Abstract: 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.
“Inventory Management, Dealers’ Connections and Prices in OTC Markets” with Jean-Edouard Colliard and Peter Hoffman. Last revised: May 2020. Forthcoming in the Journal of Finance.
Predicts that the distribution of aggregate inventories between core and peripheral dealers affect the distribution of transaction prices and bid-ask spreads in OTC markets.
Abstract: “We propose a new model of trading in OTC markets. Dealers accumulate inventories by trading with end-investors and trade among each other to reduce their inventory holding costs. Core dealers have access to a more efficient trading technology than peripheral dealers, who are heterogeneously connected to core dealers and trade with each other bilaterally. Connectedness affects prices and allocations if and only if the peripheral dealers’ aggregate inventory position differs from zero. The resulting price dispersion increases in the size of this position. The model generates new predictions about the joint effects of peripheral dealers’ connectedness and dealers’ aggregate inventories on transaction prices, both among dealers and between dealers and their clients.”