• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.627-633
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• 2020

The study attempts to analyze the impact of firm's risk on capital structure in the context of seasonal and non-seasonal businesses. We use two independent variables namely credit risk and systematic risk and one dependent variable to explore this connection. Sugar sector is taken as seasonal while the textile sector as non-seasonal businesses. The panel data of twenty-five firms from each sector are taken ranging for the period of 2012 to 2019 which has been retrieved from their annual reports for empirical analysis of the study. The results reveal the negative impact of credit risk on capital structure in both types of businesses. Increasing (decreasing) one point of credit risk causes a decrease (increase) leverage ratio by 0.27 points for seasonal while increasing (decreasing) one point of credit risk causes to decrease (increase) leverage by 0.15 points for non-seasonal businesses. Furthermore, the study shows positive impact of systematic risk on leverage ratio in non-seasonal business and no impact in seasonal business. Any increase (decrease) in the systematic risk causes an incline (decline) leverage ratio by 2.68 units for non-seasonal businesses. The study provides a guideline to managers for risk management in businesses. The research focusses on theoretical as well as managerial and policy implications on risk management in businesses.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.1-17
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• 2013

In this article, several types of hybrid forecasting models are suggested. In particular, hybrid models using the generalized additive model (GAM) are newly suggested as an alternative to those using neural networks (NN). The prediction performances of various hybrid and non-hybrid models are evaluated using simulated time series data. Five different types of seasonal time series data related to an additive or multiplicative trend are generated over different levels of noise, and applied to the forecasting evaluation. For the simulated data with only seasonality, the autoregressive (AR) model and the hybrid AR-AR model performed equivalently very well. On the other hand, if the time series data employed a trend, the SARIMA model and some hybrid SARIMA models equivalently outperformed the others. In the comparison of GAMs and NNs, regarding the seasonal additive trend data, the SARIMA-GAM evenly performed well across the full range of noise variation, whereas the SARIMA-NN showed good performance only when the noise level was trivial.

• Journal of Korea Water Resources Association
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• v.46 no.2
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• pp.183-193
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• 2013

Selecting the evaluation index to determine water resources system design yield is an important problem for water resources engineers. Reliability, resilience and vulnerability are three widely used indices for yield analysis. However, there is an overlap region between indices as well as resilience and vulnerability can show improvement in non-monotonic phenomena although yield condition becomes worse. These problems are usually not recognized and the decisions are made according to calculated estimates in real situation. The reason for this is caused by a diverse characteristics of water resources system such as seasonal variability of hydrologic characteristics and water demands. In this study, the applicability of resilience and vulnerability for multi indices application in addition to reliability which is applied generally is examined. Based on highly seasonal irrigation water demand ratio, the correlation and non-monotonic phenomena of each index are analyzed for seven selected reservoirs. Yongdam reservoir which supplies constant water supply showed the general tendency, but Chungju, Andong, Namgang and other reservoirs which supplies irrigation water showed clear non-monotonic phenomena in resilience and vulnerability.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.8
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• pp.7-17
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• 2022

The article discusses the problems caused by inflation in the developing Asia-Pacific region during the time of the worldwide pandemic and suggests innovative solutions to the problem. The reality is that some of the commodity groups from the consumer basket (e.g., non-seasonal fruits, electronics, furniture, hotel, and restaurant services, etc.) fail to reflect the needs of the low-income earners, which make the majority in developing countries. At the same time, the inflation targeting regime has become outdated and not reliable, because of uncontrolled exogenic factors (imported inflation, fluctuation in oil prices, supply chain disruption, Russia-Ukraine war, etc.) prevailing on endogenic factors and thus making it impossible to control the price stability, especially in developing countries. Since, the old-fashioned inflation index and inflation targeting mechanisms regrettably fail to fully reflect both the society and governmental/central banks' expectations, based on which we first should have better care and second create better policies; we propose to use a combination of already well-known indexes and policies, with the new statistical indicators, which reflects price fluctuations on the medication, utilities, and nutrition.

