An Automated System for Analysing the Financial News

Abstract

The 24-hour news cycle and barrage of online media is a constant drum beat. The flow of positive and negative financial news is always in flux, influencing our current perspective and reassessing our future outlook. Nowhere is this more true than in the capital markets where assets are priced and risk assessed based on future expectations. While many factors influence a trader's decision to buy or sell an asset it can be argued that the sentiment from the 24-hour news cycle greatly impacts their outlook on the future value of an asset. In this paper our method propose new methods to predict the positive or negative sentiment of financial news. Using Natural Language Processing methods, our method extract syntactic sentence patterns from financial news. From these patterns we conduct experiments using machine learning sentiment analysis approaches to predict sentiment. It find that our sentiment prediction methods are able to consistently out perform the methods. Our robust techniques give the financial practitioner a method to analyze the news sentiment factor and labeling them using a machine learning algorithm logistic regression.