An Algorithmic Framework for Systematic Literature Reviews: A Case Study for Financial Narratives
It addresses the need for more efficient and reproducible literature reviews, particularly for researchers in fields like financial economics, though it is incremental as it builds on existing NLP and clustering methods.
This paper tackled the problem of conducting systematic literature reviews by introducing an algorithmic framework that integrates NLP, clustering, and interpretability tools, applied to a case study on financial narratives, revealing fragmented conceptualization often reduced to sentiment analysis or topic modeling without a unified theoretical framework.
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.