• Knowledge Management Research
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• v.22 no.4
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• pp.119-133
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• 2021

As a large amount of data is produced in each industry, a number of time series pattern prediction studies are being conducted to make quick business decisions. However, there is a limit to predicting specific patterns in nonlinear time series data due to the uncertainty inherent in the data, and there are difficulties in making strategic decisions in corporate management. In addition, in recent decades, various studies have been conducted on data such as demand/supply and financial markets that are suitable for industrial purposes to predict time series data of irregular random walk models, but predict specific rules and achieve sustainable corporate objectives There are difficulties. In this study, the prediction results were compared and analyzed using the Chaos analysis method for roulette data and financial market data, and meaningful results were derived. And, this study confirmed that chaos analysis is useful for finding a new method in analyzing time series data. By comparing and analyzing the characteristics of roulette games with the time series of Korean stock index future, it was derived that predictive power can be improved if the trend is confirmed, and it is meaningful in determining whether nonlinear time series data with high uncertainty have a specific pattern.

• Asia Marketing Journal
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• v.14 no.1
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• pp.1-24
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• 2012

Nowadays, customer satisfaction has been one of company's major objectives, and the index to measure and communicate customer satisfaction has been generally accepted among business practices. The major issues of CSI(customer satisfaction index) are three questions, as follows: (a)what level of customer satisfaction is tolerable, (b)whether customer satisfaction and company performance has positive causality, and (c)what to do to improve customer satisfaction. Among these, the second issue is recently attracting academic research in several perspectives. On this study, the second issue will be addressed. Many researchers including Anderson have regarded customer satisfaction as core competencies, such as brand equity, customer equity. They want to verify following causality "customer satisfaction → market performance(market share, sales growth rate) → financial performance(operating margin, profitability) → corporate value performance(stock price, credit ratings)" based on the process model of marketing performance. On the other hand, Insoo Jeon and Aeju Jeong(2009) verified sequential causality based on the process model by the domestic data. According to the rejection of several hypotheses, they suggested the balance model of marketing performance as an alternative. The objective of this study, based on the existing process model, is to examine the causal relationship between customer satisfaction and corporate value performance. Anderson and Mansi(2009) proved the relationship between ACSI(American Customer Satisfaction Index) and credit ratings using 2,574 samples from 1994 to 2004 on the assumption that credit rating could be an indicator of a corporate value performance. The similar study(Sangwoon Yoon, 2010) was processed in Korean data, but it didn't confirm the relationship between KCSI(Korean CSI) and credit ratings, unlike the results of Anderson and Mansi(2009). The summary of these studies is in the Table 1. Two studies analyzing the relationship between customer satisfaction and credit ratings weren't consistent results. So, in this study we are to test the conflicting results of the relationship between customer satisfaction and credit ratings based on the research model considering Korean credit ratings. To prove the hypothesis, we suggest the research model as follows. Two important features of this model are the inclusion of important variables in the existing Korean credit rating system and government support. To control their influences on credit ratings, we included three important variables of Korean credit rating system and government support, in case of financial institutions including banks. ROA, ER, TA, these three variables are chosen among various kinds of financial indicators since they are the most frequent variables in many previous studies. The results of the research model are relatively favorable : R², F-value and p-value is .631, 233.15 and .000 respectively. Thus, the explanatory power of the research model as a whole is good and the model is statistically significant. The research model has good explanatory power, the regression coefficients of the KCSI is .096 as positive(+) and t-value and p-value is 2.220 and .0135 respectively. As a results, we can say the hypothesis is supported. Meanwhile, all other explanatory variables including ROA, ER, log(TA), GS_DV are identified as significant and each variables has a positive(+) relationship with CRS. In particular, the t-value of log(TA) is 23.557 and log(TA) as an explanatory variables of the corporate credit ratings shows very high level of statistical significance. Considering interrelationship between financial indicators such as ROA, ER which include total asset in their formula, we can expect multicollinearity problem. But indicators like VIF and tolerance limits that shows whether multicollinearity exists or not, say that there is no statistically significant multicollinearity in all the explanatory variables. KCSI, the main subject of this study, is a statistically significant level even though the standardized regression coefficients and t-value of KCSI is .055 and 2.220 respectively and a relatively low level among explanatory variables. Considering that we chose other explanatory variables based on the level of explanatory power out of many indicators in the previous studies, KCSI is validated as one of the most significant explanatory variables for credit rating score. And this result can provide new insights on the determinants of credit ratings. However, KCSI has relatively lower impact than main financial indicators like log(TA), ER. Therefore, KCSI is one of the determinants of credit ratings, but don't have an exceedingly significant influence. In addition, this study found that customer satisfaction had more meaningful impact on corporations of small asset size than those of big asset size, and on service companies than manufacturers. The findings of this study is consistent with Anderson and Mansi(2009), but different from Sangwoon Yoon(2010). Although research model of this study is a bit different from Anderson and Mansi(2009), we can conclude that customer satisfaction has a significant influence on company's credit ratings either Korea or the United State. In addition, this paper found that customer satisfaction had more meaningful impact on corporations of small asset size than those of big asset size and on service companies than manufacturers. Until now there are a few of researches about the relationship between customer satisfaction and various business performance, some of which were supported, some weren't. The contribution of this study is that credit rating is applied as a corporate value performance in addition to stock price. It is somewhat important, because credit ratings determine the cost of debt. But so far it doesn't get attention of marketing researches. Based on this study, we can say that customer satisfaction is partially related to all indicators of corporate business performances. Practical meanings for customer satisfaction department are that it needs to actively invest in the customer satisfaction, because active investment also contributes to higher credit ratings and other business performances. A suggestion for credit evaluators is that they need to design new credit rating model which reflect qualitative customer satisfaction as well as existing variables like ROA, ER, TA.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.157-176
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• 2018

