• The Journal of Small Business Innovation
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• v.19 no.3
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• pp.43-55
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• 2016

This study examines the implications of special items (SI) for future earnings using quarterly Korean data over the period from 2011 to 2014. Due to the lack of identification of SI in Korea, I choose several items as special items if they are material and non-recurring items following prior studies. Then I regressed seasonally-differenced future earnings on positive and negative SI and found that their effects on future earnings were different. While negative SI are explained by inter-period expense transfer, positive SI are not well-described by traditional prototypes. Next, I regressed seasonally-differenced future earnings on negative SI sub-types as they are heterogeneous in nature and have differing implications for future earnings. While PPE impairments and intangibles impairments are partly explained by the inter-period expense transfer, unspecified loss of other loss items are not. Interestingly, these effects are attenuated or disappear in the Kosdaq market when the markets are divided into the Kospi and Kosdaq markets.

• Proceedings of the Safety Management and Science Conference
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• 2008.11a
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• pp.87-99
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• 2008

우리는 정부의 재정지출이 주가와 콜금리 및 회사채, 산업생산 등에 미치는 영향에 대하여 분기별 데이터를 사용하여 분석하였다. 선행연구들을 살펴보면, 미국의 경우 1960년부터 2000년 기간사이에서 GDP에서 차지하는 조세징수액의 1% 표준편차 (standard deviation) 상승이 분기별로는 4% 그리고 연간 9%의 기대수익률(연율 기준) 을 낮추는 영향을 미쳤음을 알 수 있다. 한국의 경우 미국의 선행연구에서와 비슷하게 재정정책과 통화정책의 변수를 동시에 사용하였을 경우 재정정책과 통화정책변수 모 두 코스피수익률보다는 회사채수익률과의 연관성이 더 높음을 알 수 있다.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.541-559
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• 2015

VaR is now widely used as an important tool to evaluate and manage financial risks. In particular, it is important to select an appropriate volatility model for the rate of return of financial assets. In this study, both univariate and multivariate models are considered to evaluate VaR of the portfolio composed of KOSPI, Hang-Seng, Nikkei indexes, and their performances are compared through back testing techniques. Overall, multivariate models are shown to be more appropriate than univariate models to estimate the portfolio VaR, in particular DCC and ADCC models are shown to be more superior than others.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.419-425
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• 2010

Kernel machine learning is gaining a lot of popularities in analyzing large or high dimensional nonlinear data. We use this technique to estimate a GARCH model for predicting the conditional volatility of stock market returns. GARCH models are usually estimated using maximum likelihood (ML) procedures, assuming that the data are normally distributed. In this paper, we show that GARCH models can be estimated using kernel machine learning and that kernel machine has a higher predicting ability than ML methods and support vector machine, when estimating volatility of financial time series data with fat tail.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.753-771
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• 2016

Value at Risk (VaR) is widely used as an important tool for risk management of financial institutions. In this paper we discuss estimation and back testing for VaR of the portfolio composed of KOSPI, Dow Jones, Shanghai, Nikkei indexes. The copula functions are adopted to construct the multivariate distributions of portfolio components from marginal distributions that combine extreme value theory and GARCH models. Volatility models with t distribution of the error terms using Gaussian, t, Clayton and Frank copula functions are shown to be more appropriate than the other models, in particular the model using the Frank copula is shown to be the best.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.875-887
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• 2021

Value at Risk (VaR) is the most popular measure for market risk. In this paper, we consider the VaR estimation of portfolio consisting of a variety of assets based on multivariate copula model known as vine copula. In particular, sparse vine copula which penalizes too many parameters is considered. We show in the simulation study that sparsity indeed improves out-of-sample forecasting of VaR. Empirical analysis on 60 KOSPI stocks during the last 5 years also demonstrates that sparse vine copula outperforms regular copula model.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.1
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• pp.23-30
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• 2019

As deep learning with the network-based algorithms evolve, artificial intelligence is rapidly growing around the world. Among them, finance is expected to be the field where artificial intelligence is most used, and many studies have been done recently. The existing financial strategy using deep-run is vulnerable to volatility because it focuses on stock price forecasts for a single stock. Therefore, this study proposes to construct ETF products constructed through portfolio methods by calculating the stocks constituting funds by using deep learning. We analyze the performance of the proposed model in the KOSPI 100 index. Experimental results showed that the proposed model showed improved results in terms of returns or volatility.

• Asia-Pacific Journal of Business
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• v.13 no.3
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• pp.281-291
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• 2022

Purpose - We study whether the bullwhip effect is prevalent among Korean firms and how the characteristics of it differ from the ones in other countries. Design/methodology/approach - We obtained quarterly financial and operational information on KOSPI-listed firms in manufacturing, wholesale, and retail industries from 2013 to 2019. We explore the variation of the bullwhip effect across firms and validate hypotheses. Findings - First, we find that for the KOSPI-listed firms, the bullwhip effect is more prevalent compared with the production smoothing. We provide additional findings by using sub-samples of manufacturing firms, wholesaling and retailing firms, big-sized firms, small- and medium-sized firms, domestic-sales intensive firms, and export intensive firms. Second, we show that in general, the bullwhip effect of Korean firms increases with the days in inventory or the demand seasonality ratio. However, the persistence of demand shock does not affect the bullwhip effect of Korean firms. Research implications or Originality - We compare our results with those in other studies that use information on the U.S. and Chinese firms. Our findings show that factors explaining the bullwhip effect across Korean firms have similarities and differences compared with firms in the U.S. and Chinese firms.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.159-164
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• 2012

Although the display industry plays an important role in the entire Korean economy, few empirical research has analyzed the efficiency of display companies. The purpose of this paper is to measure and analyze efficiency of korean listed display firms using DEA(Data Envelopment Analysis) models. We evaluate the CCR and BCC efficiency in DEA models and the return to scale of the Korean listed display companies. The benchmarking companies and efficiency value for the display companies with inefficiency are also provided to improve their efficiency. We analyzed the 44 listed companies consisted of 7 listed on KOSPI and 37 listed on KOSDAQ at the end of 2010. The analysis results show six companies whose values of CCR are 1, and fourteen firms whose values of BCC efficiency are 1. In additions, the six companies have the scalability efficiency. Eventually the efficiency analysis can provide the valuable information for inefficient companies to find benchmarking companies and to improve their efficiency.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.63-76
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• 2016

Most previous studies found a positive relationship between the value of a firm and its R&D investments. This research measures the impact of the timescale of the R&D investment of a firm on its value using panel vector autoregression. By measuring the time required for R&D to impact the value of a firm, this study demonstrates that the lead time is an essential factor in the analysis of the effect of R&D investment on a firm's value. Our study finds that the length of the lead time varies according to the firm's size, industry concentration, and book to market ratio. Firms with a higher industry concentration show a shorter lead time. Also, firms with a larger size and higher book to market ratio generally show a shorter lead time.