Stock Price Predictions using Crossover SMA

A time series is a graphed collection of data points in chronological sequence. Time series analysis methods are used to derive relevant statistics and other data features. Traditional statistical methods of direct hands-on data analysis were employed for predictions because data was simple and volume was low. As a rise in data volume and complexity, researchers are investigating more sophisticated technologies that can act directly on data for predictions. Stocks are the most volatile investment, with a significant risk of loss, but a high return on investment if handled properly. The value of price information in stock management cannot be underestimated. Capital markets are used to acquire and sell equity and debt instruments, as well as activities such as public offering, stock trading, and stock issued by public companies. In many countries, Stock exchanges are considered major players in financial sectors. To advise client, stockbrokers (who execute stock trade) use technical, fundamental or time series analysis to predict stock prices. In time series forecasting moving averages are often used as they are straightforward and it is common type of smoothing. Moving averages are used in technical analysis of stocks to forecast market movements. Two techniques are represented in this paper for prediction, using the simple moving average approach to predict prices and the simple moving average crossover strategy to generate buy/sell signals. The predictions are done using the Ultratech cement dataset [4] from 2018 to 2021. Fortis dataset [5] is used to forecast buy/sell signals. Both datasets are taken from the ‘Yahoo Finance’ platform; because of the instability of stock prices, this technique can't anticipate exact times and can't be used as a profit standard.