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

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  • Hsien-Ming Chou

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.

Suggested Citation

  • Hsien-Ming Chou, 2024. "Predicting Turnover Rates for Short-Term Stock Index Investments Using Artificial Intelligence and Empirical Analysis," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 14(6), pages 1-18.
    • Handle: RePEc:spt:admaec:v:14:y:2024:i:6:f:14_6_18
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    References listed on IDEAS

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    1. Hyejung Chung & Kyung-shik Shin, 2018. "Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction," Sustainability, MDPI, vol. 10(10), pages 1-18, October.
    2. Hsien-Ming Chou & Cheng-Wen Lee & Tsai-Lun Cho, 2022. "The Incorporation of Service-Learning into a Management Course: A Case Study of a Charity Thrift Store," Sustainability, MDPI, vol. 14(12), pages 1-22, June.
    3. Hsien-Ming Chou, 2024. "Analyzing the Impact of COVID-19 on Short-Term Investment Behavior through Stochastic Oscillator Indicators," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 14(5), pages 1-6.
    4. Hsien-Ming Chou & Tsai-Lun Cho, 2020. "Effects of Slope Coefficients and Bollinger Bands on Short-term Investment," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 10(2), pages 1-7.
    5. Lucian A. Bebchuk & Alon Brav & Wei Jiang, 2015. "The Long-Term Effects of Hedge Fund Activism," NBER Working Papers 21227, National Bureau of Economic Research, Inc.
    6. Hsien-Ming Chou, 2023. "Using Bull and Bear Index of Deep Learning to Improve the Indicator Model on Extremely Short-term Futures Trading," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 13(6), pages 1-6.
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