Analyzing Parts of Speech and Their Impact on Stock Price

INTRODUCTION Term selection in a document is important. Not only for the author's ability to convey information precisely to an audience, but term selection can also be used to carry an emotional sentiment. This sentiment could reveal a bias in the author's treatment of a subject or uncover word choice tendencies. When applied to financial text documents, biases and word choice tendencies can have a real impact on stock price movement. The ability to predict stock market behavior has always had a certain appeal to researchers. While numerous attempts to accurately predict price have been made, the difficulty has been incomplete models of human trading behavior, which at the core rely on rational decisionmaking. These human behavioral patterns are difficult to define and are constantly changing; thus making accurate predictions quite difficult. To further add to the uncertainty, there are two entirely opposed philosophies of stock market research; fundamental and technical analysis techniques (Technical Analysis, 2005). Fundamentalists seek to leverage a security's relative data, ratios and earnings, while technicians analyze charts and modeling techniques based on historical trading volume and pricing. The entire problem thus becomes, does price history matter? As the roles of computers in electronic stock trading have grown, along with the ease of gathering information, it has been possible to not only test both the fundamental and technical trading models, but also to create electronic trading mechanisms without the problem of human bias. Many of these systems have simply followed the trend of automating existing fundamental and/or technical strategies. Their goal is to achieve better returns than human traders by removing the elements of emotion and bias from trading (Jelveh, 2006). The downside is that these systems lack intuition and will continue to buy even after unfavorable news events, such as losing a costly court battle. In order to work effectively, these systems require that news events be translated into numeric data before appropriate decisions can be made. This information-translation problem introduces serious lag-time into decisions and in some cases human analysts must override trades. The motivation of this paper is to build and test a financial news article system that investigates those terms that create the most price movement in textual financial news articles. By identifying those terms, researchers and traders alike can further refine existing quantitative models and further the science of price prediction in the stock market. This paper is arranged as follows. Section 2 provides an overview of literature concerning Stock Market prediction and textual representation techniques. Sections 3 and 4 describe our proposed approaches and the AZFinText system respectively. Section 5 provides an overview of our experimental design. Section 6 details our experimental findings and discusses their impact on price prediction. Section 7 delivers our conclusions. LITERATURE REVIEW There are two major market prediction theories; Efficient Market Hypothesis (EMH) and Random Walk Theory. In EMH, the price of a security is a reflection of complete market information and when new information is added, the market instantly adjusts the price of the security (Fama, 1964). EMH can vary the amount of information sharing throughout the market in three distinct levels; the weak form, the semi-strong and the strong form. In weak EMH, only historical data is embedded within the current price. Semi-Strong EMH incorporates both historical and current public information into its prices. Strong EMH includes all pertinent information such as history, current public information and private information, such as insider trading. From EMH theory, it is the belief that markets behave efficiently and instantaneous price corrections make any prediction model useless. Random Walk Theory is similar to Semi-Strong EMH where all information is contained within the current price and cannot be used in future prediction. …