Machine learning (ML) methods are highly flexible, but their ability to approximate the true data-generating process is fundamentally constrained by finite samples. We characterize a universal lower bound, the Limits-to-Learning Gap (LLG), quantifying the unavoidable discrepancy between a model’s ...
Keywords:
machine learning, asset pricing, predictability, big data, limits to learning, excess volatility, stochastic discount factor, kernel methods
