An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks

摘要

Neural networks have been shown to be a promising tool for forecasting financial time series. Several design factors significantly impact the accuracy of neu- ral network forecasts. These factors include selection of input variables, architecture of the network, and quantity of training data. The questions of input variable selec- tion and system architecture design have been widely researched, but the correspond- ing question of how much information to use in producing high-quality neural network models has not been adequately addressed.