FinAnchor: Aligned Multi-Model Representations for Financial Prediction

TL;DR

Across multiple financial NLP tasks, FinAnchor consistently outperforms strong single-model baselines and standard ensemble methods, demonstrating the effectiveness of anchoring heterogeneous representations for robust financial prediction.

Abstract

Financial prediction from long documents involves significant challenges, as actionable signals are often sparse and obscured by noise, and the optimal LLM for generating embeddings varies across tasks and time periods. In this paper, we propose FinAnchor(Financial Anchored Representations), a lightweight framework that integrates embeddings from multiple LLMs without fine-tuning the underlying models. FinAnchor addresses the incompatibility of feature spaces by selecting an anchor embedding space and learning linear mappings to align representations from other models into this anchor. These aligned features are then aggregated to form a unified representation for downstream prediction. Across multiple financial NLP tasks, FinAnchor consistently outperforms strong single-model baselines and standard ensemble methods, demonstrating the effectiveness of anchoring heterogeneous representations for robust financial prediction.

Figures (4)

• Figure 1: (a) Three different LLM independently predict next-day stock movement from the same earnings call transcript: Models A and B predict negative return, while Model C predicts positive return. Highlighted phrases denote text snippets deemed salient by each model. In this case, Model A and B emphasize financially relevant cues (e.g., margins, demand, guidance), while Model C is dominated by less relevant content. (b)Multi-Model Representation Alignment Procedure. We select an anchor LLM space as a common coordinate system and learn ridge regression maps that project source-model representations into the anchor space. The aligned representations are then aggregated in the anchor space to form an aligned representation for downstream prediction.
• Figure 2: Decision transitions after alignment on Stock Movement Prediction task. Counts of label transitions from Gemma to the FinAnchor under validation-chosen thresholds. The FinAnchor corrects a substantial number of false positives (FP$\rightarrow$TN), while also introducing smaller regressions.
• Figure 3: FinAnchor increases confidence on corrected cases. We measure the change in confidence assigned to the ground-truth label, $\Delta P(y)=P_{\text{Aligned}}(y)-P_{\text{Gemma}}(y)$, on the test set and stratify examples into three groups: Corrected (Gemma incorrect, Aligned correct), Degraded (Gemma correct, Aligned incorrect), and Unchanged. Boxplots show the distribution of $\Delta P(y)$ within each group (median and interquartile range; whiskers denote 1.5$\times$IQR; outliers omitted). Positive values indicate that the FinAnchor assigns higher probability to the true label than Gemma.
• Figure 4: ROC-AUC comparison across five datasets. We report ROC-AUC for six methods (Zero-shot, Few-shot, FinBERT, Longformer, Anchor (Gemma), and FinAnchor), grouped by dataset.