# Forecasting Financial Market Structure from Network Features using Machine Learning

arXiv: https://arxiv.org/abs/2110.11751 · HTML: https://mlsanity.com/paper/2110.11751/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 72 references that are in the map.
- First posted on arXiv 22 Oct 2021.

## Builds on (most cited first, 11 of 11)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Sentiment Analysis of Twitter Data for Predicting Stock Market Movements](https://mlsanity.com/paper/1610.09225/) (2016)
- [Link Prediction in Social Networks: the State-of-the-Art](https://mlsanity.com/paper/1411.5118/) (2014)
- [Parsimonious modeling with Information Filtering Networks](https://mlsanity.com/paper/1602.07349/) (2016)
- [Link Prediction in Complex Networks: A Mutual Information Perspective](https://mlsanity.com/paper/1405.4341/) (2014)
- [The multiplex dependency structure of financial markets](https://mlsanity.com/paper/1606.04872/) (2016)
- [Erratum: Link prediction in drug-target interactions network using similarity indices](https://mlsanity.com/paper/1711.00150/) (2017)
- [Review on Learning and Extracting Graph Features for Link Prediction](https://mlsanity.com/paper/1901.03425/) (2019)
- [A proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series](https://mlsanity.com/paper/1509.05475/) (2015)
- [Improving link prediction in complex networks by adaptively exploiting multiple structural features of networks](https://mlsanity.com/paper/1608.04533/) (2016)

## Built on by (most cited first, 2 of 2)

- [Machine Learning vs. Randomness: Challenges in Predicting Binary Options Movements](https://mlsanity.com/paper/2511.15960/) (2025)
- [STN-TGAT: Top-K Portfolio Construction via Prior-Guided Graph Attention with Learnable Soft-Threshold Sparsification](https://mlsanity.com/paper/2607.19385/) (2026)

## Abstract (arXiv)

We propose a model that forecasts market correlation structure from link- and node-based financial network features using machine learning. For such, market structure is modeled as a dynamic asset network by quantifying time-dependent co-movement of asset price returns across company constituents of major global market indices. We provide empirical evidence using three different network filtering methods to estimate market structure, namely Dynamic Asset Graph (DAG), Dynamic Minimal Spanning Tree (DMST) and Dynamic Threshold Networks (DTN). Experimental results show that the proposed model can forecast market structure with high predictive performance with up to 40% improvement over a time-invariant correlation-based benchmark. Non-pair-wise correlation features showed to be important compared to traditionally used pair-wise correlation measures for all markets studied, particularly in the long-term forecasting of stock market structure. Evidence is provided for stock constituents of the DAX30, EUROSTOXX50, FTSE100, HANGSENG50, NASDAQ100 and NIFTY50 market indices. Findings can be useful to improve portfolio selection and risk management methods, which commonly rely on a backward-looking covariance matrix to estimate portfolio risk.