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on Market Microstructure |
| By: | Matteo Aquilina; Eric Budish; Peter O'Neill |
| Abstract: | We use stock exchange message data to quantify the negative aspect of high-frequency trading, known as "latency arbitrage". The key difference between message data and widely familiar limit order book data is that message data contain attempts to trade or cancel that fail. This allows the researcher to observe both winners and losers in a race, whereas in limit order book data you cannot see the losers, so you cannot directly see the races. We find that latency arbitrage races are very frequent (about one per minute per symbol for FTSE 100 stocks), extremely fast (the modal race lasts 5-10 millionths of a second), and account for a remarkably large portion of overall trading volume (about 20%). Race participation is concentrated, with the top six firms accounting for over 80% of all race wins and losses. The average race is worth just a small amount (about half a price tick), but because of the large volumes the stakes add up. Our main estimates suggest that races constitute roughly one third of price impact and the effective spread (key microstructure measures of the cost of liquidity), that latency arbitrage imposes a roughly 0.5 basis point tax on trading, that market designs that eliminate latency arbitrage would reduce the market's cost of liquidity by 17%, and that the total sums at stake are on the order of $5 billion per year in global equity markets alone. |
| Keywords: | market design, high-frequency trading, financial exchanges, liquidity, latency arbitrage, trading volume, message data |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:955&r= |
| By: | Jean-Philippe Bouchaud |
| Abstract: | We attempt to reconcile Gabaix and Koijen's (GK) recent Inelastic Market Hypothesis with the order-driven view of markets that emerged within the microstructure literature in the past 20 years. We review the most salient empirical facts and arguments that give credence to the idea that market price fluctuations are mostly due to order flow, whether informed or non-informed. We show that the Latent Liquidity Theory of price impact makes a precise prediction for GK's multiplier $M$, which measures by how many dollars, on average, the market value of a company goes up if one buys one dollar worth of its stocks. Our central result is that $M$ increases with the volatility of the stock and decreases with the fraction of the market cap. that is traded daily. We discuss several empirical results suggesting that the lion's share of volatility is due to trading activity. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2108.00242&r= |
| By: | Terrence Hendershott (University of California, Berkeley - Haas School of Business); Dmitry Livdan (University of California, Berkeley); Norman Schürhoff (University of Lausanne; Swiss Finance Institute; Centre for Economic Policy Research (CEPR)) |
| Abstract: | We examine technology enabling dispersed investors to directly trade with each other in over-the-counter markets via the largest electronic trading platform in corporate bonds starting Open Trading (OT) to allow investor-to-investor trading. Over our six-year sample, OT steadily grew to win 12% of trades on the platform, with 2% being investor-to-investor trading, 3% being dealers trading with new clients, and 7% being new liquidity providers acting like dealers. This suggests that investors in corporate bonds prefer intermediation to direct trade. However, OT can enable new dealers to compete in liquidity provision. OT's steady growth facilitates measuring its effect on investors, dealers, and competition to provide liquidity using an auction model. |
| Keywords: | Over-the-counter markets, electronic trading, request for quote, open trading, corporate bonds, dealers |
| JEL: | G12 G14 G19 |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2143&r= |
| By: | Eghbal Rahimikia; Stefan Zohren; Ser-Huang Poon |
| Abstract: | We develop FinText, a novel, state-of-the-art, financial word embedding from Dow Jones Newswires Text News Feed Database. Incorporating this word embedding in a machine learning model produces a substantial increase in volatility forecasting performance on days with volatility jumps for 23 NASDAQ stocks from 27 July 2007 to 18 November 2016. A simple ensemble model, combining our word embedding and another machine learning model that uses limit order book data, provides the best forecasting performance for both normal and jump volatility days. Finally, we use Integrated Gradients and SHAP (SHapley Additive exPlanations) to make the results more 'explainable' and the model comparisons more transparent. |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2108.00480&r= |
| By: | Gündüz, Yalin; Pelizzon, Loriana; Schneider, Michael; Subrahmanyam, Marti G. |
| Abstract: | We study the impact of transparency on liquidity in OTC markets. We do so by providing an analysis of liquidity in a corporate bond market without trade transparency (Germany), and comparing our findings to a market with full posttrade disclosure (the U.S.). We employ a unique regulatory dataset of transactions of German financial institutions from 2008 until 2014 to find that: First, overall trading activity is much lower in the German market than in the U.S. Second, similar to the U.S., the determinants of German corporate bond liquidity are in line with search theories of OTC markets. Third, surprisingly, frequently traded German bonds have transaction costs that are 39-61 bp lower than a matched sample of bonds in the U.S. Our results support the notion that, while market liquidity is generally higher in transparent markets, a subset of bonds could be more liquid in more opaque markets because of investors "crowding" their demand into a small number of more actively traded securities. |
| Keywords: | Corporate Bonds,WpHG,Liquidity,Transparency,OTC markets |
| JEL: | G15 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdps:212021&r= |