Quantitative Finance

TL;DR for operators Portfolio teams do not usually fail because they have no models. They fail because the models age, the signals decay, and the process of discovering new sparse selection logic is slow, expensive, and wonderfully allergic to market regime shifts. The paper behind EFS — Evolutionary Factor Search — proposes a useful change in framing: stop asking the LLM to “pick stocks” and ask it to generate executable alpha-factor formulas that can be backtested, filtered, evolved, and used to rank assets under sparse portfolio constraints.1 That distinction matters. The LLM is not the portfolio manager. It is the factor-factory intern with suspicious stamina. The backtest loop is still the adult in the room. ...

TL;DR for operators A trader usually asks, “What is the signal now?” This paper asks a more expensive question: “What did the signal do on the way here?” That difference matters when alpha does not decay instantly, when order flow moves prices slowly, or when volatility changes the usefulness of the same forecast. ...

TL;DR for operators This paper is about risk estimation, not market prophecy. The neural network does not try to forecast returns, detect tomorrow’s winners, or become a portfolio manager with a hoodie and a GPU budget. It learns how to clean covariance information so that a global minimum-variance portfolio behaves better out of sample.1 ...

TL;DR for operators QuantBench is not another paper asking investors to believe that the newest neural architecture will finally decode markets because it has more layers and a nicer diagram. Mercifully. It is a benchmark platform for quantitative investment that tries to evaluate AI methods across the full quant workflow: factor mining, modelling, end-to-end position generation, portfolio optimisation, and order execution.1 ...