No Free Money Theorem for Machine Learning Application in Stock Market Prediction

Abstract

The "No Free Lunch" (NFL) theorem in machine learning asserts that there is no universal algorithm or model that excels across all possible problem instances. In the context of machine learning applications for stock market prediction, this theorem implies that no single predictive model can consistently outperform others under all market conditions. The NFL theorem, introduced by David Wolpert in 1996, emphasizes the importance of considering the specific characteristics of the problem domain. In this paper, we extend the NFL Theorem as “No Free Money(NFM) Theorem for Machine learning applications in stock market predictions. The data gathered from Machine Learning competition for stock market prediction are utilized for providing experimental evidence for NFM theorem.

For stock market prediction, the NFM theorem underscores the need for tailored approaches, acknowledging that the effectiveness of machine learning models is contingent on factors such as market dynamics, economic conditions, and the quality of historical data. It cautions against assuming a one-size-fits-all solution and highlights the challenge of developing models that generalize well to diverse and evolving market scenarios. The theorem prompts practitioners to approach stock market prediction with a realistic understanding of the limitations inherent in algorithmic approaches, encouraging careful consideration of the data, features, and context relevant to each specific application.