Angela Hsiang‐Ling Chen, Xiaoli Wang, Jason Zu‐Hsu Lee and Chun‐Yuan Fu
This paper aims to explore the relationship of various financial and non‐financial factors to corporate value and how these factors can be used for the purpose of firm valuation…
Abstract
Purpose
This paper aims to explore the relationship of various financial and non‐financial factors to corporate value and how these factors can be used for the purpose of firm valuation. The focus is placed on a developing high‐tech industry.
Design/methodology/approach
The authors collect and compare data from companies within the time window of 1997 through 2010. The techniques of stepwise regression and back‐propagation neural network (BPNN) are applied to analyze this data, where the variables of operating profit margin, ROE, ROA, net income ratio, Tobin's Q and stock price are chosen to indicate firm value.
Findings
Each firm value variable appears to have a different set of estimator variables consisting of financial and non‐financial factors. The estimator variable in the set that has a high influence relative to the others tends to be financial factor. However, certain non‐financial factors appear to be considered as an estimator variable for different firm value variables more often than financial factors such as employee productivity, wealth created per employee, revenue growth rate, management expense per employee, R&D expense to management expense ratio, and R&D expenditure to total assets ratio. Further, the incorporation of BPNN shows an improvement of the result of the regression method in terms of overall estimation error, especially for operating profit margin.
Originality/value
The authors' investigation highlights the importance of the use of non‐financial factors for firm valuation in developing biotech industries. The result can be helpful for investors who seek to examine information variables and indicators for the opportunity presented by the above industries. In addition, the significant estimation improvement by incorporating the BNPP method into the commonly used regression method suggests the beneficial use of BPNN in refining the traditional methods in the field.