Optimization and Risk Assessment Application of Financial Time Series Forecasting Models Based on Machine Learning

Abstract

Financial time series forecast has always been a difficult question because capital market is inherently nonlinear and noisy. Traditional econometric models can't catch the complicated patterns so people have lots of interest in machine learning (ML), deep learning. This paper studies how to optimize and apply advanced neural networks such as LSTMs and GRUs in financial prediction tasks. We propose a framework for hyperparameter optimization with Bayesian optimization, and show that this approach outperforms random parameter selection. We take daily closing prices from the SP500 index over a ten-year period and compare the results of optimized models against both traditional statistics based benchmarks (ARIMA) as well as non-optimized ML models. The Results show that hyperparameter training gives considerable improvement in forecasting accuracy as optimized GRU shows Lowest RMSE and MAE. And also, the better forecasts from this study are proven practical with the aid of a risk assessment framework. We use the prediction outputsto find out the Value -at -Risk, or VaR, and this shows that the better forecasts that we get from the model that has been adjusted are able to give us more exact and correct information about risks. This kind of information is very important when it comes to taking care of our money, and following what the bosses say. This research points out how important it is to properly fine-tune models when we use ML stuff for finance stuff and shows us that such improved models really can make our work with numbers better.