The UvA-LINKER will give you a range of other options to find the full text of a publication (including a direct link to the full-text if it is located on another database on the internet).
De UvA-LINKER biedt mogelijkheden om een publicatie elders te vinden (inclusief een directe link naar de publicatie online als deze beschikbaar is in een database op het internet).
faculty: "FEB" and publication year: "2007"
| Authors||S. Bekiros, D. Georgoutsos|
|Title||Evaluating direction-of-change forecasting: neurofuzzy models vs. neural networks|
|Journal||Mathematical and Computer Modelling|
|Faculty||Faculty of Economics and Business|
|Institute/dept.||FEB: Amsterdam School of Economics Research Institute (ASE-RI)|
|Abstract||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. The sample extends over the period 2/8/1971–4/7/1998 while the sub-period 4/8/1998–2/5/2002 has been reserved for out-of-sample testing purposes. Our results suggest that, in absence of trading costs, the return of the proposed neurofuzzy model is consistently superior to that of the recurrent neural model as well as of the buy & hold strategy for bear markets. On the other hand, we found that the buy & hold strategy produces in general higher returns than neurofuzzy models or neural networks for bull periods. The proposed neurofuzzy model which outperforms the neural network predictor allows investors to earn significantly higher returns in bear markets.|
Use this url to link to this page: http://dare.uva.nl/en/record/273895
Contact us about this recordNotify a colleague
Add to bookbag