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

Umair Khan, Farhan Aadil, Mustansar Ali Ghazanfar, Salabat Khan, Noura Metawa, Khan Muhammad, Irfan Mehmood and Yunyoung Nam
Additional contact information
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
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

Sustainability, 2018, vol. 10, issue 10, 1-20

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).

Keywords: financial management; stock exchange prediction; regression; forecasting; correlation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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