International Journal of Computational Intelligence Research (IJCIR)

Volume 2, Number 1 (2006)

 


Improving investing strategy in stock market with valuation 

technology clustering and neural network


Kevin Hsiao, Jung-Bin Li, An-Pin Chen
Institute of Information Management, College of Management, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 300, ROC

 

Abstract
Valuation technology is particularly applicable for investors who focus on long-run rather than short-term returns. Notably, a firm must have existed long enough for an accurate valuation to be possible. This study proposes an approach that combines Neural Network, clustering and valuation technology, which deals with the problem of long-period valuation. The data set comprises the MSCI 50 from the Taiwan stock market, and NOLPAT growth and ROIC of company are the key factors for segregating data by SOM. A total of 10 elements from the Pro Forma and Mackensy DCF model are inputted back to the propagation neural network. The market value is the output and the shares outstanding can decide the reasonable stock price. The function of important is to shorten the predicting period to one quarter. Investors can rely on the values of share value difference to make their medium-term investment strategy.

Key words
SOM, BPN, DCF, Free Cash Flow.

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