Stock value ratio classification
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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


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
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


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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