Maximizing Potential Returns of Investment Strategies: Strategy Optimization and Robustness Analysis Using Causal Explainable AI (Causal XAI)

In volatile markets, traditional investment strategies, based on correlations, often face unpredictable failures. Causal Explainable AI (Causal XAI) goes beyond merely predicting 'what will happen' to uncover 'why it happens,' thereby offering an innovative solution to fundamentally optimize investment strategies and robustly respond to unexpected market shocks.

1. The Challenge / Context

For a long time, quantitative investment strategies have evolved based on statistical correlations and pattern recognition. However, these strategies often revealed their limitations in the face of unexpected macroeconomic shocks such as the COVID-19 pandemic, the Russia-Ukraine war, and the advent of a high-interest rate era. As the implicit assumption that past correlations would hold in the future collapsed, many investment models malfunctioned or caused significant losses. Furthermore, deep learning-based AI models, which learn complex nonlinear patterns, offer high predictive accuracy but face the 'black box' problem, making their operational mechanisms difficult to understand. In situations demanding transparency and accountability in investment decision-making, models that cannot clearly explain why a particular investment should be made or which factors generate returns struggle to gain trust.

What we need now is an investment strategy that goes beyond simply increasing predictive accuracy, one that understands the fundamental causal relationships of market phenomena, operates robustly even in changing environments based on this understanding, and can explain its operational mechanisms. Causal Explain