Tom Wolfe died yesterday; in “The Right Stuff”, there’s a contrast between the test pilots at the Edwards Air Force Base and the astronauts selected for Project Mercury; while the test pilots where in control of their planes (until they pushed the envelope too far), the astronauts were not in control of their capsules – indeed, their fate was determined by the ground control decisions, gravity and the circumstances of the launch.
In a certain way, the shift from modeling to learning evident in today’s presentations has some parallels with the right stuff … while the model designers have to deal with issues like ensuing convergence or no-arbitrage while walking the razor-thin path between tractability and explanatory power, there’s something more akin to the role of a passenger riding through an interpolated orbit as one learns its way from past data.
A good reminder of this came with the opening presentation, where Gerd Gigerenzer discussed real world heuristics vs theory and complexity. This is best exemplified by the gaze heuristic. His research program includes:
-The Adaptive Toolbox (What are the heuristics we use, their building blocks, and the evolved capacities they exploit?)
-Ecological Rationality (What types of environments does a given heuristic work in?)
-Intuitive Design (How can heuristics and environments be designed to improve decision making?)
A good anecdote about Markowitz’s own personal portfolio allocation rule (1/N) helps to bring home these points.
Stefano Pasquali (BlackRock) discussed how the machine learning framework for liquidity risk management was implemented; my favourite slides (when and how to use machine learning, a posssible application of transfer learning) are below:
The “Frontiers in big data, machine learning and supercomputing” panel had brief presentations from the debaters; some interesting points from Marcos López de Prado on the applications of machine learning in finance: hierarchical estimates instead of covariance matrices, detection of structural breaks (particularly useful in countries like Brazil, where such breaks are rather frequent), bet sizing (meta labeling), feature importance and the detection of false investment strategies; Horst Simon stressed the importance of a balanced team when working with today’s data processing challenges.
For a critical answer to López de Prado, please see Michael Harris here: https://medium.com/@mikeharrisNY/how-some-academics-misguide-traders-and-hedge-funds-b0bfd7e12a99
Alexander Giese presented on Trade Anomaly detection; I like the auto-encoder and local outlier factor approaches for finding outliers (the problem of classifying performance among a diverse – in scale – group is similar to the local outlier approach).
Lorenzo Bergomi, the author of the must-read Stochastic Volatility Modeling ( https://www.lorenzobergomi.com/ ), developed an interesting way of breaking down theta for exotic options portfolios while also pointing out that you need to work within the same Monte Carlo simulations for theta (avoid building one calculation for the price today and another – even with the same random numbers – for the price tomorrow); this is critical for longer-dated options, which have a small theta.
Vacslav Glukhov showed how JP Morgan implemented a self-learning agent with focus on limit order placement, instead of working with the traditional dynamic programming approach:
Rama Cont presented his paper “Universal Features of Price Formation in Financial Markets: Perspectives From Deep Learning” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3141294 ; interesting results on on the model trained on all stocks outperforming the model trained on each stock.
Michael Steliaros had a lot of data the evolution of liquidity on different markets, from intraday volume profiles (please note the increasing importance and pull of the close):
To the breakdown of daily volatility into intraday vs overnight volatility:
At the same time slot, Michael Benzaquen from Écolpe Polytechnique ( http://www.off-ladhyx.polytechnique.fr/people/benzaquen/pages/publications.html ) discussed the “Recent progress in impact dynamics”; this talk featured information from “Dissecting cross-impact on stock markets: An empirical analysis” and “Unravelling the trading invariance hypothesis”.
In “Rethinking Market Impact”, Rama Cont questioned the roles that metaorder size and duration play in market impact:
An interesting day, and more commentary on these and other presentations at the end of the week.