The article systematizes empirical assessments of the impact of digitalization of trading, information, and post-trading processes on liquidity, volatility, information efficiency, and transaction costs in stock markets. The theoretical framework is based on three classes of effects: reducing information asymmetry and search costs by accelerating data processing and transmission; enhancing operational efficiency and short-term instability due to algorithmic decision-making; and reducing operational risks and security requirements by transforming post-trading infrastructure while increasing requirements for cyber resilience and data quality. Empirical identification is based on difference-in-differences, natural experiments, and fixed-effects panel models, using news arrays and regulatory statistics. The most consistent result is the narrowing of spreads, increased depth, and reduced slippage when switching to electronic order books and direct access, especially for small- and medium-cap stocks. It has been determined that algorithmization increases the frequency of quotes and the continuity of pricing, but increases the likelihood of short-term outliers; digital disclosure channels accelerate the incorporation of news into prices and reduce post-event drift.
digitalization, liquidity, volatility, information efficiency, stock market, and empirical assessments
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