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Machine Learning in Finance - course by Henri Waelbroeck


Machine Learning in Finance - course by Henri Waelbroeck

This course is being run by Henri Waelbroeck (Vice President and Director of Research for Portfolio Management & Trading solutions at FactSet) and will take place over the space of four late-afternoon/evening sessions at LSE between 26 February - 6 March 2019.

As the topics covered all interrelate, attendees are encouraged to attend all four sessions.

This course is open to all current students at LSE who are studying a postgraduate course (valid LSE ID card required) or to members of academic staff. Please note that light refreshments will be provided.


Dates and locations of the sessions are as follows:

Session #1: Tuesday 26 February 2019, 5.00pm - 7.00pm (Clement House, CLM.7.02)

Session #2: Wednesday 27 February, 6.00pm - 8.00pm (Clement House, CLM.7.02)

Session #3: Tuesday 5 March 2019, 5.00pm - 7.00pm (Parish Hall, PAR.LG.03)

Session #4: Wednesday 6 March 2019, 6.00pm - 8.00pm ((Clement House, CLM.7.02)

The course will be structured in the following manner, and will cover the topics below:

1) Introduction: A lesson in humility: the global financial market as a learning machine and the role of the individual data scientist.

The data science process:

-          Problem identification in (nearly) arbitrage-free markets

-          The science: feature engineering and expert insight

-          Near-equilibrium dynamics vs sledge hammers

-          Application domain: black swans, FOMC meeting days and Thanksgiving dinners

-          Performance metrics and expectations

-          Reserving enough test data to validate performance

-          Concept drift

-          Feature selection

-          Model selection

-          Model testing and performance reporting

2) Microstructure

-          Forecasting the order flow imbalance

-          Short-term volatility GARCH and Q-Hawkes processes

3) Trade execution algorithms: predicting the participation rate

-          Algorithm styles, spread costs and adverse selection

-          Order flow persistence

-          Not-so-naïve Bayes for regression

4) Alpha profiling: behavioural trading and coherence of manager decisions

-          Fair pricing

-          Market impact and expected cost of alternative execution strategies

-          Case studies: quant fund vs value manager

5) Volatility forecasting

-          Beating GARCH I - VIX-dependent model coefficients

-          Beating GARCH II – news and event awareness

6) Options pricing

-          Ensemble average of predictors vs Vol surface: time-series and cross-sectional viewpoints

-          What are they thinking? Learning diffusion-jump models

-          Real-time option pricing: beyond Greeks

7) Corporate bonds in a crisis

-          The Merton model as a machine learning tool

-          Application: the 2016 US shale issuers crisis

8) Exercises/discussion/review







Map CLM.7.02 / PAR.LG.03, London School of Economics and Political Science, London, United Kingdom
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