Towards Sports Betting as a Financial Asset: An Investigative Analysis of Risk, Investment Potential, and Future Perspectives

Abstract

This paper explores the potential of sports betting as a financial asset class by integrating financial theory and artificial intelligence. We frame systematic sports wagering as a form of investment, highlighting its uncorrelated returns and potential diversification benefits. Financial models such as the Kelly criterion, Sharpe ratio, and Value at Risk (VaR) are applied to assess risk-adjusted performance and portfolio optimization. Additionally, we investigate how leverage can amplify both returns and risks in betting portfolios, and we introduce an uncertainty-adjusted Kelly criterion to account for prediction errors, validating these approaches via simulations. 1 Artificial intelligence techniques-including machine learning, deep learning, and reinforcement learning-are used to forecast outcomes, manage risk, and optimize betting strategies. Real-world use cases, such as artificial intelligence-driven betting funds, algorithmic arbitrage across bookmakers, and automated market-making on exchanges, illustrate practical implementations of these models. While high volatility, regulatory barriers, and liquidity constraints remain challenges, our findings suggest that with mature analytics and governance frameworks, sports betting could emerge as a credible alternative asset for institutional portfolios.