Knowledge that Transforms

At what point in a firm's life should it go public? How do a firm's ex ante product market characteristics relate to its going-public decision? Further, what are the implications of a firm going public on its post-IPO operating and product market performance? In this article, we answer the above questions by conducting the first large sample study of the going-public decisions of U.S. firms in the literature. We use the Longitudinal Research Database (LRD) of the U.S. Census Bureau, which covers the entire universe of private and public U.S. manufacturing firms. Our findings can be summarized as follows. First, a private firm's product market characteristics (total factor productivity [TFP], size, sales growth, market share, industry competitiveness, capital intensity, and cash flow riskiness) significantly affect its likelihood of going public after controlling for its access to private financing (venture capital or bank loans). Second, private firms facing less information asymmetry and those with projects that are cheaper for outsiders to evaluate are more likely to go public. Third, as more firms in an industry go public, the concentration of that industry increases in subsequent years. The above results are robust to controlling for the interactions between various product market and firm-specific variables. Fourth, IPOs of firms occur at the peak of their productivity cycle: the dynamics of TFP and sales growth exhibit an inverted U-shaped pattern, both in our univariate analysis and in our multivariate analysis using firms that remained private throughout as a benchmark. Finally, sales, capital expenditures, and other performance variables exhibit a consistently increasing pattern over the years before and after the IPO. The last two findings are consistent with the view that the widely documented post-IPO operating underperformance of firms is due to the real investment effects of going public rather than being due to earnings management immediately prior to the IPO.

We embed a structural model of credit risk inside a dynamic continuous-time consumption-based asset pricing model, which allows us to price equity and corporate debt in a unified framework. Our key economic assumptions are that the first and second moments of earnings and consumption growth depend on the state of the economy, which switches randomly, creating intertemporal risk, which agents prefer to resolve sooner rather than later, because they have Epstein-Zin-Weil preferences. Agents optimally choose dynamic capital structure and default times. For a dynamic cross-section of firms, our model endogenously generates a realistic average term structure and time series of actual default probabilities and credit spreads, together with a reasonable levered equity risk premium, which varies with macroeconomic conditions.

This article documents differences between the Q-sensitivity of investment of stand-alone firms and unrelated segments of conglomerate firms. Unrelated segments exhibit lower Q-sensitivity of investment than stand-alone firms. This fact is driven by unrelated segments of conglomerate firms that tend to invest less than stand-alone firms in high-Q industries. This finding is robust to matching on industry, year, size, age, and profitability. The differences are more pronounced in conglomerates in which top management has small ownership stakes, suggesting that agency problems explain the investment behavior of conglomerates.

Most recent empirical option valuation studies build on the affine square root (SQR) stochastic volatility model. The SQR model is a convenient choice, because it yields closed-form solutions for option prices. We investigate alternatives to the SQR model, by comparing its empirical performance with that of five different but equally parsimonious stochastic volatility models. We provide empirical evidence from three different sources: realized volatilities, S&P500 returns, and an extensive panel of option data. The three sources of data all point to the same conclusion: the best volatility specification is one with linear rather than square root diffusion for variance. This model captures the stylized facts in realized volatilities, it performs well in fitting various samples of index returns, andit has the lowest option implied volatility mean squared error in and out of sample.

In February 2003, the U.S. Securities and Exchange Commission officially certified a fourth credit rating agency, Dominion Bond Rating Service (DBRS), for use in bond investment regulations. After DBRS certification, bond yields change in the direction implied by the firm's DBRS rating relative to its ratings from other certified rating agencies. A one-notch-higher DBRS rating corresponds to a 39-basis-point reduction in a firm's debt cost of capital. The impact on yields is driven by cases where the DBRS rating is better than other ratings and is larger among bonds rated near the investment-grade cutoff. These findings indicate that ratings-based regulations on bond investment affect a firm's cost of debt capital.

Using high-frequency stock market data and (synthetic) variance swap rates, this paper identifies and investigates the temporal variation in the market variance risk-premium. The variance risk is manifest in two salient features of financial returns: stochastic volatility and jumps. The pricing of these two components is analyzed in a general semiparametric framework. The key empirical results imply that investors' fears of future jumps are especially sensitive to recent jump activity and that their willingness to pay for protection against jumps increases significantly immediately after the occurrence of jumps. This in turn suggests that time-varying risk aversion, as previously documented in the literature, is primarily driven by large, or extreme, market moves. The dynamics of risk-neutral jump intensity extracted from deep out-of-the-money put options confirms these findings. The Author 2009. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: [email protected], Oxford University Press.

We find a positive cross-sectional relationship between expected stock returns and default risk, contrary to the negative relationship estimated by prior studies. Whereas prior studies use noisy ex post realized returns to estimate expected returns, we use ex ante estimates based on the implied cost of capital. The results suggest that investors expected higher returns for bearing default risk, but they were negatively surprised by lower-than-expected returns on high default risk stocks in the 1980s. We also extend the sample compared with prior studies and find that the evidence based on realized returns is considerably weaker in the 1952–1980 period.

Based on an analysis of the agency risk for bondholders from managerial entrenchment and fraud, we derive and test refutable hypotheses about the influence of managerial agency risk on bond covenants, using a comprehensive database of corporate bonds from the 1993–2007 period. Managerial entrenchment and the risk of managerial fraud significantly influence the use of covenants, in the direction predicted by the agency-theoretic framework. Our analysis highlights the varied effects of entrenchment on different types of agency risks faced by bondholders: Entrenched managers aggravate investment risk, but ameliorate risk from shareholder opportunism. Covenant use also responds efficiently to the quality of information available regarding the risk of managerial fraud.

This article develops and implements a new test to investigate whether sell-side analysts herd around the consensus when they make stock recommendations. Our empirical results support the herding hypothesis. Stock price reactions following recommendation revisions are stronger when the new recommendation is away from the consensus than when it is closer to it, indicating that the market recognizes analysts’ tendency to herd. We find that analysts from larger brokerages, analysts following stocks with smaller dispersion across recommendations, and analysts who make less frequent revisions are more likely to herd.

The empirical evidence on the cross-sectional relation between idiosyncratic risk and expected stock returns is mixed. We demonstrate that the omission of the previous month's stock returns can lead to a negatively biased estimate of the relation. The magnitude of the omitted variable bias depends on the approach to estimating the conditional idiosyncratic volatility. Although a negative relation exists when the estimate is based on daily returns, it disappears after return reversals are controlled for. Return reversals can explain both the negative relation between value-weighted portfolio returns and idiosyncratic volatility and the insignificant relation between equal-weighted portfolio returns and idiosyncratic volatility. In contrast, there is a significantly positive relation between the conditional idiosyncratic volatility estimated from monthly data and expected returns. This relation remains robust after controlling for return reversals.