• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.19-32
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• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.63-82
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• 2016

Individuals gather information online to resolve problems in their daily lives and make various decisions about the purchase of products or services. With the revolutionary development of information technology, Web 2.0 has allowed more people to easily generate and use online reviews such that the volume of information is rapidly increasing, and the usefulness and significance of analyzing the unstructured data have also increased. This paper presents an analysis on the lexical features of expert product reviews to determine their influence on consumers' purchasing decisions. The focus was on how unstructured data can be organized and used in diverse contexts through text mining. In addition, diverse lexical features of expert reviews of contents provided by a third-party review site were extracted and defined. Expert reviews are defined as evaluations by people who have expert knowledge about specific products or services in newspapers or magazines; this type of review is also called a critic review. Consumers who purchased products before the widespread use of the Internet were able to access expert reviews through newspapers or magazines; thus, they were not able to access many of them. Recently, however, major media also now provide online services so that people can more easily and affordably access expert reviews compared to the past. The reason why diverse reviews from experts in several fields are important is that there is an information asymmetry where some information is not shared among consumers and sellers. The information asymmetry can be resolved with information provided by third parties with expertise to consumers. Then, consumers can read expert reviews and make purchasing decisions by considering the abundant information on products or services. Therefore, expert reviews play an important role in consumers' purchasing decisions and the performance of companies across diverse industries. If the influence of qualitative data such as reviews or assessment after the purchase of products can be separately identified from the quantitative data resources, such as the actual quality of products or price, it is possible to identify which aspects of product reviews hamper or promote product sales. Previous studies have focused on the characteristics of the experts themselves, such as the expertise and credibility of sources regarding expert reviews; however, these studies did not suggest the influence of the linguistic features of experts' product reviews on consumers' overall evaluation. However, this study focused on experts' recommendations and evaluations to reveal the lexical features of expert reviews and whether such features influence consumers' overall evaluations and purchasing decisions. Real expert product reviews were analyzed based on the suggested methodology, and five lexical features of expert reviews were ultimately determined. Specifically, the "review depth" (i.e., degree of detail of the expert's product analysis), and "lack of assurance" (i.e., degree of confidence that the expert has in the evaluation) have statistically significant effects on consumers' product evaluations. In contrast, the "positive polarity" (i.e., the degree of positivity of an expert's evaluations) has an insignificant effect, while the "negative polarity" (i.e., the degree of negativity of an expert's evaluations) has a significant negative effect on consumers' product evaluations. Finally, the "social orientation" (i.e., the degree of how many social expressions experts include in their reviews) does not have a significant effect on consumers' product evaluations. In summary, the lexical properties of the product reviews were defined according to each relevant factor. Then, the influence of each linguistic factor of expert reviews on the consumers' final evaluations was tested. In addition, a test was performed on whether each linguistic factor influencing consumers' product evaluations differs depending on the lexical features. The results of these analyses should provide guidelines on how individuals process massive volumes of unstructured data depending on lexical features in various contexts and how companies can use this mechanism from their perspective. This paper provides several theoretical and practical contributions, such as the proposal of a new methodology and its application to real data.

• Journal of Distribution Science
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• v.13 no.9
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• pp.47-57
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• 2015

