Stock Closing Price Prediction using Machine Learning Techniques

Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. With the introduction of artificial intelligence and increased computational capabilities, programmed methods of prediction have proved to be more efficient in...

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Bibliographic Details
Published in:	Procedia computer science Vol. 167; pp. 599 - 606
Main Authors:	Vijh, Mehar, Chandola, Deeksha, Tikkiwal, Vinay Anand, Kumar, Arun
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 2020
Subjects:	Artificial Neural Network Random Forest Regression Stock market prediction Random Forest Regression Artificial Neural Network Stock market prediction
ISSN:	1877-0509, 1877-0509
Online Access:	Get full text
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Summary:	Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. With the introduction of artificial intelligence and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices. In this work, Artificial Neural Network and Random Forest techniques have been utilized for predicting the next day closing price for five companies belonging to different sectors of operation. The financial data: Open, High, Low and Close prices of stock are used for creating new variables which are used as inputs to the model. The models are evaluated using standard strategic indicators: RMSE and MAPE. The low values of these two indicators show that the models are efficient in predicting stock closing price.
ISSN:	1877-0509 1877-0509
DOI:	10.1016/j.procs.2020.03.326