Institutional fund intelligence

Past performance tells you what happened. AlphaPredictor tells you what's actually driving every fund.

The real factor and macro exposures behind 1,221 US equity ETFs — how each behaves when the environment shifts, and a forward view built from all of it. Fully decomposed, every number traceable to the model, validated walk-forward since 2009.

See the validation →

Tested out of sample since 2009 · up to 72% directional hit rate · top-quintile spread positive at every horizon

Exhibit 1 — Model validation Walk-forward · 2009–2026
Top quintile (model-ranked) +14.41% p.a.
Bottom quintile +5.37% p.a.
+9.04%
p.a. top–bottom spread
1-month horizon · 198 periods
non-overlapping · out of sample
Directional hit rate, by horizon
59%
1-month
66%
3-month
72%
6-month
62%
12-month

The proof — and how it's built

How the validation is built

The figures above aren't a back-test we curated — they're computed live in the platform. Every month since December 2009 the model ranks the full universe by its return signal, sorts it into five groups, and tracks what the top and bottom groups actually did next, with no look-ahead.

Out of sample

Every estimate is scored before the month it forecasts — no look-ahead, evaluated walk-forward on non-overlapping periods.

The full universe

All 1,221 equity ETFs are ranked every month — not a curated subset and not the survivors that happened to do well.

The live signal

It's the exact signal used in production scoring — recomputed in the platform, the same numbers you see in the hero exhibit.

Headline figures are shown at the 1-month horizon across the full equity-ETF universe, 198 monthly periods since December 2009. These are hypothetical research statistics — not returns achieved by any investor — shown gross of fees, costs and taxes. Past model performance does not guarantee future results. Full disclaimer.

See it in action

A two-minute tour of every tab

From the screener and validation to portfolios, the optimizer and fund-level transparency — watch how the whole platform fits together.

The difference

Most screeners rank funds on what already happened. AlphaPredictor® shows you what's actually driving every fund — and how it behaves when the environment turns.

Exposure-transparent

Every fund decomposed into the factor and macro exposures truly driving it — alpha, factor return and the risk-free rate — with each figure traceable back to the model, not a black box.

Regime-aware

See how each fund behaves across the business cycle — credit spreads, rates, the yield curve, commodities — so you know how it should hold up before the regime turns, not a year after.

Forward view, decomposed

A Bayesian engine distils it all into a forward signal — shown as a relative, conditional read, updated monthly, and validated walk-forward over 16 years. The synthesis, not a crystal ball.

Explore the platform ↓

Client-ready outputs

A report your clients can actually read

Any fund or portfolio exports to a self-contained document — the signal, its decomposition, the factor and macro exposures, risk and suggested allocations, in plain language. Preview a sample:

Beyond US ETFs

Your universe, your model

The live demo runs on US-listed ETFs — but the engine isn't limited to them. We configure the same model to the universe you actually use, and deliver the output however your workflow needs it.

Configured to your universe

Mutual funds, European & UCITS ETFs, or your own approved & recommended list — scored on the same forward signal, factor attribution and portfolio tooling, run with your inputs and constraints.

  • ▸ Mutual-fund and SMA-model universes for TAMPs and model providers
  • ▸ European / UCITS coverage for non-US mandates
  • ▸ Your house list, benchmarks and constraints — onboarded for you

Delivered via API

Pull model outputs — signals, scores and factor exposures — directly into your own research and portfolio systems. The dashboard is optional; the data integrates where you already work.

  • ▸ Programmatic signals, scores and exposures for quant and research desks
  • ▸ Feed your existing models, screens and reporting pipelines
  • ▸ API access available on request

Tell us your universe and how you'd like to consume it — and we'll scope it with you.

The macro engine

Alpha depends on the regime

Most models estimate one static alpha per fund. AlphaPredictor® splits it in two: all-weather alpha, earned in any environment, and time-varying alpha — the part a fund earns in this macro environment, driven by its sensitivity to five regime factors: default spread, term spread, short rate, dividend yield and commodities.

Below, each fund's monthly Return Signal is split into the contribution from every regime factor — green where a factor added to the signal this month, red where it subtracted. The model re-estimates these as the regime shifts.

Macro factor contributions — this period (%)
Macro factor contribution heat map — each regime factor's signed contribution (green positive, red negative) to the monthly Return Signal for XBI, VDC, VPU, XLE, SMH, SCHD, VGT and QQQ

The AlphaScore™

A single, defensible number for every fund

Every ETF's return signal for the coming month is ranked into a percentile against the full universe — 100 = the strongest signal, 0 = the weakest — and broken into the components that drive it: all-weather alpha, time-varying alpha, and factor return. Sort 1,221 funds in one view, or filter to a category and find the strongest names instantly.

  • ▸ Return signal decomposed into alpha vs beta, not a black box
  • ▸ Factor and macro exposures as z-scores against the whole universe
  • ▸ Natural-language screening — "cheapest tech with strong momentum"
ETF Screener — "cheapest tech ETFs with strong momentum"
Natural-language screening: the query 'cheapest tech ETFs with strong momentum' applied as a structured sort and Technology filter, with top-scoring funds and their expected-return decomposition

Drill into any fund

Every number traceable to the model

Click a fund and the full picture opens: the return signal broken into all-weather alpha, time-varying alpha, factor return and the risk-free rate — then the factor and macro exposures behind it, each scored against the whole universe. Here, energy's XLE earns a 98 AlphaScore from a positive alpha and value tilt, even as its equity-market beta is a drag this month.

