Implementation of deep learning models in predicting ESG index volatility  

作　　者：Hum Nath Bhandari Nawa Raj Pokhrel Ramchandra Rimal Keshab R.Dahal Binod Rimal 

机构地区：[1]Department of Mathematics,Roger Williams University,1 Old Ferry Rd.,Bristol,RI,02809,USA [2]Computer Science and Physics Department,Xavier University of Louisiana,1 Drexel Dr,New Orleans,LA,70125,USA [3]Department of Mathematical Sciences,Middle Tennessee State University,1301 E Main St,Murfreesboro,TN,37132,USA [4]Department of Mathematics,State University of New York Cortland,22 Graham Ave,Cortland,NY,13045,USA [5]Department of Mathematics,The University of Tampa,401 W Kennedy Blvd,Tampa,FL,33606,USA

出　　处：《Financial Innovation》2024年第1期821-844,共24页金融创新(英文)

摘　　要：The consideration of environmental,social,and governance(ESG)aspects has become an integral part of investment decisions for individual and institutional investors.Most recently,corporate leaders recognized the core value of the ESG framework in fulfilling their environmental and social responsibility efforts.While stock market prediction is a complex and challenging task,several factors associated with developing an ESG framework further increase the complexity and volatility of ESG portfolios compared with broad market indices.To address this challenge,we propose an integrated computational framework to implement deep learning model architectures,specifically long short-term memory(LSTM),gated recurrent unit,and convolutional neural network,to predict the volatility of the ESG index in an identical environment.A comprehensive analysis was performed to identify a balanced combination of input features from fundamental data,technical indicators,and macroeconomic factors to delineate the cone of uncertainty in market volatility prediction.The performance of the constructed models was evaluated using standard assessment metrics.Rigorous hyperparameter tuning and model-selection strategies were implemented to identify the best model.Furthermore,a series of statistical analyses was conducted to validate the robustness and reliability of the model.Experimental results showed that a single-layer LSTM model with a relatively small number of neurons provides a superior fit with high prediction accuracy relative to more complex models.

关 键 词：ESG investing ESG index Deep learning Machine learning Volatility prediction 

分 类 号：F42[经济管理—产业经济]