News to Profits: Decoding Market Signals with AI

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Author: Denis Avetisyan

A new approach leverages the power of artificial intelligence to extract meaningful events from news articles and translate them into more accurate stock market predictions.

Using large language models and attention-based deep learning, researchers demonstrate improved stock return predictability through structured event representation and interpretable analysis of news data.

Despite the increasing availability of textual data, extracting predictive signals from news remains a challenge for traditional financial modeling. This paper, ‘Structured Event Representation and Stock Return Predictability’, introduces a novel approach leveraging large language models to distill structured event representations from news articles, significantly improving stock return prediction accuracy. By combining these representations with an attention-based deep learning model, we demonstrate superior performance and enhanced interpretability compared to existing text-driven forecasting methods. Could a more nuanced understanding of event structures unlock even greater predictive power in financial markets?

Beyond Superficial Signals: Uncovering the True Drivers of Financial Markets

Conventional financial forecasting frequently prioritizes historical price patterns – time-series analysis – and broad emotional indicators, such as positive or negative news sentiment. However, this approach often proves inadequate when confronted with the intricate forces that truly govern market behavior. While identifying that sentiment exists is relatively straightforward, discerning why that sentiment is forming, and how it will translate into actual trading behavior, remains a significant challenge. The inherent limitations of these methods stem from their inability to account for the complex web of interconnected events – geopolitical shifts, technological breakthroughs, or unforeseen crises – that exert a powerful, and often unpredictable, influence on asset prices. Consequently, forecasts built solely on past performance or superficial emotional analysis frequently fail to capture the nuanced drivers of market fluctuations, leading to inaccurate predictions and missed opportunities.

Conventional financial forecasting techniques often falter because they treat stock price fluctuations as isolated responses to singular inputs, rather than acknowledging the intricate web of interconnected events that truly drive market behavior. A sudden drop in a tech stock, for instance, isn’t simply triggered by negative news; it’s a consequence of how that news interacts with existing investor sentiment, competing product launches, broader economic indicators, and even seemingly unrelated geopolitical events. These methods struggle to discern causal relationships within this complexity, often mistaking correlation for causation and leading to predictions that fail to account for the ripple effects of real-world happenings. The result is a persistent inability to anticipate significant market shifts, as models remain blind to the multifaceted interplay of factors shaping investor decisions and asset valuations.

Conventional financial prediction frequently prioritizes easily quantifiable data, such as market sentiment, often to the detriment of understanding the substantive events driving economic shifts. While gauging public feeling offers a snapshot of current mood, it fundamentally fails to address why those feelings exist or how specific real-world occurrences are impacting valuations. A surge in negative sentiment, for instance, might reflect anxieties surrounding a geopolitical crisis, a supply chain disruption, or an impending regulatory change – details lost when focusing solely on the emotional response. This emphasis on ‘how people feel’ rather than ‘what is happening’ creates a predictive blind spot, as underlying causal factors remain obscured, and models struggle to differentiate between transient emotional reactions and fundamental shifts in economic reality. Consequently, strategies built on sentiment analysis alone risk misinterpreting market signals and failing to anticipate significant price movements.

Predictive accuracy in financial markets transcends simply gauging public opinion; it necessitates a granular comprehension of the events shaping economic realities. Current models often treat market fluctuations as isolated responses to sentiment, overlooking the intricate web of causality connecting global occurrences. Superior forecasting, therefore, demands an analytical approach that maps not just how events are perceived, but what those events are – geopolitical shifts, technological breakthroughs, regulatory changes, and even natural disasters – and crucially, how these factors interrelate. By modeling these complex relationships, rather than relying solely on emotional indicators, financial predictions can move beyond superficial correlations and tap into the deeper, systemic drivers of market behavior, potentially unlocking a new era of precision and reliability.

The SER Framework: Structuring Chaos into Actionable Intelligence

The SER Framework employs Large Language Models (LLMs) to automate the process of Event Extraction from unstructured text sources, specifically raw news articles. This LLM-driven extraction focuses on identifying core event components and converting them into a structured representation. The output is not simply keyword identification, but a formalized depiction of events, detailing the actors, actions, and entities involved. This conversion facilitates computational analysis by transforming narrative text into a machine-readable format suitable for downstream tasks such as trend analysis and predictive modeling. The LLMs are trained to recognize event patterns and extract relevant information with minimal human intervention, thereby enabling scalable processing of large volumes of news data.

