Open AccessStock Prediction using Machine Learning
Author(s) -
J. Dhilipan,
D. B. Shanmugam,
Quraishi Imran
Publication year - 2021
Publication title -
international journal of linguistics and computational applications
Language(s) - English
Resource type - Journals
eISSN - 2394-6393
pISSN - 2394-6385
DOI - 10.30726/ijlca/v8.i4.2021.84008
Subject(s) - artificial intelligence , machine learning , stock market prediction , computer science , stock market , stock (firearms) , corporation , python (programming language) , stock exchange , predictive modelling , econometrics , finance , economics , engineering , mechanical engineering , paleontology , horse , biology , operating system
Stock trading is one of the foremost activity in finance world. Stock market prediction is used to find the long run values of the stock and other financial factors influenced on a financial exchange. The technical and fundamental or the statistical analysis is employed by most of the stockbrokers while making the stock predictions. Python programming language in machine learning is used for the stock market prediction. In this paper we have proposed a Machine Learning (ML) approach which trains from the available stocks data and gain intelligence and then uses the acquired knowledge for an accurate prediction. In stock market prediction, the aim is to predict the longer term value of the financial stocks of a corporation [1]. The recent trend in market prediction technologies is that the use of machine learning approach which makes predictions supported the values of current stock market indices by training on their previous values. Machine learning itself employs different models to form prediction easier and authentic. This paper focus on Regression and Long Short Term Memory (LSTM) based Machine learning to predict stock values. The factors that are being considered include re-open, close, low, high and volume [2,3].