Log In Sign Up

Forecasting Financial Market Structure from Network Features using Machine Learning

by   Douglas Castilho, et al.

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.


page 1

page 5

page 10

page 11

page 12


Forecasting financial markets with semantic network analysis in the COVID-19 crisis

This paper uses a new textual data index for predicting stock market dat...

Forecasting Market Changes using Variational Inference

Though various approaches have been considered, forecasting near-term ma...

Learning Embedded Representation of the Stock Correlation Matrix using Graph Machine Learning

Understanding non-linear relationships among financial instruments has v...

Reliability of MST identification in correlation-based market networks

Maximum spanning tree (MST) is a popular tool in market network analysis...

Long-Term Modeling of Financial Machine Learning for Active Portfolio Management

In the practical business of asset management by investment trusts and t...

Sequential Learning and Economic Benefits from Dynamic Term Structure Models

This paper explores the statistical and economic importance of restricti...