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朴荀植,徐錫完 효성여자대학교 산업경영연구소 1998 경영경제 Vol.15 No.-
Bank risk management is more than just policies and procedures covering the various segments of a business. Management must give thought to the risks to which the strategies give rise. Risk management policies which delineates serve the dual purposes of establishing patterns of behavior which delineates serve the dual purposes of establishing patterns of behavior which are consistent with of risk to be assumed along the way. The evaluation culminates in a rating knowing by its acronym CAMEL. CAMEL is derived from the first letters of the words Capital, Assets, Management, Earnings and Liquidity. Especially, bank risk management technique is the risk measurement framework and the capital requirement. As the capital requirement, the principal form of eligible capital to cover market risk consists of shareholders' equity and retained earnings (tier 1 capital) and supplementary capital (tier 2 capital) as defined in the 1988 Basle Accord. But banks may also, at the discretion of their national authority, employ a third tier of capital (tier 3 capital), consisting of short-term subordinated debt as defined for the sole purpose of meeting a proportion of the capital requirement for market risks. As risk measurement framework, bank will have flexibility in devising the precise nature of their models, but the standards will apply for the purpose of calculating their capital change. VAR(value at risk) must be computed on a daily basis. In calculating VAR, a 99th percentile, one-tailed confidence interval is to be used. An instantaneous price shock equivalent to a 10 day movement in price is to be used and so on. A VAR statement has below components, the dollar amount or the probability of the loss not exceeding the dollar amount, a methodology for using the assumptions that define normal market conditions, a methodology for using the assumptions to compute the VAR. Accordingly, the above mentioned domestic bank risk management is to request a sufficiency of the capital requirement and devising of the risk measurement framework models with value at risk.
포트폴리오 成果側定模型에 關한 考察 : Jensen과 Fama模型을 中心으로
朴筍植 대구효성가톨릭대학교 1982 연구논문집 Vol.25 No.1
Portfolio performance evaluation should try to assess whether the right assets were chosen at the right time (asset selection and timing) and how well these assets were diversified. Asset selection and timing can be evaluated by determining whether the protfolio manager achieved the level of return warranted by the level of risk assumed. Willram F. Sharpe emphasized the slope of the capital market line as the critical value to consider in measuring the performance of a well diversified protfolio. The slope of the CML is called the reward-to-variability ratio(RVAR) which expresses the relationship of the excess reutrn one achieves on his portfolio to the amount of risk he assumed. The higher this ratio is (that is, greater the slope of the line connecting risk free asset with the protfolio located in the risk-return space), the better the protfolio. Jack Treynor developed a perfromance measure based on Betas rather than standard deviation of rates of return. Its measurement of porformance is reward-to-volatility ratio(RVOL) which expresses the relationship of the excess return one achieves on any asset holdings to the amount of systematic risk(β) assumed. The higher this ratio, the greater is the excess returm for that asset, given its level of β risk. The equation of Jensen's characteristic line in risk-premium form is as follows: Rit-Rft=αi+βi(Rmt-Rft)+Uit The alpha intercept in Jensen's characteristic line is regression estimate of the excess returns from the ith asset averaged over the sample period used to estimate this time series regression, Jensen's αi is used as the measurement for the portifolio performance evaluation. If the portfolio manager can forecast market movements, the protfolio performance measure, αi, will be positive for two reasons, (1) the extra returns actually earned on the portfolio due to the manager's ability, and (2) the positive bias in the estimate of αi resulting from the negative bias in the estimate of βi. Hence, E.F. Fama suggested that Overall Performance of portfolio can be subdivided into two parts, the return from security selection (Selectivity) and the return from bearing risk (Risk). The Selectivity is defined as the difference between the return on the managed portfolio and the return on a naively selected portfolio with the same level of market risk. the Selectivity can also be subdivided into Net Selectivity and Diversification. The part of Overall Performance due to Risk can be allocated to the investor and to the portfolio manager. Accordingly, we can evaluate the performance of portfolio by each performance component.
