Publications
54 results found
Capponi A, Lehalle C-A, 2023, Machine Learning and Data Sciences for Financial Markets A Guide to Contemporary Practices, Publisher: Cambridge University Press, ISBN: 9781316516195
Learn how cutting-edge AI and data science techniques are integrated in financial markets from leading experts in the industry.
Guo X, Lehalle C-A, Xu R, 2022, Transaction cost analytics for corporate bonds, Quantitative Finance, Vol: 22, Pages: 1295-1319
Lehalle C-A, Raboun A, 2022, Financial Markets in Practice From Post-crisis Intermediation to Fintechs, Publisher: World Scientific Publishing Company, ISBN: 9789811252570
This risk-transformation oriented view is supported by the changes that followed the last global financial crisis: consumers of financial products asked for less complex risk transformations, regulators demanded limiting risks inside ...
Lehalle C-A, Neuman E, Shlomov S, 2021, Phase transitions in Kyle's model with market maker profit incentives, Publisher: arXiv
We consider a stochastic game between three types of players: an insidetrader, noise traders and a market maker. In a similar fashion to Kyle's model,we assume that the insider first chooses the size of her market-order and thenthe market maker determines the price by observing the total order-flowresulting from the insider and the noise traders transactions. In addition tothe classical framework, a revenue term is added to the market maker'sperformance function, which is proportional to the order flow and to the sizeof the bid-ask spread. We derive the maximizer for the insider's revenuefunction and prove sufficient conditions for an equilibrium in the game. Then,we use neural networks methods to verify that this equilibrium holds. We showthat the equilibrium state in this model experience interesting phasetransitions, as the weight of the revenue term in the market maker'sperformance function changes. Specifically, the asset price in equilibriumexperience three different phases: a linear pricing rule without a spread, apricing rule that includes a linear mid-price and a bid-ask spread, and ametastable state with a zero mid-price and a large spread.
Leal L, Laurière M, Lehalle C-A, 2021, Learning a functional control for high-frequency finance, Publisher: arXiv
We use a deep neural network to generate controllers for optimal trading onhigh frequency data. For the first time, a neural network learns the mappingbetween the preferences of the trader, i.e. risk aversion parameters, and theoptimal controls. An important challenge in learning this mapping is that inintraday trading, trader's actions influence price dynamics in closed loop viathe market impact. The exploration--exploitation tradeoff generated by theefficient execution is addressed by tuning the trader's preferences to ensurelong enough trajectories are produced during the learning phase. The issue ofscarcity of financial data is solved by transfer learning: the neural networkis first trained on trajectories generated thanks to a Monte-Carlo scheme,leading to a good initialization before training on historical trajectories.Moreover, to answer to genuine requests of financial regulators on theexplainability of machine learning generated controls, we project the obtained"blackbox controls" on the space usually spanned by the closed-form solution ofthe stylized optimal trading problem, leading to a transparent structure. Formore realistic loss functions that have no closed-form solution, we show thatthe average distance between the generated controls and their explainableversion remains small. This opens the door to the acceptance of ML-generatedcontrols by financial regulators.
Lehalle C-A, Simon G, 2021, Portfolio selection with active strategies: how long only constraints shape convictions, Journal of Asset Management, Vol: 22, Pages: 443-463
Raboun A, Briere M, Lehalle C-A, 2021, Liquidity Provision and Market-Making in Different Uncertainty Regimes: Evidence from the COVID-19 Market Crash, Available at SSRN 3815169
Lehalle C-A, Mounjid O, Rosenbaum M, 2021, Optimal liquidity-based trading tactics, Stochastic Systems, Vol: 11, Pages: 368-390
Li M, Lehalle C-A, 2021, Do Word Embeddings Really Understand Loughran-McDonald’s Polarities?, arXiv preprint arXiv:2103.09813
Bucci F, Mastromatteo I, Eisler Z, et al., 2020, Co-impact: crowding effects in institutional trading activity, QUANTITATIVE FINANCE, Vol: 20, Pages: 193-205, ISSN: 1469-7688
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Briere M, Lehalle C-A, Nefedova T, et al., 2020, Modeling transaction costs when trades may be crowded: A Bayesian network using partially observable orders imbalance, Machine Learning for Asset Management: New Developments and Financial Applications, Pages: 387-430
Lehalle C-A, Neuman E, 2019, Incorporating signals into optimal trading, Finance and Stochastics, ISSN: 1432-1122
Optimal trading is a recent field of research which was initiated by Almgren, Chriss, Bertsimas and Lo in the late 90's. Its main application is slicing large trading orders, in the interest of minimizing trading costs and potential perturbations of price dynamics due to liquidity shocks. The initial optimization frameworks were based on mean-variance minimization for the trading costs. In the past 15 years, finer modelling of price dynamics, more realistic control variables and different cost functionals were developed. The inclusion of signals (i.e. short term predictors of price dynamics) in optimal trading is a recent development and it is also the subject of this work.We incorporate a Markovian signal in the optimal trading framework which was initially proposed by Gatheral, Schied, and Slynko [21] and provide results on the existence and uniqueness of an optimal trading strategy. Moreover, we derive an explicit singular optimal strategy for the special case of an Ornstein-Uhlenbeck signal and an exponentially decaying transient market impact. The combination of a mean-reverting signal along with a market impact decay is of special interest, since they affect the short term price variations in opposite directions.Later, we show that in the asymptotic limit were the transient market impact becomes instantaneous, the optimal strategy becomes continuous. This result is compatible with the optimal trading framework which was proposed by Cartea and Jaimungal [10].In order to support our models, we analyse nine months of tick by tick data on 13 European stocks from the NASDAQ OMX exchange. We show that orderbook imbalance is a predictor of the future price move and it has some mean-reverting properties. From this data we show that market participants, especially high frequency traders, use this signal in their trading strategies.
