Taiwan Stock Exchange Corporation Research Articles

Exploring cash refund capital reduction in Taiwan: Empirical insights from bull and bear markets

AbstractThe paper explores the concept of cash refund capital reductions (CRCR) in the context of firms listed on the Taiwan Stock Exchange Corporation (TSEC) and GreTai Securities Market (GTSM). CRCR is a distinct and increasingly vital financial tool used by these firms. Taiwan stands alone in adopting CRCR, making it a challenge to establish both a theoretical framework and gather empirical evidence on this subject. In this study, we examined 205 CRCR announcements made between 2002 and 2022, delving into their impact on both bull and bear markets. Our investigation reveals that the market responses to CRCR announcements were more significant during bull markets compared to bear markets. Additionally, we delve into the crucial factors influencing market responses in these varying market conditions. The findings underscore the importance for firms announcing CRCR to carefully consider the prevailing market conditions.

Balancing act: how corporate accounting disclosures and social learning shape corporate image in Taiwan

This research explores how Corporate Accounting Disclosure (CAD) affects the corporate reputation (CR) of publicly traded Taiwan companies, with special emphasis on the moderating influence of social learning (SL). While Corporate Accounting Disclosure and social learning serve as the independent and moderating variables, respectively, corporate reputation is the study's dependent variable. A sample of 125 companies, listed on the Taiwan Stock Exchange Corporation (TWSE), was selected for this study. Our findings suggest that Corporate Accounting Disclosure positively correlates with corporate reputation, as does social learning. Interestingly, the anticipated moderating effect of Social Learning on the association between Corporate Accounting Disclosure and Corporate Reputation was not observed. Future research endeavors may delve deeper into the underlying reasons for this phenomenon by adopting a multifaceted approach of longitudinal studies and cross-cultural analysis. The study also offers in-depth discussions of its findings and clearly outlines its theoretical and practical implications. It concludes by offering recommendations and charting potential avenues for subsequent research.

A hybrid time series forecasting method based on neutrosophic logic with applications in financial issues

Rising market demands, economic pressures, and technological advancements have spurred researchers to seek ways to enhance business environments and scientific productivity. Predictive science, crucial in this context, has gained prominence due to the rapid progress in information technology and forecasting algorithms. Time series forecasting, widely used in fields like engineering, economics, tourism, and energy, has inherent limitations with classical statistical methods, leading researchers to explore artificial intelligence and fuzzy logic for more accurate predictions. However, despite extensive efforts to improve accuracy, challenges persist. The research introduces a model aimed at surpassing existing methods in time series forecasting accuracy. This approach combines meta-heuristic optimization algorithms and neutrosophic logic to enhance precision in uncertain and complex environments, promising improved forecasting outcomes. The study shows that the performance of the neutrosophic time series modeling approach is highly dependent on the optimal selection of the universe of discourse and its corresponding intervals. This study selects the quantum optimization algorithm (QOA), genetic algorithm (GA), and particle swarm optimization (PSO) to address this weakness. These optimization algorithms improve the performance of the NTS modeling approach by selecting the global universe of discourse and corresponding intervals from the list of locally optimal solutions. The proposed hybrid model (i.e., NTS-QOA model) is verified and validated with datasets of university enrollment of Alabama (USA), Taiwan futures exchange (TAIFEX) index, and Taiwan Stock Exchange Corporation (TSEC) weighted index. Various experimental results signified the efficiency of the proposed model over existing benchmark models in terms of average forecasting error rate (AFER). This value using the proposed NTS QOA, NTS GA, and NTS PSO method on the university dataset is 0.166, 0.167, 0.164, on the TAIFEX dataset, is 0.081, 0.081, and 0.081, and on the TSEC dataset is 0.09, 0.09, and 0.09, respectively.

Artificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction

“Going concern” is a professional term in the domain of accounting and auditing. The issuance of appropriate audit opinions by certified public accountants (CPAs) and auditors is critical to companies as a going concern, as misjudgment and/or failure to identify the probability of bankruptcy can cause heavy losses to stakeholders and affect corporate sustainability. In the era of artificial intelligence (AI), deep learning algorithms are widely used by practitioners, and academic research is also gradually embarking on projects in various domains. However, the use of deep learning algorithms in the prediction of going concern remains limited. In contrast to those in the literature, this study uses long short-term memory (LSTM) and gated recurrent unit (GRU) for learning and training, in order to construct effective and highly accurate going-concern prediction models. The sample pool consists of the Taiwan Stock Exchange Corporation (TWSE) and the Taipei Exchange (TPEx) listed companies in 2004–2019, including 86 companies with going concern doubt and 172 companies without going concern doubt. In other words, 258 companies in total are sampled. There are 20 research variables, comprising 16 financial variables and 4 non-financial variables. The results are based on performance indicators such as accuracy, precision, recall/sensitivity, specificity, F1-scores, and Type I and Type II error rates, and both the LSTM and GRU models perform well. As far as accuracy is concerned, the LSTM model reports 96.15% accuracy while GRU shows 94.23% accuracy.

FQTSFM: A fuzzy-quantum time series forecasting model

The study shows that there are two main problems that affect the performance of fuzzy time series (FTS) models, namely the selection of the universe of discourse and the determination of the fuzzy degree of the memberships. However, the selection of the appropriate universe of discourse along with the degree of memberships is simultaneously associated with the multiobjective optimization problem (MOOP). Therefore, in this study, an improved version of the quantum optimization algorithm (QOA) has been proposed to find their optimal solutions. This improved QOA is called fast forward quantum optimization algorithm (FFQOA). Finally, by integrating FFQOA with the FTS modelling approach, a hybrid model called fuzzy-quantum time series forecasting model (FQTSFM) is designed. The main objective of FFQOA in FQTSFM is to select the Pareto-optimal front (POF) from all optimal non-dominated Pareto solutions by using the archive and grid concept during simulation. The main advantages of the proposed FQTSFM are that it converges very fast compared to the existing hybridized based FTS models and is able to evolve one-step ahead forecasted results. The FQTSFM is tested with three different datasets, namely daily average temperatures of Taipei, Taiwan Futures Exchange (TAIFEX) index and Taiwan Stock Exchange Corporation (TSEC) weighted index. The performance of the FQTSFM is compared with various established well-known FTS and non-FTS models in terms of different statistical metrics. In the case of daily average temperatures, TAIFEX index and TSEC weighted index datasets, the FQTSFM achieves correlation coefficients of 0.9988, 0.9978 and 0.9768, respectively. For daily average temperatures, TAIFEX index and TSEC weighted index datasets, the Theil’s U statistic values of the FQTSFM are 0.0022, 0.0022 and 0.0054, respectively. The FQTSFM has mean squared errors of 0.3959, 22.3965 and 83.6793 for the daily average temperatures, the TAIFEX index and the TSEC-weighted index datasets, respectively. In the case of daily average temperatures, TAIFEX index and TSEC weighted index datasets, the FQTSFM shows cross entropy values of 3.3498, 189.5018 and 708.0296, respectively. These metrics, including other empirical analyses, confirm that the forecasted accuracy of the proposed FQTSFM exceeds that of well-known time series models.

Dividend Analysis of Dow Jones Asia Select Dividend 30 Index

This article examines the relationship of the Dow Jones Asia Select Dividend 30 Index (DJASD) with six Asian market indices, namely the Bombay Stock Exchange (BSE), the Jakarta Stock Exchange (JKSE) Index, the Korea Composite Stock Price Index (KOSPI), the Nikkei (Japan), the Shanghai Stock Exchange (SSE), and the Taiwan Stock Exchange Corporation (TSEC). It involves the calculation of coefficient of correlation and coefficient of determination to explain the pattern of variance of the DJASD Index with the aforesaid six independent indices. As a first step, data reduction is performed on these indices to extract a principal component that best describes the variance in the dataset. This is followed by performing an analysis for robustness using two methods. Both the methods give approximately the same results, authenticating the reliability of the analysis. The results show that the DJASD Index has a low positively correlation with the principal component (that represents BSE, JKSE, KOSPI, Nikkei, SSE, and TSEC). TOPICS:Wealth management, exchanges/markets/clearinghouses, information providers/credit ratings Key Findings • The performance of the DJASD has weak correlation with the performance of the financial markets. • The dividend payout is largely related to the performance of the companies included in the index. • The investors cannot expect higher dividends when financial markets are performing well.

