IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i10p3702-d175840.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/10/3702/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/10/3702/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rajagopal, 2015. "Market Trend Analysis," Palgrave Macmillan Books, in: The Butterfly Effect in Competitive Markets, chapter 4, pages 95-118, Palgrave Macmillan.
    2. 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.
    3. 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.
    4. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bharat Kumar Meher & Iqbal Thonse Hawaldar & Latasha Mohapatra & Cristi Spulbar & Ramona Birau, 2020. "The Effects of Environment, Society and Governance Scores on Investment Returns and Stock Market Volatility," International Journal of Energy Economics and Policy, Econjournals, vol. 10(4), pages 234-239.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lin, Yu & Yan, Yan & Xu, Jiali & Liao, Ying & Ma, Feng, 2021. "Forecasting stock index price using the CEEMDAN-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    2. Nina Biljanovska & Lucyna Gornicka & Alexandros Vardoulakis, 2019. "Optimal Macroprudential Policy and Asset Price Bubbles," IMF Working Papers 2019/184, International Monetary Fund.
    3. Roger E. A. Farmer, 2018. "Pricing Assets in a Perpetual Youth Model," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 30, pages 106-124, October.
    4. Packey, Daniel J. & Kingsnorth, Dudley, 2016. "The impact of unregulated ionic clay rare earth mining in China," Resources Policy, Elsevier, vol. 48(C), pages 112-116.
    5. He, Yiyao, 2022. "Heterogeneous stock traders, endogenous bubbles, and economic fluctuations," Finance Research Letters, Elsevier, vol. 47(PA).
    6. Diego Lopez-Bernal & David Balderas & Pedro Ponce & Arturo Molina, 2021. "Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems," Future Internet, MDPI, vol. 13(8), pages 1-14, July.
    7. Clay E Porch & Matthew V Lauretta, 2016. "On Making Statistical Inferences Regarding the Relationship between Spawners and Recruits and the Irresolute Case of Western Atlantic Bluefin Tuna (Thunnus thynnus)," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-13, June.
    8. Hirano, Tomohiro & Toda, Alexis Akira, 2024. "Bubble economics," LSE Research Online Documents on Economics 122042, London School of Economics and Political Science, LSE Library.
    9. Paloviita, Maritta & Haavio, Markus & Jalasjoki, Pirkka & Kilponen, Juha & Vänni, Ilona, 2020. "Reading between the lines : Using text analysis to estimate the loss function of the ECB," Research Discussion Papers 12/2020, Bank of Finland.
    10. Chunping Liu & Zhirong Ou, 2021. "What determines China's housing price dynamics? New evidence from a DSGE‐VAR," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3269-3305, July.
    11. Hyejung Chung & Kyung-shik Shin, 2018. "Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction," Sustainability, MDPI, vol. 10(10), pages 1-18, October.
    12. Jani KINNUNEN & Armenia ANDRONICEANU & Irina GEORGESCU, 2019. "Digitalization Of Eu Countries: A Clusterwise Analysis," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 13(1), pages 1-12, November.
    13. Pablo A. Guerron-Quintana & Tomohiro Hirano & Ryo Jinnai, 2019. "Recurrent Bubbles and Economic Growth," CARF F-Series CARF-F-457, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    14. Larin, Benjamin, 2016. "A Quantitative Model of Bubble-Driven Business Cycles," VfS Annual Conference 2016 (Augsburg): Demographic Change 145817, Verein für Socialpolitik / German Economic Association.
    15. Pintus, Patrick A. & Wen, Yi & Xing, Xiaochuan, 2022. "The inverted leading indicator property and redistribution effect of the interest rate," European Economic Review, Elsevier, vol. 148(C).
    16. Elona Marku & Manuel Castriotta & Michela Loi & Maria Chiara Di Guardo, 2021. "General Purpose Technology: The Blockchain Domain," International Journal of Business and Management, Canadian Center of Science and Education, vol. 15(11), pages 192-192, July.
    17. Asit Kumar Das & Debahuti Mishra & Kaberi Das & Pradeep Kumar Mallick & Sachin Kumar & Mikhail Zymbler & Hesham El-Sayed, 2022. "Prophesying the Short-Term Dynamics of the Crude Oil Future Price by Adopting the Survival of the Fittest Principle of Improved Grey Optimization and Extreme Learning Machine," Mathematics, MDPI, vol. 10(7), pages 1-33, March.
    18. Jianjun Miao, 2016. "Introduction to the symposium on bubbles, multiple equilibria, and economic activities," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 61(2), pages 207-214, February.
    19. Winkler, Fabian, 2020. "The role of learning for asset prices and business cycles," Journal of Monetary Economics, Elsevier, vol. 114(C), pages 42-58.
    20. Jihong Xiao & Xuehong Zhu & Chuangxia Huang & Xiaoguang Yang & Fenghua Wen & Meirui Zhong, 2019. "A New Approach for Stock Price Analysis and Prediction Based on SSA and SVM," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 287-310, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3702-:d:175840. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.