An Algorithmic Framework for Systematic Literature Reviews: A Case Study for Financial Narratives

TL;DR

This paper presents an algorithmic framework utilizing NLP and clustering to enhance the efficiency, reproducibility, and quality assessment of systematic literature reviews, demonstrated through a case study on financial narratives.

Contribution

It introduces a novel, integrated algorithmic approach for conducting systematic literature reviews, specifically applied to financial narratives, improving automation and structure in review processes.

Findings

The framework improves review efficiency and reproducibility.

Financial narratives research is fragmented and often limited to sentiment analysis.

The proposed method effectively structures literature review in complex domains.

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.