Stock Prediction using Machine Learning

Author
Dr. J. Dhilipan, D. B. Shanmugam, Imran Quraishi
Keywords
Long Short Term Memory; LSTM; Tensorflow; Neural Network Module.
Abstract
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].
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Received : 15 March 2021
Accepted : 18 December 2021
Published : 26 December 2021
DOI: 10.30726/ijlca/v8.i4.2021.84008

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