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

ANN Approach to Speculate Stock Performance for Inter-Day Traders ECE 539 Chris Churas 5/05/2000. Outline. Problem Description Data Acquisition Method Overview Neural Network Design ANN Inputs & Outputs Performance and Results Conclusions. Problem Description.

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

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  1. ANN Approach to Speculate Stock Performance for Inter-Day TradersECE 539Chris Churas5/05/2000

  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. 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 • P/E Ratio • 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: 1 0 0 0 0 = Decrease in stock price > threshold 0 1 0 0 0 = Decrease in stock price <= threshold 0 0 1 0 0 = No change in stock price 0 0 0 1 0 = Increase in stock price <= threshold 0 0 0 0 1 = 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 • Learning Rate: 0.5 • Momentum: 0.9 • Epoch: 500 • Threshold: 0.15% of stock price • Data Statistics Per Stock • Number of Training Samples: 1200-1500 • Number of Testing Samples: 300

  11. 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

  12. 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

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