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.

中文翻译：

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走向收益电话和股票价格变动

收益电话会议由上市公司的管理层主持，与分析师和投资者讨论公司的财务业绩。在收益电话会议期间披露的信息是分析师和投资者做出投资决策的重要数据来源。因此，我们利用收益电话会议记录来预测未来的股价动态。我们建议使用深度学习框架对成绩单中的语言进行建模，其中应用注意力机制将文本数据编码为向量，供判别网络分类器预测股价走势。我们的实证实验表明，所提出的模型优于传统的机器学习基线，并且收益电话信息可以提高股价预测性能。