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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.

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Presentation Transcript
classification
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
Outline
  • Define Problem
  • Data
  • Method
  • Initial Result
  • Discussion
definition
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
Problem Definition
  • Given training data, predict the ratio for the future
  • Classification vs Prediction Problem
  • Need to define the classes (more later)
problem definition1
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
outline1
Outline
  • Define Problem
  • Data
  • Method
  • Initial Result
  • Discussion
slide9
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.
slide10
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
Problem…
  • Book value is updated annually
    • 1 per year
  • Market value is updated daily
    • 365 per year
  • What can we do?
our approach
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 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
outline2
Outline
  • Define Problem
  • Data
  • Method
  • Initial Result
  • Discussion
attributes
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
Attribute Selection
  • Select a subset of attributes to use
  • Algorithms considered
    • Greedy Algorithm
    • Genetic Algorithm (genoud package in R)
evaluation function
Evaluation Function
  • Produce a score of how a particular subset of features work (error rate)
  • Minimization problem
  • Possible candidates
    • SVM
    • Neural Network
    • Etc.
outline3
Outline
  • Define Problem
  • Data
  • Method
  • Initial Result
  • Discussion
slide22

Classify on the training data

using 10 features

Error = abs(predicted - actual)

explanation of result
Explanation of Result
  • Works well on training set
  • When applied on new data, accuracy is around 40-50%
to do list
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
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