Natural Language Processing is rapidly evolving into a primary instrument for Computational Social Science, with researchers increasingly using embeddings to measure latent constructs such as novelty, creativity, and bias.However, this transition faces a fundamental validity challenge: the "Proxy Presumption," or the reliance on geometric properties (e.g., cosine distance) as direct measures of social concepts.We argue that without explicit validation, unsupervised representations remain entangled mixtures of the target construct (C) and confounding attributes (Z) like topic, style, and authorship.To bridge the gap between semantic embeddings and valid social measures, we introduce the Construct Validity Protocol (CVP).Drawing on causal representation learning and psychometrics, the CVP offers a rigorous pipeline from conceptualization to quantitative verification.We further propose Counterfactual Neutralization, a novel method using LLMs to reduce confounding in embedding space.By providing a standardized Validity Suite-including tests for discriminant, incremental, and predictive validity-this work offers the community a toolkit to transform heuristic proxies into robust, scientifically defensible instruments.

Explaining stock predictions is generally a difficult task for traditional non-generative deep learning models, where explanations are limited to visualizing the attention weights on important texts. Today, Large Language Models (LLMs) present a solution to this problem, given their known capabilities to generate human-readable explanations for their decision-making process. However, the task of stock prediction remains challenging for LLMs, as it requires the ability to weigh the varying impacts of chaotic social texts on stock prices. The problem gets progressively harder with the introduction of the explanation component, which requires LLMs to explain verbally why certain factors are more important than the others. On the other hand, to fine-tune LLMs for such a task, one would need expert-annotated samples of explanation for every stock movement in the training set, which is expensive and impractical to scale.

Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility, allowing financial institutions to price and hedge derivatives, and banks to quantify the risk in their trading books. Additionally, most financial regulators also require a liquidity horizon of several days for institutional investors to exit their risky assets, in order to not materially affect market prices. However, the task of multi-step stock price prediction is challenging, given the highly stochastic nature of stock data. Current solutions to tackle this problem are mostly designed for single-step, classification-based predictions, and are limited to low representation expressiveness. The problem also gets progressively harder with the introduction of the target price sequence, which also contains stochastic noise and reduces generalizability at test-time.