a novel crowd-sourcing mechanism in which institutional investors communicate with reputable news media to influence the crowd and accelerate return realization. Using over one million Wall Street Journal articles from 1980-2020, we create a new measure of crowd-sourcing based on institutional investor predictions in the news (InstPred). We show that for industries with higher InstPred, (i) value and momentum returns are 34% to 62% larger, and (ii) institutional investors collectively trade anomalies more. Our results are reinforced by quasi-exogenous variation in industries' investor-WSJ connections and cannot be explained by existing measures including sentiment.

Fundamental Anomalies

Erica X.N. Li
,
Cheung Kong Graduate School of Business
Guoliang Ma
,
Iowa State University
Shujing Wang
,
Tongji University
Cindy Yu
,
Iowa State University

Abstract

This paper proposes a portfolio-independent method to estimate q-theory models and examines whether an extensive set of stock market anomalies can be generated by a two-capital q-model. Model parameters are obtained using Bayesian Markov Chain Monte Carlo (MCMC) to match firm-level stock returns. Our methodology addresses Campbell (2017)’s critique on prior studies that model parameters are chosen to fit a specific set of anomalies and different values are needed to fit each anomaly. The estimated two-capital model generates large and significant size, momentum, profitability, investment, and intangibles premiums. However, it falls short in explaining the value and accruals anomalies.

Does peer-reviewed theory help predict the cross-section of stock returns?

Andrew Chen
,
Federal Reserve Board
Alejandro Lopez-Lira
,
University of Florida
Tom Zimmermann
,
University of Cologne

Abstract

We compare four groups of cross-sectional return predictors: (1) published with a risk-based explanation, (2) published with a mispricing explanation, (3) published with uncertain origins, and (4) naively data-mined from accounting variables. For all groups, predictability decays by 50% post-sample, showing theory does not help predict returns above naive backtesting. Data-mined predictors display features of published predictors including the rise in returns as in-sample periods end, the speed of post-sample decay, and themes from the literature like investment, issuance, and accruals. Our results imply peer-review systematically mislabels mispricing as risk, though only 18% of predictors are attributed to risk.

Discussant(s)
Michaela Pagel
,
Columbia University
Marina Niessner
,
University of Pennsylvania
Theis Jensen
,
Yale University
Yao Deng
,
University of Connecticut
JEL Classifications
  • G1 - General Financial Markets