FINANCIAL TIME SERIES FORECASTING USING ADAPTIVE RISK METRICS AND TRANSFORMER MODELS

Accurate financial time series forecasting is essential for risk management, portfolio optimization, and trading strategies. Traditional statistical models and classical machine learning approaches often struggle to capture the complex dependencies and volatility of financial markets. Recent advancements in deep learning, particularly transformer-based architectures, have shown significant promise in modeling sequential financial data. However, integrating dynamic risk assessment into forecasting models remains an open challenge. This study proposes a transformer-based financial time series forecasting framework that incorporates adaptive risk metrics to improve predictive accuracy and risk-aware decision-making. The model leverages self-attention mechanisms to capture long-range dependencies in financial data while integrating dynamic volatility measures, value-at-risk (VaR), and conditional value-at-risk (CVaR) as additional input features. By incorporating these adaptive risk factors, the model enhances its ability to anticipate market fluctuations and adjust forecasts accordingly. Experiments on real-world financial datasets demonstrate that the proposed approach outperforms traditional autoregressive models, recurrent neural networks (RNNs), and baseline transformer architectures in terms of predictive accuracy and risk-adjusted performance. The results highlight the importance of integrating risk-sensitive metrics into deep learning-based financial forecasting models, offering a more comprehensive approach for market analysis and investment decision-making.