The adaptive nature of liquidity taking in limit order books
By D. Taranto, G. Bormetti, Fabrizio Lillo. 2014
23 citations recorded by the source corpus.
In financial markets, the order flow, defined as the process assuming value one for buy market orders and minus one for sell market orders, displays a very slowly decaying autocorrelation function. Since orders impact prices, reconciling the persistence of the order flow with market efficiency is a subtle issue. A possible solution is provided by asymmetric liquidity, which states that the impact of a buy or sell order is inversely related to the probability of its occurrence. We empirically find that when the order flow predictability increases in one direction, the liquidity in the opposite side decreases, but the probability that a trade moves the price decreases significantly. While the last mechanism is able to counterbalance the persistence of order flow and restore efficiency and diffusivity, the first acts in the opposite direction. We introduce a statistical order book model where the persistence of the order flow is mitigated by adjusting the market order volume to the predictability of the order flow. The model reproduces the diffusive behaviour of prices at all time scales without fine-tuning the values of parameters, as well as the behaviour of most order book quantities as a function of the local predictability of the order flow.
Subfields: Economics and Econometrics, Finance
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In Economics and Econometrics
- Justin A. Sirignano
- Rama Cont
- Adrien de Larrard
- Saejoon Kim
- Alec N. Kercheval
- Konstantinos Spiliopoulos
- Antonis Papapantoleon
- Andreas Hagemann
In Finance
- Justin A. Sirignano
- Rama Cont
- Konstantinos Spiliopoulos
- Matthew F. Dixon
- Peter Tankov
- Saejoon Kim
- Adrien de Larrard
- Steven Kou
Citations in the corpus
- Universal features of price formation in financial markets: perspectives from deep learning (2018, background): “There is a trove of empirical evidence going against this hypothesis, and pointing to long-range dependence in financial time series (Mandelbrot et al. 1997, Bouchaud et al. 2004, Lillo and Farmer 2004, Bacry et al. 2008, Taranto et al. 2014).”