Kao-Yi Shen

A decision rule-based soft computing model for supporting financial performance improvement of the banking industry

This study attempts to diagnose the financial performance improvement of commercial banks by integrating suitable soft computing methods. The diagnosis of financial performance improvement comprises of three parts: prediction, selection and improvement. The performance prediction problem involves many criteria, and the complexity among the interrelated variables impedes researchers to discover patterns by conventional statistical methods. Therefore, this study adopts a dominance-based rough set approach to solve the prediction problem, and the core attributes in the obtained decision rules are further processed by an integrated multiple criteria decision-making method to make selection and to devise improvement plans. By using VIKOR method and the influential weights of DANP, decision maker may plan to reduce gap of each criterion for achieving aspired level. The retrieved attributes (i.e., criteria) are used to collect the knowledge of domain experts for selection and improvement. This study uses the data (from 2008 to 2011) from the central bank of Taiwan for obtaining decision rules and forming an evaluation model; furthermore, the data of five commercial banks in 2011 and 2012 are chosen to evaluate and improve the real cases. In the result, we found the top-ranking bank outperformed the other four banks, and its performance gaps for improvements were also identified, which indicates the effectiveness of the proposed model.

Financial modeling and improvement planning for the life insurance industry by using a rough knowledge based hybrid MCDM model

Financial modeling for the life insurance industry involves two main difficulties: (1) Selecting the minimal and critical variables for modeling while considering the impreciseness and interrelationships among the numerous attributes and (2) measuring plausible synergy effects among variables and dimensions that might cause undesirable biases for an evaluation model. To overcome these difficulties, this paper proposes a two-stage hybrid approach: Rough financial knowledge is retrieved first, and then the obtained core attributes are measured and synthesized using fuzzy-integral-based decision methods. The main innovation of this study is the use of rough knowledge retrieval procedures and fuzzy measures for exploring the synergy effects on financial performance. This approach is expected to support insurers to systematically improve their financial performance. A group of life insurance companies in Taiwan was analyzed, and the findings support the existence of interrelated synergy effects among the core criteria. In addition, five companies were examined to illustrate financial performance improvement planning with this approach. This study bridges the gap between advanced soft computing techniques and pragmatic financial modeling in a dynamic business environment.

Combining DRSA decision-rules with FCA-based DANP evaluation for financial performance improvements

AbstractThis study proposes a combined method to integrate soft computing techniques and multiple criteria decision making (MCDM) methods to guide semiconductor companies to improve financial performance (FP) – based on logical reasoning. The complex and imprecise patterns of FP changes are explored by dominance-based rough set approach (DRSA) to find decision rules associated with FP changes. Companies may identify its underperformed criterion (gap) to conduct formal concept analysis (FCA) – by implication rules – to explore the source criteria regarding the underperformed gap. The source criteria are analysed by decision making trial and evaluation laboratory (DEMATEL) technique to explore the cause-effect relationship among the source criteria for guiding improvements; in the next, DEMATEL-based analytical network process (DANP) can provide the influential weights to form an evaluation model, to select or rank improvement plans. To illustrate the proposed method, the financial data of a real semiconduc...

A new approach and insightful financial diagnoses for the IT industry based on a hybrid MADM model

Financial performance is vital for information technology (IT) companies to survive intense global competition. Because of the complexity in the business environment and the rapidly advancing technologies, companies lack specific guidance to understand the implicit relationship among crucial financial indicators for improving prospects in a contextual approach. To resolve the aforementioned concern, this study proposed a new approach by combining the variable consistency dominance-based rough set approach (VC-DRSA) with the decision-making trial and evaluation laboratory (DEMATEL) technique to explore the complex relationship among financial variables and improve future performances. In addition, a fuzzy inference system was devised on the basis of the findings of the VC-DRSA and DEMATEL technique to examine granulized knowledge and implications. A group of real IT companies listed on the Taiwan stock market were used as an empirical case to present the benefits of the new approach. The results generated a set of decision rules that can be used for forecasting future performance prospects and diagnosing the directional influences of crucial variables to gain insights; certain strong decision rules were further examined using fuzzy inference to verify the obtained implications. The findings contribute to the financial applications of decision-making science and computational intelligence in practice.

Contextual Improvement Planning by Fuzzy-Rough Machine Learning: A Novel Bipolar Approach for Business Analytics

Nearly all companies need to retrieve valuable information from business data to increase its efficiency or value, and the rising interests of research in this domain could be named as business analytics. Because most of the problems (obstacles) faced by business have to consider a group of complex and interrelated factors, conventional statistics models (e.g., regression) have constraints in resolving these interrelated and complex problems. Therefore, this study proposes a novel multiple attribute decision-making model to resolve—from ranking/selection to improvement planning—the problems of business analytics in finance, based on the similarity with positive contexts (rules) and the dissimilarity with negative ones. The proposed model not only enhances the previous method (i.e., dominance-based rough set approach, DRSA) on ranking within the same decision class, but also provides a contextual approach to guide businesses for systematic improvements. Infused with the modified VIKOR method, the proposed model could support a company to transform analytics into priority contexts, which may guide improvement planning. To show the proposed model, a group of semiconductor companies in Taiwan is analyzed as an empirical case, and three companies are taken as examples to illustrate the ranking and improvement planning processes. The obtained findings thus contribute to bridge the applications of data-driven business analytics to the field of decision science in practice.

