CS224n Final Project Stock Price Prediction Using News Articles

The basic form of efficient market hypothesis postulates that publicly available information is incorporated into stock prices. The stronger form of the hypothesis asserts that all information are immediately reflected in the market as soon as they become available; whereas the weaker form allows a certain lag period in which the information is digested by the public and stock prices move gradually towards the efficient target. Previous research suggests that there is correlation between news articles and future stock movement, and several methods have been proposed for making prediction on stock market and devising profit-generating trading strategies. In this project, building on previous research by falvi and Elkan [1], Fung et al. [3] and a similar final project from previous year by Timmons and Lee [6], we build two models using Naive Bayes and Maximum Entropy classifiers to predict stock price movement based on archived news articles from the Wall Street Journal and the Reuters Financial corpus, and evaluate their effectiveness.