Reading the Market: How Event Memory Boosts Stock Prediction

Author: Denis Avetisyan

A new framework leverages large language models to extract and track key events from financial news, improving the accuracy of stock price forecasting.

The StockMem framework establishes an overarching architecture for managing and utilizing stock market data, enabling a cohesive system for analysis and informed decision-making.

StockMem constructs structured event knowledge from news to enhance time series prediction with incremental information.

Predicting stock prices remains a formidable challenge due to market volatility and the difficulty of discerning signal from noise in real-time news. This paper introduces StockMem: An Event-Reflection Memory Framework for Stock Forecasting, a novel approach that structures financial news into a temporal knowledge base of events and tracks evolving market expectations. By mining event-price dynamics and reflecting on past experiences, StockMem retrieves analogous scenarios to enhance predictive accuracy and provide explainable reasoning. Could this event-driven memory architecture unlock more transparent and robust financial forecasting models?

Beyond Reactive Forecasting: Embracing Dynamic Event Understanding

Conventional stock forecasting frequently centers on the statistical analysis of past performance, a methodology increasingly challenged by its inability to adequately represent the impact of contemporary events. While historical data can reveal trends, it often falls short in anticipating how unforeseen circumstances – geopolitical shifts, technological breakthroughs, or even shifts in public sentiment – will influence market behavior. This reliance on purely quantitative metrics overlooks the qualitative factors driving investor decisions, creating a blind spot for disruptions not reflected in prior data sets. Consequently, models built solely on historical analysis can generate inaccurate predictions when faced with novel situations, highlighting the necessity for approaches that incorporate a broader understanding of the dynamic interplay between world events and financial markets.

Conventional stock forecasting models frequently stumble when attempting to integrate the ripple effects of real-world events into market predictions. These systems, often built on time-series analysis, treat events as isolated data points rather than nodes in a complex web of cause and effect. Consequently, a geopolitical crisis, a shift in consumer sentiment, or even a single corporate announcement isn’t processed for its potential to influence other factors – supply chains, investor confidence, or competing industries. The market doesn’t react to events in a vacuum; reactions are layered, amplified, or dampened by existing conditions and related occurrences. Failing to account for this interplay leads to models that miscalculate risk, overestimate or underestimate growth, and ultimately, deliver inaccurate predictions, particularly during periods of heightened volatility or unforeseen circumstances. The predictive power diminishes because the models lack the capacity to reason about the why behind market movements, only the what.

The efficacy of stock market forecasting hinges not simply on what information is processed, but crucially, on how that information is situated within a comprehensive understanding of unfolding events. Traditional models often treat data points in isolation, neglecting the crucial web of cause and effect that connects seemingly disparate occurrences to market fluctuations. This inability to contextualize new information – to understand its relevance within a broader event landscape – severely limits predictive power and, more importantly, hinders explainability. A model that cannot articulate why a particular event is expected to influence the market lacks robustness and invites skepticism, as genuine insight requires an appreciation for the complex interplay between world events and investor behavior. Consequently, forecasts become brittle, susceptible to unforeseen circumstances, and ultimately, less reliable for informed decision-making.

Event Memory is constructed through a workflow encompassing event extraction, merging, and tracking.

StockMem: An Event-Reflection Framework for Proactive Forecasting

StockMem addresses the challenge of incorporating external information into stock price forecasting by modeling the interconnectedness of news events and market behavior. Traditional time series analysis often overlooks the impact of discrete events; StockMem aims to quantify this impact through a dedicated framework. The system doesn’t simply ingest event data, but structures it to represent how events unfold and influence each other over time, acknowledging that the effect of an event isn’t static. This dynamic event representation is then used to model and predict stock price movements, allowing the framework to potentially capture nuances missed by methods relying solely on historical price data. The core innovation lies in its ability to track the evolving relationship between events and market responses, providing a more comprehensive approach to stock forecasting.

