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on Market Microstructure |
| By: | Hans Degryse (CEPR - Center for Economic Policy Research - CEPR); Rudy de Winne; Carole Gresse (DRM - Dauphine Recherches en Management - CNRS - Centre National de la Recherche Scientifique - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres); Richard Payne |
| Abstract: | We measure the extent to which consolidated liquidity in modern fragmented equity markets overstates true liquidity due to a phenomenon that we call Ghost Liquidity (GL). GL exists when traders place duplicate limit orders on competing venues, intending for only one of the orders to execute, and when one does execute, duplicates are cancelled. Employing data from 2013 for 91 stocks trading on their primary exchanges and three alternative platforms where order submitters are identified consistently across venues, we find that simply measured consolidated liquidity exceeds true consolidated liquidity due to the existence of GL. On average, for every 100 shares passively traded by a multi-market liquidity supplier on a given venue, around 19 shares are immediately cancelled by the same liquidity supplier on a different venue. Yet the average weight of GL in total consolidated depth, at around 4%, does not outweigh the liquidity benefits of fragmentation. GL is most pronounced for traders with a speed advantage such as high-frequency traders, in stocks exhibiting greater market fragmentation, in stocks where the tick is more likely to be binding, and on non-primary exchanges. Furthermore, GL decreases when the fraction of traders using smart order routing is large. Finally, we show that an increase in GL leads to the execution costs of slow and algo traders increasing, while those of HFTs are unaffected. |
| Keywords: | algorithmic trading,fragmentation,stocks trading |
| Date: | 2021–09–08 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03338259&r= |
| By: | Marjolein E. Verhulst; Philippe Debie; Stephan Hageboeck; Joost M. E. Pennings; Cornelis Gardebroek; Axel Naumann; Paul van Leeuwen; Andres A. Trujillo-Barrera; Lorenzo Moneta |
| Abstract: | We introduce a methodology to visualize the limit order book (LOB) using a particle physics lens. Open-source data-analysis tool ROOT, developed by CERN, is used to reconstruct and visualize futures markets. Message-based data is used, rather than snapshots, as it offers numerous visualization advantages. The visualization method can include multiple variables and markets simultaneously and is not necessarily time dependent. Stakeholders can use it to visualize high-velocity data to gain a better understanding of markets or effectively monitor markets. In addition, the method is easily adjustable to user specifications to examine various LOB research topics, thereby complementing existing methods. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.04812&r= |
| By: | Pietro Saggese; Alessandro Belmonte; Nicola Dimitri; Angelo Facchini; Rainer B\"ohme |
| Abstract: | We mine the leaked history of trades on Mt. Gox, the dominant Bitcoin exchange from 2011 to early 2014, to detect the triangular arbitrage activity conducted within the platform. The availability of user identifiers per trade allows us to focus on the historical record of 440 investors, detected as arbitrageurs, and consequently to describe their trading behavior. We begin by showing that a considerable difference appears between arbitrageurs when indicators of their expertise are taken into account. In particular, we distinguish between those who conducted arbitrage in a single or in multiple markets: using this element as a proxy for trade ability, we find that arbitrage actions performed by expert users are on average non-profitable when transaction costs are accounted for, while skilled investors conduct arbitrage at a positive and statistically significant premium. Next, we show that specific trading strategies, such as splitting orders or conducting arbitrage non aggressively, are further indicators of expertise that increase the profitability of arbitrage. Most importantly, we exploit within-user (across hours and markets) variation and document that expert users make profits on arbitrage by reacting quickly to plausible exogenous variations on the official exchange rates. We present further evidence that such differences are chiefly due to a better ability of the latter in incorporating information, both on the transactions costs and on the exchange rates volatility, eventually resulting in a better timing choice at small time scale intervals. Our results support the hypothesis that arbitrageurs are few and sophisticated users. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.10958&r= |
| By: | Masood Tadi; Irina Kortchmeski |
| Abstract: | This research aims to demonstrate a dynamic cointegration-based pairs trading strategy, including an optimal look-back window framework in the cryptocurrency market, and evaluate its return and risk by applying three different scenarios. We employ the Engle-Granger methodology, the Kapetanios-Snell-Shin (KSS) test, and the Johansen test as cointegration tests in different scenarios. We calibrate the mean-reversion speed of the Ornstein-Uhlenbeck process to obtain the half-life used for the asset selection phase and look-back window estimation. By considering the main limitations in the market microstructure, our strategy exceeds the naive buy-and-hold approach in the Bitmex exchange. Another significant finding is that we implement a numerous collection of cryptocurrency coins to formulate the model's spread, which improves the risk-adjusted profitability of the pairs trading strategy. Besides, the strategy's maximum drawdown level is reasonably low, which makes it useful to be deployed. The results also indicate that a class of coins has better potential arbitrage opportunities than others. This research has some noticeable advantages, making it stand out from similar studies in the cryptocurrency market. First is the accuracy of data in which minute-binned data create the signals in the formation period. Besides, to backtest the strategy during the trading period, we simulate the trading signals using best bid/ask quotes and market trades. We exclusively take the order execution into account when the asset size is already available at its quoted price (with one or more period gaps after signal generation). This action makes the backtesting much more realistic. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.10662&r= |
| By: | Lawrence Middleton; James Dodd; Simone Rijavec |
| Abstract: | Trading styles can be classified into either trend-following or mean-reverting. If the net trading style is trend-following the traded asset is more likely to move in the same direction it moved previously (the opposite is true if the net style is mean-reverting). The result of this is to introduce positive (or negative) correlations into the time series. We here explore the effect of these correlations on the long-run variance of the series through probabilistic models designed to explicitly capture the direction of trading. Our theoretical insights suggests that relative to random walk models of asset prices the long-run variance is increased under trend-following strategies and can actually be reduced under mean-reversal conditions. We apply these models to some of the largest US stocks by market capitalisation as well as high-frequency EUR/USD data and show that in both these settings, the ability to predict the asset price is generally increased relative to a random walk. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.08242&r= |
| By: | Alessandro Gavazza; Alessandro Lizzeri |
| Abstract: | This is an invited chapter for the forthcoming Volume 4 of the Handbook of Industrial Organization. We focus on markets with frictions, such as transaction costs, asymmetric information, search and matching frictions. We discuss how such frictions affect allocations, favor the emergence of intermediaries or dealers, and potentially create market power. Our focus is mostly on markets with many participants rather than on transactions that are bilateral or involve a small number of players. |
| JEL: | L0 L1 |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:29259&r= |