Decision Tree-Based Hedging: Risk and Return Analysis

Abstract

 Effectiveness of a decision tree-based hedging strategy in the stock market, compared to a traditional hedging approach, is explored in this research paper. Historical stock data is used to employ a decision tree model for predicting price movements and devising a corresponding hedging strategy. The performance of both strategies is evaluated based on several financial metrics, including cumulative returns, Sharpe Ratio, Maximum Drawdown, and Calmar Ratio. It is indicated by our results that, while higher returns and superior risk-adjusted performance are yielded by the decision tree-based strategy, significantly higher risk is also entailed, as evidenced by a substantial maximum drawdown. Insights into the trade-offs between risk and reward in advanced hedging strategies are provided by this study, offering valuable considerations for investors with varying risk appetites.