Portfolio Selection via Topological Data Analysis

TL;DR

This paper introduces a novel portfolio selection method using Topological Data Analysis to better capture the complex structure of stock market data, resulting in improved investment performance.

Contribution

The paper proposes a two-stage portfolio construction approach leveraging TDA-based features for representation and clustering, demonstrating its effectiveness over traditional methods.

Findings

Outperforms existing portfolio selection methods

Consistent results across different time periods

Shows the viability of TDA in financial data analysis

Abstract

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.