Stock Value Ratio Classification

1. Stock Value Ratio Classification Yan Sui Zheng 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

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?