• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.2
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• pp.1-13
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• 2019

As a way to find new and innovative technologies, many companies have invested in and acquired skilled startups. Because startups are usually small in size and have a small history of past business experience, there are many risks involved in acquiring them as they have limited technical skills and business feasibility verification methods. Thus, venture capital plays an important role in discovering and investing competitive startups. While Independent Venture Capital generally values financial returns, Corporate Venture Capital, which plays investment roles in the firm, values business synergies with the parent company from a strategic perspective. In an industry sector where development of technology is rapid and whether new technology is held determines a company's competitiveness, existing companies incorporate startups with innovative technologies into their investment portfolios, collaborate together, and take over for comprehensive cooperation. In addition, new investments and acquisitions are carried out through the management of portfolio companies to obtain and utilize industry information. In this paper, major U.S. companies listed in the U.S. verified their investment activities through corporate venture capital and their impact on parent companies and startups through regression, while the parent company's acquisition performance was analyzed through an event study based on a stock price analysis. The criteria for startup were defined as companies with less than 12 years of experience, and the analysis showed that the parent companies with corporate venture capital with a larger number of investments actively take over startups. In addition, increasing corporate venture capital's financial investment activities shows a negative impact on the parent companies' acquisition activities, and the acquisition performance increased when the parent companies took over startups in its portfolio.

• The Korean Journal of Financial Management
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• v.25 no.1
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• pp.109-140
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• 2008

The primary objective of this paper investigates whether asymmetric volatility phenomenon is caused by differences of opinion among investors and analyses information availability has an effect on asymmetric volatility. The empirical test period covers recent 6 years from January 4, 2000 to December 29, 2005. Five portfolios have been formed according to information availability(volume and market value). For the purpose of this study, We use TGARCH model, TGARCH-M model and adjusted model which include trading volume as a proxy differences of opinion among investors. The results are summarized as follows ; First, adjusted model analysis shows that asymmetric volatility phenomenon is disappeared or asymmetric coefficient and ratio is decreased than basis model. Second, portfolio analysis shows that the higher volume and market value, the more prominent asymmetric volatility phenomenon. And adjusted model analysis shows the higher volume and market value, the more decrease asymmetric ratio. Over all, assertion that differences of opinion among investors has caused asymmetric volatility phenomenon is regarded as reasonable. And, We see that information availability have great effect on asymmetric volatility phenomenon. We think that theses results can also occur opinion adjustment of optimistic investors. Namely, asymmetric volatility phenomenon can occur difference of information authenticity.

• KDI Journal of Economic Policy
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• v.14 no.4
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• pp.27-49
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• 1992

This paper stresses the role of market fundamentals rather than bubbles in explaining Korea's recent experience of large fluctuations of stock and real estate prices. The bubble story that emphasizes the self-fulfilling prophecies of investors seems to be inappropriate to explain the recent changes of assets prices in Korea. Those who argue for bubble phenomenon in Korea tend to interpret the volatile movements of assets prices as some form of bubbles, but without implementing a rigorous test on the presence of bubbles. Even when some bubble tests are carried out, such studies exhibit various econometric problems in testing. More seriously, they suffer from the misspecification problems in setting up a market model. This paper has shown that Korea's recent changes in assets prices could be explained by changes in market fundamentals according to the emergence and the subsequent fading of 'three lows'. First, it tried to explain changes in assets prices by changes in such market fundamentals as real interest rates and economic growth. Second, it showed that the real estate prices overshoot when the liquidity and exchange rates change, using the two-sector general equilibrium portfolio balance model. It is argued that the rapid rise in real estate prices during 1986-89 stems from Yen's and Won's appreciation $vis-{\grave{a}}-vis$ the U.S. dollar and liquidity expansion (or decreases in real interest rates), while the downturn in real estate prices since 1990 is associated with Yen's and Won's depreciation $vis-{\grave{a}}-vis$ the U.S. dollar and rises in real interest rates in reflection of the excess demand for liquidity.

