Banking Industry Stocks Analysis Using Econometric and Deep Learning Approach*

Analyzing stock prices is very difficult due to the uncertainty and their dynamic nature. Investors use many statistical techniques such as moving average, Relative Strength Index (RSI) and others to make buying or selling decision on stocks investment. Stocks price are times series data which is a model that deals with a sequence of measurement of a same variable based on series of time. In this paper, we evaluate the stocks using an econometric approach using granger causality which is a methodology used in econometric to analyse short- or long-term dependency in binary or multivariate data. Another technique of analysis that is also beneficial for investors is the prediction. The second of econometric used in the study is the Autoregressive Integrated Moving Average (ARIMA). Finally The study experiments a deep learning approach using the Long Short Term Memory(LSTM) which also cops with sequences of prediction using historical information and the Multilayer Perceptron (MLP) which is a class of feedforward artificial network. In this research, we conduct a predictive analysis of historical stock price data of three banking industry stocks of New York Exchange (NYSE) which are JP Morgan, Bank of America and Wells Fargo. We first evaluate their long-term relationship using their logarithm returns and follow by the prediction method. Based on the results obtained from the three methods meaning the LSTM, ARIMA and MLP, we conclude that LSTM outperforms ARIMA and MLP on the prediction