Stock Market Value Prediction Based On Machine Learning Approach - Chew, Min Wei (2023)
Stock market price prediction has been an attractive area to many researchers particularly in the field of Machine Learning (ML) and time series analysis. With a good prediction model, investors can maximize returns with minimal risks in portfolio. However, not every investment guarantees with returns. This is due to the traditional ways of stock price prediction requires every investor to acquire technical skills and financial knowledge. Hence, traditional way of predicting the stock market is slowly replaced by Artificial Intelligent (AI) to improve the accuracy of prediction model and lower the requirement for investors to be success in the stock market. As a human, mental and physical limitations constraint the ability to perform task perfectly and effectively in the entire life. Stock market is influenced by many aspects and the fluctuations will cause most of the human to do emotional and bad investment decision. Hence, an automated stock price predictor based on machine learning plays an important role in helping investors to make a good investment decision. Study and analysis the existing prediction model is crucial to build and implement a machine learning based stock price predictor. The predictor built has to produce a visual graph for validation and verification purpose to make sure the efficiency and accuracy of model so that it is easy for investors even beginners to follow. A machine learning based stock price predictor will provide guidelines that enable investors to make rational investment decision with a relatively low risk of losing money.