Stock Price Prediction Using Time Series Forecasting

Abstract

Abstract: Accurately predicting stock market returns is a very difficult task due to the volatile and non-linear nature of financial stock markets. With the advent of artificial intelligence and increasing computing power, programmed forecasting methods have proven to be more efficient in predicting stock prices. In this work, artificial neural networks and random forest techniques are used to, predicted the next day's closing prices for five companies in various industries. Financial Data: Use the opening, high, low and closing prices to create new variables that will be used as inputs to the model. The model is evaluated against the standard strategy metrics RMSE and MAPE. The low values of these two indicators indicate that the model is efficient in predicting the closing price of the stock. Accurately predicting stock market returns is a very difficult task due to the volatile and non-linear nature of financial stock markets. With the advent of artificial intelligence and increasing computing power, programmed forecasting methods have proven to be more efficient in predicting stock prices. In this work, artificial neural networks and random forest techniques are used to, predicted the next day's closing prices for five companies in various industries.