• 지능정보연구
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• 제21권3호
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• pp.117-129
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• 2015

인터넷을 활용한 정보 검색이 보편화됨에 따라, 상대적으로 정보가 부족했던 개인 투자자들이 인터넷 검색을 통해서 관심 기업의 정보를 지속적으로 '주의(attention)'하고 이를 통하여 시의 적절하고 유용한 정보를 획득할 수 있게 되었다. 본 연구에서는 능동적 정보검색을 통해 유도된 주의 효과가 투자자에게 정보전달의 역할을 하는지 개별 기업 주식의 변동성과 동조화를 중심으로 규명하고자 한다. 특히 기업의 본질적 가치에 대한 정보획득 및 판단이 쉽지 않은 정보기술 서비스 중심의 IT 기업을 대상으로 최근 10년간의 키워드 검색 데이터를 분석하였다. 분석 결과, 인터넷 검색을 통한 기업정보의 수집 및 확산의 용이성은 투자자가 기업의 가치를 보다 정확히 평가하는데 도움을 주고 결과적으로 시장에서의 탈동조화를 유인함을 알 수 있다. 즉, 투자자의 주의는 시장에 내재된 불완전성에 의해 본질적인 요소와 상관없이 주식들의 수익률이 동시에 같은 방향으로 움직이는 동조화 현상을 약화시키는데 영향을 미쳤다. 이러한 결과는 기업 규모가 클수록, 연도별 분석에서는 최근에 가까울수록 더 크게 영향을 미치는 것으로 나타났다. 잘 알려진 기업일수록 인터넷 검색으로 획득할 수 있는 정보의 양이 많고, 또한 시간이 지날수록 정보가 쌓이면서 이러한 현상은 더 심화될 것으로 예측할 수 있다. 반면, 인터넷 검색량과 기업의 변동성은 규모가 큰 기업의 경우에만 유의한 양의 관계를 보여주었다. 본 연구는 투자자의 주의효과를 인터넷 검색량을 이용하여 실증 분석하였다는데 의의가 있으며, 연구 결과는 기업 주식의 변동성 및 동조화 현상에 대한 이해를 높이고 투자자의 투자결정에 도움이 될 것으로 기대된다.

• 한국유통학회지:유통연구
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• 제9권1호
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• pp.47-65
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• 2004

본 사례 연구는 백화점과 할인점 대표 기업들의 수익성을 1999년, 2000년, 2001년 3개년도에 걸친 공시된 회계 정보와 ROIC (return on invested capital) 지표를 이용하여 분석하고 있다. 특히 미국의 대표 백화점과 대표 할인점 업체들과의 업태별 벤치마킹을 시도하였다. 연구 결과 기업 가치를 극대화하기 위해서는 미국의 Wal-Mart와 J.C. Penny의 경우와 같이 자산 회전율과 영업 이익률의 두 가지 지표를 모두 향상시키는 일이다. 그러나 현실적으로 백화점 업체들은 영업 이익률에 보다 초점을 맞추고 있음을 실증적으로 알 수 있었고 우리나라 외국계 할인점 업체들은 진출 초기의 특성으로 저조한 수익성 지표를 보여주고 있었다.

• 농촌계획
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• 제4권1호
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• pp.32-39
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• 1998

In Korea, rural village improvement has mainly been led by goverment investment. This approach, however, has its own limit since there are so many village to be improved while the budgetary sources are restricted, As an alternative, inducement of private investments to these area is considered in order to promote rural village improvement. The possibility of inducing private investments to the rural village improvement depends on the location of the village. The possibility may be highest in the sub-urban area since expected benefits from land development is usually high. One desirable approach to induce private investment to these area is the cooperative development system. Residents, private investors and governments plays its own role, independently and cooperatively, But benifits from the investment to improve rural village in general plain area are so low that it is difficult to induce the private investments to these area. In that case, indirect development system will be a proper strategy which maintaining government-led development method as usual, expanding the participation of private developers such as the construction companies. In general, rate of returns from investment to the rural sectors is lower than that to the other sectors, therefore financial support such as the long- term, low loan rate and a partial value-added tax exemption should be given to the investors to the rural village improvement projects.

