Deep Learning-based Financial Market Volatility Forecasting and Investment Strategy Optimization

Abstract

Financial market volatility exhibits non-linearity, non-stationarity and high complexity. Traditional quantitative analysis and statisti
cal models fail to accurately capture the intrinsic patterns of market volatility, resulting in latency and systemic constraints in investment deci
sion-making. Deep learning, relying on its powerful nonlinear fitting and feature mining capabilities, has become the core technical means for
addressing the problems in market volatility forecasting, and has concurrently established novel paradigm pathways for investment strategy
optimization. This paper, based on the operation laws of the financial market and the characteristics of deep learning technology, elucidates
the core logic of applying deep learning to financial market volatility forecasting, analyzes its technical advantages and practical challenges
in the volatility forecasting process, explores the specific pathways for optimizing investment strategies through deep learning, and concur
rently systematize risk mitigation imperatives throughout technological application lifecycles, aiming to promote the deep integration of deep
learning technology and financial investment practice, enhance the scientific rigor and stability of investment decision-making, and facilitate
market participants to mitigate volatility risk and optimize the return on investment.