PREDICTING EARNINGS SURPRISES THROUGH NATURAL LANGUAGE PROCESSING OF MANAGEMENT COMMUNICATIONS

Volume-09 Issue 10, October-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [428] PREDICTING EARNINGS SURPRISES THROUGH NATURAL LANGUAGE PROCESSING OF MANAGEMENT COMMUNICATIONS Amira Hassan* and Liam O’Connor Department of Computer Science, University of Warwick, United Kingdom * Correspondence: [email protected] ABSTRACT Earnings surprises, defined as deviations between actual reported earnings and analyst consensus forecasts, represent critical information events that significantly impact stock prices and investment decisions. This research investigates the application of Natural Language Processing (NLP) techniques to extract predictive signals from earnings call transcripts and corporate disclosures. We employ FinBERT, a domain-specific transformer-based language model undergoing specialized three-stage training, alongside traditional sentiment analysis approaches. Through comprehensive empirical analysis of over 5,000 earnings events, we demonstrate that NLP-derived features provide significant incremental predictive power beyond traditional financial variables. Results indicate that FinBERT achieves 88% classification accuracy, substantially outperforming traditional machine learning approaches including Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) networks. Furthermore, analysis of news volume patterns around earnings announcements reveals systematic relationships with surprise magnitude, with pronounced effects observed during the announcement period. This research contributes to the growing literature on textual analysis in finance and provides practical insights for investors seeking to incorporate qualitative information into earnings prediction models. Keywords earnings surprises, natural language processing, sentiment analysis, FinBERT, transformer models, earnings calls, financial text mining 1. INTRODUCTION Earnings announcements represent among the most significant information events in capital markets, with actual reported earnings that deviate substantially from analyst expectations generating pronounced market reactions. These deviations, commonly termed earnings surprises, have been extensively documented to trigger abnormal returns, with the magnitude and direction of price responses correlating systematically with the size and sign of the surprise [1]. The ability to anticipate earnings surprises before they materialize holds considerable value for investors, enabling superior portfolio positioning and enhanced risk-adjusted returns through informed trading decisions preceding official earnings releases [2]. In contemporary financial markets where information asymmetry persists despite regulatory efforts toward transparency, the capacity to extract predictive signals from management communications has emerged as a critical competitive advantage for institutional investors and quantitative asset managers. Volume-09 Issue 10, October-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [429] Traditional approaches to predicting earnings surprises have predominantly relied on quantitative methodologies that analyze historical financial statements, analyst forecast revisions, and market-based signals such as price momentum and option market activity. While these quantitative factors demonstrate documented predictive power, they capture only part of the information landscape relevant to earnings outcomes [3]. Corporate managements communicate extensively with stakeholders through various channels including quarterly earnings conference calls, regulatory filings, press releases, and investor presentations. These communications contain rich qualitative information about business operations, market conditions, strategic initiatives, and management expectations that may foreshadow upcoming earnings performance. The challenge lies in systematically processing and analyzing the vast quantities of unstructured textual data generated through these communication channels to extract meaningful predictive signals. The emergence of sophisticated Natural Language Processing technologies has created new opportunities to systematically analyze corporate textual disclosures. Recent advances in transformer-based language models, particularly Bidirectional Encoder Representations from Transformers, have dramatically improved machines' ability to understand nuanced language patterns and extract meaningful signals from financial texts [4]. Unlike traditional dictionary-based sentiment analysis approaches that rely on predefined word lists, transformer models employ attention mechanisms that consider contextual relationships between words, enabling more accurate interpretation of complex financial discourse [5]. The development of domain-adapted variants specifically trained on financial corpora has further enhanced performance on finance-specific tasks, with models like FinBERT demonstrating superior accuracy compared to both general-purpose language models and traditional machine learning approaches [6]. The application of NLP to management communications for earnings prediction is theoretically motivated by several considerations. First, management possesses superior information about company operations and prospects relative to outside analysts, and this information asymmetry may be partially revealed through the language choices and communication patterns exhibited in their disclosures [7]. Second, behavioral finance research suggests that linguistic cues including sentiment, certainty, and complexity can reflect management confidence levels and may correlate with subsequent performance outcomes [8]. Third, the efficient market hypothesis does not require instantaneous incorporation of all available information, suggesting that textual signals from management communications may contain underutilized predictive content that has not been fully reflected in analyst forecasts [9]. These theoretical underpinnings provide strong motivation for investigating whether systematic analysis of management language patterns can enhance earnings surprise prediction. Empirical evidence supporting the value of textual analysis in financial contexts has accumulated substantially in recent years. Studies have documented that sentiment measures derived from earnings call transcripts predict subsequent stock returns, with more positive management tone associated with superior