박순식,안영규 한국국제경영관리학회 2007 국제경영리뷰 Vol.11 No.1
본 연구에서는 행태재무론(behavioral finance)중 시장참가자들이 합리적이라는 가정에 위배되는 일상적인 오류인 투자성향효과(disposition effect)에 대하여 살펴보고자 한다. 구체적으로, 본 연구에서는 투자성향효과를 보이는 투자자가 시장에 존재할 경우 시간의 흐름에 따라 균형자산의 가격이 어떻게 변하는지에 대하여 살펴본다. 그리고 한국주식시장에서 기대수익률의 횡단면적 특성 중 행태변수(behavioral variables)의 대용치로 투자성향효과변수의 설명력을 검정한다. 이를 위해, Grinblatt & Han(2005)의 모형을 이용하여 2000년부터 2005년까지의 한국증권거래소에 상장된 기업을 대상으로 Fama & MacBeth(1973) 횡단면 회귀분석을 실시하였다. 분석결과, 과거 단․장기 수익률 반전현상과 국한된 중기수익률 지속현상을 관찰할 수 있었으며, 전체 분석기간 동안 기업규모효과가 상당히 크게 작용할 뿐만 아니라 투자성향효과에 대해서도 부분적으로 유의적인 분석결과를 도출함으로써, 한국주식시장에서 행태변수의 대용치인 투자성향효과변수의 적용가능성을 시사해 주는 분석결과를 얻을 수 있었다. This paper shows the equilibrium asset pricing model based on the disposition effect suggested by Grinblatt & Han(2005) and empirically tests the weekly returns and volumes of Korea stock market. The disposition effect is the tendency of investors to hold on to their losing stocks too long and sell their winners too soon. The empirical implications of the model are verified with Fama & MacBeth(1973) cross-sectional regressions that make use of behavioral variables to proxy for unrealized capital gains. Most of our regression specifications, returns reversal appears over the past short-term and long-term horizons, limited returns continuation occurs over the past intermediate horizons. In all of our regression specifications, the strength of the firm size effect is much more important as a determinant of expected returns. Moreover, we can find a significant explanation power of behavioral variables related to the disposition effect in Korea stock market.
포트폴리오 구성방법과 위험수준에 따른 베타계수의 예측오차 분석
박순식 대구효성가톨릭대학교 1998 연구논문집 Vol.58 No.1
The purpose of this paper is to provide an empirical assessment of the effects of basic issues in forecasting beta : the length of the holding period used in computing returns and the formation of portfolios of securities in order to reduce the random error in predicting beta. I also investigate the forecasting error of beta coefficient from different risk levels. The effectiveness of Vasicek's Bayesian adjustment and Merrill Lynch adjustment for biases in the OLS beta estimates is studied in combination with these elements. 160 manufacturing companies listed in Korean Stock Exchange were sampled for this study. The beta coefficients were computed weekly and monthly returns obtained from KIS-SMAT data system over successive four year estimation intervals from January 1985 through December 1995. Forecasting error MSE indicates that beta estimated from weekly returns is easier to predict than beta estimated from monthly returns. So betas estimated from weekly returns can improve the stability and predictability of beta than that from monthly returns. The Vasicek's Bayesian adjustment to the OLS beta estimates enable substantial reduction in forecasting MSE in all cases, so this adjustment technique can improve the predictability of beta. The method of forming portfolio in order to reduce the random error component of forecasting error appear to have some influence on MSE. Randomly selected portfolio formation method appears to be more effective in reducing forecasting error than the method based on simple ranking of firms according to beta estimates. The stability of beta coefficient is highest in medium risk level comparing to high and low risk levels. The beta adjustment techniques such as Vasicek's Bayesian and Merrill Lynch can improve beta prediction for high risk securities, but not for medium risk securities.
