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on Market Microstructure |
| By: | Maryam Farboodi; Laura Veldkamp |
| Abstract: | In most sectors, technological progress boosts efficiency. But financial technology and the associated data-intensive trading strategies have been blamed for market inefficiency. A key cause for concern is that better technology might induce traders to extract other's information from order flow data mining, rather than produce information themselves. Defenders of these new trading strategies argue that they provide liquidity by identifying uninformed orders and taking the other side of their trades. We adopt the lens of long-run growth to understand how improvements in financial technology shape information choices, trading strategies and market efficiency, as measured by price informativeness and market liquidity. We find that unbiased technological change can explain a market-wide shift in data collection and trading strategies. But our findings also cast doubt on common wisdom. First, although extracting information from order flow does crowd out production of fundamental information, this does not compromise price informativeness. Second, although taking the opposite side of uninformed trades is typically called "providing liquidity," the rise of such trading strategies does not necessarily improve liquidity in the market as a whole. |
| JEL: | E2 G14 |
| Date: | 2017–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:23457&r=mst |
| By: | Erhan Bayraktar; Alexander Munk |
| Abstract: | Oft-cited causes of mini-flash crashes include human errors, endogenous feedback loops, the nature of modern liquidity provision, fundamental value shocks, and market fragmentation. We develop a mathematical model which captures aspects of the first three explanations. Empirical features of recent mini-flash crashes are present in our framework. For example, there are periods when no such events will occur. If they do, even just before their onset, market participants may not know with certainty that a disruption will unfold. Our mini-flash crashes can materialize in both low and high trading volume environments and may be accompanied by a partial synchronization in order submission. Instead of adopting a classically-inspired equilibrium approach, we borrow ideas from the optimal execution literature. Each of our agents begins with beliefs about how his own trades impact prices and how prices would move in his absence. They, along with other market participants, then submit orders which are executed at a common venue. Naturally, this leads us to explicitly distinguish between how prices actually evolve and our agents' opinions. In particular, every agent's beliefs will be expressly incorrect. As far as we know, this setup suggests both a new paradigm for modeling heterogeneous agent systems and a novel blueprint for understanding model misspecification risks in the context of optimal execution. |
| Date: | 2017–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1705.09827&r=mst |
| By: | Eraslan, Sercan |
| Abstract: | This paper investigates the asymmetries in arbitrage trading with onshore and offshore renminbi spot rates, focusing on the time-varying driving factors behind the deviations of the two rates from their long-run equilibrium. Fundamentally, offshore and onshore renminbi rates represent the same economic quantity and hence should be driven by the same pricing mechanism. However, the two exchange rates deviate remarkably from each other, creating arbitrage opportunities over many days. For the empirical analysis, I build a three-regime threshold vector error correction model with offshore and onshore spot rates and further regime-dependent explanatory variables. The model is estimated in different periods in order to consider the impact of appreciation and depreciation expectations on possible arbitrage trading. The estimation results suggest that directional expectations, global risk sentiment, and local as well as global liquidity conditions dominate the adjustment process in the absence of arbitrage trading when the offshore rate is stronger than its onshore counterpart. However, the error correction mechanism of the offshore (onshore) rate toward its equilibrium with the onshore (offshore) rate is driven by the arbitrage trading due to a relatively weaker (stronger) offshore (onshore) rate in the upper regime in times of appreciation (depreciation) expectations. |
| Keywords: | threshold cointegration,vector error correction model,arbitrage trading,renminbi exchange rates,onshore and offshore markets |
| JEL: | C32 F31 G15 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdps:132017&r=mst |