Skip to Main content Skip to Navigation
Conference papers

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
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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.
Complete list of metadata

https://hal.inria.fr/inria-00168347
Contributor : Nicolas Navet <>
Submitted on : Monday, August 27, 2007 - 4:09:12 PM
Last modification on : Friday, February 26, 2021 - 3:28:07 PM
Long-term archiving on: : Thursday, April 8, 2010 - 9:28:45 PM

File

CIEF2007_NN_SHC_AT.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

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⟩

Share

Metrics

Record views

419

Files downloads

222