• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.4
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• pp.63-71
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• 2009

The traditional portfolio optimization problem is to find an investment plan for securities with reasonable trade-off between the rate of return and the risk. The seminal work in this field is the mean-variance model by Markowitz, which is a quadratic programming problem. Since it is now computationally practical to solve the model, a number of alternative models to overcome this complexity have been proposed. In this paper, among the alternatives, we focus on the Mean Absolute Deviation (MAD) model. More specifically, we developed an algorithm to obtain an optimal portfolio from the MAD model. We showed mathematically that the algorithm can solve the problem to optimality. We tested it using the real data from the Korean Stock Market. The results coincide with our expectation that the method can solve a variety of problems in a reasonable computational time.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.218-219
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• 2007

Silicon feed stock shortage have acted as major restraints for growth of photovoltaic industry. Concentrating photovoltaic (CPV) system will reduce the use of silicon PV materials. This paper presents the application possibility of mono-crystalline silicon solar cell, which has increased in market share, for PV concentrator. We measured the power of solar cell using sun simulator and I-V curve tracer and compared the results. The comparison of results showed that the concentrated solar cell generated the power more approximately 7 times than without concentration in spite of non-heat sink. If CPV technology included heat sink combines already developed PV tracking system, it will have a merit economically.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.857-860
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• 2014

본 연구에서는 경제 뉴스로부터 서술어 중심의 감성 사전을 구축하고, 하루 동안에 배포된 뉴스를 이용해 전일 종가 대비 당일 종가의 등락을 예측하는 모델을 제안한다. 기존의 주식 도메인 관련 감성 사전을 구축하는 방식은 주가 등락에 관련된 명사를 중심으로 사전을 구축하는 방식이나 대부분의 명사는 극성 값이 중립인 경우가 많아 극성 값을 추정하기 힘들다는 문제점이 있다. 본 연구에서는 극성 값이 잘 표현되는 서술어 중심의 감성사전을 구축하고 극성 값을 자동 추출하여 주가의 등락을 예측한다. 실험 결과 기존 감성 사전을 통한 주가 예측 방법에 비하여 본 연구에서 제안하는 서술어 중심의 감성 사전을 통한 주가 예측 정확도가 높게 나타났다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.668-671
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• 2015

이 논문은 대한민국의 주식시장에 상장한 기업의 지배구조 분석을 위해 대주주가 어떠한 형태로 주식을 보유하고 있는지에 대한 네트워크 분석이다. 분석대상은 주식시장에 상장한 기업과 그 기업의 주식의 대주주 데이터를 모두 수집하였다. 이를 기업과 대주주 행위자 간에 주식을 보유하고 있는 네트워크를 분석하여 그 보유형태의 의미를 파악하였다. 분석결과 네트워크 형태는 크게 '전체분석, 산업분석, 군집분석, 상장기업분석, 대주주분석, 계열사 분석' 여섯 가지이다. 네트워크 분석결과 주식시장은 전형적인 척도 없는 네트워크 형태를 나타내었으며 반면 그룹간의 계열사 네트워크는 전형적인 계층구조로써 좁은 세상 네트워크의 사례를 나타내었다. 따라서 투자 성향이 갖거나 대주주 간의 이해관계가 있거나 투자상품들이 포트폴리오로 조합원 경우 대주주 간의 네트워크가 밀집된 것을 확인할 수 있었다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.33
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• pp.173-183
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• 1995

The Objective of the study was to determine the usefulness of human resource accounting(HRA) information in assisting financial analysis in their investment decisions. The objective achieved by an investigation through which the reporting of HRA, combined with demographic factors that are independent or interactive, affects the decisions of financial analysts regarding the estimation of the market price of a hypothetical company's common stocks. Two kinds of research were conducted to increase the reliability of the study at the same time. Two or three sets of financial statement were prepared. Each consists of balance sheet and income statement. The actual financial statement was modified to exclude personal bias and opinion.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.1396-1399
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• 2012

