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

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

1 TRIO - Real time and interoperability
2 AI-ECON Research Center

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.

Mots clés: Entropy estimate; surrogate testing; genetic programming; financial trading rules; NYSE

Domaines: Ingénierie, finance et science [cs.CE]

Nicolas Navet, Shu-Heng Chen. Entropy rate and profitability of technical analysis: experiments on the NYSE US 100 stocks. 6th International Conference on Computational Intelligence in Economics & Finance - CIEF 2007, Jul 2007, Salt-Lake City, United States. pp.501-507, ⟨10.1142/9789812709677_0073⟩. ⟨inria-00168347⟩