• Journal of Fashion Business
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• v.24 no.2
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• pp.136-153
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• 2020

In the second half of the twentieth century, fashion shows were a long-standing promotional medium and changed form and styles as times changed. In the past, if a model were on the stage simply to showcase a brand's work, the stage would have been transformed into a more active space, displaying various performances or seasonal themes combined with art. Then in the 2000s, there was an increasing number of instances when the global fashion industry of used figurative objet in organizing a fashion show's stage. In particular, because fashion shows require audience response and satisfaction, producing displays using objet is an effective marketing method. In the early 2000s, many brands were already introducing runway displays using objet, and these cases are expected to increase further in the future. This collection of 23 ready-to-wear models, which constituted the runway display, was by utilizing the objet more than five times from the 485 brands listed in Vogue's runway category. Based on our previous research, we classified the objet expressive characteristics as reproducibility, simplicity, non-artificiality, and fantasy. Among the 207 collections that we analyzed, the collection that utilized objet in its runway display had 170 circuits. Using objet in the runway display leverages visual language which allows one to communicate the season's concept, brand identity, and desired message more easily. Futhermore, it was spatially expressed to create feeling of satisfaction.

• Journal of the Korean Society of Costume
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• v.64 no.2
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• pp.35-49
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• 2014

In cases of fashion businesses operating by consignment, base estimate on quantity of sales is the most essential part of merchandising. This study classified factors influential to sales into factors with systematic influence and factors with unsystematic influence. In order to find out influence of each factor on sales, non-linear regression was used with SPSS package on the basis of actual data on sales for 5 years for sport shoes brand. Major findings of this study are as follows. First, price level had significant negative(-) influence on sales. Second, price expectation effects had significant negative(-) influence on sales. Third, competitor's price effect showed significant negative(-) value. Fourth, day-of-the-week effect showed significant positive(+) effect. The theoretical marketing implications of this study are as follows. First, study on price leads to expansion of the researches from apparels to sport shoes. Field of study on price was enlarged through expansion of variable of study from price level and price expectation effect to promotion, day-of-the-week effect and rainfall effect. Second, quantitative scale of day-of-the-week effect was found and it could be confirmed that there was seasonal differences with day-of-the-week effect. Implications of above findings on marketing managers are as follows. First, it was found that an increase in competitiveness of brand power and a decline in absolute value of competitor's price effect can be realized when new product groups are developed to meet the unsatisfied needs in the market. Second, it was possible to find out the parameters scales of the price response function, making it possible to estimate sales for the next season, and in turn realize increase in rate of sales and profit rate. This research is based on the dynamic price response function, which is rare to find in the apparel business and it academic significance due to its expanding response model which was focused on price in conventional researches to non-systematic variables.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.244-253
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• 2013

In industries, shipping is an important issue in improving the forecasting accuracy of sales. This paper introduces a hybrid method and plural methods are compared. Focusing the equation of exponential smoothing method (ESM) that is equivalent to (1, 1) order autoregressive-moving-average (ARMA) model equation, a new method of estimating the smoothing constant in ESM had been proposed previously by us which satisfies minimum variance of forecasting error. Generally, the smoothing constant is selected arbitrarily. However, this paper utilizes the above stated theoretical solution. Firstly, we make estimation of ARMA model parameter and then estimate the smoothing constant. Thus, theoretical solution is derived in a simple way and it may be utilized in various fields. Furthermore, combining the trend removing method with this method, we aim to improve forecasting accuracy. This method is executed in the following method. Trend removing by the combination of linear and 2nd order nonlinear function and 3rd order nonlinear function is executed to the original production data of two kinds of bread. Genetic algorithm is utilized to search the optimal weight for the weighting parameters of linear and nonlinear function. For comparison, the monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non-monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.