Predicting Stock Market Returns and the Efficiency Market Hypothesis

By: Mohammad AbouzarKarthik GollapinniVijay Soppadandi

Professor: Lutz Maria Kolbe

Advisor: Patrick Urbanke

Department of Business AdministrationGeorg-August University of GoettingenCrucial Topics In Information ManagementJanuary 30th, 2015

Overview Introduction Algorithms 1st Effort: Lu et al. (2009) Analysis 2nd Effort: Ince and Trafalis (2006) Analysis 3rd Effort: Khansa and Liginlal (2011) Analysis Conclusion

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IntroductionAlgorithms

Lu et al. Ince et al.

Khansa et al.Conclusion

Introduction Motivation:

Issues in Investment Decision Making Efficiency Market Hypothesis

Why do we care? Our Goal:

Stock Market Returns Prediction Test EMH Theory

Methodology Evaluation

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IntroductionAlgorithms

Lu et al. Ince et al.

Khansa et al.Conclusion

Algorithms 1/5 - ARIMA AutoRegressive Integrated Moving Average Integrated Non-Stationary Process Identification:

Autocorrelation Function Partial Autocorrelation

Hypothesis Testing Estimation:

Maximum Likelihood Estimation Diagnostic Checking

Forecasting: Dynamic Forecast

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IntroductionAlgorithms

Lu et al. Ince et al.

Khansa et al.Conclusion

Algorithms 2/5 - VAR Vector Auto Regression Multi-Equation System An Equation for Each Variable as Dependent Variable

Yt = A + B1Yt-1 + B2Yt-2 + … + BpYt-p + εt

Why VAR? Time Series Data with Autoregressive Nature Analysis of Multivariate Time Series Describing Dynamic Behavior Financial Time Series Making Predictions

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IntroductionAlgorithms

Lu et al. Ince et al.

Khansa et al.Conclusion

Algorithms 3/5 - Neural Networks Machine Learning Algorithm Based on Human Brain How it works:

The Weights Adjustment Training Method

Goal: Hidden Layer Number Neurons Number

Learning and Momentum Parameter

(Sermpinis et al. 2012)

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IntroductionAlgorithms

Lu et al. Ince et al.

Khansa et al.Conclusion

Algorithms 4/5 - ICA Independent Component Analysis X = AS, where:

A is Unknown Matrix S is Latent Source Signals

Goal: Noise Removal and Signals Separation Two Pre-Processing Steps:

Centering Whitening

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IntroductionAlgorithms

Lu et al. Ince et al.

Khansa et al.Conclusion

Algorithms 5/5 - SVR Support Vector Regression Extension of SVM Follows Regression Problem

Linear Regression & Non-Linear Regression(Lu et al. 2009)

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IntroductionAlgorithms

Lu et al. Ince et al.

Khansa et al.Conclusion

Lu et al. (2009) Main Goal: Two Stage Model First Stage: ICA – Identify Noise and Filter out Noise Second Stage: Use Reconstructed Data as Inputs to SVR Data Set:

Nikkei 225 Opening Cash Index Prices TAIFEX Closing Cash Index Prices

Limitations: Data Availability

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IntroductionAlgorithms

Lu et al. Ince et al.

Khansa et al.Conclusion

Lu et al. (2009)Original Results of Nikkei 225 Reproduced Results of Nikkei 225

Original Results of TAIEX Reproduced Results of TAIEX(Lu et al. 2009)

(Lu et al. 2009)

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IntroductionAlgorithms

Lu et al. Ince et al.

Khansa et al.Conclusion

Lu et al. (2009)T-tests Results:

Experiments ICA + SVR SVR

Reproduced Results 0.375985 0.30949

n-components = 1 0.318908 0.30949

Standardized data 0.502299 0.446244

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IntroductionAlgorithms

Lu et al. Ince et al.

Khansa et al.Conclusion

Ince and Trafalis (2006) Main Goal: Hybrid Model First Stage: Input Selection Using ARIMA and VAR Second Stage: Prediction Using MLP and SVR Data Set:

GBP/USD, AUD/USD, JPY/USD and EUR/USD Rates Limitations: Parameters

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IntroductionAlgorithms

Lu et al. Ince et al.

