• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2993-3002
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• 2018

This case study introduces the process of developing the technology rating evaluation model for investment. The technology evaluation rating model for investment is a project that the Financial Services Commission and the Ministry of Commerce, Industry and Energy collaborated to expand the scope of technology finance from loan to investment. The technology evaluation model for investment was developed with the aim of predicting high growth companies. The model consists of a statistical model and an expert model. Here, statistical models were modeled by using logistic regression analysis. Expert models gathered opinions of experts and identified the weight of each evaluation item and set the model. The rating system of the model is composed of 10 grades. The distribution of the model was consistent with KTRS grade distribution. Interestingly, the emphasis is on technology and marketability. In the technology valuation grade model for the goddess, there is a considerable difference from the emphasis on managerial competence or business performance.

• Journal of Digital Convergence
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• v.17 no.3
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• pp.73-78
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• 2019

Recently, there is a need to introduce a Korean-style restriction sandbox system that exempts or suspends existing regulations so that new products or services based on new technologies can be commercialized without restrictions. In response, the government reorganized the relevant statutes to promptly check regulations centering on four fields, including industrial convergence, ICT, FinTech, and regional innovation growth, and to allow experimental, proof and market releases by setting certain conditions(zone, period, scale, etc.). However, despite the same regulatory sandbox application, depending on the nature of the field applied, differences in application subject, whether application of regulatory specifics, system of push ahead decision-making and whether support of financial and taxation are shown. This research is intended to present efficient operation measures for successful settling of Korean-style regulation sandboxes by comparing and analyzing, centering on the Industrial Fusion Promotion Act in the Industrial Convergence Field, ICT field's Information and Communication Convergence Act, FinTech field's Financial Innovation Act and Regional Special Zone Act in the Regional Innovation and Growth Sector.

• International Area Studies Review
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• v.22 no.1
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• pp.123-140
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• 2018

This paper analyzes herd behavior observed in the loan market from 2001 to 2014 using a panel data on commercial banks including state-owned banks, domestic private banks, foreign banks, and Islamic banks. The paper finds evidence that herd behaviors of domestic private and foreign banks have been pronounced and long-lasting around the 2008 global financial crisis when state-owned banks did not show such a behavior. This result shows that since private banks tend to be keen on maximizing profits and avoid financial risks exposed by lending during a financial crisis, their lending decisions are not independent but dependent on whether other banks increase loans or not. On the other hand, Islamic banks do not show herd behavior during the financial crisis. This finding is consistent with earlier studies that Islamic banks have different characteristics, such as profit and operation mechanisms, from other private banks. Another interesting finding is that when it comes to rural loans, all the banks' herd behavior is short-lived and the herding indexes are quite volatile. This finding is attributable to distinct features of rural loans. Usually maturities of rural loans are shorter than city loans and related to the cycle of farming. Agricultural production is heavily dependent on unpredicted factors, such as floods and droughts, not previous year's production. Lastly, the paper finds a herding across bank type that state-owned, foreign, and Islamic banks follow domestic private banks'lending decisions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.352-362
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• 2019

The study investigates one of the long-standing, but still controversial issues in modern finance from the international and domestic perspectives. That is, financial components and differences on corporate profitability are identified and compared under the primary hypotheses. Empirical research settings include the sample data as KOSPI-listed chaebol firms, time reference covering the post-era of the global financial turmoil and two differently defined profitability indices measured by the market- and the book-value bases. A majority of total 7 explanatory variables except firm size and leverage ratio reveal their statistically significant power to explain profitability indices for the chaebol firms in the first hypothesis. The results are generally compatible with those obtained from their counterparts of non-chaebol firms. In the second hypothesis applying multinomial logistic model, the chaebol firms are classified into three groups according to the level of profitability. It is then confirmed that variables to represent the market-valued debt ratio, business risk and growth potential are financially discriminating factors among the three groups. The study may provide a new vision to identify financial factors of corporate profitability for Korean chaebol firms after the global financial crisis, which can enhance the benefits of interested parties at the government or corporate level in a virtuous cycle.

