[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81968-en":3,"doc-seo-81968-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},81968,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Macroeconomic Message Passing for Anticipating Foreign Exchange Regime Changes","This research presents a graph-theoretic method for predicting foreign exchange (FX) market regimes in currency prices, using exogenous macroeconomic variables to update localized node features through message passing. The approach applies the Graph Tsetlin Machine (GraphTM) and constructs hypervectorized directed multigraphs from multivariate macro drivers and technical indicators. Structured message passing yields deep, interpretable logical clauses that detect complex sub-graph patterns for USD/JPY regime anticipation.","arXiv :2607 .067 19v 1 [ cs .CE] 7 Jul 2026  \nMacroeconomic Dynamics (2026), x, 1–26  \nRESEARCH ARTICLE  \nMacroeconomic Message Passing for Anticipating Foreign Exchange Regime Changes: A Deep Logical Learning Approach using Graph Tsetlin Machines  \nChristian Blakely*†‡ and Melanie Gilmore¶  \n†Centre for Artificial Intelligence Research, University of Agder, Norway ‡Head of AI, Bernoly AG, Zurich, Switzerland  \n¶La Jolla Private Wealth Group, Wells Fargo Advisors, La Jolla, CA, USA  \n*Corresponding author. Email: [blakely@bernoly.com](blakely@bernoly.com)  \nAbstract  \nThis paper introduces a graph-theoretic approach for predicting market regimes in foreign exchange (FX) currency prices. Specifically, the proposed model incorporates exogenous macroeconomic variables to update localized node features via message-passing operations. Utilizing the Graph Tsetlin Machine (GraphTM) framework, we empirically demonstrate the efficacy of this approach in anticipating market regimes for the US Dollar and Japanese Yen currency pair (USD/JPY) . By representing multivariate macroeconomic drivers and technical indicators as hypervectorized directed multigraphs, the GraphTM leverages structured message passing to construct deep, interpretable logical clauses capable of recognizing complex sub-graph patterns.  \nKeywords: tsetlin machines, macro regime indicators, forex markets  \n1. Introduction  \nThe identification of foreign exchange (FX) market regimes, namely distinct states of price action governed by specific macroeconomic conditions, is a challenging area of quantitative finance. Their prediction and estimation goes as far back to the 1980s with classical parametric models (Hamilton 1989; Krolzig 1997) . They successfully introduced latent state tracking, but are structurally bound by strict linearity assumptions and scale poorly in high-dimensional feature spaces. Modern machine learning implementations such as the ones found in (Botte and Bao 2021; Erdoğdu and Baycan 2025) mitigate this by utilizing unsupervised learning over macroeconomic periods labeled by four different regime types. Similarly, Bank of England produced Lee et al 2023 that evaluates FX forward hedging strategies for the Pound Sterling (GBP) across four distinct market states using an advanced regimeswitching framework. However, all these papers tackled much longer time horizons  \n2 Christian Blakely et al.  \nand thus are much slower to adapt to quickly changing shocks in the regime over time. In this paper, we present a methodology for anticipating four different regimes that operate on hourly data. We will focus on the usdjpy bid/ask prices and demonstrate that our model can adapt to new information in the market quickly.  \nRecent advancements in Tsetlin Machines (TM) have introduced interpretable, logic-based pattern recognition that competes with deep learning architectures while maintaining high computational efficiency. However, the standard TM is constrained by a boolean representation of input data and fixed-length architectures with no sense of memory to the past, unless explicitly used as input without learning when it should or shouldn’t be used and its importance or relevance (also known as “attention\") . To compensate for this setback, the Convolutional Tsetlin Machine (ConvTM) has been considered in market microstructure to do both regime detection in orderbooks in Blakely 2022 as well as fast higher-order the microprice estimates in Blakely 2024 .  \nThe Graph Tsetlin Machine (GraphTM) introduced in Granmo, Abdelwahab et al 2026 made an impact in the logic-based machine learning community by offering anew architecture which processes multimodal data represented as hypervectorized directed multigraphs. This allows for a wide range of new applications for learning from data as there is no more constraint on the relationships between different types of data.  \nIn this paper, we leverage this computational relationship structure by mapping market indicators ","cbCaiaQ53zNhOdVr","https://ap.wps.com/l/cbCaiaQ53zNhOdVr","pdf",1591854,4,1,26,"English","en",105,"# Introduction\n## The Challenge of Interest Rate-Driven Stagnation","[{\"question\":\"What problem does the paper address in FX markets?\",\"answer\":\"The paper targets the identification and prediction of foreign exchange market regimes, defined as distinct states of price action driven by specific macroeconomic conditions.\"},{\"question\":\"How does the Graph Tsetlin Machine (GraphTM) contribute to the proposed method?\",\"answer\":\"GraphTM represents macroeconomic drivers and technical indicators as hypervectorized directed multigraphs, then uses message passing to build deep, interpretable logical clauses for recognizing complex sub-graph patterns.\"},{\"question\":\"Why is the USD/JPY pair studied in this work?\",\"answer\":\"The study focuses on USD/JPY bid/ask prices because interest rate divergence has a sensitive impact on this pair, and the method is demonstrated on hourly data to adapt quickly to new market information.\"}]","Macroeconomic Message Passing for Anticipating Foreign Exchange Regime Changes | 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problem does the paper address in FX markets?","Question",{"text":76,"@type":77},"The paper targets the identification and prediction of foreign exchange market regimes, defined as distinct states of price action driven by specific macroeconomic conditions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the Graph Tsetlin Machine (GraphTM) contribute to the proposed method?",{"text":81,"@type":77},"GraphTM represents macroeconomic drivers and technical indicators as hypervectorized directed multigraphs, then uses message passing to build deep, interpretable logical clauses for recognizing complex sub-graph patterns.",{"name":83,"@type":74,"acceptedAnswer":84},"Why is the USD/JPY pair studied in this work?",{"text":85,"@type":77},"The study focuses on USD/JPY bid/ask prices because interest rate divergence has a sensitive impact on this pair, and the method is demonstrated on hourly data to adapt quickly to new market 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