This is a summary of links recently featured on Quantocracy as of Wednesday, 06/17/2026. To see our most recent links, visit the Quant Mashup. Read on readers!
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Testing an AI-Assisted Research Workflow for Multi-Asset Pullback Strategy Discovery [Quantpedia]This study investigates short-term price reversalstemporary retracements following adverse daily returnsand develops a systematic trading framework to capture this effect across multiple asset classes. Using daily data from six liquid ETFs spanning equities, fixed income, currencies, gold, and commodities over the period 20062025, the strategy applies a long-term trend filter based on a
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Honey I shrunk the weights (instead of the inputs!) [Investment Idiocy]TLDR: This is a post about something that doesn't work. So don't read if you only care about cherry picked delightful backtests. This is my fifth post in a rapid fire intense series on portfolio optimisation. In my last post I looked at the optimal amount of shrinkage to use with real data, when running a bayesian methodology for mean variance optimisation. I found two things. Firstly,
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Value-at-Risk Estimation: Improved Estimates with the Harrell-Davis Quantile Estimator [Portfolio Optimizer]In a previous blog post of this series, the main univariate Value-at-Risk (VaR) estimation methods were described. Among these, and for scenario-based VaR estimation like historical VaR or Monte Carlo VaR, the most widely used [non-parametric] estimator is the corresponding order statistic of the empirical quantile of the portfolio return distribution, or a linear combination of two subsequent