With Donald Trump's actions in Greenland, Minneapolis, and Venezuela, a foreign enemy could not invent a better chain of events to wreck the standing of the United States. Imagine you were Vladimir Putin or Xi Jinping and you woke up a year ago having magically been given command of puppet strings that control the White House. Your explicit geopolitical goal is to undermine trust in the United States on the world stage. You want to destroy the Western rules-based order that has preserved peace and security for 80 years, which allowed the US to triumph as an economic superpower and beacon of hope and innovation for the world. What exactly would you do differently with your marionette other than enact the ever more reckless agenda that Donald Trump has pursued since he became president last year?

The U.S. Federal Open Market Committee (FOMC) regularly discusses and sets monetary policy, affecting the borrowing and spending decisions of millions of people. In this work, we release Op-Fed, a dataset of 1044 human-annotated sentences and their contexts from FOMC transcripts. We faced two major technical challenges in dataset creation: imbalanced classes -- we estimate fewer than 8% of sentences express a non-neutral stance towards monetary policy -- and inter-sentence dependence -- 65% of instances require context beyond the sentence-level. To address these challenges, we developed a five-stage hierarchical schema to isolate aspects of opinion, monetary policy, and stance towards monetary policy as well as the level of context needed. Second, we selected instances to annotate using active learning, roughly doubling the number of positive instances across all schema aspects. Using Op-Fed, we found a top-performing, closed-weight LLM achieves 0.80 zero-shot accuracy in opinion classification but only 0.61 zero-shot accuracy classifying stance towards monetary policy -- below our human baseline of 0.89. We expect Op-Fed to be useful for future model training, confidence calibration, and as a seed dataset for future annotation efforts.

Forecasting central bank policy decisions remains a persistent challenge for investors, financial institutions, and policymakers due to the wide-reaching impact of monetary actions. In particular, anticipating shifts in the U.S. federal funds rate is vital for risk management and trading strategies. Traditional methods relying only on structured macroeconomic indicators often fall short in capturing the forward-looking cues embedded in central bank communications. This study examines whether predictive accuracy can be enhanced by integrating structured data with unstructured textual signals from Federal Reserve communications. We adopt a multi-modal framework, comparing traditional machine learning models, transformer-based language models, and deep learning architectures in both unimodal and hybrid settings. Our results show that hybrid models consistently outperform unimodal baselines. The best performance is achieved by combining TF-IDF features of FOMC texts with economic indicators in an XGBoost classifier, reaching a test AUC of 0.83. FinBERT-based sentiment features marginally improve ranking but perform worse in classification, especially under class imbalance. SHAP analysis reveals that sparse, interpretable features align more closely with policy-relevant signals. These findings underscore the importance of integrating textual and structured signals transparently. For monetary policy forecasting, simpler hybrid models can offer both accuracy and interpretability, delivering actionable insights for researchers and decision-makers.

Artificial intelligence (AI) has become a powerful tool for economic research, enabling large-scale simulation and policy optimization. However, applying AI effectively requires simulation platforms for scalable training and evaluation-yet existing environments remain limited to simplified, narrowly scoped tasks, falling short of capturing complex economic challenges such as demographic shifts, multi-government coordination, and large-scale agent interactions. To address this gap, we introduce EconGym, a scalable and modular testbed that connects diverse economic tasks with AI algorithms. Grounded in rigorous economic modeling, EconGym implements 11 heterogeneous role types (e.g., households, firms, banks, governments), their interaction mechanisms, and agent models with well-defined observations, actions, and rewards. Users can flexibly compose economic roles with diverse agent algorithms to simulate rich multi-agent trajectories across 25+ economic tasks for AI-driven policy learning and analysis. Experiments show that EconGym supports diverse and cross-domain tasks-such as coordinating fiscal, pension, and monetary policies-and enables benchmarking across AI, economic methods, and hybrids. Results indicate that richer task composition and algorithm diversity expand the policy space, while AI agents guided by classical economic methods perform best in complex settings. EconGym also scales to 10k agents with high realism and efficiency.

In this study, we propose a methodology to extract, index, and visualize ``climate change narratives'' (stories about the connection between causal and consequential events related to climate change). We use two natural language processing methods, BERT (Bidirectional Encoder Representations from Transformers) and causal extraction, to textually analyze newspaper articles on climate change to extract ``climate change narratives.'' The novelty of the methodology could extract and quantify the causal relationships assumed by the newspaper's writers. Looking at the extracted climate change narratives over time, we find that since 2018, an increasing number of narratives suggest the impact of the development of climate change policy discussion and the implementation of climate change-related policies on corporate behaviors, macroeconomics, and price dynamics. We also observed the recent emergence of narratives focusing on the linkages between climate change-related policies and monetary policy. Furthermore, there is a growing awareness of the negative impacts of natural disasters (e.g., abnormal weather and severe floods) related to climate change on economic activities, and this issue might be perceived as a new challenge for companies and governments. The methodology of this study is expected to be applied to a wide range of fields, as it can analyze causal relationships among various economic topics, including analysis of inflation expectation or monetary policy communication strategy.

