1. A Hybrid ARIMA and Support Vector Machines Model in Stock Price Forecasting Ping-Feng Pai, Chih-Sheng Lin The International Journal of Management Science 2004 Fanghua Lin Financial Services Analytics, UD

2. Content Models ARIMA SVM Hybrid Model Empirical Results Conclusion Start-up

3. Data-oriented approach: sensitive to data structure Traditional Finance Model in Forecasting: ARIMA Linear, cannot catch the non-linear partSVM i.e.,

4. SVM Kernel functions Non-linearly separable functionsLinearly separable function In this paper, The Gaussian kernel function is used

5. Linear partNonlinear part Hybrid Model: ARIMA & SVM SVMARIMA Data Residuals +

6. Empirical Study DataDaily Stock Closing Price （ eg. General Motors Corporation ） Stocks10 stocks Time Period10/21/2002 - 2/28/2003 Data Set SplitTraining datasetValidation data setTesting data set 10/21/2002 -12/31/20121/2/2003-1/31/20032/3/2003-2/28/2003 Goal Comparision Model 1: ARIMA(0,1,0) Model 2: SVM Model 3: ARIMA + SVM Model 4: Hybrid Model Forecasting AccuracyMAE(mean absolute error), MSE(mean square error), MAPE (mean absolute percent error), RMSE (root mean square error)

7. ARIMA models SVMs modelsHybrid models sσCsσC Eastman Kodak Company (0,1,0)0.301001.00.210 General Motors Corporation (0,1,0)1.90.31003.401 J.P.Morgan Chanse & Co. (0,1,0)1.301002.001 Altria Group, Inc. (0,1,0)1.301004.101 SBC (0,1,0)1.50.11004.201 Citigroup Inc. (0,1,0)0.601001.00.110 General Electirc Company (0,1,0)0.60.41003.20.410 Southwest Water Company (0,1,0)1.701000.30.410 American National Insurance Company (0,1,0)1.401000.70.810 ATP Oil & Gas Corporation 1.201002.00.210 Different Parameters for Ten Stocks

8. Results

9. MAEMSEMAPERMSE Model 1: ARIMA(0,1,0) 0.49050.37481.42140.6122 Model 2: SVM 0.43520.31861.26540.5644 Model 3: ARIMA +SVM 0.45860.35691.33910.5974 Model 4: Hybrid Model 0.25790.20490.75500.3162 Results Hybrid Model performs the best

10. Conclusion Hybrid Model of ARIMA and SVM performs better than single ARIMA model or SVM model Simple combination of ARIMA and SVM does not perform better than ARIMA model or SVM model

11. Start-up: SVM Capital conduct global equity investing via a synergistic combination of machine insights from big data and human insights and judgment. SVM Capital uses SVMs, and other AI techniques, in its investing process. The investment horizon is medium-term. Education: B.S., Finance & Decision Sciences (Upenn), B.A., Psychology (Upenn), Mater in Computer Science (Pontifícia Universidade Católica do Rio de Janeiro) Working Experience: 10+ years of worldwide financial markets experience in long-short equity and M&A, Emerging markets entrepreneur Raphael Rottge