박순식 한국경영교육학회 2014 경영교육연구 Vol.29 No.6
The purpose of this study is to investigate the relationship between growth opportunity and debt ratio of listed companies on the KOSDAQ market, the data is based on the 2,916 firm-year observations during the sample period from 2003 to 2011. For pannel data regression analysis, I used random effect regression model and fixed effect regression model in addition to OLS model. As a result of this analysis, growth opportunity has significantly negative effect on debt ratio in accordance with agency theory and trade off theory of financing. The relationship between growth opportunity and debt ratio is not monotonic but cubic style. When companies’ growth opportunities are low and high, the relationship between growth opportunity and debt ratio is negative. For intermediate level of growth opportunity, the result shows the existence of a positive relationship between growth opportunity and debt ratio. The results of previous studies in this area including this study are not consistent with each other. The relationship between growth opportunity and debt ratio has been an important topic in corporate finance, but an unsolved problem up to now. In addition, it is found from control variables, that firms with high profitability and liquidity have low debt. Firm size and asset tangibility are positively related to debt ratio. 본 연구는 코스닥시장 기업의 성장기회와 부채비율의 관련성을 분석하였다. 실증적 분석대상기간은 2003년 1월부터 2011년 12월까지의 9개년이며 분석기간 자료가 이용 가능한 324개 기업의 2916개 패널자료를 선정하여 최소자승법에 의한 회귀분석과 더불어 확률효과모형과 고정효과모형으로 회귀분석하였다. 연구결과, 첫째, 성장기회와 부채비율의 관련성에 관한 국내 연구에서는 상반된 연구결과를 제시하고 있는데 코스닥시장에서 성장기회와 부채비율이 부(-)의 관련성을 가지는 것으로 확인되었다. 부채사용은 재무곤경비용을 증가시키고 미래 성장기회를 감소시키기 때문에 성장기회가 큰 기업이 부채발행을 줄인다는 절충이론을 지지하는 것으로 보인다. 또한 부채사용에 따른 주주와 채권자의 대리문제로 인하여 과소투자문제와 같은 비효율성을 발생시키기 때문에 부채사용에 따른 비효율성을 회피하기 위하여 코스닥시장에서 성장기회가 높은 기업일수록 부채를 적게 사용하는 것으로 보인다. 둘째, 성장기회의 크기에 따라 분석한 결과 성장기회가 낮거나 높은 경우 성장기회와 부채비율은 부(-)의 관련성을 가지고 중간인 경우 정(+)의 관계를 나타내는 경향이 있다. 셋째, 수익성과 유동성은 부채비율과 부(-)의 관련성을 가지고 기업규모와 자산유형성은 부채비율에 정(+)의 영향을 미치는 것으로 확인되었으며 많은 국내외 선행연구 결과와 부합하였다. 비부채성 세금혜택과 부채비율의 관련성을 판단하기 어려웠다.
박순식,안영규 한국산업경영학회 2005 한국산업경영학회 발표논문집 Vol.2005 No.2
본 연구에서는 행태재무론(behavioral finance) 중 시장참가자들이 합리적이라는 가정에 위배되는 일상적인 오류인 투자성향효과(disposition effect)에 대하여 살펴보고자 한다. 구체적으로, 본 연구에서는 투자성향효과를 보이는 투자자가 시장에 존재할 경우 시간의 흐름에 따라 균형자산의 가격이 어떻게 변하는지에 대하여 살펴본다. 그리고 한국주식시장에서 기대수익률의 횡단면적 특성 중 행태변수(behavioral variables)의 대용치로 투자성향효과변수의 설명력을 검정한다. 이를 위해, Grinblatt & Han(2003)의 이론모형을 소개하고, 1993년부터 2002년까지의 한국증권거래소에 상장된 기업을 대상으로 Fama & MacBeth(1973) 횡단면 회귀분석을 실시한다.
朴荀植 효성여자대학교 산업경영연구소 1998 경영경제 Vol.15 No.-
The NPV of a project, strategy, or acquisition candidate, and what accounts to the same thing, the contribution to the MVA of the company, is by definition equal to the present value of EVA it can be expected to generate in the future. EVA(economic value added) is likely to be the most sensible basis for evaluating and rewarding the periodic performance of empowered line people. When fully implemented, EVA is the centerpiece of an integrated financial management system that encompasses the full range of corporate financial decision making - everything from capital budgeting, acquisition pricing, and the setting of corporate goals to shareholder and management incentive compensation. EVA is the internal measure management can decentralize throughout the company and use as the basis for a completely integrated financial management system. It allows all key management decisions to be clearly modelled, monitored, communicated, and rewarded according to how much value they add to shareholders' investment. Whether reviewing a capital budgeting project, value an acquisition, considering strategic plan alternatives, assessing performance, or determing bonuses, the goal of increasing EVA over time offers a clear financial mission for management and means of improving accountability and incentives. Analysis of Stern Stewart Performance 1,000 indicates that the changes in their EVAs over a five-year period account for nearly 50% of changes in their MVAs recorded over that same time. By comparison, growth in sales explained 10% of MVA changers, growth in EPS just 15% to 20%, and retern on equity only 35%. Thus, EVA is almost 50% better than its closest accounting-based competitor in explaining changers in shareholder wealth, a significant improvement.