The purpose of this research is to develop a detection model for companies designated as administrative issue in KOSDAQ market using financial data. Administration issue designates the companies with high potential for delisting, which gives them time to overcome the reasons for the delisting under certain restrictions of the Korean stock market. It acts as an alarm to inform investors and market participants of which companies are likely to be delisted and warns them to make safe investments. Despite this importance, there are relatively few studies on administration issues prediction model in comparison with the lots of studies on bankruptcy prediction model. Therefore, this study develops and verifies the detection model of the companies designated as administrative issue using financial data of KOSDAQ companies. In this study, logistic regression and decision tree are proposed as the data mining models for detecting administrative issues. According to the results of the analysis, the logistic regression model predicted the companies designated as administrative issue using three variables - ROE(Earnings before tax), Cash flows/Shareholder's equity, and Asset turnover ratio, and its overall accuracy was 86% for the validation dataset. The decision tree (Classification and Regression Trees, CART) model applied the classification rules using Cash flows/Total assets and ROA(Net income), and the overall accuracy reached 87%. Implications of the financial indictors selected in our logistic regression and decision tree models are as follows. First, ROE(Earnings before tax) in the logistic detection model shows the profit and loss of the business segment that will continue without including the revenue and expenses of the discontinued business. Therefore, the weakening of the variable means that the competitiveness of the core business is weakened. If a large part of the profits is generated from one-off profit, it is very likely that the deterioration of business management is further intensified. As the ROE of a KOSDAQ company decreases significantly, it is highly likely that the company can be delisted. Second, cash flows to shareholder's equity represents that the firm's ability to generate cash flow under the condition that the financial condition of the subsidiary company is excluded. In other words, the weakening of the management capacity of the parent company, excluding the subsidiary's competence, can be a main reason for the increase of the possibility of administrative issue designation. Third, low asset turnover ratio means that current assets and non-current assets are ineffectively used by corporation, or that asset investment by corporation is excessive. If the asset turnover ratio of a KOSDAQ-listed company decreases, it is necessary to examine in detail corporate activities from various perspectives such as weakening sales or increasing or decreasing inventories of company. Cash flow / total assets, a variable selected by the decision tree detection model, is a key indicator of the company's cash condition and its ability to generate cash from operating activities. Cash flow indicates whether a firm can perform its main activities(maintaining its operating ability, repaying debts, paying dividends and making new investments) without relying on external financial resources. Therefore, if the index of the variable is negative(-), it indicates the possibility that a company has serious problems in business activities. If the cash flow from operating activities of a specific company is smaller than the net profit, it means that the net profit has not been cashed, indicating that there is a serious problem in managing the trade receivables and inventory assets of the company. Therefore, it can be understood that as the cash flows / total assets decrease, the probability of administrative issue designation and the probability of delisting are increased. In summary, the logistic regression-based detection model in this study was found to be affected by the company's financial activities including ROE(Earnings before tax). However, decision tree-based detection model predicts the designation based on the cash flows of the company.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Distribution Science
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• v.13 no.9
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• pp.95-101
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• 2015