• 가정과삶의질연구
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• 제29권2호
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• pp.51-62
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• 2011

Financial consumers can invest their financial assets directly or indirectly. This investment type have effect on their financial well-being and may be influenced by their financial characteristics and investment attitude. The purposes of the study were to classify the consumers by direct and indirect investment behavior of their financial assets and to investigate their socio-economic characteristics and investment attitudes to give implications for financial counseling and education. The data came from the 2009 Fund Investors Survey which was conducted by Korea Investors Protection Foundation. Total 2,530 consumers were analyzed using frequency, CROSSTAB, ANOVA and Duncan's multiple range test. In general, consumer tended to be rational in choosing the investment type. Noninvestors consisted of 38.5% of the sample. The economic level was the lowest for the noninvestors. The consumers who invest both indirectly and directly consisted of 21.0% and their economic level was the highest. Their investment tendency was between direct and indirect investors'. The proportion of direct investors ws 12.1% and that of indirect investors was 28.4%. Although the economic levels of indirect investors and direct investors were not statistically different, there were differences in their demographics and investment attitudes. The proportions of those aged 30-39, female and nonmarried were greater for indirect investors. They had the tendency to invest safely and diversely for a long term with reserve money. On the other hand, direct investors tended to be male, married and aged 40-49. They tended to invest intensively for a shorter term and seek returns even with borrowing money.

• 기술혁신학회지
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• 제7권3호
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• pp.533-554
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• 2004

Real option valuation considers the managerial flexibility to make ongoing decisions regarding implementation of investment projects and deployment of real assets. The appeal of the framework is natural given the high degree of uncertainty that firms face in their technology investment decisions. This paper suggests an algorithm for estimating volatility of logarithmic cash flow returns of real asset based on Monte Carlo simulation. This research uses a binomial model to obtain point estimate of real option value with embedded expansion option case and provides also an array of numerical results to show the interval estimation of option value using Monte Carlo simulation.

• Asia pacific journal of information systems
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• 제30권1호
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• pp.31-52
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• 2020

Stock markets are popular investment avenues to people who plan to receive premium returns compared to other financial instruments, but they are highly volatile and risky due to the complex financial dynamics and poor understanding of the market forces involved in the price determination. A system that can forecast, predict the stock prices and automatically create a portfolio of top performing stocks is of great value to individual investors who do not have sufficient knowledge to understand the complex dynamics involved in evaluating and predicting stock prices. In this paper the authors propose a Stock prediction, Portfolio Generation and Selection model based on Machine learning algorithms, Artificial neural networks (ANNs) are used for stock price prediction, Mathematical and Statistical techniques are used for Portfolio generation and Un-Supervised Machine learning based on K-Means Clustering algorithms are used for Portfolio Evaluation and Selection which take in to account the Portfolio Return and Risk in to consideration. The model presented here is limited to predicting stock prices on a long term basis as the inputs to the model are based on fundamental attributes and intrinsic value of the stock. The results of this study are quite encouraging as the stock prediction models are able predict stock prices at least a financial quarter in advance with an accuracy of around 90 percent and the portfolio selection classifiers are giving returns in excess of average market returns.

• 산업경영시스템학회지
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• 제41권4호
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• pp.81-90
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• 2018

Modern investment theory has empirically proved that stock returns can be explained by several factors such as market risk, firm size, and book-to-market ratio. Other unknown factors affecting stock returns are also believed to still exist yet to be found. We believe that one of such factors is the operational efficiency of firms in transforming inputs to outputs, considering the fact that operations is a fundamental and primary function of any type of businesses. To support this belief, this study intends to empirically study the relationship between firm efficiency and stock price performance. Firm efficiency is measured using data envelopment analysis (DEA) with inputs and outputs obtained from financial statements. We employ cross-efficiency evaluation to enhance the discrimination power of DEA with a secondary objective function of aggressive formulation. Using the CAPM-based performance regression model, we test the performance of equally weighted portfolios of different sizes selected based upon DEA cross-efficiency scores along with a buy & hold trading strategy. For the empirical test, we collect financial data of domestic firms listed in KOSPI over the period of 2000~2016 from well-known financial databases. As a result, we find that the porfolios with highly efficient firms included outperform the benchmark market portfolio after controlling for the market risk, which indicates that firm efficiency plays a important role in explaining stock returns.

