Currency Forecasting using Multiple Kernel Learning with Financially Motivated Features Tristan Fletcher, Zakria Hussain and John Shawe- Taylor Fanghua Lin Financial Services Analytics

Content Motivation Empirical Study Results Conclusion & Contribution

Motivation A trader can profit from accurate prediction of currency trend: Three situations (buying price (bid) and selling price (ask) ):

Financially Motivated Features

8*16=128 feature/kernel combinations Three SVM are trained on the data: Empirical Study

100 days shifting Testing: days shifting Training:100 Testing:100 Training: 100 Time Empirical Study

Results

Percentage Accuracy of Predictions Δt: Time Horizon (Prediction)MKLF 8 K 16 F1K1F1K1 F1K3F1K Results

Conclusion The most successful individual kernels are selected by cross-validation are awarded very low weights by SimpleMKL. This reflects a common feature of trading rules where individual signals can drastically change their significance in terms of performance when use in combination. Furthermore, the effective method of combining a set of price and volume based features in order to correctly forecast the direction of price movements in a manner similar to a trading rule

Financial Forecasting with Gompertz Multiple Kernel Learning Han Qin Dejing Dou Yue Fang 2010 IEEE International Conference on Data Mining Fanghua Lin Financial Services Analytics

Content Models Garch Gompertz Function Gomperz Multiple Kernel Learning Subgradient Descent Algorithm Empirical Study Conclusion & Contribution

Garch Model Kernel Function i.e.,

Gompertz Function Assigns higher weights to most recent data

SVM Gompertz FunctionGarch Model Gomperz Multiple Kernel Learning (GMKL) Subgradient Descent Algorithm

 LMKL :training data and test data have same distributions. GMKL addresses the non-stationary problem by favoring recent data.  LMKL :single data source but different kernel functions. GMKL: different data sources with same kernel function.  LMKL : discovers which kernel function is better for a certain region of the kernel matrix. GMKL: assigns the weights to different regions by considering the order of time series data. Difference between LMKL and GMKL

DataDaily Index Closing Price （ eg, General Motors Corporation ） Index 5 major international stock indexes: Dw Jones Industrial Average, S &P 500, FTSE 100, Hengsheng, Nikei 225 Time PeriodJan 2007 – Dec 2009 Goal Comparsion Model 1: SVM Model 2: MKL Model 3: GMKL Forecasting AccuracyRelative Absolute Error (RAE) Test Many Shifting Periods and Average the Performance Empirical Study Time Testing Training Time n day shifting Training Testing n day shifting

Forecasting DJI using DJI and one other index

Forecasting using all 5 indexes

GMKL performs better than both SVM and MKL. GMKL model is more robust than MKL when considering more training data sources Conclusion & Contribution For data mining: novel model to integrate multiple financial time series data sources Propose a domain specific kernel function to leverage domain knowledge in the mining process Contribution: For financial forecasting: New method to tackle the international market integration problem address the non-stationary of the financial time series data Reveal interesting relationships among multiple international stock markets Conclusion:

Start-up: Thought Machine CEO: Paul Taylor Working Experience: Manager and Technical Lead in Google, Chief Executive Officer in Phonetic Arts, Visiting Lecture in University of Cambridge Education: PhD, Edinburgh University’s Centre for Speech Technology Research Thought Machine is building technology to revolutionize the way people do their day to day banking. Using Machine Learning to analyze transactions, to find patterns and let users better understand and manage their finances.