Sparse Portfolio Selection via Topological Data Analysis based Clustering

• URL: http://arxiv.org/abs/2401.16920v2
• Date: Fri, 13 Dec 2024 10:20:13 GMT
• Title: Sparse Portfolio Selection via Topological Data Analysis based Clustering
• Authors: Anubha Goel, Damir Filipović, Puneet Pasricha,
• Abstract summary: This paper uses topological data analysis tools and introduces a data-driven clustering-based stock selection strategy tailored for sparse portfolio construction. Our strategy to integrate TDA with the clustering algorithm significantly enhanced the performance of sparse portfolios across various performance measures in diverse market scenarios.
• Score: 4.547063832007314
• License:
• Abstract: This paper uses topological data analysis (TDA) tools and introduces a data-driven clustering-based stock selection strategy tailored for sparse portfolio construction. Our asset selection strategy exploits the topological features of stock price movements to select a subset of topologically similar (different) assets for a sparse index tracking (Markowitz) portfolio. We introduce new distance measures, which serve as an input to the clustering algorithm, on the space of persistence diagrams and landscapes that consider the time component of a time series. We conduct an empirical analysis on the S\&P index from 2009 to 2022, including a study on the COVID-19 data to validate the robustness of our methodology. Our strategy to integrate TDA with the clustering algorithm significantly enhanced the performance of sparse portfolios across various performance measures in diverse market scenarios.

Related papers

• Optimizing Portfolio Performance through Clustering and Sharpe Ratio-Based Optimization: A Comparative Backtesting Approach [0.0]
  This paper introduces a comparative backtesting approach that combines clustering-based portfolio segmentation and Sharpe ratio-based optimization to enhance investment decision-making. We segment a diverse set of financial assets into clusters based on their historical log-returns using K-Means clustering. For each cluster, we apply a Sharpe ratio-based optimization model to derive optimal weights that maximize risk-adjusted returns.
  arXiv  Detail & Related papers  (2025-01-21T12:00:52Z)
• Exploring applications of topological data analysis in stock index movement prediction [5.82416342574148]
  We construct point clouds for stock indices using three different methods. Four distinct topological features are computed to represent the patterns in the data, and 15 combinations of these features are enumerated and input into six different machine learning models. We evaluate the predictive performance of various TDA configurations by conducting index movement classification tasks on datasets such as CSI, DAX, HSI and FTSE.
  arXiv  Detail & Related papers  (2024-11-21T06:41:39Z)
• Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis [53.38518232934096]
  Multi-task learning (MTL) is a powerful machine learning paradigm designed to leverage shared knowledge across tasks to improve generalization and performance. We propose an MTL approach at the intersection between task clustering and feature transformation based on a two-phase iterative aggregation of targets and features. In both phases, a key aspect is to preserve the interpretability of the reduced targets and features through the aggregation with the mean, which is motivated by applications to Earth science.
  arXiv  Detail & Related papers  (2024-06-12T08:30:16Z)
• Lidar Panoptic Segmentation and Tracking without Bells and Whistles [48.078270195629415]
  We propose a detection-centric network for lidar segmentation and tracking. One of the core components of our network is the object instance detection branch. We evaluate our method on several 3D/4D LPS benchmarks and observe that our model establishes a new state-of-the-art among open-sourced models.
  arXiv  Detail & Related papers  (2023-10-19T04:44:43Z)
• Portfolio Selection via Topological Data Analysis [2.3901301169141056]
  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. Experimental results show that our proposed system outperforms other methods.
  arXiv  Detail & Related papers  (2023-08-15T09:36:43Z)
• Detection and Evaluation of Clusters within Sequential Data [58.720142291102135]
  Clustering algorithms for Block Markov Chains possess theoretical optimality guarantees. In particular, our sequential data is derived from human DNA, written text, animal movement data and financial markets. It is found that the Block Markov Chain model assumption can indeed produce meaningful insights in exploratory data analyses.
  arXiv  Detail & Related papers  (2022-10-04T15:22:39Z)
• Cooperative Self-Training for Multi-Target Adaptive Semantic Segmentation [26.79776306494929]
  We propose a self-training strategy that employs pseudo-labels to induce cooperation among multiple domain-specific classifiers. We employ feature stylization as an efficient way to generate image views that forms an integral part of self-training.
  arXiv  Detail & Related papers  (2022-10-04T13:03:17Z)
• Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function [106.69643619725652]
  We develop a training strategy that allows even a simple BiLSTM model, when trained with cross-entropy loss, to achieve competitive results. We report state-of-the-art results for text classification task on several benchmark datasets.
  arXiv  Detail & Related papers  (2020-09-08T21:55:22Z)
• Topology-based Clusterwise Regression for User Segmentation and Demand Forecasting [63.78344280962136]
  Using a public and a novel proprietary data set of commercial data, this research shows that the proposed system enables analysts to both cluster their user base and plan demand at a granular level. This work seeks to introduce TDA-based clustering of time series and clusterwise regression with matrix factorization methods as viable tools for the practitioner.
  arXiv  Detail & Related papers  (2020-09-08T12:10:10Z)
• CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus [62.86856923633923]
  We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements. In contrast to previous works, which resorted to hand-crafted search strategies for multiple model detection, we learn the search strategy from data. For self-supervised learning of the search, we evaluate the proposed algorithm on multi-homography estimation and demonstrate an accuracy that is superior to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-01-08T17:37:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.