Effect evaluation of macro-financial regulation policies based on deep learning and fuzzy algorithms

Abstract

Monetary, fiscal, and credit interventions are macro-financial regulation policies and are critical to ensuring financial stability. The nonlinear, high-dimensional, and uncertain macroeconomic and economic systems make it difficult to measure their effectiveness. The research builds a new Bi-directional Long Short-Term Memory with Multi-Head Attention Transformer tuned Takagi–Sugeno Fuzzy Network (Bi-LSTM-MHAT-TSFN) to deliver accurate, robust, and interpretable macro-financial policies evaluation. Pre-processed through normalization, missing-value handling, outlier filtering, and autoencoder-based dimensionality reduction, the input comprised a hybrid macro-financial dataset combining publicly available indicators with synthetically generated samples, and then dimensionality reduced in 2019–2025 Gross Domestic Product (GDP) growth, inflation, interest rates, credit, leverage, asset prices, and policy interventions. A Bi-LSTM network that could find patterns, lags, and structural shocks in macro-financial sequences was used to capture temporal dependencies. The MHAT module improved learning across variables by making crucial connections more obvious. The TSFN layer then turned these improved features into clear, understandable fuzzy rules that made it easier to evaluate policies. All implementation procedures were carried out using Python. The Bi-LSTM-MHAT-TSFN model showed an accuracy of 98.8%. The policy effectiveness classification attained the stable-state classification accuracy of 94.5%, early-warning classification accuracy of 92.1%, stress-state classification accuracy of 90.4%, and crisis-state classification accuracy of 96.2% with 31 percent sensitivity improvements on policy detection. These findings suggest that the MHAT-TSFN framework can be an interpretable and high-performance tool to evaluate the effectiveness of macro-financial policy, help implement regulatory responses to events, and guide evidence-based policy-making decisions in uncertain and complex macroeconomic conditions.