StockMem employs Large Language Models (LLMs) to process unstructured text data – such as news articles, SEC filings, and social media posts – and identify salient events relevant to stock price movements. These LLMs are utilized for tasks including named entity recognition, relationship extraction, and sentiment analysis to pinpoint event triggers, actors, and associated impacts. The extracted information is then converted into a structured event representation, typically a knowledge graph or a relational database, where events are nodes and their attributes (time, location, participants, sentiment) and relationships to other events are defined as edges. This structured format allows for quantitative analysis and facilitates the tracking of event sequences and their potential influence on stock market behavior.

StockMem incorporates two distinct memory modules to enhance forecasting capabilities. Event Memory functions as a temporal repository, continuously updating event embeddings as new information becomes available; this allows the model to track the evolving context surrounding specific events and their potential impact on stock prices over time. Complementing this, Reflection Memory stores experiences – event-outcome pairs – enabling the model to learn from past predictions and refine its understanding of event-price relationships; this historical data is used to adjust model parameters and improve the accuracy of future forecasts by identifying patterns and correlations not immediately apparent in current data. The iterative interaction between these memory modules facilitates adaptive learning and contributes to improved prediction performance.

Contextualizing Events: Leveraging Historical Resonance and LLM-Driven Insight

StockMem employs Historical Reference Experience by identifying and analyzing past events exhibiting analogous characteristics to current market conditions. This process involves constructing a database of historical events, categorized by key features such as economic indicators, geopolitical factors, and market responses. When a new event occurs, the system utilizes vector similarity searches to identify historical precedents, quantifying the degree of correlation based on shared attributes. These historical analogues are then used to project potential future outcomes, providing a probabilistic framework for assessing the likely impact of the current event on market behavior. The system doesn’t predict events, but rather assesses the probability of specific market reactions based on how similar situations unfolded in the past.

Event Extraction within StockMem leverages Large Language Models (LLMs) to identify and categorize salient details from news and financial reports. This process isn’t static; it’s continuously refined through LLM-Driven Iterative Induction. This technique involves the LLM analyzing initial extractions, identifying inconsistencies or ambiguities, and then iteratively re-evaluating and improving its understanding of event specifics. The LLM learns from each iteration, effectively building a more precise and coherent representation of the event, including key actors, locations, timing, and associated consequences. This iterative refinement ensures the framework moves beyond simple keyword identification to a nuanced comprehension of event details, minimizing misinterpretations and maximizing the accuracy of subsequent analysis.

StockMem’s assessment of market expectations relies on the continuous integration of incremental information – updates and new developments pertaining to ongoing events. This process allows the framework to move beyond initial event reporting and identify deviations from previously held assumptions. By monitoring these changes in real-time, StockMem can quantify the degree to which unfolding events differ from anticipated outcomes, providing a dynamic evaluation of market response. This capability is crucial for identifying potential mispricings or shifts in sentiment that may not be immediately apparent from static event data alone.

Prompt engineering is a critical component of StockMem’s functionality, directly influencing the Large Language Model’s (LLM) ability to accurately process and interpret financial data. This involves carefully crafting input prompts to guide the LLM’s reasoning, specifying the desired output format, and controlling the scope of analysis. Techniques include providing explicit instructions regarding the identification of relevant features, defining constraints on the LLM’s responses, and utilizing few-shot learning to demonstrate desired reasoning patterns. Optimization focuses on minimizing ambiguity and maximizing the LLM’s ability to extract pertinent information and generate coherent, factually-grounded conclusions, ultimately improving the framework’s overall performance and reliability.

Validating StockMem: Demonstrating Superior Predictive Performance

Evaluations consistently revealed StockMem’s predictive capabilities to surpass those of all benchmark models when applied to four major technology stocks. This demonstrated a marked improvement in stock prediction accuracy, suggesting the framework effectively captures and interprets market signals. Rigorous testing showed StockMem not only identified trends but also consistently outperformed traditional methods in forecasting stock movements, indicating a potential for substantial gains in investment strategies. The consistent superiority across diverse technology stocks highlights the robustness and generalizability of the StockMem approach, positioning it as a promising tool for financial analysis and forecasting.

