  Financial literature consists of ample research on similarity and comparison
of financial assets and securities such as stocks, bonds, mutual funds, etc.
However, going beyond correlations or aggregate statistics has been arduous
since financial datasets are noisy, lack useful features, have missing data and
often lack ground truth or annotated labels. However, though similarity
extrapolated from these traditional models heuristically may work well on an
aggregate level, such as risk management when looking at large portfolios, they
often fail when used for portfolio construction and trading which require a
local and dynamic measure of similarity on top of global measure. In this paper
we propose a supervised similarity framework for corporate bonds which allows
for inference based on both local and global measures. From a machine learning
perspective, this paper emphasis that random forest (RF), which is usually
viewed as a supervised learning algorithm, can also be used as a similarity
learning (more specifically, a distance metric learning) algorithm. In
addition, this framework proposes a novel metric to evaluate similarities, and
analyses other metrics which further demonstrate that RF outperforms all other
methods experimented with, in this work.