Event Representations within the SER Framework utilize a subject-action-object triplet structure to standardize the extraction of information from news articles. This methodology decomposes each event into its core components: the actor initiating the event (subject), the activity being performed (action), and the entity acted upon (object). For example, the sentence “The company acquired the startup” would be represented as “Company-Acquired-Startup”. This consistent formatting, regardless of the original phrasing, allows for computational analysis and comparison of events across diverse sources, facilitating the identification of relationships and trends that would otherwise remain obscured by linguistic variation.

The SER Framework prioritizes extracting the core event – the ‘what’ occurred – over the details of execution – the ‘how’ it occurred. This focus is based on the principle that the fundamental event is more stable and generalizable for predictive modeling. Variations in execution details, such as specific actors or methods, are considered less crucial for identifying recurring patterns and anticipating future events. By abstracting away from implementation specifics, the SER framework aims to reduce noise and improve the reliability of predictions derived from news data, creating a more robust system less susceptible to superficial changes in reporting.

Traditional news analysis often relies on keyword searches and statistical correlations, which can fail to detect relationships beyond explicitly stated connections. The SER framework’s structured event representation-specifically, the subject-action-object triplets-enables the identification of implicit relationships. By representing events as discrete units with defined components, the framework facilitates graph-based analysis and network mapping. This allows for the detection of second-order effects, causal links between seemingly unrelated events, and the emergence of patterns indicative of underlying trends that are not readily apparent through conventional methods of text analysis. The resulting structured data supports the application of advanced analytical techniques, including link prediction and anomaly detection, to uncover subtle connections previously obscured by unstructured textual data.

From Signal to Prediction: Validating the Framework’s Predictive Power

The SER Framework utilizes an attention mechanism to dynamically assess the relevance of individual events to predicted stock price movements. This mechanism assigns weights to each event based on its learned importance, allowing the model to prioritize information likely to influence stock prices. Specifically, the attention weights are calculated through a learned function that considers the event’s features and its relationship to the target stock. Events receiving higher attention weights contribute more significantly to the final price prediction, effectively filtering out noise and focusing on salient signals. This adaptive weighting process differentiates the SER Framework from models that treat all events equally, enhancing its capacity to capture complex market dynamics.

The predictive capabilities of the SER framework were statistically assessed using Fama-MacBeth regression, a cross-sectional regression technique employed to estimate the relationship between asset returns and various factors over multiple time periods. This method allows for robust hypothesis testing while accounting for potential time-series and cross-sectional dependencies. Portfolio sorting was then implemented to further evaluate the model’s performance; stocks were ranked based on their predicted returns and divided into portfolios, with subsequent analysis of the portfolios’ realized returns to determine if higher predicted returns correlated with actual performance. This process provides an out-of-sample validation of the model’s ability to identify profitable investment opportunities.

Comparative performance testing indicates the SER Framework generates superior returns relative to established baseline models. Specifically, the SER Framework achieved an annualized return of 10.93% when utilizing daily predictions and 5.23% with weekly predictions. These figures represent a measurable improvement over benchmark models including BERT-based approaches and traditional sentiment analysis techniques, demonstrating the framework’s capacity to effectively translate event data into predictive market signals.

The efficacy of the structured event representation approach employed by the SER Framework is statistically confirmed by a positive Fama-French five-factor α. This alpha represents the excess return achieved above that predicted by standard asset pricing models considering market risk, size, value, profitability, and investment factors. A statistically significant, positive α indicates that the SER Framework captures information beyond these commonly recognized factors, demonstrating its ability to identify and leverage previously uncaptured market signals for improved predictive performance. The magnitude of this alpha, combined with rigorous statistical testing, substantiates the framework’s ability to consistently generate excess returns not attributable to conventional risk premiums.

