A Security Market Plane Approach to Stock Selection

Robert Ferguson; Richard Lynn

doi:10.2469/faj.v40.n5.75

A Security Market Plane Approach to Stock Selection

Financial Analysts Journal ◽

10.2469/faj.v40.n5.75 ◽

1984 ◽

Vol 40 (5) ◽

pp. 75-80

Author(s):

Robert Ferguson ◽

Richard Lynn

Keyword(s):

Security Market ◽

Stock Selection

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Improving Returns on Stock Investment through Neural Network Selection

Artificial Neural Networks in Finance and Manufacturing ◽

10.4018/978-1-59140-670-9.ch009 ◽

2006 ◽

pp. 152-164 ◽

Cited By ~ 2

Author(s):

Tong-Seng Quah

Keyword(s):

Neural Networks ◽

Conceptual Development ◽

Bankruptcy Prediction ◽

Security Market ◽

Stock Selection ◽

Financial Variables ◽

Stock Investment ◽

And Performance

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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