NORMA eResearch @NCI Library

Stock Market Prediction Approach in United Kingdom

Pandhure, Sonali (2019) Stock Market Prediction Approach in United Kingdom. Masters thesis, Dublin, National College of Ireland.

[img]
Preview
PDF (Master of Science)
Download (1MB) | Preview
[img]
Preview
PDF (Configuration manual)
Download (3MB) | Preview

Abstract

Now a day's Stock market prediction has become an attractive and interesting thing for data analysts.In this research most of the models such as Time series and machine learning techniques are used for stock market prediction. London Stock market is the trending topic for all human beings and it will remain trending in future because each and every person has started investing in stocks. In this research London stock market data is analysed and predicted depending on Brexit discussion impact happened in month October 2019 on London stock market. Also Brexit is a very trending thing in UK and it has very high impact on parliament and on entire European unions. Methodologies are used for this London stock market predictions are ARIMA time series, Random Forest, logistic regression, multiple regression, and Naïve Bayes model. ARIMA is used fore forecasting the 3 months stocks, Logistic regression is used to find impact of Brexit discussion and other model are used to predict the accuracy of the results.
Keywords: Random Forest, Logistic Regression, ARIMA time series, Naïve Bayes, multiple regression etc.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software

H Social Sciences > HG Finance > Investment > Stock Exchange
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 16 Jun 2020 10:57
Last Modified: 16 Jun 2020 10:57
URI: http://trap.ncirl.ie/id/eprint/4293

Actions (login required)

View Item View Item