A new hybrid time series forecasting model based on the neutrosophic set and quantum optimization algorithm

This article acquaints a new method to forecast the time series dataset based on neutrosophic-quantum optimization approach. This study uses neutrosophic set (NS) theory to represent the inherited uncertainty of time series dataset with three different memberships as truth, indeterminacy and false. We refer such representations of time series dataset as neutrosophic time series (NTS). This NTS is further utilized for modeling and forecasting time series dataset. Study showed that the performance of NTS modeling approach is highly dependent on the optimal selection of the universe of discourse and its corresponding intervals. To resolve this issue, this study selects quantum optimization algorithm (QOA) and ensembles with the NTS modeling approach. QOA improves the performance of the NTS modeling approach by selecting the globally optimal universe of discourse and its corresponding intervals from the list of local optimal solutions. The proposed hybrid model (i.e., NTS-QOA model) is verified and validated with datasets of university enrollment of Alabama (USA), Taiwan futures exchange (TAIFEX) index and Taiwan Stock Exchange Corporation (TSEC) weighted index. Various experimental results signify the efficiency of the proposed NTS-QOA model over existing benchmark models in terms of average forecasting error rates (AFERs) of 0.44%, 0.066% and 1.27% for the university enrollment, TAIFEX index and TSEC weighted index, respectively.

A High-Order Neutrosophic-Neuro-Gradient Descent Algorithm-Based Expert System for Time Series Forecasting

This article proposed a novel hybrid time series forecasting model using neutrosophic set (NS) theory, artificial neural network (ANN) and gradient descent algorithm. This study deals with three main problems of time series dataset, viz., representation of time series dataset using NS, three degrees of memberships of NS together, and generation of the forecasting results. To resolve these three domain specific problems, this study advocated the application of neutrosophic-neuro-gradient approach. NS theory was utilized to represent the uncertainty associated with time series dataset and was referred to as neutrosophic time series (NTS). In NTS, various decision rules were created in the form of IF-THEN rules, which were termed as neutrosophic entropy decision rules (NEDRs). An ANN-based architecture took NEDRs as input to evolve the forecasting results. To improve the performance of ANN and to obtain optimal forecasting results, this study additionally utilized the gradient descent algorithm to minimize the differences between the calculated and target output values during the simulation. The proposed model was verified and validated with three different datasets, including TAIFEX index, university enrollment dataset of Alabama and Taiwan Stock Exchange Corporation (TSEC) weighted index. Experimental results showed that the proposed model outperformed existing benchmark models with average forecasting error rates of 1.02%, 0.74% and 1.27% for the TAIFEX, university enrollment and TSEC, respectively.

SUATU PERBANDINGAN PENGATURAN CORPORATE SOCIAL RESPONSIBILITY (CSR) DI TAIWAN DAN DI INDONESIA

Regulation comparison between Corporate Social and Environmental Responsibility (CSER) in Indonesia and Corporate Social Responsibility in Taiwan, aiming to compare the regulation of CSR in Taiwan and CSER in Indonesia. This Comparison carried out using the normative research method, which analyzed descriptively illustrating that regulation CSER at Art 74 Indonesian Law No. 40 of 2007 on Limited Company in conjuction with Government regulation Number 47 of 2012 on Social and Environmental Responsibility of limited company compared with Corporate Social Responsibility Regulation in Taiwan as required by the Taiwan Stock Exchange Corporation (TWSE) and GreTai Securities Market (GTSM)

Independent directors and earnings management: The moderating effects of controlling shareholders and the divergence of cash-flow and control rights

