{"manuscript_title":"<b>Enhanced Gold Market Forecasting Using Cross-Contextual Attention with Hippopotamus-Optimized Random-Coupled Neural Networks</b>","abstract":"The global gold market is highly impacted by numerous ever-changing financial, economic, and geopolitical elements, making accurate prediction a complex and critical task in financial forecasting. The complex temporal dependencies and contextual interactions included in traditional models are frequently difficult to describe such high-dimensional, multi-source financial data. This study proposes a new supervised deep learning model for forecasting financial markets in the global gold market using an Enhanced Gold Market Forecasting Using Cross Random Contextual Hippopotamus coupled Attention Network (Cross-RCH-CAN). The input dataset, titled \"financial gold market\", comprises 200 daily business-day observations, excluding weekends, and includes key indicators such as gold prices, USD index, oil prices, inflation and interest rates, stock market index, unemployment rate, geopolitical risk, gold production and demand, and ETF holdings. The data undergoes pre-processing using Zero-Shot Text Normalization, followed by feature extraction through the Kolmogorov-Arnold Vision Transformer, capturing complex dependencies and structural patterns. Prediction is performed using the proposed Cross Random Contextual Hippopotamus coupled Attention Network (Cross-RCH-CAN), which integrates a Random-Coupled Neural Network with a Cross-Contextual Attention Mechanism, and is optimized using the Hippopotamus Optimization (HO) algorithm to fine-tune learning parameters. This novel model achieves an outstanding prediction accuracy of 99.9%, demonstrating its robustness and precision. The proposed method enhances interpretability and effectively captures non-linear dependencies in volatile financial data, offering improved forecasting stability and reliability.","keywords":["Gold market prediction","financial forecasting","cross-contextual attention","random-coupled neural network","Hippopotamus optimization","Kolmogorov-Arnold Vision Transformer","zero-shot text normalization"]}