Predictive Analysis of S&P BSE Greenex Index: Unlocking Insights for Sustainable Investments

Abstract

The COVID-19 pandemic has led to reduced economic and industrial activities, prompting a noticeable transition towards a more sustainable way of life. This could indicate that we are on the path to reducing our carbon footprint in the long term. Consequently, analysed the performance of India's sustainability index, the S&P BSE GREENEX, which assesses the sector-wise carbon performance of stocks. It comprises stocks selected based on their energy efficiency performance using publicly disclosed financial and energy data. Forecasting the stock market is critical when formulating investment strategies. Considering the profound negative impact of the COVID-19 pandemic on global stock markets, investment decisions are becoming increasingly challenging and riskier, especially when channelling funds towards green technologies and clean energy. This study analysed the predictive accuracy of the Long Short-Term Memory (LSTM) deep learning model for Indian companies that promote sustainability through their investment decisions during and after the COVID-19 period. The empirical outcomes demonstrate the ability of the LSTM model to generate fairly precise predictions for a wide spectrum of companies across diverse sectors; during and after the crisis. These findings provide valuable insights for investors seeking to make informed decisions regarding sustainability-focused investments as represented by the S&P BSE GREENEX Index.

Keywords

• s&p bse greenex
• covid-19
• lstm
• predictive analysis
• time series forecast
• sustainability