The research results on the suppressing effect of independent directors on earnings management are not consistent in existing literature. In addition, it has been argued that using financial statements provided by top management to investigate top management's earnings management is not appropriate. Therefore, the purpose of this study is twofold. First, we used external auditors (including the auditors of Taiwan Stock Exchange Corporation and the auditors of two Big Four accounting firms – Deloitte & Touche and KPMG in Taiwan) as respondents in order to obtain less biased data. Second, we investigate the moderating effects of controlling shareholders and the divergence of cash-flow and control rights on the relationship between independent directors and earnings management. The results show that both the existence of controlling shareholders and the divergence of cash-flow and control rights have significant suppressing effects on the relationship between independent directors and earnings management. Theoretical and practical implications are also discussed.

Information disclosure and price manipulation during the pre-closing session: evidence from an order-driven market

On 20 February 2012, the Taiwan Stock Exchange Corporation launched an order-matching simulation mechanism for five minutes during the start of the pre-closing session, in order to increase information disclosure during this period (13:25–13:30). Pre-closing information disclosure significantly reduces both trading costs and closing-price volatility, as well as price manipulation. The decrease in price manipulation found in this work is due to pre-closing information disclosure, not the behaviour of investors shifting to an earlier time. Further, if a stock price rises or falls by more than 3.5% in the simulation in the last minute during the closing session, trading of the stock will be suspended for two minutes from 13:31 to reduce volatility. However, this trading mechanism (suspended-closing) does not seem to have achieved the intended goals of the authorities, as it has not been able to significantly reduce closing-price volatility and price manipulation.

The Effects of the Information Disclosure and Transparency Rankings System on Earnings Management

In an attempt to achieve greater transparency and disclosure, the Taiwan Stock Exchange Corporation (TSEC) and the GreTai Securities Market (GTSM) implemented an information disclosure and transparency rankings system (IDTRS) for all listed companies in Taiwan. This study examines whether the implementation of IDTRS reduces earnings manipulation by firms and whether the manipulation practice changes after the ranking scheme was revised in 2005. Furthermore, we analyze the impact of information disclosure rankings on earnings manipulation in different ranking schemes and investigate the relationship between changes in disclosure rankings and changes in earnings management. The empirical results indicate that accruals-based earnings management and real activities manipulation can be effectively reduced by the ranking system and that the level of information disclosure in different ranking schemes is negatively related to earnings management. Comparing firms with a transparency ranking downgrade, we show that firms with a ranking upgrade tend to exhibit a significant decrease in their earnings management activities.

A study on Co-integration of Pakistani Stock Market with selected Asian Stock Markets

Pakistani Equity Market has seen many ups and downs since the last two decades. The local investors are feeling themselves much insecure in indigenous investment. The reasons behind are political instability, severe power crises and terrorism which compelled the local investors to go across boarder and need to explore the multiple option of investment in international securities to minimize the investment risk. The main purpose and scope of this study is to explore causal and dynamic linkages of Karachi Stock Exchange, KSE-100 index (Pakistan) with emerging stock markets of Nikkei-225 (Japan), Shanghai Stock Exchange, SSE (China), Kuala Lumpur Stock Exchange, KLSE (Malaysia) and Taiwan Stock Exchange Corporation, TSEC Taiwan. The most recent data is taken from January, 2001 to December, 2013. Monthly stock index observations are taken. Descriptive statistics, Correlation Analysis, Unit Root Test, VAR, Co-integration Test, VECM Test are used to identify the presence of short-term as well as long-term associations. Empirical results indicate that KSE-100 is a volatile market and have suitable level of returns. Moreover, KSE-100 index has not long-term relationship with Japan, Malaysia, Taiwan and China but Taiwan, China and Japan has short run relationships to KSE. The findings conclude that there is a further need of future study to explore the factors of economic integration amongst these equity markets. The study overall exhibits awareness not only for economic and financial decision makers but also for international as well as regional investors about the benefit opportunities of portfolio diversification in these equity markets, funds management and trends of the stock market.

