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In recent years, the swift progress of artificial intelligence (AI) has significantly influenced trading practices, providing traders with advanced algorithms that improve decision-making and enhance trading strategies, leading to increased profits and reduced risks. The onset of the era of big data has further enriched this field, offering access to extensive financial data, such as historical stock prices, company financial statements, financial news articles, social media sentiments, and macroeconomic indicators—all publicly available. By identifying complex patterns and correlations within this vast data set, deep learning (DL) algorithms have proven their ability to predict stock prices and market trends more accurately than traditional methods. This comprehensive survey aims to provide an insightful examination of various deeplearning models employed in stock market forecasting. The primary objective is to categorize these models into two distinct types: Uni-modal and multimodal models. By exploring the nuances within each category, this literature survey provides a comprehensive understanding of these models’ strengths, applications, and contributions to the constantly evolving research landscape of stock market forecasting. Our survey adopts a systematic approach to categorize and analyze deep-learning models in stock market forecasting. Leveraging established databases and repositories, we will compile a comprehensive dataset comprising academic articles, conference papers, and other scholarly publications related to DL in finance. This dataset will span a defined period, allowing us to capture the temporal evolution of research trends in stock market prediction. The first phase involves extracting and compiling relevant literature from established databases, including but not limited to Scopus, Web of Science, and Google Scholar. This dataset will serve as the foundation for exploring the evolving landscape of DL applications in stock market forecasting. Subsequently, advanced techniques and methodologies will be employed to analyze citation patterns, model co-occurrence, and the intellectual structure of research in this domain. Our research identifies influential authors, collaboration networks, and geographical distribution of research activities to uncover emerging clusters of research excellence. The findings of this survey contribute valuable insights to both academia and industry. By categorizing and examining the strengths of uni-modal and multi-modal deep-learning models, researchers can refine their methodologies, and practitioners can make informed decisions regarding adopting predictive models in financial markets. Furthermore, the survey aims to guide future research directions, enhancing the overall effectiveness of predictive models in the dynamic landscape of stock market forecasting. In conclusion, this survey aims to provide a comprehensive overview of deeplearning models in stock market forecasting. By systematically categorizing and analyzing these models, our study aspires to contribute to the ongoing dialogue on integrating AI in financial practices, fostering a deeper understanding of the field’s evolution and future directions.

The expression of mucosa-associated lymphoid tissue 1 (MALT1) that activates nuclear factor (NF)-κB in lymphocyte lineages is rapidly inactivated in oral carcinoma cells at the invasive front and the patients with worst prognosis. However, its mechanism to accelerate carcinoma progression remains unknown, and this study was carried out to examine the role in invasion. HSC2 oral carcinoma cells stably expressing wild-type MALT1 (wtMALT1) reduced the invasion of basement membrane matrices and collagen gels, and the dominant-negative form (∆MALT1)-expressing cells aggressively invaded into collagen gels. MALT1 decelerated proliferation and migration of cells and downregulated expression of matrix metalloproteinase 2 and 9, which were confirmed by short interfering RNA transfections. Reporter assays and immunoblot analysis showed that MALT1 does not affect the NF-κB pathway but inhibits ERK/MAPK activation. This was confirmed by endogenous MALT1 expression in oral carcinoma cell lines. Orthotopic implantation of ∆MALT1-expressing HSC2 cells in mice grew rapid expansive and invasive tongue tumors in contrast to an absence of tumor formation by wtMALT1-expressing cells. These results demonstrate that MALT1 suppresses oral carcinoma invasion by inhibiting proliferation, migration, and extracellular matrix degradation and that the ERK/MAPK pathway is a target of MALT1 and further suggests a role as a suppressor of carcinoma progression. [ABSTRACT FROM AUTHOR]