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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 Discussion of findings Conclusion and Comments
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
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
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 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
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
Feed Forward Neural Network (FFNN) Radial Basis Function Network (BRFN) Adaptive Neuro Fuzzy Inference System
(ANFIS) Rough Set Based Pesudo Outer Product Rule
(RSPOP)
FFNN ANFISBRFN RSPOP
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
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
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 functionLayer 2: MultiplicationLayer 3: NormalizationLayer 4: Production of the input and a first order polynomialLayer 5: Sum
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 variableLayer 2: Each input label node represents a fuzzy member functionLayer 3: Each rule node represent an if-then fuzzy rulesLayer 4: Each output label node represents a fuzzy member functionLayer 5: Each output node represents an output linguistic variable
COMPARISON
Prior Knowledge
Layer #
Type Advantage
FFNN No need 3 Numerical Simplest
RBFN No need 3 Numerical Interpolation problem
ANFIS Need 5 Linguistic Use prior knowledge to reduce optimization search space
RSPOP No need 5 Linguistic Reduction of attributes and fuzzy rules
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
OUTLINE
Introduction and literature review Specification of neuro fuzzy networks News Coding Experiment and data Discussion of findings Conclusion and Comments
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
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)
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 (PCM)
Let L be the set of news on the company and T be the Time for which news data is classified
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
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
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
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
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
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
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 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 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 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 PREDICTION
All reductions are at points where the stock has taken a sharp jerk
It is not predictable based on historical past patterns
OUTLINE
Introduction and literature review Specification of neuro fuzzy networks News Coding Experiment and data Discussion of findings Conclusion and Comments
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
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?
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