Predict Stock Market Prices with Recurrent Neural Networks using NASDAQ Data Stream

Abstract

Prediction and modeling of stock market changes attract not only economists and other scientific professionals, but the general public as well. With the rise of blockchain, and cryptocurrencies, the interest in stock trading has surged. Stock prices are precisely recorded in frequent fixed intervals, and this data is publicly available. Due to the outstanding performance of recurrent neural networks in sequential data modeling, recurrent networks can be applied to model the stock market. Although accurate prediction of the changes is not possible due to the stock market’s highly stochastic stochastic nature, a recurrent neural network could give a good approximation of trends. National Association of Securities Dealers Automated Quotations publishes stock values every few minutes, which data stream was used to model and predict changes. This paper presents a proof of concept implementation of a stock market price prediction system using recurrent neural networks and a continuous data stream.