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inria-00168347, version 1

Entropy rate and profitability of technical analysis: experiments on the NYSE US 100 stocks

Nicolas Navet () a, 1, Shu-Heng Chen 2

6th International Conference on Computational Intelligence in Economics & Finance - CIEF 2007 (2007) 501-507

Abstract: The entropy rate of a dynamic process measures the uncertainty that remains in the next information produced by the process given complete knowledge of the past. It is thus a natural measure of the difficulty to predict the evolution of the process. The first question investigated here is whether stock price time series exhibit temporal dependencies that can be measured through entropy estimates. Then we study the extent to which the return of financial trading rules is correlated with the entropy rates of the price time series. Experiments are conducted on EOD data of the stocks composing the NYSE US 100 index during period 2000–2006, with the use of Genetic Programming to induce the trading rules.

  • a –  INRIA
  • 1:  TRIO (INRIA Lorraine - LORIA)
  • INRIA – CNRS : UMR7503 – Université Henri Poincaré - Nancy I – Université Nancy II – Institut National Polytechnique de Lorraine (INPL)
  • 2:  AI-ECON Research Center
  • National Chengchi University
  • Domain : Computer Science/Computational Engineering, Finance, and Science
  • Keywords : Entropy estimate – surrogate testing – genetic programming – financial trading rules – NYSE
 
  • inria-00168347, version 1
  • http://hal.inria.fr/inria-00168347
  • oai:hal.inria.fr:inria-00168347
  • From: Nicolas Navet <>
  • Submitted on: Monday, 27 August 2007 16:09:12
  • Updated on: Thursday, 16 April 2009 17:38:51