Khansa et al.Conclusion

Ince and Trafalis (2006)

Exchange rates MLP network SVR methodARIMA VAR ARIMA VAR

EURO/USD 0.000445 0.000478 0.000193 0.000272GDP/USD 0.004173 0.004021 0.000618 0.000210JPY/USD 1.931316 1.666312 1.144361 1.412094

AUD/USD 0.000223 0.000242 0.000152 0.000177

Original MSE of ARIMA and VAR Input Selection

Reproduced MSE of ARIMA and VAR Input Selection

Exchange Rates MLP network SVR methodARIMA VAR ARIMA VAR

EURO/USD 0.000029 0.000005 0.000004 0.000003GDP/USD 0.000012 0.000009 0.000002 0.000012JPY/USD 0.053500 0.032300 0.010600 0.015900

AUD/USD 0.000002 0.000006 0.000001 0.000004

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IntroductionAlgorithms

Lu et al. Ince et al.

Khansa et al.Conclusion

Ince and Trafalis (2006) T-tests Results:

Exchange rates MLP network SVR methodARIMA VAR ARIMA VAR

EURO/USD 0.774534 0.235134 0.121729 0.120220GDP/USD 0.482344 0.754345 0.134559 0.134967JPY/USD 0.471323 0.458653 0.139211 0.143889

AUD/USD 0.764532 0.466566 0.118899 0.116609

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IntroductionAlgorithms

Lu et al. Ince et al.

Khansa et al.Conclusion

Khansa and Liginlal (2011) Main Goal: Security Threat Influence on Stock Market Return First Stage: Aggregating Data Second Stage: ANN and VAR Analysis Data Set:

Market Return Value of Information Security Firms Market Weighted-Value Index Intensity of Daily Malicious Attacks

Limitations: Data Availability

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IntroductionAlgorithms

Lu et al. Ince et al.

Khansa et al.Conclusion

Khansa and Liginlal (2011) Results:

T-tests Results: Experiments ANN VAR

Number 1 0.959528 0.95455

Number 2 0.434657 0.57677

Conclusion Accurate Forecasting is Important in Investment Decision Making Our aim was to Predict Stock Market Returns and EMH Reproduced:

Lu et al. (2009) Ince and Trafails (2006) Khansa and Linginal (2009)

Analysis : No Valid Prediction Stock Market Returns No Statistically Significant Outperformance No Evidence that the EMH is not True

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IntroductionAlgorithms

Lu et al. Ince et al.

Khansa et al.Conclusion

References Cheung, Y.M., and Xu, L. 2001. "Independent component ordering in ICA time series analysis." Neurocomputing(41), no. 1, pp. 145-152. Hyvärinen, A. 1999. “Fast and robust fixed-point algorithms for independent component analysis.” IEEE Transactions on Neural Networks (10), pp. 626–634. Hyvärinen, A., Karhunen, A. J., and Oja, E. 2001. Independent Component Analysis, John Wiley & Sons, New York. Hyvärinen, A., and Oja, E. 2000. “Independent component analysis: algorithms and applications.” Neural Networks (13), pp. 411–430. Ince, Huseyin, and Theodore B. Trafalis. 2006. "A hybrid model for exchange rate prediction." Decision Support Systems (42), no. 2, pp. 1054-1062. Khansa, L., and Liginlal, D. 2011. “Predicting stock market returns from malicious attacks: A comparative analysis of vector autoregression and time-delayed

neural networks.” Decision Support Systems (51), pp. 745–759. Lam, Monica. 2004. “Neural network techniques for financial performance prediction: Integrating fundamental and technical analysis.” Decision Support

Systems (37), pp. 567–581. Lu, Chi-Jie, Tian-Shyug Lee, and Chih-Chou Chiu. 2009. "Financial time series forecasting using independent component analysis and support vector

regression." Decision Support Systems (47), no. 2, pp. 115-125. Sermpinis, G., Dunis, C., Laws, J., and Stasinakis, C. 2012. “Forecasting and trading the EUR/USD exchange rate with stochastic Neural Network combination

and time-varying leverage.” Decision Support Systems (54), pp. 316–329. Trafalis, T.B., and Ince, H. 2000. “Support vector machine for regression and applications to financial forecasting.” Neural Networks, IJCNN 2000,

Proceedings of the IEEEINNSENNS International joint Conference, vol. 6, IEEE, pp. 348–353. Vapnik, V.N. 2000. The Nature of Statistical Learning Theory, Springer, New York.

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Thank you.Any questions?

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Predicting Stock Market Returns and the Efficiency Market Hypothesis

By: Mohammad AbouzarKarthik GollapinniVijay Soppadandi

Professor: Lutz Maria Kolbe

Advisor: Patrick Urbanke

Department of Business AdministrationGeorg-August University of GoettingenCrucial Topics In Information ManagementJanuary 30th, 2015