future performance [10]. Research examining the volume and timing of news coverage around earnings announcements finds systematic patterns that correlate with surprise magnitude and market reactions [11]. Analysis of linguistic features including readability, specificity, and use of forward-looking language in corporate filings reveals associations with earnings quality and future performance outcomes [12]. Despite this growing body of evidence, several important questions remain regarding the optimal methodologies for extracting predictive signals from management communications and the incremental value these signals provide beyond traditional quantitative predictors. Volume-09 Issue 10, October-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [430] This paper contributes to the literature by providing comprehensive analysis of multiple NLP approaches for earnings surprise prediction, with particular focus on evaluating the performance of state-of-the-art transformerbased models against traditional methods. We examine whether linguistic features extracted from earnings call transcripts through FinBERT provide significant incremental predictive power beyond conventional financial variables. Our analytical framework employs machine learning classifiers to predict earnings surprise categories using combinations of textual and traditional features, enabling systematic assessment of the marginal contribution of NLP-derived signals. Furthermore, we investigate temporal patterns in news volume and textual features relative to earnings announcements to identify when linguistic signals emerge and how they evolve leading up to earnings releases. The practical implications extend to institutional investors seeking to enhance quantitative forecasting models, corporate managers crafting investor communications, and researchers investigating information processing in capital markets. 2. LITERATURE REVIEW The prediction of earnings surprises has been a central focus of accounting and finance research for decades, with substantial literature examining various methodologies for anticipating deviations between reported earnings and market expectations [13]. Recent years have witnessed growing interest in leveraging textual analysis techniques to extract predictive information from qualitative corporate disclosures, motivated by advances in computational linguistics and the increasing availability of digitized financial texts. The application of NLP to financial text analysis represents a rapidly evolving research domain at the intersection of finance, computer science, and linguistics, with transformer-based language models emerging as the current state-of-the-art approach. The foundational work on transformer architecture revolutionized natural language processing by introducing attention mechanisms that enable models to capture long-range dependencies and rich contextual information without relying on recurrent or convolutional structures [14]. Building on this foundation, Bidirectional Encoder Representations from Transformers established a new standard for deep contextualized language modeling through its bidirectional pre-training approach that processes text considering both preceding and following context simultaneously [15]. The success of BERT across numerous NLP benchmarks sparked development of numerous variants tailored for specific domains and tasks, with financial text analysis emerging as a particularly active application area given the specialized language and vocabulary employed in corporate communications. FinBERT represents a significant advancement in applying transformer models to financial domains through domain-adaptive pre-training on extensive financial corpora. Huang et al. developed FinBERT using a specialized three-stage training process beginning with general domain pre-training on BookCorpus and Wikipedia, followed by continued pre-training on financial texts including corporate filings and analyst reports, and concluding with fine-tuning on labeled financial sentiment data [16]. This approach enables the model to capture domain-specific semantic relationships and contextual nuances critical for accurate financial text analysis. Empirical evaluations demonstrate that FinBERT substantially outperforms the Loughran-McDonald dictionary-based approach and other machine learning algorithms including naive Bayes, support vector machines, random forests, and neural networks in financial sentiment classification tasks [17]. The superior performance stems from FinBERT's ability to utilize contextual information effectively, particularly excelling at identifying sentiment in sentences that simpler algorithms mislabel as neutral. Volume-09 Issue 10, October-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [431] Research examining the application of FinBERT and related transformer models to stock prediction and return forecasting has yielded promising results. Jiang and Zeng integrated FinBERT-based sentiment analysis within a Long Short-Term Memory network framework to forecast stock market movements, demonstrating that incorporating sentiment features significantly enhances predictive accuracy compared to standalone LSTM or traditional ARIMA models [18]. Their findings indicate that the combination of transformer-based sentiment extraction with time-series modeling captures both the qualitative aspects of financial discourse and temporal patterns in price movements. Similarly, studies applying FinBERT to analyze social media content and news headlines for market prediction have shown that financial domain-adapted models outperform both generalpurpose BERT and traditional sentiment analysis approaches [19]. The analysis of earnings conference calls represents a particularly rich application area for NLP techniques given the substantial information content researchers have documented in these communications. Management presentations and question-answer sessions during earnings calls provide insights into business operations, strategic initiatives, and management expectations that may not be fully captured in quantitative financial metrics [20]. Recent research has explored the application of various NLP approaches to earnings call transcripts, ranging from simple dictionary-based sentiment analysis to sophisticated deep learning architectures [21]. Studies examining the comparative performance of different NLP methods find that transformer-based models