• The Korean Journal of Financial Management
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• v.12 no.1
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• pp.33-56
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• 1995

본(本) 연구(硏究)는 자본구조이론과 전통적 연구에서 제시된 변수를 통합하고 횡단면(橫斷面) 요인(要因)과 시계열(時系列) 요인(要因)을 결합하여 우리나라의 자본구조결정 요인을 식별할 수 있는 이론적(理論的) 모형(模型)을 제시하여, 또한 제시된 모형을 한국증권시장(韓國證券市場)의 자료(資料)를 이용하여 실증적(實證的)으로 분석(分析)하였다. 그리고 실증적 분석에는 횡단면(橫斷面) 자료(資料)와 시계열(時系列) 자료(資料)를 결합하는 패널자료추정법(資料推定法)을 사용하였다. 본(本) 연구(硏究)에서 제시된 자본구조이론(資本構造理論)과 관련된 결정 요인으로는 기업(企業)의 성장기(成長機)을, 내부주주(內部株主)의 지분율(持分率) 그리고 내부주주수(內部株主數)의 비율 등을, 전통적 횡단면 요인으로는 경영위험(經營危險), 자산구성(資産構成), 수익성(收益性), 기업규모(企業規模) 등을, 그리고 전통적 시계열 요인으로는 법인세율(法人稅率)과 물가수준(物價水準) 등을 제시하였다. 본(本) 연구(硏究)에서 다루는 실증분석기간은 1981년 1월부터 1990년 12월까지의 10년간이었으며, 추출된 표본기업(標本企業)의 수(數)는 104개사이다. 실증적 분석결과, 본(本) 연구(硏究)에서 제시된 설명변수들이 자본구조(資本構造)의 변동(變動)을 49.91%정도 설명하고 있으며 설명변수 중 기업(企業)의 성장기회(成長機會), 내부주주(內部株主)의 지분을, 경영위험(經營危險), 수익성(收益性), 기업규모(企業規模), 물가수준(物價水準) 등은 자본구조의 결정 요인으로 통계적인 의미를 갖는 변수로 밝혀졌으며 회귀계수(回歸係數)의 부호도 기대하였던 바와 일치하고 있다. 질산으로 처리된 것이 컸고 0.75 M과 1.0 M의 질산을 사용했을 때는 작음이 확인되었다. 이상의 실험결과들로부터 친수성인 $NH_4Y$형 제올라이트를 소수성의 것으로 변환시키기 위한 수증기의 온도는 $500^{\circ}C$와 $600^{\circ}C$가, 그리고 질산의 농도는 0.5 M이 적합한 것으로 결론지을 수 있고, 이와 같은 결론은 BET비표면적과 TPV값과 같은 경향을 보인 벤젠과 톨루엔의 흡착용량측정결과로 입증되었다. 탈알루미늄된 제올라이트들의 수분에 대한 Si/Al비와 흡착용량은 각각 높은 농도의 질산으로 처리된 것일수록 증가하고 감소하여 소수성이 증가함을 나타내었다.(不適合性)이 나타났다. 본 연구는 기존의 기대수익률(期待收益率) 위주의 요일효과(曜日效果) 분석에서 주식수익률(株式收益率)의 분산(分散) 즉, 변동성(變動性)에 촛점을 두어 분석하였으며, 이는 투자자의 정확한 위험측정(危險測定)수단의 제공이라는 면에서 의의(意義)가 있을 것으로 생각된다.據金) 운용(運用)에 관한 정책수립시(政策樹立時) 금융선진국(金融先進國)의 증거금(證據金) 정책운용(政策運用)을 통한 시장관리(市場管理) 경험(經驗)을 어느 정도 참고할 수 있음을 시사한다고 할 것이다. 한다. 실증분석결과는 본문의 <표 1>에 제시되어 있으며 그 내용을 간략하게 요약하면 다음과 같다. (A) 실증분석모형 : 본 연구에서는 다기간 자산가격결정모형중에서 대표적인 Lucas (1978)모형을 직접 사용한다. $$1={\beta}\;E_t[\frac{U'(C_{t+1})\;P_t\;s_{t+1}}{U'(C_t)

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.271-280
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• 1999

