1. Predicting Stock Prices with Multi-Layer Perceptron
Raamis Hussain

2. Outline
Try to predict daily closing price of Apple stock using various features
Date, daily open, daily high, daily low, price/earnings ratio, market capitalization, volume
Predict trends as well as actual price using 1200 days of data

3. Configuring MLPRegressor
1-3 hidden layers
Rectified Linear Unit Function as activation function: f(x) = max(0, x)
Stop when validation score stops improving

4. Predicting Price Trends
Try to predict general price movement over some time period
Success = predicting positive/negative change
Use date as only feature

5. Predicting Price Trends

6. Success!
We are able to accurately predict the general trend of the price using our MLP
Next step: Train MLP with features and try to predict actual price
Train features for closing price 15 days in the future

7. 15-Day Prediction Test

8. Doesn’t Work!
The Neural network is making predictions of the past instead of the future
Apparently the features are too closely related to same-day price
MLP predicting closing price of day on same day as given features
Try with less features

9. 15-Day Prediction Test #2
Features: Date, p/e ratio, market cap, volume

10. Conclusions
Even with features that are not related to the price, the prediction isn’t very accurate
MLP predicts same-day price instead of future price
We need more general and sophisticated features to train on
Earnings per share
Debt to equity ratio
And others
Our simplistic method is not sophisticated enough to predict exact stock prices