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

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) BRFN FFNN ANFIS 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

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 • 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

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 fora small stock like DBS • Singapore is a very controlledmarket

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?