How Markets Slowly Digest Changes in Supply and Demand

By Jean-Philippe Bouchaud, J. D. Farmer, Fabrizio Lillo. 2008

512 citations recorded by the source corpus.

In this article we revisit the classic problem of tatonnement in price formation from a microstructure point of view, reviewing a recent body of theoretical and empirical work explaining how fluctuations in supply and demand are slowly incorporated into prices. Because revealed market liquidity is extremely low, large orders to buy or sell can only be traded incrementally, over periods of time as long as months. As a result order flow is a highly persistent long-memory process. Maintaining compatibility with market efficiency has profound consequences on price formation, on the dynamics of liquidity, and on the nature of impact. We review a body of theory that makes detailed quantitative predictions about the volume and time dependence of market impact, the bid-ask spread, order book dynamics, and volatility. Comparisons to data yield some encouraging successes. This framework suggests a novel interpretation of financial information, in which agents are at best only weakly informed and all have a similar and extremely noisy impact on prices. Most of the processed information appears to come from supply and demand itself, rather than from external news. The ideas reviewed here are relevant to market microstructure regulation, agent-based models, cost-optimal execution strategies, and understanding market ecologies.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/65831d637e9af4ac6743fe45c274a6a6d0929aaf

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Citations in the corpus

No-dynamic-arbitrage and market impact (2010, unknown): “In the language of Gerig (2007) and Bouchaud et al. (2008), E[St] gives the expected market impact at time t of a hidden order with rate of trading _ xs; we know how much and when we intend to trade, but other market participants do not.”
Econophysics review: II. Agent-based models (2011, unknown): “There are, of course, many reviews and books already published in this area (see, e.g., Challet et al. (2004), Coolen (2005), Chatterjee and Chakrabarti (2007), Samanidou et al. (2007), Bouchaud et al. (2009), Lux and Westerhoff (2009), Yakovenko and Rosser (2009), etc.).”
A Stochastic Model for Order Book Dynamics (2010, unknown): “The steady state profile of the order book describes the average market impact of trades Farmer et al. (2004), Bouchaud et al. (2008).”
Market Impacts and the Life Cycle of Investors Orders (2014, unknown): “• In the picture of [Bouchaud, 2009], there is no such thing as permanent impact.”
Statistical inference for ergodic point processes and application to Limit Order Book (2015, background): “While the availability of high-frequency financial data has made in-depth analysis of LOB dynamics accessible, it is now possible to use statistical tools in order to highlight either empirical facts about their shapes and their evolution [26,9], or to design and select models that are able to repro”
Computational Visual Analysis of the Order Book Dynamics for Creating High-frequency Foreign Exchange Trading Strategies (2015, background): “In the specific case of the order book dynamics, several studies had shown that order books have relevant information to improve financial price direction prediction [1, 2, 3, 4, 10, 11].”
Intelligent Data Analysis of Intelligent Systems (2010, background): “Zero intelligence models can be improved by taking some of the strategic properties of the agents into account, either by empirically characterizing the behavior of real agents, or by imposing the principle of market efficiency, which states that no agent can make riskless profits [ 28 ].”
The price impact of order book events: market orders, limit orders and cancellations (2009, methodology): “Since the dependence of impact on the volume of the trades is weak [6, 15], we have chosen to classify events not according to their volume but according to whether they change the mid-point or not.”
Systemic risk-efficient asset allocations: Minimization of systemic risk as a network optimization problem (2018, background): “Note that ‘liquidity is an elusive concept’ Amihud (2002) and different concepts of liquidity and associated price impacts do exist (Bouchaud et al., 2009; Bouchaud, 2010).”
A Mathematical Approach to Order Book Modelling (2010, background): “f actual trade prices looks much ﬂatter compared to the simulation (See ﬁgure 9.) This was discovered and discussed in detail by Bouchaud et al. in [3], and Lillo and Farmer in [13] (See also [9] and [2].) They note that actual order signs exhibit positive long-ranged correlations. They also note th”
Price Dynamics in a Markovian Limit Order Market (2011, background): “On the other hand, empirical studies on limit order books (Bouchaud et al. (2008), Farmer et al. (2004), Gourieroux et al. (1999), Holliﬁeld et al. (2004), Smith et al. (2003)) provide an extensive list of statistical features of order book dynamics that are challenging to incorporate in a single…”
Statistical Modeling of High-Frequency Financial Data (2011, background): “These data provide us with a detailed view of the complex dynamic process through which the market ‘digests’ the inﬂow of supply and demand to generate the price [14, 38].”
The adaptive nature of liquidity taking in limit order books (2014, methodology): “As shown in [4], using as sign predictor an AR(p) model when p → ∞, the statistical model of equation (6) proposed in [11] is equivalent to the propagator model (see [1, 3]).”
Ergodicity and scaling limit of a constrained multivariate Hawkes process (2013, unknown): “On the one hand, an extensive statistical study on the limit order book dynamics and limit order book information content can be found in [9, 26, 39, 19, 30, 10, 25, 14, 41]; on the other hand, many researchers have endeavored to propose different models, including equilibrium model, agent-based mod”
Crises and Collective Socio-Economic Phenomena: Simple Models and Challenges (2012, background): “In the case of financial markets, the price change itself can be seen, on a coarse-grained time scale, as an indicator of the aggregate demand (although the detailed relationship between the two might be quite subtle, see [65, 66]).”
A one-level limit order book model with memory and variable spread (2017, background): “Bouchaud et al. [3] argue that relatively large spreads may be created by monopolistic practices of market makers, high order processing costs, and large market orders.”