Overconfidence and disappointment in venture capital decision making: An empirical examination

Graves, Samuel B.; Ringuest, Jeffrey L.

doi:10.1002/mde.2931

Cited by 16 publications

(9 citation statements)

References 53 publications

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“…Zavertiaeva et al () showed that overconfident managers not only invest additional resources in R&D but also amplify the effect of financial determinants of R& D (i.e., firm liquidity or profitability), and the same study further examined the long‐term performance effects of overconfident managers' overinvestment. Graves and Ringuest () examined the behavioral consequences of overconfidence and disappointment in the funding decisions of venture capitalists through deterministic and stochastic models based on empirical data. Mertins and Hoffeld () examined the relationship between individual overconfidence and voluntary cooperation in workplace behavior and explored the driving factors behind this.…”

Section: Introductionmentioning

confidence: 99%

Managerial overconfidence, overinvestment, and R&D spillover

Zhou

2019

Manage Decis Econ

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This paper examines whether a firm will select an overoptimistic manager when a cost-reduction investment has a spillover effect. We consider a Cournot competition model where R&D investment ex ante occurs before the process of product market competition. Our analysis reveals that there exists a unique and symmetric equilibrium for firms to delegate overoptimistic managers. We show that only when the spillover effect is sufficiently high do firms benefit from delegation. Furthermore, the equilibrium confidence level and investment decision first decrease and then increase as the spillover parameter changes. As the initial production cost increases, the equilibrium performance becomes worse.1 we assume herein that the product is perfectly substitutable and that innovation spillover is symmetric. In further studies, we will extend the model to an asymmetric scenario and examine whether our results still hold.

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Section: Introductionmentioning

confidence: 99%

Managerial overconfidence, overinvestment, and R&D spillover

Zhou

2019

Manage Decis Econ

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“…This paper uses a variety of methods such as one-way ANOVA and ML prediction model to determine which industries are more favorable and which characteristics have a greater impact on investment performance. By analyzing 11 unique types of startups and 9 unique types of characteristics, it can help us more comprehensively understand the impact of industries and characteristics on investment performance, rather than a deeper study of the single characteristic of enterprises [31,32]. For example, VC prefers high technology and high added-value industries and has always invested more in economically active regions [33,34].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Investigation on the Impact of Chinese Venture Capital Institutions’ Performance: Evaluation Factors of Venture Enterprises to Venture Capital Institutions

Hu³

et al. 2022

Systems

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Studying the influencing factors of venture capital fund investment performance is crucial for the decision making of venture capital institutions. This paper explored the influencing factors of venture capital institutions from the perspective of startups, aiming to elucidate the mechanisms of these factors on the performance of venture capital funds and to propose a novel and effective predictive model of investment performance. Linear regression and one-way ANOVA were used to analyze the influence of each variable on investment performance, and the weight proportion of each influencing factor was obtained under the linear model. Two machine learning models, including the random forest algorithm and extreme learning machine algorithm, are established, and the particle swarm algorithm and machine learning algorithm were combined to optimize the random parameters in the two models. Compare the reliability and accuracy of machine learning models and multivariate linear regression models. The analysis results indicate that the PSO-ELM hybrid model has a better predictive performance than other prediction models. A convenient machine learning algorithm provided in this paper can quickly and effectively predict the investment performance of various investment portfolios and provide investors with decision-making assistance.

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“…Models based on specific investment theses can help determine which traits may be more influential in decision-making to RSC and which industries are more favorable. Our framework of analyzing 15 unique categories of startups allowed us to compare the impact of the many traits on decision-making, as opposed to a deeper dive on a single feature measured among companies [34]. However, by comparing multiple characteristics for each company, it becomes clear that many metrics on the scorecard may be influenced by each other.…”

Section: Discussionmentioning

confidence: 99%

Startup Investment Decision Support: Application of Venture Capital Scorecards Using Machine Learning Approaches

Bai

Zhao

2021

Systems

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This research aims to explore which kinds of metrics are more valuable in making investment decisions for a venture capital firm using machine learning methods. We measure the fit of developed companies to a venture capital firm’s investment thesis with a balanced scorecard based on quantitative and qualitative characteristics of the companies. Collaborating with the management team of Rose Street Capital (RSC), we explore the most influential factors of their balanced scorecard using their retrospective investment decisions of successful and failed startup companies. Our study employs six standard machine learning models and their counterparts with an additional feature selection technique. Our findings suggest that “planning strategy” and “team management” are the two most determinant factors in the firm’s investment decisions, implying that qualitative factors could be more important to startup evaluation. Furthermore, we analyzed which machine learning models were most accurate in predicting the firm’s investment decisions. Our experimental results demonstrate that the best machine learning models achieve an overall accuracy of 78% in making the correct investment decisions, with an average of 87% and 69% in predicting the decision of companies the firm would and would not have invested in, respectively. Our study provides convincing evidence that qualitative criteria could be more influential in investment decisions and machine learning models can be adapted to help provide which values may be more important to consider for a venture capital firm.

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Overconfidence and disappointment in venture capital decision making: An empirical examination

Cited by 16 publications

References 53 publications

Managerial overconfidence, overinvestment, and R&D spillover

Managerial overconfidence, overinvestment, and R&D spillover

Machine Learning-Based Investigation on the Impact of Chinese Venture Capital Institutions’ Performance: Evaluation Factors of Venture Enterprises to Venture Capital Institutions

Startup Investment Decision Support: Application of Venture Capital Scorecards Using Machine Learning Approaches

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