Lexicon-Based Evidence on Environmental Disclosure and IPO Under-pricing

The aim of the study is to examine the effect of environmental disclosures on IPO performance and investigate whether established certification mechanisms substitute the sustainability-based qualitative signals or complement the relation. The design employs the programming language Python for textual analysis of environmental disclosures in IPO prospectuses of a sample of 625 IPOs listed on the Bombay Stock Exchange, India. Raw Return (RR) and Market Adjusted Excess Returns (MAER) are measured to calculate IPO under-pricing. Thereafter, regression analysis is used in the presence of control variables (listing delay, issue size, firm age, oversubscription, return on assets (ROA), and firm size) to analyse the impact of environmental disclosure on IPO under-pricing. This study reveals important observations that can assist policymakers in formulating disclosure regulations and company managers in planning their pre-IPO strategy. A positive relationship between environmental disclosure scores and the returns on initial public offerings is discovered. Contrary to the ESG-IPO literature, the issuer-controlled pre-listing signal through environmental disclosures enhances the short-term demand, generating green demand premium. In an environment dependent on rating agencies for the sustainability scores of the listed firms, this study uses the information from the source, i.e. IPO prospectus to measure the environmental scores using lexicon-based analysis of disclosed information by the debut firms. The issuer-controlled pre-listing signal through environmental disclosures are found to be playing an important role in deciding the sustainability quotient of the organisation. The study explains the pricing of sustainable signal in emerging markets that is still transitioning on regulatory fronts. JEL Code : G32, G14, M14