---
title: "Black-Litterman"
description: "Black-Litterman is an allocation method built on MPT: it replaces the noisy expected returns with a stable equilibrium tilted toward momentum views."
canonical_url: "https://fincanva.com/glossary/black-litterman"
last_updated: "2026-10-06"
md_url: "https://fincanva.com/glossary/black-litterman.md"
---

# Black-Litterman

Black-Litterman is an [allocation method](/glossary/allocation-and-allocation-method) built on Markowitz's [Modern Portfolio Theory](/glossary/mpt-markowitz): the same mean-variance optimisation, but the noisy historical averages it uses as expected returns are replaced by a steadier estimate — an equilibrium tilted toward "views". In Fincanva the views are each [instrument](/glossary/instrument)'s momentum, computed for you at every rebalance; you enter no forecast.

**Also seen as:** Black-Litterman model, BL, Bayesian mean-variance, momentum views

The method picker lists it as **"Black-Litterman"**, right after MPT (Markowitz), and describes it as "Markowitz with corrected expected returns: a stable equilibrium tilted by momentum views". It is an [allocation method](/glossary/allocation-and-allocation-method) offered both inside a single [strategy](/glossary/strategy) and inside a [Combined](/glossary/strategy-in-a-combined).

## How does Fincanva handle it?

- **Black-Litterman is included wherever MPT is**: it has no [plan level](/glossary/plan-level) of its own, so any plan that includes MPT (Markowitz) includes it, at the strategy level and inside a [Combined](/glossary/combined) alike. See [what each plan includes](/docs/account-security/what-each-plan-includes).
- **Optimization target** defaults to **Optimal**; **Max return** is the other choice.
- **Momentum months** defaults to 12 and **View confidence** to 1.
- **Every other setting is MPT's own** and behaves as described on [Modern Portfolio Theory](/glossary/mpt-markowitz): [position direction](/glossary/direction-long-only-long-short-short-only) (strategy level only), weight limits, force diversification, the **Covariance matrix** choice and the **Resampled** option, with the plan levels stated there. With Resampled on, the Black-Litterman estimate is computed once and every resample reuses it.
- The [calculation window](/glossary/calculation-window) (**In-sample**) supplies the risk and momentum inputs, and weights are recomputed at every rebalance, so a Black-Litterman weighting moves over the life of a [backtest](/glossary/backtest).

## How does Black-Litterman correct the expected returns?

Black-Litterman blends two estimates of each instrument's expected return — an equilibrium, which is stable, and a set of views — weighting each by how much it is trusted. In its textbook form the blended ("posterior") expected returns are:

$$
\mu_{BL} = \left[({\tau}{\Sigma})^{-1} + {P}^{\top}{\Omega}^{-1}{P}\right]^{-1}\left[({\tau}{\Sigma})^{-1}{\Pi} + {P}^{\top}{\Omega}^{-1}{Q}\right]
$$

where:

- the vector of equilibrium returns
- the returns the views expect
- which instruments each view is about
- the uncertainty of the views
- the covariance matrix of the instruments
- the scale of the equilibrium's uncertainty

Its [covariance matrix](/glossary/covariance-matrix) term carries the instruments' risk. The result is a weighted average of the equilibrium and the views, where the more certain a source is, the more it pulls the estimate toward itself. The optimiser then works on $\mu_{BL}$ exactly as MPT works on the historical averages.

The app describes the method's momentum views as "Starts from a stable equilibrium and tilts it toward the instruments with momentum. The views are computed for you: you don't enter any forecast." The equilibrium it starts from is built from the instruments' risk, not from their market capitalisation.

## Which optimization targets can Black-Litterman aim at?

Black-Litterman offers two of MPT's three targets: **Optimal** (the default) and **Max return**. The app describes them as "Optimal = best risk-adjusted return. Max return = highest expected return regardless of risk."

**Min volatility** is not offered, because the lowest-risk portfolio is chosen from the covariance matrix alone and does not use expected returns — the one input Black-Litterman changes. On Min volatility it would make (almost) no difference, so the target stays with [MPT (Markowitz)](/glossary/mpt-markowitz).

**Moving between MPT (Markowitz) and Black-Litterman keeps your settings.** Choosing Black-Litterman on an MPT strategy keeps every MPT setting and moves a **Min volatility** target to **Optimal**; choosing MPT (Markowitz) again keeps them too and drops the momentum views.

**A strategy saved before Black-Litterman became its own method keeps running exactly as saved.** If it had the Black-Litterman switch on with the **Min volatility** target, it is now shown as MPT (Markowitz), because that combination uses (almost) no expected returns; the first time you edit its MPT settings, it is saved without the switch.

## What are the momentum views, and which settings control them?

The views are each instrument's recent momentum measured against the others, recomputed at every [rebalance](/glossary/rebalance) from the data available on that date, so a backtest never uses information from after the date it is simulating. The **Momentum views** section of the method has two settings:

- **Momentum months** — how far back momentum is measured; from 1 to 24, 12 by default.
- **View confidence** — "Higher = momentum weighs more against the equilibrium." From 0.1 to 10, 1 by default.

Momentum can reverse sharply, and a Black-Litterman tilt reverses with it: a higher view confidence makes the weights follow recent winners more closely, for better and for worse.

## What does it look like in practice?

Take one instrument whose equilibrium return is 6% and whose momentum view says 12%. In the textbook blend, if the equilibrium and the view are trusted equally, the expected return used is halfway between them: (6% + 12%) / 2 = **9%**. If the view is trusted twice as much as the equilibrium, it counts twice: (6% + 2 × 12%) / 3 = **10%**.

Plain MPT would have used the historical average of the window directly — which, after a strong run, can be far above either number and pile the portfolio into that one instrument. Black-Litterman's estimate moves toward momentum only as far as its confidence allows, which is why its weights change more gradually. The numbers here illustrate the textbook blend; they are not the exact figures Fincanva computes for a given confidence setting.

Choosing the momentum months or the view confidence that made a past backtest look best is [overfitting](/glossary/overfitting), and searching many combinations for that best one is [data snooping](/glossary/data-snooping-bias).

Fincanva is for education and illustration only. It is not personalised financial advice, and past or simulated results do not predict future ones. [Read the Terms Addendum](https://fincanva.com/terms/addendum#section-3)