generally outperform traditional approaches, though the optimal methodology depends on specific task requirements and data characteristics [22]. News volume patterns around earnings announcements have been shown to exhibit systematic relationships with earnings surprises and market reactions. Research documents that news coverage intensifies dramatically on earnings announcement dates, with elevated volumes persisting in the days immediately following releases [23]. The distribution of news volume shows pronounced spikes coinciding with earnings disclosures, suggesting coordinated media response to corporate information events. Analysis of the relationship between news coverage intensity and earnings surprise magnitude reveals that larger surprises generate disproportionately greater media attention, potentially amplifying market reactions through increased investor awareness and information processing [24]. These findings highlight the importance of considering not only textual content but also the volume and timing of communications when assessing information environments surrounding earnings events. The integration of machine learning techniques with NLP for financial prediction represents a methodological frontier attracting increasing research attention. Comparison studies evaluating various pre-trained language models for multi-class text classification in financial domains find that domain-adapted transformers achieve superior performance compared to general-purpose models [25]. Research on privacy-enabled financial text classification has explored techniques such as differential privacy and federated learning to enable NLP model training while preserving data confidentiality [26]. These advances address practical concerns about handling sensitive financial information while maintaining model performance. The development of specialized models for emerging markets and multilingual contexts extends the applicability of transformer-based approaches beyond English-language US markets [27]. The mechanisms through which textual analysis provides predictive power for earnings outcomes warrant theoretical consideration. Information-based explanations suggest that linguistic patterns in management communications reflect genuine private information about firm prospects, with sophisticated NLP techniques effectively decoding signals that managers embed in their disclosures [28]. Behavioral perspectives propose that Volume-09 Issue 10, October-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [432] language patterns may indicate psychological states such as confidence or anxiety that correlate with but do not necessarily cause future outcomes. Research examining market reactions to textual features finds incomplete immediate incorporation of information, suggesting either behavioral frictions or the presence of information that requires time to fully process and verify [29]. Understanding these mechanisms matters for assessing whether textual signals provide enduring predictive value or represent temporary inefficiencies subject to arbitrage. Recent methodological advances have focused on enhancing transformer model performance through architectural innovations and improved training strategies. Research on fine-tuning BERT-based models for text classification tasks has identified optimal hyperparameter configurations and training approaches that maximize accuracy while minimizing overfitting risk. Studies examining the combination of convolutional neural networks with BERT architectures demonstrate that hybrid approaches can capture both local textual patterns and broader contextual relationships, achieving performance improvements over standalone transformer models in certain applications. The development of lighter, more efficient transformer variants addresses computational constraints and enables deployment in resource-limited environments while maintaining competitive accuracy. The application of large language models beyond BERT to financial text analysis represents an emerging research direction. Studies comparing generative models such as GPT variants and ChatGPT to encoder-only transformers like FinBERT for financial sentiment analysis reveal trade-offs between model capabilities and task-specific performance. While large generative models demonstrate impressive general language understanding, domainadapted encoders often achieve superior results on focused classification tasks with smaller computational requirements. Research exploring optimal feature selection and input representation for financial prediction models highlights the importance of combining sentiment features with traditional quantitative variables to maximize predictive power. 3. METHODOLOGY 3.1 Data Collection and Preprocessing Our research analyzes a comprehensive dataset spanning multiple years of publicly traded companies' earnings events and associated management communications. The sample construction begins with identification of earnings announcement dates and consensus analyst forecasts from financial databases, ensuring representative coverage across market capitalizations and industry sectors. For each earnings event, we collect the earnings call transcript that occurs either immediately following or shortly before the earnings release, recognizing that timing conventions vary across companies but these calls typically provide the most substantive management commentary surrounding earnings. The dataset encompasses over 5,000 distinct earnings events from companies in the Standard and Poor's 500 index and Russell 3000 universe, providing sufficient statistical power for robust empirical analysis while maintaining focus on actively traded securities with adequate analyst coverage. The earnings call transcripts are obtained from professional financial data providers that aggregate and structure these communications in machine-readable format. Each transcript undergoes preprocessing to separate the management discussion section, which contains prepared remarks from executives, from the question-and-answer segment where analysts pose inquiries and management responds. This segmentation enables separate analysis of prepared versus spontaneous communication, recognizing that these portions may contain distinct information given differences in planning opportunity and interactive dynamics. Text preprocessing follows standard natural language processing protocols including tokenization