• The Journal of Asian Finance, Economics and Business
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• 제7권12호
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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.

• Communications for Statistical Applications and Methods
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• 제18권2호
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• pp.237-244
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• 2011

In call forecasting literature, both the seasonal autoregressive integrated moving average(ARIMA) type models and seasonal linear models have been popularly suggested as competing models. However, their parallel comparison for the forecasting accuracy was not strictly investigated before. This study evaluates the accuracy of both the seasonal linear models and the seasonal ARIMA-type models when predicting intra-day call arrival rates using both real and simulated data. The seasonal linear models outperform the seasonal ARIMA-type models in both one-day-ahead and one-week-ahead call forecasting in our empirical study.

• 응용통계연구
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• 제23권2호
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• pp.249-261
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• 2010

Long-term forecasting of seasonal time series is critical in many applications such as planning business strategies and resolving possible problems of a business company. Unlike the traditional approach that depends solely on dynamic models, Li and Hinich (2002) introduced a combination of stochastic dynamic modeling with filter bank approach for forecasting seasonal patterns using highly coherent(High-C) waveforms. We modify the filter selection and forecasting procedure on wavelet domain to be more feasible and compare the resulting predictor with one that obtained from the wavelet variance estimation method. An improvement over other seasonal pattern extraction and forecasting methods based on such as wavelet scalogram, Holt-Winters, and seasonal autoregressive integrated moving average(SARIMA) is shown in terms of the prediction error. The performance of the proposed method is illustrated by a simulation study and an application to the real stock price data.

• Journal of the Korean Statistical Society
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• 제36권4호
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• pp.447-456
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• 2007

On the basis of marginal likelihood of the residual vector which is free of nuisance mean parameters, we propose new Lagrange Multiplier seasonal unit root tests in seasonal autoregressive process. The limiting null distribution of the tests is the standardized ${\chi}^2-distribution$. A Monte-Carlo simulation shows the new tests are more powerful than the tests based on the ordinary least squares (OLS) estimator, especially for large number of seasons and short time spans.

• 응용통계연구
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• 제24권2호
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• pp.217-225
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• 2011

본 논문에서는 스펙트럼 분석과 계절성 선형 모델을 이용하여 intra,-day 콜센터 통화량 예측에 필요한 계절성 변수를 찾아내는 방법을 제시한다. 제시한 방법을 북미 지역의 어느 은행의 5분 단위 콜센터 통화량에 실증 적용하여 기존의 통계적 방법으로는 입증할 수 없었던 월 단위 계절성 변수가 유의함을 보인다. 새로이 찾아진 연수가 intra-day 콜센터 통화량 예측능력을 향상시키는지 확인하기 위해서 새로운 변수를 포함하는 계절성 선형 모델과 이 변수를 포함하지 않은 계절성 선형 모델의 익일 통화량 예측능력을 비교 평가한다. 평가결과 새로운 변수를 포함한 모델이 우수하다는 결과를 얻었다.

• 경영과학
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• 제20권1호
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• pp.103-124
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• 2003

The seasonal adjustment is an essential process in analyzing the time series of economy and business. There are various methods to adjust seasonal effect such as moving average, extrapolation, smoothing and X11. One of the powerful adjustment methods is X11-ARIMA Model which is popularly used in Korea. This method was delivered from Canada. However, this model has been developed to be appropriate for Canadian and American environment. Therefore, we need to review whether the Xl1-ARIMA Model could be used properly in Korea. In this study, we have applied the method to the annual sales of refrigerator sales in A electronic company. We appreciated the adjustment by result analyzing the time series components such as seasonal component, trend-cycle component, and irregular component, with the proposed method.

