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Hey guys! I’ve just released a refined, compact edition of one of my newsletter series on the foundations of quantitative trading. It is a theoretical introduction designed for aspiring quants who are beginning to explore the field. The focus is on first principles, method, and the conceptual framework behind quantitative trading.
Link in the comments. I hope you enjoy it.
The paper defines a separator as information that makes asset returns mutually conditionally independent. It then says that the maximum absolute conditional correlation is zero when the information is a separator. But that equivalence is false without additional distributional assumptions.
Evaluate the severity of the original breakdown, verify remediation through several quarters of clean filings, and confirm that restatement risk is resolved. The sweet spot is finding a solid core business whose governance discount is lifting. The setup occurs when a multi-quarter control discount disappears while operating fundamentals remain intact. Enter only after the market begins repricing the lower reporting-risk state.
The problem is that the additional reward depends on price changes and sentiment, not on how much of each asset the agent actually holds or trades.
There’s only one reason I haven’t told X to fuck off. 🤣
Auditor resignations are another data source because it can reflect governance and risks. Indeed, his job can be assessed as any other event. Here, the bearish setup carries high conviction when a resignation intersects with deteriorating liquidity.
Check last year compilation here amazon.com/stores/Quant-Beck…
The authors acknowledge in Section 3.1 that the dynamic program uses the entire realized order-flow path of the evaluation day, including information that becomes available after each trading decision 😬 in other words, look-ahead bias
The jackknife method systematically evaluates the impact of each observation by recalculating the median after its removal <3


































