• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.11
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• pp.5476-5487
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• 2013

From foreign and/or domestic investors' perspectives, it may be interesting to find any financial attributes or profile of the firms headquartered in 'Chungcheong' province concerning profitability, given that this subject so far drew less attention in the previous literature. This study performed three hypothesis tests on the profitability indicator by utilizing the models such as the 'panel data' one and the 'logistic' regression one, applying a modified 'Dupont' system. With respect to the major findings, the results identified that the proxies measuring leverage across the book-value(BVLEV1) and the market-value(MVLEV1) bases, were statistically significant constituents determining profitability. Another explanatory variable, SIZE, with its positive and statistically significant relationship to the indicator, represented that the firms in the province were smaller than their counterparts in the other regional areas in Korea. DRELY applying a modified 'Dupont' system, found to be the only statistically significant discriminating factor between these comparison groups. As one of the primary contributions of this study, the outcomes may be used by the financial institutions operated across the regions including Seoul Metropolitan area, when implementing their lending practices to provide funds for potential borrowers such as the firms belonging to 'Chungcheong' province.

• Land and Housing Review
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• v.5 no.4
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• pp.259-269
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• 2014

After global financial crisis, the ratio of household debt to GDP was decreasing in other advanced countries such as the U.S., and the U.K. and so on. But, in Korea, household debt (of which residential mortgage loan account for a large part) ratio is still increasing. This paper focuses on the scale and characteristics of households in negative housing equity (those are called House-poors in Korea), and also the socio-economic backgrounds of the formation process. In financial perspective, the problem of negative housing equity depends on financial debt repayment capability. We used DSR (Debt Service Ratio) and LTA (Loan to Asset ratio) as financial indicators to evaluate the debt repayment capability. The critical value of DSR is assumed as 40%, and LTA 100%. The socio-economic backgrounds of the House-poors are as follows : increasing households debt dependency, over lending competition of financial institutions and unreasonable loan in household economy, instability of real estate market, week regulation on mortgage loan. Finally, this paper suggests some implications about the range and the target of public intervention.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.379-389
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• 2019

The study empirically investigates financial factors that may influence on corporate R&D intensity during the post-era of the global financial turmoil (from 2010 to 2015) to mitigate possible spillover effect associated with the crisis. Concerning the empirical research settings of the study, chaebol firms listed in the KOSPI stock market are used as sample data with adopting various econometric estimation methods to enhance validity of the results. In the first hypothesis test, it is found that there exist inter-industry financial differences in terms of the ratio of R&D expenditure across all the sample years, but the statistical differences may arise from only a few domestic industries beloning to the high-growth sector. Moreover, it is also interesting to identify that, for the high-tech sector, 3 explanatory variables such as R&D intensity in a prior year, firm size and change in cash holdings are proved to be financial factors to discriminate between chaebol firms and their counterparts of non-chaebol firms, whereas a proportion of tangible assets over total assets as well as the former two variables are shown to be significant factors on the R&D intensity for the low-tech sector.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.7134-7147
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• 2014

This study examined one of the contemporary issues that may be interesting to academics and practitioners regarding the driving force of the growth rate for the firms belonging to the chaebols in the Korean capital markets. With respect to the empirical results obtained from two hypothesis tests, the first hypothesis was to identify any financial determinants on the growth rate by applying both dynamic panel data and static panel data models. The debt ratios relevant to the book- and market-value showed their positive relationships with the DV of GROWTH1, along with other significant IDVs such as one-period lagged DV of GROWTH_1, SIZE1 and FOS with statistical significance. Second, by employing conditional quantile regression (CQR) analysis, the control variables, such as ROA, SMARKET, time dummy variable of F2010 and F2011, and the industry dummies of IND3 and IND10, provided evidence of their significant influences on DV of GROWTH1.

• 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 the Korean Society for information Management
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• v.38 no.3
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• pp.23-39
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• 2021

This study aims to explore research trends in Blockchain studies in South Korea using dynamic topic modeling and network analysis. To achieve this goal, we conducted the university & institute collaboration network analysis, the keyword co-occurrence network analysis, and times series topic analysis using dynamic topic modeling. Through the university & institute collaboration network analysis, we found major universities such as Soongsil University, Soonchunhyang University, Korea University, Korea Advanced Institute of Science and Technology (KAIST) and major institutes such as Ministry of National Defense, Korea Railroad Research Institute, Samil PricewaterhouseCoopers, Electronics and Telecommunications Research Institute that led collaborative research. Next, through the analysis of the keyword co-occurrence network, we found major research keywords including virtual assets (Cryptocurrency, Bitcoin, Ethereum, Virtual currency), blockchain technology (Distributed ledger, Distributed ledger technology), finance (Smart contract), and information security (Security, privacy, Personal information). Smart contracts showed the highest scores in all network centrality measures showing its importance in the field. Finally, through the time series topic analysis, we identified five major topics including blockchain technology, blockchain ecosystem, blockchain application 1 (trade, online voting, real estate), blockchain application 2 (food, tourism, distribution, media), and blockchain application 3 (economy, finance). Changes of topics were also investigated by exploring proportions of representative keywords for each topic. The study is the first of its kind to attempt to conduct university & institute collaboration networks analysis and dynamic topic modeling-based times series topic analysis for exploring research trends in Blockchain studies in South Korea. Our results can be used by government agencies, universities, and research institutes to develop effective strategies of promoting university & institutes collaboration and interdisciplinary research in the field.