STUDY ON INVESTMENT APPROACHES IN STOCK MARKET FOR PRICE PREDICTION AND FRAUD DETECTION (WITH SPECIAL REFERENCE TO ARTIFICIAL INTELLIGENCE SYSTEMS)

Abstract

The stock market is crucial to a nation's social and economic structure. Due to the heightened risk associated with stock market investing, stock market prediction is an important and well-known research field in the financial industry. However, most uncertainty can be reduced with the development of computationally sophisticated technologies. The writing on utilizing information-digging procedures for the recognizable proof of monetary extortion is audited and classified in this exploration. The current literature on stock market price prediction using computational forecasting techniques is reviewed in this paper. The chosen papers are arranged and discussed in this article based on six primary points of view: (1) the particular stock market and the corresponding dataset, (2) the various types of input variables, (3) the pre-processing techniques, (4) the relevant feature selection techniques to select useful variables, (5) the prediction models used for Forecasting to address the stock price prediction problem, and (6) the metrics applied to assess the performance of the models. The main output of this work is to offer researchers and financial analysts a systematic way to create viable stock market forecasting methodologies. The primary commitment of this work is to offer researchers and analysts a calculated way to make sharp financial exchange estimating strategies. This report likewise incorporates the reasonable system for future work that intends to work on the viability of current strategies.