• Journal of Korea Port Economic Association
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• v.36 no.2
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• pp.63-86
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• 2020

The tax lease scheme for ships is an advanced ship financing tool that generates tax benefits through accelerated depreciation of capital allowances and transferring them to the ship operator (leasee) via reductions in rental payments. The scheme was introduced by Japan in 1978 and by France in 1998 to support their shipping and shipbuilding industries. The size of tax benefits varies by country depending on the depreciation rate for ships, corporate tax rate, and the tax system on profits from the sale of ship. This study uses a virtual model of the Korean tax lease scheme for ships based on the French tax lease scheme. The size of tax benefits is calculated and compared to those in the French and Japanese tax lease schemes. According to the analysis, the size of the tax benefit was approximately 19% for France, 14% for Japan, and 12% for Korea. This is differentiated by the country's depreciation rate and corporate tax rate, which have the greatest impact on the size of tax benefits. For the Korean virtual model, if the tax benefits are distributed by the operator and the investor at the rate of 75:25, the operator is expected to enjoy tax benefits equivalent to about 9% of the ship price and the investor to enjoy 3%. Despite limited information and data regarding the tax lease scheme for ships, this study was the first attempt in Korea to design a virtual model of the Korean tax lease scheme based on some predictable assumptions. Therefore, a group of shipping, financing, and legal experts will follow up on more professional and practical reviews of the model in the near future. Hence, this study will serve as a small contribution to the early introduction of the Korean tax lease scheme for ships.

• The Korean Journal of Financial Management
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• v.25 no.4
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• pp.53-78
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• 2008

Foreign investors who invest in the Korean stock markets are exposed to two kinds of foreign exchange rate risk, the economic exposure and the translation exposure. The former is the foreign exchange rate exposure in return generating process of the assets invested and the latter is the foreign exchange rate exposure in the translation of domestic return into foreign investors' currency. Domestic investors, however, are exposed only to foreign exchange rate exposure in the asset invested. This different situation on foreign exchange rate exposure between foreign investors and domestic investors can induce different response to exchange rate change by investor groups. Previous studies on foreign exchange rate exposure of Korean firms reported that quite a few Korean firms are exposed to foreign exchange risks and suggested to manage the foreign exchange risks. Also, many studies on the market segmentation showed that a market can be practically segmented according to the characteristics of investor groups. These studies support the hypothesis that the Korean stock market can be practically segmented by the foreign investors' attitude to the foreign exchange rate exposure. This study examines the response of both foreign investors and domestic investors to the foreign exchange rate exposures in Korean stock markets. Test results show that foreign investors increase their sell transactions when the foreign exchange rate exposure of the previous day is negative. This result can be possible when foreign investors attempt to actively manage the decrease in value of their assets due to rising of exchange rate. Analysis on the sell order data is also supportive to this interpretation. Foreign investors also increase their buy transactions when the foreign exchange rate exposure of the previous day is negative. This result can be possible when foreign investors use actively the relation between the increase in asset value and the translation gain due to declining of exchange rate. Analyses on buy order data, however, do not show the same result as the analyses on transaction data. This difference may come from the difference of information contained in transaction data and order data. In summary, the result of the paper supports the hypothesis that foreign investors response differently to foreign exchange rate exposure compared with domestic, Korean investors. Two groups do not show different response when exchange rate exposure is positive, i.e., as foreign exchange rate is increase (decrease), the asset value is increase (decrease). However, foreign investors' response is different from that of domestic investors when exchange rate exposure is negative, i.e., as foreign exchange rate is increase (decrease), the asset value is decrease (increase). These results mean that foreign investors and domestic investors are placed in different situations related to foreign exchange rate exposure, and these differences are reflected in the Korean stock markets. And domestic investors need to consider foreign investors' different attitude to the foreign exchange rate exposure when they analysis foreign investors' trading behavior.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.81-96
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• 2016

