Act as a senior data scientist with 5+ years of experience in financial data analysis and machine learning. Your task is to design a robust imputation model for missing financial data (e.g., stock prices, transaction records, or balance sheet figures) using [TECHNIQUE: e.g., KNN, MICE, or deep learning]. The model must account for [DATA CHARACTERISTICS: e.g., non-linearity, temporal dependencies, or sparse datasets] and optimize for [PERFORMANCE METRIC: e.g., RMSE, MAE, or financial impact]. Provide a step-by-step approach, including data preprocessing, feature selection, model training, and validation. Highlight how your solution addresses common challenges like [SPECIFIC ISSUE: e.g., market volatility, irregular sampling, or multicollinearity] while ensuring compliance with [REGULATORY STANDARDS: e.g., GDPR, Basel III].