1. Stock Value Ratio Classification Yan SuiZheng Chai

2. Classification MKV/BKV is an indicator of investors’ confidence in a particular company Being able to predict this ratio gives insight to predicting the stock prices

3. Outline Define Problem Data Method Initial Result Discussion

4. Definition Market Value The current quoted price at which investors buy or sell a share of common stock or a bond at a given time. Also known as "market price". Book Value The accounting value of a firm. The total value of the company's assets that shareholders would theoretically receive if a company were liquidated. Per share: total value divided by number of shares

5. Problem Definition Given training data, predict the ratio for the future Classification vs Prediction Problem Need to define the classes (more later)

6. Problem Definition Why do we can about the ratio? Book value stays relatively constant and could be estimated Could estimate market price if we know this ratio and estimated book value

8. Outline Define Problem Data Method Initial Result Discussion

9. Data Dow Jones Industrial Average (Dow 30) Consists of 30 of the largest and most widely held public companies in the United States. E.g. American Express, AT&T, Boeing, Citigroup, Exxon Mobil, GM, GE, Intel, etc.

10. Data wrds from Wharton Attributes are from CRSP/COMPUSTAT Merged database Book value and market value are from COMPUSTAT North America database High, low, and closing prices for each month are available

11. Problem… Book value is updated annually 1 per year Market value is updated daily 365 per year What can we do?

12. Our Approach Estimate “annual market price” of a stock by averaging its high, low and closing prices over 12 months. Market value = estimated market price Another possibility: Interpolate annual book values

13. Data Preprocessing Data Cleaning ~400 attributes --> 68 attributes (possibly more) Estimate annual market value Divide the MKV/BKV ratios into a number of classes Currently, there are 5 classes

14. 1995 - 2005, 330 total observations

15. ratioclass# of Ex R <= 2020 2 < R <=41114 4 < R <=6297 6 < R <= 10369 R > 10430

16. Outline Define Problem Data Method Initial Result Discussion

17. Attributes Hundreds or even thousands possible attributes Using too many attributes may result in over- fitting Want to select a subset that work best for the task

18. Attribute Selection Select a subset of attributes to use Algorithms considered Greedy Algorithm Genetic Algorithm (genoud package in R)

19. Genetic Algorithm

20. Evaluation Function Produce a score of how a particular subset of features work (error rate) Minimization problem Possible candidates SVM Neural Network Etc.

21. Outline Define Problem Data Method Initial Result Discussion

22. Classify on the training data using 10 features Error = abs(predicted - actual)

23. Number of features

24. Top features

25. Explanation of Result Works well on training set When applied on new data, accuracy is around 40-50%

26. To Do List Retain more (non-atomic) attributes Try other evaluation functions Classification on daily ratio Other feature selection algorithms? Hopefully, find out which features are more influential in predicting market price for some stocks

27. Question?