Market Mispricing and Limits to Arbitrage
Paper Session
Sunday, Jan. 9, 2022 10:00 AM - 12:00 PM (EST)
- Chair: Mikhail Simutin, University of Toronto
A Multifactor Perspective on Volatility-Managed Portfolios
Abstract
A fundamental result in finance is that return is related to risk. Moreira and Muir (2017) cast doubt on this by showing that one can time equity risk factors using their variance. However, Cederburg, O’Doherty, Wang, and Yan (2020) show these strategies fail out of sample and Barroso and Detzel (2020) show they donot survive transaction costs. We develop a conditional mean-variance multifactor portfolio whose weights change with market volatility and outperforms even out of sample and net of transaction costs both unconditional multifactor portfolios and volatility-managed individual-factor portfolios. Our strategy performs particularly well during periods of high volatility. These results resurrect concerns about the
risk-return relation.
Benchmarking Intensity
Abstract
Benchmarking incentivizes fund managers to invest a fraction of their funds’ assets in their benchmark indices, and such demand is inelastic. We construct a measure of inelastic demand a stock attracts, benchmarking intensity (BMI), computed as its cumulative weight in all benchmarks, weighted by assets following each benchmark. Exploiting the Russell 1000/2000 cutoff, we show that changes in stocks’ BMIs instrument for changes in ownership of benchmarked investors. The resulting demand elasticities are low. We document that both active and passive fund managers buy additions to their benchmarks and sell deletions. Finally, an increase in BMI lowers future stock returns.The Loan Fee Anomaly: A Short Seller’s Best Ideas
Abstract
We find that equity loan fees are the best predictor of cross-sectional returns. When compared to 102 other anomalies and other costs borne by short sellers, the loan fee anomaly has the highest monthly long-short return (1.17%), the highest monthly Sharpe Ratio (0.40), and unlike other anomalies, exhibits strong persistence throughout the sample. We show that 28% of the loan fee anomaly can be explained by its selective exposure to the best performing anomalies, while 72%is due to unique information possessed by short sellers. Future papers that examine or debate the predictability of cross sectional returns should include the single most effective predictor, loan fees.Discussant(s)
Yang Song
,
University of Washington
Oliver Boguth
,
Arizona State University
Jonathan Berk
,
Stanford University
Juhani Linnainmaa
,
Dartmouth College
JEL Classifications
- G0 - General