QuantMinds 2018 (the conference formerly known as Global Derivatives) started with two summits: Quant invest and Quant Tech, with a strong presence reflecting the increasing importance and attendance of the buy side; the traditional Volatility Workshop with Bruno Dupire also happened on Monday.
The QuantReg team (Marcos, Omar and Othmane) is attending the conference; all the impressions from the summit (and any mistakes in interpreting what was presented) come from Marcos. For pictures of some of the presentations, please check his Twitter feed.
Massimo Morini opened the Quant Tech Summit by analysing how the change in the name of the conference reflects the reality: quants these days are not only working on pricing derivatives. They have been working in machine learning (some of them, like Massimo himself, worked with these statistical methods 15/20 years ago), and also on DLT and quantum computing.
Daniele Bernardi discussed some of the opportunities that fintech brings (disruption of the existing wealth management business).
Switching to the Quant Invest Summit, a great presentation from Stefano Pasquali (BlackRock) on Liquidity Risk Management, discussing how the framework for systematic risk management was implemented, the build-up of machine learning models, the measurement of slippage, etc. In my point of view, extending this framework to the PM decision making (not only the trader) will lead to a joint analysis of price and volume in portfolio allocation (it would be easier to simulate a portfolio with a realistic entry and exit prices and schedules).
Artur Sepp discussed machine learning methods (which help in dimensionality reduction) for volatility trading, with great insights on testing: avoid generalizations from results of highly path-dependent P&L calculations; test for stability and against the forecast. Look also at minimum description length / Kolmogorov complexity.
Nick Baltas discussed how the impact of crowding is context-dependent: which strategy is crowded? A risk-premia (e.g. Momentum) divergent strategy or a price anomaly (e.g. Value) convergent strategy? They might present different outcomes in terms of systemic risk (although LTCM might be an exception to this model).
Matthew Dixon discussed “Predicting Rare Events with Long Short Term Machines”; good references are his papers on this subject and this Jupyter notebook: https://github.com/Quiota/tensorflow/blob/master/TF_LSTM_LOB.ipynb
Nicolas Mirjolet showed how small/boutique hedge funds need to find the right niche of strategies (frequency of trading, etc.) and how depth (one single strategy with a Sharpe of 2.0) is better than breadth (5 strategies with individual Sharpes of 1.0 and a combined Sharpe of 2.5).
Richard Turner simulated a market model based on Farmer and Joshi 2002 ( https://pdfs.semanticscholar.org/b28d/0f8768288959cf1d85842544c46c6fe3a4af.pdf ) on “Deep Learning for Systematic Trading”.
Overall, a great day, very good presentations and interesting insights.