Taiwan Economic Journal Research Articles

Collusion or supervision? Between auditor and audit firm – tax planning’s perspective

Purpose This study aims to investigate the impact of auditor industry specialization (AIS) on clients’ tax planning at the audit firm level and individual auditor level, respectively. Design/methodology/approach The study’s sample consists of 44,637 firm-year observations of Chinese firms listed on the Shenzhen and Shanghai Stock Exchanges during the period from 2002–2020. The data are collected from the Taiwan Economic Journal. Panel regression is used to test hypotheses. Additionally, a two-stage least squares model is used to address concerns about possible endogeneity. Findings The relationship between tax planning and AIS is significantly positive at the audit firm level, while it is significantly negative at the individual auditor level. Originality/value The authors use manually collected data to investigate the distinct impacts of two AIS metrics on tax planning: the number of clients and the scale of clients.

Women who money: the impact of female board members and institutional ownership on firms’ financial performance

Purpose This study aims to explore the relationship between board diversity and financial performance, examining how the presence of women on corporate boards affects metrics such as profitability. In addition, this study investigates how corporate governance and ownership structure influence gender diversity policies and the appointment of women to boards. Design/methodology/approach Two hypotheses were proposed and following regression models were developed for data analysis. The sample for this study consists of companies listed on the Taiwan Stock Exchange in five years (2015–2019). The data was extracted from the Taiwan Economic Journal Taiwan database with the final data set comprised 9,379 firm-year observations. Findings This study indicates a positive association between board diversity and financial performance. Moreover, the relationship between female directors on the board and financial performance is stronger for firms with a higher percentage of institutional investors. Originality/value By highlighting how gender-balanced boards and engaged institutional shareholders improve profitability, this study suggests that actively recruiting women directors and pursuing governance reforms that empower shareholders can pay dividends for Taiwanese companies. The results make an important contribution to the limited research on diversity's impact within Asian corporate settings.

Related party transactions and tax avoidance: does government ownership play a role?

Purpose This study aims to examine the association between related-party transactions (RPT) and tax avoidance. The study further investigates whether government ownership improves scrutiny of tax aggressiveness activities among Taiwanese group companies. Design/methodology/approach The authors used 16,061 firm-year observations derived from the Taiwan Economic Journal Database (TEJ) from 2005 to 2021. The authors applied GLS fixed-effect regression. Additional tests, such as a difference-in-difference examination, propensity score matching (PSM) analysis and other tests were performed to obtain more robust results. Findings The results show different consequences between eliminated and non-eliminated RPT toward tax avoidance. RPT enhances tax benefits aligned with the efficient contracting hypothesis. Under varying degrees of government control, this paper empirically reveals that government ownership has a role in mitigating tax avoidance. This implies that government control improves corporate governance by balancing opportunistic and efficiency-based tax avoidance. Practical implications This paper provides substantial practical implications since using the strategy of reducing taxes through RPT will result in greater tax savings at the business group level. Therefore, RPT is beneficial for enhancing business efficiency. Furthermore, government control increases corporate governance quality, which could lead to balancing tax aggressiveness activity. Originality/value Using a unique setting for RPT reporting in Taiwan, this paper divides RPT into eliminated and non-eliminated RPT. The findings offer significant insight for policymakers, investors and managers regarding the utilization of RPT to enhance efficiency in business groups. Additionally, this paper highlights the role of government control in preserving a harmonious balance in tax planning practices.

Efficiency evaluation of intellectual capital for value creation: a case study of the semiconductor industry in Taiwan

