Abstract

With the increase in computing power and the popularity of machine learning (ML), it has become the norm to tackle more complex problems using ML. The stock market is known to be a highly volatile environment in which stock prices can fluctuate in an erratic manner. The main goal behind this study is to use a deep learning artificial intelligence model to understand and forecast future stock prices. An analysis was also done to assess the role of social media in the stock market price variation and to what extent, it impacts stock prices. The favored approach was to use a Recurrent neural network (RNN) composed of a Long Short-Term Memory (LSTM) model to predict the prices as it is the most suitable to work with time- series data. A successful model was deployed which showed a high level of accuracy and produced low values with regards to the loss function.

References

• P. Jiao, A. Veiga, and A. Walther, Social media, news media and the stock market. Journal of Economic Behavior and Organization, 176, pp.63-90, 2020.
• S. Madge, and S. Bhatt, Predicting stock price direction using support vector machines. Independent work report spring, 45, 2015.
• S.P. Das and S. Padhy, Support vector machines for prediction of futures prices in Indian stock market. International Journal of Computer Applications, 41(3), 2012.
• K.J. Kim, Financial time series forecasting using support vector machines. Neurocomputing, 55(1-2), pp.307-319, 2003.
• M.S. Brown, M. J. Pelosi, and H. Dirska, Dynamic-radius species-conserving genetic algorithm for the financial forecasting of Dow Jones index stocks. In International Workshop on Machine Learn- ing and Data Mining in Pattern Recognition (pp. 27-41). Springer, Berlin, Heidelberg, 2013.
• M. Qiu and Y. Song, Predicting the direction of stock market index movement using an optimized artificial neural network model. PloS one, 11(5), 2016.
• K.J.Kim, and I. Han, Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert systems with Applications, 19(2), pp.125-132, 2000.
• F. Ghashami, K. Kamyar, and S. A. Riazi, Prediction of stock market index using a hybrid technique of artificial neural networks and particle swarm optimization. Applied Economics and Finance, 8(1), 2021.
• M. Hiransha, E.A. Gopalakrishnan, V. K. Menon, and K. P. Soman, NSE stock market prediction using deep-learning models. Pro- cedia computer science, 132, pp.1351-1362, 2018.
• M. Usmani, S.H. Adil, K. Raza, and S.S.A. Ali, Stock market prediction using machine learning techniques. In 2016 3rd international conference on computer and information sciences (ICCOINS) (pp. 322-327). IEEE, 2016.
• A.V. Devadoss, and T.A.A. Ligori, Stock prediction using artifi- cial neural networks. International Journal of Data Mining Techniques and Applications, 2(1), pp.283-291, 2013.
• D.M. Nelson, A.C. Pereira, and R.A.De Oliveira, Stock market’s price movement prediction with LSTM neural networks. In 2017 International joint conference on neural networks (IJCNN) (pp. 1419-1426). IEEE, 2017.
• M. Roondiwala, H.Patel, and S.Varma, Predicting stock prices using LSTM. International Journal of Science and Research (IJSR), 6(4), pp.1754-1756, 2017.
• A.A. Ariyo, A.O. Adewumi, and C.K. Ayo, Stock price prediction using the ARIMA model, UKSim-AMSS 16th international conference on computer modelling and simulation (pp. 106- 112). IEEE, 2014.
• Q. Ma, Comparison of ARIMA, ANN and LSTM for stock price prediction. In E3S Web of Conferences (Vol. 218, p. 01026). EDP Sciences, 2020.
• H. Yang, L. Chan, and I. King, Support vector ma- chine regression for volatile stock market prediction. In International conference on intelligent data engineering and automated learning (pp. 391-396). Springer, Berlin, Heidelberg, 2002.