• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.109-121
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• 2023

This paper examines whether angel investors affect startup's financial performance (profitability and growth ratios) in the Korean startup market over 10 years period from 2009 to 2018. In particular, we consider not only the behavior of angel investor such as the investment amount or the type of investments (stocks, bonds) but also the type of angle investor (individual or corporation). Our empirical results are as follows. First, we find that the startup's profitability ratios are higher after the investment of angel investors. However, the growth ratios show different results in assets and sales. Second, we identify that the investment amount of angel investors negatively affects on the startup's growth ratios. Lastly, we find that equity investment such as common stock or preferred stock and the individual angel investors such as personal investment association or professional angels show higher financial performance than bond investment or corporate angel investors. Overall, our findings imply that angel investors positively affect startup's financial performance. In particular, we infer that the superior financial performance is largely attributed to monitor startups by participating as shareholders or to select more carefully by the individual angel investors who may be exposed to more investment risk. In conclusion, our findings support that angel investors play a positive role in the Korean venture investment market.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.229-249
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• 2022

This paper investigates machine learning models for predicting the designation of administrative issues in the KOSDAQ market through various techniques. When a company in the Korean stock market is designated as administrative issue, the market recognizes the event itself as negative information, causing losses to the company and investors. The purpose of this study is to evaluate alternative methods for developing a artificial intelligence service to examine a possibility to the designation of administrative issues early through the financial ratio of companies and to help investors manage portfolio risks. In this study, the independent variables used 21 financial ratios representing profitability, stability, activity, and growth. From 2011 to 2020, when K-IFRS was applied, financial data of companies in administrative issues and non-administrative issues stocks are sampled. Logistic regression analysis, decision tree, support vector machine, random forest, and LightGBM are used to predict the designation of administrative issues. According to the results of analysis, LightGBM with 82.73% classification accuracy is the best prediction model, and the prediction model with the lowest classification accuracy is a decision tree with 71.94% accuracy. As a result of checking the top three variables of the importance of variables in the decision tree-based learning model, the financial variables common in each model are ROE(Net profit) and Capital stock turnover ratio, which are relatively important variables in designating administrative issues. In general, it is confirmed that the learning model using the ensemble had higher predictive performance than the single learning model.

• Journal of Service Research and Studies
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• v.14 no.2
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• pp.18-36
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• 2024

As interest in ESG has been increased, it is easy to find papers that empirically study that a company's ESG activities have a positive impact on the company's performance. However, research on what ESG activities companies should actually engage in is relatively lacking. Accordingly, this study systematically classifies ESG activities of companies and seeks to provide insight to companies seeking to plan new ESG activities. This study analyzes how Chinese manufacturing companies perform ESG activities based on their dynamic capabilities in the global economy and how they differ in their activities. This study used the ESG annual reports of 151 Chinese manufacturing listed companies on the Shanghai & Shenzhen Stock Exchange and ESG indicators of China Securities Index Company (CSI) as data. This study focused on the following three research questions. The first is to determine whether there are any differences in ESG activities between companies with high ESG scores (TOP-25) and companies with low ESG scores (BOT-25), and the second is to determine whether there are any changes in ESG activities over a 10-year period (2010-2019), focusing only on companies with high ESG scores. The results showed that there was a significant difference in ESG activities between high and low ESG scorers, while tracking the year-to-year change in activities of the top-25 companies did not show any difference in ESG activities. In the third study, social network analysis was conducted on the keywords of E/S/G. Through the co-concurrence matrix technique, we visualized the ESG activities of companies in a four-quadrant graph and set the direction for ESG activities based on this.

• Industry Promotion Research
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• v.9 no.2
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• pp.29-35
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• 2024

Recently, the Korean financial market has implemented a ban on unleveraged short selling, and leveraged short selling, which involves selling borrowed securities, is called general short selling. This study sought to come up with improvement measures to revitalize short selling from the perspective of individual investors. Short selling refers to selling stocks you do not own in the stock market, predicting that the stock price of the stock will fall, and borrowing stocks to sell them. Based on the results of this study, the short selling market's growth and improvement plans are as follows. First, a plan must be developed to expand short selling opportunities for individual investors. In the domestic short selling market, including KOSPI and KOSDAQ, foreign and institutional participants account for more than 95% of the market, and individual investors are very small. Therefore, its expansion is inevitable. Second, monitoring and punishment for unfair short selling transactions must be strengthened. Representative improvement measures that can minimize the side effects of short selling include strengthening monitoring of unfair trading and short selling, and raising the level of punishment. In addition, measures must be taken to further increase the level of punishment for short selling related to unfair transactions. Third, the short selling reporting and disclosure system needs to be improved. In the case of Korea, short selling transactions are not yet as active as in developed countries, but there is a need to expand the disclosure system to strengthen market transparency in preparation for future short selling transactions becoming more active. In conclusion, it is reported that if short selling regulations are excessively strengthened, losses may occur in terms of price efficiency and market liquidity, which may ultimately have a negative impact on the market. Therefore, policies related to short selling must be made while taking into account the positive aspects of regulatory effects and the negative impact on the market.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• The KIPS Transactions:PartC
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• v.10C no.5
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• pp.625-634
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• 2003

