Andrew Chen and Alejandro Lopez-Lira challenge the foundation of asset pricing theory in their recent paper, questioning the reasons behind the excess returns generated by investment factors. Their study compares anomalies with behavioral and risk-based explanations to data-mined anomalies, finding no difference in out-of-sample returns. This has significant implications for academic research and real-world investment strategies. The podcast delves into their findings, exploring the concepts of anomalies, factors, data mining, and the role of peer-reviewed theory in asset pricing.
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insights INSIGHT
History of Anomalies Research
The quest to explain market anomalies began with observations like the size and book-to-market effects.
Fama and French attempted to unify these under a risk-factor framework, but many more anomalies have since emerged.
insights INSIGHT
Behavioral vs. Risk-Based Explanations
Behavioral explanations for factor performance suggest market inefficiencies due to biases and slow information uptake.
Risk-based explanations posit that returns compensate for bearing systematic risks.
insights INSIGHT
Data Mining Redefined
Data mining involves searching for patterns in data without theoretical guidance.
Historically viewed negatively, it is now gaining acceptance thanks to machine learning.
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Those of us that invest using factors have been taught that there needs to be a reason why they work. We have been taught that for their excess returns to persist in the future, there should be a behavioral or risk-based explanation as to why they exist in the first place. If that assumption is wrong, it would call into question the validity of much of the work that has been done in asset pricing research and would also have significant implications for real world investment strategies build using the research.
Our guest this week recently published a paper that calls those core ideas of asset pricing theory into question. We speak with Andrew Chen, Principal Economist at the Federal Reserve's Capital Markets Section and Alejandro Lopez-Lira, Assistant Professor of Finance at the University of Florida about their new paper "Does Peer Reviewed Theory Predict the Cross Section of Stock Returns." The paper compared anomalies with behavioral and risk-based explanations to others that were purely data mined. They found no difference in out of sample returns among the 3 groups. In the interview, we take a deep dive into their findings and what they mean for both the world of academic research and real-world investment strategies.
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