Beyond Prediction: Uncovering the Systemic Relationships Driving Market Behavior

The Stock-Event Relationship (SER) framework demonstrates a significant phenomenon known as Entity-Based Comovement, revealing that stocks aren’t isolated entities but are intrinsically linked through shared relationships with real-world organizations. This means stocks connected to the same suppliers, customers, or even key personnel tend to exhibit correlated price movements, irrespective of industry sector. The framework identifies these connections by analyzing news and event data, pinpointing the entities – companies, people, or products – that bridge the gap between seemingly disparate stocks. Consequently, a positive event concerning one entity can ripple through the market, influencing the performance of multiple stocks connected to it, and conversely, negative news can trigger a synchronized downturn. This interconnectedness suggests that traditional diversification strategies, focused solely on industry sectors, may be incomplete, and a more nuanced understanding of entity-based relationships is crucial for accurate risk assessment and portfolio construction.

Event Topic Modeling serves as a crucial analytical layer, transforming a multitude of individual events into discernible thematic categories that illuminate overarching market trends. This technique doesn’t simply catalog occurrences; it statistically infers the underlying topics discussed within the event data, revealing connections between seemingly disparate events. For example, a surge in news regarding supply chain disruptions, coupled with reports of rising energy costs and labor shortages, might be identified as a dominant “Inflationary Pressure” topic. By aggregating events under these broader themes, analysts can move beyond reacting to isolated incidents and instead gain a holistic understanding of the forces shaping market behavior, enabling a more proactive and informed approach to investment strategy and risk assessment. The result is a shift from observing what happened to understanding why it happened, and crucially, anticipating potential future impacts.

A refined comprehension of market dynamics directly empowers more strategic investment approaches and robust risk mitigation. By moving beyond simple correlation and identifying the underlying event-driven connections influencing asset prices, investors can construct portfolios designed not just for potential gains, but also for resilience against unforeseen circumstances. This insight allows for proactive adjustments to holdings based on emerging thematic trends, potentially capitalizing on opportunities others miss, and minimizing exposure to negative impacts stemming from specific events or entity-related news. Consequently, a framework that elucidates these connections shifts the focus from reactive responses to informed, predictive strategies, ultimately enhancing long-term financial performance and bolstering overall portfolio stability.

The framework establishes a direct link between specific events and subsequent stock returns, moving beyond simple correlation to suggest potential causation. By meticulously analyzing the impact of various occurrences – from product launches and regulatory changes to executive decisions and macroeconomic shifts – it dissects the complex forces influencing financial performance. This event-driven approach allows for a granular understanding of why stocks move, identifying which events have the most significant and lasting effects. Consequently, the tool enables investors to move beyond reactive strategies and instead proactively assess the potential impact of unfolding events, ultimately fostering more informed and potentially profitable investment decisions and refining risk mitigation practices.

The study meticulously details how transforming unstructured textual data into structured event representations enhances predictive power-a process echoing Georg Wilhelm Friedrich Hegel’s assertion that “The truth is the whole.” Just as Hegel believed understanding required grasping the totality of a concept, this research demonstrates that comprehensive event structuring, facilitated by large language models, moves beyond isolated data points. By carefully checking data boundaries to avoid spurious patterns, the attention-based deep learning model reveals the interconnectedness of events driving stock returns, offering a more holistic and interpretable forecast than traditional methods. The model doesn’t merely predict; it elucidates why returns fluctuate, aligning with a Hegelian approach to knowledge.

Where Do We Go From Here?

The capacity to distill structured representations from unstructured text, and subsequently leverage those representations for predictive modeling, appears promising – yet rests on a foundation of computational alchemy. The current work demonstrates a correlation, a pattern observed between textual events and market behavior. However, correlation does not imply causation, and the ‘why’ remains stubbornly opaque. Future efforts must move beyond merely identifying predictive signals, and attempt to model the mechanisms by which these signals influence investor sentiment and, ultimately, stock prices. A deeper engagement with behavioral finance, and a willingness to incorporate domain-specific knowledge, will be crucial.

Furthermore, the reliance on large language models introduces a layer of inherent uncertainty. These models, while impressive in their ability to mimic human language, are essentially black boxes. The interpretability gains offered by attention mechanisms are valuable, but insufficient. A truly robust system will require methods for verifying the reliability of the extracted event representations, and for quantifying the uncertainty associated with their impact on stock returns. The challenge lies in discerning genuine predictive power from spurious correlations amplified by model complexity.

Ultimately, the field must acknowledge that predictive modeling, particularly in a complex system like the stock market, is an exercise in pattern recognition. If a pattern cannot be reproduced or explained, it doesn’t exist.

Original article: https://arxiv.org/pdf/2512.19484.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-23 14:28