This systematic literature review analyses the recent advances of machine learning and
deep learning in finance. The study considers six financial domains: stock markets, portfolio …

The application of Artificial Intelligence (AI) to financial investment is a research area that
has attracted extensive research attention since the 1990s, when there was an accelerated …

Applications of deep learning in financial market prediction have attracted widespread
attention from investors and scholars. From convolutional neural networks to recurrent …

Investors are constantly aware of the behaviour of stock markets. This affects their emotions
and motivates them to buy or sell shares. Financial sentiment analysis allows us to …

Multi-criteria decision-making (MCDM) models are well-suited for solving portfolio selection
problems. Diversified financial indices and complex subjective preferences are important …

Predicting financial trends of stock indexes is important for investors to reduce risk on
investment and efficient decision making if the prediction is made accurately. Researchers …

Real-time market prediction tool tracking public opinion in specialized newsgroups and
informative market data persuades investors of financial markets. Previous works mainly …

Financial Sentiment Analysis (FSA) is an important domain application of sentiment analysis
that has gained increasing attention in the past decade. FSA research falls into two main …

Sentiment analysis of short informal texts, such as tweets, remains a challenging task due to
their particular characteristics. Much effort has been made in the literature of Twitter …

Large language models (LLMs) have drastically changed the possible ways to design
intelligent systems, shifting the focus from massive data acquisition and new model training …