Andres Azqueta-Gavaldon, Borja Ureta
arXiv 5 Jun 2026 · Econometrics
arXiv:2606.07049 · PDF · DOI · OpenAlex · Extracted main text
We introduce CausalAlpha, an open-source framework that constructs a high-frequency Geopolitical Risk (GPR) index from Telegram OSINT channels using natural language processing, and applies causal discovery methods to identify the directed causal structure between geopolitical uncertainty and financial market variables. Unlike standard sentiment indices or Granger-causality approaches, CausalAlpha employs the Peter-Clark (PC) algorithm to recover the directed acyclic graph (DAG) of causal dependencies between five category-specific GPR indicators and a set of financial variables spanning commodity prices, equity indices, and credit instruments, estimated across four DAG specifications and three significance levels with 500 block-bootstrap resamples. Two findings emerge as globally robust across all DAG specifications at alpha = 0.10: political instability and energy media coverage independently and causally precede conflict coverage, establishing conflict as the primary causal sink of geopolitical narrative escalation in real-time OSINT channels. At the strictest significance level (alpha = 0.05), conflict coverage causally precedes energy sector equity returns (delta XLE), consistent with geopolitical escalation transmitting to energy markets. A Structural VAR on the core macro panel confirms that dynamic transmission from geopolitical NLP signals to financial market prices is statistically weak at daily frequency, suggesting that geopolitical news signals operate primarily within the media narrative system. The framework is deployed as a production application on Google Cloud Run with automated data collection and index construction, representing a step toward real-time macrofinancial risk monitoring using OSINT.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Caldara, Dario and Iacoviello, Matteo (2022) Measuring Geopolitical Risk | 1.000 | 9 | 5 | 100% |
| 2 | Spirtes, Peter and Glymour, Clark and Scheines, Richard (2000) Causation, Prediction, and Search | 0.843 | 3 | 3 | 100% |
| 3 | Verduzco-Bustos, Guillermo and Zanetti, Francesco (2026) The Effects of Geopolitical Oil Price Shocks | 0.811 | 4 | 2 | 100% |
| 4 | Baker, Scott R. and Bloom, Nicholas and Davis, Steven J (2016) Measuring Economic Policy Uncertainty | 0.737 | 3 | 2 | 100% |
| 5 | Soula, Emerson and Dubow, Ben and Osadchuk, Roman (2024) Another Battlefield: Telegram as a Digital Front in Russia's War Against Ukraine | 0.644 | 2 | 2 | 100% |
| 6 | Chowdhury, Mohammad Ashraful Ferdous and Hassan, M. Kabir and Abdull… (2025) Geopolitical risk transmission dynamics to commodity, stock, and energy markets | 0.511 | 2 | 1 | 100% |
| 7 | Genç, Timur (2026) Iran War Becomes $500 Million Betting Frenzy on Polymarket | 0.511 | 2 | 1 | 100% |
| 8 | ACLED (2025) Europe and Central Asia Overview: December 2025 | 0.405 | 1 | 1 | 100% |
| 9 | Alesina, Alberto and Perotti, Roberto (1996) Income Distribution, Political Instability, and Investment | 0.405 | 1 | 1 | 100% |
| 10 | Bollen, Johan and Mao, Huina and Zeng, Xiaojun (2011) Twitter Mood Predicts the Stock Market | 0.405 | 1 | 1 | 100% |
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