Purpose - This study provides evidence of the impact of the mandatory adoption of Korean equivalents to International Financial Reporting Standards (K-IFRS) on accounting quality. K-IFRS uses fair value as a basis of measurement and is characterized by principle-based standards. These characteristics can lead to a decrease in conservatism. Therefore, this study aims to examine whether or not there is a change in the level of conservatism before and after the enforcement of K-IFRS (2007~2014). By comparing 2007 through 2008 and 2013 through 2014 (excluding 2009 to 2012), we test "the temporary adjustment phenomenon" and document an overall decline in the degree of conservatism after the adoption of K-IFRS. Research design, data, and methodology - Our sample is comprised of data of all listed Korea Composite Stock Price Index (KOSPI) manufacturing distribution companies in Korea from 2007 to 2014, which yields the pooled sample of 4,412 (panel A) and 1,915 (panel B) firm-year observations for hypotheses 1 and 2. In line with recent literature, we adopt the Givoly and Hayn (2000) model, which recomputes the non-operating accruals, excluding two components that are most likely to capture the effect of restructuring activities: special items and gains or losses from discontinued operations. In addition, we also use these variables: SIZE, LEV, INV_CYCLE, ROA, OWN, and FOR. Results - Our sample period spans 2007 to 2014. This offers evidence on the effect of the mandatory adoption of IFRS on conservatism. Our findings can be summarized as follows. First, in panel A, for mandatory K-IFRS adoption (2011), we do not find any significant evidence of conservatism. We can guess that the "temporary adjustment phenomenon" is the reason that we do not find significant evidence of conservatism. Second, we investigate panel B from 2009 to 2012. We document an overall decline in the degree of conservatism after the adoption of K-IFRS. We can assume that these results are due to "the temporary adjustment phenomenon." Conclusions - This study finds that conservatism significantly decreased after IFRS adoption. In particular, this study makes the initial effort to elucidate "the temporary adjustment phenomenon" to analyze the effect of K-IFRS on conservative accounting. We argue that K-IFRS are conceptually conservative but that inappropriate application of the conservatism principles is likely to prevent financial reporting from reaching the level of conservatism targeted by the IASB. Overall, this paper contributes to the literature on IFRS and can be useful to capital market supervisors who are monitoring the trends of the firms implementing K-IFRS. Additionally, our results inform stakeholders of the potentially negative effect of the greater flexibility permitted by IFRS and/or lack of appropriate enforcement on key dimensions of accounting quality. This has important implications for Korean regulators and standard setters as they review the cost and benefits of IFRS. Our study also sheds light on the importance of the institutional environment in achieving the targeted objectives for improving financial reporting quality.

• Journal of Korea Technology Innovation Society
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• v.21 no.4
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• pp.1577-1605
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• 2018

This study analyzes China's technology billionaires. However, this study does not show the accumulating process of wealth in each of the Chinese billionaires, because there are many technology billionaires, but only deals with the macro analysis of the technology billionaire; the pattern of existence, comparison with other industries, the process of wealth creation reflecting China's particularity, and comparison with the world's technology billionaires. The findings of this study are as follows. First, more than 10 billion yuan of Chinese billionaires will emerge from 2004. Second, in the early days, illegal and corruption made rich, but the wealth of own efforts has gradually increased. Third, the real estate and manufacturing billionaires are still strong overall, but the growth of billionaires in technology, medicine and finance industry is remarkable. Fourth, in the case of the top 10 richest, four are from real estate, four from technology, and two from manufacturing and distribution. Most technology billionaires are in Guangdong, Zhejiang, Beijing and Shanghai. The determinants of the number of billionaires are GDP, exchange rate to US Dollar and Shenzen Stock Index, and those of technology billionaire are GDP and exchange rate. Given the relationship with existing theories, this study can be called the fifth type of billionaire research. Conceptually, the main reason for accumulating wealth is the search for policy opportunities, market opportunities and technology opportunities.

• Journal of Korea Trade
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• v.24 no.4
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• pp.71-94
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• 2020

Purpose - Multi-National Enterprises (MNEs) tend to face a high level of institutional pressures in regions with high institutional development level. When complying with institutional pressures, firms try to make decisions to maximize profit while minimizing the risks to them. The purpose of this study is to investigate the influence of the institutional development level on institutional compliance timing by MNEs and the relationship between compliance speed and corporate performance. Design/methodology - The research focuses on three main variables, which are the institutional development level (as a determination of the institutional pressure level), the firm's compliance speed (as a determination of the compliance timing), and the firm's financial performance (as a determination of the corporate performance). We collected 19,869 firm-level data from CSMAR (the China Stock Market and Accounting Research), 6,922 CSR data from RKS (the Rankins CSR Ratings), and province and city-level data from the NERIM (National Economic Research Institute Index of Marketization) and NBSC (National Bureau of Statistics of China). The firms in China were chosen for analysis, and the analysis period was from 2008 to 2017. Random Effects GLS Regression was used to test the relationships among the variables. Findings - This study examined the effect of the institutional development level on the firm's compliance speed, together with the effect of compliance speed on the firm's financial performance of the MNEs in China. We found that the institutional development level positively influenced firms' financial performances, which means the firms' financial performances are better in the region with a high institutional development level. The compliance speed of institutional practice by firms was faster in the higher level of institutional development. However, the firm's delayed compliance led to better financial performance. Originality/value - Studies in the resource dependence view of Institutional Theory often fall short in understanding the theory by overlooking the firm's active decision-making. Thus, the findings do not present a full scope of corporate performance in this regard. This study not only found a way to test the role of a firm's independent decision-making (i.e., compliance timing) when facing the institutional pressure but also prove the significant role of the compliance timing on corporate performance. Also, we were able to test the effect of institutional development level, controlling location-specific variables because we used CSR performance data for MNEs operating in China. Lastly, by doing the above, the findings of this study suggest practical implications to the industry practitioners in MNEs.