Analysis of the Fed's communication by using textual entailment model of
Zero-Shot classification
- URL: http://arxiv.org/abs/2306.04277v1
- Date: Wed, 7 Jun 2023 09:23:26 GMT
- Title: Analysis of the Fed's communication by using textual entailment model of
Zero-Shot classification
- Authors: Yasuhiro Nakayama, Tomochika Sawaki
- Abstract summary: We analyze documents published by central banks using text mining techniques.
We compare the tone of the statements, minutes, press conference transcripts of meetings, and the Fed officials' speeches.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this study, we analyze documents published by central banks using text
mining techniques and propose a method to evaluate the policy tone of central
banks. Since the monetary policies of major central banks have a broad impact
on financial market trends, the pricing of risky assets, and the real economy,
market participants are attempting to more accurately capture changes in the
outlook for central banks' future monetary policies. Since the published
documents are also an important tool for the central bank to communicate with
the market, they are meticulously elaborated on grammatical syntax and wording,
and investors are urged to read more accurately about the central bank's policy
stance. Sentiment analysis on central bank documents has long been carried out,
but it has been difficult to interpret the meaning of the documents accurately
and to explicitly capture even the intentional change in nuance. This study
attempts to evaluate the implication of the zero-shot text classification
method for an unknown economic environment using the same model. We compare the
tone of the statements, minutes, press conference transcripts of FOMC meetings,
and the Fed officials' (chair, vice chair, and Governors) speeches. In
addition, the minutes of the FOMC meetings were subjected to a phase analysis
of changes in each policy stance since 1971.
Related papers
- Detection of Temporality at Discourse Level on Financial News by Combining Natural Language Processing and Machine Learning [8.504685056067144]
Finance-related news such as Bloomberg News, CNN Business and Forbes are valuable sources of real data for market screening systems.
We propose a novel system to detect the temporality of finance-related news at discourse level.
We have tested our system on a labelled dataset of finance-related news annotated by researchers with knowledge in the field.
arXiv Detail & Related papers (2024-03-30T16:40:10Z) - FMPAF: How Do Fed Chairs Affect the Financial Market? A Fine-grained
Monetary Policy Analysis Framework on Their Language [3.760301720305374]
We propose the Fine-Grained Monetary Policy Analysis Framework (FMPAF), a novel approach that integrates large language models (LLMs) with regression analysis.
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.
arXiv Detail & Related papers (2024-03-10T07:21:31Z) - Off-Policy Evaluation for Large Action Spaces via Policy Convolution [60.6953713877886]
Policy Convolution family of estimators uses latent structure within actions to strategically convolve the logging and target policies.
Experiments on synthetic and benchmark datasets demonstrate remarkable mean squared error (MSE) improvements when using PC.
arXiv Detail & Related papers (2023-10-24T01:00:01Z) - Trillion Dollar Words: A New Financial Dataset, Task & Market Analysis [1.933681537640272]
We construct the largest tokenized and annotated dataset of Federal Open Market Committee (FOMC) speeches, meeting minutes, and press conference transcripts.
Using the best-performing model (RoBERTa-large), we construct a measure of monetary policy stance for the document release days.
Our dataset, models, and code are publicly available on Huggingface and GitHub under CC BY-NC 4.0 license.
arXiv Detail & Related papers (2023-05-13T17:32:39Z) - Investigating Fairness Disparities in Peer Review: A Language Model
Enhanced Approach [77.61131357420201]
We conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs)
We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date.
We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige.
arXiv Detail & Related papers (2022-11-07T16:19:42Z) - Entity Graph Extraction from Legal Acts -- a Prototype for a Use Case in
Policy Design Analysis [52.77024349608834]
This paper presents a prototype developed to serve the quantitative study of public policy design.
Our system aims to automate the process of gathering legal documents, annotating them with Institutional Grammar, and using hypergraphs to analyse inter-relations between crucial entities.
arXiv Detail & Related papers (2022-09-02T10:57:47Z) - Weak Supervision in Analysis of News: Application to Economic Policy
Uncertainty [0.0]
Our work focuses on studying the potential of textual data, in particular news pieces, for measuring economic policy uncertainty (EPU)
Economic policy uncertainty is defined as the public's inability to predict the outcomes of their decisions under new policies and future economic fundamentals.
Our work proposes a machine learning based solution involving weak supervision to classify news articles with regards to economic policy uncertainty.
arXiv Detail & Related papers (2022-08-10T09:08:29Z) - Aspect-based Sentiment Analysis in Document -- FOMC Meeting Minutes on
Economic Projection [0.0]
Aspect-based Sentiment Analysis is not widely used on financial data due to the lack of large labeled dataset.
I propose a model to train ABSA on financial documents under weak supervision and analyze its predictive power on various macroeconomic indicators.
arXiv Detail & Related papers (2021-08-09T14:29:58Z) - Similarity metrics for Different Market Scenarios in Abides [58.720142291102135]
Markov Decision Processes (MDPs) are an effective way to formally describe many Machine Learning problems.
This paper analyzes the use of three similarity metrics based on conceptual, structural and performance aspects of the financial MDPs.
arXiv Detail & Related papers (2021-07-20T09:18:06Z) - Mundus vult decipi, ergo decipiatur: Visual Communication of Uncertainty
in Election Polls [56.8172499765118]
We discuss potential sources of bias in nowcasting and forecasting.
Concepts are presented to attenuate the issue of falsely perceived accuracy.
One key idea is the use of Probabilities of Events instead of party shares.
arXiv Detail & Related papers (2021-04-28T07:02:24Z) - Super-App Behavioral Patterns in Credit Risk Models: Financial,
Statistical and Regulatory Implications [110.54266632357673]
We present the impact of alternative data that originates from an app-based marketplace, in contrast to traditional bureau data, upon credit scoring models.
Our results, validated across two countries, show that these new sources of data are particularly useful for predicting financial behavior in low-wealth and young individuals.
arXiv Detail & Related papers (2020-05-09T01:32:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.