Systematic investment approach update: August 2024

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.

Focusing on the hit rate: not every trade needs to be a winner. Like Federer’s 54% point success that translates into 80% match wins, flipping losing positions can amplify returns. Our latest Quanta Byte explores how even modest improvements in selection can propel consistent portfolio outperformance across market cycles.

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.

In our latest Quanta Byte, delve into the details and uncover the mathematical foundations of convolutions, their links to AI, and how advanced techniques like the Fast Fourier Transform are driving new efficiencies in computation and quantitative 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.