Stock Value Ratio Classification

Yan Sui Zheng Chai

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

Outline

Define Problem Data Method Initial Result Discussion

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

Problem Definition

Given training data, predict the ratio for the future

Classification vs Prediction Problem Need to define the classes (more later)

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

Outline

Define Problem Data Method Initial Result Discussion

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.

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

Problem…

Book value is updated annually 1 per year

Market value is updated daily 365 per year

What can we do?

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

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

1995 - 2005, 330 total observations

ratio class # of Ex

R <= 2 0 20

2 < R <=4 1 114

4 < R <=6 2 97

6 < R <= 10 3 69

R > 10 4 30

Outline

Define Problem Data Method Initial Result Discussion

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

Attribute Selection

Select a subset of attributes to use Algorithms considered

Greedy Algorithm Genetic Algorithm (genoud package in R)

Genetic Algorithm

Evaluation Function

Produce a score of how a particular subset of features work (error rate)

Minimization problem Possible candidates

SVM Neural Network Etc.

Outline

Define Problem Data Method Initial Result Discussion

Classify on the training data

using 10 features

Error = abs(predicted - actual)

Number of features

Top features

Explanation of Result

Works well on training set When applied on new data, accuracy is

around 40-50%

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

Question?