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Home > Archives > Volume 20, No 7 (2022) > Article

DOI: 10.14704/nq.2022.20.7.NQ33471

Prediction and Analysis of Stock Market using ARIMA Model and Machine Learning Techniques

Ankit Bansal, Ghada Elkady, Ram Bhawan Singh, Dr Swati

Abstract

Stock markets are one of the most fascinating domains for economic growth and GDP growth - not only because of the trade and human capital that is associated with it, but also due to the unpredictability of the profits changing hands. Predicting the future and performance of any stock market can hoard fortunes in the good times and minimize losses when the winds have changed. Predicting the future value of exchange-traded company stock or other financial instruments is known as "stock market prediction". Furthermore, if news sentiment for a particular stock will be taken into consideration, the results are expected to be better. In order to forecast future market behavior, it makes use of market psychology, behavioral economics, and quantitative analysis. A good prediction of the future price of a stock could yield considerable benefit. In this paper we tend to find an ideal combination of different prediction models - LSTM, Prophet and ARIMA so that the prediction can be as close to the original as possible. This will help in gauging the market better for particular stocks and reduce the error gap with the use of technology. It is majorly focused on determining whether or not a current trend will continue and, if not, when it will reverse.

Keywords

Stock Market, Machine Learning, LSTM, Network Infrastructure, Architecture, ARIMA, Prophet by Facebook, News Sentiment Analysis, Prediction, Integrated Model, Economic Growth

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