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Improving Returns on Stock Investment through Neural Network Selection
Abstract
Artificial neural networks’ (ANNs’) generalization powers have in recent years received admiration of finance researchers and practitioners. Their usage in such areas as bankruptcy prediction, debt-risk assessment, and security-market applications has yielded promising results. With such intensive research and proven ability of the ANN in the area of security-market application and the growing importance of the role of equity securities in Singapore, it has motivated the conceptual development of this work in using the ANN in stock selection. With their proven generalization ability, neural networks are able to infer the characteristics of performing stocks from the historical patterns. The performance of stocks is reflective of the profitability and quality of management of the underlying company. Such information is reflected in financial and technical variables. As such, the ANN is used as a tool to uncover the intricate relationships between the performance of stocks and the related financial and technical variables. Historical data, such as financial variables (inputs) and performance of the stock (output) is used in this ANN application. Experimental results obtained thus far have been very encouraging.
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