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Rajni Bala, Sandeep Singh, Kiran Sood and Simon Grima : Sustainable Finance and Investment
Analytics: A Systematic Literature Review and Meta-Analysis Approach

Keywords: Sustainable Finance, Meta-analysis, Systematic Review, Investment
Analytics
Jel classification: G11, Q01, Q56

INTRODUCTION
The global financial landscape is undergoing a substantial transformation
characterised by a paradigm shift toward sustainable finance. Investors, regulators,
and stakeholders increasingly demand financial practices that account not only for
profitability but also for long-term environmental and social impacts. Sustainable
finance incorporates ESG principles, serving as a framework for evaluating the
broader implications of financial decision-making (PLOS ONE, 2023). Over the past
decade, the incorporation of ESG factors has evolved from being a niche strategy to a
mainstream necessity, driven by evidence linking responsible investment to financial
performance and resilience (Heliyon, 2022). Alongside this development is the rise of
investment analytics. Modern analytics methods, ranging from econometric
modelling to artificial intelligence, enable sophisticated evaluation of ESG
performance and predictive portfolio structuring (Environment, Development and
Sustainability, 2022). This integration of sustainability and analytics paves the way
for a more inclusive and performance-driven investment philosophy. Despite the
growth of literature in this area, there remains a need for a systematic synthesis of
knowledge that consolidates trends, assesses empirical outcomes, and provides
guidance for future inquiry (Suliman Elmahdi & Seyullayev, 2021).
The world economy is currently experiencing a paradigm shift based on the rising
credit between the environment, stewardship, and social understanding. The rise of
sustainable finance is the reaction to the inability of conventional financial
instruments to price externalities connected to environmental destruction and social
injustices (Sullivan & Mackenzie, 2017). Simultaneously, there have been hyped
investment analytical tools which include big data, machine learning and artificial
intelligence and allow investors to scale and precise ESG data (Capelle-Blancard &
Petit, 2019). The use of ESG criteria is no longer considered a sacrifice to financial
accomplishments. Instead, it is becoming more regarded as a means to long-term value
creation (Friede, Busch, & Bassen, 2015). The need to synthesise existing evidence
through systematic means is increasing as academic research and industry practice in
this area are expanding rapidly (Kunz, Pospíšil, & Kročil, 2018). Sustainable,
analytics finance and investment (Pan and Yang 2018a, 2018b), is an emerging field
that this chapter attempts to analyse and map its evolution, trends, and the empirical
implications of the same using a systematic literature review (SLR) and meta-analysis
(MA).

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