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Machine learning methods for financial forecasting and trading profitability: Evidence during the Russia–Ukraine war

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Title: Machine learning methods for financial forecasting and trading profitability: Evidence during the Russia–Ukraine war
Authors: Yaohao Peng, João Gabriel de Moraes Souza
Source: REGE Revista de Gestão, Vol 31, Iss 2, Pp 152-165 (2024)
Publisher Information: Emerald Publishing, 2024.
Publication Year: 2024
Collection: LCC:Commerce
LCC:Business
Subject Terms: Time-series forecasting, Algorithmic trading, Support vector machines, Russia–Ukraine war, Efficient market hypothesis, Trading profitability, Commerce, HF1-6182, Business, HF5001-6182
More Details: Purpose – This study aims to evaluate the effectiveness of machine learning models to yield profitability over the market benchmark, notably in periods of systemic instability, such as the ongoing war between Russia and Ukraine. Design/methodology/approach – This study made computational experiments using support vector machine (SVM) classifiers to predict stock price movements for three financial markets and construct profitable trading strategies to subsidize investors’ decision-making. Findings – On average, machine learning models outperformed the market benchmarks during the more volatile period of the Russia–Ukraine war, but not during the period before the conflict. Moreover, the hyperparameter combinations for which the profitability is superior were found to be highly sensitive to small variations during the model training process. Practical implications – Investors should proceed with caution when applying machine learning models for stock price forecasting and trading recommendations, as their superior performance for volatile periods – in terms of generating abnormal gains over the market – was not observed for a period of relative stability in the economy. Originality/value – This paper’s approach to search for financial strategies that succeed in outperforming the market provides empirical evidence about the effectiveness of state-of-the-art machine learning techniques before and after the conflict deflagration, which is of potential value for researchers in quantitative finance and market professionals who operate in the financial segment.
Document Type: article
File Description: electronic resource
Language: Portuguese
ISSN: 2177-8736
1809-2276
Relation: https://doaj.org/toc/1809-2276; https://doaj.org/toc/2177-8736
DOI: 10.1108/REGE-05-2022-0079/full/pdf
DOI: 10.1108/REGE-05-2022-0079
Access URL: https://doaj.org/article/d15fe28b410b4547bd59a110114fd286
Accession Number: edsdoj.15fe28b410b4547bd59a110114fd286
Database: Directory of Open Access Journals
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  Data: <searchLink fieldCode="AR" term="%22Yaohao+Peng%22">Yaohao Peng</searchLink><br /><searchLink fieldCode="AR" term="%22João+Gabriel+de+Moraes+Souza%22">João Gabriel de Moraes Souza</searchLink>
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  Data: REGE Revista de Gestão, Vol 31, Iss 2, Pp 152-165 (2024)
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  Data: Emerald Publishing, 2024.
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  Data: 2024
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  Data: LCC:Commerce<br />LCC:Business
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  Data: <searchLink fieldCode="DE" term="%22Time-series+forecasting%22">Time-series forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithmic+trading%22">Algorithmic trading</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Russia–Ukraine+war%22">Russia–Ukraine war</searchLink><br /><searchLink fieldCode="DE" term="%22Efficient+market+hypothesis%22">Efficient market hypothesis</searchLink><br /><searchLink fieldCode="DE" term="%22Trading+profitability%22">Trading profitability</searchLink><br /><searchLink fieldCode="DE" term="%22Commerce%22">Commerce</searchLink><br /><searchLink fieldCode="DE" term="%22HF1-6182%22">HF1-6182</searchLink><br /><searchLink fieldCode="DE" term="%22Business%22">Business</searchLink><br /><searchLink fieldCode="DE" term="%22HF5001-6182%22">HF5001-6182</searchLink>
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  Label: Description
  Group: Ab
  Data: Purpose – This study aims to evaluate the effectiveness of machine learning models to yield profitability over the market benchmark, notably in periods of systemic instability, such as the ongoing war between Russia and Ukraine. Design/methodology/approach – This study made computational experiments using support vector machine (SVM) classifiers to predict stock price movements for three financial markets and construct profitable trading strategies to subsidize investors’ decision-making. Findings – On average, machine learning models outperformed the market benchmarks during the more volatile period of the Russia–Ukraine war, but not during the period before the conflict. Moreover, the hyperparameter combinations for which the profitability is superior were found to be highly sensitive to small variations during the model training process. Practical implications – Investors should proceed with caution when applying machine learning models for stock price forecasting and trading recommendations, as their superior performance for volatile periods – in terms of generating abnormal gains over the market – was not observed for a period of relative stability in the economy. Originality/value – This paper’s approach to search for financial strategies that succeed in outperforming the market provides empirical evidence about the effectiveness of state-of-the-art machine learning techniques before and after the conflict deflagration, which is of potential value for researchers in quantitative finance and market professionals who operate in the financial segment.
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        Value: 10.1108/REGE-05-2022-0079/full/pdf
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      – Text: Portuguese
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        PageCount: 14
        StartPage: 152
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      – SubjectFull: Time-series forecasting
        Type: general
      – SubjectFull: Algorithmic trading
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      – SubjectFull: Support vector machines
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      – SubjectFull: Russia–Ukraine war
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