DETECTION AND PREVENTION OF MARKET MANIPULATION AND INSIDER TRADING IN STOCK MARKETS: RISK INDICATORS AND CONTROL MODELS

Abstract

The integrity of global financial markets is perpetually challenged by evolving methods of manipulation and the illicit exploitation of private information. As trading mechanisms transition from human-centric exchanges to highfrequency, algorithmic ecosystems, the nature of market abuse has shifted towards complex, microsecond-level anomalies and obscured network-based collusion. This research report provides an exhaustive examination of the theoretical and practical frameworks used to detect and prevent market manipulation and insider trading. Grounded in market microstructure theory, the analysis explores the efficacy of risk indicators such as Kyle’s Lambda and the Volume-Synchronized Probability of Informed Trading (VPIN) in quantifying order flow toxicity. It further scrutinizes the evolution of surveillance models, contrasting traditional rule-based logic with state-of-the-art Deep Learning architectures, including Transformers for Limit Order Book (LOB) forecasting and Graph Neural Networks (GNNs) for identifying non-linear insider networks. A comparative legal analysis of the United States and European Union regulatory regimes highlights the divergence in enforcement philosophies and pre-trade risk controls. By synthesizing empirical data, algorithmic methodologies, and regulatory statutes, this report offers a roadmap for the next generation of market surveillance, emphasizing the necessity of adaptive, context-aware artificial intelligence in preserving market fairness.