A Funding Source Decision on Corporate Bond - Private Placements vs Public Bond - (기업의 회사채 조달방법 선택에 관한 연구 - 사모사채와 공모사채 발행을 중심으로 -)
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- The Korean Journal of Financial Management
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- v.21 no.2
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- pp.99-123
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- 2004
We focus in this study on incremental financing decisions and estimate a logit model for the probability a firm will choose a private placement over a public bond issue. We hypothesize that information asymmetry, financial risk, agent cost, and proprietary information may affect a firm's choice between public debt and private placements. We find that as the size of firm increases, the probability of choosing a private placement declines significantly. The age of the firm, however, is not a significant factor affecting the firm's choice between public and privately-placed bond. The coefficients on the firm's leverage and non-investment grade dummy are significantly positive, meaning firms with high financial risk and credit risk select private placements. The findings regarding agency-related variables, PER and Tobin's Q, are somewhat complex. We find significant evidence that firms with high PER prefer private placements to public bonds, suggesting that borrowers with options to engage in asset substitution or underinvestment are more likely to choose private placements. The coefficient of Tobin's Q is negative, but not significant, which weakly support the hold-up hypothesis. When we construct an interaction term on the Tobin's Q with a non-investment rating dummy, however, the Tobin's Q interaction term becomes positive and significant. Thus, high Tobin's Q firms with a speculative rating are significantly more likely to choose a private placement, regardless of the potential hold-up problems. The ratio of R&D to sales, proxy for proprietary information, is positively significant. This result can be interpreted as evidence in favor of a role for proprietary information in the debt sourcing decision process for these firms.
Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.
The study examines how the environmental factors of store influence service brand personality and repurchase intention in the service environment. The service industry has been experiencing the intensified competition with the industry's continuous growth and the influence from rapid technological advancement. Under the circumstances, it has become ever more important for the brand competitiveness to be distinctively recognized against competition. A brand needs to be distinguished and differentiated from competing companies because they are all engaged in the similar environment of the service industry. The differentiation of brand achievement has become increasingly important to highlight certain brand functions to include emotional, self-expressive, and symbolic functions since the importance of such functions has been further emphasized in promoting consumption activities. That is the recent role of brand personality that has been emphasized in the service industry. In other words, customers now freely and actively express their personalities or egos in consumption activities, taking an important role in construction of a brand asset. Hence, the study suggests that it is necessary to disperse the recognition and acknowledgement that the maintenance of the existing customers contributes more to boost repurchase intention when it is compared to the efforts to create new customers, particularly in the service industry. Meanwhile, the store itself can offer a unique environment that may influence the consumer's purchase decision. Consumers interact with store environments in the process of,virtually, all household purchase they make (Sarel 1981). Thus, store environments may encourage customers to purchase. The roles that store environments play are to provide informational cues to customers about the store and goods and communicate messages to stimulate consumers' emotions. The store environments differentiate the store from competing stores and build a unique service brand personality. However, the existing studies related to brand in the service industry mostly concentrated on the relationship between the quality of service and customer satisfaction, and they are mostly generalized while the connective studies focused on brand personality. Such approaches show limitations and are insufficient to investigate on the relationship between store environment and brand personality in the service industry. Accordingly, the study intends to identify the level of contribution to the establishment of brand personality made by the store's physical environments that influence on the specific brand characteristics depending on the type of service. The study also intends to identify what kind of relationships with brand personality exists with brand personality while being influenced by store environments. In addition, the study intends to make meaningful suggestions to better direct marketing efforts by identifying whether a brand personality makes a positive influence to induce an intention for repurchase. For this study, the service industry is classified into four categories based on to the characteristics of service: experimental-emotional service, emotional -credible service, credible-functional service, and functional-experimental service. The type of business with the most frequent customer contact is determined for each service type and the enterprise with the highest brand value in each service sector based on the report made by the Korea Management Association. They are designated as the representative of each category. The selected representatives are a fast-food store (experimental-emotional service), a cinema house (emotional-credible service), a bank (credible-functional service), and discount store (functional-experimental service). The survey was conducted for the four selected brands to represent each service category among consumers who are experienced users of the designated stores in Seoul Metropolitan City and Gyeonggi province via written questionnaires in order to verify the suggested assumptions in the study. In particular, the survey adopted 15 scales, which represent each characteristic factor, among the 42 unique characteristics developed by Jennifer Aaker(1997) to assess the brand personality of each service brand. SPSS for Windows Release 12.0 and LISREL were used in the analysis of data verification. The methodology of the structural equation model was used for the study and the pivotal findings are as follows. 