Counterfactual Explanation-Based Cryptocurrency Price Prediction.

Area of Science:

• Artificial Intelligence
• Financial Technology
• Computational Finance

Background:

• Deep learning models excel at cryptocurrency forecasting but lack interpretability and trustworthiness.
• Cryptocurrency markets exhibit high volatility and complex non-linear dynamics, necessitating robust risk management.
• Understanding model sensitivity to historical data variations is crucial for reliable financial predictions.

Purpose of the Study:

• To propose the Cryptocurrency Counterfactual Explanation (CryptoForecastCF) model for enhancing interpretability in cryptocurrency forecasting.
• To address the trustworthiness gap in deep learning models used for financial predictions.
• To provide actionable insights for traders and risk managers in volatile cryptocurrency markets.

Main Methods:

• Developed CryptoForecastCF, a model utilizing gradient-based optimization for counterfactual explanations.
• Identified minimal, norm-constrained perturbations (l1 or l2) to historical market features (e.g., price).
• Generated counterfactual explanations to steer model predictions into user-specified target intervals.

Main Results:

• CryptoForecastCF elucidates key driving factors and decision boundaries of opaque forecasting models.
• The model identifies specific market shifts needed to achieve desired predictive outcomes.
• Actionable insights are provided for navigating high-stakes scenarios and mitigating unfavorable predictions.

Conclusions:

• CryptoForecastCF enhances the interpretability and trustworthiness of deep learning models for cryptocurrency forecasting.
• The approach facilitates improved risk management by revealing model sensitivities.
• Actionable counterfactual explanations empower financial professionals to make more informed decisions in cryptocurrency trading.