Financial performance prediction model based on firms’ internal capability determinants: evidence from listed firms in Thailand during the transition period of going public

Abstract

This study attempts to develop a financial performance prediction model, which explores the relationship between the internal capability determinants and the financial performance of public firms both before and after the firms participated in the Stock Exchange of Thailand (SET). The regression and logistic regression methods are employed in this model based on two internal capability determinants, namely firm financial strengths and firm characteristics. The evaluation is based on 111 listed firms that entered the SET between 2003 and 2014, inclusively. Decision makers can make use of the developed model to predict their firms’ financial performance in relative groups (high, medium and low performance groups) during the transition years when they become public. Unlike prior studies, this study includes the effect of time by considering the relative transition years (compared to the year that the firms join the stock market), which are not the calendar years. As a result, the true timing effect on the firm performance before and after going public can be discovered. The empirical evidence shows that the firms have to maintain different levels of determinants during different years of operation to yield a better financial performance. In addition, they can use the results of the prediction to prepare the most effective investment plan with the strategy of when and how much should be invested for each relative type of firms in order to upgrade its financial performance in relative to other firms in the marke

Keywords:

• internal capability determinants
• firm characteristics
• firm financial strengths
• firm performance
• financial prediction model
• logistic regression

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

The work was supported by the Thammasat University Research Fund [Contract Number: TUFT 33/2566}.