This is a summary of links recently featured on Quantocracy as of Monday, 08/03/2026. To see our most recent links, visit the Quant Mashup. Read on readers!
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Quantitativo weekly #4 [Quantitativo]The only sustainable competitive advantage is to learn faster than your competition. Arie de Geus Implementing research papers can sometimes work, though a perfect replication often fails. Its never wasted effort, though: the ideas in the paper end up feeding new ideas and good conversations with other researchers. Heres the 4th edition of the Quantitativo weekly, featuring papers that
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Supertrend, flipped to death: the most-taught indicator on the internet [The Refutation]If you learned to trade from YouTube, you probably learned the Supertrend. It topped our folklore hunt being the single most-taught beginner tool on the internet, complete with the green-line/red-line screenshots and the promise that the trend will tell you when to start riding the new trend. So we did the honest thing: we tested it exactly the way it's taught. Figure 1 is the tool itself, on
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Down June & July: 11 of 13 Septembers Closed Lower [Quantifiable Edges]Both June and July saw SPX close lower. Seasonality for those two months it is generally favorable. But since 1950, two of the weakest months are August (3rd worst) and September (worst). So in this weekends letter I decided to see how August and September fared when we already had June and July slipping. The table below gives us an answer. After down closes in both June and July. August has
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Conformal Prediction in Quantitative Finance [Vertox Quant]In one of our previous articles, we built a neural-network-based volatility forecaster that beats baseline models in all volatility regimes: Volatility Forecasting using Neural Networks Vertox Jun 22 Volatility Forecasting using Neural Networks Today, we are gonna look at something weve never done in any article before: Neural Networks. Read full story Now no need to be sceptical about the
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Making Option Pricing Models More Practical [Relative Value Arbitrage]The Black-Scholes-Merton model is one of the cornerstones of modern quantitative finance. Despite its elegance and widespread use, its simplifying assumptions limit its ability to capture many features of real financial markets. As a result, researchers continue to extend the model to make it more realistic and applicable in practice. In this post, we discuss two such extensions. The first