Systematic Approach

Two mantras exist among investors when it comes to equity factors. First, you hear that “factors tend to outperform the broader market in the long run”. True. Second, you hear that “timing factors is almost impossible, and that you would be better off with a static allocation”. False. In fact, dynamic timing approaches to factor premia management adds significant benefits over static allocation approaches and in this piece, we show you why

I fattori tradizionali sono in ritardo? In questo Quanta Byte di Expl(AI)ning, esploriamo come l'AI possa dare nuova vita agli investimenti guidati dai fattori. Scoprite il concetto di fattori potenziati dall'AI: un approccio più intelligente e adattivo che trasforma i modelli statici in motori predittivi.

Factor investing has long explained market behavior, but its flaws—timing dependencies, rigid linearity, and backward-looking assumptions—limit predictive power. Can AI overcome these? This Expl(AI)ning episode dissects factor evolution, their pitfalls, and how machine learning enables dynamic models. Dive into quantitative investing’s future.

AI has dominated games like chess and Go, but the markets are far more complex. In our latest Expl(AI)ning article, we explore why investing’s complexity remains unmatched, where AI stands today, and what’s next for finance.

Gain an intuition on how complex AI architectures actually work. You will see that one model cannot fit all kinds of data. The key to solving problems with AI and surpassing mathematical challenges is to use the right model and the appropriate, high-quality data.

Explore the fascinating parallels between biological intelligence and artificial neural networks. This article dives into how AI mimics the brain’s neuron structures to solve complex problems, revealing the power of neural networks and their potential to reshape our understanding of intelligence and decision-making.