Predicting Turnover Rates for Short-Term Stock Index Investments Using Artificial Intelligence and Empirical Analysis

Abstract

Abstract Short-term investments, particularly in stock index futures, have attracted significant interest among day traders seeking quick returns. However, consistently generating profits remains challenging due to suboptimal trading policies. To address this, our study explores the potential of artificial intelligence, specifically deep learning, in predicting optimal turnover rates for short-term stock index transactions. Through empirical methods and an extensive analysis of over 30,000 datasets, we examine the impact of turnover rates on prediction performance. Our findings highlight the substantial influence of higher turnover rates on day traders' profitability in short-term investments. Notably, our deep learning algorithm achieves an exceptional accuracy rate of 93.25% in predicting longer turnover rates. By elucidating the relationship between turnover rates and financial forecasting, this research offers a novel perspective to the existing literature. Traders can leverage these insights to make informed decisions, enhancing the potential for more consistent and profitable outcomes in their short-term investment strategies. Ultimately, this study empowers day traders with valuable knowledge, providing a pathway to navigate the challenges of achieving sustained success in short-term investments. Keywords: Short-term Investments, Stock Index Futures, Artificial Intelligence Techniques, Turnover Rates, Financial Forecasting.