Knowledge that Transforms

We introduce a novel measure for investors' Degree of Rejoicing and Regret (DRR) and test its power to explain cross-sectional stock returns. Consistent with investors demanding compensation for anticipated regret, a portfolio of low-DRR stocks outperforms that of high-DRR stocks by 16.45% annually in the U.S. market. This DRR effect is present globally across 44 markets and is stronger in countries characterized by higher individualism, greater uncertainty avoidance, and weaker investor protection. Our analysis highlights the crucial role of rejoicing, a previously underemphasized component of regret theory, and demonstrates that our DRR measure subsumes the pricing power of existing regret-only proxies.

We study the production effects of one of the largest bank branch expansion programs in history, implemented by the government of India during the 1980s. Combining policy-driven variation with newly-digitized data on bank lending and crop prices at the district-year level, we do not find evidence for a significant shift in agricultural output and inputs on average. Greater bank presence does promote resilience to climate risk, however, by attenuating the effect of lagged rainfall shocks on output. This effect operates via changes in the incidence of cropping during the dry winter season, which makes use of costly irrigation resources.

This research investigates the influence of methodological choices in portfolio sorts on the size of the carbon premium. By analyzing more than 100,000 portfolio construction paths, we find that differences in the construction of brown-minus-green portfolios create a substantial non-standard error. From 2009 to 2022, the mean carbon premium is −0.16% per month, with a non-standard error of 0.26%. Methodological choices regarding the carbon transition risk proxy, the portfolio weighting scheme, and double sorting induce the largest variation, while controlling for common risk factors reduces it. Estimates of the carbon premium from firm-level regressions are similarly sensitive to methodological choices. Finally, we show that carbon allowance prices are related to the level of the carbon premium, whereas unexpected climate change concerns help explain periods of lower methodological uncertainty.

We study the trade-off between signal selection and diversification in asset pricing when many return predictors are available. Using the data-mining framework of Yan and Zheng (2017), we form long–short portfolios from financial ratio signals and evaluate performance relative to the CAPM and the Fama–French six-factor model. Although null signals are prevalent, portfolio performance is largely insensitive to their inclusion. Portfolios restricted to the most statistically significant signals underperform more diversified strategies. Out-of-sample information ratios are highest at p -value thresholds between 5% and 10%, well above levels typically advocated for false-discovery-controlled inference. The results indicate that diversification is more effective than strict inference-oriented signal selection for portfolio construction.

Traditional negotiation trading dominates the electronic limit order book (LOB) of the X-Bond platform in the Chinese government bond market. Using a unique dataset, we conduct the first systematic study of the X-Bond’s role. We find that: (1) trades via LOB are notably more cost effective than via negotiation, with cost differences influenced by factors such as trader groups, bond types, trade sizes, and on-/off-the-run status; (2) electronic trading reduces the costs of negotiation trades through increased liquidity, an information channel, and a liquidity’s externality; and (3) due to the absence of an interdealer market, the X-Bond platform primarily serves as a liquidity source of last resort for managing inventory risk.

Changes in volatility contain valuable information about the likelihood of positive versus negative returns. We propose a new approach to modeling financial returns that exploits this insight by decomposing returns into sign and magnitude (absolute value) components, with magnitude closely related to volatility. The joint distribution used to compute expected returns combines a model for the marginal distribution of magnitude with a model for the distribution of the sign, conditional on the contemporaneous magnitude. Unlike traditional linear predictive regressions, this decomposition framework captures nonlinear predictability in return dynamics. An out-of-sample forecasting evaluation using monthly U.S. stock market excess returns demonstrates substantial statistical and economic gains relative to linear regression and complete subset regression, while delivering performance that is competitive with copula-based return-decomposition methods and other nonlinear benchmarks.