Machine Learning vs. Traditional Models: Predicting Stock Volatility

Predicting how much stock prices swing day to day is tough. It matters a lot for investors, banks, and even government rules. Most past research looked at big market indexes like the VIX. But this study zooms in on individual stocks instead. It compares old-school stats methods with newer AI tools. The goal? See which one guesses future volatility better.

The study picked ten famous U.S. companies. They came from tech, finance, and energy sectors. Data ran from January 2018 to December 2024. That’s over 1,700 trading days of info. Five models were tested. Two were classic: GARCH(1,1) and GJR-GARCH(1,1,1). These use math formulas to spot patterns in past prices. The other three used machine learning. One was Random Forest, a tree-based AI method. The other two were deep learning models: LSTM and GRU. Both are types of neural networks built to handle time-based data.

Each model tried to predict volatility over five days. The output was annualized realized volatility. To check accuracy, the team used several scorecards. RMSE and MAE measured average error. QLIKE checked how well models handled extreme moves. VaR and ES tested risk levels. Kupiec backtest saw if predictions matched real outcomes. They also grouped market moods using K-Means clustering. An ablation study showed which inputs mattered most. Lastly, they timed how fast each model trained.

Results showed Random Forest won on speed and accuracy. Its mean RMSE was just 0.0892. That beat both LSTM and GRU. Plus, it trained about seven times faster than the deep learning models. GRU came in second place overall. It did better than LSTM on most tests. The classic GARCH models still held value. They worked well for setting risk limits and checking VaR. But they lagged behind machine learning when it came to raw prediction power.

So what does this mean? Machine learning can beat traditional stats when guessing stock swings. Especially when you care about speed and low error. But old methods aren’t dead yet. They still help with safety checks and rule-following tasks. The best choice depends on what you need: fast guesses or solid risk control.