Perform the Stock Prediction Using the Sentiment Analysis and Time Series Forecasting Approaches to Determine the Optimal One

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Authors
Thimmaiah, Kavya
Issue Date
2024
Degree
MSc in Financial Analytics
Publisher
Dublin Business School
Rights
Abstract
Stock returns are affected by a variety of factors, among which the social media remarks of public figures are one of the more important aspects on the stock market trend. On top of that, latest news about the product of the stock also matters. In this paper, we determine the sentiment type of public figures' social media remarks from the perspective of textual sentiment, and compare them with the stock chart of the day to analyse the connection between the two. Specifically, we first construct a dataset of public figures' social remarks and classify the sentiment types, and then we use the network model BERT for training to be able to judge the sentiment type of a new remark when it is inputted, which serves as a basis for stock prediction. The experiment shows that the public figure's speech and the news will have a strong impact on the stock trading on the same day, but the impact is small for a long time, at the same time, the more influential the public figure is, the more obvious the impact on the stock. The development and wealth of countries depend heavily on the stock market. Data mining and artificial intelligence methods are required to analyse stock market data. The financial success of particular businesses is one of the important factors that has a significant impact on stock price volatility. However, news reports also have a significant impact on how the stock market moves. In this research, we use sentiment classification to use non-measurable data, such as financial news articles, to forecast a company's future stock trend. We seek to cast light on the effect of news reports on the stock market by analysing the connection between news and stock movement. Our study seeks to advance knowledge of the function of news sentiment in forecasting stock market trends. The dataset used in this study consists of news headlines from the financial news website, Financial Times, and the prediction task is to classify the direction of the stock price changes as either positive or negative. The purpose of this study is to evaluate the effectiveness of sentiment analysis for stock prediction and to compare the performance of different algorithms.