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Stock market prediction via deep learning techniques: A survey
Existing surveys on stock market prediction often focus on traditional machine learning
methods instead of deep learning methods. This motivates us to provide a structured and …
methods instead of deep learning methods. This motivates us to provide a structured and …
Choosing the best of both worlds: Diverse and novel recommendations through multi-objective reinforcement learning
Since the inception of Recommender Systems (RS), the accuracy of the recommendations in
terms of relevance has been the golden criterion for evaluating the quality of RS algorithms …
terms of relevance has been the golden criterion for evaluating the quality of RS algorithms …
On the current state of deep learning for news recommendation
The exponential outbreak of news articles makes it troublesome for the readers to find,
select and read the most relevant ones and alleviate the resulting information and cognitive …
select and read the most relevant ones and alleviate the resulting information and cognitive …
Group-based personalized news recommendation with long-and short-term fine-grained matching
Personalized news recommendation aims to help users find news content they prefer, which
has attracted increasing attention recently. There are two core issues in news …
has attracted increasing attention recently. There are two core issues in news …
An integrated early warning system for stock market turbulence
This study constructs an integrated early warning system (EWS) that identifies and predicts
stock market turbulence. Based on switching ARCH (SWARCH) filtering probabilities of the …
stock market turbulence. Based on switching ARCH (SWARCH) filtering probabilities of the …
A mayfly algorithm for cardinality constrained portfolio optimization
Portfolio optimization is an essential issue in quantitative investing, which aims to find the
best set of portfolios by allocating the proportion of assets. One of the most widely studied …
best set of portfolios by allocating the proportion of assets. One of the most widely studied …
Extracting structured insights from financial news: An augmented llm driven approach
Financial news plays a crucial role in decision-making processes across the financial sector,
yet the efficient processing of this information into a structured format remains challenging …
yet the efficient processing of this information into a structured format remains challenging …
NLP in FinTech applications: past, present and future
Financial Technology (FinTech) is one of the worldwide rapidly-rising topics in the past five
years according to the statistics of FinTech from Google Trends. In this position paper, we …
years according to the statistics of FinTech from Google Trends. In this position paper, we …
Assessment of the applicability of large language models for quantitative stock price prediction
In accordance with the findings presented in [34], this study examines the applicability of
Machine Learning (ML) models and training strategies from the Natural Language …
Machine Learning (ML) models and training strategies from the Natural Language …
AI in Stock Market Forecasting: A Bibliometric Analysis
In recent years, the swift progress of artificial intelligence (AI) has significantly influenced
trading practices, providing traders with advanced algorithms that improve decision-making …
trading practices, providing traders with advanced algorithms that improve decision-making …