인공신경망을 시계열예측에 적용하는 경우에 고려되어야 할 문제중, 특히 모형에 적합한 입력변수의 생성이 중요시되고 있는데, 이러한 분야는 인공신경망의 모형생성과정에서 입력변수에 대한 전처리기법으로써 다양하게 제시되어 왔다. 가장 최근의 입력변수 전처리기법으로써 제시되고 있는 신호처리기법은 전통적 주기분할처리방법인 푸리에변환기법(Fourier transforms)을 비롯하여 이를 확장시킨 개념인 웨이블릿변환기법(wavelet transforms) 등으로 대별될 수 있다. 이는 기본적으로 시계열이 다수의 주기(cycle)들로 구성된 상이한 시계열들의 집합이라는 가정에서 출발하고 있다. 전통적으로 이러한 시계열은 전기 또는 전자공학에서 주파수영역분할, 즉 고주파 및 저주파수를 분할하기 위한 기법에 적용되어 왔다. 그러나, 최근에는 이러한 연구가 다양한 분야에 활발하게 응용되기 시작하였으며, 그 중의 대표적인 예가 바로 경영분야의 재무시계열에 대한 분석이다. 전통적으로 재무시계열은 장, 단기의사결정을 가진 시장참여자들간의 거래특성이 시계열에 각기 달리 가격으로 반영되기 때문에 이러한 상이한 집단들의 고요한 거래움직임으로 말미암아 예를 들어, 주식시장이 프랙탈구조를 가지고 있다고 보기도 한다. 이처럼 재무시계열은 다양한 사회현상의 집합체라고 볼 수 있으며, 그만큼 예측모형을 구축하는데 어려움이 따른다. 본 연구는 이러한 시계열의 주기적 특성에 기반을 둔 신호처리분석으로서 기존의 시계열로부터 노이즈를 줄여 주면서 보다 의미있는 정보로 변환시켜줄 수 있는 웨이블릿분석 방법론을 새로운 필터링기법으로 사용하여 현재 많은 연구가 진행되고 있는 인공신경망의 모형결합을 통해 기존연구과는 다른 새로운 통합예측방법론을 제시하고자 한다. 본 연구에서는 제시하는 통합방법론은 크게 2단계 과정을 거쳐 예측모형으로 완성이 된다. 즉, 1차 모형단계에서 원시 재무시계열은 먼저 웨이브릿분석을 통해서 노이즈가 필터링 되는 동시에, 과거 재무시계열의 프랙탈 구조, 즉 비선형적인 움직임을 보다 잘 반영시켜 주는 다차원 주기요소를 가지는 시계열로 분해, 생성되며, 이렇게 주기에 따라 장단기로 분할된 시계열들은 2차 모형단계에서 신경망의 새로운 입력변수로서 사용되어 최종적인 인공 신경망모델을 구축하는 데 반영된다. 기존의 주기분할방법론은 모형개발자입장에서 여러 가지 통계기준치중에서 최적의 기준치를 합리적으로 선택해야 하는 문제가 추가적으로 발생하며, 본 연구에서는 이상의 제반 문제들을 개선시키기 위해 통합방법론으로서 기존의 인공신경망모형을 구조적으로 확장시켰다. 이 모형에서 기존의 입력층 이전단계에 새로운 층이 정의된다. 이렇게 해서 생성된 새로운 통합모형은 기존모형에서 생성되는 기본적인 학습파라미터와 더불어, 본 연구에서 새롭게 제시된 주기분할층의 파라미터들이 모형의 학습성과를 높이기 위해 함께 고려된다. 한편, 이러한 학습과정에서 추가적으로 고려해야 할 파라미터 갯수가 증가함에 따라서, 본 모델의 학습성과가 local minimum에 빠지는 문제점이 발생될 수 있다. 즉, 웨이블릿분석과 인공신경망모형을 모두 전역적으로 최적화시켜야 하는 문제가 발생한다. 본 연구에서는 이 문제를 해결하기 위해서, 최근 local minimum의 가능성을 최소화하여 전역적인 학습성과를 높여 주는 인공지능기법으로서 유전자알고리즘기법을 본 연구이 통합모델에 반영하였다. 이에 대한 실증사례 분석결과는 일일 환율예측문제를 적용하였을 경우, 기존의 방법론보다 더 나운 예측성과를 타나내었다.

• Journal of Korea Port Economic Association
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• v.39 no.4
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• pp.269-283
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• 2023

Based on daily data from January 4, 2016 to September 27, 2022, the impact of extreme movements of BDI on shipping companies' network connectivity was analyzed using CoVaR network connectivity. The main results and policy implications are as follows. First, according to the copula model results, the Student-t copula was selected as the most suitable model for COSCO, HMM, HRAG, MAERSK, and WAN. EVER was selected as a time-varying Gumbel copula, and YANG was selected as a time-varying rotated-Gumbel copula. Second, as a result of analysis using the TVP-VAR model, the linkage between shipping companies tended to increase when the BDI turned into an extreme risk state. In the comparison of net connectivity, the roles of COSCO and EVER changed. In addition, in the analysis of net pairwise connectivity, it was found that the change in the extreme risk state of BDI also affected the connectivity of shipping companies. In particular, EVER, WAN, and COSCO showed large changes. Taken together, the extreme fluctuations in BDI changed the role of Asian shipping companies, intensifying competition among shipping companies and strengthening risk delivery. It was confirmed that BDI has a great influence on the network connectivity of shipping companies and has an important influence on the stability of the stock market network. Therefore, the results of this study should consider not only the connectivity of shipping companies according to market conditions, but also the connectivity in extreme situations.

