Time Series Forecasting of the Microsoft Corporation Stock Price

Forecasting and computation refer to the technique of predicting future market stock prices and the prices of other economic assets during the trade. Effectively estimating the stock price of the company could benefit the business in terms of higher circulation. The efficient-market theory claims that the stock market's current price reflects all the information that is currently accessible and that any small changes in stock market price are therefore intrinsically unpredictable and erratic. Others disagree, and individuals with this perspective have a wide range of models, techniques, and skills that ostensibly give them the ability to predict future price trends. Artificial Neural Networks (ANNs), Statistical Regressors, and other kinds of Neural Network training techniques can all be conceived of as approximations of mathematical functions. The feed-forward network, which uses the reverse transmission of errors methodology to modify the system's weights, is the most well-known type of ANN for forecasting stocks. The MSFT (Microsoft Inc.) stock price database, which contains historical everyday price information, served as the source for the information needed for the suggested research. The task under consideration is reliant on the MSFT stock price being predicted. We stripped the MSFT stock price database for the time frame “13-03-1986” to “24-03-2022”. Our forecast horizon is of 24 months ahead.