A New Approach to Neural Network Based Stock Trading Strategy

摘要

The paper presents an idea of using an MLP neural network for determining the optimal buy and sell time on a stock exchange. The inputs in the training set consist of past stock prices and a number of technical indicators. The buy and sell moments on the training data that will become the output to the neural network can be determined either automatically or manually by a user on past data. We discuss also the input space transformation and some improvements to the backpropa-gation algorithms.