• Journal of Intelligence and Information Systems
• /
• v.21 no.3
• /
• pp.117-129
• /
• 2015

As the Internet becomes ubiquitous, a large volume of information is posted on the Internet with exponential growth every day. Accordingly, it is not unusual that investors in stock markets gather and compile firm-specific or market-wide information through online searches. Importantly, it becomes easier for investors to acquire value-relevant information for their investment decision with the help of powerful search tools on the Internet. Our study examines whether or not the Internet helps investors assess a firm's value better by using firm-level data over long periods spanning from January 2004 to December 2013. To this end, we construct weekly-based search volume for information technology (IT) services firms on the Internet. We limit our focus to IT firms since they are often equipped with intangible assets and relatively less recognized to the public which makes them hard-to measure. To obtain the information on those firms, investors are more likely to consult the Internet and use the information to appreciate the firms more accurately and eventually improve their investment decisions. Prior studies have shown that changes in search volumes can reflect the various aspects of the complex human behaviors and forecast near-term values of economic indicators, including automobile sales, unemployment claims, and etc. Moreover, search volume of firm names or stock ticker symbols has been used as a direct proxy of individual investors' attention in financial markets since, different from indirect measures such as turnover and extreme returns, they can reveal and quantify the interest of investors in an objective way. Following this line of research, this study aims to gauge whether the information retrieved from the Internet is value relevant in assessing a firm. We also use search volume for analysis but, distinguished from prior studies, explore its impact on return comovements with market returns. Given that a firm's returns tend to comove with market returns excessively when investors are less informed about the firm, we empirically test the value of information by examining the association between Internet searches and the extent to which a firm's returns comove. Our results show that Internet searches are negatively associated with return comovements as expected. When sample is split by the size of firms, the impact of Internet searches on return comovements is shown to be greater for large firms than small ones. Interestingly, we find a greater impact of Internet searches on return comovements for years from 2009 to 2013 than earlier years possibly due to more aggressive and informative exploit of Internet searches in obtaining financial information. We also complement our analyses by examining the association between return volatility and Internet search volumes. If Internet searches capture investors' attention associated with a change in firm-specific fundamentals such as new product releases, stock splits and so on, a firm's return volatility is likely to increase while search results can provide value-relevant information to investors. Our results suggest that in general, an increase in the volume of Internet searches is not positively associated with return volatility. However, we find a positive association between Internet searches and return volatility when the sample is limited to larger firms. A stronger result from larger firms implies that investors still pay less attention to the information obtained from Internet searches for small firms while the information is value relevant in assessing stock values. However, we do find any systematic differences in the magnitude of Internet searches impact on return volatility by time periods. Taken together, our results shed new light on the value of information searched from the Internet in assessing stock values. Given the informational role of the Internet in stock markets, we believe the results would guide investors to exploit Internet search tools to be better informed, as a result improving their investment decisions.

• Journal of Distribution Research
• /
• v.9 no.1
• /
• pp.47-65
• /
• 2004

The purpose of this study is to analyze the investment returns and profitability of major Korean retail companies and to discuss the implications of their published accounting data. We have found that Korean department store companies shows the sound ROIC (return on invested capital) compared with U,5. counterparts. However, major discount store companies have problems in terms of poor ROIC ratios in spite of their rapid growth for the last decade. The results of ROIC performance of 3 major department stores and 3 discount stores are compared and the implications of the study are discussed. Furthermore, the published accounting data of major U.S. department stores and discount stores are analyzed in the context of benchmarking for Korean companies.

• Journal of Korean Society of Rural Planning
• /
• v.4 no.1
• /
• pp.32-39
• /
• 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.

• Journal of Families and Better Life
• /
• v.29 no.2
• /
• pp.51-62
• /
• 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.

• Journal of Korea Technology Innovation Society
• /
• v.7 no.3
• /
• pp.533-554
• /
• 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
• /
• v.30 no.1
• /
• pp.31-52
• /
• 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.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.41 no.4
• /
• pp.81-90
• /
• 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
• /
• v.8 no.9
• /
• pp.45-52
• /
• 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.

• Journal of Information Technology Services
• /
• v.15 no.2
• /
• pp.51-63
• /
• 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

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.1
• /
• pp.50-56
• /
• 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.