We examine the value and efficiency of analyst recommendations through the lens of capital market anomalies. We find that analysts do not fully use the information in anomaly signals when making recommendations. Analysts tend to give more favorable consensus recommendations to stocks classified as overvalued and, more important, these stocks subsequently tend to have particularly negative abnormal returns. Analysts whose recommendations are better aligned with anomaly signals are more skilled and elicit stronger recommendation announcement returns. Our findings suggest that analysts’ biased recommendations could be a source of market friction that impedes the efficient correction of mispricing.

We examine the extent to which institutional investors herd in the U.S. corporate bond market and the price impact of their herding behavior. We find that the level of institutional herding in corporate bonds is substantially higher than what is documented for equities, and that sell herding is much stronger and more persistent than buy herding. The price impact of herding is also highly asymmetric. While buy herding facilitates price discovery, sell herding causes transitory yet large price distortions. Such price destabilizing effect of sell herding is particularly pronounced for speculative-grade, small, and illiquid bonds, and during the financial crisis.

This paper provides the first empirical evidence of the externalities of credit default swaps (CDS). We find that a firm's leverage is lower when a larger proportion of its revenue is derived from CDS-referenced customers. This finding is robust to alternative samples and measures, placebo tests, and the selection of customers by suppliers. Moreover, firms affected by customer CDS trading issue equity to lower leverage, and their equity issuance costs are lower. These findings are consistent with the view that CDS trading on customers improves the information environment for suppliers. Therefore, while many firms are not directly linked to CDS trading, CDS trading on their customers has spillover effects on these firms’ financial policies.

We structurally estimate an investment-based asset pricing model, in which firms' exposure to macroeconomic risk is unknown. Bayesian beliefs about this parameter are updated from firms' and industry peers' comovement between their productivity and consumption growth. The model implies that discount rates rise endogenously with the perceived risk exposure of firms, thereby depressing investment and valuation ratios. We test these predictions in the data and find strong support for them. We also confirm that cross-sectional learning from peers is crucial and that alternative Bayesian risk estimates, which ignore peer observations, do not predict firm variables.

Using a structural model, we estimate the liquidity multiplier of an interbank network and banks’ contributions to systemic risk. To provide payment services, banks hold reserves. Their equilibrium holdings can be strategic complements or substitutes. The former arises when payment velocity and multiplier are high. The latter prevails when the opportunity cost of liquidity is large, incentivising banks to borrow neighbors’ reserves instead of holding their own. Consequently, the network can amplify or dampen shocks to individual banks. Empirically, network topology explains cross-sectional heterogeneity in banks’ systemic-risk contributions while changes in the equilibrium type drive time-series variation.

We derive a model with qualitative implications for options pricing under counterparty credit risk and provide empirical evidence using the data from the Hong Kong derivatives market during 2005–2014. We find that the log-price difference between a derivative warrant with counterparty credit risk and an otherwise identical option without counterparty credit risk is significantly and negatively associated with the credit default swap spread on the warrant issuer. We also find that the prices of out-of-the-money put warrants are more sensitive to credit risk than those of other warrants. Our results show counterparty credit risk matters for derivative pricing.

Affine jump-diffusion models have been the mainstream in options pricing because of their analytical tractability. Popular affine jump-diffusion models, however, are still unsatisfactory in describing the options data and the problem is often attributed to the diffusion term of the unobserved state variables. Using prices of variance-swaps (i.e., squared VIX) implied from options prices, we provide fresh evidence regarding the misspecification of affine jump-diffusion models, as variance-swap prices are affine functions of the state variables in a broader class of models that do not restrict the diffusion term of the state variables. We apply the nonparametric methodology used by Aït-Sahalia (1996b), supplemented with bootstrap tests and other parametric tests, to the S&P 500 index options data from January 1996 to September 2008. We find that, while the affine diffusion term of the state variables may contribute to the misspecification as the literature has suggested, the affine drift of the state variables, jump intensities, and risk premiums are also sources of misspecification.

Motivated by psychological evidence on limited investor attention and anchoring, we propose two proxies for the degree to which traders under- and overreact to news, namely, the nearness to the Dow 52-week high and the nearness to the Dow historical high, respectively. We find that nearness to the 52-week high positively predicts future aggregate market returns, while nearness to the historical high negatively predicts future market returns. We further show that our proxies contain information about future market returns that is not captured by traditional macroeconomic variables and that our results are robust across G7 countries. Comprehensive Monte Carlo simulations and comparisons with the NYSE/Amex market cap index confirm the significance of these findings.

Extant research examines the extent to which bankruptcy has intra-industry valuation consequences. This study broadens the investigation by examining the wealth effects of distress and bankruptcy filing for suppliers and customers of filing firms. On average, important wealth effects occur prior to and at bankruptcy filings and extend beyond industry competitors along the supply chain. Specifically, distress related to bankruptcy filings is associated with negative and significant stock price effects for suppliers. Supplier wealth effects are more negative when intra-industry contagion is more severe. We also investigate the importance of industry structure, specialized product nature, and leverage on supply chain effects.