Analysis of stock prices and its associated volume of social network activity

Abstract

Stock market prediction has been a convenient testing ground and a highly cited example for applying machine learning techniques to real-life scenarios. However, most of these problems using twitter feeds to analyze stock market prices make use of techniques such as sentimental analysis, mood scoring, financial behavioral analysis, and other such similar methods. In this paper, we propose to discover a correlation between the stock market prices and their associated twitter activity. It is always observed that whenever there are spikes in the stock market prices, there is a preceding twitter activity indicating the imminent spike in the aforementioned stock price. Our objective is to discover this existence of a correlation between the volumes of tweets observed when a market indices’ stock price spikes and the amount by which the stock price changes, with the help of machine learning techniques. If this correlation does exist, then an attempt is made to figure out a mathematical relationship between the two factors. © Springer Nature Singapore Pte Ltd. 2018.