News-based Business Sentiment and its Properties as an Economic Index

• URL: http://arxiv.org/abs/2110.10340v1
• Date: Wed, 20 Oct 2021 02:20:53 GMT
• Title: News-based Business Sentiment and its Properties as an Economic Index
• Authors: Kazuhiro Seki, Yusuke Ikuta, and Yoichi Matsubayashi
• Abstract summary: 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.
• Score: 1.0323063834827415
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper presents an approach to measuring business sentiment based on textual data. Business sentiment has been measured by traditional surveys, which are costly and time-consuming to conduct. To address the issues, we take advantage of daily newspaper articles and adopt a self-attention-based model to define a business sentiment index, named S-APIR, where outlier detection models are investigated to properly handle various genres of news articles. Moreover, we propose a simple approach to temporally analyzing how much any given event contributed to the predicted business sentiment index. To demonstrate the validity of the proposed approach, an extensive analysis is carried out on 12 years' worth of newspaper articles. The analysis shows that the S-APIR index is strongly and positively correlated with established survey-based index (up to correlation coefficient r=0.937) and that the outlier detection is effective especially for a general newspaper. Also, S-APIR is compared with a variety of economic indices, revealing the properties of S-APIR that it reflects the trend of the macroeconomy as well as the economic outlook and sentiment of economic agents. Moreover, to illustrate how S-APIR could benefit economists and policymakers, several events are analyzed with respect to their impacts on business sentiment over time.

Related papers

• Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
  "Context is Key" (CiK) is a time series forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context. We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters. Our experiments highlight the importance of incorporating contextual information, demonstrate surprising performance when using LLM-based forecasting models, and also reveal some of their critical shortcomings.
  arXiv  Detail & Related papers  (2024-10-24T17:56:08Z)
• Predicting Financial Market Trends using Time Series Analysis and Natural Language Processing [0.0]
  This study was to assess the viability of Twitter sentiments as a tool for predicting stock prices of major corporations such as Tesla, Apple. Our findings indicate that positivity, negativity, and subjectivity are the primary determinants of fluctuations in stock prices.
  arXiv  Detail & Related papers  (2023-08-31T21:20:58Z)
• Sentiment Analysis in the Era of Large Language Models: A Reality Check [69.97942065617664]
  This paper investigates the capabilities of large language models (LLMs) in performing various sentiment analysis tasks. We evaluate performance across 13 tasks on 26 datasets and compare the results against small language models (SLMs) trained on domain-specific datasets.
  arXiv  Detail & Related papers  (2023-05-24T10:45:25Z)
• Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model [50.06663781566795]
  We consider a dynamic model with the consumers' preferences as well as price sensitivity varying over time. We measure the performance of a dynamic pricing policy via regret, which is the expected revenue loss compared to a clairvoyant that knows the sequence of model parameters in advance. Our regret analysis results not only demonstrate optimality of the proposed policy but also show that for policy planning it is essential to incorporate available structural information.
  arXiv  Detail & Related papers  (2023-03-28T00:23:23Z)
• Causal Intervention Improves Implicit Sentiment Analysis [67.43379729099121]
  We propose a causal intervention model for Implicit Sentiment Analysis using Instrumental Variable (ISAIV) We first review sentiment analysis from a causal perspective and analyze the confounders existing in this task. Then, we introduce an instrumental variable to eliminate the confounding causal effects, thus extracting the pure causal effect between sentence and sentiment.
  arXiv  Detail & Related papers  (2022-08-19T13:17:57Z)
• 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)
• Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities [12.101446195463591]
  We describe a case-based reasoning approach to predicting stock market returns using only historical pricing data. A key contribution of this work is the development of a novel similarity metric for comparing historical pricing data.
  arXiv  Detail & Related papers  (2021-07-07T17:26:32Z)
• REST: Relational Event-driven Stock Trend Forecasting [76.08435590771357]
  We propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods. To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts. To address the second shortcoming, we construct a stock graph and design a new propagation layer to propagate the effect of event information from related stocks.
  arXiv  Detail & Related papers  (2021-02-15T07:22:09Z)
• A Sentiment Analysis Approach to the Prediction of Market Volatility [62.997667081978825]
  We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. The sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information.
  arXiv  Detail & Related papers  (2020-12-10T01:15:48Z)
• S-APIR: News-based Business Sentiment Index [0.9137554315375919]
  We adopt a recurrent neural network (RNN) with Gated Recurrent Units to predict the business sentiment of a given text. An RNN is initially trained on Economy Watchers Survey and then fine-tuned on news texts for domain adaptation. A one-class support vector machine is applied to filter out texts deemed irrelevant to business sentiment.
  arXiv  Detail & Related papers  (2020-03-06T00:18:50Z)

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.