Enhancing forthcoming trend estimation in trading platform based on multiphase combination of sentiment analysis and LSTM
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Authors
Trehan, Mohita
Author ORCID Profiles (clickable)
Degree
Master of Applied Technologies (Computing)
Grantor
Unitec, Te Pūkenga - New Zealand Institute of Skills and Technology
Date
2024
Supervisors
Varastehpour, Soheil
Shakiba, Masoud
Shakiba, Masoud
Type
Masters Thesis
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
New Zealand Stock Exchange (NZX)
New Zealand
stock markets
stock movement prediction
stock price analysis
sentiment analysis (SA)
Long Short-Term Memory (LSTM)
computer modelling
deep-learning algorithms
algorithms
New Zealand
stock markets
stock movement prediction
stock price analysis
sentiment analysis (SA)
Long Short-Term Memory (LSTM)
computer modelling
deep-learning algorithms
algorithms
ANZSRC Field of Research Code (2020)
Citation
Trehan, M. (2024). Enhancing forthcoming trend estimation in trading platform based on multiphase combination of sentiment analysis and LSTM (Unpublished document submitted in partial fulfilment of the requirements for the degree of Master of Applied Technologies (Computing)). Unitec, Te Pūkenga - New Zealand Institute of Skills and Technology https://hdl.handle.net/10652/6470
Abstract
Since its inception, the stock market has piqued the curiosity of researchers, and several attempts have been made to forecast its future patterns. The market is dynamic, and the value of an organisation’s stock price is influenced by a variety of elements, including previous stock data and public attitudes and opinions. Researchers have shown that the "public sentiment" which split them into two factions, one in favor and the other against is the most prevalent of these characteristics. It got simpler to analyse the relationship between them as Artificial Intelligence (AI) advanced and strong Machine Learning (ML) and Deep Learning (DL) algorithms were introduced.
The number of outlets where the public can express their opinions has grown exponentially as the world entered the internet era. The researchers have experimented with several data sources, such as social networking websites, financial news websites, online global newsrooms, Yahoo finance websites, and other forums for exchanging opinions, in order to gather information about investor and public feelings as well as stock historical data. The forecasts have become more accurate over time as a result of testing various algorithms. Most of these studies have focused on larger stock markets, such as those in the USA, India, China, Japan, and Europe. Comparatively smaller stock markets cannot use the same strategies since they are virtually independent and heavily influenced by local news and public opinion, which might differ greatly from those of the rest of the globe.
In this research, this gap has been addressed and focus has been made on the New Zealand Stock Exchange (NZX). This thesis explores the field of stock market forecasting with a particular emphasis on the NZX, a market that has received relatively little attention due to its distinct architecture and autonomous operations. In order to improve Future Trend estimation in trading platforms, this dissertation makes use of a multiphase machine learning and deep learning methods. The suggested method combines sentiment analysis with historical stock data from Yahoo Finance and financial news data from "sharechat.co.nz". Five of the largest New Zealand organisations stock data sets has been obtained. These companies has been selected because the textual data had more mentions of them than of others, which allows the sentiment scores to accurately reflect changes in the company.
In this research, Future Trend prediction models are built employing deep learning techniques, specifically Long Short-Term Memory (LSTM) networks, and five different subprocesses. Across various sub-processes, the impact of sentiment news and stock history data on Future Trend estimates is examined. Furthermore, feature selection strategies are used to reduce the possibility of overfitting.
A thorough literature analysis, a thorough explanation of the research methods, and the nuances of data collecting and pre-processing have all been included in the thesis format. It goes on to explain how the suggested prediction model was put together and clarifies the ramifications of the study’s findings.
Therefore, the principal contributions of this thesis are outlined as follows: 1) development of an innovative multiphase framework for Future Trend prediction for NZX by integrating sentiment analysis data from regional news with historical stock data; 2) deployment and comparative evaluation of both LSTM and machine learning models namely RF and SVM throughout five sub-processes, showcasing their efficacy in forecasting future patterns for five well-known New Zealand businesses and insights into how well they work and how they could potentially leveraged for boosting the accuracy of stock market trend predictions; 3) analysis of the special characteristics of the NZX, emphasising how sensitive it is to regional news and public opinion in contrast to other markets; 4) demonstration of how pre-processing methods like feature selection and data normalisation enhanced the predictive models accuracy and effectiveness; 5) recognising the difficulties and developments in acquiring and applying highquality financial news data for NZ; 6) highlighting how the prediction framework aid in improving accuracy for Future Trend prediction being used for the same goal at various sub-processes.
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