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Open AccessArticle

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

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Department of Computer Science, COMSATS University Islamabad, Attock Campus, Punjab 43600, Pakistan
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Department of Software Engineering, U.E.T Taxila, Punjab 47080, Pakistan
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Anderson College of Business, Regis University, Denver, CO 80221-1099, USA
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Faculty of Commerce, Mansoura University, Mansoura 1101, Egypt
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Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul 143-747, Korea
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Department of Software, Sejong University, Seoul 143-747, Korea
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Department of Computer Science and Engineering, Soonchunhyang University, Asan 31538, Korea
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Authors to whom correspondence should be addressed.
Sustainability 2018, 10(10), 3702; https://doi.org/10.3390/su10103702
Received: 14 August 2018 / Revised: 5 October 2018 / Accepted: 8 October 2018 / Published: 15 October 2018
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). View Full-Text
Keywords: financial management; stock exchange prediction; regression; forecasting; correlation financial management; stock exchange prediction; regression; forecasting; correlation
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Khan, U.; Aadil, F.; Ghazanfar, M.A.; Khan, S.; Metawa, N.; Muhammad, K.; Mehmood, I.; Nam, Y. A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets. Sustainability 2018, 10, 3702.

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