This is a summary of links recently featured on Quantocracy as of Thursday, 05/15/2025. To see our most recent links, visit the Quant Mashup. Read on readers!
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Are you blind to the tail risks lurking in calm markets? [Trading the Breaking]Algorithmic trading systems can give you this sleek, high-tech confidencelike the robots have everything under control. Theyre fast, precise, and backtested to death, right? But thats where the trap snaps shut. When your risk metrics are built on things like standard deviation or recent drawdowns, youre basically judging a hurricane by the breeze in your backyard. Sure, those stats
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Are Sector-Specific Machine Learning Models Better Than Generalists? [Quantpedia]Can machine learning models better predict stock returns if they are tailored to specific industries, or is a one-size-fits-all (generalist) approach sufficient? This question lies at the heart of a recent research paper by Matthias Hanauer, Amar Soebhag, Marc Stam, and Tobias Hoogteijling. Their findings suggest that the optimal solution lies somewhere in between: a Hybrid machine learning
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The Virtue of Complexity in Return Prediction [Alpha Architect]In the realm of investment strategies, simplicity has long been favored. Traditional models with a limited number of parameters are prized for their interpretability and ease of use. However, recent research challenges this convention, suggesting that embracing complexity can lead to more accurate return predictions. The study The Virtue of Complexity in Return Prediction by Bryan T. Kelly,