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Predictive AI uses advanced deep learning architectures to analyze structured and unstructured datasets to forecast financial market volatility. These models can identify complex relationships across large amounts of market data. Whether that translates into consistently accurate forecasts and whether predictive AI is inherently better than traditional models is another question.
Predictive AI can predict market volatility to an extent, and we're seeing it happen through machine learning algorithms such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU) that process and analyze nonlinear patterns in historical prices and trading volumes to predict future volatility and trading outcomes.
Natural Language Processing models such as FinBERT analyze financial news headlines and economic announcements to identify sentiment that can be incorporated into volatility forecasts.
How consistently accurate they are is a different story. Financial markets can react to unexpected events, and patterns identified in historical data don't always hold when market conditions change.
Research by Eghbal Rahimikia and Ser-Huang Poon found that high-performance machine learning models can provide better forecasting ability than traditional HAR models in normal market conditions. Their study tested volatility forecasting across 23 NASDAQ stocks using data from 2007 to 2022, and the results showed that more advanced models outperformed the HAR benchmark during approximately 90% of the out-of-sample period.
However, machine learning performance deteriorated during periods of extreme volatility, precisely when accurate forecasts can be particularly important.
All that shows is room for development, and predictive AI is constantly improving. And the models can still identify nonlinear relationships between financial variables that simpler statistical models may miss, potentially improving volatility forecasts.
AI can also analyze much larger and more varied datasets, allowing traders and analysts to consider information beyond historical price movements.
Traders still rely on classic technical and fundamental analysis frameworks to analyze historical price movements and trends (technical) or company earnings and economic conditions (fundamental). Both are the backbone of trading strategies, whether it's CFD trading and analyzing price behavior and transaction costs or the underlying financial health and business model of stocks before buying company shares.
What can be said, however, is that for CFD trading, which trades price movements of an underlying asset without actually owning it, predictive AI has a lot of potential in tracking historical price movements and technical indicators.
That said, machine learning is still not at the point of interpreting unfamiliar events and understanding whether historical patterns remain relevant.
Traditional models such as GARCH generate time-dependent volatility models in financial markets through mathematical expressions. The conditional volatility forecasts it generates help traders identify market risks and price fluctuations.
HAR is also still essential, analyzing historical realized volatility over daily, weekly, and monthly periods to estimate future conditions. Both can be particularly useful when assessing risk in leveraged products.
For nonlinear financial dynamics, predictive AI will outperform traditional statistical models under normal market conditions. But limitations remain in highly volatile markets, which is why the technology is not yet fully reliable.
