Approach

A multi-strategy method, conservatively combined.

Vayo combines well-established risk premia with proprietary alpha signals developed through statistical research, quantitative modelling, and applied AI. Strategies are sized by volatility, governed by portfolio-level limits, and admitted only after surviving a rejection-heavy research pipeline.

Multi-strategy philosophy

No single signal explains markets. Vayo treats the portfolio as a deliberate combination of partially independent sources of return: well-established risk premia documented in the academic literature, augmented with proprietary alpha signals developed in-house. The objective is breadth without dilution — enough genuinely uncorrelated bets that the aggregate behaves differently from any of its parts.

Construction and sizing

Every strategy is volatility-sized so that no individual line of research can dominate the book. Allocations are tested against portfolio-level risk limits before deployment and rebalanced on a disciplined schedule rather than at discretion.

  • Volatility targeting at the strategy and portfolio level
  • Hard allocation ceilings per strategy and per factor exposure
  • Drawdown-aware exposure reduction rules

Investable universe

The framework is designed to take long and short exposure across global equities, bonds, ETFs, futures and options. Breadth of instruments is what makes a multi-strategy approach possible — not every signal expresses itself well in the same wrapper.

Risk governance

Risk is treated as an input, not a consequence. The portfolio is built to deliver risk-adjusted returns with limited correlation to traditional asset classes; that requires explicit constraints on gross exposure, concentration, factor tilt, and tail behaviour, set in advance and monitored continuously.

Research-to-deployment pipeline

Candidate strategies are sourced from academic literature and from distributed research networks of professional and former-professional fund managers and quantitative researchers. AI-augmented tooling is used for summarization, coding, and back-testing — accelerating evaluation, not replacing judgement.

The dominant outcome of the pipeline is rejection. The large majority of candidates do not survive out-of-sample testing, robustness checks, or implementation-cost analysis. Only those that do are integrated into the portfolio, and only at volatility-controlled weights.

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