• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.101-113
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• 2021

This paper inquires the macro-economic factors that may affect the corporate venture capital (CVC) from an industrial organization theory perspective. Unlike existing studies focusing CVC investments related to parent corporates' strategic intention, we identified CVC firm as an independent financial investor affected by macro environment and industrial structure. Specifically, we empirically investigate whether and how industry's boom, exogenous crisis, economic growth, and competition intensity affect the CVC investment for a data set of investment in the U.S. based corporate venture capital industry, 1996-2017. The empirical data analyzed in the study contained a total of 84 U.S. based CVC firms and their 2,306 investments from 1996 until 2017. After conducting a time-series negative binomial analysis, our empirical analyses suggest that the CVC investments are affected negatively by exogenous crisis and competition intensity, and positively by industrial boom and economic growth. we found the significance and direction of our independent variables strongly supported all of our four hypotheses in a highly robust manner. The results of this study are expected to contribute the literatures of corporate venture capital and venture investment by illustrating which macro-economic and industrial structure factors affect CVC investment decision to adapt to dynamic environmental change beside strategic intention of CVC firm's parent corporates.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.320-332
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• 2021

This paper investigates the influence of stock-level left-tail risk, which is defined using Value-at-Risk(VaR) estimates of past one-year daily stock returns, in the expected stock returns in the Korean stock market. Our results are summarized as follows: First, monthly-constructed zero-cost portfolios that buy (shortsell) the highest (lowest) left-tail risk decile in the previous month exhibit an average monthly return (called left-tail risk premium) of -2.29%. Second, Fama-MacBeth cross-sectional regressions suggest that left-tail risk in the previous month shows significant and negative explanatory power over return in this month, after controlling for various firm characteristics such as firm size, B/M, market beta, liquidity, maximum daily return, idiosyncratic volatility, and skewness. Third, the stocks with larger recent month loss have lower returns in the next month. Fourth, the magnitude of left-tail risk premium is negatively related with lagged market-level volatility. These results support the hypothesis from a perspective of behavioral finance that the overpricing of stocks with left-tail risk is attributed to the investors' underreaction to it.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.1-26
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• 2020

The term 'Unicorns' in the corporate ecosystem was firstly introduced by Aileen Lee in 2013. It has been actively discussed in South Korea particularly to compare the level of the 'start-up ecosystem' from a global perspective. Accordingly, the Korean government has recently set a policy goal 'to nurture 20 Korean unicorn companies by 2022'. While the phenomenon of 'Unicorn Club Company' has been brought to the level of policy objectives and spread more widely to the public, existing academic research to understand its substantial and underlying implications has been insufficient. First, in this study, the characteristics of 479 'Unicorn Club' companies in 2019 were analyzed in-depth. Previous research has focused on the general status and trend by analyzing the number of unicorn companies by country and industry classifications. However, this study conducted a qualitative exploratory analysis by investigating descriptive statistics about unicorn companies, including their investors, while providing case studies. Also, cluster analysis, ANOVA, and multi-level regression were employed for quantitative exploration. The characteristics of individual companies were examined based on the "ERIS Model (Entrepreneur - Industry(Market) - Resource - Strategy Model)". Secondly, factors influencing its valuations were examined in connection with the previously analyzed characteristic variables and investor characteristics. Finally, based on these, the future direction of the "Unicorn Phenomenon" from the perspective of "Enterprise Ecosystem" and productively using it from the perspective of the public policy is suggested.

