1. Buy or Sell? The age old question

2. Introduction Goals: • Predict stocks one year out with a MLP • Prove you only need small selection of data to forecast the market

3. Data One of my primary goals with this assignment was to prove that you only needed a handful of different pieces of data to predict that stock market.

4. Price • The price you pay for a stock and the price you sell a stock at determine how well you do in the stock market.

5. PE Ratio • The PE ratio allows for the simplest comparison between different shares, as companies within a particular industry generally fall within a certain PE range. Comparisons between companies in different industries, however, are generally not appropriate using the Price to Earnings ratio. I learned the hard way during the dot bust that a PE ratio of a 100 really isn’t a deal.

6. Volume Volume plainly put is the number of shares bought and sold for a given period. A large percentage price increase accompanied by a higher than average volume is a strong indicator of future price movements. A large percentage price movement accompanied by lower than average volume is a very weak indicator of higher prices, and is, in fact, an indicator that a correction in prices is possible.

7. Williams %R: Popular way for measuring overbought and oversold levels. The scale ranges from 0 to -100 with readings from 0 to -20 considered overbought, and readings from -80 to -100 considered oversold.

8. Neural Networks Used • Back Prorogation • K-Nearest Neighbor • BP proved to be the most successful

9. Results

10. Baseline test • K-Nearest Neighbor was used as a baseline.

11. Conclusions • On average 75% prediction rate vs. a baseline of 67%. • Proved you can use MLPs to predict the stockmarket • If you pick the right data, you don’t need much of it.

12. Buy or Sell? For the stocks I predicted here are results: Sell GE Buy 3M, Microsoft, and Wal-Mart