Paper: arXiv 2601.03794

Authors: Gabin Taibi, Joerg Osterrieder

Abstract

This paper introduces an algorithmic framework for conducting systematic literature reviews (SLRs), designed to improve efficiency, reproducibility, and selection quality assessment in the literature review process. The proposed method integrates Natural Language Processing (NLP) techniques, clustering algorithms, and interpretability tools to automate and structure the selection and analysis of academic publications. The framework is applied to a case study focused on financial narratives, an emerging area in financial economics that examines how structured accounts of economic events, formed by the convergence of individual interpretations, influence market dynamics and asset prices. Drawing from the Scopus database of peer-reviewed literature, the review highlights research efforts to model financial narratives using various NLP techniques. Results reveal that while advances have been made, the conceptualization of financial narratives remains fragmented, often reduced to sentiment analysis, topic modeling, or their combination, without a unified theoretical framework. The findings underscore the value of more rigorous and dynamic narrative modeling approaches and demonstrate the effectiveness of the proposed algorithmic SLR methodology.

Complexity vs Empirical Score

  • Math Complexity: 2.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: This paper proposes an algorithmic framework to conduct systematic literature reviews using NLP, clustering, and embedding techniques, applying it to financial narratives. While the mathematical formulations are minimal, the empirical application is well-structured and offers a clear, highly reproducible methodology for quantitative meta-analyses.

Research Flowchart

  flowchart TD
    A[Research Goal: Automate Systematic Literature Review for Financial Narratives] --> B[Data Collection: Retrieve Scopus Database Literature]
    B --> C[NLP & Clustering Pipeline: Text Processing, Embedding & Clustering]
    C --> D[Interpretability Analysis: Topic Extraction & Selection Quality Assessment]
    D --> E[Outcome 1: Validated Algorithmic SLR Framework]
    D --> F[Outcome 2: Revealed Fragmented Landscape in Financial Narrative Research]

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1 of 136 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.