# An Exploratory Study of Stock Price Movements from Earnings Calls

arXiv: https://arxiv.org/abs/2203.12460 · HTML: https://mlsanity.com/paper/2203.12460/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2022.

## Built on by (most cited first, 14 of 14)

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- [Incorporating Heterophily into Graph Neural Networks for Graph Classification](https://mlsanity.com/paper/2203.07678/) (2022)
- [DeFine: Decision-Making with Analogical Reasoning over Factor Profiles](https://mlsanity.com/paper/2410.01772/) (2024)
- [CSPO: Cross-Market Synergistic Stock Price Movement Forecasting with Pseudo-volatility Optimization](https://mlsanity.com/paper/2503.22740/) (2025)
- [Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey](https://mlsanity.com/paper/2308.04947/) (2023)
- [Trading through Earnings Seasons using Self-Supervised Contrastive Representation Learning](https://mlsanity.com/paper/2409.17392/) (2024)
- [FLAG: Financial Long Document Classification via AMR-based GNN](https://mlsanity.com/paper/2410.02024/) (2024)
- [MiMIC: Multi-Modal Indian Earnings Calls Dataset to Predict Stock Prices](https://mlsanity.com/paper/2504.09257/) (2025)

## Abstract (arXiv)

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.