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on Market Microstructure |
| By: | Ying Chen; Ulrich Horst; Hoang Hai Tran |
| Abstract: | We derive an explicit solution for deterministic market impact parameters in the Graewe and Horst (2017) portfolio liquidation model. The model allows to combine various forms of market impact, namely instantaneous, permanent and temporary. We show that the solutions to the two benchmark models of Almgren and Chris (2001) and of Obizhaeva and Wang (2013) are obtained as special cases. We relate the different forms of market impact to the microstructure of limit order book markets and show how the impact parameters can be estimated from public market data. We investigate the numerical performance of the derived optimal trading strategy based on high frequency limit order books of 100 NASDAQ stocks that represent a range of market impact profiles. It shows the strategy achieves significant cost savings compared to the benchmark models of Almgren and Chris (2001) and of Obizhaeva and Wang (2013). |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1912.06426&r=all |
| By: | Ao Kong; Hongliang Zhu; Robert Azencott |
| Abstract: | Predicting the intraday stock jumps is a significant but challenging problem in finance. Due to the instantaneity and imperceptibility characteristics of intraday stock jumps, relevant studies on their predictability remain limited. This paper proposes a data-driven approach to predict intraday stock jumps using the information embedded in liquidity measures and technical indicators. Specifically, a trading day is divided into a series of 5-minute intervals, and at the end of each interval, the candidate attributes defined by liquidity measures and technical indicators are input into machine learning algorithms to predict the arrival of a stock jump as well as its direction in the following 5-minute interval. Empirical study is conducted on the level-2 high-frequency data of 1271 stocks in the Shenzhen Stock Exchange of China to validate our approach. The result provides initial evidence of the predictability of jump arrivals and jump directions using level-2 stock data as well as the effectiveness of using a combination of liquidity measures and technical indicators in this prediction. We also reveal the superiority of using random forest compared to other machine learning algorithms in building prediction models. Importantly, our study provides a portable data-driven approach that exploits liquidity and technical information from level-2 stock data to predict intraday price jumps of individual stocks. |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1912.07165&r=all |
| By: | Greppmair, Stefan; Theissen, Erik |
| Abstract: | We analyze how the introduction of a mini futures contract affects the liquidity of the regular contract. We use a panel data set that covers more than 20 years and more than 20 contracts. We use a traditional difference-in-differences methodology as well as a synthetic control group approach (Abadie and Gardeazabal (2003), Abadie, Diamond and Hainmueller (2015)). We find that the liquidity of the regular contracts increases and the volatility decreases upon the introduction of a mini futures contract when the regular contract is traded electronically whereas the reverse is true when it is floor-traded. While total trading volume increases upon the introduction of the mini contract, the volume of the regular contracts does not change significantly. Overall, our results imply that the introduction of mini futures contracts is beneficial. They also confirm the superiority of electronic trading over floor-based trading. |
| Keywords: | Stock index futures,Mini futures,Liquidity,Market quality |
| JEL: | G10 G15 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfrwps:1906&r=all |
| By: | Henry Hanifan; John Cartlidge |
| Abstract: | We explore the competitive effects of reaction time of automated trading strategies in simulated financial markets containing a single exchange with public limit order book and continuous double auction matching. A large body of research conducted over several decades has been devoted to trading agent design and simulation, but the majority of this work focuses on pricing strategy and does not consider the time taken for these strategies to compute. In real-world financial markets, speed is known to heavily influence the design of automated trading algorithms, with the generally accepted wisdom that faster is better. Here, we introduce increasingly realistic models of trading speed and profile the computation times of a suite of eminent trading algorithms from the literature. Results demonstrate that: (a) trading performance is impacted by speed, but faster is not always better; (b) the Adaptive-Aggressive (AA) algorithm, until recently considered the most dominant trading strategy in the literature, is outperformed by the simplistic Shaver (SHVR) strategy - shave one tick off the current best bid or ask - when relative computation times are accurately simulated. |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1912.02775&r=all |