Financial Prediction Using Neural Networks
Financial Prediction Using Neural Networks
Neural Networks in the Capital Markets
Neural Networks in the Capital Markets
Neural Networks for Financial Forecasting
Neural Networks for Financial Forecasting
Selecting superior securities: using discriminant analysis and neural networks to differentiate between 'winner' and 'loser' stocks
Stock market prediction using artificial neural networks with optimal feature transformation
Neural Computing and Applications
Neural network for modeling financial time series: a new approach
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartIII
Enhancing Existing Stockmarket Trading Strategies Using Artificial Neural Networks: A Case Study
Neural Information Processing
The prediction of Taiwan government bond yield by neural networks
ICS'09 Proceedings of the 13th WSEAS international conference on Systems
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
The prediction of Taiwan 10-year government bond yield
WSEAS TRANSACTIONS on SYSTEMS
Enhancing stockmarket trading performance with ANNs
Expert Systems with Applications: An International Journal
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Trading team composition for the intraday multistock market
Decision Support Systems
Information Systems Frontiers
Mathematics and Computers in Simulation
Hi-index | 12.05 |
A great deal of work has been published over the past decade on the application of neural networks to stockmarket trading. Individual researchers have developed their own techniques for designing and testing these neural networks, and this presents a difficulty when trying to learn lessons and compare results. This paper aims to present a methodology for designing robust mechanical trading systems using soft computing technologies, such as artificial neural networks. This paper describes the key steps involved in creating a neural network for use in stockmarket trading, and places particular emphasis on designing these steps to suit the real-world constraints the neural network will eventually operate in. Such a common methodology brings with it a transparency and clarity that should ensure that previously published results are both reliable and reusable.