We show in a model of over-the-counter trading that customers in equilibrium may choose to contact very few dealers to incentivize maximum liquidity provision—“less is more.” This happens when dealers’ liquidity supply is sufficiently elastic to competition. This mechanism is orthogonal to conventional concerns, such as contacting or search cost, private information, and relationship. A social planner would mandate even fewer contacts than the market outcome, where customers induce excessive dealer competition. The model predicts endogenous market power, yields implications for regulation and design of electronic platforms, and speaks to customers’ search behavior and their execution quality.

This paper rationalizes the LASSO algorithm based on uncertain fat-tail priors and max-min robust optimization. Our rationalization excludes heuristic learning or restrictive prior assumptions in the original interpretation of LASSO (Tibshirani (1996)). In our setting, economic agents (arbitrageurs) face ambiguity about fat-tail shocks and in equilibrium, they ignore a reasonable range of ambiguous signals but respond linearly to almost unambiguous signals. With this LASSO equivalent strategy, arbitrageurs can amass extra market power which induces a “cartel” to protect their aggregate profit from being competed away. This result shows a new mechanism for limited arbitrage.

Information arrives at securities markets through price quotes and trades. Informed traders impose adverse-selection costs on quote suppliers. This creates incentives for the latter to identify relatively uninformed groups and trade with them off-exchange. The marketplace turns hybrid, at the cost of thinner, highly informed (toxic) volume at the center. This pattern has largely eluded econometricians, because the standard approach to measuring information shares is biased against finding it. We show why this is the case, and design a bias-free approach. The novel approach shows that, indeed, the conjectured pattern is strongly present in the data.