[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119076-en":3,"doc-seo-119076-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119076,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Predicting Economic Trends and Stock Market Prices with Deep Learning and Advanced Machine Learning Techniques - Paper","The volatile, non-linear nature of stock market data—especially in the post-pandemic period—creates major difficulties for reliable financial forecasting. This study develops deep learning and supervised machine learning approaches to predict financial trends, quantify risks, and forecast stock prices, with a focus on the technology sector. Two RNN models, LSTM and GRU, are evaluated for efficiency, while ARIMA and Facebook Prophet plus XGBoost are used to improve robustness, showing GRU’s advantages in accuracy and training time.","electronics   \nArticle  \nPredicting Economic Trends and Stock Market Prices with Deep Learning and Advanced Machine Learning Techniques  \nVictor Chang 1, *, Qianwen Ariel Xu 1, Anyamele Chidozie 2 and Hai Wang 3  \nCitation: Chang, V.; Xu, Q.A.; Chidozie, A.; Wang, H. Predicting Economic Trends and Stock Market Prices with Deep Learning and Advanced Machine Learning Techniques. Electronics 2024, 13, 3396 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)electronics13173396  \nAcademic Editor: Simeone Marino  \nReceived: 17 June 2024  \nRevised: 15 August 2024  \nAccepted: 22 August 2024  \nPublished: 26 August 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Operations and Information Management, Aston Business School, Aston University, Birmingham B4 7ET, UK; [qianwen.ariel.xu@gmail.com](qianwen.ariel.xu@gmail.com)  \n2 School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK  \n3 School of Computer Science and Digital Technologies, Aston University, Birmingham B4 7ET, UK; [h.wang10@aston.ac.uk](h.wang10@aston.ac.uk)  \n* [Correspondence: v.chang1@aston.ac.uk or victorchang.research@gmail.com](Correspondence: v.chang1@aston.ac.uk or victorchang.research@gmail.com)  \nAbstract: The volatile and non-linear nature of stock market data, particularly in the post-pandemic era, poses significant challenges for accurate financial forecasting. To address these challenges, this research develops advanced deep learning and machine learning algorithms to predict financial trends, quantify risks, and forecast stock prices, focusing on the technology sector. Our study seeks to answer the following question: “Which deep learning and supervised machine learning algorithms are the most accurate and efficient in predicting economic trends and stock market prices, and under what conditions do they perform best?” We focus on two advanced recurrent neural network (RNN) models, long short-term memory (LSTM) and Gated Recurrent Unit (GRU), to evaluate their efficiency in predicting technology industry stock prices. Additionally, we integrate statistical methods such as autoregressive integrated moving average (ARIMA) and Facebook Prophet and machine learning algorithms like Extreme Gradient Boosting (XGBoost) to enhance the robustness of our predictions. Unlike classical statistical algorithms, LSTM and GRU models can identify and retain important data sequences, enabling more accurate predictions. Our experimental results show that the GRU model outperforms the LSTM model in terms of prediction accuracy and training time across multiple metrics such as RMSE and MAE. This study offers crucial insights into the predictive capabilities of deep learning models and advanced machine learning techniques for financial forecasting, highlighting the potential of GRU and XGBoost for more accurate and efficient stock price prediction in the technology sector.  \nKeywords: stock prices; deep learning; artificial neural networks; recurrent neural networks; long short-term memory (LSTM); gated recurrent unit (GRU)  \n1. Introduction  \nThe finance sector is a crucial domain for applying advanced deep learning (DL) and machine learning (ML) models due to its dynamic nature and the significant stakes involved in financial decision-making. Accurate financial forecasting in this sector can lead to substantial economic benefits, reduced risks, and more informed decisions. In the complex and constantly evolving world of finance, forecasting has been a key focus for many researchers over the years. The volatility and unpredictability of the stock market present significant c","cbCaieQPVaHV61nG","https://ap.wps.com/l/cbCaieQPVaHV61nG","pdf",4585135,1,27,"English","en",105,"# Abstract\n# Keywords\n# 1. Introduction\n## Finance forecasting challenges\n## Stock price drivers\n## Traditional methods and time-series models","[{\"question\":\"Why is forecasting stock market prices difficult in this study?\",\"answer\":\"Stock market data is volatile and non-linear, and the environment after the pandemic further increases unpredictability, making accurate forecasting challenging.\"},{\"question\":\"Which models are compared for predicting technology-sector stock prices?\",\"answer\":\"The study evaluates two recurrent neural network models, LSTM and GRU, for efficiency and prediction performance.\"},{\"question\":\"How do the statistical and machine learning methods contribute to the predictions?\",\"answer\":\"ARIMA and Facebook Prophet are integrated to enhance robustness, and XGBoost is used as an additional machine learning approach to improve predictive capability.\"}]","Predicting Economic Trends and Stock Market Prices with Deep Learning and Advanced Machine Learning Techniques - 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is forecasting stock market prices difficult in this study?","Question",{"text":76,"@type":77},"Stock market data is volatile and non-linear, and the environment after the pandemic further increases unpredictability, making accurate forecasting challenging.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which models are compared for predicting technology-sector stock prices?",{"text":81,"@type":77},"The study evaluates two recurrent neural network models, LSTM and GRU, for efficiency and prediction performance.",{"name":83,"@type":74,"acceptedAnswer":84},"How do the statistical and machine learning methods contribute to the predictions?",{"text":85,"@type":77},"ARIMA and Facebook Prophet are integrated to enhance robustness, and XGBoost is used as an additional machine learning approach to improve predictive 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