• The Journal of Asian Finance, Economics and Business
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• 제8권9호
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• pp.45-52
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• 2021

Stock price fluctuations affect investor returns, particularly, in this pandemic situation that has triggered stock market shocks. As a result of this situation, investors prefer to move their money into a safer portfolio. Therefore, in this study, we approach an efficient portfolio model using smart beta and combining others to obtain a fast method to predict investment stock returns. Smart beta is a method to selects stocks that will enter a portfolio quickly and concisely by considering the level of return and risk that has been set according to the ability of investors. A smart beta portfolio is efficient because it tracks with an underlying index and is optimized using the same techniques that active portfolio managers utilize. Using the logistic regression method and the data of 100 low volatility stocks listed on the Indonesia stock exchange from 2009-2019, an efficient portfolio model was made. It can be concluded that an efficient portfolio is formed by a group of stocks that are aggressive and actively traded to produce optimal returns at a certain level of risk in the long-term period. And also, the portfolio selection model generated using the smart beta, beta, alpha, and stock variants is a simple and fast model in predicting the rate of return with an adjusted risk level so that investors can anticipate risks and minimize errors in stock selection.

• 한국IT서비스학회지
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• 제15권2호
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• pp.51-63
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• 2016

In this study, we conducted a comparative analysis of R&D investment efficiency and operational efficiency of IT firms using Data Envelopment Analysis (DEA). We categorized thirteen sample firms into two groups-IT manufacturing and IT service-after an extensive literature review on IT industry classification. We adopted an output-oriented two-stage DEA model suggested by Banker et al. (1984) with total asset and R&D investment as input variables. Then, we constructed investment efficiency and operational efficiency by using Return on Equity (ROE) and Return on Asset (ROA) as intervening variables and operating income and Earnings Per Share (EPS) as output variables. The outcome of the analysis is summarized as follows. First of all, IT manufacturing firms were more efficient (57% on average) than IT service firms. To be specific, IT service firms showed decreasing returns to scale (DRS) with diseconomy of scale. In contrast, IT service firms showed higher operational efficiency (81.5% on average) than IT manufacturing firms. Also, we conducted a Mann-Whitney U test to compare the output of IT service firms and IT manufacturing firms. Lastly, we found a negative correlation ($R^2$ = -.754) between R&D investment efficiency and operational efficiency which infers the trade-off between two constructs

• 산업경영시스템학회지
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• 제40권1호
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• pp.50-56
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• 2017

The IRR (internal rate of return) is often used by investors for the evaluation of engineering projects. Unfortunately, it is widely known that it has serial flaws. Also, External rate of returns (ERRs) such as ARR (Average Rate of Return) or MIRR (MIRR, Modified Internal Rate of Return) do not differentiate between the real investment and the expenditure. The PRR (Productive rate of return) is faithful to the conception of the return on investment. The PRR uses the effective investment instead of the initial investment. In this paper, we examined two cases of the engineering project. the one is a traditional engineering project with financing activity, another is the project with R&D. Although the IRR has only one value, it overestimates or underestimate profitabilities of Engineering Projects. The ARR and the MARR assume that a returned cash reinvest other projects or assets instead of the project currently executing. Thus they are only one value of a project's profitability, unlike the IRR. But the ARR does not classify into the effective investment and non-investment expenditure. It only accepts an initial expenditure as for an investment. The MIRR also fails to classify into the investment and the expenditure. It has an error of making a loss down as the investment. The IRR works as efficiently as a NPW (Net Present Worth). It clearly expresses a rate of return in respect of an investment in an engineering project with a loan. And it shows its ability in an engineering project with a R&D investment.