Modeling for forecasting stock market using the discrete least square method and the Lagrange Interpolation method / Maznah Banu Habiboo Raman ... [et al.] - Habiboo Raman, Maznah Banu; Mohd Azahar, Ahmad Husaini; Adam, Nur Dayana; Abdullah, Siti Aisyah (UiTM Cawangan Negeri Sembilan Kampus Seremban, 2022)
The least-squares method was used as an estimation method developed independently in 1796 by Gauss, Legendre, and Adrain in 1805 and 1808 respectively (Abazid et al., 2018). It is a method that involves statistical means by evaluating in-depth regression analysis to estimate the solution of a more determined system in which there is more unknown in a set of equations. Next, the Lagrange Interpolation method is the technique to estimate the value of a mathematical function for any intermediate value of the independent variables. The stock market is also well-known as the equity market or share market. The stock market is widely known as a platform to engage in economic transactions of selling and buying stocks or shares that are ownership claims over a business (Asalatha, 2019). The stock market, without a doubt, is an important part of a country's economy. Furthermore, stock market movements are influenced by many macro-economic factors, such as political events, firm policies, general economic conditions, commodity price indices, bank rates, and more (Patel et al., 2015). We obtained the data from Yahoo Pinance and the two mathematical modeling methods used in this study were the discrete least-square method and the Lagrange interpolation method. Microsoft Excel 2019 is used to analyze and evaluate the data to find the stock market forecast value of the discrete least-squares method as well as for the Lagrange interpolation method. Next, the sum of each data is evaluated to find errors.