Implementation of ANN and GARCH for Stock Price Forecasting

Abstract

For simulating intricate goalfunctions, neural networks are a technology that is employed in artificial intelligence. The usage of artificial neural networks is becoming more popular.(ANNs) to certain sorts of tasks, for example learning to comprehend complicated sensor data collected in the real world, is one of the most effective methods of learning approaches available. The usage of time series models in financial time series prediction has grown significantly over the past decade, and their relevance in this area continues to expand. To be more specific, the goal of this research is to determine whether neural networks have the ability to predict financial time series in general, or, more specifically, whether they have the ability to predict future patterns i The stock market in the United States is characterized by the European Union, and Brazil, among other things. They are compared to a well-known forecasting approach, generalized autoregressive conditional heteroskedasticity, in this research, and their accuracy is shown to be superior (GARCH). Aside from that, the optimal network design for each data sample is developed for each data sample. According to this article, ANNs are capable of forecasting the stock markets under examination, and their resilience may be increased by varying the network topology utilized to construct them. Aside from that, the results of this research demonstrate that ANNs outperform GARCH models in terms of efficiency of statistical performance.