Graph database while computationally efficient filters out quickly the ESG integrated equities in investment management

TL;DR

This study compares SQL, No-SQL, and graph databases for efficiency in filtering ESG-integrated equities, finding graph databases particularly effective for resource-efficient investment analytics.

Contribution

It demonstrates that graph databases outperform traditional databases in ESG equity filtering, proposing a new framework for resource-efficient investment decision-making.

Findings

Graph databases are more efficient for ESG equity filtering.

Graph ML with RAG architecture reduces computational resource use.

Graph databases enable extended ESG analytics in investment management.

Abstract

Design/methodology/approach This research evaluated the databases of SQL, No-SQL and graph databases to compare and contrast efficiency and performance. To perform this experiment the data were collected from multiple sources including stock price and financial news. Python is used as an interface to connect and query databases (to create database structures according to the feed file structure, to load data into tables, objects, to read data , to connect PostgreSQL, ElasticSearch, Neo4j. Purpose Modern applications of LLM (Large language model) including RAG (Retrieval Augmented Generation) with Machine Learning, deep learning, NLP (natural language processing) or Decision Analytics are computationally expensive. Finding a better option to consume less resources and time to get the result. Findings The Graph database of ESG (Environmental, Social and Governance) is comparatively better and can be considered for extended analytics to integrate ESG in business and investment. Practical implications A graph ML with a RAG architecture model can be introduced as a new framework with less computationally expensive LLM application in the equity filtering process for portfolio management. Originality/value Filtering out selective stocks out of two thousand or more listed companies in any stock exchange for active investment, consuming less resource consumption especially memory and energy to integrate artificial intelligence and ESG in business and investment.