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on Market Microstructure |
| By: | Alexis Stenfor (University of Portsmouth); Masayuki Susai (Nagasaki University) |
| Abstract: | We investigate if and how other traders react to algorithmic order-splitting tactics. Studying over 1.4 million limit orders in the EUR/USD foreign exchange (FX) spot market, we find that stealth-trading strategies adopted by algorithmic traders seem to go detected and are perceived as more market-moving than orders of the corresponding size typically submitted by human traders. We also document that algorithmic traders appear to be more sensitive to limit orders submitted from the opposite side (free-option risk) than to the same side of the order book (non-execution risk). Once human traders have had time to react, however, the pattern reverses. |
| Keywords: | algorithmic trading, foreign exchange, limit order book, market microstructure, order splitting, stealth trading |
| JEL: | D4 F3 |
| Date: | 2021–02–12 |
| URL: | http://d.repec.org/n?u=RePEc:pbs:ecofin:2021-02&r=all |
| By: | Roberto Riccó; Barbara Rindi; Duane J. Seppi |
| Abstract: | We determine optimal market access pricing for an exchange or Social Planner. Exchanges optimally use rebate-based pricing (vs. strictly positive fees) when ex ante gains-from-trade and trading activity are low (high). Exchange rebate-based pricing increases (decreases) welfare when investor valuation dispersion and trading activity are low (high). A Social Planner increases welfare using rebate-based pricing. High-frequency traders strengthen exchange incentives for rebate-based pricing; a new explanation for widespread Maker-Taker and Taker-Maker pricing. With HFTs, rebate-based pricing improves total welfare, but Pareto transfers are needed to improve investor welfare. Sequential bargaining games between competing exchanges setting fees have pure-strategy equilibria. JEL classification: G10, G20, G24, D40 Keywords: Market access fees, make-take, limit order markets, liquidity, market microstructure |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:igi:igierp:675&r=all |
| By: | Giuseppe Buccheri (DEF Università di Roma "Tor Vergata"); Stefano Grassi (DEF Università di Roma "Tor Vergata"); Giorgio Vocalelli (DEF Università di Roma "Tor Vergata") |
| Abstract: | We propose a model of price formation in which the trading price varies only if the value of the information signal is large enough to guarantee a profit in excess of transaction costs. Using transaction data only, we extract: (i) the conditional volatility of the underlying security, which is thus cleaned out by market frictions, (ii) an estimate of transaction costs. Our analysis reveals that, after correcting for frictions, the risk of illiquid securities is substantially different from what predicted by traditional volatility models. Furthermore, in periods of high volatility, our estimate of transaction costs remains highly correlated with bid-ask spreads, whereas alternative illiquidity proxies, such as the number of zero returns, loose their explanatory power. |
| Keywords: | Illiquidity,Market Microstructure,Volatility,Risk assessment. |
| JEL: | B26 C22 C58 |
| Date: | 2021–01–30 |
| URL: | http://d.repec.org/n?u=RePEc:rtv:ceisrp:506&r=all |
| By: | Zihao Zhang; Bryan Lim; Stefan Zohren |
| Abstract: | Market by order (MBO) data - a detailed feed of individual trade instructions for a given stock on an exchange - is arguably one of the most granular sources of microstructure information. While limit order books (LOBs) are implicitly derived from it, MBO data is largely neglected by current academic literature which focuses primarily on LOB modelling. In this paper, we demonstrate the utility of MBO data for forecasting high-frequency price movements, providing an orthogonal source of information to LOB snapshots. We provide the first predictive analysis on MBO data by carefully introducing the data structure and presenting a specific normalisation scheme to consider level information in order books and to allow model training with multiple instruments. Through forecasting experiments using deep neural networks, we show that while MBO-driven and LOB-driven models individually provide similar performance, ensembles of the two can lead to improvements in forecasting accuracy -- indicating that MBO data is additive to LOB-based features. |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2102.08811&r=all |