Should regulators encourage the migration of trade from over-the-counter (OTC) to centralized markets? To address this question, we study a model in which banks make costly decisions to participate in an OTC market, a centralized market, or both markets at the same time. Banks differ in their ability to take large positions, what we call their trading capacity. In equilibrium, intermediate-capacity banks find it optimal to participate in the centralized market. In contrast, low-and high-capacity banks find it optimal to participate in the OTC market, due to an endogenous complementarity. Namely, low capacity banks receive worse terms of trade than in the centralized market but better risk sharing, thanks to the intermediation services offered by high-capacity banks. High-capacity banks receive worse risk sharing than in the centralized market, but profit from the provision of intermediation services to low-capacity banks. While the social optimum has qualitatively similar participation patterns, it prescribes that more customers migrate to the centralized market, and that more dealers enter the OTC market.
We analyze how computing power and data abundance affect speculators' search for predictors. In our model, speculators search for predictors through trials and optimally stop searching when they find a predictor with a signal-to-noise ratio larger than an endogenous threshold. Greater computing power raises this threshold, and therefore price informativeness, by reducing search costs. In contrast, data abundance can reduce this threshold because (i) it intensifies competition among speculators and (ii) it increases the average number of trials to find a predictor. In the former (latter) case, price informativeness increases (decreases) with data abundance. We derive implications of these effects for the distribution of asset managers' skills and trading profits.
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