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MM-3: Private Information and Trade Informativeness
Clients’ Connections: Measuring the Role of Private Information in Decentralised Markets
1Bank of England, United Kingdom; 2London School of Economics
We propose a new measure of private information in decentralised markets – connections – defined as the number of dealers with whom a client trades in a time period. Using proprietary data for the UK government bond market, we show that clients have systematically better performance when having more connections, and this effect is stronger during macroeconomic announcements. Time-variation in market-wide connections also helps explain yield dynamics. Given our novel measure, we present two applications suggesting that (i) dealers pass on information, acquired from their informed clients, to their subsidiaries, and (ii) informed clients better predict the order-flow intermediated by their dealers.
Insider Trading Under the Microscope
Wilfrid Laurier University, Canada
Recent theory suggests that company insiders may time liquidity, and therefore their trading should be difficult to detect. Empirical studies have not yet come to an agreement on this issue, largely due to data limitations. I examine a multi-year intraday dataset that identifies all insider orders and trades processed by a major stock exchange. The results suggest that insiders primarily focus on return timing rather than liquidity timing and therefore are easily discovered by other market participants. As such, prices adjust to insider trades quickly, in a matter of minutes, and before insiders finish trading. Most of this adjustment is driven by order imbalances generated by insiders themselves and by traders who mimic them.
Dynamic Trade Informativeness
1INSEAD, France; 2INSEAD, Singapore
This paper develops a structural model to examine high-frequency price dynamics. The key innovation is to allow trades’ permanent price impact to be time-varying—dynamic trade informativeness. A distribution-free ﬁltering technique pins the real-world data to the model. The ﬁltered series signiﬁcantly recover the eﬃcient price innovation through the dynamics of trade informativeness; improve trades’ explanatory power for future returns; gauge informed investors’ patience; and capture the general intraday trend, as well as systematic patterns around speciﬁc events. The framework contributes to the better utilization of high-frequency trading data.
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