The New Standard in Portfolio Management: Combining Multimanagement, Absolute Return, Trend Following, and Quantitative Discipline

Thierry Crovetto, TCSF (TC Stratégie Financiere / TC Formation), Independent financial analyst specialising in fund selection and risk management

In an era of heightened volatility and structural shifts, portfolio resilience demands more than diversification, it requires a strategic blend of manager selection, adaptive exposure, and emotion-free decision-making. The integration of Multimanagement, absolute return strategies, and trend following, enhanced by quantitative models, offers a compelling framework for optimizing risk-adjusted returns.

Multimanagement: Allocating to Talent, Not Just Assets

Modern Multimanagement is about selecting talented fund managers with complementary styles and proven adaptability. Rather than relying on macro forecasts or benchmark constraints, this approach builds portfolios around: 

• High flexibility and independence
• Complementary styles and low correlation
• Proven adaptability across regimes

By combining diverse sources of alpha, Multimanagement enhances resilience and reduces redundancy, especially when paired with systematic allocation filters.

Absolute Return Strategies: Stability Over Market Direction 

Absolute return strategies aim to deliver consistent performance regardless of market direction. Their value lies in:

• Low correlation to traditional assets
• Regular returns above the risk-free rate
• Flexibility to exploit niche opportunities & inefficiencies

When selected for complementarity and combined prudently, these strategies offer smoother performance profiles and lower drawdowns than traditional equity or credit exposures. Selectivity is key, as dispersion among managers is wide.

J.P. Morgan’s analysis of hedge fund performance confirms a structural shift. The “Alpha Winter” (2011–2019) was marked by suppressed interest rates, low volatility, and high correlations—conditions that stifled hedge fund alpha. Since 2020, all three drivers have reversed: rates exceed 2%, volatility has normalized, and intra-stock correlations have declined. Hedge funds are once again delivering strong excess returns, with platform performance exceeding 7% annually since the regime shift. The timing seems to be positive to increase allocation in absolute return strategies.

Trend Following: A Systematic Compass for Exposure 

Trend following, particularly when applied to excess return (above the risk-free rate), provides a disciplined overlay to guide exposure that:

• Filters out macro noise and beta drift
• Enhances timing of entries and exits
• Reduces drawdowns and emotional bias

Empirical evidence shows that applying trend-following filters to credit, absolute return or Niche Equity funds (such as Frontier Markets) funds significantly improves Sharpe, Sortino, Sterling & Calmar ratios, while reducing the risk.

Quantitative Models: Reducing Emotion, Enhancing Discipline

Quantitative approaches are essential to eliminate emotional bias, the investor’s “worst enemy,” as Benjamin Graham put it. Models based on risk allocation, optimization under constraints and trend following strategies, allow for:

• Objective fund selection and exposure management
• Constrained optimizations under risk budgets
• Systematic reallocation based on performance signals

These tools transform portfolio management from reactive to proactive, replacing intuition with data-driven conviction.

Strategic Synergy

When these four components are integrated:

• Multimanagement provides the talent engine
• Absolute return strategies offer stability and diversification
• Trend following ensures adaptive, rules-based exposure
• Quantitative models enforce discipline and reduce behavioral risk

The result is a portfolio that is not only performance-seeking but also risk-conscious, capable of navigating uncertainty with discipline and control.

In short, this quartet transforms portfolio management from a static allocation exercise into a dynamic, conviction-driven process grounded in risk control and empirical robustness.