We analyze financial contracting in start-ups backed by corporate venture capitalists (CVCs). CVCs' strategic goals can economically hurt or benefit the start-ups, depending on product market relationships between start-ups and CVC parents. Empirically, start-ups receive funding from both complementary and competitive CVC parents. However, start-up insiders commonly limit the influence of competitive CVCs, awarding them lower board power, while retaining higher board representation for themselves. Second, lead CVCs receive lower board representation, indicating heightened concerns about their greater influence in start-ups' early stages. Finally, start-ups extract higher valuations from competitive CVCs, reflecting greater moral hazard problems. Overall, CVC strategic objectives affect their early inclusion in VC syndicates, their control rights and share pricing.

We propose a novel approach to optimizing portfolios with large numbers of assets. We model directly the portfolio weight in each asset as a function of the asset's characteristics. The coefficients of this function are found by optimizing the investor's average utility of the portfolio's return over the sample period. Our approach is computationally simple and easily modified and extended to capture the effect of transaction costs, for example, produces sensible portfolio weights, and offers robust performance in and out of sample. In contrast, the traditional approach of first modeling the joint distribution of returns and then solving for the corresponding optimal portfolio weights is not only difficult to implement for a large number of assets but also yields notoriously noisy and unstable results. We present an empirical implementation for the universe of all stocks in the CRSP–Compustat data set, exploiting the size, value, and momentum anomalies.

This paper considers inference in a broad class of nonregular models. The models considered are nonregular in the sense that standard test statistics have asymptotic distributions that are discontinuous in some parameters. It is shown in Andrews and Guggenberger (2009a) that standard fixed critical value, subsampling, and m out of n bootstrap methods often have incorrect asymptotic size in such models. This paper introduces general methods of constructing tests and confidence intervals that have correct asymptotic size. In particular, we consider a hybrid subsampling/fixed-critical-value method and size-correction methods. The paper discusses two examples in detail. They are (i) confidence intervals in an autoregressive model with a root that may be close to unity and conditional heteroskedasticity of unknown form and (ii) tests and confidence intervals based on a post-conservative model selection estimator. Copyright 2009 The Econometric Society.

ABSTRACT The Sarbanes‐Oxley Act (SOX) mandates management evaluation and independent audits of internal control effectiveness. The mandate is costly to firms but may yield benefits through lower information risk that translates into lower cost of equity. We use unaudited pre–SOX 404 disclosures and SOX 404 audit opinions to assess how changes in internal control quality affect firm risk and cost of equity. After controlling for other risk factors, we find that firms with internal control deficiencies have significantly higher idiosyncratic risk, systematic risk, and cost of equity. Our change analyses document that auditor‐confirmed changes in internal control effectiveness (including remediation of previously disclosed internal control deficiencies) are followed by significant changes in the cost of equity that range from 50 to 150 basis points. Overall, our cross‐sectional and intertemporal change test results are consistent with internal control reports affecting investors' risk assessments and firms' cost of equity.

We study asset pricing in economies featuring both risk and uncertainty. In our empirical analysis, we measure risk via return volatility and uncertainty via the degree of disagreement of professional forecasters, attributing different weights to each forecaster. We empirically model the typical risk-return trade-off and augment these models with our measure of uncertainty. We find stronger empirical evidence for an uncertainty-return trade-off than for the traditional risk-return trade-off. Finally, we investigate the performance of a two-factor model with risk and uncertainty in the cross section.

We examine whether institutional investors follow each other into and out of the same industries. Our empirical results reveal strong evidence of institutional industry herding. The cross-sectional correlation between the fraction of institutional traders buying an industry this quarter and the fraction buying last quarter, for example, averages 40%. Additional tests suggest that correlated signals primarily drive institutional industry herding. Our results also provide empirical support for “style investing” models.

Given the key role of liquidity in finance research, identifying high quality proxies based on daily (as opposed to intraday) data would permit liquidity to be studied over relatively long timeframes and across many countries. Using new measures and widely employed measures in the literature, we run horseraces of annual and monthly estimates of each measure against liquidity benchmarks. Our benchmarks are effective spread, realized spread, and price impact based on both Trade and Quote (TAQ) and Rule 605 data. We find that the new effective/realized spread measures win the majority of horseraces, while the Amihud [2002. Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets 5, 31–56] measure does well measuring price impact.