forecasting eur/usd monthly exchange rate using support vector machine

Forecasting direction of EUR/USD monthly exchange rate using support vector machine

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

G53IDS 09/10

Presenter: Li Junlong

Supervisor: Ho Sooi Hock

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Project Objective

• Apply SVM to forecast the nominal monthly exchange rate of EUR/USD

• Review SVM forecast performance


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What does the software do?


Learning Forecasting Analysis

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How is this useful?

• For government– Defense against possible impending financial disaster

• Imagine foreseeing the next currency crisis

• For private– Trading decisions based on good market predictions

tend to perform better• Make money


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What is forecasting?

• It is to estimate, predict or calculate in advance, through the use of various forecasting methods, to determine the future expectations.


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Which kind of forecast methodology?


SVM

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Why 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.


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Why 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.


EUR/USD27%

USD/JPY13%

GBP/USD12%

Others48%

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Why 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 basis• such as quarterly release of %∆ of GDP.

– Aggregation function needed for certain data released shorter than monthly basis

• such as ∆ of price.– Combination of both function for certain data released without

fixed timings• such as ∆ of central bank discount rate.


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Why not shorter periods?

• The FX market is relatively nosier in very short term.– Greater exposure to errors

• Economic policies usually require certain period of time for results to be observable, due to the problem of time lags.– Recognition lag– Decision lag– Effect lag


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Why direction, not value?

• Limitation of SVM, a binary classifier.• Higher complexity to predict value.• Less probability of error

– Possibilities of direction = 2– Possibilities 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)


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Past related researches?

• Stocks– Autoregressive Integrated Moving Average (ARIMA)– Random Forests / Decision Trees (DT)– Artificial Neural Networks (ANN)– Support Vector Machine (SVM)– Support Vector Regression (SVR)

• FX– Autoregressive Integrated Moving Average (ARIMA)– Artificial Neural Networks (ANN)– Support Vector Regression (SVR)


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Why 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.


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What is the expected accuracy?

• Wei Huang, Yoshiteru Nakamori, Shou-Yang Wang, “Forecasting stock market movement direction with support vector machine”, October 2005– Hit 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%


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Where are the evidences?


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What is SVM?

• Method of supervised learning for machines• Learn to linearly classify data

– Construct separation condition– Calculate separating hyperplane


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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 kernels– Polynomial– Radial Basis Function


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How SVM works?

Note: Above are just simplified explanations, actual work is much more complicated.


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Demo?


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Plans for implementation?


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What are your input data?

• Any data that has been generally observed to show strong relationship with the direction of EUR/USD exchange rate– Backed 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)*


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Where do you get your data?

• Internet– Government websites

• U.S. Bureau of Economic Analysis• U.S. Bureau of Labor Statistics• Eurostat of European Commission• The European Central Bank

– Private organizations• Exchange-Rates.org• Google Finance


http://www.bea.gov/

http://www.bls.gov/CPI/

http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/themes

http://www.ecb.int/ecb/html/index.en.html

http://www.exchange-rates.org/history/USD/EUR/G/30

http://www.google.com/finance?q=EURUSD

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Challenges 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.


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What are the challenges?

• Knowledge– High complexity of the theories.– Large amount of theories from different fields.– Steep learning curve for me.

• Experience– First time

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


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What are the drawbacks of SVM?

• Does not assess probabilistic confidence of classification.

• Only provides binary classification.

• Counterarguments• Multiclass SVM though combinations of multiple SVMs• SVM 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]


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“

Humans 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?


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• [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.JPG• 2nd image http://www-kairo.csce.kyushu-u.ac.jp/~norikazu/svm.png• 3rd image

http://4.bp.blogspot.com/_Hyi86mcXHNw/SG3VNOjch9I/AAAAAAAAADs/BGm_olKmArA/s400/SVM+Process+Outline.png

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References


http://www.svms.org/finance/CaoTay2003.pdf

http://www.svms.org/finance/CaoTay2001.pdf

http://www.svms.org/finance/YangChanKing2002.pdf

http://www.springerlink.com/index/7ECATQ5CYRUHD97J.pdf

http://svms.org/finance/ChenShihWu2006.pdf

http://svms.org/finance/ChenShihWu2006.pdf

http://www.cac.science.ru.nl/people/ustun/SVM.JPG

http://www-kairo.csce.kyushu-u.ac.jp/~norikazu/svm.png



http://www.animationfactory.com/

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