IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v201y2025ip2s0960077925013657.html

Predicting stock prices using permutation decision trees and strategic trailing

Author

Listed:
  • Ramraj, Vishrut
  • Nagaraj, Nithin
  • N.B., Harikrishnan

Abstract

In this paper, we explore the application of Permutation Decision Trees (PDT) and strategic trailing for predicting stock market movements and executing profitable trades in the Indian stock market. We focus on high-frequency data using 5-minute candlesticks for the top 50 stocks listed in the NIFTY 50 index and Forex pairs such as XAUUSD and EURUSD. We implement a trading strategy that aims to buy stocks at lower prices and sell them at higher prices, capitalizing on short-term market fluctuations. Due to regulatory constraints in India, short selling is not considered in our strategy. The model incorporates various technical indicators and employs hyperparameters such as the trailing stop-loss value and support thresholds to manage risk effectively. We trained and tested data on a 3 month dataset provided by Yahoo Finance. Our bot based on Permutation Decision Tree achieved a profit of 1.1802% over the testing period, where as a bot based on LSTM gave a return of 0.557% over the testing period and a bot based on RNN gave a return of 0.5896% over the testing period. All of the bots outperform the buy-and-hold strategy, which resulted in a loss of 2.29%.

Suggested Citation

  • Ramraj, Vishrut & Nagaraj, Nithin & N.B., Harikrishnan, 2025. "Predicting stock prices using permutation decision trees and strategic trailing," Chaos, Solitons & Fractals, Elsevier, vol. 201(P2).
    • Handle: RePEc:eee:chsofr:v:201:y:2025:i:p2:s0960077925013657
    DOI: 10.1016/j.chaos.2025.117352
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077925013657
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2025.117352?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Gurjeet Singh, 2022. "Machine Learning Models in Stock Market Prediction," Papers 2202.09359, arXiv.org.
    2. Vanshu Mahajan & Sunil Thakan & Aashish Malik, 2022. "Modeling and Forecasting the Volatility of NIFTY 50 Using GARCH and RNN Models," Economies, MDPI, vol. 10(5), pages 1-20, April.
    3. Syed Hasan Jafar & Shakeb Akhtar & Hani El-Chaarani & Parvez Alam Khan & Ruaa Binsaddig, 2023. "Forecasting of NIFTY 50 Index Price by Using Backward Elimination with an LSTM Model," JRFM, MDPI, vol. 16(10), pages 1-23, September.
    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. Namitha Yeldho & Dany Thomas & Vimal George Kurian & Chandralekha Arathy & Ajithakumari Vijayappan Nair Biju, 2025. "Are machine learning models effective in predicting emerging markets? Investigating the accuracy of predictions in emerging stock market indices," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(1), pages 839-904, February.
    2. Haojun Pan & Yuxiang Tang & Guoqiang Wang, 2024. "A Stock Index Futures Price Prediction Approach Based on the MULTI-GARCH-LSTM Mixed Model," Mathematics, MDPI, vol. 12(11), pages 1-15, May.
    3. Syed Hasan Jafar & Shakeb Akhtar & Hani El-Chaarani & Parvez Alam Khan & Ruaa Binsaddig, 2023. "Forecasting of NIFTY 50 Index Price by Using Backward Elimination with an LSTM Model," JRFM, MDPI, vol. 16(10), pages 1-23, September.
    4. Moumita Barua & Teerath Kumar & Kislay Raj & Arunabha M. Roy, 2024. "Comparative Analysis of Deep Learning Models for Stock Price Prediction in the Indian Market," FinTech, MDPI, vol. 3(4), pages 1-18, November.
    5. Aida Nabilah Sadon & Shuhaida Ismail & Azme Khamis & Muhammad Usman Tariq, 2024. "Heteroscedasticity effects as component to future stock market predictions using RNN-based models," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-18, May.
    6. Himanshu Kautkar & Sudeep Das & Himanshi Gupta & Sajal Ghosh & Kakali Kanjilal, 2026. "Leveraging an Integrated First and Second Moments Modeling Approach for Optimal Trading Strategies: Evidence From the Indian Pharma Sector in the Pre‐ and Post‐COVID‐19 Era," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(2), pages 563-588, March.
    7. Angelo Leogrande & Carlo Drago & Massimo Arnone, 2024. "Analyzing Regional Disparities in E-Commerce Adoption Among Italian SMEs: Integrating Machine Learning Clustering and Predictive Models with Econometric Analysis," Working Papers hal-04700413, HAL.
    8. Bhakti Bhushan Mishra & Namita Sahay & Preeti Sharma, 2025. "Investigating the spillover effect of implied volatility on nifty return in different time periods with reference to index options: a multivariate GARCH approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(6), pages 1993-2004, June.
    9. Longsheng Cheng & Mahboubeh Shadabfar & Arash Sioofy Khoojine, 2023. "A State-of-the-Art Review of Probabilistic Portfolio Management for Future Stock Markets," Mathematics, MDPI, vol. 11(5), pages 1-34, February.
    10. Avi Thaker & Leo H. Chan & Daniel Sonner, 2024. "Forecasting Agriculture Commodity Futures Prices with Convolutional Neural Networks with Application to Wheat Futures," JRFM, MDPI, vol. 17(4), pages 1-15, April.
    11. Srivastava, Mrinalini & Rao, Amar & Parihar, Jaya Singh & Chavriya, Shubham & Singh, Surendar, 2023. "What do the AI methods tell us about predicting price volatility of key natural resources: Evidence from hyperparameter tuning," Resources Policy, Elsevier, vol. 80(C).
    12. M N, Nikhil & Chakraborty, Suman & B M, Lithin & Ledwani, Sanket, 2022. "Modeling Indian Bank Nifty volatility using univariate GARCH models," MPRA Paper 116824, University Library of Munich, Germany, revised 06 Feb 2023.
    13. Saima Latif & Faheem Aslam & Paulo Ferreira & Sohail Iqbal, 2024. "Integrating Macroeconomic and Technical Indicators into Forecasting the Stock Market: A Data-Driven Approach," Economies, MDPI, vol. 13(1), pages 1-28, December.
    14. Fazlollah Soleymani & Qiang Ma & Tao Liu, 2025. "Managing the Risk via the Chi-Squared Distribution in VaR and CVaR with the Use in Generalized Autoregressive Conditional Heteroskedasticity Model," Mathematics, MDPI, vol. 13(9), pages 1-16, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:chsofr:v:201:y:2025:i:p2:s0960077925013657. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.