Article: Forecasting artificial intelligence assets' future volatility: evidence from explainable machine learning Journal: International Journal of Business and Systems Research (IJBSR) 2026 Vol.20 No.2 pp.151 - 183 Abstract: The rapid adoption of artificial intelligence (AI) has made it essential to forecasting of AI-related assets volatility. This study employs explainable artificial intelligence methods, specifically decision tree, extra tree, random forest, gradient boosting models to forecast future volatility in AI assets. The study results reveal that the gradient boosting model achieves the highest predictive directional accuracy 80% to 90% for volatility. Furthermore, SHAP analysis identifies key predictors, which including technical indicators such as the moving average, exponential moving average, and momentum, which effectively capture short-term market sentiment, while macroeconomic variables reflect broader economic conditions. This research bridges a gap in the literature by integrating explainable machine learning with time-series forecasting of AI asset volatility, offering insights into which model and variables most effectively forecast volatility in both blockchain-based and traditional AI asset markets, which is essential for effective portfolio optimisation and risk management. Inderscience Publishers - linking academia, business and industry through research

Title: Forecasting artificial intelligence assets' future volatility: evidence from explainable machine learning

Authors: Rizwan Ali; Jin Xu; Shahbaz Hussain; Mushahid Hussain Baig

Addresses: School of Economics and Management, Southwest Jiaotong University, Chengdu, China ' School of Economics and Management, Southwest Jiaotong University, Chengdu, China; Service Science and Innovation Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu, China ' Department of Management Sciences, University of Okara, Okara, Pakistan ' School of Economics and Management, Southwest Jiaotong University, Chengdu, China

Abstract: The rapid adoption of artificial intelligence (AI) has made it essential to forecasting of AI-related assets volatility. This study employs explainable artificial intelligence methods, specifically decision tree, extra tree, random forest, gradient boosting models to forecast future volatility in AI assets. The study results reveal that the gradient boosting model achieves the highest predictive directional accuracy 80% to 90% for volatility. Furthermore, SHAP analysis identifies key predictors, which including technical indicators such as the moving average, exponential moving average, and momentum, which effectively capture short-term market sentiment, while macroeconomic variables reflect broader economic conditions. This research bridges a gap in the literature by integrating explainable machine learning with time-series forecasting of AI asset volatility, offering insights into which model and variables most effectively forecast volatility in both blockchain-based and traditional AI asset markets, which is essential for effective portfolio optimisation and risk management.

Keywords: AI asset volatility; explainable artificial intelligence; portfolio; SHAP analysis.

DOI: 10.1504/IJBSR.2026.152910

International Journal of Business and Systems Research, 2026 Vol.20 No.2, pp.151 - 183

Received: 02 Mar 2025
Accepted: 09 Jun 2025
Published online: 14 Apr 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Forecasting artificial intelligence assets' future volatility: evidence from explainable machine learning

Keep up-to-date