Qualified Financial Advisor – regulated by the AMF (registered CIF within the CNCIF; n° ORIAS: 13000399 - www.orias.fr)
Executive Head of Research - Professor in Financial Economics

With respect to my professional career (as an academic researcher and a practitioner of financial markets), I think I am now ready for new challenges…

After ​more than ​15 years in the asset management industry within the same company, and 20 years studying, researching and lecturing ​in different universities, my various experiences (successful business projects, top-rated academic articles and a full professor tenure) make me very open to new solicitations, either in the academic world in a well-established institution, and (or), in a new managing director position ​/ scientific advisor, working in ​an asset management ​company.

I am thus ready to study new proposals, with a very flexible point of view (on the various aspects: job description, localization, language, role, responsibilities…), and will be pleased to sign...

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You will find below a short bio, a short CV and a more detailed one (and, also, a complete application form for a Professor position on demand).

Short Biography
(in French)

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Short Biography
(in English)

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Short Curriculum Vitae
(in English)

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Curriculum Vitae
(in English)

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Some versions of my articles may be available on the following websites:

Google Scholar


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Social Science Research Network

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Research Gate


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RePEc


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Microsoft Academic Search

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Dblp


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Cairn.INFO


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You will also find below some of our dedicated collaborative websites:

on Systemic Risk


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on Performance Measures


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on Extreme Risks*


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* preliminary beta version - work in progress; User: GRI - Password: Variances. www.extreme-risk.ca


Here are some books I have participated in...

in progress **
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** work in progress (project); please kindly do not quote, cite or diffuse without an explicit authorization. Release expected in 2015...

Quantitative Trading Strategies

Below are provided the details of several (four at the time - more are coming soon) real (out-of-sample dynamic market-valued) strategies (i.e. portfolios) that follow different trading rules: Robust Minimum Volatility, V-ratio Performance-tilted, Dynamic Proportion Portfolio Insurance and Flexible Betas.

Please feel free to click on the images to download the (raw) excel files (up-dated every month or so), that correspond to the input data and strategy financial results (including simulated costs, with benchmark comparisons and some financial risk follow-ups).

These analyses are for information purposes only and do not represent an invitation to buy or sell any security or financial product. Please kindly have first a look at the complete disclaimer below the strategies.

Robust Minimum Volatility Portfolio
(from 03/01/2003 to 06/05/2016)

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Last Update: 06/05/2016.

Definition: This portfolio is constructed by using a dynamic minimum volatility optimization (out-of-sample exercise) in a large universe of European equities (Euro STOXX 600 ex UK). Short sales are not allowed and a maximum weight constraint of 20% per asset has been used.

Warning: A minimum volatility portfolio is designed to provide protection of the invested capital, and should be able to diminish the losses in bear markets; but it may well underperform during bull markets. Furthermore, if this strategy is followed by a large part of the investors, we may end up in an asset bubble (with over-priced assets contained in the basket of the lowest risky assets).

This simulation is hypothetical and does not reflect the results or risks associated with actual trading.

To download you can click on the image or click here.

Reference article:
Maillet B. (with Tokpavi S. and B. Vaucher), (2015), “Global Minimum Variance Portfolio Optimisation under some Model Risk: A Robust Regression-based Approach”, 35 pages – forthcoming in the European Journal of Operational Research.

Other References:
M. Britten-Jones, (1999), “The Sampling Error in Estimates of Mean-variance Efficient Portfolio Weights”, The Journal of Finance 54(2), 655-671.

V-Ratio - Performance Tilted Strategy
(from 03/01/2003 to 06/05/2016)

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Last Update: 06/05/2016.

Definition: This strategy selects on a weekly basis (out-of-sample), from the STOXX Europe 600 ex UK universe, the three stocks with the best characteristics in a return/risk trade-off framework (equal weights).

Reading: The left axis represents the performance of the V-Ratio strategy (blue line). The right axis, represents the performance of the STOXX Europe 600 ex UK Index (grey line).

