Empirical study regarding the analysis of investment behavior on high-frequency trading at the Romanian stock exchange
DOI: https://doi.org/10.3846/tede.2026.28512Abstract
The role and effects of high-frequency trading in financial markets are manifold, spanning from improved (true) price discovery to predatory mechanisms amongst stock market trading agents. The effects of using different types of AI and Machine Learning – while technically capable to react to the accelerated pace of trading opportunities – remain largely unclear. We concentrate on designing potentially useful “generalized prompts” for AI-based trading models, which is the main task from a market analyst’s point of view. With a sufficient number of such relevant and often also structurally highly diverse inputs, the computationally intensive modeling and validation part can then be relegated to an otherwise autonomously working AI application. We offer a principled approach of proposing derived time series and diverse filters for time- and resolution-localized information for high-frequency stock market analysis and trading models.
First published online 8 October 2026
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high-frequency, financial data, stock exchange, fuzzy decision, emerging economies, sentiment indexHow to Cite
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Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
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Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
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