1. ANN Approach to Speculate Stock Performance for Inter-Day Traders ECE 539 Chris Churas 5/05/2000

2. Outline Problem Description Data Acquisition Method Overview
Neural Network Design
ANN Inputs & Outputs
Performance and Results
Conclusions

3. Problem Description
Given information pertaining to a particular stock. I want to be able to predict whether a stock will increase or decrease in price.
The time interval of prediction will be between one and twenty minutes. The information will include stock price, current trading volume, P/E ratio and other factors.

4. Data Acquisition Gathered data from http://entrypoint.com
Downloading of data from entrypoint
Wrote Perl program getstocks.pl to download and parse stock data. This program also stored stock data in Mysql Database
The Perl programs getdata.pl and setdata.pl took data from Database and wrote to ascii file for input into Neural Network

5. Method Overview Use of 2 Layer Artificial Neural Network
Input various stock attributes into ANN
Output binary value that denotes whether stock will increase or decrease in future

6. Using Sigmoid Activation Function
Neural Network Design
Using Sigmoid Activation Function
Output Layer 5 Neurons
Hidden Layer 8 Neurons
Direction of ANN
Input Layer 11 Neurons

7. ANN Inputs Earnings Per Share Day High P/E Ratio Day Low
Avg. Daily Volume
Common Shares Out
52 Week High
52 Week Low
Day High
Day Low
Previous Day Close
Current Volume
Last Price

8. ANN 5 Outputs
The ANN output is 5 binary bits. Which translate to the following:
= Decrease in stock price > threshold
= Decrease in stock price <= threshold
= No change in stock price
= Increase in stock price <= threshold
= Increase in stock price > threshold
Note: Threshold is set by user in setdata.pl

9. ANN 3 Outputs
The ANN output is 3 binary bits. Which translate to the following:
1 0 0 = Decrease in stock price
0 1 0 = No change in stock price
0 0 1 = Increase in stock price

10. ANN Configuration ANN Parameters Used Data Statistics Per Stock
Learning Rate: 0.5
Momentum: 0.9
Epoch: 500
Threshold: 0.15% of stock price
Data Statistics Per Stock
Number of Training Samples:
Number of Testing Samples: 300

13. Results 5 Output ANN performance marginal
Only predictions on WDC stock had satisfactory results
3 Output ANN did not fair much better
Once again WDC was only stock to be predicted with some accuracy

14. Conclusions
Perhaps with improved data set that was not missing so much data all stocks could have results similar to WDC stock
If the performance could be improved to that of WDC stock. Then ANN could be implemented as part of a stock ticker for Inter-Day Stock Traders