A Novel Bipolar MCDM Model Using Rough Sets and Three-Way Decisions for Decision Aids

This study proposes a bipolar model for resolving multiple criteria decision-making (MCDM) problems, based on the integration of rough set theory and three-way decisions, for forming a hybrid bipolar model. It begins by dividing a decision space into three disjoint regions: negative, neutral, and positive states, in the next, a rough set theory based rule induction mechanism generates two sets of rules associated with the positive and the negative states respectively, termed as the positive and the negative rules. The two groups of rules are regarded the bipolar rough experience (knowledge) in the form of rules and granulized knowledge, and decision makers can then define a threshold to select the covered number of rules in the bipolar decision model. This novel approach not only supports decision makers to select or rank alternatives, but also identify rough knowledge for the addressed problem. As a result, this novel bipolar model transforms soft computing analytics into a comprehensible bipolar model for decision aids.

Knowledge Supported Refinements for Rough Granular Computing: A Case of Life Insurance Industry

Dominance-based rough set approach (DRSA) has been adopted in solving various multiple criteria classification problems with positive outcomes; its advantage in exploring imprecise and vague patterns is especially useful concerning the complexity of certain financial problems in business environment. Although DRSA may directly process the raw figures of data for classifications, the obtained decision rules (i.e., knowledge) would not be close to how domain experts comprehend those knowledge—composed of granules of concepts—without appropriate or suitable discretization of the attributes in practice. As a result, this study proposes a hybrid approach, composes of DRSA and a multiple attributes decision method, to search for suitable approximation spaces of attributes for gaining applicable knowledge for decision makers (DMs). To illustrate the proposed idea, a case of life insurance industry in Taiwan is analyzed with certain initial experiments. The result not only improves the classification accuracy of the DRSA model, but also contributes to the understanding of financial patterns in the life insurance industry.

Decision Rules-Based Probabilistic MCDM Evaluation Method – An Empirical Case from Semiconductor Industry

Dominance-based rough set approach has been widely applied in multiple criteria classification problems, and its major advantage is the inducted decision rules that can consider multiple attributes in different contexts. However, if decision makers need to make ranking/selection among the alternatives that belong to the same decision class—a typical multiple criteria decision making problem, the obtained decision rules are not enough to resolve the ranking problem. Using a group of semiconductor companies in Taiwan, this study proposes a decision rules-based probabilistic evaluation method, transforms the strong decision rules into a probabilistic weighted model—to explore the performance gaps of each alternative on each criterion—to make improvement and selection. Five example companies were tested and illustrated by the transformed evaluation model, and the result indicates the effectiveness of the proposed method. The proposed evaluation method may act as a bridge to transform decision rules (from data-mining approach) into a decision model for practical applications.

Stable Rules Evaluation for a Rough-Set-Based Bipolar Model: A Preliminary Study for Credit Loan Evaluation

The modern business environment is full of uncertain and imprecise circumstances that require decision makers (DMs) to conduct informed and circumspect decisions. In this regard, rough set theory (RST) has been widely acknowledged as capable to resolve these complicated problems while relevant knowledge can be extracted—in the form of rules—for decision aids. By using those learned rules, an innovative bipolar decision model that comprises the positive (preferred) and negative (unwanted) rules, can be applied to rank alternatives based on their similarity to the positive and the dissimilarity to the negative ones. However, in some business cases (e.g., personal credit loan), applicants need to provide information (values) on all the attributes, requested by a bank. Sometimes, experienced evaluators (e.g., senior bank staff) might question the validity of some values (direct or indirect evidences) provided by an applicant. In such a case, evaluators may assign additional values to those attributes (regarded as non-deterministic ones) in a bipolar model, to examine the stability of a rule that is supported by questionable instances. How to select those rules with satisfactory stability would be an important issue to enhance the effectiveness of a bipolar decision model. As a result, the present study adopts the idea of stability factor, proposed by Sakai et al. [1], to enhance the effectiveness of a bipolar decision model, and a case of credit loan evaluation, with partially assumed values on several non-deterministic attributes, is illustrated with the discussions of potential application in practice.

Conjoint effects of R&D on the financial performance of semiconductor companies: Rule-based granular computing

Research and development (R&D) is inherently important to high-tech industry, such as semiconductor companies. However, the complex and ambiguous relationship between R&D and the subsequent financial performance varies and is still underexplored. Previous studies based on statistical analyses are not able to provide individual guidance considering the specific circumstance/status of a company. Therefore, the aim of this study is to explore the inherent patterns or rules - by a two-stage soft computing model - to guide semiconductor companies to improve its financial performance, considering R&D and the other factors. This research uses a group of semiconductor companies in Taiwan as an empirical case, and the research period ranges from 2006 to 2013. Compare with the conventional discriminant analysis approach, the obtained decision rules from the proposed rule-based analytic model shows superior results that may classify the financial performance of semiconductor companies by a set of financial and R&D indicators with 75% accuracy in average. In addition, we also show how to use the retrieved knowledge (i.e., rules) to guide a real company to improve its performance in the future. The findings contribute to the understanding of R&D influences to the financial performance of semiconductor companies with managerial implications in practice.