Abstract

This study presents a novel financial performance forecasting method that combines the threshold technique with Artificial Neural Networks (ANN). It applies the threshold regression method to identify the factors within the board of directors that influence the financial performance of traditional industries in Taiwan. The findings indicate that the ANN method effectively predicts financial performance by using relevant board structure data. Furthermore, the empirical results suggest that boards with more members demonstrate increased profitability. Additionally, a more significant presence of board members with accounting expertise contributes to more consistent profits. In contrast, an increased presence of members with financial expertise has a more pronounced impact on profitability.

Introduction

Traditional industries, including sectors such as metal machinery, the chemical industry, and subsistence industries, have played pivotal roles in Taiwan’s economic development, trade, and industrial evolution over the years. However, in recent years we have witnessed the emergence of economic powerhouses and the rapid progress of regional economic and trade partnerships like ASEAN, resulting in significant changes to the global economic and trade landscape. As a result, these industries are now operating within a new and highly competitive environment. In this era of rapid transformation, these industries must identify the essential factors that will enable them to maintain and enhance their positions on the international stage.

Investors seek to achieve multiple objectives through the election of a company’s board of directors. They aim to enhance corporate governance, safeguard the rights and interests of shareholders, and anticipate that the board will contribute expertise to improve the decision-making prowess of the board itself. Ultimately, this is expected to enhance the company’s overall performance. Consequently, whether the board of directors can positively impact corporate performance has remained a significant research focus in management and academia.

While many studies have explored the relationship between the board of directors’ characteristics and company performance, a notable need exists for more in-depth articles examining the professional backgrounds of the board members and CEOs and how their leadership qualities influence financial performance. This gap in research underscores the need for a more comprehensive understanding of the intricate dynamics between leadership, professional expertise, and corporate financial outcomes.

Conclusions

The research results show that, for companies in traditional industries that are listed on the Taiwanese stock market, the composition of the board of directors impacts the business’s financial performance.

Boards with more members may have a greater capacity for overseeing corporate governance. A greater representation of board members with accounting expertise tends to lead to more consistent corporate profits, whereas an increased presence of members with financial expertise results in more pronounced profit fluctuations. Additionally, in the case of companies with lower profits, managers may face pressure from loan contracts or receive limited attention from investors, potentially leading to heightened practices related to profit management. Managers may aim to maximize their benefits. At this time, the return on assets is high, and the managers obtain the expected benefits. Thus, to restrain the profit management behavior of managers, enterprises need to have a broader view and have specific judgments and analyses in each enterprise to build organizational structure and procedures. The operation of the board of directors is more effective because of the industry’s characteristics, the enterprise’s size, the capital structure, and the performance of the enterprise. Finally, this study used deep learning and big data techniques to build an Artificial Neural Network (ANN) model that specializes in predicting the ROA of companies based on most factors related to board structure, with a prediction accuracy of over 78%.