This page has only limited features, please log in for full access.

Unclaimed
Sin-Jin Lin
Department of Accounting, Chinese Culture University, Taipei 11114, Taiwan

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 04 August 2021 in Axioms
Reads 0
Downloads 0

Compared to widely examined topics in the related literature, such as financial crises/difficulties in accurate prediction, studies on corporate performance forecasting are quite scarce. To fill the research gap, this study introduces an advanced decision making framework that incorporates context-dependent data envelopment analysis (CD-DEA), fuzzy robust principal component analysis (FRPCA), latent Dirichlet allocation (LDA), and stochastic gradient twin support vector machine (SGTSVM) for corporate performance forecasting. Ratio analysis with the merits of easy-to-use and intuitiveness plays an essential role in performance analysis, but it typically has one input variable and one output variable, which is unable to appropriately depict the inherent status of a corporate’s operations. To combat this, we consider CD-DEA as it can handle multiple input and multiple output variables simultaneously and yields an attainable target to analyze decision making units (DMUs) when the data present great variations. To strengthen the discriminant ability of CD-DEA, we also conduct FRPCA, and because numerical messages based on historical principles normally cannot transmit future corporate messages, we execute LDA to decompose the accounting narratives into many topics and preserve those topics that are relevant to corporate operations. Sequentially, the process matches the preserved topics with a sentimental dictionary to exploit the hidden sentiments in each topic. The analyzed data are then fed into SGTSVM to construct the forecasting model. The result herein reveals that the introduced decision making framework is a promising alternative for performance forecasting.

ACS Style

Hsueh-Li Huang; Sin-Jin Lin; Ming-Fu Hsu. An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages. Axioms 2021, 10, 179 .

AMA Style

Hsueh-Li Huang, Sin-Jin Lin, Ming-Fu Hsu. An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages. Axioms. 2021; 10 (3):179.

Chicago/Turabian Style

Hsueh-Li Huang; Sin-Jin Lin; Ming-Fu Hsu. 2021. "An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages." Axioms 10, no. 3: 179.

Original article
Published: 12 May 2021 in International Journal of Machine Learning and Cybernetics
Reads 0
Downloads 0

This research introduces a fusion architecture that integrates balanced scorecards (BSCs) and network data envelopment analysis (NDEA) to conduct a performance evaluation task from multiple perspectives. The architecture is able to capture the dynamics of production processes and sub-processes, uncover some of the components behind successful business practices, and shed light on needed actions for decision makers. Furthermore, the architecture not only can support decision makers to plan for improvement, but also equip them with forecasting ability. To enhance its forecasting quality, this study goes beyond quantitative ratios and extends them to qualitative ratios (i.e., readability: the complexities of disclosure) borrowed from computational linguistics. The results indicate that a poor readability score is highly associated with bad operations. Finally, to enlarge the mechanism’s applicable fields, the study executes the genetic algorithm (GA) to extract the inherent decision logics and represents them in a human-readable manner. The mechanism, examined by real cases, is a promising alternative for performance evaluation and forecasting.

ACS Style

Ming-Fu Hsu; Sin-Jin Lin. A BSC-based network DEA model equipped with computational linguistics for performance assessment and improvement. International Journal of Machine Learning and Cybernetics 2021, 12, 2479 -2497.

AMA Style

Ming-Fu Hsu, Sin-Jin Lin. A BSC-based network DEA model equipped with computational linguistics for performance assessment and improvement. International Journal of Machine Learning and Cybernetics. 2021; 12 (9):2479-2497.

Chicago/Turabian Style

Ming-Fu Hsu; Sin-Jin Lin. 2021. "A BSC-based network DEA model equipped with computational linguistics for performance assessment and improvement." International Journal of Machine Learning and Cybernetics 12, no. 9: 2479-2497.

Research article
Published: 17 February 2021 in Cybernetics and Systems
Reads 0
Downloads 0

The dramatic deterioration in a corporate’s profitability not only threatens its own interests, employees, and investors, but can also impact external entities and people through financial losses and high risk exposure. Thus, in today’s turbulent market environments an essential issue arises as to how to set up an effective pre-warning model that provides managers with specific avenues to avoid financial troubles from getting worse and offers investors useful directions to adjust their investment portfolios. However, extant forecasting models are not yet capable of fully explaining the relationships between past and future performances, which may be due to the omission of some critical information. To capture the multidimensional nature of performance assessment, this study extends a singular data envelopment analysis (DEA) specification to multiple DEA specifications and further incorporates them with a risk-adjusted metric so as to present an overarching reflection of corporates’ operations. To make the outcome much more accessible to non-specialists, we utilize a visualization technique to represent the data’s main structure and then feed the analyzed data into a twin parametric-margin support vector machine (TPSVM) to construct the forecasting model. Due to the obscure nature of the SVM-based model, this study executes the multiple instances learning (MIL) algorithm to extract the inherent decision logics and to represent them in human readable way. After examining it with real cases, the proposed model is a promising alternative for performance assessment and forecasting.

ACS Style

Sin-Jin Lin. Integrated Artificial Intelligence and Visualization Technique for Enhanced Management Decision in Today’s Turbulent Business Environments. Cybernetics and Systems 2021, 52, 274 -292.

AMA Style

Sin-Jin Lin. Integrated Artificial Intelligence and Visualization Technique for Enhanced Management Decision in Today’s Turbulent Business Environments. Cybernetics and Systems. 2021; 52 (4):274-292.

Chicago/Turabian Style

Sin-Jin Lin. 2021. "Integrated Artificial Intelligence and Visualization Technique for Enhanced Management Decision in Today’s Turbulent Business Environments." Cybernetics and Systems 52, no. 4: 274-292.

