1. Stock Price Prediction with News Articles Qicheng Ma CS224n Final Project

2. What is built Build classifiers to predict {News Articles} => {Stock Up/Down/Flat on one day} Actually build {paragraph mentioning Stock} => {+/-/0} Tried Naïve Bayes and Max Ent classifier. Using WSJ/ReuterFF news 1994-1996, daily quotes from Y!Finance Trading based on Up/Down signals, fixed-value daily long/short weighted by Up/Down signal. More paragraphs with +/- signal for one stock => trade stock more distribute fixed investment amount for one day proportional to |sum(signals)| Compare to baseline buy-and-hold strategy, 5% vs 2% monthly Based on last year’s project (Timmons and Lee) and other research papers some subtle differences, tried different things.

3. Results Training on 29 months data, test on 1 month. Repeat.

4. Results – monthly returns NB not as good as Baseline (same return, higher volatility) MaxEnt 5.2% vs baseline 2.6%, statistically significant at 95% confidence Precision more important than Recall => want most signals to be profitable, less important to discover all profitable opportunities with risk of false positive.

5. Future Work Build a generic model first (e.g. “STOCK earning exceeds expectation”), then train stock-specific on features Predict real-value returns, instead of discrete classes Look at excess return (stock minus market), adjust for beta, inflation, etc. Use more frequent data. Real time stock quotes, news article streams (search engine quotes and feeds) Profit!