We evaluate the efficacy of price discovery in the round-the-clock U.S. Treasury market. Using a comprehensive intraday database, we explore informational role of trades over the 24-hour day. We find that information asymmetry is generally highest in the preopen period and lowest in the postclose period. Information asymmetry in the overnight period is comparable to that in the regular trading period. However, on days with macroeconomic announcements, information asymmetry peaks shortly after the news release at 8:30. Moreover, information asymmetry is higher on Monday morning and higher immediately before than after the open of U.S. Treasury futures trading. Although volume is low after hours and trading cost is relatively high, overnight trading generates significant price discovery. Results suggest that overnight trading activity is an important part of the Treasury price discovery process.

We conduct an international study on the effect of climate policy uncertainty on the systemic risk of banks from G20 countries. We find that climate policy uncertainty is associated with lower bank systemic risk. This relation is more pronounced in countries with high innovation capacity, climate readiness, more systemically important banks, and a more competitive banking system. Climate-related information disclosure and sustainable investments are critical economic channels through which the effect of climate policy uncertainty works. Our findings alleviate the concern that climate transition risk may contribute to financial instability and provide practical implications for regulators to design climate transition policies.

This paper examines the pricing of volatility risk and idiosyncratic volatility in the cross-section of corporate bond returns for the period of 1994–2016. Results show that bonds with high volatility betas have low expected returns, and this negative relation appears in all segments of corporate bonds. Further, bonds with high idiosyncratic bond (stock) volatility have high (low) expected returns, and this relation strengthens as ratings decrease. Conventional risk factors and bond/issuer characteristics cannot account for these cross-sectional relations. There is evidence that the effect of idiosyncratic stock volatility on expected bond returns works through the channel of contemporaneous stock returns.

Numerous trading strategies examined in options research exhibit remarkably high mean returns and Sharpe ratios. We show some of these seemingly “good deals” are due to look-ahead biases. These biases stem from using information unavailable at the portfolio formation time to filter out observations suspected of being noisy or erroneous. Our results suggest that elevated Sharpe ratios may serve as potential indicators of such look-ahead biases. Furthermore, deviating from previous literature findings, we show that illiquidity is not strongly priced in stock options and that only a small set of stock characteristics are in fact associated with option expected returns.

We develop methods for testing factor models when the weights in portfolios of factors and test assets can vary with lagged information. We derive and evaluate consistent standard errors and finite sample bias adjustments for unconditional maximum squared Sharpe ratios and their differences. Bias adjustment using a second-order approximation performs well. We derive optimal zero-beta rates for models with dynamically trading portfolios. Factor models’ Sharpe ratios are larger but standard test asset portfolios’ maximum Sharpe ratios are larger still when there is dynamic trading. As a result, most of the popular factor models are rejected.

We examine the effects of liquidity, default and personal taxes on the relative yields of Treasuries and municipals using a generalized model with liquidity risk. The municipal yield model includes liquidity as a state factor. Using a unique transaction dataset, we estimate the liquidity risk of municipals and its effect on bond yields. Empirical evidence shows that municipal bond yields are strongly affected by all three factors. The effects of default and liquidity risk on municipal yields increase with maturity and credit risk. Liquidity premium accounts for about 9–13% of municipal yields for AAA bonds, 9–15% for AA/A bonds and 8–19% for BBB bonds. A substantial portion of the maturity spread between long- and short-maturity municipal bonds is attributed to the liquidity premium. Ignoring the liquidity risk effect thus results in a severe underestimation of municipal bond yields. Conditional on the effects of default and liquidity risk, we obtain implicit tax rates very close to the statutory tax rates of high-income individuals and institutional investors. Furthermore, these implicit income tax rates are quite stable across bonds of different maturities. Results show that including liquidity risk in the municipal bond pricing model helps explain the muni puzzle.

To attenuate an inherent errors-in-variables bias, portfolios are widely employed to test asset pricing models; but portfolios might mask relevant risk- or return-related features of individual stocks. We propose an instrumental variables approach that allows the use of individual stocks as test assets, yet delivers consistent estimates of ex post risk premiums. This estimator also yields well-specified tests in small samples. The market risk premium under the capital asset pricing model (CAPM) and the liquidity-adjusted CAPM, premiums on risk factors under the Fama–French three- and five-factor models, and the Hou et al. (2015) four-factor model are all insignificant after controlling for asset characteristics.

An AI analyst trained to digest corporate disclosures, industry trends, and macroeconomic indicators surpasses most analysts in stock return predictions. Nevertheless, humans win ‘‘Man vs. Machine’’ when institutional knowledge is crucial, e.g., involving intangible assets and financial distress. AI wins when information is transparent but voluminous. Humans provide significant incremental value in ‘‘Man + Machine’’, which also substantially reduces extreme errors. Analysts catch up with machines after ‘‘alternative data’’ become available if their employers build AI capabilities. Documented synergies between humans and machines inform how humans can leverage their advantage for better adaptation to the growing AI prowess.

This paper investigates the relation between media coverage and offering yield spreads using a comprehensive dataset of 5,338 industrial bonds issued from 1990 to 2011. We find that media coverage is negatively associated with firms’ cost of debt. This association is robust to controlling for standard yield determinants, different model specifications, and endogeneity. We identify 4 economic channels through which media coverage influences the cost of debt: Information asymmetry, governance, liquidity, and default risk. Importantly, media coverage has an independent influence beyond the effects of these economic mechanisms and is not a proxy for other firm attributes.