PurposeThis study constructs an efficiency evaluation framework for assessing the human, structural and relational capital in the semiconductor industry of Taiwan. Furthermore, we analyze whether there are significant differences in efficiency across different levels concerning the industry supply chain (upstream, midstream and downstream), employee service tenure, capital scale and company establishment years.Design/methodology/approachThis study focuses on Taiwanese semiconductor companies, utilizing data sourced from the Taiwan Economic Journal (TEJ) Database for the period spanning 2017 to 2021, encompassing a total of five years. Due to the nondisclosure of intangible asset values by all companies, an effort was made to ensure a comparable baseline by excluding companies with incomplete or missing data. Finally, empirical analysis was conducted on a sample of 64 companies using the dynamic network data envelopment analysis method.Findings(1) Overall efficiency demonstrates structural capital as the most prominent, followed by relational capital, while human capital shows relatively poorer efficiency. (2) To enhance the efficiency of intellectual capital, priority should be given to improving the efficiency of outputs related to intellectual property rights such as patents. (3) The midstream segment exhibits the best efficiency in both structural and relational capital. (4) Companies with longer employee service tenures exhibit superior efficiency in human capital in the long run. (5) Companies with extended establishment years and larger capital scales demonstrate superior efficiency in both human and structural capital.Originality/valueReflecting on past literature, scholars have primarily focused on the relationship between intellectual capital and firm efficiency, often emphasizing the overall efficiency of intellectual capital. However, within organizations, human capital, structural capital, and relational capital are interrelated. This study, for the first time, assesses the efficiency of these three components within an organization. The research addresses the challenges in analyzing the efficiency of intellectual capital and introduces a highly contemporary approach – dynamic network data envelopment analysis (DNDEA). Using the semiconductor industry in Taiwan as a case study, this paper conducts empirical analysis in a captivating and worthy industry. Therefore, the ideas presented in this paper are original.

Exploring the impact of auditor industry specialization on income smoothing

Since the late 1970s’ reform and opening-up, China’s economy has achieved remarkable success, maintaining an average annual GDP growth of 9.7% from 1978 to 2006. In 2007, China achieved a GDP of 3.38 trillion USD, surpassing Germany to become the world’s third-largest economy, and by the second quarter of 2010, it had become the second-largest economy globally. These developments have attracted international investors’ attention to Chinese equities. However, China’s poor reputation for corporate governance may deter investors, despite their positive outlook on China’s economy. Corporate managers in China are known to engage in income smoothing, taking actions to dampen fluctuations in their firms’ publicly reported net income. In 2004, the OECD proposed that a strong disclosure regime promoting real transparency can help attract capital and maintain confidence in the capital market, with income smoothing being a key concern for both institutional and individual investors. This study investigates how auditor industry specialization (AIS) affects income smoothing behavior in Chinese listed companies. Using a sample of 27,208 firm-year observations from 2006 to 2018 collected from the Taiwan Economic Journal Database, the results indicate that AIS is negatively related to managers’ income smoothing behavior, underscoring the importance of industry expertise in mitigating such practices. Additionally, an independent t-test highlights the difference between Big Four and non-Big Four auditors in influencing client companies’ income smoothing behaviors. The study further demonstrates the significant moderating effect of Big Four auditors on the relationship between accounting firms with top industry expertise and client companies’ income smoothing, reflecting the unique characteristics of the Chinese market. This study verifies the suppressive effect of AIS on firms’ income smoothing and indirectly suggests that governments (or the Chinese Institute of Certified Public Accountants, CICPA) should regularly disclose accounting firms/auditors with industry expertise publicly. This regime can help global investors make informed investment decisions.

Participative leadership congruence and employee task performance: the intermediate roles of person-unit fit and unit-member exchange

PurposeParticipative leadership's influence on employee task performance has garnered significant attention in a rapidly evolving organizational landscape. This study explores the multilevel dynamics of participative leadership congruence between unit managers and direct supervisors and its effects on employee task performance.Design/methodology/approachDrawing on two longitudinal studies based on the firms randomly selected from the Taiwan Economic Journal (TEJ) database, the research observes the mediating role of Person-Unit fit and the moderating influence of Unit-Member Exchange in the participative leadership-performance relationship.FindingsThe findings reveal how participative leadership congruence enhances person-unit fit, which in turn benefits employee task performance. Unit-member exchange plays a critical role in augmenting the participative leadership congruence? Person-unit fit? Employee task performance relationship.Originality/valueThe study extends leadership literature by highlighting the significance of leadership alignment across levels and the interplay between psychological and social factors in improving employee performance.

Paradoxical leadership on firm performance: What role can guanxi HRD practices play?

