Forecast and Analysis of Stock Market Volatility Using Deep Learning Algorithms

Abstract

Stock markets serve as a platform where individuals and institutional investors can come together to buy and sell shares in a public venue and ultimately impact the economy. With the advent of digital technology these markets or exchanges exist as electronic marketplaces. These markets are very volatile thus making the stock market prediction a highly challenging problem. These predictions of stock value offer great profits which serve as a huge motivation for extensive research in this area. Identifying and predicting a stock value beforehand by even a fraction of a second can result in very high profits. Similarly, a near to precise prediction can be extremely profitable in the amortized case. This attractiveness of finding a solution has motivated researchers in both industry and academia to devise techniques despite the complications due to volatility, seasonality and time dependency, economy, and other such factors. Lately, AI/ML techniques like Fuzzy Logic and Support Vector Machines (SVMs) have been used to arrive at different solutions. In this paper, we explore and develop an ensemble predictive system to forecast the market prices using deep learning algorithms. Here we consider the fractional change in stock value and the intra-day high and low values of the stock to train and employ a neural network for obtaining the trading strategy that leads to relatively superior market returns. The focus here is on the use of regression and LSTM based deep learning strategies used to predict stock values. Factors considered are open, close, low, high, and volume.