1. Stock Price Prediction from Natural Language Understanding of News Headlines Machine learning experiment: Task is to predict whether a stock will rise or fall significantly in reaction to the appearance of some news headline about the stock. The agent is trained on a year's worth of dated headlines and closing prices for 100 stocks. Performance is measured on the number of accurately classified test headlines. Does the headline predict a RISE, FALL or NOTHING?

2. Some related prior work Thomas Fawcett and Foster John Provost (1999) - Activity Monitoring, monitor streams of data for something interesting. Lavrenko et al (2000) – Ænalyst system, combines two time series data streams, headlines and quotes. Sofus Macskassy (2003) - Information Filtering, Prospective training data Yu, H. Hatzivassiloglou, V (2003) - Towards Answering Opinion Questions, separate facts and opinions, understand polarity of opinions, using Bayesian Classifier

3. Naïve Bayes Classifier P(D|h) P(h) P(D) P(h|D) = h  H, H is the set {RISE, FALL, NOTHING} priors: P(RISE) = 0.013055 P(FALL) = 0.007976 P(NOTHING) = 0.978969 -The occurrences of rises and falls are sparse. - Each word in the dictionary collected has counts of when they appeared and when they occurred during a RISE, FALL - P(D|h) estimated by multiplying the probabilities of the occurrence of each word in a headline during the RISE, FALL and NOTHING

4. Dictionary generated Human-understandable model Can be used for further agent design

5. Results % Accuracy vs. # of Training headlines

6. Further work Bug fixes Something more than “bag of words” Per-symbol language models Simple word-based decision-stubs as inputs to the Boosting algorithm