This is a summary of links recently featured on Quantocracy as of Friday, 08/29/2025. To see our most recent links, visit the Quant Mashup. Read on readers!
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Neural Nets and Factor Models [Falkenblog]Gu, Kelly, and Xiu (2020) – "Empirical Asset Pricing via Machine Learning" and Chen, Pelger, and Zhu (2019) – "Deep Learning in Asset Pricing" examine various machine learning and neural net algorithms. Both find significant improvements to standard factor models. Several hidden parameter choices are not directly learned during training but significantly impact model
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How Can We Explain the Low-Risk Anomaly? [Quantpedia]The low-risk anomaly in financial markets has puzzled researchers and investors, challenging the traditional risk-return paradigm (higher risk->higher return). This phenomenon, where low-risk assets outperform their high-risk counterparts on a risk-adjusted basis, has been observed across various asset classes, including stocks and mutual funds. What may be the possible explanation?
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Cross-Sectional Momentum: Results from Commodities and Equities [Relative Value Arbitrage]Momentum strategies can be divided into two categories: time series and cross-sectional. In a previous newsletter, I discussed time series momentum. In this post, I focus on cross-sectional momentum strategies. Cross-Sectional Momentum in the Commodity Market Momentum trading is often divided into 2 categories: time-series momentum and cross-sectional momentum. Time-series based trading strategies
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Weekly Research Recap [Quant Seeker]Asymmetry and Crude Oil Returns (Liu, Zhang, and Bouri) This paper introduces a new distribution-based asymmetry factor (OIS) for crude oil that strongly predicts WTI futures returns. A one-standard-deviation rise in OIS, signaling right-tail clustering, forecasts a 3.15% drop in next-month returns (t3.1, R=4.1%). Out-of-sample, OIS achieves an R of 4.2%, far exceeding standard