Wrapper-Enabled Feature Selection and CPLM-Based NARX Model for Stock Market Prediction

Abstract

Abstract The prices in the stock market are dynamic in nature, thereby pretend as a hectic challenge to the sellers and buyers in predicting the trending stocks for the future. To ensure effective prediction of the stock market, the chronological penguin Levenberg–Marquardt-based nonlinear autoregressive network (CPLM-based NARX) is employed, and the prediction is devised on the basis of past and the recent rank of market. Initially, input data are subjected to the features extraction that is based on the technical indicators, such as WILLR, ROCR, MOM, RSI, CCI, ADX, TRIX, MACD, OBV, TSF, ATR and MFI. The technical indicator is adapted for predicting the stock market. The wrapper-enabled feature selection is employed for selecting the highly significant features that are generated using the technical indicators. The highly significant features of the data are fed to the prediction module, which is developed using the NARX model. The NARX model uses the CPLM algorithm that is formed using the integration of the chronological-based penguin search optimization algorithm and the Levenberg–Marquardt algorithm. The prediction using the proposed CPLM-based NARX shows the superior performance in terms of mean absolute percentage error and root mean square error with values of 0.96 and 0.805, respectively.