The venture capital entry model on game options with jump-diffusion process

Ko, Chuan-Chuan; Lin, Tyrone T.; Yang, Chyan

doi:10.1016/j.ijpe.2011.02.016

Cited by 9 publications

(7 citation statements)

References 22 publications

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“…Balibek and K€ oksalan 2010Multi-objective stochastic programming Consiglio and Staino (2012) Stochastic programming Valladão, Veiga, and Veiga (2014) Stochastic programming Consiglio, Lotfi, and Zenios (2018) Linear programming Venture capital and initial public offerings Ko, Lin, and Yang (2011) Game theory Aouni, Colapinto, and Torre (2014) Fuzzy goal programming Bast i, Kuzey, and Delen (2015) Support vector machines Afful-Dadzie and Afful-Dadzie 2016Multicriteria analysis Quintana, Ch avez, Luque Baena, and Luna (2018) ANFIS, genetic optimization Tian, Xu, and Fujita (2018) Fuzzy systems Zhong, Liu, Zhong, and Xiong (2018) Bayesian inference, Markov Chain Monte Carlo Operational and liquidity risk modeling Chavez-Demoulin, Embrechts, and Ne slehov a 2006Extreme value theory Shevchenko (2009) Bayesian inference Aquaro et al 2010Bayesian networks Shevchenko 2011Bayesian inference Sanford and Moosa (2012) Bayesian networks Janabi, Hernandez, Berger, and Nguyen (2017) Copula modeling Eling and Jung (2018) Copula modeling Peña, Bonet, Lochmuller, Chiclana, and G ongora (2018) Adaptive fuzzy inference model Azar and Dolatabad 2019Fuzzy cognitive maps Derivatives and volatility modeling Bandi and Bertsimas (2014) Linear programming Quek, Pasquier, and Kumar (2007) Neural networks Liu, Cao, Ma, and Shen (2019) Wavelets Neural networks Kim and Won (2018) Deep learning Bezerra and Albuquerque (2017) Support vector machines Zeng and Klabjan (2019) Support vector machines Financial fraud detection Gaganis (2009) Multicriteria analysis, machine learning Dikmen and K€ uc¸€ ukkocao glu (2010) Integer programming Glancy and Yadav (2011) Text mining Abbasi, Albrecht, Vance, and Hansen 2012Stacked generalization Sahin, Bulkan, and Duman (2013) Decision trees Balla, Gaganis, Pasiouras, and Zopounidis (2014) Multicriteria analys...…”

Section: Study Methodologymentioning

confidence: 99%

Computational approaches and data analytics in financial services: A literature review

Andriosopoulos

Doumpos

Pardalos

et al. 2019

Journal of the Operational Research Society

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The level of modeling sophistication in financial services has increased considerably over the years. Nowadays, the complexity of financial problems and the vast amount of data require an engineering approach based on analytical modeling tools for planning, decision making, reporting, and supervisory control. This article provides an overview of the main financial applications of computational and data analytics approaches, focusing on the coverage of the recent developments and trends. The overview covers different methodological tools and their uses in areas, such as portfolio management, credit analysis, banking, and insurance.

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Section: Study Methodologymentioning

confidence: 99%

Computational approaches and data analytics in financial services: A literature review

Andriosopoulos

Doumpos

Pardalos

et al. 2019

Journal of the Operational Research Society

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“…A more general framework was proposed by Dixit and Pindyck [29] to discuss the optimal R&D investment timing in an uncertain market. In recent years, related work also includes Bouis et al [34], Ko et al [35], and Leung and Kwok [36], etc. In addition, the latest work includes an option pricing framework proposed by Martzoukos and Zacharias [37] to demonstrate how to optimally make costly strategic pre-investment R&D decisions.…”

Section: Literature Reviewmentioning

confidence: 99%

Risk Dominance Analysis of R&D Investment Cooperation in Dynamic Option Game

Liu

Qian

et al. 2022

Sustainability

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Research and development (R&D) investment is very important for firms to gain competitive advantages and sustainable development. Due to the uncertainty of the market and competitors, R&D investment is usually costly and high risk. In such circumstances, firms not only have to figure out the optimal investment timing, but also consider whether to cooperate with competitors to share the risks and costs. In this paper, a two-stage dynamic exchange option game model is proposed for two symmetric competing firms to analyze their R&D investment decision and cooperation. The results show that under uncertainty, the R&D investment timing and cooperation strategy of the two firms depend on the market fluctuation, R&D cost, opportunity benefit of free riding, and the externality of cooperation. If the opportunity benefit of free riding is less than or equal to half of the cooperative research cost, the two firms will invest as early as possible and cooperate. The technology spillover and profits of new products will positively affect the willingness of the competing firms to invest and cooperate in R&D. Moreover, we also calculate the market value thresholds of the investment strategies for the two firms. When the market value is small, the two firms wait for the R&D investment; when the value increases, the firm with a high successful R&D probability will lead the investment, and the other firm follows the investment; when the value is large enough, the two firms will invest at the beginning of the period.

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“…This study attempts to analyze the valuation of venture capital investments and to explore links to real option theory. Venture capital is one possible form of private equity financing (Landström, 2007;Metrick-Yasuda, 2011) and a significant financial source for innovative and high-risk startups (Cassar, 2004;Ko et al, 2011) or from another aspect for high-tech, new or young SMEs (Chotigeat et al, 1997;Gompers-Lerner, 1999). In the case of venture capital, the investor appears as a financial intermediary of a non-financial corporation (Maula, 2007), the primary reward available to the investor are the capital gains (Lorenz, 1989).…”

Section: Introductionmentioning

confidence: 99%

Real Option Analysis of Venture Capital Investments

Posza

2020

Balkans JETSS

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Venture capital investments play an important role in the development and growth of start-up companies that are characterized by a high degree of uncertainty and growth potential, and venture capital is also one of the major sources of financing for entrepreneurial businesses. In the case of venture capital investment, staging has a huge potential, so the venture capitalists keep the right to participate in further financing rounds. The real option approach as an evaluation method provides an opportunity to evaluate this kind of investment with the help of flexibility in the case of a high degree of uncertainty. The paper puts the emphasis on the evaluation and the effectiveness of venture capital investments primarily from the aspect of real option theory tested on Hungarian venture capital cases. The paper concludes that the option-based valuation methods are more suitable for evaluating venture capital investments than others, especially the discounted cash flow method.

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The venture capital entry model on game options with jump-diffusion process

Cited by 9 publications

References 22 publications

Computational approaches and data analytics in financial services: A literature review

Computational approaches and data analytics in financial services: A literature review

Risk Dominance Analysis of R&D Investment Cooperation in Dynamic Option Game

Real Option Analysis of Venture Capital Investments

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