A correlation matrix is a pair-by-pair table of how closely return series moved together over a period: each cell holds a correlation between −1 and +1, where +1 means lockstep, 0 no linear relationship and −1 opposite directions. Fincanva shows one for a Combined, computed on daily returns, on the Correlations page of its analysis.
Also seen as: correlation table; pairwise correlations.
How does Fincanva handle it?
A Combined's analysis has a Correlations page, and the matrix is the table it opens with, How they move together: the lower triangle of every strategy-against-strategy pair, plus a column for each strategy against the Combined itself, all computed on daily returns. Only the lower triangle is drawn, because the upper half repeats every cell and the diagonal is 1 by definition, so neither is data. Beside it, Against the big markets sets each strategy — and the Combined — against six of the factor roster, switching between two readings of the same pair, Correlation and Adjusted beta — the second of which is the adjusted beta. A period selector above both scopes them to the whole run or to a single calendar year; a year the engine has no correlations for falls back to the whole period rather than emptying the tables.
r² is deliberately not a column. On Fincanva's figures r² is exactly the square of the correlation printed beside it, so a column of it would restate its neighbour rather than add anything — the reading what r² adds describes is done from the correlation itself. A rolling correlation, the through-time form of the same measure, is not shown either.
The page is included from the Advanced plan, the same plan level as Strategy analytics: Free and Starter meet a refusal drawn over a sample strategy rather than their own figures. See what each plan includes.
How is correlation calculated?
Correlation is the covariance of two return series divided by the product of their standard deviations, which rescales the relationship into the fixed −1 to +1 range so any two pairs can be compared.
- the correlation coefficient
- the covariance of the two return series
- the product of their standard deviations
- r², the coefficient squared
What does r² add to a correlation?
r² is the correlation squared, and it says how much of one series' variation is explained by the other. A correlation of 0.9 gives an r² of 0.81 — a strong shared story; a correlation of 0.3 gives an r² of 0.09, so roughly nine tenths of the movement is unexplained by the pair. Its practical job in a matrix is to flag which cells deserve attention: a low r² means the pair's relationship is weak, so any beta-style figure reported beside it would rest on that weak relationship and should be read as noise rather than as a reliable sensitivity.
In a matrix that carries them, adjusted beta figures run in both directions — A on B and B on A — because a sensitivity is not symmetric even though the correlation is. How that adjustment is defined is documented on its own page.
How do you read a correlation matrix?
A matrix is read as a grid of pairs, and three habits make it useful:
- The diagonal is always 1 — every series is perfectly correlated with itself, so those cells carry no information.
- The matrix is symmetric, so each pair appears once: the cell for A-and-B is the cell for B-and-A.
- Where the cells are colour-graded, the intensity tracks the size of the correlation, not its usefulness — a block of deeply-shaded cells is a cluster of things that move together, and the faint cells, easy to skip past, are the ones that behave independently.
Strategy analytics answers a neighbouring question one strategy at a time: tracking error says how differently a strategy moved from its parent Combined, where the matrix says how differently the strategies moved from each other.
The Correlation matrix setting of Min correlation is a different thing: it chooses how that allocation method estimates the correlations it weighs on inside a single strategy, and it does not change this page.
What counts as a good value?
Low correlation is what makes a group of holdings behave differently from each other; high correlation means they are, in effect, expressing the same bet in different clothing. Two cautions come with reading the number: correlation only captures the linear relationship between two series, and it is not stable — pairs that look independent in calm periods often move together in a market shock, which is precisely when their independence was supposed to help — see rolling correlation for the through-time reading of the same pair.
What does it look like in practice?
A Combined holds three strategies. Strategies A and B show a correlation of 0.9: an r² of 0.81, so most of what one did the other did too — holding both bought roughly one exposure twice, and the Combined's risk is more concentrated than the count of strategies suggests. Strategy C pairs with A at 0.2, an r² of 0.04: their paths are almost unrelated, so C is the strategy actually doing something different inside the Combined. Then read the same row for a market factor: if A also correlates 0.9 with the broad market, most of A's story is the market's story, not A's.