Multilayer feedforward networks are universal approximators
Neural Networks
IEEE Spectrum
Neural networks: applications in industry, business and science
Communications of the ACM
Comparing BP and ART II neural network classifiers for facility location
Computers and Industrial Engineering
Neural Networks for Statistical Modeling
Neural Networks for Statistical Modeling
Neural Networks in Computer Intelligence
Neural Networks in Computer Intelligence
Neural Networks for Financial Forecasting
Neural Networks for Financial Forecasting
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Journal of Management Information Systems - Special section: Realizing value from information technology investment
Neural network models for a resource allocation problem
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Knowledge discovery techniques for predicting country investment risk
Computers and Industrial Engineering
Decision Support Systems - Special issue: Data mining for financial decision making
Expert Systems with Applications: An International Journal
A Query-Driven Approach to the Design and Management of Flexible Database Systems
Journal of Management Information Systems
A Kernel-Based Technique for Direction-of-Change Financial Time Series Forecasting
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part II
A neural-network-based nonlinear metamodeling approach to financial time series forecasting
Applied Soft Computing
Commercial Internet filters: Perils and opportunities
Decision Support Systems
Expert Systems with Applications: An International Journal
Feature selection techniques, company wealth assessment and intra-sectoral firm behaviours
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
When to choose an ensemble classifier model for data mining
International Journal of Business Intelligence and Data Mining
MISMIS - A comprehensive decision support system for stock market investment
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Select the size of training set for financial forecasting with neural networks
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Hybridizing data stream mining and technical indicators in automated trading systems
MDAI'11 Proceedings of the 8th international conference on Modeling decisions for artificial intelligence
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
A D-GMDH model for time series forecasting
Expert Systems with Applications: An International Journal
An approach of bio-inspired hybrid model for financial markets
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Methodological triangulation using neural networks for business research
Advances in Artificial Neural Systems
Multiple Kernel Learning with Fisher Kernels for High Frequency Currency Prediction
Computational Economics
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Neural networks have been shown to be a promising tool for forecasting financial time series. Several design factors significantly impact the accuracy of neural network forecasts. These factors include selection of input variables, architecture of the network, and quantity of training data. The questions of input variable selection and system architecture design have been widely researched, but the corresponding question of how much information to use in producing high-quality neural network models has not been adequately addressed. In this paper, the effects of different sizes of training sample sets on forecasting currency exchange rates are examined. It is shown that those neural networks-given an appropriate amount of historical knowledge-can forecast future currency exchange rates with 60 percent accuracy, while those neural networks trained on a larger training set have a worse forecasting performance. In addition to higher-quality forecasts, the reduced training set sizes reduce development cost and time.