Marco Bianchi
Protecting Capital Against Market Meltdowns:
A Machine-Learning Approach to Systematic Risk Management
This talk presents Turning Point Dates Capital Protection (TP Dates CP), a risk-management
overlay designed for long-only, long-term institutional investors. The system uses machine
learning, pattern recognition, and behavioral-finance metrics to distil roughly 250 trading
days per year into a small set of ex-ante candidate dates on which the probability of a major
market correction is elevated. These turning-point windows are identified ahead of time,
are registered and non-revisable (±1 day), and are mapped to the corresponding time-based price thresholds that act as support/resistance levels.
The methodology is rooted in the observation that investor greed and fear generate recurring,
synchronized cyclical patterns that can be projected forward. Over more than two decades
of back-testing, the approach has highlighted high-risk periods surrounding the COVID crash
of 2020, the 2022 bear market, and the 2025 volatility episodes, while also providing opportunistic entry signals. The resulting overlay strategy is implemented with minimal intervention—typically via liquid futures and options—without liquidating core holdings or incurring major transaction costs.
The talk will cover the conceptual framework, the ML/AI pipeline, the track record across
multiple asset classes (equity indices, single stocks, bonds, commodities, and FX) and a concrete workflow for integrating the tool into existing portfolio management processes.
Dr. Marco Bianchi is the founder of MMB Advisers, London, and has spent more than thirty
years in quantitative research and systematic trading. He holds a PhD in Statistics and Econometrics from the London School of Economics and is a Eurohedge Award winner (2003) and runner-up (2006) for systematic equity strategies. His career spans senior roles at the Bank of England, Citigroup, and Barclays Capital, and he was co-founder and Head of Quantitative Research at Ethus (2017–2025). He has published in the American Economic Review, the European Economic Review, the Journal of Applied Econometrics, and the Journal of Business & Economic Statistics. His current work focuses on AI-driven crash-prediction models, time-series econometrics, and risk-management tools for institutional portfolios.