More than a decade after its initial proposal, deployment of IP Multicast has been limited due to the problem of traffic control in multicast routing, multicast address allocation in global internet, reliable multicast transport techniques etc. Lately, according to increase of multicast application service such as internet broadcast, real time security information service etc., overlay multicast is developed as a new internet multicast technology. In this paper, we describe an overlay multicast protocol and propose fast join mechanism that considers switching of the tree. To find a potential parent, an existing search algorithm descends the tree from the root by one level at a time, and it causes long joining latency. Also, it is try to select the nearest node as a potential parent. However, it can't select the nearest node by the degree limit of the node. As a result, the generated tree has low efficiency. To reduce long joining latency and improve the efficiency of the tree, we propose searching two levels of the tree at a time. This method forwards joining request message to own children node. So, at ordinary times, there is no overhead to keep the tree. But the joining request came, the increasing number of searching messages will reduce a long joining latency. Also searching more nodes will be helpful to construct more efficient trees. In order to evaluate the performance of our fast join mechanism, we measure the metrics such as the search latency and the number of searched node and the number of switching by the number of members and degree limit. The simulation results show that the performance of our mechanism is superior to that of the existing mechanism.

• The Korean Journal of Financial Management
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• v.25 no.1
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• pp.1-22
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• 2008

This study is to evaluate, to the extent to, which advanced currency futures and emerging currency futures markets can predict accurately the future spot rate. To this end, Johansen's the maximum-likelihood cointegration method(1988, 1991) is adopted to test the unbiasedness and efficiency hypothesis. Also, this study is to estimate and compare a quantitative measure of relative efficiency as a ratio of the forecast error variance from the best-fitting quasi-error correction model to the forecast error variance of the futures price as predictor of the spot price in advanced currency futures with in emerging currency futures market. Advanced currency futures is British pound and Japan yen. Emerging currency futures includes Korea won, Mexico peso, and Brazil real. The empirical results are summarized as follows : First, the unbiasedness hypothesis is not rejected for Korea won and Japan yen futures exchange rates. This indicates that the emerging currency Korea won and the advanced currency Japan yen futures exchange rates are likely to predict accurately realized spot exchange rate at a maturity date without the trader having to pay a risk premium for the privilege of trading the contract. Second, in emerging currency futures markets, the unbiasedness hypothesis is not rejected for Korea won futures market apart from Mexico peso and Brazil real futures markets. This indicates that in emerging currency futures markets, Korea won futures market is more efficient than Mexico peso and Brazil real futures markets and is likely to predict accurately realized spot exchange rate at a maturity date without risk premium. Third, this findings show that the results of unbiasedness hypothesis tests can provide conflicting finding. according to currency futures class and forecasts horizon period, Fourth, from the best-fitting quasi-error correction model with forecast horizons of 14 days, the findings suggest the Japan yen futures market is 27.06% efficient, the British pound futures market is 26.87% efficient, the Korea won futures market is 20.77% efficient, the Mexico peso futures market is 11.55%, and the Brazil real futures market is 4.45% efficient in the usual order. This indicates that the Korea won-dollar futures market is more efficient than Mexico peso, and Brazil real futures market. It is therefore possible to concludes that the Korea won-dollar currency futures market has relatively high efficiency comparing with Mexico peso and Brazil real futures markets of emerging currency futures markets.

