The purpose of this study is to construct a valid and rigorous fraudulent financial statement detection model. The research objects are companies which experienced both fraudulent and non-fraudulent financial statements between the years 2002 and 2013. In the first stage, two decision tree algorithms, including the classification and regression trees (CART) and the Chi squared automatic interaction detector (CHAID) are applied in the selection of major variables. The second stage combines CART, CHAID, Bayesian belief network, support vector machine and artificial neural network in order to construct fraudulent financial statement detection models. According to the results, the detection performance of the CHAID–CART model is the most effective, with an overall accuracy of 87.97 % (the FFS detection accuracy is 92.69 %).

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.

The purpose of this study is to establish an effective financial distress prediction model by applying hybrid machine learning techniques. The sample set is 262 financially distressed companies and 786 non-financially distressed companies, listed on the Taiwan Stock Exchange between 2012 and 2018. This study deploys multiple machine learning techniques. The first step is to screen out important variables with stepwise regression (SR) and the least absolute shrinkage and selection operator (LASSO), followed by the construction of prediction models, as based on classification and regression trees (CART) and random forests (RF). Both financial variables and non-financial variables are incorporated. This study finds that the financial distress prediction model built with CART and variables screened by LASSO has the highest accuracy of 89.74%.

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Supplier evaluation is very important for supply chain management, procurement management, cost accounting, and management accounting. The most critical step involved in constructing a supplier evaluation model is selecting supplier evaluation indicators and analyzing the corresponding weights. However, an effect will occur where supplier ratings moderate the correlation between supplier evaluation indicators and supplier performance. This study surveys 104 supplier evaluation specialists and collects evaluation data from 1028 suppliers. Statistical data analysis shows that, in the absence of supplier ratings, the supplier evaluation indicator can be considered as a reflective scale that represents supplier performance. If a supplier rating is in place, then the correlations between supplier evaluation indicators are significantly weakened, and supplier evaluation indicators are recognized as a formative scale. The study also finds, through an examination of six different weight models, that the importance of supplier evaluation indicators is ordered as: quality, price, delivery performance, customer service, and flexibility. The aforementioned research findings make a meaningful contribution to the literature on supply chain management and also serve as insightful references for business practices in supplier evaluation.

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.

This study focuses on accrual-based earnings management. The purpose of this study is to establish an innovative and high-accuracy model for detecting earnings management using hybrid machine learning methods integrating stepwise regression, elastic net, logistic regression (Logit regression), and decision tree C5.0. Samples of this study are the electronic companies listed on the Taiwan Stock Exchange, and data are derived from the Taiwan Economic Journal (TEJ) for a period of ten years from 2008 to 2017. Results show that the earnings management detection model, as established by elastic net and C5.0, provides the best classification performance, and its average accuracy reaches 97.32%.

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