• 패션비즈니스
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• 제11권1호
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• pp.1-14
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• 2007

This study was aimed at giving help to the people intending to change their own hair color design and also providing the guide line to the cosmetic circles for developing new hair color design and promoting sales by statistically analyzing seasonal changes of hair colors puplished on women's magazines(Vogue Korea, Estetica Korea, Woman Chosun, Ce.ci) from 2003 to 2006. The researching methods were as follows; (1) hair colors published on women's magazines from september 2003 to August 2006 were measured by N.C.S. color reader(4 magazines $\times$10 main hair colors/magazine $\times$ 12 months $\times$ 3 years = 1,440 colors). (2) N.C.S. tone is made of percentage, so measured values and chromas were statistically analyzed by mean, standard deviation, and seasonal deferences were statistically analyzed by t-test and specified on high significant values. But hues were not made of percentage, so these were statistically analyzed by cross tabulation analysis, $x^2$ -test and specified on high significant values. These all had been analyzed by SPSS program(ver. 11.0). The results were as follows; (1) Usually seasonal changes of hair values were significant, specially in foreign licensed magazines, and bright values appeared in S/S and dark values in F/W. (2) Seasonal changes of hair hues were significant only on foreign women's magazines. Therefore seasonal changes of korean hair colors were not significant compared by foreign hair colors because of hardness of color changes of dark black hair and hair damages by hair tints and bleaches and trends of well being and hair care. But hair color changes have been developed gradually and will developed furthermore. So korean hair cosmetic circles have to present hair color trends deferenciated by seasons. And S/S hair values have to be brignt and F/W have to be dark. And new seasonal hair hues matched by korean have to be developed and presented.

• 한국도로학회논문집
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• 제20권4호
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• pp.47-58
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• 2018

PURPOSES : Seoul introduced public bicycles to reduce environmental pollution and create a healthy society. Because the use of bicycles is highly weather dependent, and bicycles are rented by the people, member characteristics and seasonal influences should be considered. This study analyzed bicycle traffic characteristics considering seasonal and member characteristics and highlighted some implications. METHODS : The Yeouido and Sangam districts, which have multiple business districts, were taken as the areas of interest. In order to reflect seasonal and membership characteristics, the traffic volume, time of use, and characteristics of each zone were categorized by season (spring, summer, autumn, winter) and membership type (season, daily, group). In addition, we analyzed the pattern of traffic volume and usage time according to the traffic purpose after separating rental locations into residential, business, subway, and park, reflecting the land characteristics. RESULTS : The results revealed that seasonal characteristics were high for bicycle traffic, time of use, and occupancy rate for park locations in spring and autumn. In terms of membership characteristics, group and daily users appeared as major visitors for park locations, and the trends of commuter pass users showed that bicycle use meets the purpose of introducing public bicycles. CONCLUSIONS : Traffic characteristics differed according to seasonal and membership characteristics. It is necessary to involve and extend the users of the commuter pass. Situations in which commuter pass users cannot function as a group or in which daily users monopolize bicycles (especially near parks, near subway stations, etc.) must be avoided.

• The Journal of Asian Finance, Economics and Business
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• 제8권4호
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• pp.741-749
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• 2021

The research paper endeavors to investigate the presence of seasonal anomalies in the Indian equity market. It also aims to verify the notion that equity markets are for long-term investors. The study employs daily index data of Sensex, Bombay Stock Exchange, to understand its volatility for the period ranging from January 2001 to August 2020. To analyze the seasonal effects in the stock market of India, multiple regression techniques along with descriptive analysis, graphical analysis and various statistical tests are used. The study also employs the rolling returns at different time intervals in order to understand the underlying risks and volatility involved in equity returns. The results from the analysis reveal that daily and monthly seasonality is not present in Sensex returns i.e., investors cannot earn abnormal returns by timing their investment decisions. Hence, the major finding of this study is that the Indian stock market performance is random, and the returns are efficient. The other major conclusion of the research is that the equity returns are profitable in the long run providing investors a hope that they can make gains and compensate for the loss in one period by a superior performance in some other periods.

• 한국신뢰성학회지:신뢰성응용연구
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• 제3권1호
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• pp.13-40
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• 2003

The seasonal adjustment is an essential process in analyzing the time series of economy and business. One of the powerful adjustment methods is X11-ARIMA Model which is popularly used in Korea. This method was delivered from Canada. However, this model has been developed to be appropriate for Canadian and American environment. Therefore, we need to review whether the X11-ARIMA Model could be used properly in Korea. In this study, we have applied the method to the annual sales of refrigerator sales in A electronic company. We appreciated the adjustment by result analyzing the time series components such as seasonal component, trend-cycle component, and irregular component, with the proposed method. Additionally, in order to improve the result of seasonal adjusted time series, we suggest the demand forecasting method base on autocorrelation and seasonality with the X11-ARIMA PROC.