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A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets

Author

Listed:
  • Umair Khan

    (Department of Computer Science, COMSATS University Islamabad, Attock Campus, Punjab 43600, Pakistan)

  • Farhan Aadil

    (Department of Computer Science, COMSATS University Islamabad, Attock Campus, Punjab 43600, Pakistan)

  • Mustansar Ali Ghazanfar

    (Department of Software Engineering, U.E.T Taxila, Punjab 47080, Pakistan)

  • Salabat Khan

    (Department of Computer Science, COMSATS University Islamabad, Attock Campus, Punjab 43600, Pakistan)

  • Noura Metawa

    (Anderson College of Business, Regis University, Denver, CO 80221-1099, USA
    Faculty of Commerce, Mansoura University, Mansoura 1101, Egypt)

  • Khan Muhammad

    (Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul 143-747, Korea)

  • Irfan Mehmood

    (Department of Software, Sejong University, Seoul 143-747, Korea)

  • Yunyoung Nam

    (Department of Computer Science and Engineering, Soonchunhyang University, Asan 31538, Korea)

Abstract

Knowledge-based decision support systems for financial management are an important part of investment plans. Investors are avoiding investing in traditional investment areas such as banks due to low return on investment. The stock exchange is one of the major areas for investment presently. Various non-linear and complex factors affect the stock exchange. A robust stock exchange forecasting system remains an important need. From this line of research, we evaluate the performance of a regression-based model to check the robustness over large datasets. We also evaluate the effect of top stock exchange markets on each other. We evaluate our proposed model on the top 4 stock exchanges—New York, London, NASDAQ and Karachi stock exchange. We also evaluate our model on the top 3 companies—Apple, Microsoft, and Google. A huge (Big Data) historical data is gathered from Yahoo finance consisting of 20 years. Such huge data creates a Big Data problem. The performance of our system is evaluated on a 1-step, 6-step, and 12-step forecast. The experiments show that the proposed system produces excellent results. The results are presented in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).

Suggested Citation

  • Umair Khan & Farhan Aadil & Mustansar Ali Ghazanfar & Salabat Khan & Noura Metawa & Khan Muhammad & Irfan Mehmood & Yunyoung Nam, 2018. "A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets," Sustainability, MDPI, vol. 10(10), pages 1-20, October.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3702-:d:175840
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    References listed on IDEAS

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    1. Jianjun Miao & Pengfei Wang & Zhiwei Xu, 2015. "A Bayesian dynamic stochastic general equilibrium model of stock market bubbles and business cycles," Quantitative Economics, Econometric Society, vol. 6(3), pages 599-635, November.
    2. Xi, Yanhui & Peng, Hui & Qin, Yemei & Xie, Wenbiao & Chen, Xiaohong, 2015. "Bayesian analysis of heavy-tailed market microstructure model and its application in stock markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 117(C), pages 141-153.
    3. Su, Zhenming & Peterman, Randall M., 2012. "Performance of a Bayesian state-space model of semelparous species for stock-recruitment data subject to measurement error," Ecological Modelling, Elsevier, vol. 224(1), pages 76-89.
    4. Rajagopal, 2015. "Market Trend Analysis," Palgrave Macmillan Books, in: The Butterfly Effect in Competitive Markets, chapter 4, pages 95-118, Palgrave Macmillan.
    5. Hongjun Guan & Zongli Dai & Aiwu Zhao & Jie He, 2018. "A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-15, February.
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    Cited by:

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