IJARP SJIF(2018): 4.908

International Journal of Advanced Research and Publications!

LQ45 Stock Price Predictions Using The Deep Learning Method

Volume 4 - Issue 4, April 2020 Edition
[Download Full Paper]

Author(s)
Nurmalitasari, Sri Sumarlinda, Nyoto Supriyanto, Davina Kusuma Putri
Keywords
Stock Price, Prediction, Deep Learning, LSTM
Abstract
The stock price prediction is very useful for investors to see how the company's investment prospects in the future. Stock price predictions can be used to anticipate stock price deviations and assist investors in making decisions. Prediction of stock price index movements can be categorized as a problem that is quite challenging in financial predictions However, the complexity of the stock market has made it very difficult to develop predictive models that can be said to be effective. We offer a systematic analysis of the use of deep learning neural networks to analyze and predict the stock market. The ability of this deep learning neural network method is able to extract features from a large amount of data without relying on prior knowledge from predictors, thus making this deep learning method suitable for predicting stock prices at high frequencies.
References
[1]. Phan, D. H. B., Sharma, S. S., & Narayan, P. K. (2015). Stock return forecasting: Some new evidence. International Review of Financial Analysis, 40, 38–51.

[2]. Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there? Review of Fi- nancial studies, 20(3), 651–707.

[3]. Bacchetta, P., Mertens, E., & Van Wincoop, E. (2009). Predictability in financial mar- kets: What do survey expectations tell us? Journal of International Money and Finance, 28(3), 406–426.

[4]. Campbell, J. Y., & Thompson, S. B. (2008). Predicting excess stock returns out of sample: Can anything beat the historical average? Review of Financial Studies, 21(4), 1509–1531.

[5]. Bollerslev, T., Marrone, J., Xu, L., & Zhou, H. (2014). Stock return predictability and variance risk premia: Statistical inference and international evidence, Journal of Financial and Quantitative Analysis, 49(03), 633–661.

[6]. Ferreira, M. A., & Santa-Clara, P. (2011). Forecasting stock market returns: The sum of the parts is more than the whole. Journal of Financial Economics, 100(3), 514–537.

[7]. Kim, J. H., Shamsuddin, A., & Lim, K.-P. (2011). Stock return predictability and the adaptive markets hypothesis: Evidence from century-long us data. Journal of Empirical Finance, 18(5), 868–879.

[8]. Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Comparison of arima and artifi- cial neural networks models for stock price prediction. Journal of Applied Mathematics.

[9]. Enke, D., & Mehdiyev, N. (2013). Stock market prediction using a combination of stepwise regression analysis, differential evolution-based fuzzy clustering, and a fuzzy inference neural network. Intelligent Automation & Soft Computing, 19(4), 636–648.

[10]. Guresen, E., Kayakutlu, G., & Daim, T. U. (2011a). Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 38(8), 10389–10397.

[11]. Kara, Y., Boyacioglu, M. A., & Baykan, Ö. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the istanbul stock exchange. Expert Systems with Applications, 38(5), 5311–5319.

[12]. Kazem, A., Sharifi, E., Hussain, F. K., Saberi, M., & Hussain, O. K. (2013). Support vec- tor regression with chaos-based firefly algorithm for stock market price fore- casting. Applied Soft Computing, 13(2), 947–958.

[13]. Monfared, S. A., & Enke, D. (2014). Volatility forecasting using a hybrid gjrgarch neural network model. Procedia Computer Science, 36, 246–253. Kim, Y., & Enke, D. (2016a). Developing a rule change trading system for the futures market using rough set analysis. Expert Systems with Applications, 59, 165–173.

[14]. Kim, Y., & Enke, D. (2016b). Using neural networks to forecast volatility for an asset allocation strategy based on the target volatility. Procedia Computer Science, 95, 281–286.

[15]. Wang, J.-Z., Wang, J.-J., Zhang, Z.-G., & Guo, S.-P. (2011). Forecasting stock indices with back propagation neural network. Expert Systems with Applications, 38(11), 14346–14355.

[16]. Chourmouziadis, K., & Chatzoglou, P. D. (2016). An intelligent short term stock trading fuzzy system for assisting investors in portfolio management. Expert Systems with Applications, 43, 298–311.