The StockMem framework achieves heightened prediction accuracy by moving beyond simple textual analysis and instead focusing on a structured representation of events impacting stock prices. This utilizes models like BGE-M3 to dissect complex information into discrete, interconnected events – for example, a new product launch, an earnings report, or a shift in regulatory policy – and their associated sentiment. By organizing information in this manner, the framework can more effectively discern the causal relationships between events and stock market fluctuations, going beyond superficial correlations. This structured format allows the system to identify nuanced patterns that would be lost in unstructured text, ultimately leading to more reliable predictions and a demonstrable advantage over baseline models relying on traditional methods.

Investigations into the core components of StockMem revealed a critical dependence on structured event representation for accurate stock prediction. Researchers conducted ablation studies, systematically removing the framework’s use of formalized event data and replacing it with alternative approaches – namely, simple text summaries and aggregated opinion clusters. These substitutions consistently led to a demonstrable decline in predictive performance. This outcome underscores that the precise organization of information – detailing what happened, when, and how – is more valuable than merely conveying the general sentiment or a condensed narrative. The framework’s ability to dissect complex information into discrete, structured events allows for a nuanced understanding of market-moving factors, ultimately enhancing its capacity to forecast stock behavior with greater precision.

The analytical strength of StockMem relies fundamentally on DeepSeek-V3, a large language model distinguished by its capacity for nuanced comprehension of financial text and its ability to discern subtle shifts in market sentiment. This model isn’t merely processing words; it’s interpreting the contextual implications of news, reports, and social media, allowing StockMem to move beyond simple keyword analysis. DeepSeek-V3’s architecture facilitates the extraction of critical relationships between events and stock movements, and its predictive capabilities are crucial in forecasting potential price fluctuations. The model’s inherent understanding of complex financial language significantly enhances the framework’s ability to identify relevant information and generate accurate stock predictions, effectively serving as the ‘brain’ behind StockMem’s sophisticated analytical process.

The predictive power of StockMem relies heavily on its capacity to monitor events over time and integrate new information as it emerges. Research demonstrated a significant decline in accuracy when longitudinal tracking and the incorporation of incremental data were removed from the model; this indicates that simply analyzing static data points is insufficient for effective stock prediction. By continuously tracking the evolution of events and identifying deviations from anticipated market behavior, the framework can adapt to changing circumstances and refine its forecasts. This dynamic approach, which contrasts with traditional methods focused on historical data alone, allows StockMem to capture nuanced shifts in sentiment and anticipate market reactions with greater precision, ultimately bolstering its performance and highlighting the importance of temporal awareness in financial forecasting.

Future Directions: Expanding the Horizon of Event-Based Reasoning

The architecture underpinning StockMem – encompassing robust event extraction, a structured knowledge representation, and the crucial integration of historical context – extends far beyond the realm of financial forecasting. These principles are readily adaptable to diverse fields grappling with time-series data and the need to interpret sequential events; consider applications in medical diagnosis, where patient histories and symptom progression are key, or in legal reasoning, where precedents and case timelines dictate outcomes. Similarly, in supply chain management, understanding past disruptions and their cascading effects is vital for proactive risk mitigation. The system’s ability to not just identify events, but to organize them within a meaningful structure and connect them to relevant past occurrences, provides a powerful analytical foundation applicable to any domain where understanding what happened, when, and why is paramount for informed decision-making.

The StockMem framework’s continued development prioritizes improved robustness in the face of real-world data complexities. Current research concentrates on refining the system’s capacity to interpret events described with imprecise language or partial information, a common challenge in fields like news analysis and market intelligence. This involves exploring novel methods for probabilistic reasoning and incorporating techniques from natural language processing, such as semantic similarity and contextual disambiguation. By developing algorithms that can effectively infer missing details and resolve ambiguities, the framework aims to move beyond reliance on perfectly structured data and achieve a more nuanced understanding of dynamic events, ultimately enhancing its predictive capabilities and broadening its applicability to diverse domains.

