Predicting stock market movements using artificial neural networks

Abstract

Knowing about the future returns attract every investor. Investors can take appropriate decisions once they know what would be the future returns based on their investments. To know the returns, the earlier studies have proposed basic models like Efficient Marker Hypothesis and Random Walk Model, whereas these theories have their own limitations in predicting the direction of the stock and the next day value of the stock. Later with the evolution of Machine learning and Deep Learning Techniques, there were many experiments which were made to study the stock markets. The present research paper aims at applying the Deep Learning technique of Artificial Neural Network to predict the direction of the stock index. The data consist of daily open price, close price, high price, low price and volume of NIFTY 50, S&P 500, New York Stock Index, Korean Stock Index, Dow Jones Index and Shanghai Stock Index from Jan 2015 to May 2020. The open price of the index is fed as input to the Artificial Neural Network. The model is evaluated on different performance metrics of Accuracy, Precision, Recall and F1-Score.