• The Korean Journal of Financial Management
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• v.20 no.1
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• pp.233-260
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• 2003

This paper compares the underpricing of IPOs listed on KOSADQ with that of IPOs listed on KSE. When we consider the last day of upper price limit of IPOs, IPOs on KSE show higher initial excess return than IPOs on KOSDAQ. And AR2 which is the abnormal return based on the stock price of the last day recording upper limit after listing, IPOs on KOSDAQ exhibit larger abnormal return than IPOs on KSE. Our study also reports that the long-term performance of IPOs in two markets does not show any difference. That is, IPOs of both markets under performed in the long-run. The wealth relatives of IPOs are a little higher than market portfolio. We explored the reasons of the underpricing of IPOs in both markets through the multiple regression analysis. The business history is examined asstatistically significant variable to explain the underpricing.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.697-703
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• 2022

Due to the recent economic downturn caused by Covid-19 and the unstable international situation, many investors are choosing the derivatives market as a means of investment. However, the derivatives market has a greater risk than the stock market, and research on the market of market participants is insufficient. Recently, with the development of artificial intelligence, machine learning has been widely used in the derivatives market. In this paper, reinforcement learning, one of the machine learning techniques, is applied to analyze the scalping technique that trades futures in minutes. The data set consists of 21 attributes using the closing price, moving average line, and Bollinger band indicators of 1 minute and 3 minute data for 6 months by selecting 4 products among futures products traded at trading firm. In the experiment, DNN artificial neural network model and three reinforcement learning algorithms, namely, DQN (Deep Q-Network), A2C (Advantage Actor Critic), and A3C (Asynchronous A2C) were used, and they were trained and verified through learning data set and test data set. For scalping, the agent chooses one of the actions of buying and selling, and the ratio of the portfolio value according to the action result is rewarded. Experiment results show that the energy sector products such as Heating Oil and Crude Oil yield relatively high cumulative returns compared to the index sector products such as Mini Russell 2000 and Hang Seng Index.

• Korean small business review
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• v.41 no.1
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• pp.29-51
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• 2019

This study investigates how SMEs' (small and medium-sized enterprises) financing strategies affect firm valuation. Given that information asymmetry is engaged in firm valuation in the stock market, investors interpret the meanings of debt financing depending on how SMEs construct the portfolio of financing strategies (retained earnings vs debt financing), thereby making investment decision. Specifically, given that SMEs' debt financing has two meanings in the market signals, called "benefit" and "cost", this study postulates that firm valuation will be differently made by investors, depending on how they interpret the meanings of debt financing under choice competition between retained earnings and debt financing. In this study, we argue that under choice competition, as a SME's debt proportion increases, the "cost" signal outweighes the "benefit" signal, thereby decreasing firm valuation. Moreover, the effect of such signal can be contingent on the SME's characteristics-firm visibility. These ideas are examined using 363 U.S. SMEs ranging from 1971 to 2010. The fixed-effects models estimating Tobin's q show that under choice competition, a SME's debt proportion has a negative impact on firm valuation and that the firm's high visibility mitigates the effect of "cost" signal. In conclusion, this study sheds new light on how investors' interpretations of SMEs' financing strategies affect firm valuation.

• 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.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.63-75
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• 2023

This study conducted empirical analysis on the factors affecting the investment performance of 205 domestic venture funds (with a total liquidation amount of 7.25 trillion KRW) newly formed from 2007 to 2017 and completely liquidated as of the end of 2022. Due to the nature of private equity funds, obtaining empirical data is extremely challenging, especially for data post-COVID-19 era liquidations. Nevertheless, despite these challenges, it is meaningful to analyze the impact on the investment returns of domestic venture funds using the most recent data available from the past 10 years. This study categorized the factors influencing venture fund performance into external environmental factors and internal factors. External environmental factors included "economic cycles," "stock markets," "venture markets," and "exit markets," while internal factors included the fund management company's capabilities in terms of "experience," "professional personnel," and "assets under management (AUM)." The fund structure was also categorized into "fund size" and "fund length" for comparative analysis. In summary, the analysis yielded the following results: First, the 3-year government bond yield, which represents economic cycles well, was found to have a significant impact on fund performance. Second, the average 3-month KOSDAQ index return after fund formation had a statistically significant positive effect on fund performance. Third, the number of IPOs, indicating the competition intensity at the time of venture fund liquidation, was shown to have a negative effect on fund performance. Fourth, it was observed that the larger the AUM of the fund management company, the better the fund's returns. Finally, venture fund returns showed variations depending on the year of formation (Vintage). Therefore, when individuals consider investing in venture funds, it is considered a highly effective investment strategy to construct an investment portfolio taking into account not only external environmental factors and internal fund factors but also the vintage year.