A comparative study of predictive algorithms for business analytics and decision support systems: Finance as a case study

A novel hybrid model based on Hodrick–Prescott filter and support vector regression algorithm for optimizing stock market price prediction

“…To assess the performance of the overall three algorithms, several experiments have been conducted using real world financial time series. According to our comparative study we could clearly conclude that the support vector regression is the best prediction algorithm that can be execute to provide the financial time series forecasting [24,28]. The architecture of our proposed framework is shown in Fig.…”

“…There are different families of regression algorithms and different ways of measuring the error [23,24].…”

“…Constructing a decision tree is all about finding attribute that returns the highest standard deviation reduction (i.e., the most homogeneous branches) [24,25].…”

“…16 can be written as where β is the vector of regression coefficients and X is the model matrix. The model matrix has columns corresponding to the regressor variables x 1 , x 2 , …, x m , columns for interaction terms of any order, and a column of one's defining the intercept [24,26].…”

“…The selection of kernel function is important to the effectiveness of Support Vector Regression. However, there is no mature theory in the selection of kernel function of SVR [24,27].…”