Weekly Recap
Research on quant investing and trading.
Welcome to this week’s collection of links to the latest research and insights on quant investing. Below, you’ll find a curated list where each title links to the source for more information. Thank you for reading!
Crypto
Microstructure and Market Dynamics in Crypto Markets (Easley et al.)
The paper demonstrates that standard market microstructure metrics can predict cryptocurrency price dynamics, indicating market inefficiencies.
Empirical Asset Pricing
Granular Information and Sectoral Movements (Jiang et al.)
The paper demonstrates that individual stock price movements can predict sector ETF returns, and incorporating structural economic information enhances the predictive power.
Crowded Spaces and Anomalies (Lazo-Paz et al.)
This paper studies crowding of institutional investors, finding a positive association between crowding and future returns, and a significant alpha.
The Price of Downside and Upside Correlation Risk: cross-sectional evidence (Li et al.)
The authors present new measures of correlation risks and find downside risks to be priced in the cross-section of stocks.
Time-Varying Betas in Foreign Exchange Returns: An IPCA Approach (Fu et al.)
This paper uses Instrumented Principal Component Analysis and a set of country characteristics and finds significant forecasting power of G10 currency returns,
Bond Risk Premiums at the Zero Lower Bound (Andreasen et al.)
The predictive power of the yield spread for bond returns is found to be significantly stronger when the short rate is at the zero lower bound, here defined as being below 1%.
ESG
Social Premiums (Briscoe-Tran et al.)
The paper finds that while individual components of a firm's social score affect stock returns, their combined impact is neutralized.
Lecture Notes
Big Data Asset Pricing (Lasse Pedersen)
Great course offered by Lasse Pedersen at Copenhagen Business School. Topics include: Empirical asset pricing, working with asset-pricing data, multiple testing issues and the factor zoo, machine learning, and more.
Machine Learning
Machine Learning Mutual Fund Flows (Fausch et al.)
The authors explore the ability of various machine learning models to predict future mutual fund flows, finding significant predictive power.
A Dynamic Regime-Switching Model Using Gated Recurrent Straight-Through Units (Antulov-Fantulin et al.)
The authors propose a new method for detecting regime-shifts based on deep learning, and market timing strategy applied to the S&P500 outperforms buy and hold.
Volatility
Factor Modeling for Volatility (Ding et al.)
Building on the empirical finding that volatilities across stocks tend to co-move, the authors propose a single factor model that captures key empirical properties of volatilities.
Blogs and Podcasts
Inventory scores and metal futures returns (Macrosynergy)
Bootstrap Simulations with Exact Sample Mean Vector and Sample Covariance Matrix (Portfolio Optimizer)
What Many Buy-And-Hold Investors Get Wrong: Professor Hendrik Bessembinder Explains (Meb Faber)
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