Multimodal Sentiment Analysis of Earnings Calls and SEC Filings: A Deep Learning Approach to Financial Disclosures

Abstract

The financial landscape is characterized by an immense vol- ume of information, much of which is unstructured and com- plex. Among the most critical sources of information for investors and analysts are corporate financial disclosures, par- ticularly earnings call transcripts and SEC filings (e.g., 10-K, 10-Q reports). These documents contain a wealth of data, including quantitative figures, qualitative discussions, and forward-looking statements, all of which can significantly influence market perceptions and asset valuations. However, extracting meaningful insights from these diverse and often lengthy documents presents a substantial challenge. Tra- ditional sentiment analysis, often relying solely on textual cues, may overlook the subtle yet impactful signals conveyed through other modalities, such as the tone of voice in earnings calls or the visual presentation of data in filings.
Sentiment analysis, the computational study of opinions, emotions, and subjectivity expressed in text, has become an indispensable tool in financial research. Its application ranges from predicting stock market movements to assessing corporate reputation. While significant progress has been made in text-based sentiment analysis, particularly with the advent of deep learning models, these approaches often treat different data sources in isolation. For instance, an earnings call is not just a transcript; its` also an audio event where vocal inflections, pauses, and emphasis can convey sentiment that text alone cannot capture. Similarly, SEC filings, while primarily textual, often include tables, charts, and specific formatting that can subtly influence interpretation.
This paper proposes a novel deep learning framework for multimodal sentiment analysis, specifically tailored for finan- cial disclosures. Our approach integrates textual data from earnings call transcripts and SEC filings with acoustic fea- tures extracted from earnings call audio. By combining these distinct modalities, we aim to develop a more comprehensive and accurate understanding of the sentiment embedded within corporate communications. The motivation behind this mul- timodal approach stems from the hypothesis that a holistic analysis, leveraging complementary information from differ- ent data streams, will yield superior predictive power and richer insights compared to unimodal methods. Our rigorous evaluation demonstrates that the multimodal model achieves an F1-score of 0.88, significantly outperforming text-only (0.82) and audio-only (0.71) baselines. This is particularly relevant in the high-stakes environment of financial markets, where even marginal improvements in sentiment detection can lead to significant advantages.
We will detail the architecture of our deep learning model, which is designed to effectively process and fuse information from both text and audio modalities. The paper will cover the data collection and preprocessing steps, including the extraction of relevant features from each modality. Further- more, we will present a rigorous evaluation of our models` performance against unimodal baselines, demonstrating the efficacy of our multimodal fusion strategy. Finally, we will discuss the implications of our findings for financial analysis, highlight the limitations of the current approach, and outline avenues for future research in this rapidly evolving field.
Multimodal Sentiment Analysis, Deep Learning, Financial Disclosures, Earnings Calls, SEC Filings, Natural Language Processing, Acoustic Analysis, Financial Technology