Neural networks trading returns are compared out-of-sample with traditional ARIMA returns for corn, silver, and deutsche mark. Results show that neural network and ARIMA models had positive returns, and at about the same levels. However, deutsche mark was less profitable and returns were not statist
Forecasting futures trading volume using neural networks
β Scribed by Iebeling Kaastra; Milton S. Boyd
- Publisher
- John Wiley and Sons
- Year
- 1995
- Tongue
- English
- Weight
- 828 KB
- Volume
- 15
- Category
- Article
- ISSN
- 0270-7314
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
## Abstract Forecasting river flow is important to water resources management and planning. In this study, an artificial neural network (ANN) model was successfully developed to forecast river flow in Apalachicola River. The model used a feedβforward, backβpropagation network structure with an opti
We would like to acknowledge N. Marovac and R. Swiniarski of the Department of Mathematical Sciences at San Diego State University for introducing us to neural networks. Also, the helpful comments of E. Ornberg and N. Varaiya of the Department of Finance, San Diego State University, and two anonymou
## Abstract Forecasting currency exchange rates is an important financial problem that has received much attention especially because of its intrinsic difficulty and practical applications. The statistical distribution of foreign exchange rates and their linear unpredictability are recurrent themes
## Abstract The primary objective of this study is to investigate the possibility of including more temporal and spatial information on shortβterm inflow forecasting, which is not easily attained in the traditional timeβseries models or conceptual hydrological models. In order to achieve this objec