The study tested different ways to predict stock market volatility using neural networks. They compared 16 models and found that combining a specific volatility model with a neural network gave the best results. They also discovered that using a certain type of volatility model and normal distribution worked better than others. Overall, using a neural network improved the accuracy of predicting stock market volatility.