Forecasting EUR/USD monthly exchange rate using support vector machine

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This is my 2009 presentation on development of a forecast machine.

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Forecasting USD/EUR daily average exchange rate direction using support vector machine

Forecasting direction of EUR/USD monthly exchange rate using support vector machineUNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusG53IDS 09/10Presenter: Li JunlongSupervisor: Ho Sooi HockProject ObjectiveApply SVM to forecast the nominal monthly exchange rate of EUR/USDReview SVM forecast performance2UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusWhat does the software do?3UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusHow is this useful?For governmentDefense against possible impending financial disasterImagine foreseeing the next currency crisisFor privateTrading decisions based on good market predictions tend to perform betterMake money4UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusWhat is forecasting?It is to estimate, predict or calculate in advance, through the use of various forecasting methods, to determine the future expectations.5UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusWhich kind of forecast methodology?6UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia Campus

SVMWhy FOREX, not stocks?Less insider information compared with stocks.Seems to be more closely related to public information.such as of GDP growth, balance of payment, interest rate, inflation rate, etc.Need more complete package information to make better forecasts.The information package of FOREX is more complete as many are publicly available.7UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusWhy EUR/USD?According to the Bank for International Settlements study in 2007, the most heavily traded products on the spot market were:

EUR/USD was chosen because of its popularity.8UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusWhy monthly forecast?Many statistics are released on monthly basis, such as of US & Europe inflation rate, balance of payment etc. Interpolation function for certain data released longer than monthly basissuch as quarterly release of % of GDP.Aggregation function needed for certain data released shorter than monthly basissuch as of price.Combination of both function for certain data released without fixed timingssuch as of central bank discount rate.9UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusWhy not shorter periods?The FX market is relatively nosier in very short term.Greater exposure to errorsEconomic policies usually require certain period of time for results to be observable, due to the problem of time lags.Recognition lagDecision lagEffect lag10UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusWhy direction, not value?Limitation of SVM, a binary classifier.Higher complexity to predict value.Less probability of errorPossibilities of direction = 2Possibilities of value

Trading driven by a certain forecast with a small forecast error may not be as profitable as trading guided by an accurate prediction of the direction of movement. (W. Huang et al. 2005)11UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusPast related researches?StocksAutoregressive Integrated Moving Average (ARIMA)Random Forests / Decision Trees (DT)Artificial Neural Networks (ANN)Support Vector Machine (SVM)Support Vector Regression (SVR)FXAutoregressive Integrated Moving Average (ARIMA)Artificial Neural Networks (ANN)Support Vector Regression (SVR)

12UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusWhy SVM, not others?DT learners tend to create over-complex trees that do not generalize the data well. This is called overfitting.[1]NN learners does not produce unique results.[2]Empirical tests suggest that SVMs tend to produce lower error rate in stock forecasting compared to ANN and ARIMA methods.[3]SVR is used for variable forecasts.13UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusWhat is the expected accuracy?Wei Huang, Yoshiteru Nakamori, Shou-Yang Wang, Forecasting stock market movement direction with support vector machine, October 2005Hit ratio of SVM 73%Hit ratio of Combining model 75%Jingtao Yao, Chew Lim Tan, A Case Study on Using Neural Networks to Perform Technical Forecasting of Forex, 2000.Hit ratio of ANN 70%

14UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusWhere are the evidences?15UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia Campus

What is SVM?Method of supervised learning for machinesLearn to linearly classify dataConstruct separation conditionCalculate separating hyperplane

16UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia Campus

How 'bout non-linearly separable data?Feature map the input space to a usually high dimensional feature space where the data points become linearly separable, called kernelsPolynomialRadial Basis Function

17UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia Campus

How SVM works?