The effects of information transparency on analysts& forecasts: evidence from the information disclosure and transparency ratings system in Taiwan

In an attempt to reduce the information asymmetry between corporate insiders and outsiders, the Taiwan Stock Exchange Corporation (TSEC) and the Gre Tai Securities Market (GTSM) requested the Securities and Futures Institute to implement an information disclosure and transparency ratings system (IDTRS) for all publicly traded companies listed on the TSEC and the GTSM. This study investigates the effects of the IDTRS on the properties of analysts’ forecasts. The results indicate that analysts’ earnings forecasts are more accurate and the earnings forecast dispersion among analysts decreases after the implementation of the IDTRS. Additionally, the results estimated from the post IDTRS period show that the analysts’ earnings forecast errors and dispersion of the “more transparent” companies are smaller than those of the “less transparent” companies.

The Correlation between R&D Activities and Income Rate in Addition to Gross Profit Rate of the Enterprise Operation

As the trading volume by institutional investors in Taiwans stock market increasing in recent years according to information of Taiwan Stock Exchange Corporation, the influence on financial performance by the institutional investors is getting more and more important although institutional investors play a monitoring role to the company. Thus the impact of R & D activities on the financial performance of enterprises is studied frequently. This study focuses on the impact of R & D activities on income rate in addition to gross profit rate of the Enterprise Operation. The implication in practice is that business must attract research and development intensity (RDI) relative to research and development density (RDD) to improve future business value.

Capability contingent: the impact of organisational learning styles on innovation performance

In today's highly competitive and rapidly changing business environment, organisational learning can ensure an enterprise's survival and develop new opportunities. However, considering the different resources and capabilities available to an organisation, what kind of organisational learning style should an enterprise adopt to achieve its goal of organisational learning and innovation? This study aimed to determine the best learning styles and innovation performance under different contingencies. This study thus developed eight relevant hypotheses and then conducted an empirical study involving the top managers of 650 manufacturing companies listed on the Taiwan Stock Exchange Corporation and the Over The Counter Market in Taiwan. The result of the path analysis of 154 valid samples indicated that the learning styles required for both incremental and radical innovation are substantially different between the four distinct contingencies when combining internal capacity and external relationships.

Using a hybrid evolution approach to forecast financial failures for Taiwan-listed companies

Bankruptcy has been an important topic in finance and accounting research for a long time. Recent major bankruptcies have included seemingly robust companies such as Enron, Kmart, Global Crossing, WorldCom, and Lehman Brothers. These cases have become of serious public concern due to the huge influence these companies have on the real economy. This research proposes a hybrid evolution approach to integrate particle swarm optimization (PSO) with the support vector machine (SVM) technique for the purpose of predicting financial failures. The preparation phase collected an initial sample of 68 companies listed by the Taiwan Stock Exchange Corporation (TSEC). The financial datasets were constructed based on 33 financial ratios, four non-financial ratios and one combined macroeconomic index. To select suitable indicators for the input vector, the principle component analysis (PCA) technique was applied to reduce the data and determine how groupings of indicators measure the same concept. In the swarming phase, PSO was applied to obtain suitable parameters for SVM modeling without reducing the classification accuracy rate. In the modeling phase, the SVM model was used to build a training set that was used to calculate the model's accuracy and fitness value. Finally, these optimized parameters were used in the hybrid PSO–SVM model to evaluate the model's predictive accuracy. This paper provides four critical contributions. (1) Using the PCA technique, the statistical results indicate that the financial prediction performance is mainly affected by financial ratios rather than non-financial and macroeconomic ratios. (2) Even with the input of nearly 70% fewer indicators, our approach is still able to provide highly accurate forecasts of financial bankruptcy. (3) The empirical results show that the PSO–SVM model provides better classification accuracy (i.e. normal vs. bankrupt) than the grid search (Grid–SVM) approach. (4) For six well-known UCI datasets, the PSO–SVM model also provides better prediction accuracy than the Grid–SVM, GA–SVM, SVM, SOM, and SVR–SOM approaches. Therefore, this paper proposes that the PSO–SVM approach is better suited for predicting potential financial distress.

