Predictive Analytics in Algorithmic Trading: Evaluating Model Robustness Under Market Volatility

Predictive analytics has emerged at the heart of the development of algorithmic trading allowing traders to identify trends in intricate financial data sets, predict market volatility and maximize execution algorithms. Though these models tend to perform well in steady states, their resilience is commonly challenged when markets are hit with sudden shocks or regime changes. This article analyses the performance of various prediction methods ranging from traditional statistical models to complex machine learning and deep learning architectures under unstable environments. Previous research indicates that AI-based trading systems are capable of providing higher returns and better risk management than traditional methods. Yet, empirical evidence indicates that algorithmic strategies could feed back instability due to feedback loops, data drift and investor sentiment dynamics. Novel adaptive and ensemble models show promise to enhance stability and predictive performance under uncertainty. Through integration of recent literature insights, this paper presents a framework to assess model robustness with a focus on trade-offs between accuracy, interpretability and adaptability. The results identify predictive analytics not as a promise of certainty but as a means to develop resilient strategies capable of navigating turbulent financial markets.