Stock Market Price Trend Prediction using an Outlier Data Mining Algorithm

The stock market is an area where the stock prices are not constant; however, they are dynamic, nonlinear and volatile in nature. The prediction of the stock price is a challenging task with the factors such as the financial performance of the organization, politics, unexpected natural and man-made causes, and worldwide economic conditions. In concern to these issues, several analytic techniques are being developed by researchers, financial analysts, and data scientists for exploring the nature of stock market trends a novel approach is known as the Heuristic optimization-based hybrid approach for the pre-processing of data collected from the stocks. The GANN square technique for the prediction of stock price exactly. The data pre-processing involves four stages (i) ignoring the data with missing values of features, (ii) data imputation initialization, (iii) detection and deletion of outliers, and Hybrid Archimedes-based SALP Swarm Algorithm based future selection. Thus the stated approach effectively removes the unwanted noises and features from the dataset. this made it easier for the GANN square technique the prediction of stock prices effectively