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on Market Microstructure |
| By: | Ioane Muni Toke (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec); Nakahiro Yoshida (Graduate school of mathematics - The University of Tokyo) |
| Abstract: | We introduce a Cox-type model for relative intensities of orders flows in a limit order book. The model assumes that all intensities share a common baseline intensity, which may for example represent the global market activity. Parameters can be estimated by quasi likelihood maximization, without any interference from the baseline intensity. Consistency and asymptotic behavior of the estimators are given in several frameworks, and model selection is discussed with information criteria and penalization. The model is well-suited for high-frequency financial data: fitted models using easily interpretable covariates show an excellent agreement with empirical data. Extensive investigation on tick data consequently helps identifying trading signals and important factors determining the limit order book dynamics. Several illustrations are provided. |
| Keywords: | Hawkes processes,ratio models,Cox processes,order book models,point processes,Cox model,spread,imbalance,ratio model,trading signals |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-01799398&r=all |
| By: | Di Maggio, Marco; Franzoni, Francesco; Massa, Massimo; Tubaldi, Roberto |
| Abstract: | We study empirically informed traders' reaction to the presence of short sellers in the market. We find that investors with positive views on a stock strategically slow down their trades when short sellers are present in the same stock. Moreover, they purchase larger amounts to take advantage of the price decline induced by short sellers. Furthermore, they break up their buy trades across multiple brokers, suggesting that they wish to hide from the short sellers. This behavior may impact price discovery, as we find a sizeable reduction of positive information impounding for stocks more exposed to short selling during information sensitive periods. The evidence is confirmed exploiting exogenous variation in short interest provided by the Reg SHO Pilot Program. The findings have relevance for the regulatory debate on the market impact of short selling. |
| Keywords: | Informed trading; institutional investors; Market Efficiency; Short selling; Strategic traders |
| JEL: | G30 M41 |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:13812&r=all |
| By: | Oleh Danyliv; Bruce Bland |
| Abstract: | Working on different aspects of algorithmic trading we empirically discovered a new market invariant. It links together the volatility of the instrument with its traded volume, the average spread and the volume in the order book. The invariant has been tested on different markets and different asset classes. In all cases we did not find significant violation of the invariant. The formula for the invariant was used for the volatility estimation, which we called the instantaneous volatility. Quantitative comparison showed that it reproduces realised volatility better than one-day-ahead GARCH(1,1) prediction. Because of the short-term prediction nature, the instantaneous volatility could be used by algo developers, volatility traders and other market professionals. |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1908.02847&r=all |
| By: | David Byrd; Tucker Hybinette Balch |
| Abstract: | In finance, the weak form of the Efficient Market Hypothesis asserts that historic stock price and volume data cannot inform predictions of future prices. In this paper we show that, to the contrary, future intra-day stock prices could be predicted effectively until 2009. We demonstrate this using two different profitable machine learning-based trading strategies. However, the effectiveness of both approaches diminish over time, and neither of them are profitable after 2009. We present our implementation and results in detail for the period 2003-2017 and propose a novel idea: the use of such flexible machine learning methods as an objective measure of relative market efficiency. We conclude with a candidate explanation, comparing our returns over time with high-frequency trading volume, and suggest concrete steps for further investigation. |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1908.08168&r=all |