Stock Value Ratio Classification

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# Stock Value Ratio Classification - PowerPoint PPT Presentation

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.

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### 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
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)
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)

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