[When a firm exercises discretion to disclose or withhold information (strategic disclosure), risk-averse investors command higher expected returns when expected cash flows decrease, producing a negative correlation between these expectations. Moreover, stock returns exhibit stronger reversal than they do when full disclosure is enforced. We propose a model that makes these predictions and provide consistent evidence using a panel of foreign firms that list American Depositary Receipts (ADRs). We find significant shifts in the time-series properties of stock returns for firms that undergo large changes in disclosure environments, such as those cross-listing on the NYSE/AMEX/NASDAQ and those from less-developed/emerging markets and code-law countries]

In portfolio risk minimization, the inverse covariance matrix prescribes the hedge trades in which a stock is hedged by all the other stocks in the portfolio. In practice with finite samples, however, multicollinearity makes the hedge trades too unstable and unreliable. By shrinking trade sizes and reducing the number of stocks in each hedge trade, we propose a “sparse” estimator of the inverse covariance matrix. Comparing favorably with other methods (equal weighting, shrunk covariance matrix, industry factor model, nonnegativity constraints), a portfolio formed on the proposed estimator achieves significant out-of-sample risk reduction and improves certainty equivalent returns after transaction costs.

When a firm exercises discretion to disclose or withhold information (strategic disclosure), risk-averse investors command higher expected returns when expected cash flows decrease, producing a negative correlation between these expectations. Moreover, stock returns exhibit stronger reversal than they do when full disclosure is enforced. We propose a model that makes these predictions and provide consistent evidence using a panel of foreign firms that list American Depositary Receipts (ADRs). We find significant shifts in the time-series properties of stock returns for firms that undergo large changes in disclosure environments, such as those cross-listing on the NYSE/AMEX/NASDAQ and those from less-developed/emerging markets and code-law countries.

Firm managers of defined-benefit (DB) pension plan sponsors reveal their primary motives — risk-shifting or risk-management — through their assumed expected rates of return (ERRs) on the plan assets. Managers with risk-shifting motives choose high ERRs to exploit flexible internal financing from employees via pension underfunding. Those with risk-management motives choose low ERRs to reduce future cash-flow uncertainty by improving the pension funding status. We examine if ERRs predict the firms’ future cash-flow allocation between pension funding and corporate investments, in a Japanese sample that mitigates the selection bias concern for US DB plan sponsors. Using dynamic panel regressions that control for lagged dependent variables, firms’ business prospects, and unobserved fixed effects, we show that higher ERRs precede higher capital investments, R&D expenses, and net pension obligations while revealing managerial aggression, especially among firms with high external financing costs. Higher ERRs predict higher market-to-book ratios for the firms with larger R&Ds and/or underfunding, suggesting that the risk-shifting channel of internal financing with high ERRs can help alleviate underinvestment problems.

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.

With superior information about their customers’ prospects, suppliers extend trade credit to capture future profitable business. We show that this information advantage generates significant return predictability. After controlling for major firm characteristics, firms that rely more on trade credit relative to debt financing have higher subsequent stock returns. The return predictability by trade credit is stronger among firms with lower borrowing capacity or profitability, and is more significant for firms with a higher degree of information asymmetry. Our findings suggest that trade credit extension reveals suppliers’ information that diffuses gradually across the investing public.