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The stock market's inherent volatility and complexity pose significant challenges for investors seeking to optimize their strategies. This thesis addresses the critical need for improved forecasting methods in stock price prediction by proposing a hybrid approach that combines traditional machine learning (ML) techniques, specifically Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) networks, with sentiment analysis derived from financial news and social media platforms. The research establishes a theoretical framework integrating quantitative data, such as historical stock prices, with qualitative sentiment data to enhance prediction accuracy. The study involves the collection of a comprehensive dataset covering stock prices and sentiment scores from various sources, including news articles and social media posts, from January 2010 to December 2023. Rigorous data preprocessing techniques, including normalization and feature engineering, are employed to prepare the data for analysis. A comparative analysis of the SVM and LSTM models uses multiple performance metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and classification accuracy. The findings reveal that the LSTM model significantly outperforms the SVM model in predictive accuracy, demonstrating its capability to capture complex temporal dependencies inherent in financial time series data. Furthermore, integrating sentiment analysis significantly enhances the predictive performance of both models. Notably, transformer-based sentiment analysis techniques, such as BERT and DistilBERT, provide superior sentiment classifications compared to traditional methods like VADER and TextBlob. The empirical results indicate that incorporating sentiment data leads to an average accuracy improvement of 12.8% over models that rely solely on historical price data. This research contributes to the evolving field of financial forecasting by emphasizing the importance of a hybrid approach that amalgamates quantitative and qualitative data. The implications of these findings extend beyond academic research, offering valuable insights for investors and financial analysts seeking to leverage advanced predictive models to navigate market uncertainties. Ultimately, this dissertation advocates adopting sophisticated hybrid models that enhance stock investment strategies and decision-making processes in the finance sector.
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There are many systematic reviews on predicting stock. However, each reveals a different portion of the hybrid AI analysis and stock prediction puzzle. The principal objective of this research was to systematically review the existing systematic reviews on Artificial Intelligence (AI) models applied to stock market prediction to provide valuable inputs for the development of strategies in stock market investments. Keywords that would fall under the broad headings of AI and stock prediction were looked up in Scopus and Web of Science databases. We screened 69 titles and read 43 systematic reviews, including more than 379 studies, before retaining 10 for the final dataset. This work revealed that support vector machines (SVM), long short-term memory (LSTM), and artificial neural networks (ANN) are the most popular AI methods for stock market prediction. In addition, the time series of historical closing stock prices are the most commonly used data source, and accuracy is the most employed performance metric of the predictive models. We also identified several research gaps and directions for future studies. Specifically, we indicate that future research could benefit from exploring different data sources and combinations, while we also suggest comparing different AI methods and techniques, as each may have specific advantages and applicable scenarios. Lastly, we recommend better evaluating different prediction indicators and standards to reflect prediction models’ actual value and impact.
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Predicting stock prices is difficult because of their multiple input variables, volatility, and unpredictable nature. To provide a suitable model for forecasting the global stock market, this study conducts an exploratory analysis comparing two models based on Artificial Intelligence: Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) Neural Networks. The work considers a publicly accessible dataset and uses feature engineering to extract time-series features. Stock price predictions are made using the SVM and LSTM algorithms. For this purpose, Accuracy (ACC) and Root Mean Squared Error (RMSE) are considered accuracy and performance measures. According to the results, LSTM with mean accuracy (ACC) = 0.9061 achieved better accuracy than SVM with mean accuracy (ACC) = 0.881. SVM with mean RMSE = 0.729 achieved better performance and the degree of fit to the data than LSTM with mean RMSE = 427.1. According to the results, the study demonstrates the effectiveness and applicability of machine learning methods for estimating the values of the global stock market and providing valuable models for researchers, analysts, and investors.
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Artificial intelligence (AI) and deep learning (DL) are advancing in stock market prediction, attracting the attention of researchers in computer science and finance. This bibliometric review analyzes 525 articles published from 1991 to 2024 in Scopus-indexed journals, utilizing VOSviewer software to identify key research trends, influential contributors, and burgeoning themes. The bibliometric analysis encompasses a performance analysis of the most prominent scientific contributors and a network analysis of scientific mapping, which includes co-authorship, co-occurrence, citation, bibliographical coupling, and co-citation analyses enabled by the VOSviewer software. Among the 693 countries, significant hubs of knowledge production include China, the US, India, and the UK, highlighting the global relevance of the field. Various AI and DL technologies are increasingly employed in stock price predictions, with artificial neural networks (ANN) and other methods such as long short-term memory (LSTM), Random Forest, Sentiment Analysis, Support Vector Machine/Regression (SVM/SVR), among the 1399 keyword counts in publications. Influential studies such as LeBaron (1999) and Moghaddam (2016) have shaped foundational research in 8159 citations. This review offers original insights into the bibliometric landscape of AI and DL applications in finance by mapping global knowledge production and identifying critical AI methods advancing stock market prediction. It enables finance professionals to learn about technological developments and trends to enhance decision-making and gain market advantage.
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