Research on Financial Investment Risk Assessment Techniques and Applications

Abstract

In recent years, the financial markets have undergone rapid development and significant volatility, leading to a pronounced urgency and complexity in investment risk assessment. This study explores the technologies and applications of financial investment risk evaluation, offering an in-depth analysis of the theoretical foundations and practical operations of quantitative, qualitative, and hybrid assessment methodologies. Traditional quantitative techniques, such as the Value at Risk (VaR) model, provide crucial objective data support, while qualitative approaches enrich the breadth of risk identification through expert opinions and seasoned experience. Hybrid methods synergistically combine the strengths of both, adapting to the intricacies of the market environment. The integration of big data and artificial intelligence technologies injects innovative momentum into risk assessment, enabling more precise and timely risk monitoring. Case applications span multi-layered risk management strategies within banks, insurance companies, and hedge funds, offering valuable insights for diverse financial institutions. The study posits that efficient risk assessment relies not only on technological advancements but also necessitates consideration of market dynamics and regulatory environment fluctuations. Continual innovation and practical optimization are pivotal in addressing future challenges.