to break documents into constituent words and phrases, Volume-09 Issue 10, October-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [433] removal of stop words that contribute minimal semantic content, handling of numerical values and special characters, and normalization procedures to standardize text representation. Our dependent variable, earnings surprise, is calculated as the difference between actual reported earnings per share and the mean analyst consensus forecast prevailing immediately prior to the announcement, scaled by the stock price to normalize across firms with different valuation levels. We classify surprises into discrete categories including large negative surprises exceeding one standard deviation below expectations, small negative surprises, meets expectations defined as deviations within one standard deviation, small positive surprises, and large positive surprises exceeding one standard deviation above expectations. This categorical approach facilitates machine learning classification while preserving economically meaningful distinctions across surprise outcomes, enabling targeted prediction of extreme events that generate most pronounced market reactions. 3.2 FinBERT Architecture and Training Process The central methodological innovation underlying our textual analysis approach involves employing FinBERT, a domain-adapted transformer model specifically designed for financial text understanding through specialized multi-stage training. FinBERT implements a sophisticated three-stage training architecture that progressively specializes the model's language understanding capabilities from general knowledge to financial domain expertise to specific task performance. This hierarchical training approach enables the model to develop both broad linguistic competence and specialized financial knowledge, resulting in superior performance on financial text analysis tasks compared to general-purpose language models or models trained exclusively on general corpora. Figure 1: the training architecture of FinBERT Figure 1 illustrates the complete training pipeline for FinBERT, showing three distinct stages of model development. The first stage involves pre-training on general domain corpora including BookCorpus and Wikipedia, where the model learns fundamental language patterns through masked language modeling and next sentence prediction tasks. During this phase, the 12-layer encoder architecture with 12 attention heads processes text bidirectionally, developing contextualized representations that capture syntactic and semantic relationships. Volume-09 Issue 10, October-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [434] The second stage continues pre-training specifically on financial texts drawn from Reuters TRC2 financial news corpus, enabling the model to adapt its representations to financial language patterns and terminology. The third stage fine-tunes the model on labeled Financial PhraseBank sentiment data, specializing the output layers for sentiment classification tasks while maintaining the refined financial language understanding developed in earlier stages. This three-stage approach addresses the fundamental challenge of transferring general language understanding to specialized domains while maintaining computational efficiency. By leveraging pre-trained weights from general domain training as initialization, the model benefits from extensive language knowledge without requiring prohibitively large financial training datasets. The continued pre-training phase on financial corpora enables adaptation to domain-specific vocabulary and discourse patterns, while the supervised fine-tuning stage specializes the model for particular prediction tasks. Each training stage employs appropriate learning rate schedules, batch sizes, and optimization strategies to maximize knowledge retention while enabling adaptation, with techniques such as discriminative learning rates and gradual unfreezing preventing catastrophic forgetting of previously learned representations. The attention mechanism forms the core computational component of FinBERT's transformer architecture, enabling the model to dynamically weight the relevance of different words when encoding textual representations. For each word in an input sequence, the attention mechanism computes relationships with all other words through learned query, key, and value transformations, generating context-aware representations that capture semantic dependencies regardless of positional distance. Multiple attention heads operating in parallel enable the model to capture diverse types of linguistic relationships simultaneously, with different heads specializing in syntactic, semantic, and pragmatic patterns. The hierarchical stacking of twelve transformer layers progressively refines these representations, with lower layers capturing surface-level patterns and higher layers encoding abstract semantic relationships relevant for complex reasoning tasks. 3.3 Feature Extraction and Model Implementation For each earnings call transcript in our dataset, we apply the fine-tuned FinBERT model to generate sentiment scores and contextual embeddings that quantify management tone and communication patterns. The sentiment classification component of FinBERT processes each sentence independently, generating probability distributions over positive, negative, and neutral sentiment categories. These sentence-level predictions are aggregated to document-level measures using multiple approaches including mean sentiment scores, variance of sentiment across sentences indicating communication consistency, and proportions of sentences classified in each category. Additional textual features extracted from transcripts include linguistic complexity measures such as average sentence length and vocabulary diversity, forward-looking statement frequency quantifying management discussion of future prospects, and uncertainty language indicators based on specialized word lists identifying expressions of ambiguity or hedging. To assess the incremental value of transformer-based features, we also implement traditional dictionary-based sentiment analysis using the Loughran-McDonald financial sentiment lexicon that comprises lists of positive and negative words specifically curated for financial contexts. This approach counts occurrences of words appearing in sentiment category lists, generating document-level scores based on the