Knowledge that Transforms

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.