Abstract

An artificial neural network is an intelligent system using computers that allows users to improve performance through pattern recognition. Neural networks benchmark their predictions with actual results and constantly revise their predictions, improving forecasting capability. The purpose of this paper is to support the use of neural networks as a detection mechanism tool to discover market inefficiencies in financial markets. Using the Australian capital market as an example, this study investigates the question of the existence of market inefficiencies using artificial neural networks as the investigative tool. This study also focuses on whether additional publicly sourced information when used as input through a neural network, can provide investors with a trading advantage over traditional financial models. In finance, any forecasting advantage obtained through the use of publicly available information even if internal, external or both indicate some form of inefficiency in the financial markets. In this paper, we explore the efficiency of the four capital markets, the United States, Japan, Hong Kong and Australia, but focusing on the Australian capital market using the Australian Stock Exchange’s 200 (ASX 200) index. Our research demonstrates how the inclusion of external information to our neural network model can provide a significant trading advantage. Although our preliminary analysis suggests the Australian market is as efficient as the US market, other results suggest the Australian market may be less efficient, when we take into account of external information from other financial markets. Our results show that accounting for external market signals can significantly improve forecasts on the ASX200 index but show little benefit on forecasts for the Dow Jones Industrial Average (DJIA) index. Using external market signals, our neural network model’s prediction accuracy of the ASX200 index increases by an additional 10 percent from 50 to 60 percent accuracy. This suggests the inclusion of publicly available external signals can significantly improve neural network forecasts. By exploiting this additional information, this method can improve returns for investors who incorporate such signals in their neural network models.

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