In this paper, we investigate the influence of claims in analyst reports and
earnings calls on financial market returns, considering them as significant
quarterly events for publicly traded companies. To facilitate a comprehensive
analysis, we construct a new financial dataset for the claim detection task in
the financial domain. We benchmark various language models on this dataset and
propose a novel weak-supervision model that incorporates the knowledge of
subject matter experts (SMEs) in the aggregation function, outperforming
existing approaches. Furthermore, we demonstrate the practical utility of our
proposed model by constructing a novel measure ``optimism". Furthermore, we
observed the dependence of earnings surprise and return on our optimism
measure. Our dataset, models, and code will be made publicly (under CC BY 4.0
license) available on GitHub and Hugging Face.

这篇论文研究了分析师报告和盈利电话对金融市场回报的影响，考虑到它们作为上市公司的重要季度事件。通过构建一个新的金融数据集，我们对财务领域的索赔检测任务进行了全面的分析，并在该数据集上进行了各种语言模型的基准测试，提出了一种新颖的弱监督模型，将专业主题专家的知识纳入到聚合函数中，优于现有方法。此外，我们通过构建一个新的 `` 乐观度 '' 指标展示了我们提出模型的实际实用性，并观察到盈利意外和回报与我们的乐观度指标之间的关联。我们的数据集、模型和代码将在 GitHub 和 Hugging Face 上以 CC BY 4.0 许可证公开提供。

金融中的数字索赔检测：一个新的金融数据集、弱监督模型和市场分析

Numerical Claim Detection in Finance: A New Financial Dataset,  Weak-Supervision Model, and Market Analysis

Earnings calls are hosted by management of public companies to discuss the
company's financial performance with analysts and investors. Information
disclosed during an earnings call is an essential source of data for analysts
and investors to make investment decisions. Thus, we leverage earnings call
transcripts to predict future stock price dynamics. We propose to model the
language in transcripts using a deep learning framework, where an attention
mechanism is applied to encode the text data into vectors for the
discriminative network classifier to predict stock price movements. Our
empirical experiments show that the proposed model is superior to the
traditional machine learning baselines and earnings call information can boost
the stock price prediction performance.

本文提出使用深度学习框架，通过对收益电话记录进行建模，应用注意力机制将文本数据编码为向量以预测股票价格的变化，实证实验表明该模型优于传统机器学习基准，并且收益电话信息可以提升股票价格预测性能。

关于盈利电话会议和股价走势的研究

Towards Earnings Call and Stock Price Movement

Every fiscal quarter, companies hold earnings calls in which company
executives respond to questions from analysts. After these calls, analysts
often change their price target recommendations, which are used in equity
research reports to help investors make decisions. In this paper, we examine
analysts' decision making behavior as it pertains to the language content of
earnings calls. We identify a set of 20 pragmatic features of analysts'
questions which we correlate with analysts' pre-call investor recommendations.
We also analyze the degree to which semantic and pragmatic features from an
earnings call complement market data in predicting analysts' post-call changes
in price targets. Our results show that earnings calls are moderately
predictive of analysts' decisions even though these decisions are influenced by
a number of other factors including private communication with company
executives and market conditions. A breakdown of model errors indicates
disparate performance on calls from different market sectors.

分析与证实了分析师在收益电话中的决策行为，以及将语言内容与前后市场数据相关联的能力，以帮助投资者做出决策。