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Machine Learning Based Design and Implementation of Stock Market Price Prediction Using Classification Method

Deepika Warathe, A. P. Thakare

Abstract


Deep learning for predicting stock market prices and trends has become even more popular than before in the era of big data. We analysed two years of data from the Chinese stock market and proposed a feature engineering and deep learning-based model for predicting stock market price trends. The suggested approach is comprehensive because it incorporates stock market dataset pre-processing, several feature engineering techniques, and a proprietary deep learning-based system for stock market price trend prediction. We conducted extensive tests on commonly used machine learning models and found that our proposed solution outperforms them.Because of the comprehensive feature engineering that we built, our solution outperforms. In terms of stock market trend prediction, the system has a high overall accuracy. This study adds to the stock analysis research community in both the financial and technological areas by providing extensive design and evaluation of prediction term lengths, feature engineering, and data pre-processing approaches.

 

Keywords: Machine learning (ML), stock market prediction


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References


Freeman, J. A., & Skapura, D. M. (1991). Neural networks: algorithms, applications, and programming techniques. Addison Wesley Longman Publishing Co., Inc..

Vijh, A. M. (1994). S&P 500 trading strategies and stock betas. The Review of Financial Studies, 7(1), 215-251.

Watkins, C. (1999). Dynamic alignment kernels. Advances in neural information processing systems, 39-50.

Asness, C. S., Krail, R. J., & Liew, J. M. (2001). Do hedge funds hedge?. The journal of portfolio management, 28(1), 6-19.

Agarwal, V., & Naik, N. Y. (2004). Risks and portfolio decisions involving hedge funds. The Review of Financial Studies, 17(1), 63-98.

Ang, A., Gorovyy, S., & Van Inwegen, G. B. (2011). Hedge fund leverage. Journal of Financial Economics, 102(1), 102-126.

Heckerling, P. S., Canaris, G. J., Flach, S. D., Tape, T. G., Wigton, R. S., & Gerber, B. S. (2007). Predictors of urinary tract infection based on artificial neural networks and genetic algorithms. International Journal of Medical Informatics, 76(4), 289-296.

Dybowski, R., & Gant, V. (Eds.). (2001). Clinical applications of artificial neural networks. Cambridge University Press.

Team, R. C. (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Ali, A., Magnor, O., & Schultalbers, M. (2009). Misfire detection using a neural network based pattern recognition. In International Conference on Artificial Intelligence and Computational Intelligence (Vol. 2, No. 3).


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