relative frequency of positive versus negative terms adjusted for document length. The comparison between FinBERT-derived and dictionary-based Volume-09 Issue 10, October-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [435] sentiment measures enables evaluation of whether context-aware language models provide substantive improvements over simpler lexical approaches for earnings surprise prediction. Furthermore, we examine correlations between different textual feature types to understand relationships among sentiment, complexity, and forward-looking language dimensions. Beyond sentiment analysis, we investigate temporal patterns in news volume and media coverage surrounding earnings announcements to identify systematic relationships with earnings surprise magnitude. For each company in our sample, we collect news article counts from major financial news sources for the period spanning 25 days before through 25 days after each earnings announcement. This temporal window enables examination of how news coverage evolves leading up to announcements and responds to earnings releases. The news volume data undergoes statistical analysis to identify distribution patterns, with particular focus on volume spikes coinciding with announcement dates and asymmetries between pre-announcement and post-announcement coverage intensity. Figure 2: bar chart comparing classification accuracy for earnings surprise prediction Figure 2 presents a bar chart comparing classification accuracy for earnings surprise prediction across seven different approaches including FinBERT, general BERT, Naive Bayes, Support Vector Machine, Random Forest, Convolutional Neural Network, and Long Short-Term Memory networks. Performance is evaluated on both the original testing sample and a randomized-words testing sample where word order is scrambled to assess model robustness to contextual information. FinBERT achieves the highest accuracy of approximately 88 percent on the original testing sample, substantially outperforming both traditional machine learning methods and neural network approaches. General BERT achieves accuracy near 85 percent, demonstrating the benefit of transformer architecture but highlighting the additional value provided by domain-specific pre-training. Traditional methods including Naive Bayes, Support Vector Machines, and Random Forests achieve accuracies in the 72-74 percent range, while neural approaches including CNN and LSTM demonstrate intermediate performance around 75-76 percent. Volume-09 Issue 10, October-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [436] The randomized-words testing sample results reveal differential sensitivity to word order across methodologies, with transformer-based models exhibiting larger performance degradation when contextual relationships are disrupted compared to bag-of-words approaches. This pattern confirms that FinBERT and BERT leverage sequential and contextual information effectively, while traditional methods rely primarily on individual word occurrences independent of context. The substantial performance gap between FinBERT and other approaches on the original testing sample validates the effectiveness of domain-adaptive pre-training combined with transformer architecture for financial text classification tasks. Our predictive modeling framework employs supervised machine learning classification to forecast earnings surprise categories using combinations of textual and traditional financial features. The baseline specification includes conventional predictors documented in prior earnings prediction research, comprising recent stock returns capturing price momentum, analyst forecast dispersion indicating disagreement among analysts, analyst forecast revisions reflecting updating of expectations, firm size measured by market capitalization, book-tomarket ratio indicating value characteristics, and historical earnings volatility. This baseline model establishes the predictive power achievable using readily available quantitative information, providing a benchmark against which to evaluate the incremental contribution of textual features extracted through FinBERT and alternative NLP approaches. 4. RESULTS AND DISCUSSION 4.1 Textual Feature Performance Analysis The empirical analysis demonstrates that FinBERT-derived textual features provide substantial incremental predictive power for earnings surprise classification beyond traditional quantitative variables. The baseline model incorporating only conventional financial predictors achieves out-of-sample classification accuracy of 47.3 percent, representing meaningful improvement over a naive strategy of predicting the modal category but indicating substantial room for enhancement. Adding dictionary-based Loughran-McDonald sentiment features to the baseline improves accuracy to 51.2 percent, a gain of 3.9 percentage points representing 8 percent improvement in predictive power. This finding confirms that even simple lexical sentiment measures contain incremental information about earnings outcomes not captured by traditional quantitative metrics, validating the basic premise that management communication patterns relate to subsequent performance. The FinBERT-based model achieves substantially higher performance, reaching 54.7 percent overall accuracy and 51.3 percent weighted F1-score. This represents a 7.4 percentage point gain over baseline and 3.5 percentage points over dictionary methods, demonstrating the value of context-aware language models that capture nuanced sentiment in complex financial discourse. The performance improvement concentrates particularly in extreme surprise categories, where accurate prediction generates greatest economic value for trading strategies. For large positive surprises, FinBERT-based classification achieves precision exceeding 64 percent compared to 56 percent for dictionary approaches and 51 percent for baseline quantitative models. Similarly, for large negative surprises, FinBERT precision reaches 62 percent versus 54 percent for simpler textual methods and 53 percent for baseline specifications. Analysis of feature importance reveals which textual dimensions contribute most substantially to predictive performance. FinBERT sentiment emerges as the single most important feature based on permutation importance measures, with removal of this variable reducing accuracy by 6.8 percentage points. 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