Modified Neural Network Algorithms for Predicting Trading Signals of Stock Market Indices

摘要

The aim of this paper is to present modified neural network algorithms to predict whether it isbest to buy, hold, or sell shares (trading signals) of stock market indices. Most commonly usedclassification techniques are not successful in predicting trading signals when the distributionof the actual trading signals, among these three classes, is imbalanced. The modified networkalgorithms are based on the structure of feedforward neural networks and a modified OrdinaryLeast Squares (OLSs) error function. An adjustment relating to the contribution from the historicaldata used for training the networks and penalisation of incorrectly classified trading signals wereaccounted for, when modifying the OLS function. A global optimization algorithm was employedto train these networks. These algorithms were employed to predict the trading signals of theAustralian All Ordinary Index. The algorithms with the modified error functions introduced bythis study produced better predictions.