Author: Denis Avetisyan
A new approach directly translates the relational knowledge embedded in language models into graph structures, enhancing predictions of financial market behavior.
This paper introduces Relational Probing, a framework for jointly training language model-induced graphs with downstream predictors to improve financial trend prediction without complex pipelines.
While language models excel at extracting relationships from text, adapting these insights for structured prediction often introduces computational bottlenecks and decouples representation learning from downstream optimization. This paper, ‘Relational Probing: LM-to-Graph Adaptation for Financial Prediction’, introduces a novel framework that directly induces relational graphs from language model hidden states and jointly trains them with a predictor for improved financial trend forecasting. By bypassing autoregressive decoding, Relational Probing facilitates efficient knowledge transfer and preserves the inherent structure of relational data. Could this approach unlock new avenues for integrating unstructured textual data into quantitative financial modeling and beyond?
The Illusion of Isolated Prediction: Unmasking Financial Interdependence
Conventional financial forecasting frequently prioritizes time-series analysis, a technique that examines historical data points of a single asset to predict future performance. However, this approach often overlooks the intricate web of relationships that exist between different assets-stocks don’t operate in isolation. A stock’s trajectory isn’t solely determined by its past; it’s significantly influenced by the performance of related companies, industry trends, and broader economic factors. By focusing narrowly on individual asset histories, these methods can miss critical signals arising from interconnectedness, leading to inaccurate predictions and potentially flawed investment strategies. The limitations of this isolated view highlight the need for models that can capture and quantify these relational dependencies, acknowledging that financial markets are fundamentally driven by interactions, not just individual trajectories.
Conventional financial forecasting techniques frequently underperform due to a limited capacity to process the burgeoning volume of unstructured data – specifically, the nuanced information embedded within news articles, social media feeds, and regulatory filings. These methods, largely reliant on historical price movements, fail to adequately capture the sentiment, emerging narratives, and contextual factors that significantly influence asset behavior. The inability to integrate these real-time insights creates a blind spot, leading to predictions that are often reactive rather than anticipatory. Consequently, portfolio managers and investors may miss critical signals, hindering their ability to make informed decisions and optimize returns; a more comprehensive approach that leverages natural language processing and machine learning to extract meaningful patterns from unstructured data is increasingly vital for achieving superior predictive accuracy.
Accurate financial forecasting demands a shift beyond isolated asset analysis towards understanding the complex relationships between stocks. Traditional methods often treat each stock in a vacuum, failing to recognize that price movements are rarely independent; rather, they are influenced by a web of interconnected factors and reciprocal impacts. A holistic approach acknowledges this dynamic interplay, seeking to model not just individual stock behavior, but the cascading effects of events and information across the entire market. This necessitates incorporating techniques that can identify and quantify these dependencies, moving beyond simple correlation to uncover causal links and anticipate systemic shifts – ultimately offering a more robust and reliable predictive capability than approaches focused solely on historical price data.
Relational Probing: A Graph-Based Framework for Financial Inference
Relational Probing utilizes an end-to-end framework integrating a Language Model (LM) with a Graph Attention Network (GAT) for financial trend prediction. The LM processes financial data, generating hidden state representations. These representations are then directly input into the GAT, enabling the model to learn relationships between financial instruments. This combined architecture allows for simultaneous feature extraction from sequential data and relational reasoning on the connections between assets, ultimately improving predictive accuracy compared to models relying solely on time-series analysis or isolated asset evaluation. The framework is designed for direct training, eliminating the need for pre-defined relationship features or manual graph construction.
The Relation Head is a novel architectural component that substitutes the typical final layer of a language model used in financial forecasting. Instead of predicting the next token in a sequence, the Relation Head transforms the language model’s hidden state representation into an adjacency matrix. This matrix explicitly encodes relationships between different financial instruments, where each element A_{ij} represents the strength of the connection between stock i and stock j. The output adjacency matrix facilitates the application of graph attention networks, enabling the model to directly leverage the interconnectedness of stocks and potentially capture non-linear dependencies beyond those identified through traditional time-series analysis. This allows for modeling of systemic risk and cross-asset dependencies.
Traditional financial forecasting often relies on analyzing the historical performance of individual assets – time-series correlations – which may not capture systemic risks or interdependencies. Relational Probing addresses this limitation by enabling the model to directly learn relationships between financial instruments. The model achieves this by representing these relationships as an adjacency matrix, effectively constructing a graph where nodes are assets and edges represent learned correlations. This allows the model to propagate information between related assets, capturing effects beyond simple sequential patterns and potentially improving predictive accuracy by considering the broader financial network.
Efficient Adaptation: Leveraging Small Language Models for Relational Inference
The presented framework is designed to facilitate efficient adaptation of Small Language Models (SLMs), specifically utilizing the Qwen3 series including 0.6B, 1.7B, and 4B parameter models. A key feature is the ability to perform joint fine-tuning of the SLM alongside a relation head – a component used for identifying relationships between entities – all within the memory constraints of a single GPU. This is achieved through optimized implementation and efficient parameter handling, allowing for practical experimentation and deployment of relation extraction models without requiring substantial computational resources typically associated with larger language models.