AbstractResearch shows that paradoxical leadership has a strong positive but inconsistent relationship with firm performance. Drawing on leadership contingency theory, we provide a theoretical model explaining how business unit level (BU‐level) paradoxical leadership positively impacts BU‐level performance mediated by BU‐level corporate entrepreneurship (CE). However, we also show that this relationship can turn negative when the degree of firm‐level Guanxi on human resource development (Guanxi HRD) practices is high. By relying on the responses from 276 BUs and performance archival data from the Taiwan Economic Journal database, we found that BU‐level CE mediates the relationship between BU‐level paradoxical leadership and BU‐level performance. Firm‐level Guanxi HRD practices diminish this effect and turn the positive relationship between BU‐level paradoxical leadership and BU‐level performance through BU‐level CE negative. Our study reveals the dark side of firm‐level Guanxi HRD practices and provides new theoretical and empirical insights that reconcile the relationship between paradoxical leadership and firm performance.

Application of hybrid fuzzy interval-based machine learning models on financial time series - A case study of Taiwan biotech index during the epidemic period.

In recent years, the use of machine learning to predict stock market indices has emerged as a vital concern in the FinTech domain. However, the inherent nature of point estimation in traditional supervised machine learning models leads to an almost negligible probability of achieving perfect predictions, significantly constraining the applicability of machine learning prediction models. This study employs 4 machine learning models, namely BPN, LSTM, RF, and ELM, to establish predictive models for the Taiwan biotech index during the COVID-19 period. Additionally, it integrates the Gaussian membership function MF from fuzzy theory to develop 4 hybrid fuzzy interval-based machine learning models, evaluating their predictive accuracy through empirical analysis and comparing them with conventional point estimation models. The empirical data is sourced from the financial time series of the "M1722 Listed Biotechnology and Medical Care Index" compiled by the Taiwan Economic Journal during the outbreak of the COVID-19 pandemic, aiming to understand the effectiveness of machine learning models in the face of significant disruptive factors like the pandemic. The findings demonstrate that despite the influence of COVID-19, machine learning remains effective. LSTM performs the best among the models, both in traditional mode and after fuzzy interval enhancement, followed by the ELM and RF models. The predictive results of these three models reach a certain level of accuracy and all outperform the BPN model. Fuzzy-LSTM effectively predicts at a 68% confidence level, while Fuzzy-ELM and Fuzzy-RF yield better results at a 95% confidence level. Fuzzy-BPN exhibits the lowest predictive accuracy. Overall, the fuzzy interval-based LSTM excels in time series prediction, suggesting its potential application in forecasting time series data in financial markets to enhance the efficacy of investment analysis for investors.

Impact of Outside Blockholders on Related Party Transactions and Book-tax Conformity Link

This study assesses the effects of transfer pricing audit rules in 2005 on related party transactions and book-tax conformity. Further, the presence of outside blockholder beneficially owning at least five percent of the company’s outstanding common stocks is examined. Data from 2003 to 2007 and 2018 of Taiwan-listed firms were collected from the Taiwan Economic Journal database and estimated by ordinary least squares using EViews. This study finds that book-tax conformity has no relation to related party transactions. The presence of outside blockholders is significant in the 2005 sample which might have resulted from an implementation of Transfer Pricing Assessment Rules. However, the presence of outside blockholder as an effective monitor over related party transactions – book-tax conformity link has no evidence. This paper offers evidence in an Asian context to enrich the discussion on related party transactions, book-tax conformity and outside blockholders. The finding is robust to an alternate measure of book-tax conformity.

The Research of Influencing Factors that Possibly lead to Company Bankruptcy

Previous research has established correlations between corporate bankruptcy and internal factors such as financial ratios, cash reserves, company liquidity, and capital structure, as well as external factors, such as macroeconomic conditions, industry competition, and regulatory policies. However, several unidentified factors still warrant investigation. This study employs the Binary Probit Model to analyze data from Taiwan Economic Journal, encompassing 6,819 companies over the period from 1999 to 2009. The findings indicate that corporate bankruptcy is not significantly associated with 8 variables such as Operating Gross Margin, Cash flow rate, Operating Profit Growth Rate, Total Asset Growth Rate, Current Ratio, etc. However, it exhibits particularly strong correlations with 8 variables such as Pre-tax net Interest Rate, Operating Expense Rate, ROA(C) before interest and depreciation before interest, Non-industry income and expenditure/revenue, Net Value Per Share (B), Total Asset Turnover, etc. This research provides valuable insights into factors that may lead to corporate bankruptcy, which is instrumental in identifying warning signals, mitigating risks, and formulating effective preventive measures. Moreover, it points to the direction for further research in this field.

