Temporal Evolution of Sentiment in Earnings Calls and Its Relationship with Financial Performance
Study analyzes temporal sentiment evolution in earnings calls, correlating trends with financial performance using deep learning methods and multimodal approaches.
What it examines
This study explores how earnings call sentiment changes over time and associates with financial outcomes. Using a multimodal framework combining lexicon-based and deep learning (WMSA-Bi-LSTM) methods on 18,240 transcripts, it uncovers industry-specific sentiment trends and their predictive power on operational performance.
What it concludes
The findings show that evolving sentiment patterns in earnings calls can predict operational results, offering practical use for investors and companies. Despite limitations like data sparsity and model challenges, future research should add more communication channels and refine causal methods for better long-term forecasting.
Evidence objects
Researchers unveil a multimodal framework combining lexicon-based methods with deep learning WMSA-Bi-LSTM, efficiently extracting nuanced sentiment features from earnings calls and accurately forecasting financial performance over twenty quarters across time.
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Analysts uncover that sentiment evolution drastically differs by sector, with technology and communications exhibiting volatile shifts unlike the stable trends evident in utilities and consumer staples, redefining industry communication patterns.
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Introducing sentiment momentum, the study correlates sentiment trajectories with financial metrics, achieving $$79.3%$$ accuracy in quarterly forecasts while recognizing challenges capturing nuanced language and addressing temporal sparsity biases for improvement.
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The paper innovates by introducing a longitudinal perspective to earnings call sentiment analysis. Combining lexicon-based techniques with advanced deep learning methods, including the WMSA-Bi-LSTM architecture and multi-head self-attention, its fresh approach opens new insights. Though building upon established methods, its nuanced integration offers compelling potential for improving financial sentiment analysis.
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Raw abstract and provenance
- This study investigates the temporal evolution patterns of sentiment in earnings call transcripts and their relationship with subsequent financial performance. While existing …
Source row: 1905 · abstract type: snippet