Performance evaluations indicate that incorporating hidden states from both input tokens and generated tokens (Input+Gen) consistently outperforms methods that utilize only input token hidden states (Input-Only). This improvement stems from the model’s ability to leverage contextual information derived from the generated output during the relational probing process. By considering both the initial input and the model’s subsequent response, the system gains a more complete understanding of the relationships being assessed, resulting in enhanced accuracy and overall performance metrics.
When utilizing the Qwen3-4B Small Language Model, the Relational Probing methodology attained a macro F1 score of 0.3272, indicating performance in identifying relational triples. Accuracy reached 0.5705, representing the overall correctness of predictions. The Matthews Correlation Coefficient (MCC) measured 0.0562, providing a balanced assessment of performance even with imbalanced datasets. Finally, the Area Under the Curve (AUC) was 0.5571, quantifying the model’s ability to discriminate between positive and negative examples.
Comparative analysis demonstrates a measurable performance increase achieved through the implementation of Relational Probing. Utilizing the Qwen3-0.6B model, the macro F1 score improved from 0.2831 when employing a co-occurrence baseline to 0.3171 with the introduction of Relational Probing. This represents a quantifiable gain in performance, indicating the effectiveness of the proposed method in enhancing relational understanding within the Small Language Model.
Beyond Prediction: A Networked Understanding of Financial Systems
Relational Probing distinguishes itself from traditional financial forecasting by moving beyond simple statistical correlations to explicitly map the interconnectedness of stocks. Instead of treating assets as isolated entities, the framework constructs a network representing relationships derived from historical price movements and external factors. This yields a model that isn’t merely predictive, but also interpretable; analysts can trace the pathways of influence between stocks, understanding why a particular trend is unfolding, not just that it is. The transparency inherent in this relational mapping allows for a more nuanced assessment of risk and opportunity, offering insights into the underlying mechanisms driving market behavior and potentially uncovering vulnerabilities hidden within complex financial systems.
Relational Probing distinguishes itself by integrating real-world context, specifically through the analysis of news articles, to uncover the subtle forces influencing asset behavior. Traditional financial models often rely solely on historical price data, potentially overlooking crucial external factors that drive market fluctuations. This framework, however, actively scans and processes news content, identifying connections between events, entities, and subsequent asset price movements. By quantifying the impact of previously unconsidered narratives – such as shifts in public sentiment, regulatory changes, or even geopolitical events – Relational Probing reveals the hidden drivers of asset dynamics. This allows for a more nuanced understanding of why assets move, rather than simply how they move, potentially unlocking opportunities for more informed investment decisions and proactive risk management.
A deeper comprehension of interconnected financial ecosystems, facilitated by relational probing, offers the potential to refine investment strategies and proactively lessen portfolio risks. Traditional models often isolate assets, obscuring the ripple effects of information and events; however, by recognizing how news and contextual factors influence relationships between stocks, investors gain the capacity to anticipate market shifts beyond simple predictive modeling. This holistic perspective allows for a more nuanced assessment of both opportunities and vulnerabilities, moving beyond reactive responses to a preemptive approach grounded in understanding the complex interplay of forces driving asset dynamics. Consequently, portfolios can be structured not only to capitalize on emerging trends but also to buffer against unforeseen consequences stemming from interconnected market behaviors, ultimately fostering more resilient and informed investment decisions.
The pursuit of robust prediction, as demonstrated in this work with Relational Probing, echoes a fundamental tenet of computational correctness. This paper’s direct induction of relational graphs from language model representations-circumventing cumbersome pipelines-stresses the elegance of a provable system. Alan Turing observed, “Sometimes people who are unhappy tend to look at the world as if there is something wrong with it.” Similarly, traditional financial modeling often attempts to fix noisy data, rather than establishing a mathematically sound foundation for prediction. Relational Probing, by prioritizing the inherent structure within data and leveraging the power of Graph Neural Networks, moves closer to that ideal of demonstrable, rather than empirically verified, correctness.
What Remains to Be Proven?
The presented framework, while demonstrating empirical efficacy, skirts the crucial question of why relational induction, as opposed to a purely linear projection, yields improved predictive power. The authors correctly sidestep the morass of hyperparameter tuning inherent in pipeline approaches, yet fail to provide a formal justification for the chosen relational structure itself. A rigorous analysis, perhaps through information-theoretic bounds on representational capacity, is required to move beyond observation to understanding. The current reliance on empirically derived graph structures feels… provisional.
Furthermore, the limitation to small language models, though pragmatically motivated, introduces a significant constraint. The claim of avoiding complexity appears somewhat disingenuous when the method’s scalability to larger, more expressive models remains unaddressed. A truly elegant solution should not inherently shrink from the challenge of increased dimensionality. Proving the method’s robustness to model size-or demonstrating a clear theoretical limit-is paramount.
Ultimately, the true test lies not in forecasting market fluctuations, but in establishing a provable link between language model representations, relational structure, and the underlying dynamics of financial time series. Until such a connection is formally demonstrated, this remains a promising, yet incomplete, line of inquiry. The pursuit of demonstrable truth, after all, is a far more reliable endeavor than the prediction of irrational exuberance.
Original article: https://arxiv.org/pdf/2604.10212.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2026-04-14 09:51