• Economic Analysis
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• v.27 no.1
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• pp.67-90
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• 2021

This study found an interesting fact that the nonlinear relationship structure between volatility and trading volume changed before and after the COVID-19 pandemic according to empirical analysis using Bitcoin (BTC) market data that sensitively reflects investors' trading behavior. That is, their relationship appeared positive (+) in a stable market state before COVID-19 pandemic, as in theory based on the information flow paradigm. In a state under severe market stress due to COVID-19 pandemic, however, their dependence structure changed and even negative (-). This can be seen as a consequence of increased market stress caused by COVID-19 pandemics from a behavioral economics perspective, resulting in structural changes in the asset market and a significant impact on the nonlinear dependence of volatility and trading volume (in particular, their dependence at extreme quantiles). Hence, it should be recognized that in addition to information flows, psychological phenomena such as behavioral biases or herd behavior, which are closely related to market stress, can be a key in changing their dependence structure. For empirical analysis, this study performs a test of Ross (2015) for detecting a structural change, and proposes a Copula Regression Quantiles (CRQ) approach that can identify their nonlinear relationship structure and the asymmetric dependence in their distribution tails without the assumption of i.i.d. random variable. In addition, it was confirmed that when the relationship between their extreme values was analyzed by linear models, incorrect results could be derived due to model specification errors.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.5
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• pp.31-47
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• 2022

The TIPS, a representative public-private cooperative project to revitalize the start-up ecosystem, is a government supported policy that promotes successful commercialization through various start-up support for technology-based startups. The purpose of this study is to analyze the investment decision factors of the TIPS program and to derive priorities. In order to achieve the research purpose, first, the investment decision factors were derived through literature analysis, a Delphi surveys were conducted on investors and experts participating in the evaluation of the TIPS program, and an AHP analysis was conducted on 20 VCs to empirically analyze the priority of factors on investment decisions. As a result of the analysis, the importance of critical factors was confirmed in the order of entrepreneurs(team) > market > product/service > finance > network. The importance of detailed factors was found in the order of entrepreneur's reliability and authenticity > market growth and scalability > team members' expertise and capabilities > adequacy of current market size > new market creation. This study presented the capabilities of technology-based startups preparing to participate in the TIPS program by deriving factors that influence investment decisions from an investor's perspective and comparing and analyzing the importance. It is also meaningful that basic data on determinants of private-led investment decision-making were presented to stake-holders such as venture capital, accelerator, and start-up support institutions.

• The Journal of Society for e-Business Studies
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• v.25 no.1
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• pp.65-82
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• 2020

Start-ups often use crowdfunding platforms such as Wadiz to get financing from corporate investors and general public. These platforms include functions to increase participant activity through the number of comments, interest and online word-of-mouth. This study aims to elucidate how these functions related to a participant activity exhibit mediating effects on a success of crowdfunding and an achievement rate of targeted investment amount. To this end, the individual variables such as progress period, target amount, venture company, experience of attracting investment, possession of intellectual property rights, a career of the representative and an award experience of the representative were classified into the project characteristics, the company characteristics and the representative characteristics through not only previous studies but also interviews with investment professionals and platform operators. Afterwards, this study went through an empirical verification process using a structural equation model that has both crowdfunding performance and participant activity, which is the independent and mediating factors of three perspectives, as well as the dependent variables. In other words, this study analyzes how the characteristics of three perspectives affect the participant activity and how the participant activity had the mediating effects on the crowdfunding performance. In addition, it derives how the analysis results mentioned above vary according to business types (internet services, culture/art, manufacturing/distribution) and investment styles (stock type and bond type). It is expected that this study will help not only in deriving the factors affecting the performance of an investment type crowdfunding but also in preparing the measures to increase a participant activity.

• Information Systems Review
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• v.21 no.4
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• pp.125-141
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• 2019

With the advent of deep learning technology, which is represented by AlphaGo, artificial intelligence (A.I.) has quickly emerged as a key theme of digital transformation to secure competitive advantage for businesses. In order to understand the trends of A.I. based digital transformation, a clear comprehension of the A.I. business ecosystem should precede. Therefore, this study analyzed the A.I. business ecosystem from the multi-actor network perspective and identified the A.I. platform strategy type. Within internal three layers of A.I. business ecosystem (infrastructure & hardware, software & application, service & data layers), this study identified four types of A.I. platform strategy (Tech. vertical × Biz. horizontal, Tech. vertical × Biz. vertical, Tech. horizontal × Biz. horizontal, Tech. horizontal × Biz. vertical). Then, outside of A.I. platform, this study presented five actors (users, investors, policy makers, consortiums & innovators, CSOs/NGOs) and their roles to support sustainable A.I. business ecosystem in symbiosis with human. This study identified A.I. business ecosystem framework and platform strategy type. The roles of government and academia to create a sustainable A.I. business ecosystem were also suggested. These results will help to find proper strategy direction of A.I. business ecosystem and digital transformation.

