Metaheuristic enabled intelligent model for stock market prediction via integrating volatility spillover: India and its Asian and European counterparts-Reference-Cited by-同舟云学术

Metaheuristic enabled intelligent model for stock market prediction via integrating volatility spillover: India and its Asian and European counterparts

Published:2023-03 Issue: Volume:144 Page:102127
ISSN:0169-023X
Container-title:Data & Knowledge Engineering
language:en
Short-container-title:Data & Knowledge Engineering

Author:

Tripathi Deepak Kumar,Chadha Saurabh,Tripathi Ankita

Publisher

Elsevier BV

Subject

Information Systems and Management

Reference54 articles.

1. On the global financial market integration swoosh and the trilemma;Bekaert;J. Int. Money Finance,2019

2. The role of trading frictions in financial markets;Huber;J. Econom. Dynam. Control,2019

3. Eduard Baumöhl, Syed Jawad Hussain Shahzad, Quantile coherency networks of international stock markets, Finance Res. Lett. 1 (31) 119–129.

4. From academic abilities to occupation: What drives stock market participation;Vaarmets;Emerg. Mark. Rev,2019

5. The influence of cultural distance on the volatility of the international stock market;Zhou;Econ. Model.,2019

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