This is a summary of links recently featured on Quantocracy as of Sunday, 04/05/2026. To see our most recent links, visit the Quant Mashup. Read on readers!
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A Junior Quant’s Guide to Event-Driven Trading [Quant Galore]You have to know that it didnt get that way overnight. And more often than not, it didnt get that way quietly. On every step of the way down, companies like this are forced by regulators to publicly share every detail on exactly how business is going and what theyve got planned. All you have to do is look for it. So, thats exactly what we did. A Primer on Advanced Event-Driven Trading
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Two Calendar Effects at the Month Boundary [Beyond Passive]This article examines two distinct effects that share the same calendar window and the same tickers. The first is a pure bond seasonality: TLT tends to weaken in the first week of each month and rally in the last few days, regardless of what equities do. The second is a conditioned reversal trade: when stocks outperform bonds during the first half of the month, the underperformer tends to recover
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Uncertainty [Quantitativo]Doubt is not a pleasant condition, but certainty is a ridiculous one. Voltaire Voltaire was arguably the most influential intellectual of 18th-century France. More than that, he was a provocateur. He spent his life as a one-man war against dogma, against anyone who claimed to know the truth with absolute certainty. The Age of Enlightenment didnt begin with answers: it began with
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When Elon Musk Can t Sleep, Your Portfolio Feels It [Tommi Johnsen]It is 3:47 AM Pacific Time. Elon Musk, reportedly on his fourth espresso and second viewing of a documentary about Roman emperors, picks up his phone. He types something. He posts it. Within eleven minutes, Teslas stock has moved. Within forty, three semiconductor companies have been dragged along for the ride. By morning, a fund manager in Oslo is explaining to her clients why their portfolio
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How imputation helps statistical learning for macro trading signals [Macrosynergy]Systematic trading strategies with macroeconomic information often rely on panel data that aggregate cross-country experiences over time. Panel regression is more information-efficient than single-time-series regression and allows for easier detection and assertion of the predictive power of macro factors. However, panels are often unbalanced, with factors missing for certain periods in specific
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One Year Later: Is ChatGPT Finally Worth Using for Quantitative Analysis? [Quantpedia]One year ago, in our article Can We Finally Use ChatGPT as a Quantitative Analyst?, we explored the feasibility of leveraging ChatGPT for quantitative analysis. Since then, a lot has changed: newer models are now available (from OpenAI and also other vendors), and the ecosystem around AI-assisted analysis has evolved significantly. Back then, we encountered numerous challenges, ranging from