• Journal of Arbitration Studies
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• v.20 no.1
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• pp.133-167
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• 2010

The enforcement of commercial arbitration awards in the People's Republic of China is one the controversial obstacles faced by foreign investors in China. The foreign investor will fail to enforce the arbitration award, if the Chinese court refuses the enforcement in China, even if the arbitration tribunal rules the award in favor of foreign investor who is in dispute with Chinese partners. In Korea, we have not many researches in the enforcement of foreign related awards and awards ruled by other jurisdiction. In recent times, Professor Kyung-Ja Cha(2005) and Professor Sun-Jeong Kim(2008) analyzed the enforcement of arbitration awards in China. Professor Kyung-Ja Cha(2005) reports the details of the enforcement statistics of CIETAC during 1990s. Professor Sun-Jeong Kim(2008) analyzed the obstacles of the enforcement of foreign related awards in China. This paper extends their researches in the field of the enforcement of arbitration awards in China. First, this paper extends Professor Kyung-Ja Cha(2005)'s study by introducing the Chinese enforcement situation during the period of 2000-2007. Second, this paper extends Professor Sunjung Kim(2008) emphasizes the local protectionism and the weakness of judiciary as key factors of obstacles to enforce the foreign related awards in People's Republic of China. This paper, additionally, highlights the role of the Guanxi and the antagonism of court toward arbitration institution to enforce the foreign related awards in People's Republic of China. Third, this study provides the recent developments of Supreme People's Court(SPC)'s rules to narrow down the gap between the practices of international arbitration and those of People's Republic of China. The Implications of this study are as follows. First, it is desirable for foreign investors to appoint the CIETAC or BAC as the arbitration commission in China. Second, the local competent attorney is the best choice to solve the respondent's insolvency in China. Third, foreign investors is required to monitor the provisions on the electronic instruments such as EDI and Email in Chinese law.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.2
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• pp.16-26
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• 2010

The transmission valuation methodology we propose here captures the interaction between generation and transmission investment decisions recognizing that a transmission expansion can impact the profitability of new resources investment, so that a methodology should consider both the objectives of investors in resources and the transmission planner. In this perspective, this paper purports to develop the mixed-integer programming based transmission expansion planning optimization software, which is well designed to determine the construction time and place of new generators, transmission lines, and substations as well as their capacities to minimize total expenditures related to their investment and operations while meeting technical constraints such as capacity margin, constitution ratio of power resources, spinning reserves, energy and fuel constraints, transmission line outages and losses, pi-type branching, and so on. Finally, Garver's simple system is adopted to validate not simply the accuracy but the efficiency of the proposed model in this paper.

• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.702-714
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• 2020

Stock investment is a personal investment technique that has gathered tremendous interest since the reduction in interest rates and tax exemption. However, it is risky especially for those who do not have expert knowledge on stock volatility. Therefore, it is well understood that accurate stock trend prediction can greatly help stock investment, giving birth to a volume of research work in the field. In order to compare different research works and to optimize hyper-parameters for prediction models, it is required to have an evaluation standard that can accurately assess performances of prediction models. However, little research has been done in the area, and conventionally used methods have been employed repeatedly without being rigorously validated. For this reason, we first analyze performance evaluation of stock trend prediction with respect to performance metrics and data composition, and propose a fair evaluation method based on prediction disparity ratio.