1. By Charles A. Clark1 ANN Approach to Revenue or Profit Estimation University of Wisconsin-Madison

2. By Charles A. Clark2 Initial Design Considerations Initially, I attempted to create a ANN that would predict future stock prices based upon: –Previous Stock Growth Percentages –Financial Statistics of a firm I quickly found that in today’s market, stock prices may have little in common with a firm’s financial outlook and health. Examples: –Ebay,Amazon,Biotech Industry, etc.

3. By Charles A. Clark3 Initial Design Considerations The next move was to narrow the focus of my project to a growth indicator that could be considered a derivative of financial health. –I settled on Revenue growth –Note: This network could easily be changed to support profit growth prediction as well other financial components. The only restriction is the need for enough supporting data.

4. By Charles A. Clark4 Initial Design Considerations The final consideration was upon the data itself. –First, I could only find inexpensive (read FREE) historical financial data for the last three year. –Second, I knew that the training of the network would require data for only one industry at a time. Basically, each industry operates efficiently with slightly different expectations on capital asset liability, expected ratios of short-term to long-term debt, and various other indications. To include a firm from a separate industry would be to train or test that firm unfairly.

5. By Charles A. Clark5 Data Gathering I needed to gather standardized and extensive financial data on each firm for this project. For this I ended up using two sources. –http://biz.yahoo.com/research/indgrp/ provided me with stock grouping by industryhttp://biz.yahoo.com/research/indgrp/ –http://www.money.net provided me with the information resource of historical financial information. Unfortunately their data only went back 3 years. A more cumulative training would have been interesting.http://www.money.net

6. By Charles A. Clark6 Network Design I choose to use a MLP for this project because of my familiarity with it as well as it’s flexibility. –Notes on the specific design of the MLP  Sigmoid Activation Function  25 inputs for year against year training  50 inputs for comparative training (includes data from both firms)  1 output for all designs

7. By Charles A. Clark7 Input Choice I choose my input directly from the financial information offered on money.net The following data was taken from each firm’s Income Statement, Balance Statement, & Cash Flow Statement. Revenue Operating Expenses Operating Income Income Before Taxes Income Taxes Pri/Bas EPS Ex. Xord Dilutd EPS Ex. Xord Primary/Basic Av. Share Total Current Assets Total Assets Total Current Liabilities Total Liabilities Total Equity Net Income Depreciation & Amort. Total Operating Cash Flow Total Investing Cash Flow Total Financing Cash Flow Net Change in Cash Receivables Accounts Payable Common Dividends/Shr. Outstanding Shares

8. By Charles A. Clark8 Output Choice For the year vs. year testing and training, each firm’s revenue is compared with that fir’s revenue for the following year. The percentage growth is calculated and if it is higher than a threshold X, in this case 10% we set that firms target output to be 1. In comparative testing, we must take the ratio of one firm’s revenue growth versus another firm’s revenue growth. If firms A (located in the outer loop) has a higher % of growth the target output will be 1, otherwise if firm B has the higher % than the target output will be 0.

9. By Charles A. Clark9 Results While training with both the 1997 and the 1998 data I was able to obtain an average revenue prediction rate of 61.3% over about a hundred trials. The max of my testing was as high as 70 % and the low was about 52 %. Training with the 1997 data and testing on 1998, my network obtained an average of 63.4% with a range of 70.2% to 54 %. Training with the 1998 data and testing on 1997, the network scored an average of 60.5% with a range of 65% to 52%.

10. By Charles A. Clark10 Conclusions & New Insights The results that I obtained were competitive in the sense that the pointed to the ability of a neural net to pull viable conclusions for the data. While these conclusions are not of the investment caliber, I believe the fault of this resides with the data itself. Adding the element of anticipation or expectation into the data by adding a whisper number or expected increase data column should help to push the data in the right direction. The reason that I did not initially propose this is because retrieving the PAST data on whisper number on a large number of stocks would’ve been nearly impossible. Second whisper data tends to be extremist. Often it overshoots the true growth or decline of a firm. However with the bulk of the data predicting conservatively, a whisper data field might be a good tweak. (as an aside, many of the mis-predictions were clustered around the target boundary line. This points to the validity of the data for not predicting wild results and further suggests a whisper data point might help.

11. By Charles A. Clark11 Conclusions & New Insights The second change that I would like to make to the network is the target space. The real point at which a firm becomes investment grade is when it’s growth is expected at > than 15%. Also there is a fair amount of clumping at 10% growth since that is the industry norm. Splitting the Output space into perhaps three outcomes of –Firm X <5% (sell) –5%< Firm X <15% (hold) –Firm X > 15% (buy)