  • ▸ Expected-return waterfall: where every basis point comes from
  • ▸ Beta and macro sensitivities as ±σ z-scores vs all 1,221 funds
  • ▸ Trailing performance, AUM, expense ratio and benchmark in one place
XLE — fund detail
Fund detail panel for XLE showing AlphaScore 98, expected-return breakdown, factor exposure z-scores and macro sensitivities

Factor heat maps

Every exposure, mapped against the universe

See each fund's tilt to the equity-market, size, value and momentum factors as z-scores against all 1,221 funds. Blue runs above average, orange below, so a peer group's positioning reads in a single glance.

Factor exposures — z-score vs universe
Beta factor exposure heat map for XBI, VDC, VPU, XLE, SMH, SCHD, VGT and QQQ

See the overlap

Know how different your funds really are

A factor-structured covariance shows how any set of funds actually co-move — and how much of each fund's risk is explained by the four equity factors. Below, VGT and QQQ move closely together at 0.91, while energy and utilities genuinely diversify the book — the higher the number, the less two funds diversify each other. It's the co-movement check most screeners can't do.

How these funds move together
Factor-implied correlation matrix for XLE, VPU, VDC, SCHD, XBI, VGT, QQQ and SMH

Portfolio analyzer

Build it, stress-test it, see what to change

Paste any portfolio and measure it against a benchmark: expected active return, tracking error, and a per-holding breakdown of which positions drive your active risk — and which way each trade moves it. The optimizer proposes the highest-impact changes within a turnover budget.

Tracking error by position
Per-holding tracking-error contribution for a portfolio of IWB, SCHD, VGT, XLE, VPU, VNQ and QQQ

Portfolio construction

One peer group, four ways to weight it

The optimizer proposes allocations under four objectives at once — max Sharpe, max return within a volatility cap, minimum volatility, and risk parity — over a factor-aware covariance. Funds that earn weight under every objective are the more robust candidates; weight only under max-return signals a forecast-dependent position.

Suggested allocations by objective
Suggested allocations across max Sharpe, max return, minimum volatility and risk parity objectives

This month

When the model changes its mind, you see it

Each vintage, scores move because the model revised its beliefs — new returns and macro readings, not just price action. The biggest moves, funds entering and leaving the top decile, and category-level shifts, in one view. Star the funds you follow and track them month to month.

This Month
Biggest month-on-month AlphaScore gains

The engine

A model that learns every month

Rather than re-running a regression over a fixed window, AlphaPredictor® holds a belief about each fund and updates it as new data arrives — sharpening its forecasts and tracking its own confidence.

Prior belief

What the model already expects for a fund, informed by its own history and the wider universe of similar funds.

New data

Each month's returns and macro readings update that belief into a sharper posterior — and adjust how confident the model should be.

Forward view

The posterior becomes the next month's return signal — adapting to regime shifts instead of lagging a fixed lookback.

Who's behind it

A partnership of academics and practitioners

AlphaPredictor® comes out of Parala Capital, a London research firm that pairs finance professors with senior market practitioners. The methodology behind it is published and peer-reviewed — and applied in research that advises institutional investors, not confined to journals.

Peer-reviewed

Methodology published in the Journal of Finance, Review of Financial Studies and Journal of Financial Economics

Practitioners

Senior market practitioners work alongside the academics — research built to be deployed in portfolios, not just published

Fed-validated

Service on the US Federal Reserve's Model Validation Council, plus an NYSE award for best paper on equity trading

$2.9B

Parala's research advises institutional investors on roughly $2.9 billion of assets

Meet the team at parala.com →

Built for

Professionals across the ETF ecosystem

Model portfolio providers & TAMPs

A transparent, documented basis for the ETF sleeve of every model — what each fund is exposed to, how it behaves across the cycle, and a repeatable, defensible selection process advisors can stand behind.

  • ▸ Rank 1,221 ETFs on their return signal, not the trailing three years
  • ▸ Client-ready Insights reports with the model's reasoning
  • ▸ Benchmark and tracking-error analysis on any model

Multi-asset & selection desks

Go past expense ratios and star ratings to the factor and macro drivers behind every fund — on a model whose track record you can inspect yourself.

  • ▸ Optimize across five objectives on a factor-aware covariance
  • ▸ Max information ratio vs a benchmark within a TE budget
  • ▸ Inspect the live, out-of-sample validation in full

Product development & research desks

At asset managers and investment banks — surface new ideas, decompose what's driving any equity ETF or theme, and show how each behaves across the cycle.

  • ▸ Surface new ideas — funds with strong forward signals across the universe
  • ▸ Decompose any peer group into alpha, factor return and macro drivers
  • ▸ Stress-test thematic baskets against any benchmark for tracking error and exposure gaps

ETF sales & marketing teams

For ETF issuers — position your funds against the competitive set on a transparent, model-based scale and arm the field with concrete, peer-anchored talking points.

  • ▸ Position any of your ETFs against its peer set on the same model-based scale
  • ▸ Side-by-side decomposition vs direct competitors — factors, macro, correlation
  • ▸ Track peers each month as the regime shifts and exposures drift

See AlphaPredictor® on your universe

Request access for a walkthrough of the platform and the validation behind it.

Review the track record