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PREDICTING IMPACT OF NEWS ON STOCK PRICE: AN EVALUATION OF NEURO FUZZY SYSTEM Congress on Evolutionary Computation (CEC 2007) Presented by CUI, Weiwei In COMP630P 2009 - HKUST OUTLINE Introduction and literature review Specification of neuro fuzzy networks News Coding Experiment and data

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predicting impact of news on stock price an evaluation of neuro fuzzy system

PREDICTING IMPACT OF NEWS ON STOCK PRICE: AN EVALUATION OF NEURO FUZZY SYSTEM

Congress on Evolutionary Computation (CEC 2007)

Presented by CUI, Weiwei

In COMP630P 2009 - HKUST

outline
OUTLINE
  • Introduction and literature review
  • Specification of neuro fuzzy networks
  • News Coding
  • Experiment and data
  • Discussion of findings
  • Conclusion and Comments
introduction
INTRODUCTION
  • News implicitly affects financial markets
    • News  investors  stock price
    • Political, economic, financial, macro, micro…
    • Released when the security markers are open or closed
    • No attempt to study the impact of all news in total
  • Neural Fuzzy (NF) Systems
    • Predicting complex, non-linear relationships
    • Multiple variables
    • No specific pattern of distribution of data
  • NF systems are different
    • Different levels of competences and capabilities
objective of paper
OBJECTIVE OF PAPER
  • Evaluate the effectiveness of four NF systems
    • Feed Forward Neural Network (FFNN)
    • Adaptive Neuro Fuzzy Inference System (ANFIS)
    • Radial Basis Function Network (BRFN)
    • Rough Set Based Pesudo Outer Product Rule (RSPOP)
  • Apply these four NF systems on the same dataset
  • Recommend a system for more detailed analysis based on the experimental results
past studies
PAST STUDIES
  • Pure expert analysis
    • “The number of Dow Jones announcements and the aggregate measures of securities market activity such as trading volumes and market returns are related”

- Mitchell and Mulherin (1994)

    • “The arrival of public information in the U.S. Treasury Market sets off a two stage adjustment process for prices, trading volume, and bid-ask spreads”

- Fleming and Remolona (1999)

    • “Investors in Asian markets tend to react more significantly to negative stock news originating from US sources than they do to positive news”

- Doong et al. (2005)

nf systems v s statistical models
NF SYSTEMS V.S. STATISTICAL MODELS
  • NF networks have proven to be better
    • Soft computing approaches synthesizing human ability to process uncertain, imprecise, and incomplete information to make decisions
    • High-level linguistic model instead of low-level complex mathematical expressions
    • Ability to self-adjust the parameters and derive intrinsic relationships between selected inputs and outputs
outline7
OUTLINE
  • Introduction and literature review
  • Specification of neuro fuzzy networks
  • News Coding
  • Experiment and data
  • Discussion of findings
  • Conclusion and Comments
specification of nf systems
SPECIFICATION OF NF SYSTEMS
  • Feed Forward Neural Network (FFNN)
  • Radial Basis Function Network (BRFN)
  • Adaptive Neuro Fuzzy Inference System (ANFIS)
  • Rough Set Based Pesudo Outer Product Rule (RSPOP)

BRFN

FFNN

ANFIS

RSPOP

feed forward neural network
FEED FORWARD NEURAL NETWORK
  • Multilayer Perceptron (MLP)
    • Most popular type of neural networks
    • Back-propagation to update the weights
  • Simplest form of a MLP model
    • Benchmark?
  • Not good at prediction of a time series data
    • Influence of the anterior data?
radial basis function network
RADIAL BASIS FUNCTION NETWORK
  • First used to solve interpolation problems
  • Fitting a curve exactly through a set of points
    • Weighted distances are computed between the input x and a set of prototypes
    • These scale distances are then transformed through a set of nonlinear basis functions h, and these outputs are summed up in a linear combination with the original inputs and a constant.
adaptive neuro fuzzy inference system
ADAPTIVE NEURO FUZZY INFERENCE SYSTEM
  • Combine world-fuzzy logic systems and neural networks
    • Representing prior expert knowledge into a set of fuzzy membership functions
    • Reducing the optimization search space
    • Adapting the back-propagation to automate fuzzy controller parametric tuning
    • tuning

Layer 1: Fuzzy member function

Layer 2: Multiplication

Layer 3: Normalization

Layer 4: Production of the input and

a first order polynomial

Layer 5: Sum

rough set based pesudo outer product rule
ROUGH SET BASED PESUDO OUTER PRODUCT RULE
  • Combine the concept of rough set theory and presudo outer product rule
    • Automatically formulate the fuzzy rules from the numberical training data
    • No initial rule base needs to be specified