Note: Above are just simplified explanations, actual work is much more complicated.18UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusDemo?19UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusPlans for implementation?20UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusWhat are your input data?Any data that has been generally observed to show strong relationship with the direction of EUR/USD exchange rateBacked by evidence. (rare)*****Backed by research publications. (handful)***Theories based on challengeable assumptions which may or may not hold true in the real world. (many)*21UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusWhere do you get your data?InternetGovernment websitesU.S. Bureau of Economic AnalysisU.S. Bureau of Labor StatisticsEurostat of European CommissionThe European Central BankPrivate organizationsExchange-Rates.orgGoogle Finance22UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusChallenges with data selection?Financial theories based on generalization, observable correlations may not be perfect.Financial theories are hard to verify because the movement of market is constantly being influenced by other factors.Choosing the market determinants can be seen as a qualitative art.23UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusWhat are the challenges?KnowledgeHigh complexity of the theories.Large amount of theories from different fields.Steep learning curve for me.

ExperienceFirst timeDoing something you have never done before is always harder than doing something that you already have some experience of.

24UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusWhat are the drawbacks of SVM?Does not assess probabilistic confidence of classification.Only provides binary classification.

CounterargumentsMulticlass SVM though combinations of multiple SVMsSVM for regression was proposed in 1996 by Vladimir Vapnik, Harris Drucker, Chris Burges, Linda Kaufman and Alex Smola.[4]More accurate to predict direction than certain value.[5]25UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusHumans are essentially complex bio-machines.With the constant increasing complexity of man-made machines, someday they will be able to learn and make decisions independently without the need of their creators.

Lesson learnt?26UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia Campus[1]Bramer, Max. Principles of Data Mining. London: Springer, 2007. ISBN 978-1-84628-765-7.[2],[3],[5] Wei Huang, Yoshiteru Nakamori, Shou-Yang Wang, Forecasting stock market movement direction with support vector machine, Computers & Operations Research, Volume 32, Issue 10, Applications of Neural Networks, October 2005[4] Harris Drucker, Chris J.C. Burges, Linda Kaufman, Alex Smola and Vladimir Vapnik (1997). "Support Vector Regression Machines". Advances in Neural Information Processing Systems 9, NIPS 1996, 155-161, MIT Press.KIM, Kyoung-jae, 2003. Financial time series forecasting using support vector machines, Neurocomputing, Volume 55, Issues 1-2 (September 2003), Pages 307-319.CAO, L. J. and Francis E. H. TAY, 2003. Support Vector Machine With Adaptive Parameters in Financial Time Series Forecasting, IEEE Transactions on Neural Networks, Volume 14, Issue 6, November 2003, Pages 1506-1518.CAO, Lijuan and Francis E. H. TAY, 2001. Financial Forecasting Using Support Vector Machines. Neural Computing & Applications, Volume 10, Number 2 (May 2001), Pages 184-192.YANG, Haiqin, Laiwan CHAN and Irwin KING, 2002. Support Vector Machine Regression for Volatile Stock Market Prediction. In: Intelligent Data Engineering and Automated Learning: IDEAL 2002, edited by Hujun Yin, et al., pages 391--396, Springer.CAO, L. J. and Francis E. H. TAY, 2000. Feature Selection for Support Vector Machines in Financial Time Series Forecasting. In: Intelligent Data Engineering and Automated Learning - IDEAL 2000: Data Mining, Financial Engineering, and Intelligent Agents, edited by Kwong Sak Leung, Lai-Wan Chan and Helen Meng, pages 268-273.CHEN, Wun-Hua, Jen-Ying SHIH and Soushan WU, 2006. Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets, International Journal of Electronic Finance, Volume, Issue 1, pages 49-67.1st image http://www.cac.science.ru.nl/people/ustun/SVM.JPG2nd image http://www-kairo.csce.kyushu-u.ac.jp/~norikazu/svm.png3rd image http://4.bp.blogspot.com/_Hyi86mcXHNw/SG3VNOjch9I/AAAAAAAAADs/BGm_olKmArA/s400/SVM+Process+Outline.pngTemplate Provided By:

References27UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia CampusQuestionsorSuggestions?28UNIMKL-004363, School of Computer Science, Faculty of Science, The University of Nottingham Malaysia Campus

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