As the internet has become widespread and easy to access everywhere, it is common for people to search information via online search engines such as Google and Naver in everyday life. Recent studies have used online search volume of specific keyword as a measure of the internet users' attention in order to predict disease outbreaks such as flu and cancer, an unemployment rate, and an index of a nation's economic condition, and etc. For stock traders, web search is also one of major information resources to obtain data about individual stock items. Therefore, search volume of a stock item can reflect the amount of investors' attention on it. The investor attention has been regarded as a crucial factor influencing on stock price but it has been measured by indirect proxies such as market capitalization, trading volume, advertising expense, and etc. It has been theoretically and empirically proved that an increase of investors' attention on a stock item brings temporary increase of the stock price and the price recovers in the long run. Recent development of internet environment enables to measure the investor attention directly by the internet search volume of individual stock item, which has been used to show the attention-induced price pressure. Previous studies focus mainly on Dow Jones and NASDAQ market in the United States. In this paper, we investigate the relationship between the individual investors' attention measured by the internet search volumes and stock price changes of individual stock items in the KOSDAQ market in Korea, where the proportion of the trades by individual investors are about 90% of the total. In addition, we examine the difference between industries in the influence of investors' attention on stock return. The internet search volume of stocks were gathered from "Naver Trend" service weekly between January 2007 and June 2015. The regression model with the error term with AR(1) covariance structure is used to analyze the data since the weekly prices in a stock item are systematically correlated. The market capitalization, trading volume, the increment of trading volume, and the month in which each trade occurs are included in the model as control variables. The fitted model shows that an abnormal increase of search volume of a stock item has a positive influence on the stock return and the amount of the influence varies among the industry. The stock items in IT software, construction, and distribution industries have shown to be more influenced by the abnormally large internet search volume than the average across the industries. On the other hand, the stock items in IT hardware, manufacturing, entertainment, finance, and communication industries are less influenced by the abnormal search volume than the average. In order to verify price pressure caused by investors' attention in KOSDAQ, the stock return of the current week is modelled using the abnormal search volume observed one to four weeks ahead. On average, the abnormally large increment of the search volume increased the stock return of the current week and one week later, and it decreased the stock return in two and three weeks later. There is no significant relationship with the stock return after 4 weeks. This relationship differs among the industries. An abnormal search volume brings particularly severe price reversal on the stocks in the IT software industry, which are often to be targets of irrational investments by individual investors. An abnormal search volume caused less severe price reversal on the stocks in the manufacturing and IT hardware industries than on average across the industries. The price reversal was not observed in the communication, finance, entertainment, and transportation industries, which are known to be influenced largely by macro-economic factors such as oil price and currency exchange rate. The result of this study can be utilized to construct an intelligent trading system based on the big data gathered from web search engines, social network services, and internet communities. Particularly, the difference of price reversal effect between industries may provide useful information to make a portfolio and build an investment strategy.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.1
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• pp.1-19
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• 2021

The purpose of this research is to improve the investment success rate for startups in the water industry for the development of the entrepreneurial environment of the Korean water industry. In this research, we identified investment determinants through prior research and stratified them, and then surveyed the investor group at the beginning of the start-up using the FGI method, and determined the order of the investment determinants of investors. At the same time, we classified 41 start-ups related to the water industry into two groups: the group that received investment and the group that did not in the early stages of the start-up. Then we investigated the understanding of the investor's investment determinants, ranked them, and compared them by using the AHP technique. Through this, this research proposes five implications. First, it is important for start-ups in the early stages to receive seed investment to revitalize investment for startups in the water industry. For this, startups need to understand investors and prepare to attract investment with the perspective of angel investors rather than the perspective of VC investors. Second, Start-ups in the water sector should consider that the characteristics of the founder are important in order to receive seed investment, and also need to define their business at the industry and market level, and provide relevant rationale to meet the expectations of investors who value industry expertise and experience, and to increase the possibility of seed investment, which is important in the early stages of a startup. Third, institutions, such as K-water(Korea Water Resources Corporation), that support water industry startups need to conduct open innovation business opportunities discovery programs linked to startups so that startups currently participating in the startup support program could have business opportunities from the business infrastructure of platform-forming companies in the water industry. In particular, such institutions should help founders develop their industrial expertise and careers by supporting this type of start-up preparation process through the participation of in-house venture founders. Fourth, when K-water uses the government start-up support fund to discover and foster founders, it should increase initial contact with seed investors, conduct more thorough verification of business plans, and develop programs that use government start-up support funds to prepare a business suitable for seed angel investors. Fifth, K-water should support seed by connecting funds for initial investment among funds operated by itself. It is also necessary to develop a program that links the company receiving the seed investment with VC investment, not angel investment in cooperation with the VC fund operation entity participating as an LP so that companies that have attracted seed investment could attract follow-up VC investment.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.6
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• pp.57-70
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• 2023

