DOC PREVIEW
UW-Madison ECE 539 - Artificial Neural Networks Approach to Stock Prediction

This preview shows page 1-2-3-4 out of 12 pages.

Save
View full document
View full document
Premium Document
Do you want full access? Go Premium and unlock all 12 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 12 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 12 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 12 pages.
Access to all documents
Download any document
Ad free experience
Premium Document
Do you want full access? Go Premium and unlock all 12 pages.
Access to all documents
Download any document
Ad free experience

Unformatted text preview:

Artificial Neural Networks Approach to Stock PredictionProject OutlineProject DescriptionMethodFormatting of DataMatlab ImplementationResultsOutput with closing price used for trainingOutput with volume used for trainingOutput with product of closing price and volume used for trainingConclusionReferencesArtificial Neural Networks Approach to Stock PredictionPresented by Justin JaeckProject OutlineProject DescriptionExplanation of neural network usage and procedureMethodFormatting of DataMatlab implementationResults from experimentationConclusionProject DescriptionBeing a very interested and active trader in the stock market, I thought it would be informative to apply particular stock data to a neural network and extrapolate predictions to use in my own investments.MethodAfter some research and some trial and error, I decided to use a feed-forward neural network. This network has one hidden layer and is trained with a back-propagation algorithm. The network was implemented with Matlab and the neural network toolbox.I picked three possible inputs with which to train the network. The first is the closing price of the stock. The second is the volume traded of the stock. The third is the product of the closing price and the volume.The output of the network is the day to day difference of the closing price.All data used was obtained from http://www.amex.com. However, this data was not suitable for direct implementation into Matlab. I therefore wrote a java program which formats the data into a useful format.Formatting of DataData is taken from the website for the stock of interest. It can be saved to a text file. A java program takes this text file, formats the dates, removes extra white space, and scales the volume accordingly.Matlab ImplementationMy matlab program takes the output of the java program and does some additional formatting. This includes storing the date in serial format as well as calculating the product of volume and closing price.The user can then select what data is to be used for training. Upon selection, he/she selects the amount of data to be used, the number of points used to predict the next point, and the number of neurons in the hidden layer.ResultsOnce the user has input the file and selected the options, training of the network is begun. The trained network is then used to simulate and form predictions. These predictions are plotted along with the actual values. An additional plot is also generated which shows the difference between the actual and predicted valuesOutput with closing price used for trainingOutput with volume used for trainingOutput with product of closing price and volume used for trainingConclusionIn general, I found that the product of volume and closing price consistently yielded the best results. I experimented with stocks that are highly volatile and others which are very stable. Regardless of the stock, the results were the same.I had hoped to be able to predict future values of the stock, but was unable to do this. The best I could do was to predict one day into the future. However, this was found to have no accuracy whatsoever.I feel that there are too many outside factors which effect the price of a stock to simply pick a few and expect a good prediction. Many of the most influential factors are also such that they cannot be characterized into data for training purposes. Such things include press releases and earnings reports, as well as the actions of the Federal Reserve Board.References1. Neural Networks Toolbox documentation. http://www.mathworks.com/access/helpdesk/help/toolbox/nnet/nnettoc.shtml2. ECE 539 Class Notes on feed forward networks and back-propagation algorithms.3. American Stock Exchange Web page http://www.amex.com4. Multi-Task Learning for Stock Selection, Joumana Ghosn


View Full Document

UW-Madison ECE 539 - Artificial Neural Networks Approach to Stock Prediction

Documents in this Course
Load more
Download Artificial Neural Networks Approach to Stock Prediction
Our administrator received your request to download this document. We will send you the file to your email shortly.
Loading Unlocking...
Login

Join to view Artificial Neural Networks Approach to Stock Prediction and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view Artificial Neural Networks Approach to Stock Prediction 2 2 and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?