What question did this study set out to answer?

This research aims to forecast the volatility of artificial intelligence-related assets using explainable machine learning techniques.

April 17, 2026

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

Key Points

This research aims to forecast the volatility of artificial intelligence-related assets using explainable machine learning techniques.
Employed explainable AI methods such as decision tree, extra tree, random forest, and gradient boosting.
Utilized SHAP analysis to identify key predictors of volatility.
Analyzed both technical indicators and macroeconomic variables.
Gradient boosting model achieved predictive directional accuracy of 80% to 90% for volatility.
Identified key predictors, including moving average and momentum indicators, that capture market sentiment.
Demonstrated effectiveness of integrating explainable AI with time-series forecasting for AI asset volatility.

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.

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