1) The environmental factors ware classified as design factors, ambient factors, and social factors. Therefore, the validity of measurement scale of Baker et al. (1994) was proved. 2) The service brand personalities were subdivided as sincerity, excitement, competence, sophistication, and ruggedness, which makes the use of the brand personality scales by Jennifer Aaker(1997) appropriate in the service industry as well. 3) One-way ANOVA analysis on the scales of store environment and service brand personality showed that there exist statistically significant differences in each service category. For example, the social factors were highest in discount stores, while the ambient factors and design factors were highest in fast-food stores. The discount stores were highest in the sincerity and excitement, while the highest point for banks was in the competence and ruggedness, and the highest point for fast-food stores was in the sophistication, The consumers will make a different respond to the physical environment of stores and service brand personality that are inherent to the corresponding service interface. Hence, the customers will make a different decision-making when dealing with different service categories. In this aspect, the relationships of variables in the proposed hypothesis appear to work in a different way depending on the exposed service category. 4) The store environment factors influenced on service brand personalities differently by category of service. The factors of store's physical environment are transferred to a brand and were verified to strengthen service brand personalities. In particular, the level of influence on the service brand personality by physical environment differs depending on service category or dimension, which indicates that there is a need to apply a different style of management to a different service category or dimension. It signifies that there needs to be a brand strategy established in order to positively influence the relationship with consumers by utilizing an appropriate brand personality factor depending on different characteristics by service category or dimension. 5) The service brand personalities influenced on the repurchase intention. Especially, the largest influence was made in the sophistication dimension of service brand personality scale; the unique and characteristically appropriate arrangement of physical environment will make customers stay in the service environment for a long time and will lead to give a positive influence on the repurchase intention. 6) The store environment factors influenced on the repurchase intention. Particularly, the largest influence was made on the social factors of store environment. The most intriguing finding is that the service factor among all other environment factors gives the biggest influence to the repurchase intention in most of all service types except fast-food stores. Such result indicates that the customers pay attention to how much the employees try to provide a quality service when they make an evaluation on the service brand. At the same time, it also indicates that the personal factor is directly transmitted to the construction of brand personality. The employees' attitude and behavior are the determinants to establish a service brand personality in the process of enhancing service interface. Hence, there should be a reinforced search for a method to efficiently manage the service staff who has a direct contact with customers in order to make an affirmative improvement of the customers' brand evaluation at the service interface. The findings suggest several managerial implications. 1) Results from the empirical study indicated that store environment factors have a strong positive impact on a service brand personality. To increase customers' repurchase intention of a service brand, the management is required to effectively manage store environment factors and create a friendly brand personality based on the corresponding service environment. 2) Mangers and researchers must understand and recognize that the store environment elements are important marketing tools, and that brand personality influences on consumers' repurchase intention. Based on such result of the study, a service brand could be utilized as an efficient measure to achieve a differentiation by enforcing the elements that are most influential among all other store environments for each service category. Therefore, brand personality established involving various store environments will further reinforce the relationship with customers through the elevated brand identification of which utilization to induce repurchase decision can be used as an entry barrier. 3) The study identified the store environment as a component of service brand personality for the store's effective communication with consumers. For this, all communication channels should be maintained with consistency and an integrated marketing communication should be executed to efficiently approach to a larger number of customers. Mangers and researchers must find strategies for aligning decisions about store environment elements with the retailers' marketing and store personality objectives. All ambient, design, and social factors need to be orchestrated so that consumers can take an appropriate store personality. In this study, the induced results from the previous studies were extended to the service industry so as to identify the customers' decision making process that leads to repurchase intention and a result similar to those of the previous studies. The findings suggested several theoretical and managerial implications. However, the situation that only one service brand served as the subject of analysis for each service category, and the situation that correlations among store environment elements were not identified, as well as the problem of representation in selection of samples should be considered and supplemented in the future when further studies are conducted. In addition, various antecedents and consequences of brand personality must be looked at in the aspect of the service environment for further research.