Machine learning and data sciences for financial markets a guide to contemporary practices

Speaker: 

Charles Albert Lehalle, Abu Dhabi Investment Authority

Date and Time: 

Thursday, September 30, 2021 - 11:45am to 12:45pm

Abstract: 

The last 10 years have seen an increase in the use of alternative data by market participants. These data are not financial data, for instance satellite images, texts, credit card rockets, geolocation of mobile phones, etc. They can give insights on the health of economic entities like companies, cities, or production of commodities. I will explain how to address these new types of data that have very specific characteristics compared to the ones previously used to inform financial decisions : unlike prices or traded quantities, they are not well structured in collections of time series. Unlike financial statements, they may be available on a small subset of entities and linking these entities to tradable instruments can be a challenge.

I will review them and explain how the tools provided by machine learning can be used to address them, enabling to provide nowcasting (as opposed to forecasting) indicators. Enhancing financial decisions with nowcasting provides a better connection with the real economy, that is well described by alternative data. Last but not least, if I have time I will focus on the use of texts on financial datasets.

For more details, please have a look at "Do Word Embeddings Really Understand Loughran-McDonald's Polarities?" (https://arxiv.org/abs/2103.09813) and wait a few months to read two forthcoming books "The Financial Ecosystem in Practice: From Post-Crisis Intermediation To FinTechs" (World Scientific 2022, C-A. L and Amine Raboun) and "Machine Learning And Data Sciences For Financial Markets: A Guide To Contemporary Practices" (Cambridge University Press 2022, Agostino Capponi and C-A. L editors).

Research

Here is a list of my latest research.

Research papers

Published/accepted papers

• Cartea, Á., Perez Arribas, I., & Sánchez-Betancourt, L. (2022+). Double-Execution Strategies using Path Signatures. SIAM Journal on Financial Mathematics (forthcoming) Link.
• Cartea, Á., & Sánchez-Betancourt, L. (2022+). Optimal Execution with Stochastic Delay. Finance and Stochastics (forthcoming) Link.
• Cartea, Á., Jaimungal, S., & Sánchez-Betancourt, L. (2022+). Deep Reinforcement Learning for Algorithmic Trading. In Machine Learning in Financial Markets: A guide to contemporary practices (forthcoming). Edited by C.-A. Lehalle and A. Capponi. Cambridge University Press. Link.
• Cartea, Á., Jaimungal, S., & Sánchez-Betancourt, L. (2021). Latency and Liquidity Risk. International Journal of Theoretical and Applied Finance 24 (06n07), 1-37. Link.
• Forde, M., Sánchez-Betancourt, L., & Smith, B. (2021). Optimal Trade Execution for Gaussian Signals with Power-law Resilience. Quantitative Finance, 22 (3), 585-596. Link.
• Bouzianis, G., Hughston, L. P., Jaimungal, S., & Sánchez-Betancourt, L. (2021). Lévy-Ito Models in Finance. Probability Surveys 18, 132-178. Link.
• Cartea, Á., & Sánchez-Betancourt, L. (2021). The Shadow Price of Latency: Improving Intraday Fill Ratios in Foreign Exchange Markets. SIAM Journal on Financial Mathematics 12 (1), 254-294. Link.
• Hughston, L. P., & Sánchez-Betancourt, L. (2020). Pricing with Variance Gamma Information. Risks 8 (4), 105. Link

Submitted for publication

• Jaimungal, S., Pesenti, S. M., & Sánchez-Betancourt, L. (2022). Minimal Kullback-Leibler Divergence for Constrained Levy-Ito Processes. Link.
• Bouzianis, G., Hughston, L. P., & Sánchez-Betancourt, L. (2022). Information-based Trading. Link.

Working papers

• Bellani, C., Brigo, D., Pakkanen, M. S., & Sánchez-Betancourt, L. (2022). Non-average price impact in order-driven markets. Link.
• Jerome, J., Sánchez-Betancourt, L., Savani, R., & Herdegen, M. (2022). Model-based gym environments for limit order book trading. Link.
• Cartea, Á., & Sánchez-Betancourt, L. (2022). Brokers and Informed Traders: dealing with toxic flow and extracting trading signals. Link.

DPhil Thesis

• Sánchez-Betancourt, L. (2021). Uncertain Execution in Order-driven Markets. PhD Thesis, University of Oxford. Link.

Working projects

• There are a number of projects that I am pursuing in collaboration with other researchers. Some of the topics we are currently exploring are: (i) internalisation and externalisation of toxic flow, (ii) market simulators, (iii) classification of agents, and (iv) benchmarks for algorithmic trading strategies.

very proud to have sent earlier this week to Cambridge University Press the manuscript of "Machine Learning And Data Sciences For Financial Markets: A Guide To Contemporary Practices" that we co-edited Agostino Capponi and myself. 800 pages by 60 contributors on how to use ML either to improve the quality of the service, the risk management or the link with the real economy in Financial Markets. We wanted to show the continuity between quantitative finance and machine learning in this field because leveraging on existing scientific progresses is far better than restarting from scratch a data only-driven approach. #machinelearning #financialmarkets #quantitativefinance Check the table of content and stay tuned: the book should be available in mid 2022: