Abstract
The stock market forecast includes forecasting the future value of the company's shares or other financial instruments traded on the stock exchange. Different types of trading can be performed on the stock exchange. It can be a short-term trade or even a long-term trade, but if someone can predict the value or valuation of this company it can bring a very good return on investment. Before the development of the digital world, predictors continued to use paper-based working methods such as fundamental and technical analysis. Various useful technical indicators such as SMA, EMA, and MACD have proven to be very useful; however, with the advent of computer technology and prediction algorithms, they have moved into the technological field. Analysts began by building a prediction system with a neural network, a tracking vector machine, decision trees, and a hidden Markov model. Prediction accuracy is improved by using an algorithmic approach and evolutionary data mining techniques used to predict the stock market. Keywords: Stock Market, Data mining, Support Vector Machine, Neural Network. DOI: 10.7176/MTM/11-6-02 Publication date: December 31 st 2021
Highlights
Stock market forecasts have always attracted researchers
This article concludes that several approaches and techniques are available to increase the return on investment in the stock market, each method has its advantages and limitations
Simple Moving Averages (SMA) smooths out price movements by removing most counterfeits, which delays buy and sell signals
Summary
Many scientific experiments have been conducted, no method has been found that can accurately predict movements in stock price. The basic analysis assumes that the price movement in the stock market is based on the relative data of the security. Fundamentalists use numerical data such as income, trade, and management efficiency to determine future projections. Technicians use graphical and technical models to identify prices and quantitative trends.
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