Abstract:
The classic mean-variance portfolio optimization approach is criticized in large part for its
propensity to overstate estimate error. An estimated inaccuracy of a few percent can skew
the entire package. The Black-Litterman technique (Bayesian method) and the resampling
method are two common approaches to solving this problem. A more recent approach to the
issue’s solution is the clustering method. By clustering, we initially combine the stocks that
have a strong correlation and handle the group as a single stock. Following the grouping
of the stocks, we will have a few stock clusters. For these clusters, we do the standard
mean-variance portfolio optimization. By using the clustering approach, the influence of
estimating error may be minimized and the portfolio’s stability can be increased. In this
project, we’ll examine how it functions and run experiments to see if clustering techniques
enhance the portfolio’s performance and stabilities.
