Arthur G. Korteweg

Risk and Return Characteristics of Venture Capital-Backed Entrepreneurial Companies

Valuations of entrepreneurial companies are only observed occasionally, albeit more frequently for well-performing companies. Consequently, estimators of risk and return must correct for sample selection to obtain consistent estimates. We develop a general model of dynamic sample selection and estimate it using data from venture capital investments in entrepreneurial companies. Our selection correction leads to markedly lower intercepts and higher estimates of risks compared to previous studies.

Attracting Early-Stage Investors: Evidence from a Randomized Field Experiment

This paper uses a randomized field experiment to identify which start-up characteristics are most important to investors in early stage firms. The experiment randomizes investors’ information sets of fund-raising start-ups. The average investor responds strongly to information about the founding team, but not to firm traction or existing lead investors. In contrast, inexperienced investors respond to all information categories. Our results suggest that information about human assets is causally important for the funding of early stage firms, and hence, for entrepreneurial success.

Skill and Luck in Private Equity Performance

We evaluate the performance of private equity (“PE�?) funds, using a variance decomposition model to separate skill from luck. We find a large amount of long-term persistence, and skilled PE firms outperform by 7% to 8% annually. But this performance is noisy, with a large amount of luck, so top-quartile performance does not necessarily imply top-quartile skills, making it difficult for investors (“LPs�?) to identify skilled PE firms. Buyout (“BO�?) firms show the largest skill differences, implying the greatest long-term persistence. Venture capital (“VC�? ) performance is the most noisy, making good VC firms hardest to identify, and implying the smallest amount of investable persistence.

Estimating Loan‐To‐Value Distributions

We estimate a model of house prices, combined loan-to-value ratios (CLTVs), and trade and foreclosure behavior. House prices are only observed for traded properties, and trades are endogenous, creating sample-selection problems for existing approaches to estimating CLTVs. We use a Bayesian filtering procedure to recover the price path for individual properties and produce selection-corrected estimates of historical CLTV distributions. Estimating our model with transactions of residential properties in Alameda, CA, we find that 35% of single-family homes are underwater, compared to the 19% estimated by existing approaches. Further, our results reduce the index revision problem and have applications for pricing mortgage-backed securities.

Markov Chain Monte Carlo Methods in Corporate Finance

This chapter introduces Markov Chain Monte Carlo (MCMC) methods for empirical corporate finance. These methods are very useful for researchers interested in capital structure, investment policy, financial intermediation, corporate governance, structural models of the firm, and other areas of corporate finance. In particular, MCMC can be used to estimate models that are difficult to tackle with standard tools such as OLS, Instrumental Variables regressions and Maximum Likelihood. Starting from simple examples, this chapter exploits the modularity of MCMC to build sophisticated discrete choice, self-selection, panel data and structural models that can be applied to a variety of topics. Emphasis is placed on cases for which estimation by MCMC has distinct benefits compared to the standard methods in the field. I conclude with a list of suggested applications. Matlab code for the examples in this chapter is available on the author’s personal homepage.

An Empirical Target Zone Model of Dynamic Capital Structure

We develop and estimate a general (S, s) model of capital structure to investigate the relation between target leverage, refinancing thresholds, and firm characteristics in a dynamic environment. We find that firms’ target leverage is pro-cyclical, consistent with dynamic capital structure models, but in contrast to traditional regression results. The target leverage zone, in which companies optimally allow leverage to float, widens during recessions. Most of the time series variation in capital structure policy variables is due to aggregate macroeconomic factors, rather than changes in firm-specific variables.

How Often Do Firms Rebalance Their Capital Structures? Evidence from Corporate Filings

We use new hand-collected data from corporate filings to study the drivers of corporate capital structure adjustment. Classifying firms by their adjustment frequencies, we reveal previously unknown patterns in their reasons for financing and financial instruments used. Some are consistent with existing theory, while others are understudied. Many leverage changes are outside of the firms control (e.g., executive option exercise) or incur negligible adjustment costs (e.g., credit line usage). This implies a lower frequency of proactive leverage adjustments than indicated by prior research using accounting data, suggesting that costs of adjustment are higher, or the benefits lower, than previously thought.This paper shows that the frequency of capital structure adjustment varies significantly across firms. The most active 25% of firms account for 51% of leverage adjustments, while the least active 50% of firms account for only 19% of such events. Using new hand-collected data from detailed corporate filings, we find that frequently rebalancing firms tend to use lines of credit to fund operating losses and working capital needs. In contrast, infrequently rebalancing firms use long-term debt and equity to fund investment and rebalance capital structure. These findings underscore the importance of adjustment costs in financing decisions and show that the reasons for rebalancing are much broader than those covered by contemporary capital structure theories. Our results demonstrate the advantage of complementing accounting data with rich textual data from corporate filings.