Quanta Byte

AI is expanding what systematic investing can achieve — from unlocking deeper insights in complex data to building more adaptive, responsive portfolios. This piece explores the shift from classical quant to Quant 2.0 and what a more adaptive, data-driven future could look like.

Government bonds are primarily driven by moves in their underlying interest rates – and these rates are highly correlated. In this QuantaByte, we show how correlated interest rates really are, and what conclusions an investor can draw to tackle this problem.

AI models don’t have to be black boxes. In the final article of our Expl(AI)ning series, discover how SHAP values reveal the contributions behind model predictions, enhancing transparency and trust in AI-powered investing. See how we use these insights within our AI Powered process.

Rising concentration in the S&P 500 has made markets more sensitive to a handful of mega-cap stocks driving performance. While technology remains dominant, this imbalance raises questions around volatility, diversification, and market resilience. The Solutions team from the Quantitative Investments Boutique explores how concentration has evolved and what it means for investors navigating today’s equity landscape.

Can the top 2% make all the difference? The Quantitative Investments team tested an AI-powered stock picker across 20 years of data. By analysing hit rates alongside market concentration and dispersion, they uncovered fresh insights into portfolio management. Discover how AI reshapes investing in our latest article.

Backtests can mislead—or unlock real edge. In Part II of our mini-series, we show how to avoid common pitfalls, cut through bias, and turn backtests into a reliable tool for decision-making. Practical, actionable, and built for real-world investing.

Backtesting can reveal powerful insights—or create dangerous illusions. In Part I of our mini-series, we show how biases and shortcuts can inflate results, using a simple momentum strategy as an example. Learn why careful design matters before moving from simulation to real-world investing.

Have you been following Digital Asset Treasuries (DATs)? Once promoted as “intelligent leverage” on Bitcoin, they gave investors simple exposure through listed shares. But with ETFs now offering easier and cleaner access, the model faces growing pressure. In this piece, we explore whether DATs are here to stay—or fading fast.

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.

FX is fragmented by nature — and that’s exactly what makes it valuable. Our latest Quanta Byte explores how FX trends offer independent return streams that passive hedging misses. Discover what happens when FX diversification starts doing more than just reducing volatility.

Our research shows sectors deliver both top and bottom returns, while factors—especially Quality, Dividend Strength and Minimum Volatility—offer more predictable, diversified, and stable performance. We use sectors tactically based on business cycle signals, but our strategic focus remains firmly on factors for consistent, risk-adjusted returns. Discover the Hybrid Investment team’s view on Minimum Volatility equity strategies over the business cycle at the Quantitative Investment Boutique.