Risk-controlled Alpha

Senior Quantitative Portfolio Manager using advanced Portfolio Management Analytics integrating risk monitoring and attribution.

Alpha forecasting, custom risk modeling, monitoring and attribution, portfolio construction/optimisation, performance monitoring and attribution, backtesting strategies.

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Clermont Alpha™

The Science of Investment Management

Clermont Alpha™ is not active anymore. Some educational content has been left on the site for the benefits of the investment community.

Dominic Clermont started in the investment management industry in the early 1990s. He initiated, promoted and built the first Canadian-based Quantitative Portfolio Management group at a leading investment management organization. Using advanced Portfolio Management Analytics integrating risk monitoring and attribution, he developed systematic investment strategies delivering superior risk-adjusted returns.

Since 2014, he has been working with leading European managers, assisting them in their investment management process. He covered large assets managers with assets under management up to over $1 Trillion. He advised clients in London, Dublin, Paris, Brussels and Amsterdam. He discussed large capacity strategies with mega funds (AUM of hundreds of Billion $)

His quantitative investment management covered equities, fixed income, and asset allocation. He also advised on the use of a more scientific approach to illiquid asset classes like private equity.

Typical quantitative processes involves:

Forecasting returns on all assets using advanced forecasting techniques, robust statistics and econometrics
Using risk models to integrate the risk monitoring and controls to the portfolio construction process. Risk models can be commercially available or custom built for perfect alpha-risk factor alignment.
Using optimal portfolio construction techniques (optimization) to build a portfolio that optimally balance forecast returns on all assets with the risk expected - thus ensuring that the risk budget is respected at any time.
Correlation-based Risk attribution to truly understand the underlying sources of risk in the portfolio and making sure that all risks are intended.
Performance attribution to understand the sources of return and ensuring that they are in line with the risks we expected to be rewarded for.
Backtests/simulation to test investment ideas and strategies on paper before testing them in real life with real money.

The above quantitative process can also be applied to traditional/fundamental investment management where forecast returns or forecast views are coming from fundamental managers. He advised managers on such approach which led to significant improvement in the return to risk profile (IR) of the funds managed (over 150% improvement in IR).

Expertise and knowledge:

Developed active/quantitative investment strategies in all markets delivering superior risk-adjusted returns
Developed Alpha forecasting models using robust statistics and econometrics.
Advise Portfolio Managers (Fundamental, Index or Quantitative PM) to:

Enhance their current investment process by better using information from their

Risk Attribution report (to properly identify and quantify the various sources of risk).
Performance Attribution report (to identify the ex-post statistical significance of the returns from various sources of risk (intended or not),
Backtests (to test and validate enhancement ideas)

Improve their Index tracking
Research and manage Smart Betas and Minimum Volatility Strategies.
Research and manage Multi-factor strategies
Research and manage Risk Parity strategies
Research and manage investment strategies integrating ESG objectives
Overlay Alpha strategies to any strategies, or tilt portfolios towards Smart Beta factors.
Customizing any of the above strategies to their specific or clients’ needs.