|
on Market Microstructure |
| By: | Ilija I. Zovko |
| Abstract: | We show that filling an order with a large number of distinct counterparts incurs additional market impact, as opposed to filling the order with a small number of counterparts. For best execution, therefore, it may be beneficial to opportunistically fill orders with as few counterparts as possible in Large-in-scale (LIS) venues. This article introduces the concept of concentrated trading, a situation that occurs when a large fraction of buying or selling in a given time period is done by one or a few traders, for example when executing a large order. Using London Stock Exchange data, we show that concentrated trading suffers price impact in addition to impact caused by (smart) order routing. However, when matched with similarly concentrated counterparts on the other side of the market, the impact is greatly reduced. This suggests that exposing an order on LIS venues is expected to result in execution performance improvement. |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2012.10262&r=all |
| By: | Antonio Mele (University of Lugano; Swiss Finance Institute; Centre for Economic Policy Research (CEPR)); Francesco Sangiorgi (Frankfurt School of Finance & Management gemeinnützige GmbH) |
| Abstract: | We analyze the effects of trading disclosure requirements in markets with insider traders and professional investors. The insiders garble their trading throughout a mixed strategy. A number of differentially informed professional investors acquire information and contribute to increased mar- ket efficiency. A “reform” introducing post-trade transparency leads these professional investors to acquire less information and, then, to trade less, contributing to less price discovery. This information crowding-out may be so strong to neutralize the generally positive effects related to public disclosure or to harm market quality, resulting in diminished liquidity and informationally less efficient market. |
| Keywords: | post-trade transparency, information crowding-out |
| JEL: | D82 G14 G18 |
| Date: | 2020–08 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp20118&r=all |
| By: | Nittai Bergman (Tel Aviv University - Berglas School of Economics); Ohad Kadan (Washington University in St. Louis - John M. Olin Business School); Roni Michaely (University of Geneva - Geneva Finance Research Institute (GFRI); Swiss Finance Institute); Pamela C. Moulton (Cornell University - SC Johnson College of Business) |
| Abstract: | Proprietary traders’ role in capital markets has received heightened attention with the debate over the Volcker Rule following the 2008-09 financial crisis. To date, there is little evidence on whether proprietary traders provide or take liquidity and how their behavior evolves over the business cycle. Using a unique dataset of proprietary trading activity, we show that proprietary traders concentrate their trades in a subset of stocks that are liquid to begin with. On average, proprietary traders provide liquidity in their trades, but they do so selectively, in large and liquid stocks, and when intermediary balance sheets are strong. Finally, proprietary traders do not increase their liquidity provision during periods of low returns when liquidity dries up. |
| Keywords: | Liquidity, Proprietary Traders, Volcker Rule |
| JEL: | G12 G14 G18 G28 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp20109&r=all |
| By: | Bastien Baldacci; Jerome Benveniste; Gordon Ritter |
| Abstract: | A hypothetical risk-neutral agent who trades to maximize the expected profit of the next trade will approximately exhibit long-term optimal behavior as long as this agent uses the vector $p = \nabla V (t, x)$ as effective microstructure alphas, where V is the Bellman value function for a smooth relaxation of the problem. Effective microstructure alphas are the steepest-ascent direction of V , equal to the generalized momenta in a dual Hamiltonian formulation. This simple heuristics has wide-ranging practical implications; indeed, most utility-maximization problems that require implementation via discrete limit-order-book markets can be treated by our method. |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2012.12945&r=all |