Layer 1: Each input node represents an input linguistic variable

Layer 2: Each input label node represents a fuzzy member function

Layer 3: Each rule node represent an if-then fuzzy rules

Layer 4: Each output label node represents a fuzzy member function

Layer 5: Each output node represents an output linguistic variable

time series prediction using nn
TIME SERIES PREDICTION USING NN
  • Represent target values by the successive relative changes in prices since the previous time point rather than absolute prices after a fixed time horizon
  • General n-dimensional discrete time dynamic system:
    • Reconstruct the phase space form the time series data by delay coordinates
outline15
OUTLINE
  • Introduction and literature review
  • Specification of neuro fuzzy networks
  • News Coding
  • Experiment and data
  • Discussion of findings
  • Conclusion and Comments
news coding
NEWS CODING
  • 2-Value News Coding Method (2-NCM)
    • Binary coding: There is news for the day or there is no news
  • Penta Coding Method (PCM)
    • Categorical info: Classify the contents of news and to ascertain the impact of different categories of news items
2 value news coding method
2-VALUE NEWS CODING METHOD
  • Let L be the set of news on the company and T be the Time for which news data is classified
    • The coding is decided manually based on the headlines extracted from database
penta coding method pcm
PENTA CODING METHOD (PCM)
  • News category (priority in ascending order):
    • No news
    • LC – News pertaining directly to Company operations
      • Splits, dividends, bonus, successfulness of product launch
    • LP – Performance related news
      • Quarterly or annual financial report
    • LM – Macro-environmental changes
      • Interest rate change
      • Government or regulatory policy news
    • LO – Other news
      • Major stock index rise/fall without any particular reason
      • Natural or man-made disasters
penta coding method pcm19
PENTA CODING METHOD (PCM)
  • Let L be the set of news on the company and T be the Time for which news data is classified
outline20
OUTLINE
  • Introduction and literature review
  • Specification of neuro fuzzy networks
  • News Coding
  • Experiment and data
  • Discussion of findings
  • Conclusion and Comments
data stock prices and news
DATA: STOCK PRICES AND NEWS
  • DBS = Development Bank of Singapore
  • UBO = United Overseas Bank
  • ExMobile = Exxon Mobil

(News was obtained by running a single keyword search with the company names)

experiment
EXPERIMENT
  • Two measures of performance were used:
    • Root mean square error
    • Pearson’s coefficient of correlation
  • Two results of 2-NCM and PCM were benchmarked against the results form their corresponding setup with only stock prices as inputs
outline23
OUTLINE
  • Introduction and literature review
  • Specification of neuro fuzzy networks
  • News Coding
  • Experiment and data
  • Discussion of findings
  • Conclusion and Comments
results on dbs and uob
RESULTS ON DBS AND UOB
  • 2-NCM: No significant advantage
    • Low interpretability of the news input: a binary input along with a set of prices
  • PCM: Also no significant advantage
    • Small amount of training data available to the network
    • Databases do not keep sufficient information fora small stock like DBS
    • Singapore is a very controlledmarket
pcm on apple and exxon mobile
PCM ON APPLE AND EXXON MOBILE
  • Results are positive
    • FFNN is a primitive model
    • Consistent improvement across RBFN, ANFIS, and RSPOP
    • Error down by 1.1% for Apple, 1.49% for Exxon
change in stock price prediction
CHANGE IN STOCK PRICE PREDICTION
  • Legend C: error reduction by $1.72 on 19 Oct.
    • Code 3 news: performance related news
    • Benchmark model is right about the movement direction
change in stock price prediction27
CHANGE IN STOCK PRICE PREDICTION
  • Legend A: error reduction by $1.13 on 28 Dec.
    • Code 5 news: other news
      • ‘US stock Index Futures Decline; Home Depot, Apple Fall’
    • Stock price had moved up by $4.03, but benchmark model shows none
change in stock price prediction28
CHANGE IN STOCK PRICE PREDICTION
  • Legend K: error reduction by $0.4 on 29 Jun.
    • Code 4 news: Macro-environmental changes
      • Apple started investigating stock option grants
    • Not inputting impact direction, it might be dicey for the network to predict correctly
change in stock price prediction29
CHANGE IN STOCK PRICE PREDICTION
  • Error increase:
    • Legend H: lawsuit
    • Legend D: ‘Reports Findings of Stock Option’
    • Legend E: ‘Google Inc. CEO Joins Apple Computer’
change in stock price prediction30
CHANGE IN STOCK PRICE PREDICTION
  • All reductions are at points where the stock has taken a sharp jerk
  • It is not predictable based on historical past patterns
outline31
OUTLINE
  • Introduction and literature review
  • Specification of neuro fuzzy networks
  • News Coding
  • Experiment and data
  • Discussion of findings
  • Conclusion and Comments
conclusion
CONCLUSION
  • Propose, implement , and evaluate the impact of news on stock prices on a short term
  • News input could increase accuracy in most cases, or at least maintain the performance of the current models.
  • Two facts increase the prediction accuracy:
    • Large database of news
    • Volatility exhibited by price fluctuations
  • FFNN degrade results, RSPOP is best
comments
COMMENTS
  • Many pages for introduction; a few words about experiments; almost no experimental details; results and conclusion are too obvious
  • Poorly written (typos, missing labels, copied sentences from references)
  • Problems:
    • Manual coding?
    • PCM Categories are based on?
    • News can override one another?
    • Just considering the news type? What about sentiment?