• The Korean Journal of Air & Space Law and Policy
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• v.7
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• pp.101-134
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• 1995

Article 3 Paragraph 1 of the Warsaw Convention regulates the requirements of passenger tickets, Article 4 Paragraph 3, the requirements of baggage tickets, Article 8, the requirements of airway bills. In this article the writer has discussed the legal nature of the documents of air carriage, such as air waybills, passenger tickets and baggage checks. Further, the writer has also discussed several issues relating to the use of the documents of air carriage under the Warsaw Convention. Article 3 Paragraph 2, as well as Article 4 Paragraph 4 and 9 provides that the carrier shall not be entitled to avail himself of the provisions of the Convention which evade or limit his liability. In particular, the Montreal Agreement of 1966 provides that the notification on the carrier's liability in passenger ticket should be printed in more than 10 point type size with contrasting ink colors. However, another question is whether the carrier shall not be entitled to avail himself of the liability limit under the Convention in case the type size is below 10 points. The Convention does not specify the type size of certain parts in passenger tickets and only provides that the carrier shall not be entitled to avail himself of liability limit, when a carrier fails to deliver the ticket to passenger. However, since the delivery of passenger tickets is to provide an opportunity for passengers to recognize the liability limit under the Convention and to map out a subsequent measures, the carrier who fails to give this opportunity shall not be entitled to avail himself of the liability limit under the Convention. But some decisions argue that when the notice on the carrier's liability limit is presented in a fine print in a hardly noticeable place, the carrier shall not be entitled to avail himself under the Convention. Meanwhile, most decisions declare that regardless of the type size, the carrier is entitled to avail himself of liability limit of the provisions of the Convention. The reason is that neither the Warsaw Convention nor the Montreal Agreement stipulate that the carrier is deprived from the right to avail himself of liability limit of the provisions of the Convention when violating the notice requirement. In particular, the main objective of the Montreal Agreement is not on the notice of liability limit but on the increase of it. The latest decisons also maintain the same view. This issue seems to have beeen settled on the occasion of Elisa Chan, et al. vs. Korean Airlines Ltd. The U.S. Supreme Court held that the type size of passenger ticket can not be a target of controversy since it is not required by law, after a cautious interpretation of the Warsaw Convention and the Montreal Agreement highlighting the fact that no grounds for that are found both in the Warsaw Convention and the Montreal Agreement. Now the issue of type size can hardly become any grounds for the carrier not to exclude himself from the liability limit. In this regard, any challenge to raise issue on type size seems to be defeated. The same issue can be raised in both airway bills and baggage tickets. But this argument can be raised only to the tranportation where the original Convention is applied. This creates no problem under the Convention revised by the Hague Protocol, because the Hague Protocol does not require any information on weight, bulk, size, and number of cargo or baggage. The problem here is whether the carrier is entitled to avail himself of the liability limit of the provisions of the Convention when no information on number or weight of the consigned packages is available in accordance with Article 4 of the Convention. Currently the majority of decisions show positive stance on this. The carrier is entitled to avail himself of the liability limit of the provisions of the Convention when the requirement of information on number and weight of consigned packages is skipped, because these requirements are too technical and insubstancial. However some decisions declare just the opposite. They hold that the provisions of the Convention Article 4 is clear, and their meaning and effect should be imposed on it literally and that it is neither unjust nor too technical for a carrier to meet the minimum requirement prescribed in the Convention. Up to now, no decisions by the U.S. Supreme Court on this issue is available.

• The Korean Journal of Financial Management
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• v.23 no.2
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• pp.109-141
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• 2006

The central factor in the pecking order theory of financial structure is the asymmetric distribution of information between managers and less-informed outside investors. Myers and Majluf (1984) show that this asymmetry leads managers to prefer internal funds to external funds. Funds are raised through equity issues only after the capacity to issue debt has been exhausted. In contrast, according to static tradeoff theory, an optimum financial structure exists by the tradeoff between tax saving by debt and bankruptcy costs. This study examines the recent changes of Korean firms' financial structure and financing behavior and the determinants of financial structure. The sample of firms comes from the period of $1996{\sim}2004$, and the number of firms is 32,003. The major findings are as follows. First, in contrast with previous studies using US firms as sample, Korean firms have been using debt financing as their major financing instrument. Especially, the firms in the fund deficit situation relies much more on $long{\sim}term$ and $short{\sim}term$ debts rather than on equity issues. Second, as is the case with previous studies using US firms sample indicates, the financing deficit variable can not explain perfectly the net debt issue. However, compared with net equity issue variable, net debt issue variable is more closely related to the financing deficit variable. Third, when financing deficit variable is added to the current list of explanatory variables of financial structure determinants model, it has a significant and positive explanatory power. In addition, the coefficients of determinants are much improved. Thus, it is concluded that although pecking order theory is not perfect, it appears to be more useful compared to static tradeoff theory, at least in explaining the recent financing behavior of Korean manufacturing firms.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.