Journal article
Published: 25 October 2018 in Sustainability
Reads 0
Downloads 0

Corporate social responsibility (CSR) implementation has been widely acknowledged as playing a key part in enhancing firm value as well as achieving sustainable development. However, up to now the extant works in the literature have yielded non-conclusive results regarding the relationships between CSR and firm value. One of the possible reasons is that the studies ignore the multi-dimensional characteristics of CSR—that is, they merely utilize a singular synthesized indicator as a proxy to represent the corporate’s CSR performance as being unreliable and problematic. Thus, this study breaks down CSR into numerous dimensions and further examines each dimension’s impact on firm value. By doing so, managers can allocate their firm’s valuable resources to suitable areas so as to increase its reputation and value. In addition, this research sets up an artificial intelligence (AI)-based fusion model, grounded by fusion learning theory that aims at complementing the error made by a singular model, to examine the relationship between CSR’s multidimensional characteristics and firm value. Through different combinations of adopted strategies, users can realize the most representative features from an over-abundant database.

ACS Style

Kuang-Hua Hu; Sin-Jin Lin; Jau-Yang Liu; Fu-Hsiang Chen; Shih-Han Chen. The Influences of CSR’s Multi-Dimensional Characteristics on Firm Value Determination by a Fusion Approach. Sustainability 2018, 10, 3872 .

AMA Style

Kuang-Hua Hu, Sin-Jin Lin, Jau-Yang Liu, Fu-Hsiang Chen, Shih-Han Chen. The Influences of CSR’s Multi-Dimensional Characteristics on Firm Value Determination by a Fusion Approach. Sustainability. 2018; 10 (11):3872.

Chicago/Turabian Style

Kuang-Hua Hu; Sin-Jin Lin; Jau-Yang Liu; Fu-Hsiang Chen; Shih-Han Chen. 2018. "The Influences of CSR’s Multi-Dimensional Characteristics on Firm Value Determination by a Fusion Approach." Sustainability 10, no. 11: 3872.

Journal article
Published: 15 May 2018 in Sustainability
Reads 0
Downloads 0

It is widely recognized that a firm’s well-established corporate governance (CG) has a considerable impact on its corporate social responsibility (CSR) performance. How to determine the main trigger among CG’s indicators for strengthening CSR performance is thus an urgent and complicated task due to its (i.e., CSR) multi-dimensional and numerous perspectives. In order to solve this critical problem, the study breaks down CSR into four dimensions and further examines the impact of CG’s indicators on each CSR dimension by joint utilization of rough set theory (RST) and decision tree (DT). By doing so, users can realize which one CG indicator is the most essential to CSR performance. Managers can take the results as a reference to allocate valuable and scarce resources to the right place so as to enhance CSR performance in the future. To solidify our research finding, we transform the CSR forecasting model selection into a multiple criteria decision making (MCDM) task and execute a MCDM algorithm. By implementing the MCDM algorithm, users can achieve a much more reliable and consensus decision in today’s highly turbulent economic environment. The proposed mechanism, examined by real cases, is a promising alternative for CSR performance forecasting.

ACS Style

Kuang-Hua Hu; Sin-Jin Lin; Ming-Fu Hsu. A Fusion Approach for Exploring the Key Factors of Corporate Governance on Corporate Social Responsibility Performance. Sustainability 2018, 10, 1582 .

AMA Style

Kuang-Hua Hu, Sin-Jin Lin, Ming-Fu Hsu. A Fusion Approach for Exploring the Key Factors of Corporate Governance on Corporate Social Responsibility Performance. Sustainability. 2018; 10 (5):1582.

Chicago/Turabian Style

Kuang-Hua Hu; Sin-Jin Lin; Ming-Fu Hsu. 2018. "A Fusion Approach for Exploring the Key Factors of Corporate Governance on Corporate Social Responsibility Performance." Sustainability 10, no. 5: 1582.

Journal article
Published: 03 September 2014 in International Journal of Machine Learning and Cybernetics
Reads 0
Downloads 0

Corporate governance mechanisms ensure that investors get a fair return on their investment. A well-established governance mechanism reduces the information asymmetry and agency cost between a firm’s management and stakeholders, but decision makers find it difficult to assess the corporate governance status of publicly-listed firms before the annual official announcement the following year. This study proposes a hybrid ensemble learning forecasting mechanism (HELM), whose single-component candidates from the extreme learning machine (ELM) algorithm with dissimilar ensemble strategies (that is, data diversity, parameter diversity, kernel diversity, and pre-processing diversity) form one initial dataset. We implement locally linear embedding into the proposed mechanism to handle the dimensionality task and then utilize the weighted voting taken from the base components’ cross-validation performance on a training dataset as the integration mechanism. Experimental results show that the proposed HELM significantly outperforms the other classifiers, but its superior performance under many real-life application domains comes with a critical drawback: it is incapable of providing an explanation for the underlying reasoning mechanisms. Thus, this study advances the utilized rough set theory with its explanation capability to extract the inherent knowledge from the ensemble mechanism (HELM). The informative rules can be used as a guideline for decision makers to make a reliable judgment under turbulent financial markets.

ACS Style

Yu-Shan Hsu; Sin-Jin Lin. An emerging hybrid mechanism for information disclosure forecasting. International Journal of Machine Learning and Cybernetics 2014, 7, 943 -952.

AMA Style

Yu-Shan Hsu, Sin-Jin Lin. An emerging hybrid mechanism for information disclosure forecasting. International Journal of Machine Learning and Cybernetics. 2014; 7 (6):943-952.

Chicago/Turabian Style

Yu-Shan Hsu; Sin-Jin Lin. 2014. "An emerging hybrid mechanism for information disclosure forecasting." International Journal of Machine Learning and Cybernetics 7, no. 6: 943-952.