As a core branch of financial forecasting, stock forecasting plays a crucial role for financial
analysts, investors, and policymakers in managing risks and optimizing investment …

Quantitative stock selection is one of the most challenging FinTech tasks due to the non-
stationary dynamics and complex market dependencies. Existing studies rely on channel …

Stock market prediction (SMP) is a challenging task due to its uncertainty, nonlinearity, and
volatility. Machine learning models, such as artificial neural networks (ANNs) and support …

Machine learning models have demonstrated remarkable efficacy and efficiency in a wide
range of stock forecasting tasks. However, the inherent challenges of data scarcity, including …

Predicting stock ranking is a complex and challenging task due to the intricate nature of real
stock market systems. There are two main obstacles for current methods to directly using …

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 …

Deep learning techniques for predicting stock market prices is an popular topic in the field of
data science. Customized feature engineering arises as pre-processing tools of different …

Recent years have witnessed increasing attention being paid to AI-based quantitative
investment. Compared to traditional low-frequency data (eg, daily, weekly), high-frequency …

Stock price forecasting is a fundamental yet challenging task in quantitative investment.
Various researchers have developed a combination of neural network models (eg, RNNs …

In Web3 social platforms, ie social web applications that rely on blockchain technology to
support their functionalities, interactions among users are usually multimodal, from common …