Natural language based financial forecasting: a survey

By Frank Xing, Erik Cambria, Roy E. Welsch. 2017

388 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Natural Language Processing, Time-Series & Forecasting

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Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, neutral): “[23] surveyed natural language-based financial forecasting studies.”
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos (2018, background): “Emotion detection from such resources can benefit numerous fields like counseling (De Choudhury et al., 2013), public opinion mining (Cambria et al., 2017), financial forecasting (Xing et al., 2018), and intelligent systems such as smart homes and chatbots (Young et al., 2018).”
State-of-the-art in artificial neural network applications: A survey (2018, background): “But will enable predictions that will be plausible regarding model’s correlation and robustness in any range of conditions [6, 7].”
Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling (2018, background): “…community, leading to many exciting open challenges, as well as in the business world, due to the remarkable beneﬁts to be had from ﬁnancial [25] and political [26] forecasting, e-health [27] and e-tourism [28], user proﬁling [29] and community detection [30], manufacturing and supply chain…”
BloombergGPT: A Large Language Model for Finance (2023, background): “Financial Technology (FinTech) is a large and growing area with NLP technologies having an increasingly important role (Xing et al., 2018; Fisher et al., 2016; Dredze et al., 2016).”
Applications of deep learning in stock market prediction: recent progress (2020, background): “More recently, Xing et al. (2018) reviews the application of cutting-edge NLP techniques for financial forecasting, which would be concerned when text including the financial news or twitters is used as input for stock market prediction.”
A survey on classification techniques for opinion mining and sentiment analysis (2017, methodology): “Graph-based methods Graph-based learning has been focused by researchers in the last decade (Wang et al. 2015b; Lu 2015) and has been confirmed to be effective for many NLP tasks (Xing et al. 2018).”
A survey on sentiment analysis methods, applications, and challenges (2022, background): “In work of Xing et al. (2018) used to determine whether the trend will be rising or decreasing.”
Large Language Models in Finance: A Survey (2023, neutral): “Evaluation metrics can be categorized into two main groups: accuracy and performance, based on the taxonomy provided by [57].”
AI in Finance: Challenges, Techniques, and Opportunities (2021, background): “In EcoFin, the sources of long economic-financial text include EcoFin news, announcements, financial reports, annual reports, project proposals, and statements [59, 82, 86, 110, 117, 139, 171].”