ETFs

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"]

ProteuS: A Generative Approach for Simulating Concept Drift in Financial Markets ArXiv ID: 2509.11844 “View on arXiv” Authors: Andrés L. Suárez-Cetrulo, Alejandro Cervantes, David Quintana Abstract Financial markets are complex, non-stationary systems where the underlying data distributions can shift over time, a phenomenon known as regime changes, as well as concept drift in the machine learning literature. These shifts, often triggered by major economic events, pose a significant challenge for traditional statistical and machine learning models. A fundamental problem in developing and validating adaptive algorithms is the lack of a ground truth in real-world financial data, making it difficult to evaluate a model’s ability to detect and recover from these drifts. This paper addresses this challenge by introducing a novel framework, named ProteuS, for generating semi-synthetic financial time series with pre-defined structural breaks. Our methodology involves fitting ARMA-GARCH models to real-world ETF data to capture distinct market regimes, and then simulating realistic, gradual, and abrupt transitions between them. The resulting datasets, which include a comprehensive set of technical indicators, provide a controlled environment with a known ground truth of regime changes. An analysis of the generated data confirms the complexity of the task, revealing significant overlap between the different market states. We aim to provide the research community with a tool for the rigorous evaluation of concept drift detection and adaptation mechanisms, paving the way for more robust financial forecasting models. ...

Using Internal Bar Strength as a Key Indicator for Trading Country ETFs ArXiv ID: 2306.12434 “View on arXiv” Authors: Unknown Abstract This report aims to investigate the effectiveness of using internal bar strength (IBS) as a key indicator for trading country exchange-traded funds (ETFs). The study uses a quantitative approach to analyze historical price data for a bucket of country ETFs over a period of 10 years and uses the idea of Mean Reversion to create a profitable trading strategy. Our findings suggest that IBS can be a useful technical indicator for predicting short-term price movements in this basket of ETFs. ...

ETF Risk Models ArXiv ID: 2110.07138 “View on arXiv” Authors: Unknown Abstract We discuss how to build ETF risk models. Our approach anchors on i) first building a multilevel (non-)binary classification/taxonomy for ETFs, which is utilized in order to define the risk factors, and ii) then building the risk models based on these risk factors by utilizing the heterotic risk model construction of https://ssrn.com/abstract=2600798 (for binary classifications) or general risk model construction of https://ssrn.com/abstract=2722093 (for non-binary classifications). We discuss how to build an ETF taxonomy using ETF constituent data. A multilevel ETF taxonomy can also be constructed by appropriately augmenting and expanding well-built and granular third-party single-level ETF groupings. ...