• Environmental and Resource Economics Review
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• v.31 no.2
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• pp.233-260
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• 2022

In this paper, we analyzed how oil price fluctuations affect stock price by industry using the non-parametric quantile causality test method. We used weekly data of WTI spot price, KOSPI index, and 22 industrial stock indices from January 1998 to April 2021. The empirical results show that the effect of changes in oil prices on the KOSPI index was not significant, which can be attributed to mixed responses of diverse stock prices in several industries included in the KOSPI index. Looking at the stock price response to oil price by industry, the 9 of 18 industries, including Cloth, Paper, and Medicine show a causality with oil prices, while 9 industries, including Food, Chemical, and Non-metal do not show a causal relationship. Four industries including Medicine and Communication (0.45~0.85), Cloth (0.15~0.45), and Construction (0.5~0.6) show causality with oil prices more than three quantiles consecutively. However, the quantiles in which causality appeared were different for each industry. From the result, we find that the effects of oil price on the stock prices differ significantly by industry, and even in one industry, and the response to oil price changes is different depending on the market situation. This suggests that the government's macroeconomic policies, such as industrial and employment policies, should be performed in consideration of the differences in the effects of oil price fluctuations by industry and market conditions. It also shows that investors have to rebalance their portfolio by industry when oil prices fluctuate.

• The Korean Journal of Financial Management
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• v.10 no.1
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• pp.249-280
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• 1993

Recent financial theory views insurance policies as financial instruments that are traded in markets and whose prices reflect the forces of supply and demand. This article analyzes individual's insurance purchasing behavior along with capital market investment activities, which will provide a more realistic look at the tradeoff between insurance and investment in the individual's budget constraint. It is shown that the financial economic concept of insurance cost should reflect the opportunity cost of insurance premium. The author demonstrates the importance of riskless and risky financial assets in reaching an equilibrium insurance premium. In addition, the paper also investigates how the investment income could affect the four established theorems on traditional insurance literature. At the present time in Korea, the price deregulation is being debated as the most important current issue in insurance industry. In view of the results of this paper, insurance companies should recognize investment income in pricing their coverage if insurance prices are deregulated. Otherwise. price competition may force insurance companies to restrict coverage or to leave the market.

• The Korean Journal of Financial Management
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• v.6 no.1
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• pp.31-62
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• 1989

By using deterministic dynamic models, we observe the behavior of the foreign exchange rate of a small open economy with rational expectation formation and different restrictions on the international economic integrations. First, an economy connected to the world by purchasing power parity and uncovered interest parity is studied in the next section. In both sections, financial assets available in the economy are domestic money and bonds. Stocks are added as a financial instrument in the next section, and real capital accumulation is also taken into account. Furthermore, the economy concerned there is fairly autonomous, and not directly governed by either purchasing power parity or uncovered interest parity. The expectation formation used throughout the whole paper is complete perfect foresight, which is the certainty version of rational expectation and free from any forecast errors. It is found that upon monetary expansion the short run depreciation of the foreign exchange rate is a fairly robust result regardless of the degree of the international economic integration, while it is not true for fiscal expansion. The expectation on the long run state significantly affects the short run response of the exchange rate. All of our models postulate that the current account should be balanced eventually. As the result, the short run behavior of the exchange rate is affected by the expectation on the long run balance and may well be a blend of the traditional flow view and modem asset view. The initial overshooting of the exchange rate is easily observed even in the fairly autonomous economy Furthermore, the initial overshooting is not reduced over time, but augmented for some time before it is eventually eliminated. As long as we maintain rational expectaion, introducing time delay in the adjustment of the foreign goods price to the foreign exchange rate does not make much difference.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.25-58
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• 2021

