This paper examines post-revision return drift, or PRD, following analysts’ revisions of their stock recommendations. PRD refers to the finding that the analysts’ recommendation changes predict future long-term returns in the same direction as the change (i.e., upgrades are followed by positive returns, and downgrades are followed by negative returns). During the high-frequency algorithmic trading period of 2003–2010, average PRD is no longer significantly different from zero. The new findings agree with improved market efficiency after declines in real trading cost inefficiencies. They are consistent with a reduced information production role for analysts in the supercomputer era.

We assess investment banks’ influence over the agreement between their analysts’ research behavior and their clients’ interests, in the post-reform era. Competing banks discipline their analysts with worse career outcomes for producing biased reports, issuing shirking reports, and for involvement in the earnings guidance game, showing meaningful monitoring of their analysts. Highly reputable banks provide more monitoring discipline of their analysts and bonding of their moral hazard than other banks. The findings agree with the banks taking responsibility for aligning analysts’ behavior with clients’ interests.

We provide the first direct empirical support for the importance of signalling in monetary policy by testing two key predictions from a novel structural model. First, all policymaker types should become less tough on inflation over time and secondly, types that weigh output more should have a more pronounced shift. Voting data from the Bank of England's Monetary Policy Committee strongly support both predictions. Counterfactual results indicate signalling has a substantial impact on interest rates over the business cycle, and improves the committee designer's welfare. Implications for committee design include allowing regular member turnover and transparency regarding publishing individual votes.

We review developments in conducting inference for model parameters in the presence of intertemporal and cross‐sectional dependence with an emphasis on panel data applications. We review the use of heteroskedasticity and autocorrelation consistent (HAC) standard error estimators, which include the standard clustered and multiway clustered estimators, and discuss alternative sample‐splitting inference procedures, such as the Fama–Macbeth procedure, within this context. We outline pros and cons of the different procedures. We then illustrate the properties of the discussed procedures within a simulation experiment designed to mimic the type of firm‐level panel data that might be encountered in accounting and finance applications. Our conclusion, based on theoretical properties and simulation performance, is that sample‐splitting procedures with suitably chosen splits are the most likely to deliver robust inferential statements with approximately correct coverage properties in the types of large, heterogeneous panels many researchers are likely to face.

We create an analytical structure that reveals the long-run risk-return relationship for nonlinear continuous-time Markov environments. We do so by studying an eigenvalue problem associated with a positive eigenfunction for a conveniently chosen family of valuation operators. The members of this family are indexed by the elapsed time between payoff and valuation dates, and they are necessarily related via a mathematical structure called a semigroup. We represent the semigroup using a positive process with three components: an exponential term constructed from the eigenvalue, a martingale, and a transient eigenfunction term. The eigenvalue encodes the risk adjustment, the martingale alters the probability measure to capture long-run approximation, and the eigenfunction gives the long-run dependence on the Markov state. We discuss sufficient conditions for the existence and uniqueness of the relevant eigenvalue and eigenfunction. By showing how changes in the stochastic growth components of cash flows induce changes in the corresponding eigenvalues and eigenfunctions, we reveal a long-run risk-return trade-off.