The Orange Revolution in Ukraine

Khodunov, Alexander

doi:10.1007/978-3-030-86468-2_19

Cited by 17 publications

(1 citation statement)

References 22 publications

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“…A revote was ordered by the Ukraine Supreme Court and took place in December 2004, with Yushchenko being declared the winner (Karatnycky, 2005). The Orange Revolution boosted Ukraine’s press freedom but also highlighted the existence of a polarization between groups that argued in favor of an approximation to Russia and enthusiasts of an approximation to Western coalitions such as the European Union and North Atlantic Treaty Organization (NATO) (Khodunov, 2022). This polarization was reflected in the very next elections for the Ukrainian parliament that took place in 2007 and escalated after Yanukovych was eventually elected for the presidency in 2010 (White & McAllister, 2009).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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

Peng,

Souza

2024

REGE

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PurposeThis 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/approachThis 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.FindingsOn 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 implicationsInvestors 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/valueThis 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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Section: Theoretical Backgroundmentioning

confidence: 99%