Quanta Byte – Technical Note

How can machine learning ensembles be built in practice? From Bagging and Boosting to Stacking, this article explores different ensemble strategies and the role of model diversity. We also examine how ensembles are being applied in financial forecasting, drawing on both academic research and industry experience. Discover how combining models tends to deliver stronger, more resilient predictions in finance.

Why rely on one model when many do better? By combining multiple forecasts, ensembles tend to deliver more accurate and resilient results. This first article in our two-part series explores the theory behind model ensembles, linking classic forecasting research with today’s financial machine learning. Discover why combining models consistently outperforms relying on a single predictor.

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.

Stacking tensors is a routine task in machine learning, and it's included in most open-source packages. The newest Quanta Byte from Quantitative Investments provides an in-depth look at what tensor stacking is and its various uses.