Quant-Investing

Markets have a talent for embarrassing elegant models. A factor model says a company looks cheap, profitable, revised upward, less volatile, or attractively positioned. A news headline says the company just changed guidance, delayed a merger, won a contract, received a regulatory opinion, or did something else that refuses to fit politely into a spreadsheet. The obvious modern temptation is to feed both into a large language model, add some attention, and let the machine discover alpha. Naturally, because this is finance, the obvious temptation is not quite correct. ...

TL;DR for operators A portfolio does not care whether your signal has a beautiful causal origin story. It cares whether the signal points in roughly the right direction, ranks assets usefully, and is scaled well enough not to produce absurd weights. That is the useful, slightly impolite message of Alejandro Rodriguez Dominguez’s paper, Is Causality Necessary for Efficient Portfolios?1 The paper challenges a strong claim in recent causal factor-investing work: that causal factor models are necessary for investment efficiency. Its answer is narrower and more operational. Within static mean-variance and related quadratic optimisation frameworks, causal identification is not the necessary condition. The necessary operating conditions are geometric. ...