FIN-MIND: A Multi-Dimensional TCN Framework for Joint Stock Price Forecasting and Financial Risk Assessment

Forecasting stock prices and assessing financial risks are two intertwined challenges that remain central to modern quantitative finance. This paper presents \textbf{FIN-MIND}, a novel multi-dimensional temporal convolutional framework that jointly predicts stock prices and financial risk measures through decoupled attention mechanisms. Unlike unified-attention architectures that suffer from feature interference, FIN-MIND introduces two independent attention streams dedicated to price and risk modeling, thereby disentangling short-term momentum dynamics from long-term volatility structures. The model enforces strict temporal causality and employs a leakage-aware rolling evaluation protocol to ensure reliable out-of-sample performance. Extensive experiments on large-cap equities (AAPL and TSLA) from 2015 to 2024 demonstrate consistent improvements over classical econometric, recurrent, and transformer-based baselines. FIN-MIND achieves superior accuracy in both price forecasting and risk estimation, with significant gains across multiple random seeds. Beyond empirical performance, this framework provides a generalizable foundation for interpretable, causality-preserving financial forecasting and offers new insights into multi-task learning under non-stationary market conditions.