Quantitative Investments

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.

Are traditional factors falling behind? In this Expl(AI)ning Quanta Byte, we explore how AI can breathe new life into factor investing. Discover the concept of AI-powered factors—a smarter, more adaptive approach that turns static models into predictive engines.

Traditional volatility models often lag real market shifts, leaving portfolios vulnerable. Our new Quanta Byte introduces State-dependent Risk (SDR) - a dynamic approach that adapts faster to evolving conditions. By leveraging historical patterns and similarity measures, SDR more accurately forecasts volatility, helping investors navigate risk and protect their ascent.

Challenging entrenched biases, this special International Women's Day Quanta Byte decodes the myth of meritocracy in finance. Featuring insights from Prof. Renée Adams and Ola Mahmoud, discover how data dismantles stereotypes and reveals why perceptions of women in finance often stray from reality.

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.