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“Equity-Investment Accelerators Are Cherry Pickers” (Job Market Paper)

The dilemma of going big or staying small challenges entrepreneurs to wisely decide whether to trade equity in exchange for capital investment and have a more valuable equity share or retain a majority equity stake and look for noteworthy opportunities in later stages. I discuss a major heterogeneity among accelerators in terms of equity investment and early-stage seed funding. Using matched sample analysis, I provide evidence that although participating in an equity-investment accelerator has beneficial effects on the post-accelerator performance of portfolio companies to reach the next round of fundraising faster, they raise less capital in 12-month, 18-month, and 24-month time horizons after graduation in comparison to graduates of equity-free accelerators. In fact, in contrast to Yu (2020) who argues that equity-investment accelerators host lower-quality ventures, I find that founders with more years of corporate experience and higher-quality business ideas tend to shorten the path to success by participating in equity-investment accelerators. On the other hand, consistent with Hallen et al. (2020) who believe that a learning mechanism is the primary cause of accelerator effects on venture development, I find that, while ventures are heterogeneous and they learn at different rates and different depths, some entrepreneurs have already learned what to do, and it is accelerator equity design and seed investment that negatively impacts acceleration and slows down venture growth.

“What Do ICO White Papers Disclose?” with Rahmani Moghaddam, M.

We study information exchanged between ICOs and investors via disclosed information in 2149 ICO white papers. We employ natural language processing (NLP) to quantify the structure of white papers and demonstrate their contribution to the success of ICOs. Following the content analysis by Toubia et al. (2021), we elicit information from ICO textual sources by calculating three measures namely, speed, volume, and circuitousness. Our findings show that ICO projects with a well-written white paper that disclose risk and routinely return to previously discussed topics (i.e., circuitousness) are likely to raise more funds. Consistent with this, white papers that cover more ground (i.e., volume) contribute positively to the successful outcomes of ICO fundraising campaigns. In contrast, white papers that transit quickly between topics (i.e., speed) are less likely to have a positive impact.

“Smart Fund versus Dumb Capital” with Melanie Wallskog and Amrita Nain

Over the past decade, celebrities have been turning towards investing in early-stage ventures. Drawing on investor legitimacy theory, celebrities may lend credibility and legitimacy and help provide recognition and associational prestige to improve stakeholders, customers, and venture alignment. Due to significant career concerns to their core occupation, celebrities may be cognizant of their investment decisions. Using matched sample analysis and controlling for a period of association of celebrity with the venture, we find that celebrity investors increase the number of media articles, and this association is further strengthened by the diversity of prior investment characteristics of the celebrity. The effect of celebrity investors on the latest funding amount operates through increased news articles strengthened by the diversity of their investment categories. The findings have implications for the value of celebrity investors for ventures..

“The Dark Side of Venture Capital Investments, The Brain Drain” with Amrita Nain & Jue Wang

This paper provides the first large-sample comparison of all patent holders to investigate the move and relocation of inventors to VC hubs in order to finance their innovative initiatives. In general, besides the positive impacts that VCs have on local economies, they also serve as a magnet to attract entrepreneurs to relocate nearby. Indeed, the growth in some regions (i.e., VC hubs) may occur at the expense of other regions. Moreover, in some states, the net pattern can be dominated by a single urban region which can result in a misleading representation of rural areas. VC hubs have clearly hurt some regions, such as the Midwest, where there is less venture capital available to retain high ability founders, who can generate high paying jobs and contribute to economic growth. To fill the current gap in the literature, this paper will conduct a rigorous statistical analysis to determine the effect of VC investments on economic inequality. The argument of this chapter is a cyclical process of investment by VCs in entrepreneurial activities which causes talented inventors to aggregate in a few regions, and this drives economic inequality.

“Should You Become an Entrepreneur or A Corporate Employee?” with Melanie Wallskog 

This research leverages variation in the characteristics of firm founders and the accelerator programs in which they participate to study predictors of entrepreneurial success, including firm growth and innovative behavior. Based on hand-collected data on accelerator program participants, first we study how the work experience and project-orientation (e.g., problem- vs. solution-based) of new firms predicts their success. Second, we study how the structure of accelerator programs, for instance whether they provide direct access to venture capital (VC) funding, to understand the relative benefits of different accelerators. Because entrepreneurs may select into particular accelerator programs to suit their needs, we attempt to account for this selection (endogeneity) by selecting as controls entrepreneurs who are similar but have different experiences or participate in different programs; we do this using a variety of methods, including matching and synthetic controls. In order to limit the concern of multiple hypothesis testing, we additionally estimate LASSO and other restricted regressions that identify the key predictors of entrepreneurial success in our setting.

“Process versus Product Innovation” With Amrita Nain

Several studies in financial economics explore the influence of organization structure and the market for corporate control on the quantity of corporate innovative output as well as the value and novelty of the innovative output. Phillips and Zhdanov (2012) argue that an active acquisition market encourages innovation, particularly in small firms. They show that mergers can be used to acquire innovation instead of conducting in-house innovation. Firms’ incentive to conduct R&D increases with the probability of being taken over. However, existing theory relating to an industry or product’s life cycle draws a distinction between product innovation and process innovation. Empirical research on product and process patents is limited because of the difficulty of classifying patents. USPTO issued more than 300,000 patents in 2017 alone. Reading patent descriptions and classifying a patent as a new product development or a process improvement is a challenging task given the large number of patents issued in the United States each year. We propose to use machine-learning techniques to read all patent claims and use text-analysis methods to classify patents as process innovation or product innovation. Our objective is to improve our understanding of the difference between process and product innovation and testing whether assumptions made in existing theory are valid.

“Killer Startup Pitch: A Machine Learning Approach”

Data and video collection – early video, image, and speech to word analysis

“Loser Founders or Winner Entrepreneurs; Unexpected Outcomes of a Successful Exit”

Data Analysis (USA IPOs and M&A after 1970)