Stock Market Prediction with LSTM-based Neural Networks: An Empirical Analysis
Keywords:
Long short-term memory (LSTM), initial public offering (IPO), national stock exchange (NSE), stock prices, stock exchange, forecasting algorithmsAbstract
A stock market is a composite of markets and exchanges where the buying and selling of publicly traded company stocks occur on a regular basis. It serves as a marketplace for the stocks of corporations that are available to the public. Companies opt for an initial public offering (IPO) on the primary market as a means to generate capital. People buy stocks primarily in the hope that they may rise in price in the future. However, there is always uncertainty in the stock market, and people are reluctant to invest their money in the stock market. As a result, we require technology that can forecast stock values so that investors can invest in the finest stocks. As a web application, this project deals with stock market price prediction using LSTM models and uses Dash to visualise stock market information including actual and forecast values. LSTM, which stands for Long Short-Term Memory, is an iterative neural network designed specifically to capture and understand long-term dependencies. It is commonly utilized in the processing and forecasting of time series data. Many LSTMs have a chain-like form. Instead of a single neural network layer, we have four interacting layers that communicate with each other in a very specific way. Dash is a great library framework that Python can use to create interactive web application dashboards. To implement dash, we need to install dash components.
References
Ahangar RG, Yahyazadehfar M, Pournaghshband H. The comparison of methods artificial neural network with linear regression using specific variables for prediction stock price in Tehran stock exchange. arXiv preprint arXiv:1003.1457. 2010 Mar 7.
Anghel GD. Stock market efficiency and the MACD. Evidence from countries around the world. Procedia Econ Financ. 2015 Jan 1; 32: 1414–31.
Baker M, Wurgler J. Investor sentiment in the stock market. J Econ Perspect. 2007 May 1; 21(2): 129–51.
Chen K, Zhou Y, Dai F. A LSTM-based method for stock returns prediction: A case study of China stock market. In 2015 IEEE international conference on big data (big data). 2015 Oct 29; 2823–2824.
Bao W, Yue J, Rao Y. A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one. 2017 Jul 14; 12(7): e0180944.
Roondiwala M, Patel H, Varma S. Predicting stock prices using LSTM. International Journal of Science and Research (IJSR). 2017 Apr; 6(4): 1754–6. 7. Fischer T, Krauss C. Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res. 2018 Oct 16; 270(2): 654–69.
Qiu J, Wang B, Zhou C. Forecasting stock prices with long-short term memory neural network based on attention mechanism. PloS one. 2020 Jan 3; 15(1): e0227222.
Lanbouri Z, Achchab S. Stock market prediction on high frequency data using long-short term memory. Procedia Comput Sci. 2020 Jan 1; 175: 603–8. 10. Yadav A, Jha CK, Sharan A. Optimizing LSTM for time series prediction in Indian stock market. Procedia Comput Sci. 2020 Jan 1; 167: 2091–100.
Benhabib J, Wang P, Wen Y. Sentiments and aggregate demand fluctuations. Econometrica. 2015 Mar; 83(2): 549–85.
Bernanke BS, Kuttner KN. What explains the stock market's reaction to Federal Reserve policy? J Finance. 2005 Jun; 60(3): 1221–57.
Bock J. Quantifying macroeconomic expectations in stock markets using Google Trends. arXiv preprint arXiv:1805.00268. 2018 May 1.
Sadia KH, Sharma A, Paul A, Padhi S, Sanyal S. Stock market prediction using machine learning algorithms. Int J Eng Adv Technol. 2019 Apr; 8(4): 25–31.
Deepak RS, Uday SI, Malathi D. Machine learning approach in stock market prediction. Int J Pure Appl Math. 2017; 115(8): 71–77.
Bhandari HN, Rimal B, Pokhrel NR, Rimal R, Dahal KR, Khatri RK. Predicting stock market index using LSTM. Machine Learning with Applications (MLWA). 2022 Sep 15; 9: 100320.
Hegde MS, Krishna G, Srinath R. An ensemble stock predictor and recommender system. In 2018 IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI). 2018 Sep 19; 1981–1985.
