Pattern recognition and financial time-series

This paper investigates financial time-series from the perspective of a practitioner in artificial intelligence methods and pattern recognition. It presents results from statistical experiments which suggest that financial markets operate with a measure of inefficiency and predictability. However, identifying the nature of any regularities and patterns presents a difficult challenge to the artificial intelligence community, in that established techniques make assumptions about the underlying process that mostly prove to be invalid for this class of data. Copyright © 2007 John Wiley & Sons, Ltd.

[1]  D. Sornette.  Why Stock Markets Crash: Critical Events in Complex Financial Systems , 2017 .

[2]  Betty A. Toole,et al.  Ada, the enchantress of numbers , 1992 .

[3]  D. Sornette,et al.  The US 2000‐2002 market descent: How much longer and deeper? , 2002, cond-mat/0209065.

[4]  Benoit B. Mandelbrot,et al.  Fractals and Scaling in Finance , 1997 .

[5]  John L. Kelly,et al.  A new interpretation of information rate , 1956, IRE Trans. Inf. Theory.

[6]  Alistair Moffat,et al.  Implementing the PPM data compression scheme , 1990, IEEE Trans. Commun..

[7]  Takuji Nishimura,et al.  Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator , 1998, TOMC.

[8]  A. Shleifer,et al.  Inefficient Markets: An Introduction to Behavioral Finance , 2002 .

[9]  William R. Kinney,et al.  Capital market seasonality: The case of stock returns , 1976 .

[10]  THE PRACTITIONER'S PERSPECTIVE Unconscious Herding Behavior as the Psychological Basis of Financial Market Trends and Patterns , 2001 .

[11]  R. R. Prechter.  Unconscious Herding Behavior as the Psychological Basis of Financial Market Trends and Patterns , 2001 .

[12]  Mohinder S. Grewal,et al.  Kalman Filtering: Theory and Practice , 1993 .

[13]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[14]  E. Fama.  EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

[15]  A. Tversky,et al.  Choices, Values, and Frames , 2000 .

[16]  Armin Shmilovici,et al.  Using a Stochastic Complexity Measure to Check the Efficient Market Hypothesis , 2003 .

[17]  Claude E. Shannon,et al.  General treatment of the problem of coding , 1953, Trans. IRE Prof. Group Inf. Theory.

[18]  M. E. Muller,et al.  A Note on the Generation of Random Normal Deviates , 1958 .

[19]  Lance Fortnow,et al.  Prediction and dimension , 2005, J. Comput. Syst. Sci..