By internal systematic benchmarks, we mean index-grade systematic strategies that are not necessarily published, licensed or transformed into investment products, but that are built with the same methodological discipline as official indices: explicit rules, transparent definitions, scheduled rebalancing, robust historical simulations and continuous calculation.

These benchmarks may be long-only, long-short or eventually cross-asset. They can represent proprietary factor definitions, strategic allocation policies, thematic baskets, custom beta exposures, hedging references or specific risk frameworks.

Until recently, creating and maintaining large numbers of such internal benchmarks required substantial quantitative resources, data infrastructure and operational effort. As a result, most institutions were limited to a handful of standard benchmarks and a collection of research portfolios or spreadsheets.

Self-service indexing changes these economics. When methodologies can be configured through modular rules, simulated rapidly and calculated continuously, organizations can maintain not a few benchmarks, but thousands of systematically managed reference strategies. Not as isolated backtests, but as a living and governed benchmark ecosystem.

The quantitative tools of passive investing, made available to active investors

The implications extend far beyond index product manufacturing.

For active asset managers, an internal benchmark universe provides a much richer framework for understanding performance. Instead of comparing a portfolio only to its official benchmark, managers can evaluate it against a library of internally defined factor portfolios, sector-neutral variants, thematic baskets, defensive strategies or long-short signals.

This makes it easier to answer fundamental questions. Was performance driven by genuine stock-picking alpha? By an implicit value, quality, growth or momentum exposure? By sector allocation decisions? By a specific market regime?

The broader the benchmark universe, the more precisely these drivers can be identified. Performance analysis becomes less dependent on narratives and more grounded in measurable evidence. Alpha, risk and portfolio construction can be assessed through a much richer set of analytical lenses than traditional benchmark frameworks allow.

The opportunity is equally significant for asset owners. Institutional investors can create internal reference portfolios that better reflect their beliefs, constraints and objectives: custom strategic allocations, factor-aware policy portfolios, regional tilts, defensive overlays or liquidity-aware investment universes. Rather than relying exclusively on external standards, they can build benchmark architectures aligned with the way they actually allocate capital and evaluate managers.

A new internal infrastructure for investment intelligence

The objective is not that every internal benchmark becomes an investable product.

Most will not. Their primary value lies elsewhere: they become an internal analytical infrastructure that helps institutions observe markets, understand exposures, test investment ideas and improve decision-making.

In this sense, self-service indexing may do for active and institutional investors what industrial index infrastructure has already done for passive investing. It transforms systematic investment definitions into robust, scalable and continuously observable objects.

The difference is that these benchmarks are no longer limited to a small catalogue of public indices. They can be proprietary, highly customized and tailored to an institution's research process, investment philosophy and risk framework.

Over time, organizations may maintain extensive libraries of internal benchmarks that serve as reference points for portfolio construction, manager evaluation, risk monitoring and market analysis. The benchmark itself becomes a strategic information asset.

Folio already provides a glimpse of this future

With MCFT Folio, users can already design, simulate, calculate and monitor a broad range of equity-based long-only and long-short systematic benchmarks. The platform provides access to hundreds of smart-beta and factor-index templates built around fundamental, market, thematic or risk metrics. Together, they form a live analytical environment that allows users to observe and compare the behaviour of a large benchmarks/factors universe through time.

This feature is an important additional benefit of self-service indexing. The institutions that recognize this shift early will not simply have more benchmarks. They will have a better map of the investment world they are navigating, a deeper understanding of their alphas, risks and allocation decisions.

The MCFT Founders - Romain Charlassier, Jonathan Klein and Lucas Mouilleron