A Comprehensive Survey of Predicting stock market prices: an analysis of traditional statistical models and machine-learning techniques

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dc.contributor.authorPatel, Divy
dc.contributor.authorPatel, Warish
dc.contributor.authorKoyuncu, Hakan
dc.date.accessioned2024-05-27T13:01:32Z
dc.date.available2024-05-27T13:01:32Z
dc.date.issued2024en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractThe stock market has witnessed a remarkable surge in popularity in recent years, attracting investors from all walks of life. However, predicting stock values remains a daunting task due to financial markets' inherent unpredictability and complexity. Despite these challenges, the stock market offers a dynamic and ever-changing platform for traders to invest in shares, with the potential for significant gains and losses. For investors, accurate forecasting of stock prices is crucial as it provides invaluable insights into a company's financial health and growth prospects. With this information, investors can make informed decisions, mitigate risks, and capitalize on lucrative opportunities in the market. As a result, extensive research has been dedicated to developing effective prediction methods, leveraging various mathematical models and machine-learning techniques. This research paper delves into the realm of stock market prediction, explicitly focusing on evaluating different machine-learning styles. The primary objective is to comprehensively analyze and compare the performance of these techniques in forecasting stock market behavior. By understanding the strengths and limitations of each method, investors, financial analysts, and market participants can gain critical knowledge to optimize their trading strategies and decision-making processes. To achieve this goal, the study explores an array of machine-learning algorithms, ranging from traditional linear regression models to sophisticated deep-learning approaches. These algorithms leverage historical stock market data, macroeconomic indicators, company financials, and sentiment analysis, among other factors, to predict future price movements and market trends. In addition to performance comparison, the research paper examines the impact of various factors that influence the effectiveness of these machine-learning techniques. Factors such as data quality, feature engineering, model selection, hyperparameter tuning, and market conditions play pivotal roles in the accuracy of predictions. Understanding these factors will aid in refining the model-building process and enhancing overall forecasting capabilities. The study encompasses an extensive dataset spanning multiple stock markets and periods, ensuring robustness and reliability in the findings. Performance evaluation metrics, including mean squared error, accuracy, precision, recall, and F1 score, will be employed to assess the predictive power of the machine-learning techniques objectively. Furthermore, the paper investigates the potential of ensemble methods, combining the strengths of multiple models to achieve enhanced prediction accuracy. Ensemble techniques, such as bagging, boosting, and stacking, have proven effective in diverse domains and are expected to demonstrate their value in stock market prediction. By the end of this research, readers will have a comprehensive understanding of the landscape of machine-learning techniques applied to stock market prediction. The findings will offer insights into which methods are most suitable for different market conditions and will aid in establishing best practices for effective and reliable stock market forecasting. In conclusion, this research paper serves as a valuable resource for investors, financial analysts, and researchers, thoroughly assessing machine-learning techniques' efficacy in predicting stock market behavior. It contributes to the growing body of knowledge in financial technology. It underscores the critical role of data-driven decision-making in navigating the complexities of the modern stock market.en_US
dc.identifier.citationPatel, D., Patel, W., Koyuncu, H. (2024). A Comprehensive Survey of Predicting stock market prices: an analysis of traditional statistical models and machine-learning techniques. AIP Conference Proceedings, 3107(1). 10.1063/5.0208904en_US
dc.identifier.issn0094-243X
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85193034515
dc.identifier.scopusqualityQ4
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4703
dc.identifier.volume3107en_US
dc.indekslendigikaynakScopus
dc.institutionauthorKoyuncu, Hakan
dc.language.isoen
dc.publisherAmerican Institute of Physicsen_US
dc.relation.ispartofAIP Conference Proceedings
dc.relation.isversionof10.1063/5.0208904en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBILSTMen_US
dc.subjectDeep learning approachesen_US
dc.subjectHistorical stock market dataen_US
dc.subjectLinear regression modelsen_US
dc.subjectLSTMen_US
dc.subjectMacroeconomic indicatorsen_US
dc.subjectStock price predictionen_US
dc.subjectStock Marketen_US
dc.titleA Comprehensive Survey of Predicting stock market prices: an analysis of traditional statistical models and machine-learning techniques
dc.typeConference Object

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