Towards Automated Share Investment System

Predictability of financial time series (FTS) is a well-known dilemma. A typical approach to this problem is to apply a regression model, built on the historical data and then further extend it into the future. If however the goal for FTS prediction would be to support or even make investment decisions, prediction generated by regression-based models are inappropriate as on top of being uncertain and unnecessarily complex, they require lots of investor attention and further analysis to make an investment decision. Rather than precise time series prediction, a busy investor may prefer a simple decision on the current day transaction: buy, wait, sell, that would maximise his return from the investment. Based on such assumptions a classification model is proposed that learns the transaction patterns from optimally labelled historical data and accordingly gives the profit-driven decision for the current-day transaction. The model is embedded into an automated client-server platform which automatically handles data collection and maintains client models on the database. The prototype of the system was tested over 20 years of NYSE:CSC share price history showing substantial improvement of the long-term profit compared to a passive long-term investment.

[1]  Ah Chung Tsoi,et al.  Noisy Time Series Prediction using Recurrent Neural Networks and Grammatical Inference , 2001, Machine Learning.

[2]  Berlin Wu,et al.  A fuzzy identification procedure for nonlinear time series: With example on ARCH and bilinear models , 1999, Fuzzy Sets Syst..

[3]  Patrick Brézillon,et al.  Lecture Notes in Artificial Intelligence , 1999 .

[4]  Steven W. Poser.  Applying Elliott Wave theory profitably , 2003 .

[5]  F. Girosi,et al.  Nonlinear prediction of chaotic time series using support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[6]  David G. Stork,et al.  Pattern Classification , 1973 .

[7]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[8]  Francis Eng Hock Tay,et al.  Modified support vector machines in financial time series forecasting , 2002, Neurocomputing.

[9]  C. Granger,et al.  Efficient Market Hypothesis and Forecasting , 2002 .

[10]  Ruey S. Tsay,et al.  Analysis of Financial Time Series , 2005 .

[11]  Dymitr Ruta.  Visualisation of the Classifier Fusion Models , 2005, Artificial Intelligence and Applications.

[12]  Vojislav Kecman,et al.  A data mining approach to financial time series modelling and forecasting , 2001, Intell. Syst. Account. Finance Manag..

[13]  Rudolf Kruse,et al.  Neuro Fuzzy Systems for Data Analysis , 1999 .

[14]  Sameer Singh.  Noisy Time‐Series Prediction using Pattern Recognition Techniques , 2000, Comput. Intell..

[15]  Katharina Morik,et al.  A Multistrategy Approach to the Classification of Phases in Business Cycles , 2002, ECML.

[16]  Bogdan Gabrys,et al.  Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting , 2001, Multiple Classifier Systems.

[17]  Shumeet Baluja,et al.  A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .

[18]  Eric R. Ziegel,et al.  Analysis of Financial Time Series , 2002, Technometrics.

[19]  Martin Casdagli,et al.  Nonlinear prediction of chaotic time series , 1989 .