We hypothesize that short selling has a disciplining role vis-à-vis firm managers that forces them to reduce earnings management. Using firm-level short-selling data for thirty-three countries collected over a sample period from 2002 to 2009, we document a significantly negative relationship between the threat of short selling and earnings management. Tests based on instrumental variable and exogenous regulatory experiments offer evidence of a causal link between short selling and earnings management. Our findings suggest that short selling functions as an external governance mechanism to discipline managers.

[This article develops a Kalman filter model to track dynamic mutual fund factor loadings. It then uses the estimates to analyze whether managers with market-timing ability can be identified ex ante. The primary findings are as follows: (i) Ordinary least squares (OLS) timing models produce false positives (nonzero alphas) at too high a rate with either daily or monthly data. In contrast, the Kalman filter model produces them at approximately the correct rate with monthly data; (ii) In monthly data, though the OLS models fail to detect any timing among fund managers, the Kalman filter does; (iii) The alpha and beta forecasts from the Kalman model are more accurate than those from the OLS timing models; (iv) The Kalman filter model tracks most fund alphas and betas better than OLS models that employ macroeconomic variables in addition to fund returns.]

Several papers use a fractional specification (net inflow/ assets under management) to infer a convex relation between flow and past performance. However, heterogeneous linear response functions combined with the pooled analysis commonly used in these studies can yield false convexity estimates. We show that such heterogeneity obtains in practice. Along these same lines, the paper also finds that several previously unexamined implications of a convex flow-performance relation fail to hold. Moreover, convexity with fractional flows (which we confirm) largely disappears in a conditional analysis that controls for heterogeneity. Market shares offer an alternative specification for flow that is more resilient to heterogeneity. Using this alternative specification, we again find no evidence of convexity in the flow-performance relation. We conclude that the widely held belief that the flow response function is convex is due solely to misspecification of the empirical model. The flow-return relation is linear.

The global ETF industry provides more complicated investment vehicles than low-cost index trackers. Instead, we find that the real investments of ETFs may deviate from their benchmarks to leverage informational advantages (which leads to a surprising stock-selection ability) and to help affiliated OEFs through cross-trading. These effects are more prevalent in ETFs domiciled in Europe. Moreover, ETF flows seem to respond to additional risk. These results have important normative implications for consumer protection and financial stability. Received March 18, 2017; Editorial decision October 14, 2018 by Editor Raman Uppal.

We examine the impact of managerial entrenchment on firm value using a dynamic model with firm fixed effects. To estimate the model, we employ the long-difference technique, which is shown by our simulation to deliver the least biased estimates. Based on a large sample of U.S. companies, we document a significantly negative and causal effect of managerial entrenchment on firm value after taking into account omitted variables, reverse causality, and highly persistent endogenous variables. Additional analysis suggests that the causality running from managerial entrenchment to firm value is more pronounced than that for reverse causality.

Policy uncertainty can undermine the power of government subsidies to stimulate environmentally friendly research and development. We show that Chinese firms’ green R&D falls as the uncertainty of environmental subsidies rises: Exogenous, weather-driven air pollution variability induces subsidies to fluctuate, and firms in areas with high weather-driven subsidy variability undertake less green R&D and hire fewer technical employees, controlling for the average level of subsidies. Heavy emitters and environmental technology firms are more affected. The results also illustrate how policy uncertainty can arise when policymakers are influenced by conditions that are salient but with causes that are difficult to disentangle.

This paper examines how market frictions influence the managerial incentives and organizational structure of new hedge funds. We develop a stylized model in which new managers search for accredited investors and have stronger incentives to acquire managerial skill when encountering low investor demand. Fund families endogenously arise to mitigate frictions and weaken the performance incentives of affiliated new funds. Empirically, based on a TASS‐HFR‐BarclayHedge merged database, we find that ex ante identified cold inceptions facing low investor demand outperform existing hedge funds and hot inceptions facing high demand and that cold stand‐alone inceptions outperform all types of family‐affiliated inceptions.

We hypothesize that poor country-level governance, which makes public information less reliable, induces fund managers to increase their use of semipublic information. Utilizing data from international mutual funds and stocks over the 2000–2009 period, we find that semipublic information-related stock rebalancing can be five times higher in countries with the worst quality of governance than in countries with the best. The use of semipublic information increases price informativeness but also increases information asymmetry and reduces stock liquidity. It also intensified the price impact and liquidity crunch during the recent global financial crisis.

This paper proposes a simple back testing procedure that is shown to dramatically improve a panel data model's ability to produce out of sample forecasts. Here the procedure is used to forecast mutual fund alphas. Using monthly data with an OLS model it has been difficult to consistently predict which portfolio managers will produce above market returns for their investors. This paper provides empirical evidence that sorting on the estimated alphas populates the top and bottom deciles not with the best and worst funds, but with those having the greatest estimation error. This problem can be attenuated by back testing the statistical model fund by fund. The back test used here requires a statistical model to exhibit some past predictive success for a particular fund before it is allowed to make predictions about that fund in the current period. Another estimation problem concerns the use of a single statistical model for all available mutual funds. Since no one statistical model is likely to fit every fund, the result is a great deal of misspecification error. This paper shows that the combined use of an OLS and Kalman filter model increases the number of funds with predictable out of sample alphas by about 60%. Overall, a strategy that uses very modest ex-ante filters to eliminate funds whose parameters likely derive primarily from estimation error produces an out of sample risk-adjusted return of over 4% per annum.

Firms in global markets often belong to business groups. We argue that this feature can have a profound influence on international asset pricing. In bad times, business groups may strategically reallocate risk across affiliated firms to protect core “central firms.” This strategic behavior induces co-movement among central firms, creating a new intertemporal risk factor. Based on a novel data set of worldwide ownership for 2002–2012, we find that central firms are better protected in bad times and that they earn relatively lower expected returns. Moreover, a centrality factor augments traditional models in explaining the cross section of international stock returns.