Lehalle C-A, 2019, La finance de marché à l’ère de l’intelligence bon marché, Revue d’économie financière, Pages: 67-84
Lehalle C-A, Mouzouni C, 2019, A mean field game of portfolio trading and its consequences on perceived correlations, arXiv preprint arXiv:1902.09606
Briere M, Lehalle C-A, Nefedova T, et al., 2019, Stock market liquidity and the trading costs of asset pricing anomalies, Université Paris-Dauphine Research Paper
Mounjid O, Lehalle C-A, 2019, Improving reinforcement learning algorithms: towards optimal learning rate policies, Mathematical Finance
Cardaliaguet P, Lehalle C-A, 2018, Mean field game of controls and an application to trade crowding, Mathematics and Financial Economics, Vol: 12, Pages: 335-363
Lehalle C-A, Laruelle S, others, 2018, Monitoring the Fragmentation at Any Scale, World Scientific Book Chapters, Pages: 33-115
De March H, Lehalle C-A, 2018, Optimal trading using signals, arXiv preprint arXiv:1811.03718
Lehalle C-A, Laruelle S, 2017, Market Microstructure in Practice 2nd Edition, ISBN: 9789813231122
Revised edition of Market microstructure in practice, [2014]
Geeraert S, Lehalle C-A, Pearlmutter BA, et al., 2017, Mini-symposium on automatic differentiation and its applications in the financial industry, ESAIM: Proceedings and Surveys, Vol: 59, Pages: 56-75
Lehalle C-A, Mounjid O, 2017, Limit order strategic placement with adverse selection risk and the role of latency, Market Microstructure and Liquidity, Vol: 3, Pages: 1750009-1750009
Megarbane N, Saliba P, Lehalle C-A, et al., 2017, The behavior of high-frequency traders under different market stress scenarios, Market Microstructure and Liquidity, Vol: 3, Pages: 1850005-1850005
Lachapelle A, Lasry J-M, Lehalle C-A, et al., 2016, Efficiency of the price formation process in presence of high frequency participants: a mean field game analysis, Mathematics and Financial Economics, Vol: 10, Pages: 223-262
Huang W, Lehalle C-A, Rosenbaum M, 2015, How to predict the consequences of a tick value change? Evidence from the Tokyo Stock Exchange pilot program
The tick value is a crucial component of market design and is often considered the most suitable tool to mitigate the effects of high frequency trading. The goal of this paper is to demonstrate that the approach introduced in Dayri and Rosenbaum (2015) allows for an ex ante assessment of the consequences of a tick value change on the microstructure of an asset. To that purpose, we analyze the pilot program on tick value modifications started in 2014 by the Tokyo Stock Exchange in light of this methodology. We focus on forecasting the future cost of market and limit orders after a tick value change and show that our predictions are very accurate. Furthermore, for each asset involved in the pilot program, we are able to define (ex ante) an optimal tick value. This enables us to classify the stocks according to the relevance of their tick value, before and after its modification.
Bacry E, Iuga A, Lasnier M, et al., 2015, Market impacts and the life cycle of investors orders, Market Microstructure and Liquidity, Vol: 1, Pages: 1550009-1550009
Lehalle C-A, 2015, Mathematical Models to Study and Control the Price Formation Process
Azencott R, Beri A, Gadhyan Y, et al., 2014, Real-time market microstructure analysis: online transaction cost analysis, QUANTITATIVE FINANCE, Vol: 14, Pages: 1167-1185, ISSN: 1469-7688
Besson P, Lehalle C-A, 2014, The deal/book split analysis: A new method to disentangle the contribution to market and limit orders in any price change, Available at SSRN 2377965
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