This is a summary of links recently featured on Quantocracy as of Sunday, 03/29/2026. To see our most recent links, visit the Quant Mashup. Read on readers!
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Brave New Backtest [Robot Wealth]My last two articles on AI and trading research got more engagement than almost anything Ive written. More of the Disease, Faster argued that LLMs cant answer the critical question: who pays you and why? AI Will Create Millions of Quants went deeper on the why: AI makes beautiful backtests trivially easy to produce, which means more false discoveries, more overfitting dressed up
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Data transformations: Preprocessing time series [Trading the Breaking]Raw market data is the closest thing we have to the markets observable dynamics, so the instinct to leave it untouched feels sensible. But predictive models consume numerical representations. Between the tape and the optimizer there is always a translation step, whether explicit or hidden, and that translation determines which structures remain visible, which ones are attenuated, and which ones
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Fridays for Gold, Tuesdays for Stocks: Two Sides of the Same Fear Cycle [Beyond Passive]The previous article showed that gold drifts higher on Fridays specifically when the VIX term structure compresses into the extended fear zone, driven by institutional risk managers adding weekend protection on Thursday afternoons. The same day-of-week chart that opened that article contains a second significant bar on the opposite side of the cycle. Equities on Tuesday. This article examines
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When Crypto Stopped Diversifying: The ETF Regime Shift [Quantpedia]Can crypto still help diversify an equity portfolioor has that edge disappeared? Thats the practical question behind Crypto Contagion. The paper looks at how shocks move between crypto and U.S. equities, and more importantly, how that relationship changed after the launch of crypto ETFs. Instead of relying on simple correlations, the authors use a combination of jump detection (to isolate
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Unlocking Hidden Patterns: How Daily Returns Predict Future Stock Performance [Alpha Architect]Nusret Cakici, Christian Fieberg, Gabor Neszveda, Robert Bianchi, and Adam Zaremba, authors of the January 2026 study A Unified Framework for Anomalies based on Daily Returns, challenged how we think about short-term return patterns in stock markets. Their research reveals that the wealth of information contained in daily stock returns has been hiding in plain sightand when properly
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Evaluating Option-Based Strategies and Dollar-Cost Averaging [Relative Value Arbitrage]In past issues, we discussed popular investment strategies such as covered calls and collars. In this post, we continue by examining other strategies, focusing on their performance, limitations, and how they behave under different market conditions. Reexamining the Performance of Passive Options Strategies More than 40 years ago, Merton et al. published two papers [1,2] examining the performance
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From Hype to Reality: Building a Hybrid Transformer-MVO Pipeline [Jonathan Kinlay]A Five-Way Decomposition of What Actually Drives Risk-Adjusted Returns in an AI Portfolio The quantitative finance space is currently flooded with claims of deep learning models generating massive, effortless alpha. As practitioners, we know that raw returns are easy to simulate but risk-adjusted outperformance out-of-sample is exceptionally hard to achieve. In this post, we build a complete,
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Increases CAPE Ratio Predictability with a Simple Adjustment [Alpha Architect]CAPE has long been a cornerstone of long-horizon return forecasting. High valuations imply lower future returns. Low valuations imply higher future returns. Critics argue that its predictive power has faded in recent decades. This paper pushes back. It shows that the apparent decline is largely a measurement problem. When CAPE is constructed using aligned index constituents and market-cap weights,