# Demo

Body:
Classification and regression are two of the most popular uses for neural networks.  In the previous demo you learned how to test the classification performance of a trained neural network.  You found that when the "Report Type" of the NeuroSolutions for Excel "Test" process is set to "Classification", a confusion matrix is automatically generated providing an easy way to evaluate a network's classification performance.  This demo will show you how to use the same "Test" process to evaluate the performance of a neural network trained to perform regression.


Body:
This demo will develop a very simple model for predicting the S&P 500 one week in advance.  You can use this demo as a starting point for developing your own more complex financial models.  The inputs to the model consist of the 1 year Treasury Bill Yield,  the earnings per share and dividend per share for the S&P 500, and the current week's S&P 500.   The desired output is the next week's S&P 500.  The data has been pre-tagged and is stored within the worksheet named "Financial Data" located behind this slide.  There are 507 weeks worth of data which cover approximately a ten year period.  We will use a simple MLP to model this data.


Body:
The data has been pre-sampled such that the first 254 exemplars contain the data for weeks 1, 3, 5,…, 505, 507 and the last 253 exemplars contain the data for weeks 2, 4, 6,..., 504, 506.  The first 254 exemplars will be used for training and the last 253 exemplars will be used for evaluating the trained networks performance.


Body:
An MLP has been pre-trained on this data and the training results are shown in the worksheet named "TrainMLP Report" located behind this slide.  In this step we will test the networks performance on the 253 weeks of data tagged as "Testing".  Click  the "Test Network" button now to run the NeuroSolutions for Excel "Test" process.  A dialog box will be displayed with the correct options pre-set such that the testing process will load the best weights (automatically saved during pre-training), run the testing data set through the network, and produce a report summarizing the network's regression performance.  Examine the settings then click OK.


Body:
Because the "Report Type" of the "Test" process was set to "Regression", the resulting report contains a plot of the desired and actual network output in place of the confusion matrix.  Furthermore, the "Percent Correct" performance measure has been replaced by the correlation coefficient.  The other performance measures are the same as those computed for classification  since they are useful in evaluating the performance of either type of problem.  Since this a financial application, the true value of this model can only be determined by applying the model to a trading strategy and computing the profits over several time spans.  This type of analysis can easily be done within Excel.


Body:
This demo has shown that NeuroSolutions for Excel can be used to model and test time series data.  To model the S&P500 data we used a very simple MLP with only 4 inputs.  Perhaps the results could have been improved by using more inputs, including more lags of the inputs that were used, or by using some form of time lagged recurrent network.  The Gamma,  Laguerre, and Time Delay are three forms of time lagged recurrent networks included in the Consultants  version of NeuroSolutions.  If you are interested in using neural networks for financial modeling, be sure to check out TradingSolutions -- a full-featured technical analysis tool incorporating the power of neural networks and genetic algorithms.  A free evaluation copy can be downloaded from our web site (www.nd.com).  Click "Next" to return to the main demo panel.


