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An integrated new threshold FCMs Markov chain based forecasting model for analyzing the power of stock trading trend

Author

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  • Kavitha Ganesan

    (Hindustan Institute of Technology & Science)

  • Udhayakumar Annamalai

    (Hindustan Institute of Technology & Science)

  • Nagarajan Deivanayagampillai

    (Hindustan Institute of Technology & Science)

Abstract

This paper explores the power of stock trading trend using an integrated New Threshold Fuzzy Cognitive Maps (NTFCMs) Markov chain model. This new model captures the positive as well as the negative jumps and predicts the trend for different stocks over 4 months which follow an uptrend, downtrend and a mixed trend. The mean absolute per cent error (MAPE) tolerance limits, the root mean square error (RMSE) tolerance limits are determined for various stock indices over a multi-timeframe period and observed for the existing methods lying within the defined limits. The results show for every ‘n’ number of predictions made, the predicted close value of the day’s stock price was within tolerance limit with 0 % error and with 100% accuracy in predicting the future trend.

Suggested Citation

  • Kavitha Ganesan & Udhayakumar Annamalai & Nagarajan Deivanayagampillai, 2019. "An integrated new threshold FCMs Markov chain based forecasting model for analyzing the power of stock trading trend," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-19, December.
    • Handle: RePEc:spr:fininn:v:5:y:2019:i:1:d:10.1186_s40854-019-0150-4
    DOI: 10.1186/s40854-019-0150-4
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    References listed on IDEAS

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    Cited by:

    1. Tai-Liang Chen & Ching-Hsue Cheng & Jing-Wei Liu, 2019. "A Causal Time-Series Model Based on Multilayer Perceptron Regression for Forecasting Taiwan Stock Index," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(06), pages 1967-1987, November.
    2. Zhou, Yu & Kou, Gang & Xiao, Hui & Peng, Yi & Alsaadi, Fawaz E., 2020. "Sequential imperfect preventive maintenance model with failure intensity reduction with an application to urban buses," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    3. Peng, Rui & He, Xiaofeng & Zhong, Chao & Kou, Gang & Xiao, Hui, 2022. "Preventive maintenance for heterogeneous parallel systems with two failure modes," Reliability Engineering and System Safety, Elsevier, vol. 220(C).

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