Stock Selection and Prediction Using Deep Learning

Abstract

This paper offers systematic analysis on selection process of stocks and prediction of same using Deep Learning. The ability to learn huge raw dataset and extract important data from it, without any prior knowledge makes it attractive. The performance of Deep Learning Model is highly dependent on optimizer, loss function, network structure, activation function, and other parameters. This paper is an attempt to study the performance of Autoencoder Neural Network in stock selection process, and future prediction of these stocks using Long Short-Term Memory (LSTM) model. The study involves static 5 years daily data from 2013 to 2018 of each company in S & P 500. Assumption for this study is that user is willing to invest a fix amount in stocks and owns a stock already. Autoencoder has been applied on stocks which are primarily filtered according to the amount user wants to invest and the last day closing price of all the stocks to consider affordability. RMSE score, between original data and recreated data, will be used to select stocks. Considering behaviour of user is neutral, top 50 stocks with least RMSE score and bottom 50 stocks with most RMSE score have been selected. Latent Features of these selected stocks will be examined. Among these 100 stocks, 20 stocks from different sectors will be selected by checking the correlation of them with the stock user already owns. LSTM model will predict the next day future price of these 20 stocks with high accuracy using adam optimiser. Empirical results suggest that top 50 stocks with least RMSE score will give low return with low risk. This implies that top 50 stocks from the result of autoencoder gives blue chip stocks. Similarly bottom 50 stocks with high RMSE score will give high return with high risk. This implies that top 50 stocks from the result of autoencoder gives small cap stocks. Correlation estimation is noticeably improved after Autoencoder Neural Network. Future one day stock prediction using LSTM gives high accuracy with Root Mean Square Error 2.22 by using adam optimizer. Our study offers handful insights and useful road-map for further investigation of stock market analysis and prediction using Deep Learning Networks.