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Combining Time Series and Sentiment Analysis for Stock Market Forecasting

  • This thesis analyzes whether financial news articles can predict the stock prices of companies and the S&P 500 Index. The selected companies for the analysis are Facebook Inc., Apple Inc., Microsoft Corp., Google (Alphabet Inc.), and Amazon.com Inc. This thesis evaluates the predictive power of financial news by comparing the sentiment of financial news articles published on a business day to the corresponding closing value of stock prices and the S&P 500 Index. For the analysis, financial news from various resources is used for a time period between January 2018 and May 2018. The sentiment of the news articles is analyzed using a lexicon-based approach. Then, regression models are used to predict the stock market. The models used for forecasting the stock prices and the S&P 500 Index are Auto-Regressive Integrated Moving Average (ARIMA), Support Vector Regression (SVR), and Linear Regression (LR). The results of the research in overall indicate that ARIMA performs better for predicting the stock prices and the S&P 500 Index.

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Metadaten
Author:Emre Övey
Referee:Roland Müller
Advisor:Markus Schaal
Document Type:Master's Thesis
Language:English
Date of first Publication:2019/12/19
Publishing Institution:Hochschulbibliothek HWR Berlin
Granting Institution:Hochschule für Wirtschaft und Recht Berlin
Date of final exam:2019/08/05
Release Date:2019/12/19
Tag:ARIMA modelling; Sentiment analysis; Stock index prediction; Stock price forecasting,; Time-series Forecasting
Page Number:63
Institutes:FB I - Wirtschaftswissenschaften / Business Intelligence and Process Management M.Sc.
Licence (German):License LogoUrheberrechtsschutz