Modeling the Effect of Intelligent Financial Technologies on Capital Market Efficiency Using a Dynamic Econometric Approach and High-Frequency Data
Keywords:
Financial technology, capital market efficiency, dynamic econometrics, high-frequency data, algorithmic trading, thematic analysis, Tehran capital marketAbstract
This study aimed to develop a conceptual model explaining the effects of intelligent financial technologies on capital market efficiency, with emphasis on dynamic econometrics, high-frequency data, market microstructure, and mechanisms for reducing informational inefficiency. This applied qualitative study employed an exploratory model-building design. The participants consisted of capital market experts, financial technology specialists, financial data analysts, managers of financial institutions, finance and econometrics academics, and market supervision experts in Tehran. Purposeful and snowball sampling strategies were used, and theoretical saturation was achieved after 22 semi-structured interviews. Data were collected through in-depth interviews and analyzed using thematic analysis in NVivo software. To enhance trustworthiness, member checking, peer review, analytical memoing, and constant comparison of codes were applied. The analysis resulted in five main categories: “high-frequency data infrastructure and market microstructure quality,” “intelligent information processing and price discovery,” “dynamic econometrics and real-time efficiency monitoring,” “technological, algorithmic, and regulatory risks,” and “data-driven governance and intelligent market policymaking.” The findings indicated that intelligent financial technologies may enhance market efficiency by reducing information latency, increasing transparency, improving liquidity, lowering information search costs, and strengthening volatility forecasting. However, weak data infrastructure, insufficient supervisory standards, algorithmic bias, and unequal technological access may generate new forms of market fragility, liquidity instability, correlated algorithmic behavior, and informational asymmetry. Capital market efficiency in the era of intelligent financial technologies is not a static condition; rather, it is the dynamic outcome of interactions among data quality, processing speed, algorithm design, supervisory transparency, and institutional capacity to manage emerging risks. The proposed model suggests that effective use of high-frequency data and dynamic econometrics can provide a foundation for real-time monitoring of efficiency, identification of temporary inefficiencies, and design of intelligent supervisory systems in capital markets.
Keywords: Financial technology, capital market efficiency, dynamic econometrics, high-frequency data, algorithmic trading, thematic analysis, Tehran capital market.
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