AI-Driven Explainable Predictive Analytics Framework for Fair Value Measurement and Earnings Quality Prediction Using HighDimensional Financial Text and Market Signals

Abstract

The increasing complexity of financial markets and the growing volume of unstructured financial information have created significant challenges for accurately measuring fair value and assessing earnings quality. Traditional financial analysis techniques rely heavily on structured accounting data and often fail to capture the informational value embedded in highdimensional financial text, such as corporate disclosures, earnings call transcripts, analyst reports, and regulatory filings. Recent advances in artificial intelligence, natural language processing, and explainable machine learning provide new opportunities to integrate textual financial signals with market indicators for more robust predictive analytics in financial reporting and valuation.
This review paper examines the development of an AI-driven explainable predictive analytics framework designed to improve fair value measurement and earnings quality prediction by leveraging high-dimensional financial text and market-based signals. The study synthesizes existing literature on machine learning-based financial prediction models, natural language processing techniques for financial document analysis, and explainable artificial intelligence methods that enhance transparency in algorithmic decision systems. The paper further evaluates how textual sentiment, linguistic complexity, narrative tone, and semantic patterns extracted from financial disclosures can complement traditional quantitative indicators such as stock price volatility, trading volume, and accounting ratios in predictive modeling.
The proposed conceptual framework integrates transformer-based language models, financial sentiment analysis, and multimodal machine learning architectures with explainability mechanisms including SHAP values, attention visualization, and feature attribution analysis. These mechanisms enable analysts, auditors, and regulators to interpret model predictions and understand how textual and market variables influence fair value estimation and earnings quality assessments. By combining structured financial data with unstructured textual signals, the framework improves prediction accuracy, reduces model opacity, and supports more reliable financial decision-making. The review highlights the implications of explainable predictive analytics for corporate governance, financial reporting transparency, audit analytics, and regulatory oversight. The findings suggest that integrating AI-driven textual analysis with market intelligence can significantly enhance early detection of earnings manipulation, valuation anomalies, and financial reporting risks. The paper concludes by identifying research gaps related to model generalization, cross-market applicability, and regulatory integration of explainable AI systems within financial reporting and audit practices.