Warning: This strategy is highly sensitive to the state of the market. Since the strategy is based on return/risk characteristics, it may perform very badly during a general downturn in the markets.

This simulation is hypothetical and does not reflect the results or risks associated with actual trading.

To download you can click on the image or click here.

Reference article:
Maillet B. (with Billio M., G. Jannin and L. Pelizzon), (2015), “A New Generalized Utility-based N-moment Measure of Performance”, Working Paper, 88 pages.

Other References:
Maillet B. (with Caporin M., M. Costola and G. Jannin), (2014), “On the (Ab)Use of Omega?”, Working Paper, 71 pages.

Dynamic Proportion Portfolio Insurance
(from 12/1999 to 06/05/2016)

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Last Update: 06/05/2016.

Definition: This strategy (out-of-sample) aims to build a portfolio of European equities when applying a portfolio insurance method conditional to the state of the market (bull/bear).

Warning: This strategy is highly sensitive to the definition of the crisis periods. There is also a fair probability of missing part of a rebound in the market when the insurance protection has been applied to the portfolio. Since the strategy is path-dependant (per nature), the departure date of the strategy is crucial (no crisis should not be experienced when launching the strategy).

This simulation is hypothetical and does not reflect the results or risks associated with actual trading.

To download you can click on the image or click here.

Reference article:
Maillet B. (with Hamidi B. and J.-L. Prigent), (2014), “A Dynamic AutoRegressive Expectile for Time-Invariant Portfolio Protection Strategies”, Journal of Economic Dynamics and Control 46, 1-29.

Other References:
Ameur H.B. and J.-L. Prigent, (2014), “Portfolio Insurance: Gap Risk Under Conditional Multiples”, European Journal of Operational Research 236(1), 238–253.

Flexible Beta Strategy
(from 01/02/2001 to 06/05/2016)

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Last Update: 06/05/2016.

Definition: This dynamic strategy (out-of-sample) is based on the building of a portfolio with a flexible beta conditional to the state of the market (low versus high beta portfolios). The market states are differentiated based on a classification algorithm applied to a series of macroeconomic indicators commonly used in economic forecasting.

Warning: This strategy is highly sensible to market conditions and to the determination of the crisis periods that are used to define the strategic beta of the portfolio. Efficiency of the strategy is subject to leverage possibility.

This simulation is hypothetical and does not reflect the results or risks associated with actual trading.

To download you can click on the image or click here.

Reference article:
Maillet B. (with Boucher C., A. Jasinski and P. Kouontchou), (2015), “Identify and Forecast the Risk-Return Trade-off Breaks”, Working Paper (in progress), 24 pages.

Other References:
Giglio S., B. Kelly and S. Pruitt, (2015), "Systemic Risk and the Macroeconomy: An Empirical Evaluation", Chicago Booth Research Paper #12-49, 63 pages.


Disclaimer. These analyses are for information purposes only and do not represent an invitation to buy or sell any security or financial product. Backtested (simulated) returns are surely hypothetical and do not reflect the results or risks associated with actual trading. NAV are NOT here audited. The numbers are to be used as projections only; past performances do not guarantee future results. All simulations presented above are probably subject to various biases, such as data mining, data snooping, variable selection, bias of survivorship.... and mainly rely on the hypothesis that the future market conditions do not differ too much from what we have observed in the past. Therefore, while we make reasonable efforts to obtain information from sources which we believe to be reliable, we make no representation or warranty of any kind, either express or implied as to the accuracy, reliability, up-to-dateness or completeness of the information contained above. All figures presented are out-of-sample (based on dynamic forecasts), accurately computed and regularly updated. All benchmarks are dividends included. Finally, for all simulations, a transaction cost of 20 bps is considered for ETFs and 25 bps for equities; an annual management fee of 1.20% is taken into account (institutional part). Incentive compensation is equal to 20% and is subject to a high water mark computation.