Portfolio Selection via Topological Data Analysis
Petr Sokerin Kristian Kuznetsov Elizaveta Makhneva Alexey Zaytsev
Portfolio management is an essential part of investment decision-making.
However, traditional methods often fail to deliver reasonable performance. This
problem stems from the inability of these methods to account for the unique
characteristics of multivariate time series data from stock markets. We present
a two-stage method for constructing an investment portfolio of common stocks.
The method involves the generation of time series representations followed by
their subsequent clustering. Our approach utilizes features based on
Topological Data Analysis (TDA) for the generation of representations, allowing
us to elucidate the topological structure within the data. Experimental results
show that our proposed system outperforms other methods. This superior
performance is consistent over different time frames, suggesting the viability
of TDA as a powerful tool for portfolio selection.