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

Article


Khan, U., Aadil, F., Ghazanfar, M., Khan, S., Metawa, N., Muhammad, K., Mehmood, I. and Nam, Y. 2018. A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets. Sustainability. 10 (Art. 3702). https://doi.org/10.3390/su10103702
AuthorsKhan, U., Aadil, F., Ghazanfar, M., Khan, S., Metawa, N., Muhammad, K., Mehmood, I. and Nam, Y.
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).

JournalSustainability
Journal citation10 (Art. 3702)
ISSN2071-1050
Year2018
PublisherMDPI
Publisher's version
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File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.3390/su10103702
Web address (URL)https://doi.org/10.3390/su10103702
Publication dates
Online15 Oct 2018
Publication process dates
Accepted08 Oct 2018
Deposited12 Feb 2020
Copyright holder© 2018 The Authors
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