Markets and policymakers around the world hang on the consequential monetary policy decisions made by the Federal Open Market Committee (FOMC). Publicly available textual documentation of their meetings provides insight into members' attitudes about the economy. We use GPT-4 to quantify dissent among members on the topic of inflation. We find that transcripts and minutes reflect the diversity of member views about the macroeconomic outlook in a way that is lost or omitted from the public statements. In fact, diverging opinions that shed light upon the committee's "true" attitudes are almost entirely omitted from the final statements. Hence, we argue that forecasting FOMC sentiment based solely on statements will not sufficiently reflect dissent among the hawks and doves.

The effectiveness of central bank communication is a crucial aspect of monetary policy transmission. While recent research has examined the influence of policy communication by the chairs of the Federal Reserve on various financial variables, much of the literature relies on rule-based or dictionary-based methods in parsing the language of the chairs, leaving nuanced information about policy stance contained in nonverbal emotion out of the analysis. In the current study, we propose the Fine-Grained Monetary Policy Analysis Framework (FMPAF), a novel approach that integrates large language models (LLMs) with regression analysis to provide a comprehensive analysis of the impact of the press-conference communications of chairs of the Federal Reserve on financial markets. We conduct extensive comparisons of model performance under different levels of granularity, modalities, and communication scenarios. Based on our preferred specification, a one-unit increase in the sentiment score is associated with an increase of the price of S\&P 500 Exchange-Traded Fund by approximately 500 basis points, a 15-basis-point decrease in the policy interest rate, while not leading to a significant response in exchange rates.

Business and consumer surveys are an essential tool used by policy-makers and practitioners to monitor and forecast the economy. Their most valuable feature is to provide timely information about the current and expected state of economic activity that is relevant to integrate the sluggish release of macroeconomic indicators. Interestingly, surveys are often interpreted as measures of economic sentiment in the sense of providing the pulse of different aspects of the economy, such as the consumers' attitude toward spending or the expectation of purchasing managers about inflation. Some prominent examples are represented by the Survey of Consumers of the University of Michigan (MCS) for the United States (Curtin and Dechaux, 2015) and the Business and Consumer Survey (BCS) for the European Union (European Commission, 2016). Although surveys are very valuable and accurate proxies of economic activity, they are typically released at the monthly frequency which might limit their usefulness in high-frequency nowcasting of economic variables (Aguilar et al., 2021; Algaba et al., 2023).

The statement is a relatively short have a broad and significant impact on financial market document of about two pages that summarizes current trends, pricing of risky assets, and spillover to the real economic perceptions, the monetary policy determined, economy, market participants are trying to better and the names of the voters. The transcripts of the press understand the changes in the future monetary policy conference consist of a transcript to be read by the outlook of central banks. In particular, the monetary policy chairperson at the beginning of the conference, as well as of the Central Bank of the United States (Federal Reserve questions and answers with reporters, and are System, hereinafter Fed) is positioned as the most approximately 20 ~ 30 pages in volume. In some cases, important because it influences the movement of the dollar, information that is not included in the statement but is of the key currency. One of the means by which central banks interest to market participants (specific information and engage in dialogue with the market and conduct smooth future prospects) is recorded. The minutes are a document policy management is the publication of various that confirms the content of the economic analysis documents, including statements and minutes issued after reported by the Fed economists, the process of discussion policy meetings, and transcripts of speeches and that led to the decision of the policy, and the variation of congressional testimony attended by senior officials. The opinion among the members, and the volume is around Federal Open Market Committee (FOMC), a meeting at 10~20 pages. Outside of the FOMC meetings, transcripts which U.S. monetary policy is formulated, meets eight of speeches and interviews by FOMC participants (Fed times a year with members of the Federal Reserve Board officials) and statements in congressional testimony will (FRB) and the presidents of the regional Fed banks as be released at each meeting.

It is an issue of great importance for policy and investment decision makers (Schwert, 1989; Fama, 1990; Corradi et al., 2013; Chauvet et al., 2013). Empirical studies have been used to examine whether stock market volatility, which behaves differently in expansion and recession periods, can be predicted by macroeconomic variables (Schwert, 1989; Hamilton and Lin, 1996). Research has also established a link between stock market volatility and macroeconomic fundamentals (Engle and Rangel, 2008; Diebold and Yilmaz, 2008; Corradi et al., 2013; Choudhry et al., 2016). However, despite recent successes in developing machine learning (ML) models for predicting financial prices of different assets (see e.g., Gu et al. (2020); Heaton et al. (2017); Gu et al. (2021); Bianchi et al. (2021)), there is little literature discussing the impact of business cycles and market volatility on stock price forecasting with ML models. This paper fills this gap and explores the data-shifting effects of market volatility resulted from recessions on ML models. Specifically, we focus on answering the following three research questions in this work: 1. Do ML models perform differently during the recession compared to non-recession? 2. Does including recession data in the in-sample (training) period improve ML performance?