Forecasting Financial Market Structure from Network Features using Machine Learning

10/22/2021
by   Douglas Castilho, et al.
0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 5

page 10

page 11

page 12

09/09/2020

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...
11/28/2019

U-CNNpred: A Universal CNN-based Predictor for Stock Markets

The performance of financial market prediction systems depends heavily o...
07/21/2020

Efficiency of the financial markets during the COVID-19 crisis: time-varying parameters of fractional stable dynamics

This paper investigates the impact of COVID-19 on financial markets. It ...
03/23/2021

Reliability of MST identification in correlation-based market networks

Maximum spanning tree (MST) is a popular tool in market network analysis...
05/09/2016

Stochastic Portfolio Theory: A Machine Learning Perspective

In this paper we propose a novel application of Gaussian processes (GPs)...
09/07/2020

Capturing dynamics of post-earnings-announcement drift using genetic algorithm-optimised supervised learning

While Post-Earnings-Announcement Drift (PEAD) is one of the most studied...
03/10/2020

CNNpred: CNN-based stock market prediction using a diverse set of variables

Feature extraction from financial data is one of the most important prob...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.