Stock market prediction using Firefly algorithm with evolutionary framework optimized feature reduction for OSELM method

Forecasting future trends of the stock market using the historical data is the exigent demand in the field of academia as well as business. This work has explored the feature optimization capacity of firefly with an evolutionary framework considering the biochemical and social aspects of Firefly algorithm, along with the selection procedure of objective value in evolutionary notion. The performance of the proposed model is evaluated using four different stock market datasets, such as BSE Sensex, NSE Sensex, S&P 500 index and FTSE index. The datasets are regenerated using the proper mathematical formulation of the fundamental part belonging to technical analysis, such as technical indicators and statistical measures. The feature reduction through transformation is carried out on the enhanced dataset before employing the experimented dataset to the prediction models such as Extreme Learning Machine (ELM), Online Sequential Extreme Learning Machine (OSELM) and Recurrent Back Propagation Neural Network (RBPNN). For feature reduction, both statistical and optimized based feature reduction strategies are considered, where Principal Component Analysis (PCA) and Factor Analysis (FA) are examined for statistical based feature reduction and Firefly Optimization (FO), Genetic Algorithm (GA) and Firefly algorithm with evolutionary framework are well thought out for optimized feature reduction techniques. An empirical comparison is established among the experimented prediction models considering all the feature reduction techniques for the time horizon of 1 day, 3 days, 5 days, 7 days, 5 days and 30 days in advance, applying on all the datasets used in this study. From the simulation result, it can be clearly figured out that firefly with evolutionary framework optimized feature reduction applying to OSELM prediction model outperformed over the rest experimented models.