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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.

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Presentation on theme: "PREDICTING IMPACT OF NEWS ON STOCK PRICE: AN EVALUATION OF NEURO FUZZY SYSTEM Congress on Evolutionary Computation (CEC 2007) Presented by CUI, Weiwei."— Presentation transcript:

1 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

2 OUTLINE Introduction and literature review Specification of neuro fuzzy networks News Coding Experiment and data Discussion of findings Conclusion and Comments

3 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

4 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

5 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)

6 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

7 OUTLINE Introduction and literature review Specification of neuro fuzzy networks News Coding Experiment and data Discussion of findings Conclusion and Comments

8 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) FFNNANFIS BRFN RSPOP

9 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?

10 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.

11 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

12 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

13 COMPARISON Prior Knowledge Layer # TypeAdvantage FFNNNo need3NumericalSimplest RBFNNo need3NumericalInterpolation problem ANFISNeed5LinguisticUse prior knowledge to reduce optimization search space RSPOPNo need5LinguisticReduction of attributes and fuzzy rules

14 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

15 OUTLINE Introduction and literature review Specification of neuro fuzzy networks News Coding Experiment and data Discussion of findings Conclusion and Comments

16 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

17 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

18 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

19 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

20 OUTLINE Introduction and literature review Specification of neuro fuzzy networks News Coding Experiment and data Discussion of findings Conclusion and Comments

21 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)

22 EXPERIMENT Two measures of performance were used: Root mean square error Pearsons 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

23 OUTLINE Introduction and literature review Specification of neuro fuzzy networks News Coding Experiment and data Discussion of findings Conclusion and Comments

24 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 for a small stock like DBS Singapore is a very controlled market

25 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

26 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

27 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

28 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

29 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

30 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

31 OUTLINE Introduction and literature review Specification of neuro fuzzy networks News Coding Experiment and data Discussion of findings Conclusion and Comments

32 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

33 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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