Application Of Deep Learning Techniques In Financial Forecasting And Portfolio Management

Abstract

This study, titled "Application of Deep Learning Techniques in Financial Forecasting and Portfolio Management," evaluates the predictive accuracy, model architecture allocations, risk-adjusted returns, and financial feasibility of artificial neural networks in asset management. Modern investment portfolios operate under high-dimensional market noise, complex cross-asset correlations, and regime shifts. A five-year project lifecycle (2021-2025) of a deep learning portfolio optimization platform is evaluated using capital budgeting parameters: Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), and Benefit-Cost Ratio (BCR). Quantitative analysis indicates that Long Short-Term Memory (LSTM) networks represent 42% of deep learning model deployments. Deploying attentionbased Transformer models reduces return forecasting Mean Absolute Error (MAE) to 0.6% compared to 4.8% under traditional ARIMA models. Improved forecasting accuracy raises portfolio forecasting precision from 72.4% to 97.5%, lowering maximum drawdown from 22.5% to 2.8% and supporting an Assets Under Management (AUM) growth to 4,200 Crores alongside a Sharpe ratio of 2.82 by 2025. The financial model yields a positive NPV of 284.5 Crores and an IRR of 38.6%, far exceeding the 10% discount hurdle rate. The study concludes that investing in deep learning portfolio platforms is highly viable, enabling institutional fund managers to maximize risk-adjusted alpha. Keywords: Deep Learning, Financial Forecasting, Portfolio Management, LSTM Networks, Transformer Models, Sharpe Ratio, Capital Budgeting, Financial Feasibility.