uyanilalabiwi.web.fc2.com | Web server is down

Please note that Internet Explorer version 8. Please refer to this blog post for more information.

This paper investigates the nonlinear predictability of technical trading rules based on a recurrent neural network as well as a neurofuzzy model. The efficiency of the trading strategies was considered upon the prediction of the direction of the market in case of NASDAQ and NIKKEI returns.

International Financial Crises

The proposed neurofuzzy model which outperforms the neural network predictor allows investors to earn significantly higher returns in bear markets.

Journals Books Register Sign in Sign in using your ScienceDirect credentials Username.

Download - UpdateStar - uyanilalabiwi.web.fc2.com

Forgotten username or password? Sign in via your institution OpenAthens Other institution Recent Institutions.

Sign in using your ScienceDirect credentials Username. JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page.

Mathematical and Computer Modelling Volume 46, Issues 1—2 , July , Pages Author links open the author workspace. Opens the author workspace Opens the author workspace Dimitris A.

Department of Accounting and Finance, Athens University of Economics and Business, 76 Patission street, 34 Athens, Greece. Under an Elsevier user license.

Abstract This paper investigates the nonlinear predictability of technical trading rules based on a recurrent neural network as well as a neurofuzzy model. Elsevier About ScienceDirect Remote access Shopping cart Contact and support Terms and conditions Privacy policy. Cookies are used by this site. For more information, visit the cookies page.