Purpose - The aim of this paper is to investigate the explanatory power of the Pecking-order theory (the cost of financing increases with asymmetric information) among Korean retail firms from the perspective of debt capacity. According to the Pecking-order theory, a firm's first preference is to use internal funds for its capital needs, its next preference is the issuance of debt, and its last preference is the issuance of equity; this is due to the information asymmetry problem between existing shareholders and investors. However, prior empirical studies, such as Lemmon and Zender (2010), argue that the entire sample test for the Pecking-order theory could be misleading due to the different levels of debt issuance capability of each of the individual firms; in fact, they confirm that the explanatory power of the Pecking-order theory improves after taking into account the differences in debt capacity of the U.S. firms they examined. This paper implements a case study approach among Korean retail firms to examine the relationship between debt capacity and the explanatory power of the Pecking-order theory in Korea. Research design, data, and methodology - This study uses the sample of public retail firms on the Korea Composite Stock Price Index (KOSPI) from the time period of 1990 to 2013. We gather related financial and accounting statements from the financial information firm WISEfn. Credit rating information is provided by the Korea Investor Service. We employ the models of Lemmon and Zender (2010) and Son and Kim (2013) to measure a firm's debt capacity. Their logit models use the rating dummy variable as a dependent variable and incorporate other firm characteristics as independent variables to estimate debt capacity. To test the Pecking-order theory, we adopt variants of the financing deficit model of Shyam-Sunder and Myers (1999). In the test of the Pecking-order theory, we consider all of the changes in total debt obligations, current debt obligations, and long-term debt obligations. Results - Our main contribution to the literature is our confirmation of the predicted relationship between debt capacity and the explanatory power of the Pecking-order theory among Korean retail firms. The coefficients on financing deficits become greater as a firm's debt capacity improves. This is consistent with the results of Lemmon and Zender (2010). The coefficients on the square of the financing deficits are also negative for the firms in the largest debt capacity group, which is also consistent with the predictions in prior literature. Conclusions - This study takes a case study approach by examining Korean retail firms. We confirm that the Pecking-order theory explains the capital structure of retail firms more appropriately, after taking into account the debt capacity of each firm. This result suggests the importance of debt capacity consideration in the testing of the Pecking-order theory. Our result also implies that there has been a potential underestimation of the explanatory power of the Pecking-order theory in existing studies.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.243-249
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• 2020

This study analyzes the earnings management that can occur in the process of public offering in the process of SMEs reducing cost of capital, risks and seeking opportunities for direct financing. Since a company is subject to strict supervision during the IPO process, it is possible to prevent the phenomenon that the company value evaluated in the market is underestimated, or to perform earnings management in consideration of overestimation. This study attempted to verify the degree of earnings management through discretionary accruals and actual earnings management values that can affect the earnings ratio of the IPO of a company. For this study, total accruals were calculated and analyzed through discretionary accruals, sales, costs, and actual earnings management adjustments from production activities. As a result of the analysis, discretionary accruals, which are the countermeasures for earnings management during the listing process, have a positive(+) relationship in both the stock price return and the sales adjustment value, which can be viewed as a factor that induces high valuation. As a result of this, there may be a risk of adverse selection for the benefit amount, and information asymmetry may exist for public offering stocks. This study can provide useful guidelines for evaluating corporate value to domestic SMEs and investors that do business with Chinese companies as well as China through the current and type of earnings management of Chinese listed companies.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.33-53
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• 2021

In the crossroads of social change caused by the spread of the Fourth Industrial Revolution and the prolonged COVID-19, the Korean government announced the Digital New Deal policy on July 14, 2020. The Digital New Deal policy's primary goal is to create new businesses by accelerating digital transformation in the public sector and industries around data, networks, and artificial intelligence technologies. However, in a rapidly changing social environment, information asymmetry of the future benefits of technology can cause differences in the public's ability to analyze the direction and effectiveness of policies, resulting in uncertainty about the practical effects of policies. On the other hand, the media leads the formation of discourse through communicators' role to disseminate government policies to the public and provides knowledge about specific issues through the news. In other words, as the media coverage of a particular policy increases, the issue concentration increases, which also affects public decision-making. Therefore, the purpose of this study is to verify the dynamic relationship between the media coverage and the stock market on the Korean government's digital New Deal policy using Granger causality, impulse response functions, and variance decomposition analysis. To this end, the daily stock turnover ratio, daily price-earnings ratio, and EWMA volatility of digital technology-based companies related to the digital new deal policy among KOSDAQ listed companies were set as variables. As a result, keyword search volume, daily stock turnover ratio, EWMA volatility have a bi-directional Granger causal relationship with media coverage. And an increase in media coverage has a high impact on keyword search volume on digital new deal policies. Also, the impulse response analysis on media coverage showed a sharp drop in EWMA volatility. The influence gradually increased over time and played a role in mitigating stock market volatility. Based on this study's findings, the amount of media coverage of digital new deals policy has a significant dynamic relationship with the stock market.