An Exploratory Study of Stock Price Movements from Earnings Calls
Study explores predicting stock price movements using earnings call transcripts, outperforming traditional data like EPS and sales.
What it examines
This study explores the relationship between earnings calls, company sales, stock performance, and analysts' recommendations using a decade of data from 6,300 public companies. It aims to predict stock price movements by analyzing the semantic features of earnings call transcripts using graph neural networks.
What it concludes
The research suggests that semantic analysis of earnings calls can improve stock price movement predictions, offering potential applications in investment strategies. Future research could further refine these methods and explore additional data sources to enhance predictive accuracy.
Evidence objects
The research suggests that semantic analysis of earnings calls can improve stock price movement predictions, offering potential applications in investment strategies. Future research could further refine these methods and explore additional data sources to enhance predictive accuracy.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Abstract: Financial market analysis has focused primarily on extracting signals from accounting, stock price, and other numerical hard data reported in P&L statements or earnings per share reports. Yet, it is well-known that the decision-makers routinely use soft text-based documents that interpret the hard data they narrate. Recent advances in computational methods for analyzing unstructured and soft text-… ▽ More Financial market analysis has focused primarily on extracting signals from accounting, stock price, and other numerical hard data reported in P&L statements or earnings per share reports. Yet, it is well-known that the decision-makers routinely use soft text-based documents that interpret the hard data they narrate. Recent advances in computational methods for analyzing unstructured and soft text-based data at scale offer possibilities for understanding financial market behavior that could improve investments and market equity. A critical and ubiquitous form of soft data are earnings calls. Earnings calls are periodic (often quarterly) statements usually by CEOs who attempt to influence investors' expectations of a company's past and future performance. Here, we study the statistical relationship between earnings calls, company sales, stock performance, and analysts' recommendations. Our study covers a decade of observations with approximately 100,000 transcripts of earnings calls from 6,300 public companies from January 2010 to December 2019. In this study, we report three novel findings. First, the buy, sell and hold recommendations from professional analysts made prior to the earnings have low correlation with stock price movements after the earnings call. Second, using our graph neural network based method that processes the semantic features of earnings calls, we reliably and accurately predict stock price movements in five major areas of the economy. Third, the semantic features of transcripts are more predictive of stock price movements than sales and earnings per share, i.e., traditional hard data in most of the cases. △ Less
Source row: 186 · abstract type: unknown