DEVELOPING A SMART INTEGRATED MODEL BASED ON DEEP LEARNING TOOLS AND TECHNIQUES IN THE EFFICACIOUS PREDICTION OF STOCK PRICES
Saniya Malik
DAV Police Public School, Gurugram
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Recent advances in deep learning have inspired researchers to apply neural networks to stock prediction. Much research has been done on stock price prediction, but academics have yet to find a good solution. To predict the S&P 500 index's future value, we present a convolution-based neural network model in this paper. The proposed model can predict the index's direction the following day based on the index's previous values. As demonstrated by experiments, our model outperforms several benchmarks with an accuracy rate of over 55%.
Keywords:
neural networks; stock price prediction; Machine Learning (ML)
References
- Atsalakis, G. S., & Valavanis, K. P. (2009). Surveying stock market forecasting techniques—Part II: Soft computing methods. Expert Systems with Applications.
- Barra S., Carta S. M., Corriga A., Podda A. S. (2020). Recupero D. R., ”Deep learning and time series-to-image encoding for financial forecasting,” in IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 3, pp. 683-692.
- Cavalcante R.C., Brasileiro R.C., Souza V.L.F., Nobrega J.P.,& Oliveira A.L.I., Computational Intelligence and Financial Markets: A Survey and Future Directions. Expert Systems with Applications. 2016 Aug; 55 (15):194–211.
- Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669.
- Kamalov, F. (2020). Kernel density estimation-based sampling for imbalanced class distribution. Information Sciences, 512, 1192-1201.
- Kamalov, F., & Leung, H. H. (2020). Outlier detection in high dimensional data. Journal of Information & Knowledge Management, 2040013.
- Kamalov, F. (2020). Forecasting significant stock price changes using neural networks. Neural Computing and Applications. 1-13.
- Lee J, & Kang J (2020) Effectively training neural networks for stock index prediction: Predicting the S&P 500 index without using its index data. PLoS ONE 15(4): e0230635.
- Liew, J. K. S., & Mayster, B. (2017). Forecasting etfs with machine learning algorithms. The Journal of Alternative Investments, 20(3), 58- 78.
- Papadimitriou, T., Gogas, P. & Athanasiou, A.F. (2020). Forecasting S&P 500 spikes: an SVM approach. Digital Finance.
- Shen Z., Zhang Y., Lu J., Xu J., & Xiao G. (2020). A novel time series forecasting model with deep learning, Neurocomputing. 396 302–313.
- Strader, T. J., Rozycki, J. J., Root, T. H., & Huang, Y. (. (2019). Machine learning stock market prediction studies: Review and research directions. Journal of International Technology and Information Management,28(4), 63-83.
- Thabtah, F., Hammoud, S., Kamalov, F., & Gonsalves, A. (2020). Data imbalance in classification: Experimental evaluation. Information Sciences, 513, 429-441.
- Zhi S. U., Man L. U., & Dexuan L. I. (2017). Deep Learning in Financial Empirical Applications: Dynamics, Contributions and Prospects. Journal of Financial Research.
- Zhong, X., & Enke, D. (2019). Predicting the daily return direction of the stock market using hybrid machine learning algorithms. Financial Innovation, 5(1), 4.
