This study shows how venture capital investors can identify potential biases in multi-year management forecasts before an investment decision and derive significantly more accurate failure predictions. By advancing a cross-sectional projection method developed by prior research and using...

Using a big data set of venture capital financing and related startup firms from Crunchbase, this paper develops a machine-learning model called CapitalVX (for "Capital Venture eXchange") to predict the outcomes for startups, i.e., whether they will exit successfully through an IPO or...

This research applies findings from the field of factor investing to address the question of what drives private equity deal returns. I provide empirical evidence that the most comprehensively documented factor anomalies have explanatory power of cross sectional variations in returns of private...

Startups are a key force driving economic development, and the success of these high-risk ventures can bring huge profits to venture capital firms. The ability to predict the success of startups is a major advantage for investors to outperform their competitors. In this study, we explore the...

We study the relationship between buyout and venture capital (VC) funds' returns, and more typically available proxies - exits via M&A or IPO. We further explore the effects of filters on the selection of M&As and IPOs (to emphasize successes), on the relationship. We show that some of these...