This paper studies how bias in nonaccounting and in accounting information should be related. Bias in accounting information is modeled, as in some recent literature, as an alteration in the relative information content of accounting numbers. The optimal bias in one type of information is shown to be a complement of the bias in the other type. This result can be applied in various settings to explain a number of phenomena.

Using data from Chinese stock markets, we examine the effect of speculative trading on stock returns. We develop a volume-related variable, abnormal turnover ratio (ATR), by isolating speculative trading from liquidity and other components in trading volume. After a group of tests verifying that ATR indeed represents speculative trading, we show that ATR negatively predicts future stock returns. The average monthly return spread between the top and bottom ATR deciles is −1.87%, suggesting a highly significant negative ATR premium. The return predictability of ATR survives after controlling for common risk factors and event-driven information shocks. These findings indicate that speculative trading affects asset prices.

This paper examines the distortionary effects of inflation volatility on the allocation of bank loans. We argue that inflation volatility would render bank managers to behave more conservatively in issuing new loans. In contrast, when inflation volatility is low, bank managers would have the latitude to lend more idiosyncratically. Using a large panel of commercial bank data gathered from 15 countries, we provide support for our hypothesis by demonstrating a strong negative relation between inflation volatility and the dispersion of loans-to-assets ratio. Similar results are obtained when we split the sample between EU and non-EU country groups. The robustness of our findings is confirmed by a battery of sensitivity checks.

We examine the relation between political uncertainty and cash holdings for firms in China. We document that, during the first year of a new city government official's appointment, a firm, on average, holds less cash, which is consistent with the grabbing hand hypothesis of politician. Our results are robust to alternative measures of cash holdings, instrumental variable estimation, sub-samples without firms in four major cities, a matched sample approach, and placebo tests. In addition, our additional analyses suggest that a firm holds significantly less cash if: (a) the newly appointed official is from a different city relative to that from the same city, (b) it faces high political extraction risk, and (c) it has strong twin agency conflicts. Lastly, our extended results suggest that the market value of cash holdings is significantly negative during periods of political uncertainty and firms hide their cash by moving it to related firms via related party transactions.

Investment banks and issuers of Chinese domestic IPOs became fully responsible for IPO offer prices only on June 10, 2009. Before this regulatory reform, the optimistic bias in post-IPO earnings forecasts is highly comparable across affiliated and unaffiliated analysts. Afterward, the forecasts of affiliated analysts are 33 percentage points more positively distorted on average. In the first 90 days after an IPO, this relative forecast bias even increases to 63 percentage points and enlarges further when the issuer׳s stock price drops in the aftermarket. Affiliated analysts distort especially their forecasts for fiscal years further away from the forecast release date.

We examine signing bonuses awarded to executives hired for or promoted to named executive officer (NEO) positions at Standard & Poor's 1500 companies during the period 1992–2011. Executive signing bonuses are sizable and increasing in use, and they are labeled by the media as “golden hellos.” We find that executive signing bonuses are mainly awarded at firms with greater information asymmetry and higher innate risks, especially to younger executives, to mitigate the executives’ concerns about termination risk. When termination concerns are strong, signing bonus awards are associated with better performance and retention outcomes.

We study hedge fund performance evaluation under the stochastic discount factor framework of Farnsworth, Ferson, Jackson, and Todd (FFJT). To accommodate dynamic trading strategies and derivatives used by hedge funds, we extend FFJT’s approach by considering models with option and time-averaged risk factors and incorporating option returns in model estimation. A wide range of models yield similar conclusions on the performance of simulated long/short equity hedge funds. We apply these models to 2,315 actual long/short equity funds from the Lipper TASS database and find that a small portion of these funds can outperform the market.

In large-scale panel data models with latent factors the number of factors and their loadings may change over time. Treating the break date as unknown, this article proposes an adaptive group-LASSO estimator that consistently determines the numbers of pre- and post-break factors and the stability of factor loadings if the number of factors is constant. We develop a cross-validation procedure to fine-tune the data-dependent LASSO penalties and show that after the number of factors has been determined, a conventional least-squares approach can be used to estimate the break date consistently. The method performs well in Monte Carlo simulations. In an empirical application, we study the change in factor loadings and the emergence of new factors in a panel of U.S. macroeconomic and financial time series during the Great Recession.