Article Preview
Top2. Literature Review
There are numerous research studies that use similar indicators to forecast the direction of the stock market index. Much related work has been done on time series and modelling. A few reviews validated that machine learning and modelling have been the best technique to predict stock prices.
Ngabesong and McLauchlan (2019) “Implementing ‘R’ Programming for Time Series Analysis and Forecasting of Electricity Demand for Texas, USA” forecasted electric supply for Texas on the basis of historical data of one year on a one-point data from September 2016 to August 2017. The Auto Regressive Integrated Moving Average (ARIMA) model was used to estimate future predictions of electricity demand for Texas. It was concluded that the electricity demand would be on the rise for the next year and could also predict when peak shaving would be required.
Chauhan (2019), in his article on “Stock market forecasting using Time Series analysis” used the dataset consists of stock market data of Altaba Inc. which was retrieved from kaggle.com. from the year 1996 to 2017 for analysis. The Box Jenkins methodology (ARIMA model) was trained and predicted the stock prices on the test dataset.
Waqar et al (2017), “Prediction of Stock Market by Principal Component Analysis” conducted experiments on high dimensional spectral of 3 stock exchanges namely New York Stock Exchange, London Stock Exchange and Karachi Stock Exchange. The trend of three stock exchanges by using linear regression as a classification model and further to test the accuracy Principal component analysis, PCA was applied to predict the trend.
Roy et al. (2015), in their research paper “Stock Market Forecasting Using LASSO Linear Regression Model” proposed that the unique method of predicting financial market behaviour which was found to be far superior to the ridge linear regression model was through the Least Absolute Shrinkage and Selection Operator (LASSO) method based on a linear regression model. The model was experimented on the Goldman Sachs Group Inc. stock.