IDEAS home Printed from https://ideas.repec.org/a/spt/admaec/v14y2024i6f14_6_18.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.scienpress.com/Upload/AMAE%2fVol%2014_6_18.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hsien-Ming Chou & Tsai-Lun Cho & Chihli Hung, 2023. "Home-based Self-health Management Strategies of COVID-19 for the Elderly in Applied Economics," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 13(1), pages 1-1.
    2. 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.
    3. Aslan, Hadiye & Kumar, Praveen, 2016. "The product market effects of hedge fund activism," Journal of Financial Economics, Elsevier, vol. 119(1), pages 226-248.
    4. Chen, Yong & Kelly, Bryan & Wu, Wei, 2020. "Sophisticated investors and market efficiency: Evidence from a natural experiment," Journal of Financial Economics, Elsevier, vol. 138(2), pages 316-341.
    5. Chao Liu & Fengfeng Gao & Mengwan Zhang & Yuanrui Li & Cun Qian, 2024. "Reference Vector-Based Multiobjective Clustering Ensemble Approach for Time Series Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 181-210, July.
    6. Zhou, Zhongbao & Gao, Meng & Liu, Qing & Xiao, Helu, 2020. "Forecasting stock price movements with multiple data sources: Evidence from stock market in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    7. Loureiro, Gilberto & Mendonça, Cesar, 2024. "Do large registered investment funds undermine shareholder activism? Evidence from hedge fund proposals," Journal of Banking & Finance, Elsevier, vol. 162(C).
    8. Fos, Vyacheslav & Almeida, Heitor & Ersahin, Nuri & Irani, Rustom M & ,, 2019. "Do Short-Term Incentives Affect Long-Term Productivity?," CEPR Discussion Papers 13894, C.E.P.R. Discussion Papers.
    9. Oehler, Andreas & Schmitz, Jonas Tobias, 2021. "Does intensified communication of hedge funds with letters affect abnormal returns?," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 127-142.
    10. Caselli, Stefano & Gatti, Stefano & Chiarella, Carlo & Gigante, Gimede & Negri, Giulia, 2023. "Do shareholders really matter for firm performance? Evidence from the ownership characteristics of Italian listed companies," International Review of Financial Analysis, Elsevier, vol. 86(C).
    11. Strong, John S., 2022. "The evolution of activist investors in the U.S. railroad industry," Research in Transportation Economics, Elsevier, vol. 96(C).
    12. Siti Fardaniah Abdul Aziz & Norashikin Hussein & Nor Azilah Husin & Muhamad Ariff Ibrahim, 2022. "Trainers’ Characteristics Affecting Online Training Effectiveness: A Pre-Experiment among Students in a Malaysian Secondary School," Sustainability, MDPI, vol. 14(17), pages 1-24, September.
    13. Gantchev, Nickolay & Sevilir, Merih & Shivdasani, Anil, 2020. "Activism and empire building," Journal of Financial Economics, Elsevier, vol. 138(2), pages 526-548.
    14. Kostaris, Konstantinos & Andrikopoulos, Andreas, 2023. "Brokers in beneficial ownership: A network approach," International Review of Financial Analysis, Elsevier, vol. 88(C).
    15. Tamas Barko & Martijn Cremers & Luc Renneboog, 2022. "Shareholder Engagement on Environmental, Social, and Governance Performance," Journal of Business Ethics, Springer, vol. 180(2), pages 777-812, October.
    16. Liping Wang & Jiawei Li & Lifan Zhao & Zhizhuo Kou & Xiaohan Wang & Xinyi Zhu & Hao Wang & Yanyan Shen & Lei Chen, 2023. "Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey," Papers 2308.04947, arXiv.org.
    17. Wang, Yijun & Andreeva, Galina & Martin-Barragan, Belen, 2023. "Machine learning approaches to forecasting cryptocurrency volatility: Considering internal and external determinants," International Review of Financial Analysis, Elsevier, vol. 90(C).
    18. Flávia S. Maranho & Patrícia M. Bortolon & Ricardo P. C. Leal, 2020. "The firm–investor level characteristics of institutional investor engagement in Brazil," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 17(4), pages 267-281, December.
    19. Jonathan M. Karpoff, 2021. "On a stakeholder model of corporate governance," Financial Management, Financial Management Association International, vol. 50(2), pages 321-343, June.
    20. Maffett, Mark & Nakhmurina, Anya & Skinner, Douglas J., 2022. "Importing activists: Determinants and consequences of increased cross-border shareholder activism," Journal of Accounting and Economics, Elsevier, vol. 74(2).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spt:admaec:v:14:y:2024:i:6:f:14_6_18. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Eleftherios Spyromitros-Xioufis (email available below). General contact details of provider: http://www.scienpress.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.