Active Traded Indexes (ATI):

Powered by Expanded Portfolio Theory

Overview.

We design, maintain, and license rules-based Active Traded Indexes (ATI) built on Expanded Portfolio Theory (EPT). Under classic assumptions, EPT collapses to traditional MPT; once those assumptions loosen, EPT adds realistic risk targets, richer signals, and trade-aware construction—without giving up transparency.

Why an ATI (and why EPT)

  • ETF-ready from day one. Liquidity tiers, turnover budgets, and basket feasibility are built into the method, supporting tight primary-market execution.

  • Flexible modeling. Blend fundamentals, factors, thematics, and machine-learned signals—no need to force everything into variance-only math.

  • Mission-specific risk. Optimize directly to tracking error, volatility targets, CVaR/drawdown, or multi-objective blends.

  • Continuity with MPT. When returns are normal and correlations are stable, EPT reproduces MPT—same intuition, broader toolkit when needed.

Where MPT can break—and how EPT fixes it

Common MPT failure modes

  • Numerical fragility. Near-singular covariances and tiny changes in inputs can produce extreme, unstable weights.

  • Infeasibility under real constraints. Add turnover budgets, liquidity floors, or exposure caps and the classical solver can stall or become overly concentrated.

  • Ad-hoc patches. Resampling, arbitrary caps/floors, heuristic trimming—useful but inconsistent and hard to audit.

EPT’s robustness by design

  • Regularized, convex formulations. Penalization (L2/L1), shrinkage/robust dependence models, and distributional-robust objectives stabilize solutions.

  • Constraint softening with guarantees. Feasibility-preserving penalties and banding/buffers prevent solver dead-ends and reduce churn.

  • Deterministic fallback ladder. If the fully featured objective can’t be satisfied tightly, EPT automatically relaxes to a simpler, convex subproblem and, if needed, to a benchmark-aware minimum-risk portfolio—so the process always outputs a portfolio without ad-hoc hacks.

How we deliver an ATI for your ETF

  • Mandate & objective. Benchmark (if any), objective (TEB, vol target, income), constraints (issuer/sector caps, leverage/derivatives rules).

  • Universe & investability. Listing, price, ADV/spread, and free-float screens; corporate-action handling.

  • Signals & risk model. Factor/thematic/fundamental/ML signals with robust or dynamic dependence (shrinkage, regime-aware, or copula-based when appropriate).

  • Portfolio construction (EPT). Optimize expected benefit minus cost/impact with explicit turnover and liquidity controls.

  • Rebalance policy. Defined cadence plus bands & buffers to minimize churn and keep baskets predictable.

  • Index calculation & dissemination. Daily index level, constituents, weights, and pro-forma baskets; versioned methodology and audit trails.

  • Operational handoff. Licensing package for issuers/APs, factsheet templates, compliance language, and launch support.

What you (and your ETF) receive

  • Transparent rulebook and governance framework.

  • Daily files: index level, constituents/weights, and suggested baskets.

  • Creation/redemption alignment through liquidity-aware basket design.

  • Cost-controlled turnover via banding, buffers, and trade-aware optimization.

  • Backtested history (clearly disclosed as hypothetical) and live calculation procedures.

  • Scalable variants: core, factor-tilted, income-focused, volatility-targeted, or thematic.

Bottom line

MPT defined the efficient-frontier era—but it can break down under real-world constraints and noisy inputs. EPT preserves MPT where it works and extends it where it doesn’t, delivering computationally robust, always-feasible portfolio weights without ad-hoc methods—exactly what an ETF-ready Active Traded Index requires.