systems Article Startup Investment Decision Support: Application of Venture Capital Scorecards Using Machine Learning Approaches Sarah Bai 1 and Yijun Zhao 2, *   Citation: Bai, S.; Zhao, Y. Startup Investment Decision Support: Application of Venture Capital Scorecards Using Machine Learning Approaches. Systems 2021, 9, 55. https://doi.org/10.3390/ systems9030055 Academic Editor: Oleg Pavlov Received: 22 June 2021 Accepted: 19 July 2021 Published: 22 July 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Gabelli School of Business, Fordham University, New York, NY 10023, USA; sbai7@fordham.edu 2 Department of Computer and Information Sciences, Fordham University, New York, NY 10023, USA * Correspondence: yzhao11@fordham.edu Abstract: This research aims to explore which kinds of metrics are more valuable in making invest- ment 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. Keywords: machine learning; venture capital; startups; investment decision support; predictability; risk factor analysis 1. Introduction Despite the rosy outlook surrounding the venture capital industry, most funds are not profitable and may barely break even; the top 2% of venture capital (“VC”) funds receive 95% of the returns in the industry [1]. Evaluating startups can be largely subjective and earlier stage companies are relatively more unpredictable as there is less historical data. Additionally, venture capital is highly concentrated both geographically and de- velopmentally, and it is arguably very difficult to predict a successful startup let alone determine whether an early-stage company fits a VC firm’s investment thesis. There- fore, VC firms may consider a wide range of quantitative and qualitative factors when evaluating an early-stage startup such as the industry sector, amount of funding, return on investment trends, management team, relevant qualifications of the founders, market potential, and competitive advantage. These considerations can be broken down further into specific indicators that VC firms are looking to invest in based on their investment thesis. Each firm’s investment thesis may be different, and decisions to invest or not invest in a startup will differ based on these theses. The venture capital scorecard method is one such startup evaluation method of many which consider a diverse range of criteria. Initially motivated by developing a systematic approach for managers to measure the value of intangible assets to improve Systems 2021, 9, 55. https://doi.org/10.3390/systems9030055 https://www.mdpi.com/journal/systems