본 논문은 주식 투자 포트폴리오를 구성하기 위해 클러스터링 기법을 이용하는 방법을 제안한다. 클러스터링 기법은 패턴 공간 상의 특징 벡터로 표현된 패턴 데이터를 몇 개의 부분집합으로 나누는 작업을 의미한다. 본 연구에서는 주식시장 네트워크에 클러스터링 기법을 적용하여 안정성과 수익률이 높은 포트폴리오를 구성하는 방법을 제안한다. 그리고 추천 클러스터의 투자 적합여부를 데이터를 통해 확인한다. 2007년 주식 데이터를 대상으로 실험한 결과, 추천 클러스터의 수익률이 전체 수익률을 상회함을 확인할 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.394-395
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• 2023

"이성적이며 이상적인 합리적 인간"을 가정하는 기존 경제학의 이론이 항상 실제 상황과 일치하지는 않는 것으로 알려져 있다. 이의 대안으로 나온 행동경제학은, 인간의 경제적 의사결정에 심리, 인지, 감정, 사회문화적 배경 등이 영향을 미친다고 본다. 본 연구에서는 행동경제학에 의거하여, 개인의 감정과 경험이 경제적 의사결정에 영향을 미치는지 여부를 빅데이터 모델을 활용하여 분석하였다. SNS 여론으로는 Reddit, 주가지수로는 S&P 500 을 선정하였다. 수집한 텍스트 데이터를 전처리와 감정분석을 통해 독립변수 값으로 사용했고, 주가지수 등락의 방향성을 종속변수로 사용하여 로지스틱 모형을 구성했다. 모델을 활용하여 분석한 결과 Public sentiment 와 Market sentiment 간 양의 상관관계를 확인할 수 있었다. 또한, lag 를 설정하는 모델이 정확도가 더욱 높음을 확인해, 기존 경제학의 EMH 와 대립되는 바를 확인할 수 있었다. 하지만 최적의 lag 산정을 위해, 더 광범위한 데이터를 바탕으로 한 후속연구가 필요하다.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• Journal of Korea Trade
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• v.23 no.6
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• pp.145-158
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• 2019

Purpose - Alliance members have constantly revised market strategies over time by withdrawing membership from a current alliance, joining another alliance, or constructing a new alliance. From the perspective of the signaling effect, the purpose of this study is to analyze the impacts of partner volatility (new member, old member, and new group) on firm value. Design/methodology - To analyze the impact of partner volatility on firm value, companies in strategic alliances are classified into the three groups of new partner, existing partner, and new alliance, and the effects on company value are verified through an event study and the signaling effect analysis. Findings - This study proved that new partners and newly formed strategic alliances have higher expectation effects than old partner company groups, and have a more positive effect on the relevant firms' stock prices. In addition, the result of the study showed the same valid results as the alliance levels, and showed that investors' expectations were higher with new partners and new alliances than with old partners. Research Implications - A new perspective on the signaling effects of strategic alliances among shipping lines was presented in this study by grouping alliance types including new member, old member, and new group. The results provide useful insights for selecting partners and firm values of alliance announcement times. Originality/value - This study analyzed partner volatility on relevant companies' stock prices from the perspective of investors from the global shipping conference reorganization in 2017. Strategic alliances were classified into the three categories of new partner, old partner, and new alliance, and the effects on firm value were verified.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.5
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• pp.2096-2109
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• 2012

This study investigates empirical issues that have received little attention in the previous research in the Korean capital market. It is to find any financial determinants on the capital structure for the firms listed in the KOSDAQ(Korea Securities Dealers Automated Quotation). Another test is performed to find any possible discriminating factors by utilizing a robust methodology, which may distinguish between the firms belonging the 'prime section' and the 'venture section' in terms of their financial aspects. Moreover, the null hypothesis that the changing trend or movement of a firm's capital structure with respect to its industry mean (or median) may be random, is also tested. For the book-value based debt ratios, size(INSIZE), growth(GROWTH), Market to book value of equity(MVBV), volatility(VOLATILITY), market value of equity (MVE) and section dummy (SECTION) showed their statistically significant effects on the book-value based leverage ratios, respectively, while size(INSIZE), growth(GROWTH), market value of equity(MVE), beta(BETA) and section dummy (SECTION) showed their statistically significant effects on the market-value based leverage ratios. This study also found an interesting result that a firm belonging to each corresponding industry has a tendency for reversion toward its mean and median leverage ratios over the five-year tested period.