Machine Learning Model Using Extreme Gradient Boosting (XGBoost) Feature Importance and Light Gradient Boosting Machine (LightGBM) to Improve Accurate Prediction of Bankruptcy

Abstract. Humans have limitations in processing and analyzing large amounts of data in a short time, including in terms of analyzing bankruptcy data. Bankruptcy data is one of the data that has complex information, so it requires technology that can assist in the process of analyzing and processing data more quickly and efficiently. Data science technology enables data processing and analysis on a large scale, using parallel processing techniques. Parallel processing can be implemented in machine learning models.
 Purpose: Using parallel processing techniques, data science technologies enable data processing and analysis at scale. Parallel processing can be implemented in machine learning models. Therefore, this study aims to implement a machine learning model using the Light Gradient Boosting Machine (LightGBM) classification algorithm which is optimized using Extreme Gradient Boosting (XGBoost) Feature Importance to increase the accuracy of bankruptcy prediction.
 Methods/Study design/approach: Bankruptcy prediction is carried out by applying LightGBM as a classification model and optimized using the XGBoost algorithm as a Feature Importance technique to improve model accuracy. the dataset used is the Taiwanese Bankruptcy dataset collected from the Taiwan Economic Journal for 1999 to 2009 and has 6,819 data. Taiwanese Bankruptcy is unbalanced data, so this study applies random oversampling.
 Result/Findings: The results obtained after going through the model testing process using the confusion matrix obtained an accuracy of the performance of LightGBM+XGBoost Feature Importance of 99.227%.
 Novelty/Originality/Value: So it can be concluded that the implementation of XGBoost Feature Importance can be used to improve LightGBM's performance in bankruptcy prediction.

Company Bankruptcy Prediction

Following the founding and operation of a corporation, the biggest challenge it faces is the risk of bankruptcy, so how to prevent, controlling, and analyze the causes of bankruptcy is one of the most critical tasks for every company. As we all know, many factors affect corporate bankruptcy, and most studies are based on a unilateral or small range of factors set to study its impact on bankruptcy. Based on the data provided by Taiwan Economic Journal from 1999 to 2009, this paper adopts univariate and multivariate analysis models to analyze the data. A 3Comparative analysis, Normality test, T-test, non-parametric test, ANOVA test, Factor analysis and logistics were carried out using SPSS auxiliary tools Regression (Regression) controls relatively comprehensive factors that affect a company's bankruptcy from an overall perspective. It aims to demonstrate the selection of bankruptcy factors and analysis ideas. After analysis, we conclude that through the research and analysis, we have a new understanding of the causes of corporate bankruptcy. We find the four most important factors affecting corporate bankruptcy. Namely, ROAC before interest and depreciation before interest, Liability to Equity, Total assets to GNP price, and No credit interval, In addition, many single factors may have no impact on a company's bankruptcy. However, when they coexist with other factors, they will have a chain effect, resulting in even multiple amplification of the risk of bankruptcy. Therefore, different from the specific analysis of the impact of single factors on the bankruptcy of a company, this paper extracts the primary factors and takes the analysis of the relationship between individual factors as a specific example by showing the research perspective of innovation to propose the actual operation of a company from the macro and micro aspects, that is, to clarify which departments and decisions to pay special attention to in daily operation.

