19,011 research outputs found
Stock market price prediction using sentiment analysis: a case study of Nairobi stock exchange market
Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore UniversityStock market price prediction has become an area of research and interest for several years now due to the many challenges in making accurate price predictions due to the volatility of the data. However, the stock market is not easily predicted. Movement in the stock market is influenced by various factors such as personal fortunes, political events, individual tastes, preferences and natural disasters. People can express all these through their sentiments and opinions on the social media platforms, financial news, and blogs. The stock price does not only rely on the law of demand and supply. People’s opinions and moods also have a substantial impact on the movement of the stock prices of a company.
Recently, efforts to increase the accuracy of stock market predictions by including data from social media such as Facebook and Twitter has received a lot of attention. Social media can be regarded as an indicator of sentiments, and these are known to influence the stock market. Current models lack a clear interpretation, and it is also difficult to determine, which data is relevant for stock market prediction since there is an abundance of the same on social media.
This study proposed the use of machine learning algorithms that will be utilized in Natural Language Processing (NLP) to get opinions and sentiments from social media on a particular company's stock to predict the stock market prices. Previous studies show that public mood, opinion, and stock market price have some relation to an extent. The research used Support Vector Machine with bigram feature to perform sentiment analysis which exhibited and accuracy of 83 percent and Artificial Neural Network in Stock price prediction which had a mean squared error of 5.6. This research has proven that sentiment analysis can be incorporated in stock price prediction
The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges
Recently, large language models (LLMs) like ChatGPT have demonstrated
remarkable performance across a variety of natural language processing tasks.
However, their effectiveness in the financial domain, specifically in
predicting stock market movements, remains to be explored. In this paper, we
conduct an extensive zero-shot analysis of ChatGPT's capabilities in multimodal
stock movement prediction, on three tweets and historical stock price datasets.
Our findings indicate that ChatGPT is a "Wall Street Neophyte" with limited
success in predicting stock movements, as it underperforms not only
state-of-the-art methods but also traditional methods like linear regression
using price features. Despite the potential of Chain-of-Thought prompting
strategies and the inclusion of tweets, ChatGPT's performance remains subpar.
Furthermore, we observe limitations in its explainability and stability,
suggesting the need for more specialized training or fine-tuning. This research
provides insights into ChatGPT's capabilities and serves as a foundation for
future work aimed at improving financial market analysis and prediction by
leveraging social media sentiment and historical stock data.Comment: 13 page
Predicting the Effects of News Sentiments on the Stock Market
Stock market forecasting is very important in the planning of business
activities. Stock price prediction has attracted many researchers in multiple
disciplines including computer science, statistics, economics, finance, and
operations research. Recent studies have shown that the vast amount of online
information in the public domain such as Wikipedia usage pattern, news stories
from the mainstream media, and social media discussions can have an observable
effect on investors opinions towards financial markets. The reliability of the
computational models on stock market prediction is important as it is very
sensitive to the economy and can directly lead to financial loss. In this
paper, we retrieved, extracted, and analyzed the effects of news sentiments on
the stock market. Our main contributions include the development of a sentiment
analysis dictionary for the financial sector, the development of a
dictionary-based sentiment analysis model, and the evaluation of the model for
gauging the effects of news sentiments on stocks for the pharmaceutical market.
Using only news sentiments, we achieved a directional accuracy of 70.59% in
predicting the trends in short-term stock price movement.Comment: 4 page
Non-Parametric Causality Detection: An Application to Social Media and Financial Data
According to behavioral finance, stock market returns are influenced by
emotional, social and psychological factors. Several recent works support this
theory by providing evidence of correlation between stock market prices and
collective sentiment indexes measured using social media data. However, a pure
correlation analysis is not sufficient to prove that stock market returns are
influenced by such emotional factors since both stock market prices and
collective sentiment may be driven by a third unmeasured factor. Controlling
for factors that could influence the study by applying multivariate regression
models is challenging given the complexity of stock market data. False
assumptions about the linearity or non-linearity of the model and inaccuracies
on model specification may result in misleading conclusions.
In this work, we propose a novel framework for causal inference that does not
require any assumption about the statistical relationships among the variables
of the study and can effectively control a large number of factors. We apply
our method in order to estimate the causal impact that information posted in
social media may have on stock market returns of four big companies. Our
results indicate that social media data not only correlate with stock market
returns but also influence them.Comment: Physica A: Statistical Mechanics and its Applications 201
A Novel Distributed Representation of News (DRNews) for Stock Market Predictions
In this study, a novel Distributed Representation of News (DRNews) model is
developed and applied in deep learning-based stock market predictions. With the
merit of integrating contextual information and cross-documental knowledge, the
DRNews model creates news vectors that describe both the semantic information
and potential linkages among news events through an attributed news network.
Two stock market prediction tasks, namely the short-term stock movement
prediction and stock crises early warning, are implemented in the framework of
the attention-based Long Short Term-Memory (LSTM) network. It is suggested that
DRNews substantially enhances the results of both tasks comparing with five
baselines of news embedding models. Further, the attention mechanism suggests
that short-term stock trend and stock market crises both receive influences
from daily news with the former demonstrates more critical responses on the
information related to the stock market {\em per se}, whilst the latter draws
more concerns on the banking sector and economic policies.Comment: 25 page
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