  This work applies Matrix Completion (MC) -- a class of machine-learning
methods commonly used in the context of recommendation systems -- to analyse
economic complexity. MC is applied to reconstruct the Revealed Comparative
Advantage (RCA) matrix, whose elements express the relative advantage of
countries in given classes of products, as evidenced by yearly trade flows. A
high-accuracy binary classifier is derived from the application of MC, with the
aim of discriminating between elements of the RCA matrix that are,
respectively, higher or lower than one. We introduce a novel Matrix cOmpletion
iNdex of Economic complexitY (MONEY) based on MC, which is related to the
predictability of countries' RCA (the lower the predictability, the higher the
complexity). Differently from previously-developed indices of economic
complexity, the MONEY index takes into account the various singular vectors of
the matrix reconstructed by MC, whereas other indices are based only on one/two
eigenvectors of a suitable symmetric matrix, derived from the RCA matrix.
Finally, MC is compared with a state-of-the-art economic complexity index
(GENEPY). We show that the false positive rate per country of a binary
classifier constructed starting from the average entry-wise output of MC can be
used as a proxy of GENEPY.
