چکیده مقاله
Financial market forecasting has consistently remained one of the most challenging yet captivating domains for researchers, investors, and policymakers alike The inherent complexity, high volatility, non stationarity, and non linear dynamics of financial time series present significant hurdles for traditional analytical methods However, with the advent and rapid advancements in deep learning, powerful new paradigms have emerged, offering unprecedented capabilities to model these intricate relationships and significantly enhance prediction accuracy This comprehensive review systematically explores the latest innovations in applying deep learning techniques to forecast financial markets, covering a period predominantly from 2020 onwards, as evidenced by the provided literature We meticulously analyze core deep learning architectures, including Convolutional Neural Networks CNNs for feature extraction, Recurrent Neural Networks RNNs for sequential data processing, and their advanced variants such as Long Short Tem Memory LSTM networks and Gated Recurrent Units GRUS , which have proven particularly effective in capturing long term dependencies Furthermore, the review delves into hybrid and ensemble models that integrate multiple deep learning approaches or combine them with traditional statistical methods to leverage their respective strengths Our objective is to identify key trends, critically compare the performance and applicability of various deep learning models across different financial instruments e g , stocks, cryptocurrencies, economic indices , highlight prevailing challenges such as interpretability, data noise, and model robustness, and delineate promising avenues for future research The findings underscore the remarkable potential of deep learning to revolutionize financial forecasting, while also emphasizing the critical need for continued research into model stability, explainability, and the effective management of noisy and high dimensional financial data
کلیدواژهها
نویسندگان
Hamid Saqa Gandom Abadi
Department of Computer, Ya C , Islamic Azad University, Yazd, Iran
Mohammad Amini
Department of Computer, Ya C , Islamic Azad University, Yazd, Iran
شیوه ارجاع
Saqa Gandom Abadi, Hamid and Amini, Mohammad,1404,Deep Learning Innovations in Financial Market Forecasting: A Comprehensive Review,Conference on Artificial Intelligence in Emerging Economies and Digital Businesses (AIEEDB),Yazd
ارائهشده در
مجموعه مقالات کنفرانس هوش مصنوعی در اقتصادهای نوظهور و کسب وکارهای دیجیتال21 آبان 1404 · یزد