|
on Market Microstructure |
| By: | Poutré, Cédric (Université de Montréal); Dionne, Georges (HEC Montreal, Canada Research Chair in Risk Management); Yergeau, Gabriel (HEC Montreal, Canada Research Chair in Risk Management) |
| Abstract: | We explore latency arbitrage activities with a new arbitrage strategy that we test with high-frequency data during the first six months of 2019. We study the profitability of mean-reverting arbitrage activities of 74 cross-listed stocks involving three exchanges in Canada and the United States. Our arbitrage strategy is a hybrid between triangular arbitrage and pairs trading. We synchronize the high-frequency data feeds from the three exchange venues considering explicitly the latency that comes from the transportation of information between the exchanges and its treatment time. Other trading costs and arbitrage risks are also considered. The annual net profit of an HFT firm that uses limit orders is around CAD $8 million (USD $6 million), a result that we consider reasonable when compared with the previous literature. International latency arbitrage with market orders is never profitable. |
| Keywords: | Latency arbitrage; cross-listed stock; high-frequency trading; limit order; market order; synthetic hedging instrument; mean-reverting arbitrage; international arbitrage; supervised machine learning |
| JEL: | G02 G10 G11 G14 G15 G22 |
| Date: | 2021–07–20 |
| URL: | http://d.repec.org/n?u=RePEc:ris:crcrmw:2021_004&r= |
| By: | Mathieu Rosenbaum; Mehdi Tomas |
| Abstract: | Trading a financial asset pushes its price as well as the prices of other assets, a phenomenon known as cross-impact. We consider a general class of kernel-based cross-impact models and investigate suitable parameterisations for trading purposes. We focus on kernels that guarantee that prices are martingales and anticipate future order flow (martingale-admissible kernels) and those that ensure there is no possible price manipulation (no-statistical-arbitrage-admissible kernels). We determine the overlap between these two classes and provide formulas for calibration of cross-impact kernels on data. We illustrate our results using SP500 futures data. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.08684&r= |
| By: | Minh-Lý Liêu (University of Paderborn) |
| Abstract: | Social trading platforms allow investors to interact with each other. This paper studies the impact of peer attention on social trading platforms on investors' disposition effect. Using a difference-in-differences approach, I find a significant increase in the disposition effect when investors receive attention from their peers. This disposition effect increases as the number of other investors distributing likes to one another's trading decisions increases. This effect is driven both by holding on to losing positions longer and by closing winning positions faster. This finding may be explained by social facilitation theory. In the presence of others, investors want to achieve superior outcomes and limit their losses. |
| Keywords: | Social trading, transparency, disposition effect, online trading platforms. |
| JEL: | D14 G11 G23 G24 |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:pdn:dispap:81&r= |