Ari Gilder and Kevin Lerman

Faculty Advisor: Dr. Fernando Pereira

Project Advisor: Mark Dredze

Abstract

We present a system for predicting price fluctuations in Prediction Markets, such as TradeSports and the Iowa Electronic Markets. Our approach utilizes both market history and public news articles, published before the beginning of trading each day, to produce a set of recommended investment actions. Since there is evidence that prediction markets are very good indicators of future events, we hypothesize that the converse is true: past/present events can potentially assist in predicting future prices in these markets. We empirically show that these markets are surprisingly predictable, even by purely market-historical techniques. Furthermore, analyzing relevant news articles captures information independent of the market’s history, and combining the two methods significantly improves results. Capturing this signal from news articles requires some linguistic sophistication – the standard naïve bag-of-words approach does not yield predictive features. Instead, we use part-of-speech tagging, dependency parsing and semantic role labeling to generate features that improve system accuracy.

We evaluate our system on eight political markets from 2004 and show that we can make effective investment decisions based on our system’s predictions, whose profits greatly exceed those generated by a baseline system. Additionally, our market prediction system can be applied to any Prediction Market with a known end date and for which a set of relevant entities (people, places, or things) can be defined.