---
title: Predictive Model Performance in Covid-19 and Russian-Ukrainian War
url: https://www.ml-quant.com/papers/repec/pfq-journl-v-69-y-2023-i-2-p-118-132/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:pfq:journl:v:69:y:2023:i:2:p:118-132
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Funipub.lib.uni-corvinus.hu%2F8457%2F%3Bh%3Drepec%3Apfq%3Ajournl%3Av%3A69%3Ay%3A2023%3Ai%3A2%3Ap%3A118-132
featured: 2023-10-12
citations: unknown
topic: Derivatives & Volatility
---


# Predictive Model Performance in Covid-19 and Russian-Ukrainian War

The study explores the use of artificial intelligence in predicting stock market trends in volatile situations, showing its superior performance over linear regression, particularly after the Russian-Ukrainian war.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Funipub.lib.uni-corvinus.hu%2F8457%2F%3Bh%3Drepec%3Apfq%3Ajournl%3Av%3A69%3Ay%3A2023%3Ai%3A2%3Ap%3A118-132
- Identifier: RePEc:pfq:journl:v:69:y:2023:i:2:p:118-132
- Released: 2023-10-12
- First featured: Quant Letter No. 20 (2023-10-12): https://www.ml-quant.com/issues/2023-10-12/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

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