Algorithms for Learning Graphs in Financial Markets

• URL: http://arxiv.org/abs/2012.15410v1
• Date: Thu, 31 Dec 2020 02:48:35 GMT
• Title: Algorithms for Learning Graphs in Financial Markets
• Authors: Jos\'e Vin\'icius de Miranda Cardoso and Jiaxi Ying and Daniel Perez Palomar
• Abstract summary: We investigate the fundamental problem of learning undirected graphical models under Laplacian structural constraints. We present natural justifications, supported by empirical evidence, for the usage of the Laplacian matrix as a model for the precision matrix of financial assets. We design numerical algorithms based on the alternating direction method of multipliers to learn undirected, weighted graphs.
• Score: 5.735035463793008
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the past two decades, the field of applied finance has tremendously benefited from graph theory. As a result, novel methods ranging from asset network estimation to hierarchical asset selection and portfolio allocation are now part of practitioners' toolboxes. In this paper, we investigate the fundamental problem of learning undirected graphical models under Laplacian structural constraints from the point of view of financial market times series data. In particular, we present natural justifications, supported by empirical evidence, for the usage of the Laplacian matrix as a model for the precision matrix of financial assets, while also establishing a direct link that reveals how Laplacian constraints are coupled to meaningful physical interpretations related to the market index factor and to conditional correlations between stocks. Those interpretations lead to a set of guidelines that practitioners should be aware of when estimating graphs in financial markets. In addition, we design numerical algorithms based on the alternating direction method of multipliers to learn undirected, weighted graphs that take into account stylized facts that are intrinsic to financial data such as heavy tails and modularity. We illustrate how to leverage the learned graphs into practical scenarios such as stock time series clustering and foreign exchange network estimation. The proposed graph learning algorithms outperform the state-of-the-art methods in an extensive set of practical experiments. Furthermore, we obtain theoretical and empirical convergence results for the proposed algorithms. Along with the developed methodologies for graph learning in financial markets, we release an R package, called fingraph, accommodating the code and data to obtain all the experimental results.

Related papers

• Evaluating Financial Relational Graphs: Interpretation Before Prediction [4.421486904657393]
  We introduce the SPNews dataset, collected based on S&P 500 Index stocks, to facilitate the construction of dynamic relationship graphs. By using the relationship graph to explain historical financial phenomena, we assess its validity before constructing a graph neural network. Our evaluation methods can effectively differentiate between various financial relationship graphs, yielding more interpretable results.
  arXiv  Detail & Related papers  (2024-09-28T22:43:00Z)
• Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning [53.81365215811222]
  We provide a review and summary of the latest approaches, strategies, and insights that address distribution shifts within the context of graph learning. We categorize existing graph learning methods into several essential scenarios, including graph domain adaptation learning, graph out-of-distribution learning, and graph continual learning. We discuss the potential applications and future directions for graph learning under distribution shifts with a systematic analysis of the current state in this field.
  arXiv  Detail & Related papers  (2024-02-26T07:52:40Z)
• DGDNN: Decoupled Graph Diffusion Neural Network for Stock Movement Prediction [8.7861010791349]
  We propose a novel graph learning approach implemented without expert knowledge to address these issues. First, our approach automatically constructs dynamic stock graphs by entropy-driven edge generation from a signal processing perspective. Last, a decoupled representation learning scheme is adopted to capture distinctive hierarchical intra-stock features.
  arXiv  Detail & Related papers  (2024-01-03T17:36:27Z)
• A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions [64.84521350148513]
  Graphs represent interconnected structures prevalent in a myriad of real-world scenarios. Effective graph analytics, such as graph learning methods, enables users to gain profound insights from graph data. However, these methods often suffer from data imbalance, a common issue in graph data where certain segments possess abundant data while others are scarce. This necessitates the emerging field of imbalanced learning on graphs, which aims to correct these data distribution skews for more accurate and representative learning outcomes.
  arXiv  Detail & Related papers  (2023-08-26T09:11:44Z)
• Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction [14.056579711850578]
  We propose a temporal and heterogeneous graph neural network-based (THGNN) approach to learn the dynamic relations among price movements in financial time series. We conduct extensive experiments on the stock market in the United States and China.
  arXiv  Detail & Related papers  (2023-05-09T11:17:46Z)
• Graph-Regularized Tensor Regression: A Domain-Aware Framework for Interpretable Multi-Way Financial Modelling [23.030263841031633]
  We develop a novel Graph-Regularized Regression (GRTR) framework, whereby knowledge about cross-asset relations is incorporated into the model in the form of a graph Laplacian matrix. By virtue of tensor algebra, the proposed framework is shown to be fully interpretable, both coefficient-wise and dimension-wise. The GRTR model is validated in a multi-way financial forecasting setting and is shown to achieve improved performance at reduced computational costs.
  arXiv  Detail & Related papers  (2022-10-26T13:39:08Z)
• Bayesian Graph Contrastive Learning [55.36652660268726]
  We propose a novel perspective of graph contrastive learning methods showing random augmentations leads to encoders. Our proposed method represents each node by a distribution in the latent space in contrast to existing techniques which embed each node to a deterministic vector. We show a considerable improvement in performance compared to existing state-of-the-art methods on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-12-15T01:45:32Z)
• Unbiased Graph Embedding with Biased Graph Observations [52.82841737832561]
  We propose a principled new way for obtaining unbiased representations by learning from an underlying bias-free graph. Based on this new perspective, we propose two complementary methods for uncovering such an underlying graph.
  arXiv  Detail & Related papers  (2021-10-26T18:44:37Z)
• Model-Agnostic Graph Regularization for Few-Shot Learning [60.64531995451357]
  We present a comprehensive study on graph embedded few-shot learning. We introduce a graph regularization approach that allows a deeper understanding of the impact of incorporating graph information between labels. Our approach improves the performance of strong base learners by up to 2% on Mini-ImageNet and 6.7% on ImageNet-FS.
  arXiv  Detail & Related papers  (2021-02-14T05:28:13Z)
• Learning Undirected Graphs in Financial Markets [13.47131471222723]
  We show that Laplacian constraints have meaningful physical interpretations related to the market index factor and to the conditional correlations between stocks. Those interpretations lead to a set of guidelines that users should be aware of when estimating graphs in financial markets.
  arXiv  Detail & Related papers  (2020-05-20T10:48:21Z)
• Gaussian process imputation of multiple financial series [71.08576457371433]
  Multiple time series such as financial indicators, stock prices and exchange rates are strongly coupled due to their dependence on the latent state of the market. We focus on learning the relationships among financial time series by modelling them through a multi-output Gaussian process.
  arXiv  Detail & Related papers  (2020-02-11T19:18:18Z)

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.