Informed Trading

Trading on Terror? ArXiv ID: ssrn-4652027 “View on arXiv” Authors: Unknown Abstract Recent scholarship shows that informed traders increasingly disguise trades in economically linked securities such as exchange-traded funds (ETFs). Linking that Keywords: Informed Trading, Market Microstructure, ETFs, Information Asymmetry, Arbitrage, Equities Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper relies on statistical event studies and rank-order analysis rather than advanced mathematical modeling, placing it at the lower end of math complexity; however, it employs high-quality financial data (FINRA, TASE, SEC) and robust empirical methods (placebo tests, counterfactuals, statistical significance thresholds) to analyze real-world trading patterns, warranting high empirical rigor. flowchart TD A["Research Goal: How do informed traders disguise<br>trading in securities linked to terror events?"] --> B["Method: Event Study &<br>Multi-Asset Analysis"] B --> C["Data: Global Terror Events &<br>Equity/ETF Transaction Data"] C --> D["Process: Identify Abnormal Trading<br>in Linked Securities vs. Equities"] D --> E["Analysis: Cross-Sectional Regressions<br>controlling for Arbitrage Constraints"] E --> F["Finding: Increased informed trading<br>in linked ETFs during terror events"] F --> G["Outcome: Displacement of<br>information asymmetry via market linking"]

Optimal Signal Extraction from Order Flow: A Matched Filter Perspective on Normalization and Market Microstructure ArXiv ID: 2512.18648 “View on arXiv” Authors: Sungwoo Kang Abstract We demonstrate that the choice of normalization for order flow intensity is fundamental to signal extraction in finance, not merely a technical detail. Through theoretical modeling, Monte Carlo simulation, and empirical validation using Korean market data, we prove that market capitalization normalization acts as a ``matched filter’’ for informed trading signals, achieving 1.32–1.97$\times$ higher correlation with future returns compared to traditional trading value normalization. The key insight is that informed traders scale positions by firm value (market capitalization), while noise traders respond to daily liquidity (trading volume), creating heteroskedastic corruption when normalizing by trading volume. By reframing the normalization problem using signal processing theory, we show that dividing order flow by market capitalization preserves the information signal while traditional volume normalization multiplies the signal by inverse turnover – a highly volatile quantity. Our theoretical predictions are robust across parameter specifications and validated by empirical evidence showing 482% improvement in explanatory power. These findings have immediate implications for high-frequency trading algorithms, risk factor construction, and information-based trading strategies. ...

Liquidity Competition Between Brokers and an Informed Trader ArXiv ID: 2503.08287 “View on arXiv” Authors: Unknown Abstract We study a multi-agent setting in which brokers transact with an informed trader. Through a sequential Stackelberg-type game, brokers manage trading costs and adverse selection with an informed trader. In particular, supplying liquidity to the informed traders allows the brokers to speculate based on the flow information. They simultaneously attempt to minimize inventory risk and trading costs with the lit market based on the informed order flow, also known as the internalization-externalization strategy. We solve in closed form for the trading strategy that the informed trader uses with each broker and propose a system of equations which classify the equilibrium strategies of the brokers. By solving these equations numerically we may study the resulting strategies in equilibrium. Finally, we formulate a competitive game between brokers in order to determine the liquidity prices subject to precommitment supplied to the informed trader and provide a numerical example in which the resulting equilibrium is not Pareto efficient. ...

Uncertain Regulations, Definite Impacts: The Impact of the US Securities and Exchange Commission’s Regulatory Interventions on Crypto Assets ArXiv ID: 2412.02452 “View on arXiv” Authors: Unknown Abstract This study employs an event study methodology to investigate the market impact of the U.S. Securities and Exchange Commission’s (SEC) classification of crypto assets as securities. It explores how SEC interventions influence asset returns and trading volumes, focusing on explicitly named crypto assets. The empirical analysis highlights significant adverse market reactions, notably returns plummeting 12% over one week post-announcement, persisting for a month. We demonstrate that the severity of market reaction depends on sentiment and asset characteristics such as market size, age, volatility, and illiquidity. Further, we identify significant ex-ante trading volume effects indicative of pre-announcement informed trading. ...