Machine Learning delivers significant value-add for informed investment decisions, by transcending human biases and traditional quant limits in efficiently inefficient markets.
Market Dynamics & Alpha
Our understanding of capital markets aligns with the concept of "efficiently inefficient markets" (Pedersen, 2015). This theory posits that while intense competition among professional investors makes markets nearly efficient, enough inefficiencies persist to reward skilled active managers for their costs and risks. Within such markets, alpha can be generated through two primary approaches: informed decisions by highly experienced human experts, or by models and quantitative methods run by machines.
Model Superiority & Quant's Evolution
Models consistently surpass human investment decisions due to inherent noise and biases in human judgment. As Nobel laureate Daniel Kahneman points out, models are free from this "noise" and are thus consistently more accurate. Though fundamental since the 1980s for various analytical and optimization tasks, traditional quantitative models face limitations in high-dimensional markets: they struggle with a large number of variables (prone to overfitting), primarily map complex relationships linearly, and are slow to adapt to structural market breaks.
The ML Advantage
This is where Machine Learning (ML) provides a distinct advantage. Unlike traditional linear models that make strong assumptions about data, flexible ML techniques allow us to learn complex relationships without imposing restrictive parametric assumptions (Hastie et al., "The Elements of Statistical Learning", 2009). This directly mitigates the limitations of traditional models. ML enables us to analyze more influencing factors simultaneously, better understand complex non-linear relationships, and significantly enhance prediction quality through holistic modeling and adaptive approaches.