This paper investigates the Bayesian decision-theoretic foundations of the Wall Street adage that `timing is everything'. One might think that a `small' risk-neutral trader wishes to act immediately upon any private information he possesses. I begin with a counterintuitive nding that trade timing doesn't matter for an Arrow security, as one's expected return per dollar invested is a martingale. This timing irrelevance discovery motivates an analysis of general compound securities. While timing there is ambiguous, I nd that natural monotone likelihood ratio assumptions on both private and public information restore the intuition that one should trade with all due dispatch.(This abstract was borrowed from another version of this item.)

In Becker's (1973) neoclassical marriage market model, matching is positively assortative if types are complements: i.e., match output f(x, y) is suipermoddlar in x and y. We reprise this famous result assuming time-intensive partner search and transferable output. We prove existence of a search equilibrium with a continuum of types, and then characterize matching. After showing that Becker's conditions on match output no longer suffice for assortative matching, we find sufficient conditions valid for any search frictions and type distribution: supermodularity not only of output f, but also of log f, and log f Symmetric submodularity conditions imply negatively assortative matching. Examples show these conditions are necessary.

This paper explores how Bayes-rational individuals learn sequentially from the discrete actions of others. Unlike earlier informational herding papers, we admit heterogeneous preferences. Not only may type-specific ‘herds’ eventually arise, but a new robust possibility emerges: confounded learning. Beliefs may converge to a limit point where history offers no decisive lessons for anyone, and each type's actions forever nontrivially split between two actions. To verify that our identified limit outcomes do arise, we exploit the Markov-martingale character of beliefs. Learning dynamics are stochastically stable near a fixed point in many Bayesian learning models like this one.