Bankruptcy Prediction Using Genetic Algorithm-Support Vector Machine (GA-SVM) Feature Selection and Stacking

Bankruptcy is an impact caused by a company's financial failure. Financial failure in the company must be avoided so as not to cause losses to the company. In the research that was carried out utilizing a data set from the Taiwan Economic Journal as many as 6,819 to be trained using machine learning algorithms using classification techniques. The goal obtained from the research conducted is to obtain a classification technique with the best accuracy results. The method used in this research is preprocessing using the synthetic minority over-sampling technique to handling unbalanced data sets. Then, the results of the balanced data set will be processed using a genetic algorithm-support vector machine feature selection algorithm to reduce the attributes of the data set. Data sets that have experienced reduced attributes will be trained using the stacking method with a single classifier base learner in the form of k-nearest neighbors, naïve bayes, decision trees with classification and regression tree models, gradient boosting decision trees, and light gradient boosting. The meta-learner used in the stacking method is extreme gradient boosting. The results of the accuracy obtained from the research conducted were 99.22%.

Does corporate social responsibility provide protection against systemic risks? Evidence from Taiwan during the US-China trade war

This paper examines the protection against systemic risks of the financial and stock performance of firms in receipt of ‘corporate social responsibility’ (CSR) awards. The data we obtained from the Taiwan Economic Journal (TEJ) in 2016-2018, comprised of CSR-award-recipient firms (CSR firms) voted for by the Common Wealth and Global Views magazines, for a sample period running from the third quarter of 2017 to the third quarter of 2018. Our empirical results reveal that in terms of their financial performance, as compared to non-CSR-award-recipient (non-CSR) firms, CSR firms failed to demonstrate any better protection against systemic risks (such as the US-China trade war). However, the stock performance of CSR firms clearly provided better protection than that of non-CSR firms; the reason for this observation is assumed to be the higher operational costs faced by CSR firms seeking to continue to pursue their CSR goals when encountering systemic risks. Nevertheless, participation in CSR is found to have an insurance-like effect on firm value, which clearly helps to increase the confidence of investors and reduce stock volatility

Sustainable Innovation and Firm Performance Driven by FinTech Policies: Moderating Effect of Capital Adequacy Ratio

This study empirically investigated the role played by government policy in the financial industry in promoting sustainable innovation, business performance, and risk management. An original dataset, comprising data from the Taiwan Economic Journal (TEJ), Taiwan Patent Search System, and company annual reports from the period 2015–2019 was used to analyze the effects of government policy on the financial industry in Taiwan. The research results showed that a firm’s sustainable commitment is conducive to its business growth and does not increase its risk in the financial industry. The financial industry can report on FinTech news that highlights business growth, while companies with high capital adequacy rates are better equipped to manage the risks associated with innovation commitment. Financial companies are suggested to engage in sustainable innovation and thus improve their profitability. In addition, policymakers should mandate that financial companies increase their capital adequacy ratios, improve their risk-bearing capacity, and maintain financial market stability.

The pace of international expansion for digital multinational enterprises

Firms that engage in digital transformation are different from traditional firms when it comes to internationalization, because they are not designed to operate that way. This research explores the pace of internationalization of digitally-operated firms by comparing the relationship of their internal resources and external growth options with the pace of digital multinational enterprises' (DMNEs') internationalization process. We further investigate the moderating effect of international diversification via a group sample collected from the Taiwan Economic Journal (TEJ) database. Statistical results support that firms' internal resources have a U-shape relationship with their pace of internationalization, and under the moderation of international diversification this shape becomes flatter. Growth options positively influence the pace, and international diversification strengthens this relationship. This study enhances the literature in comprehending digital firms' process of expanding globally, and with the aid of diversification, DMNEs have enhanced opportunities to increase their international presence. The findings contribute to the current knowledge in the global business field about the determinants of firms that affect the pace of internationalization. Specifically, internal resources have a curvilinear relationship with the pace, while growth options have a positive relationship with it.

