Forecasting Stock Market with Support Vector Regression and Butterfly Optimization Algorithm
This work addresses stock market forecasting for financial analysts, but it is incremental as it combines existing methods (BOA and SVR) for parameter tuning.
The paper tackled the problem of forecasting stock market behavior by optimizing Support Vector Regression (SVR) parameters using the Butterfly Optimization Algorithm (BOA), resulting in a model that outperformed eleven other meta-heuristic algorithms in terms of prediction accuracy and time consumption on NASDAQ stocks.
Support Vector Regression (SVR) has achieved high performance on forecasting future behavior of random systems. However, the performance of SVR models highly depends upon the appropriate choice of SVR parameters. In this study, a novel BOA-SVR model based on Butterfly Optimization Algorithm (BOA) is presented. The performance of the proposed model is compared with eleven other meta-heuristic algorithms on a number of stocks from NASDAQ. The results indicate that the presented model here is capable to optimize the SVR parameters very well and indeed is one of the best models judged by both prediction performance accuracy and time consumption.