Energy Landscape-Driven Financial Prediction: Explainable Design and Cross-Architecture Validation

Financial market prediction remains a key challenge in quantitative research, especially under conditions of high volatility and uncertainty. Traditional point prediction methods struggle to capture the complex dynamics of markets, while interval predictions, although providing uncertainty estimates, typically lack theoretical foundations and explainability. This paper proposes a financial prediction framework based on energy landscape theory, which introduces the concept of energy from physics into financial forecasting. By constructing energy functions that reflect the stability of market states, our approach achieves reliable predictions of price movement ranges. Our method not only provides prediction results but also offers intuitive, explainable energy representations, enabling analysts to understand the reasoning behind model decisions. Experiments show that the proposed energy landscape framework enhances prediction performance across most tested machine learning architectures. On key metrics such as coverage, interval width, and calibration, our method generally outperforms standard approaches. Through extensive testing across different market conditions and asset classes, we validate the applicability and potential value of this framework. This research not only provides a new theoretical perspective for financial prediction but also delivers practical tools for building more reliable and explainable prediction models.