Investigations into historical data referencing strategies revealed a significant advantage in predictive accuracy when incorporating information from multiple companies, rather than relying solely on a single entity’s past performance. This suggests that patterns and relationships indicative of future outcomes are often discernible only when viewed across a broader economic landscape. The framework demonstrated an ability to identify subtle, interconnected signals-such as correlated market reactions or industry-wide shifts-that would have been missed by analyses limited to a single company’s history. Consequently, this cross-company approach highlights the value of expanding data horizons to capture a more comprehensive understanding of complex systems and improve the reliability of predictive modeling, offering a pathway toward more informed decision-making in dynamic environments.

The predictive capabilities of StockMem are poised for significant advancement through the incorporation of external knowledge sources and the refinement of its event reasoning algorithms. Current research focuses on integrating diverse datasets – encompassing geopolitical events, macroeconomic indicators, and even social media sentiment – to provide a more holistic understanding of market-moving forces. Simultaneously, the development of more nuanced algorithms will enable StockMem to move beyond simple correlation and towards causal reasoning, discerning not just that events occur, but how they influence stock performance. This includes exploring techniques like Bayesian networks and reinforcement learning to model complex event dependencies and optimize predictive accuracy, ultimately allowing the framework to anticipate market shifts with greater precision and reliability.

The development of StockMem represents a significant step toward artificial intelligence systems equipped to navigate and react to the intricacies of real-world scenarios. By successfully modeling event-based reasoning with historical context, the framework transcends simple prediction and begins to approximate human-like comprehension of unfolding situations. This capability extends far beyond financial analysis; the principles underpinning StockMem are readily adaptable to fields demanding dynamic assessment, such as disaster response, geopolitical forecasting, or even personalized healthcare. The system’s ability to synthesize information from disparate sources and construct a coherent narrative of events promises a future where AI can not only identify patterns but also understand why those patterns emerge, ultimately enabling more robust and adaptable decision-making in complex, ever-changing environments.

The pursuit of accurate stock prediction, as detailed in StockMem, reveals a system where interconnected events, not isolated data points, drive outcomes. This resonates with Marvin Minsky’s observation: “You can’t solve problems using the same kind of thinking that created them.” StockMem avoids relying solely on traditional time-series analysis; instead, it constructs a dynamic event-reflection memory. The framework’s ability to track incremental information from financial news and represent it as structured event knowledge suggests that a holistic understanding – where context dictates behavior – is crucial. If the system survives on duct tape, it’s probably overengineered; StockMem’s approach indicates a shift toward elegant design, prioritizing clarity and interconnectedness over sheer computational complexity.

Future Trajectories

The introduction of StockMem, while demonstrating the potential of event-reflection mechanisms for stock prediction, subtly underscores a persistent tension. Simply ingesting more information, even when structured as ‘knowledge,’ does not inherently resolve the fundamental ambiguity of financial markets. The framework’s success hinges on the LLM’s ability to discern signal from noise, a capacity that remains contingent on the quality and bias embedded within the training data. Consequently, the architecture’s ultimate performance will be determined not by the elegance of its event tracking, but by the inherent limitations of the underlying language model.

Future work will likely focus on refining the event representation itself. Moving beyond simple event tracking toward a more nuanced understanding of event causality-how one event reliably influences another-presents a significant, and perhaps necessary, challenge. A system that merely registers ‘what’ happened is less valuable than one capable of anticipating ‘why’ it happened, and more importantly, ‘what’ is likely to follow.

The broader implication lies in the realization that predictive power in complex systems isn’t about accumulating facts, but about accurately modeling the relationships between them. StockMem offers a promising step in that direction, but the path toward true financial foresight demands a more holistic understanding of market dynamics – a framework where the structure of knowledge, not just its volume, dictates predictive capability.

Original article: https://arxiv.org/pdf/2512.02720.pdf

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