Aspect-based Sentiment Analysis in Document -- FOMC Meeting Minutes on Economic Projection

• URL: http://arxiv.org/abs/2108.04080v2
• Date: Mon, 24 Apr 2023 12:35:39 GMT
• Title: Aspect-based Sentiment Analysis in Document -- FOMC Meeting Minutes on Economic Projection
• Authors: Sarah-Yifei-Wang
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Federal Open Market Committee within the Federal Reserve System is responsible for managing inflation, maximizing employment, and stabilizing interest rates. Meeting minutes play an important role for market movements because they provide the birds eye view of how this economic complexity is constantly re-weighed. Therefore, There has been growing interest in analyzing and extracting sentiments on various aspects from large financial texts for economic projection. However, Aspect-based Sentiment Analysis is not widely used on financial data due to the lack of large labeled dataset. In this paper, I propose a model to train ABSA on financial documents under weak supervision and analyze its predictive power on various macroeconomic indicators.

Related papers

• Cross-Lingual News Event Correlation for Stock Market Trend Prediction [0.1398098625978622]
  This study addresses the gap in comprehending financial dynamics across diverse global economies by creating a structured financial dataset. We conducted an analytical examination of news articles to extract, map, and visualize financial event timelines. Our method demonstrated a meaningful correlation between stock price movements and cross-linguistic news sentiments.
  arXiv  Detail & Related papers  (2024-09-16T06:45:40Z)
• AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework [48.3060010653088]
  We release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data. We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task.
  arXiv  Detail & Related papers  (2024-03-19T09:45:33Z)
• Transforming Sentiment Analysis in the Financial Domain with ChatGPT [0.07499722271664146]
  This study investigates the potential of large language models, particularly ChatGPT 3.5, in financial sentiment analysis. ChatGPT exhibited approximately 35% enhanced performance in sentiment classification and a 36% higher correlation with market returns. By underlining the significance of prompt engineering, particularly in zero-shot contexts, this study spotlights ChatGPT's potential to substantially boost sentiment analysis in financial applications.
  arXiv  Detail & Related papers  (2023-08-13T09:20:47Z)
• Analysis of the Fed's communication by using textual entailment model of Zero-Shot classification [0.0]
  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.
  arXiv  Detail & Related papers  (2023-06-07T09:23:26Z)
• Enabling and Analyzing How to Efficiently Extract Information from Hybrid Long Documents with LLMs [48.87627426640621]
  This research focuses on harnessing the potential of Large Language Models to comprehend critical information from financial reports. We propose an Automated Financial Information Extraction framework that enhances LLMs' ability to comprehend and extract information from financial reports. Our framework is effectively validated on GPT-3.5 and GPT-4, yielding average accuracy increases of 53.94% and 33.77%, respectively.
  arXiv  Detail & Related papers  (2023-05-24T10:35:58Z)
• 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)
• Causal Fairness Analysis [68.12191782657437]
  We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings. The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms. Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
  arXiv  Detail & Related papers  (2022-07-23T01:06:34Z)
• News-based Business Sentiment and its Properties as an Economic Index [1.0323063834827415]
  Business sentiment has been measured by traditional surveys, which are costly and time-consuming to conduct. We take advantage of daily newspaper articles and adopt a self-attention-based model to define a business sentiment index, named S-APIR. To illustrate how S-APIR could benefit economists and policymakers, several events are analyzed with respect to their impacts on business sentiment over time.
  arXiv  Detail & Related papers  (2021-10-20T02:20:53Z)
• FinQA: A Dataset of Numerical Reasoning over Financial Data [52.7249610894623]
  We focus on answering deep questions over financial data, aiming to automate the analysis of a large corpus of financial documents. We propose a new large-scale dataset, FinQA, with Question-Answering pairs over Financial reports, written by financial experts. The results demonstrate that popular, large, pre-trained models fall far short of expert humans in acquiring finance knowledge.
  arXiv  Detail & Related papers  (2021-09-01T00:08:14Z)
• Gaussian process imputation of multiple financial series [71.08576457371433]
  Multiple time series such as financial indicators, stock prices and exchange rates are strongly coupled due to their dependence on the latent state of the market. We focus on learning the relationships among financial time series by modelling them through a multi-output Gaussian process.
  arXiv  Detail & Related papers  (2020-02-11T19:18:18Z)
• Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States [71.54651874063865]
  Portfolio management (PM) aims to achieve investment goals such as maximal profits or minimal risks. In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data Heterogeneous data -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
  arXiv  Detail & Related papers  (2020-02-09T08:10:03Z)

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.