Bankruptcy prediction in firms with statistical and intelligent techniques and a comparison of evolutionary computation approaches

In this paper, we compare some traditional statistical methods for predicting financial distress to some more “unconventional” methods, such as decision tree classification, neural networks, and evolutionary computation techniques, using data collected from 200 Taiwan Stock Exchange Corporation (TSEC) listed companies. Empirical experiments were conducted using a total of 42 ratios including 33 financial, 8 non-financial and 1 combined macroeconomic index, using principle component analysis (PCA) to extract suitable variables. This paper makes four critical contributions: (1) with nearly 80% fewer financial ratios by the PCA method, the prediction performance is still able to provide highly-accurate forecasts of financial bankruptcy; (2) we show that traditional statistical methods are better able to handle large datasets without sacrificing prediction performance, while intelligent techniques achieve better performance with smaller datasets and would be adversely affected by huge datasets; (3) empirical results show that C5.0 and CART provide the best prediction performance for imminent bankruptcies; and (4) Support Vector Machines (SVMs) with evolutionary computation provide a good balance of high-accuracy short- and long-term performance predictions for healthy and distressed firms. Therefore, the experimental results show that the Particle Swarm Optimization (PSO) integrated with SVM (PSO–SVM) approach could be considered for predicting potential financial distress.

A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering

In recent years, newly-developed data mining and machine learning techniques have been applied to various fields to build intelligent information systems. However, few of these approaches offer online support or are able to flexibly adapt to large and complex financial datasets. Therefore, the present research adopts particle swarm optimization (PSO) techniques to obtain appropriate parameter settings for subtractive clustering (SC) and integrates the adaptive-network-based fuzzy inference system (ANFIS) model to construct a model for predicting business failures. Experiments were conducted based on an initial sample of 160 electronics companies listed on the Taiwan Stock Exchange Corporation (TSEC). Experimental results show that the proposed model is superior to other models, providing a lower mean absolute percentage error (MAPE) and root mean squared error (RMSE). The proposed one-order momentum method is able to learn quickly through one-pass training and provides high-accuracy short-term predictions, while the proposed two-order momentum provides high-accuracy long-term predictions from large financial datasets. Therefore, the proposed approach fulfills some important characteristics of the proposed model: the one-order momentum method is suitable for online learning and the two-order momentum method is suitable for incremental learning. Thus, the PS-ANFIS approach could provide better results in predicting potential financial distress.

Options analysis and knowledge management: Implications for theory and practice

Real options can be a powerful tool for quantifying the value of strategic and operational flexibility associated with uncertain IT investments. They also constitute a new way of thinking as to how knowledge management (KM) can be implemented and managed to maximize the upside potential while minimizing any downside risk. This paper explored how practitioners can incorporate options analysis into contemporary knowledge management. Options analysis is recognizing real options and then determines how they add value. The main issue here is to manage knowledge so that the theoretical option value is realized in practice. This paper developed a new metric, knowledge management performance index (KMPI), for evaluating the performance of a firm in its KM at any given point in time as follows: knowledge creation, knowledge conversion, knowledge circulation and knowledge completion. The higher the efficiency of the KM process, the higher the KMPI, thereby is enabling firms to become increasingly knowledgeable. To prove the contribution of the KMPI, a questionnaire survey was conducted among 121 firms listed on the Taiwan Stock Exchange Corporation (TSEC). This paper makes five important contributions: (1) it shows that if the KM process improves, the KMPI is enhanced. This finding is based on a survey of the questionnaires. As a result, the KMPI can effectively and efficiently represent the KM process; (2) it shows that when the KMPI increases, a company’s performance will be enhanced in five different areas This indicates a significant relationship between the KMPI and the company’s performance; (3) it also shows that the higher the KMPI, the higher the performance of the organization. This is especially valuable to any organization where knowledge is not being used optimally; (4) it provides an option analysis that may be helpful to managers for making the right decision in an uncertain environment; and (5) it presents the first application of the Black–Scholes model to use an actual business situation involving KM as its test bed. The results proved that the option pricing model can act as a measurement guideline for the entire knowledge of the whole company.