This study investigated the corporate growth with more emphasis on longitudinal characteristics, not the results of companies with relatively more emphasis on cross-sectional, in the 21st-century entrepreneurial context. As of the end of 2019, sampled 479 global unicorn companies, and 333 high-growth companies with revenue of more than $100 million among 5,000 private companies in the U.S. with a compound annual growth rate (CAGR) exceeding 15% for the past three years. They were examined with 3 perspectives in terms of corporate growth that 1) the growth of enterprise value, 2) the pace of growth, and 3) the effectiveness of growth. As a result of our study, the corporate growth of the perspective of creating enterprise value had a relatively higher relationship with the characteristics of industries and markets. The pace of growth was more fully explained by the characteristics of the industry and the market environment and the choice of strategies that make up a valid combination. In addition, growth in terms of the effectiveness of corporate performance was influenced by the choice of strategy, the characteristics of the industry and market environment, and its business age, the proxy variable of resource accumulation, comprehensively. This study through a sample based on companies with an enterprise value of more than $1 billion and annual revenue of more than $100 million can be a valid reference in terms of creating milestones and roadmaps for scale-up of early-stage startups, particularly in terms of practitioners' point of view. It also provides a critical reference for overcoming the limitations of mainstream theories of the 20th century and developing the theory of corporate growth that fits the 21st-century entrepreneurial context.

• Journal of Service Research and Studies
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• v.5 no.1
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• pp.91-103
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• 2015

We examine the information transmission between the KT Spot and the KT Futures Index, the SK Telecom Spot and the SK Telecom Futures Index, based on the returns data offered by the Korea Exchange. The data includes daily return data from 1 January 2012 to 31 December 2014. Utilizing a dynamic analytical tool-the VAR model, Granger Causality test, Impulse Response Function and Variance Decomposition have been implemented. The results of the analysis are as follows. Firstly, results of Granger Causality test suggests the existence of mutual causality the KT Futures Index and the SK Telecom Futures Index precede and have explanatory power the KT Spot and the SK Telecom Spot However the results also identified a greater causality and explanatory power of the KT Spot and the SK Telecom Spot over the KT Futures Index and the SK Telecom Futures Index. Secondly, the results of impulse response function suggest that the KT Futures Index show immediate response to the KT Spot and are influenced by till time 4. From time 2, the impact gradually disappears. Also the SKT Futures Index show immediate response to the SKT Spot and are influenced by till time 4. From time 2, the impact gradually disappears. Lastly, the variance decomposition analysis shows that the changes of return of the KT Spot and SKT Spot are dependent on those of the KT Futures Index and the SK Telecom Futures Index. This implies that returns on the KT Spot and SKT Spot have a significant influence over returns on the KT Futures Index and the SK Telecom Futures Index. It contributes to the understanding of market price formation function through analysis of detached the KT Spot and the KT Futures Index, the SK Telecom Spot and the SK Telecom Futures Index.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.4 no.4
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• pp.45-70
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• 2009

This article investigates which types of the strategies announced by the listed firms contribute to enhancing the long-term performance of the companies. Since 2002, Korean Exchange adopted the "faire disclosure policy" which mandates that all publicly traded companies must disclose material information to all investors at the same time. Thanks to the policy, Korean investors can, now, easily access the board's decision on management strategies on the same day the decision is made. If the companies trustfully carry out their announced strategies, we can decide which types of strategies actually enhance or deteriorate the long-term performance, simply by comparing the announced strategies and the firm's performance. The sample companies are confined to 60 firms that became listed in the KOSDAQ market through back-door listing from 2003 to 2005. Using only the newly listed companies, we can avoid the interference on the long-term performance of the strategies pursued before the event date. This often holds true, for many companies radically modify their strategies after the listing. Furthermore, the back-door listing companies serve our purpose better than IPO companies do, because the former tend to have a variety of announcement within a given period of time beginning the listing date. Using these sample companies, this article analyzes the effect on one year buy-and-hold returns and abnormal buy-and-hold returns after the listing of the various types of strategies announced during the same period of time. The results show that those evidences of restructuring such as 'reduction of capital' and 'resignation of incumbent board members', actually contribute to the increase in adjusted long-term stock returns. Those strategies which can be view as evidence of new investment such as 'increase in tangible assets', 'acquisition of other companies', do also helps the stockholders better off. On the contrary, 'increase in bank loans', 'changes of CEO' and 'merger' deteriorate the equity value. The last findings let us to presume that the back-door listing companies appear to use the bank loans for value-reducing activities; the change in CEO is not a sign of restructuring, but rather a sign of failure of the restructuring; another merger carried out after back-door listing itself is also value-reducing activity. This article's findings on reduction of capital, merger and bank loans oppose the results of the former empirical studies which analyze only the short-term effect on stock price. Therefore, more long-term performance studies on public disclosures are in order.