Trading and Information Diffusion in Over-the-Counter Markets

Abstract

We propose a model of trade in over‐the‐counter (OTC) markets in which each dealer with private information can engage in bilateral transactions with other dealers, as determined by her links in a network. Each dealer's strategy is represented as a quantity‐price schedule. We analyze the effect of trade decentralization and adverse selection on information diffusion, expected profits, trading costs, and welfare. Information diffusion through prices is not affected by dealers' strategic trading motives, and there is an informational externality that constrains the informativeness of prices. Trade decentralization can both increase or decrease welfare. A dealer's trading cost is driven by both her own and her counterparties' centrality. Central dealers tend to learn more, trade more at lower costs, and earn higher expected profit.

Trading networks

“…Overall, we reject the null hypothesis of random trading patterns in transactions among market participants and we support trading models motivated by information flows (Babus and Kondor, 2016), information percolation (Duffie et al, 2015) and trading strategies (Kyle et al, 2016), which predict persistent trading patterns. To highlight this dynamic, we simulate an agent-based trading model, which replicates the contemporaneous correlations between financial and network variables, but exhibits no Granger-causality.…”

“…This suggests that the Granger-causality results that we find in the actual market data arise as a result of the strategic behaviour of traders (Cohen-Cole et al, 2015) or information flows or percolation -as in Babus and Kondor (2016) and Duffie et al (2015) -and are not simply artefacts of the order matching process. 30 Indeed, Granger-causality tests among network and financial variables are generally insignificant and yield few feedback effects, indicating a very poor fit.…”

“…Babus and Kondor (2016) and Duffie et al (2015). We construct a time series of more than 12,000 trading networks, each comprised of 600 sequential trades and we use established network analysis tools to empirically link network variables with financial variables that describe market conditions.…”

“…1 More recent works directly connect network metrics to information flows (Babus and Kondor, 2016), dealer structure (Li and Schürhoff, 2014), order shredding/inventory management (Kyle et al, 2016) and 1 See, for instance, Copeland and Galai (1983), Glosten and Milgrom (1985), Kyle (1985), Admati and Pfleiderer (1988) and Foster and Viswanathan (1990, 1993, 1994. Indeed, numerous models show that trading outcomes -prices, volume, volatility and liquidity -emerge from the complex mix of information flows and liquidity demands of individual buyers and sellers.…”

Dealer Networks

“…To isolate the potential effect of adverse selection on markups, we test predictions from Babus and Kondor (, BK hereafter). BK put forward a network model of information diffusion in OTC markets in which market participants possess private information.…”