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Application and performance of data mining techniques in stock market: A review

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  • Jasleen Kaur
  • Khushdeep Dharni

Abstract

Prediction and the stock market go hand in hand. Due to the inherent limitations of traditional forecasting methods and the pursuit to uncover the hidden patterns in stock market data, stock market prediction using data mining techniques has caught the fancy of academicians, researchers, and investors. Based on a systematic review of more than 143 research studies spanning 25 years, the present paper brings to light the major issues concerning forecasting of stock markets based on data mining techniques, such as usage of data mining techniques in the stock market, input data types, single versus hybrid techniques, instruments and stock markets researched, types of software and algorithms used, measures of forecast accuracy, and performance of various data mining techniques. Emerging patterns related to various dimensions have been critically analyzed by highlighting the existing limitations and suggesting future research paradigms. This analysis can be useful for academicians, researchers and investors looking for futuristic directions in a given research domain.

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  • Jasleen Kaur & Khushdeep Dharni, 2022. "Application and performance of data mining techniques in stock market: A review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 219-241, October.
    • Handle: RePEc:wly:isacfm:v:29:y:2022:i:4:p:219-241
    DOI: 10.1002/isaf.1518
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    1. Ezgi Erturk & Ebru Akcapinar Sezer, 2016. "Software fault prediction using Mamdani type fuzzy inference system," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 8(1), pages 14-28.
    2. Fernando Perez-cruz & Julio Afonso-rodriguez & Javier Giner, 2003. "Estimating GARCH models using support vector machines," Quantitative Finance, Taylor & Francis Journals, vol. 3(3), pages 163-172.
    3. Hong, Harrison & Torous, Walter & Valkanov, Rossen, 2007. "Do industries lead stock markets?," Journal of Financial Economics, Elsevier, vol. 83(2), pages 367-396, February.
    4. Geet Lahoti & Dilip Kumar Pratihar, 2017. "Recurrent neural networks to model input-output relationships of metal inert gas (MIG) welding process," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 9(3), pages 248-282.
    5. Mojtaba Sedighi & Hossein Jahangirnia & Mohsen Gharakhani & Saeed Farahani Fard, 2019. "A Novel Hybrid Model for Stock Price Forecasting Based on Metaheuristics and Support Vector Machine," Data, MDPI, vol. 4(2), pages 1-28, May.
    6. Jana Tuckova & Martin Sramka, 2012. "ANN application in emotional speech analysis," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 4(3), pages 256-276.
    7. Christopher J. Neely & Paul A. Weller, 2011. "Technical analysis in the foreign exchange market," Working Papers 2011-001, Federal Reserve Bank of St. Louis.
    8. Kunta Ramu & V. Ravi, 2009. "Privacy preservation in data mining using hybrid perturbation methods: an application to bankruptcy prediction in banks," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 1(4), pages 313-331.
    9. Singhal, Shelly & Choudhary, Sangita & Biswal, Pratap Chandra, 2019. "Return and volatility linkages among International crude oil price, gold price, exchange rate and stock markets: Evidence from Mexico," Resources Policy, Elsevier, vol. 60(C), pages 255-261.
    10. Deepak Gupta & Mahardhika Pratama & Zhenyuan Ma & Jun Li & Mukesh Prasad, 2019. "Financial time series forecasting using twin support vector regression," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-27, March.
    11. Jenni L. Bettman & Stephen J. Sault & Emma L. Schultz, 2009. "Fundamental and technical analysis: substitutes or complements?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 49(1), pages 21-36, March.
    12. Barin N. Nag & Chaodong Han & Dong-qing Yao, 2015. "Information enhancement in data mining: a study in data reduction," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 7(1), pages 3-20.
    13. Jingtao Yao & Chew Lim Tan & Hean-Lee Poh, 1999. "Neural Networks For Technical Analysis: A Study On Klci," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 2(02), pages 221-241.
    14. Gallegati, Marco, 2008. "Wavelet analysis of stock returns and aggregate economic activity," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3061-3074, February.
    15. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    16. S.M. Monzurur Rahman & F.A. Siddiky & Uma Shrestha, 2011. "A statistical data mining approach in bacteriology for bacterial identification," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 3(2), pages 117-142.
