UCB and Thompson Sampling Algorithms in Long-Term Investing

In the rapidly evolving financial markets, traditional investment strategies are increasingly being replaced by advanced algorithms. Among these, the Upper Confidence Bound (UCB) and TS algorithms, which originate from the multi-armed bandit framework, have demonstrated significant potential across various sectors. This study utilizes historical stock data from companies during the period from 2019 to 2024 to evaluate the performance of these algorithms in long-term investment strategies. The research aims to identify how these algorithms manage investment risks and maximize returns under varying market conditions. The results reveal distinct behaviors of UCB and TS in the financial markets: UCB performs better in markets with lower volatility, while TS exhibits stronger adaptability in high-volatility environments, thereby enhancing potential returns. Through a detailed comparison of UCB and TS in long-term investment scenarios, this study provides valuable insights into the strategic application of these algorithms. It offers investment strategy guidance for investors and financial strategists and supplies effective information for economic decision-making in relevant fields.