• International Area Studies Review
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• v.21 no.1
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• pp.221-243
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• 2017

In this study, I examine overall conditions and problems of personal asset management processes by the old age people in Korea from the global perspectives. Major recommended policy implications for those are as follows.. First, the IRR (income replacement ratio) of public pensions in Korea is found to rank nearly the lowest among the OECD member countries. The relatively low fund performance compared to that of developed countries as well as this low IRR can be pointed out as major problems of public pension in Korea. It is recommended to reinforce specialty in fund management as a top priority to solve out these problems related with public pensions in Korea. Second, it is needed to set retirement pensions to be mandatory for almost all the firms in Korea to substitute for the above lower IRR of public pensions and to recover from the highest elderly poverty ratio among the OECD countries. Third, it is required to discuss about the expansion of tax refund policy application in the individual pension sector and many financial investment products under the correction of current budget control to motivate voluntary subscription for individual pension planning and to stabilize elderly lives of ordinary people in Korea. Fourth, it is required to induce market mechanism in controling price and longevity risk of reverse mortgages for the long-run sustainability.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.315-328
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• 2021

The study identified 1,500 adult consumers aged 25-54 years with life insurance within the last year as three groups, top, middle and bottom of need recognition, and demonstrated differences in insurance and finance perception and socioeconomic value perception. In particular, the study sought to identify the influence of socioeconomic value recognition factors in addition to overall recognition factors related to insurance and finance, the number of insurance held and insurance satisfaction. Overall recognition factors related to insurance and finance were classified as 'recognition of insurance as a means of professional management and finance', 'self-directed insurance design and contract' and 'recognition of economic burden on insurance'. Socioeconomic value recognition factors were divided into 'socioeconomic self-sufficiency', 'work-life value pursuit' and 'economic value pursuit'. We identified factors that affect the recognition of a higher need for insurance needs as a higher recognition of need for insurance needs. In particular, the most influential factor for the median group was the recognition of insurance as a professional management asset-tech product, and the upper group was found to be a work-life balance factor. The second influential factor was self-directed insurance design and contract factors for both groups. In order to increase the rate of insurance subscription in the future, insurance should be recognized as an essential product to pursue work-life value, and continuous improvement in information exploration conditions for consumers to explore information and compare products will be important to revitalize the insurance market.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.257-262
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• 2013

In mobile market which has been developed drastically, short distance mobile Near Field Communication is becoming the conversation topic. Google adopted this NFC technology to Android 플랫폼 for securing the leadership and many another countries including domestic companies are putting spurs to develop service and technology by connecting mobile carrier and the financial. Within this circumstance, most noticeable issue is securing stability of nfc application service. Android 플랫폼 is operating system of mobile device and also a software stack which is required limited hardware and immediate response. However, since its structural characteristic which is suitable for limited hardware, the response is not quite stable for real time process. That is, this paper researches by analyzing real time response of NFC related library provided from Android 플랫폼 and applying the result to NFC application for securing stable data process and response.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.1
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• pp.33-50
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• 2014

Though South Korea has world-class volume of Venture Capital Investment, as a share of GDP, early stage venture investments are still short, and investments are concentrated in high technology area and Capital area. Because of the high barriers to entry of the new IPO and M&A market, the venture capital companies undergo difficulties in profit. High-tech ventures face difficulties in raising money from outside investors due to information asymmetry between venture investors and venture companies. To resolve these problems, developed countries's government make a co-funding investment scheme with private sectors and design incentive mechanism such as receiving knowledge of the reputable investors' joint venture. Korean central and local government can benchmark those of things. For example, the expansion of the investment volume with private sector, region-specific matching fund and venture capital's exit path diversification such as M&A through the establishment of a business venture eco-system. At the same time, venture companies are to make an efforts to enhance the ability of screening for venture companies and the value for investment activities through a joint venture investments.