The investigation delved into 457 pieces of scholarly work, encompassing articles, published theses, and dissertations from the National Research Foundation of Korea, spanning the period of the 1997 IMF financial crisis up to 2022. The materials were sourced using terms such as 'angel investment', 'angel investor', and 'angel investment attraction'. The initial phase involved filtering out redundant entries from the preliminary collection of 267 works, leaving aside pieces that didn't pertain directly to angel investment as indicated in their abstracts. The next stage of the analysis involved a more rigorous selection process. Out of 43 papers earmarked in the preceding cut, only 32 were chosen. The criteria for this focused on the exclusion of conference presentations, articles that were either not submitted or inconclusive, and those that duplicated content under different titles. The final selection of 32 papers underwent a thorough systematic literature review. These documents, all pertinent to angel investment in South Korea, were scrutinized under five distinct categories: 1) publication year, 2) themes of research, 3) strategies employed in the studies, 4) participants involved in the research, and 5) methods of research utilized. This meticulous process illuminated the existing landscape of angel investment studies within Korea. Moreover, this study pinpointed gaps in the current body of research, offering guidance on future scholarly directions and proposing social scientific theories to further enrich the field of angel investment studies and analysis also seeks to pinpoint which areas require additional exploration to energize the field of angel investment moving forward. Through a comprehensive review of literature, this research intends to validate the establishment of future research trajectories and pinpoint areas that are currently and relatively underexplored in Korea's angel investment research stream. This study revealed that current research on domestic angel investment is concentrated on several areas: 1) the traits of angel investors, 2) the motivations behind angel investing, 3) startup ventures, 4) relevant institutions and policies, and 5) the various forms of angel investments. It was determined that there is a need to broaden the scope of research to aid in enhancing and stimulating the scale of domestic angel investing. This includes research into performance analysis of angel investments and detailed case studies in the field. Furthermore, the study emphasizes the importance of diversifying research efforts. Instead of solely focusing on specific factors like investment types, startups, accelerators, venture capital, and regulatory frameworks, there is a call for research that explores a variety of associated variables. These include aspects related to crowdfunding and return on investment in the context of angel investing, ensuring a more holistic approach to research in this domain. Specifically, there's a clear need for more detailed studies focusing on the relationships with variables that serve as dependent variables influencing the outcomes of angel investments. Moreover, it's essential to invigorate both qualitative and quantitative research that delves into the theoretical framework from multiple perspectives. This involves analyzing the structure of variables that have an impact on angel investments and the decisions surrounding these investments, thereby enriching the theoretical foundation of this field. Finally, we presented the direction of development for future research by confirming that the effect on the completeness of the business plan is high or low depending on the satisfaction of the entrepreneurs in addition to the components.

• 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 Digital Convergence
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• v.10 no.10
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• pp.19-29
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• 2012

We made a regression analysis on the early IPO of venture capital investments in Korean IPO market. First, we found that it was likely to shorten the period to IPO in companies which were fast growing with a good operating cash flow, but these companies had a higher possibility of the earning management. Second, companies with more assets and larger size of the board of directors did not take companies public any earlier. Third, a better corporate governance also had no impact on the time period to IPO in the newly public firms. The findings above clearly show that venture-backed companies in Korea pursue the tendency of an early IPO. This phenomenon was much clearer when the companies were invested in by multiple venture capital firms than by a single investor. In general, venture capital firms invest in companies which are fast growing and which have a good operating cash flow. On the other side, venture capitals make investee companies go public earlier by manipulating operating earnings, so that they themselves may exit early. In conclusion, this research has shown that venture capitals in Korea do not play a positive role in the corporate transparency. This is the paradox of venture capital investment and this also shows the current status of Korean venture capital firms.

• Management & Information Systems Review
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• v.34 no.5
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• pp.23-47
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• 2015

In the Korea stock market, individual investors have perceived stock as short arbitrage investment, not long-term investment strategy. In order to reinforce stock market transparency and soundness, it is important to enforce the measures for stock market management. Especially, stock market event caused by financial policy can be given individual investors negative information regarding a stock trading. Thus, it is a need for investigating whether comprehensive review of listing eligibility is influenced on individual investors' responses and stock behaviors in respect of effectiveness. The purpose of this study to examine the relations between such stock market management and transitional aspect of individual investors' trading types and response on the based of pre- and post-event occurrence. Using an dataset of user's text messages on 9 firms posted on the firm-based social media (i.e., Naver, Daum, Paxnet) over the period 2009 to 2014. And we performed text-clustering and topic modeling according to keywords for classifying into investors group and non-investors groups and two types of investors were categorized depending on main topic transition by event windows in Comprehensive review of listing eligibility. The results indicated that a variety of stockholders existed in the stock. And the ratio of non-investors group was on the decrease, on the other hand, the proportion of investors group veer onto the side of pre-pattern after comprehensive review of listing eligibility. A distinctive feature of our study is to explain the influence of stock market management on response changes of individual investors as well as to categorize in accordance with time progression. Implications an suggestions for future research were also discussed.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.2
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• pp.113-120
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• 2016

Technology based would-be entrepreneurs have constantly increased as coming with increasing demands for technology based startup. However, technology based startup need to go through throne field in the preparation and launching process. This paper help technology based would be entrepreneurs recognizing and pivoting all potential fatal flaws covering entrepreneurs to BM with strategies by providing self check-up lists. This paper have developed all check list based upon the previous literature reviews about technology commercialization with carrying Focused Group Interview with mentor and investor involved in the early stage of venture growth. In particular, this paper have applied these tools over 104 participants(would-be entrepreneur and entrepreneurs in the early startup) attending in Hanbat Startup Item Validation Program and Startup Leading University program. This paper developed the mega categories of list as follows: Entrepreneurship, technology and patent, target customer and market, product, BM and strategy. It also developed 17 different concept of components and 58 specific sub-lists under maga list. The research results of paper will provide solid foundation of communication with participants about checking up their state of preparation for startup as applying to mentoring for would-be entrepreneurs and to entrepreneurship education.