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Hybrid deep learning models with multi-classification investor sentiment to forecast the prices of China's leading stocks.

PloS one
The prediction of stock prices has long been a captivating subject in academic research. This study aims to forecast the prices of prominent stocks in five key industries of the Chinese A-share market by leveraging the synergistic power of deep learn...

Comprehensive financial health assessment using Advanced machine learning techniques: Evidence based on private companies listed on ChiNext.

PloS one
This study develops a specific and measurable framework for assessing the financial health (FH) of privately-owned companies listed on ChiNext, aimed at identifying financially sound enterprises and helping investors avoid losses caused by financial ...

Predicting the volatility of Chinese stock indices based on realized recurrent conditional heteroskedasticity.

PloS one
The realized recurrent conditional heteroscedasticity (RealRECH) model improves volatility prediction by integrating long short-term memory (LSTM), a recurrent neural network unit, into the realized generalized autoregressive conditional heteroskedas...

Period-aggregated transformer for learning latent seasonalities in long-horizon financial time series.

PloS one
Fluctuations in the financial market are influenced by various driving forces and numerous factors. Traditional financial research aims to identify the factors influencing stock prices, and existing works construct a common neural network learning fr...

Factor-GAN: Enhancing stock price prediction and factor investment with Generative Adversarial Networks.

PloS one
Deep learning, a pivotal branch of artificial intelligence, has increasingly influenced the financial domain with its advanced data processing capabilities. This paper introduces Factor-GAN, an innovative framework that utilizes Generative Adversaria...

Heteroscedasticity effects as component to future stock market predictions using RNN-based models.

PloS one
Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility ...

The value of artificial intelligence for the treatment of mechanically ventilated intensive care unit patients: An early health technology assessment.

Journal of critical care
PURPOSE: The health and economic consequences of artificial intelligence (AI) systems for mechanically ventilated intensive care unit patients often remain unstudied. Early health technology assessments (HTA) can examine the potential impact of AI sy...

Innovative entrepreneurial market trend prediction model based on deep learning: Case study and performance evaluation.

Science progress
In the current economic landscape, the growing importance of innovation and entrepreneurship underscores an urgent need for accurate market trend prediction. Addressing this challenge, our study introduces an innovative entrepreneurial market trend p...

Generative Artificial Intelligence for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations: An ISPOR Working Group Report.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
OBJECTIVES: To provide an introduction to the uses of generative artificial intelligence (AI) and foundation models, including large language models, in the field of health technology assessment (HTA).

Enhanced forecasting of rice price and production in Malaysia using novel multivariate fuzzy time series models.

Scientific reports
A significant portion of the world's population relies on rice as a primary source of nutrition. In Malaysia, rice production began in the early 1960s, which led to the cultivation of the country's most significant food crop up till the present day. ...