    17. ?enol Emir & Hasan Din?er & Mehpare Timor, 2012. "A Stock Selection Model Based on Fundamental and Technical Analysis Variables by Using Artificial Neural Networks and Support Vector Machines," Review of Economics & Finance, Better Advances Press, Canada, vol. 2, pages 106-122, August.
    18. Bicchetti, David & Maystre, Nicolas Maystre, 2013. "The synchronized and long-lasting structural change on commodity markets: Evidence from high frequency data," Algorithmic Finance, IOS Press, vol. 2(3-4), pages 233-239.
    19. Mohammed Alweshah & Hasan Rashaideh & Abdelaziz I. Hammouri & Hanadi Tayyeb & Mohammed Ababneh, 2017. "Solving time series classification problems using support vector machine and neural network," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 9(3), pages 237-247.
    20. Taylor, Mark P. & Allen, Helen, 1992. "The use of technical analysis in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 11(3), pages 304-314, June.
    21. Phichhang Ou & Hengshan Wang, 2009. "Prediction of Stock Market Index Movement by Ten Data Mining Techniques," Modern Applied Science, Canadian Center of Science and Education, vol. 3(12), pages 1-28, December.
    22. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    23. Weiwen Yang & Linchi Kwok, 2017. "Improving blog spam filters via machine learning," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 9(2), pages 99-121.
    24. Dwiti Krishna Bebarta & Birendra Biswal & P.K. Dash, 2012. "Comparative study of stock market forecasting using different functional link artificial neural networks," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 4(4), pages 398-427.
    25. Giorgos Tigas & Panagiotis Lefakis & Konstantinos Ioannou & Athanasios Hasekioglou, 2013. "Evaluation of artificial neural networks as a model for forecasting consumption of wood products," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 5(1), pages 38-48.
    26. Nafis Ahmad & Tomohisha Tanaka & Yoshio Saito, 2014. "Machining parameter optimisation by genetic algorithm and artificial neural network," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 6(3), pages 261-274.
    27. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    28. Zoran Vojinovic & Vojislav Kecman & Rainer Seidel, 2001. "A data mining approach to financial time series modelling and forecasting," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(4), pages 225-239, December.
    29. Quinn Taylor & Christophe Giraud-Carrier & Charles D. Knutson, 2010. "Applications of data mining in software engineering," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 2(3), pages 243-257.
    30. Wun-Hua Chen & Jen-Ying Shih & Soushan Wu, 2006. "Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets," International Journal of Electronic Finance, Inderscience Enterprises Ltd, vol. 1(1), pages 49-67.
    31. M. Saimurugan & K.I. Ramachandran, 2014. "A comparative study of sound and vibration signals in detection of rotating machine faults using support vector machine and independent component analysis," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 6(2), pages 188-204.
    32. Mohamed Essaied Hamrita & Abdelkader Trifi, 2011. "The Relationship between Interest Rate, Exchange Rate and Stock Price: A Wavelet Analysis," International Journal of Economics and Financial Issues, Econjournals, vol. 1(4), pages 220-228.
    33. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
    34. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    35. Dudyala Anil Kumar & V. Ravi, 2008. "Predicting credit card customer churn in banks using data mining," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 1(1), pages 4-28.
    36. Paramjeet & V. Ravi, 2011. "Bacterial foraging trained wavelet neural networks: application to bankruptcy prediction in banks," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 3(3), pages 261-280.
    37. Y.R. Ramesh Kumar & A. Govardhan & R.B.V. Subramanyam, 2013. "Predicting intraday prices in stock market transactions using similarity profiled temporal associations," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 5(3), pages 303-322.
    38. Dev Shah & Haruna Isah & Farhana Zulkernine, 2019. "Stock Market Analysis: A Review and Taxonomy of Prediction Techniques," IJFS, MDPI, vol. 7(2), pages 1-22, May.
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    2. Margrét Vilborg Bjarnadóttir & Louiqa Raschid, 2023. "Modeling Financial Products and Their Supply Chains," INFORMS Joural on Data Science, INFORMS, vol. 2(2), pages 138-160, October.

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