Analyses of the operating performance of information service companies based on indicators of financial statements

This study aims to discuss the operating performance of information service companies based on three major indicators of financial statements, namely, cost and expense, solvency, and operating capacity, in order to provide a basis for management when making operational decisions. This study used the data of financial statements, extracted from the Taiwan Economic Journal (TEJ) database, regarding listed and OTC companies that operated in the information service industry from 2010 to 2019. A total of 328 sets of valid reference data were obtained, including 109 sets of data about listed companies and 219 sets of data about OTC companies. Operating performance was measured by gross profit margin. Operation management capability was measured by the operating expense ratio, labor cost ratio, R&D cost ratio, quick ratio, cash flow ratio, debt to equity ratio, accounts receivable turnover, inventory turnover, and fixed asset turnover. The business scale was used as a control variable. According to the empirical results, the gross profit margin of an information service company was higher if its operating expense ratio, labor cost ratio, R&D cost ratio, quick ratio, cash flow ratio, and accounts receivable turnover were higher, its debt to equity ratio and fixed asset turnover was lower, and its business scale was larger. Moreover, the regression results of principal elements on the operating performance show that the gross profit margin of an information service company was higher if it had better operation management, R&D capabilities, and solvency; while the gross profit margin of an information service company was lower if it had higher fixed asset turnover and inventory turnover (more frequent turnover operations). The empirical results of the principal elements of operating performance are consistent with the regression results. Finally, in order to improve market competitiveness through R&D and innovation, the managers of information service companies were recommended to increase their investments in R&D. In addition, it is suggested that the management of information service companies maintain high solvency liquidity to protect creditors’ equity, mitigate the risk of a financial strait, and further stabilize operations to improve operating performance.

An Ensemble Stacking Algorithm to Improve Model Accuracy in Bankruptcy Prediction

Bankruptcy analysis is needed to anticipate bankruptcy. Errors in predicting bankruptcy often cause bankruptcy. Machine learning with high accuracy to analyze reversal must continuously improve its accuracy. Many machine learning models have been applied to predict bankruptcy. However, model improvisation is still needed to improve prediction accuracy. We propose a combination model to improve the accuracy of bankruptcy prediction based on a genetic algorithm-support vector machine (GA-SVM) and stacking ensemble method. This study uses the Taiwanese Bankruptcy dataset from the Taiwan Economic Journal. Then we implement a synthetic minority over-sampling technique for handling imbalanced datasets. We select the best feature using GA-SVM, adopt a new strategy by stacking the classifier, and use extreme gradient boosting as a meta-learner. The results show superior accuracy obtained by the stacking model-based GA-SVM with an accuracy of 99.58%. The accuracy obtained is higher than just applying a single classifier. Thus, this study shows that the proposed method can predict bankruptcy with superior accuracy.

Financial Distress Prediction: A Hybrid Tracking Model Approach

The purpose of this study was to build a highly accurate corporate financial distress tracking and prediction model based on hybrid machine learning technology. The research data were from Taiwan Economic Journal, and the research subjects were enterprises with financial distress risk announced in September 2022. In consideration of enterprise features, this study excluded the finance and insurance industries. The research period was three years (2019, 2020, and 2021) before the distress announcement. This study matched enterprises with financial distress and enterprises without financial distress (normal enterprises) at a ratio of 1:1 for each year. The sample size for each year included 374 enterprises with financial distress and 374 enterprises without financial distress. This study applied several machine learning technologies. At first, important variables were screened by applying artificial neural networks (ANNs). Next, prediction models were built based on decision tree C5.0 and random forest (RF) and were compared. According to the empirical result, the ANN-RF model provided a higher accuracy.

Determinants of corporate social responsibility performance in emerging markets: An international orientation perspective

AbstractFor emerging market multinational enterprises (EMNEs), corporate social responsibility (CSR) is a foreign business practice. Because EMNEs are late movers in the international market, their attempts to engage in sustainable development and to improve CSR performance (CSP) are hindered by their limited relevant knowledge and resources. Few studies have investigated how EMNEs achieve high CSP. In this study, a sample of 177 Taiwanese companies in Taiwan listed on the Taiwan Stock Exchange from 2013 to 2015 was examined. Data were collected from the Taiwan Economic Journal database. The effects of international orientation on the level of internationalization, market entry in advanced economies, and adherence to international CSR principles on the CSP of these EMNEs were investigated. The results revealed that higher levels of internationalization, greater market entry in advanced economies, and adherence to international CSR principles are positively related to CSP. In addition, a higher level of internationalization enhances the relationship between adherence to international CSR principles and CSP.