Portfolio Selection via Topological Data Analysis
This addresses portfolio management for investors by offering a novel approach, though it appears incremental as it builds on existing TDA techniques.
The paper tackled portfolio selection by using Topological Data Analysis to capture topological structures in stock market data, resulting in a method that consistently outperforms others across different time frames.
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.