A0392
Title: AI-based predictive system for bull-side put spread trading strategy using decision tree algorithm
Authors: ChiFang Chao - National Chengchi University (Taiwan)
Ming-Hua Hsieh - National Chengchi University (Taiwan)
Hsiu-Yuan Liang - National Chengchi University (Taiwan)
Wei-Jie Chang - VIS International School (Taiwan) [presenting]
Abstract: Options trading has garnered significant attention from both investors and academics due to its flexible and strategic nature. The aim is to enhance the performance of a bull-side put spread options trading strategy by leveraging machine learning techniques. The approach integrates raw data attributes, implied volatility metrics, and technical indicators into predictive models to forecast the profitability of trades. The effectiveness of the decision tree algorithm and the logistic classification model is compared within this framework. The findings reveal that while the logistic classification model achieves a higher cumulative payoff at the cost of executing less reliable trades, the decision tree algorithm demonstrates superior precision, making it more effective in identifying profitable trades. Future research could further refine predictive accuracy by exploring advanced machine learning algorithms and extending their application to a broader range of options strategies.
