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 …

A significant correlation between financial news with stock market trends has been explored
extensively. However, very little research has been conducted for stock prediction models …

In the context of the 2008 financial crisis, this paper focuses on the Chinese A-share stock
prices in ten years (from January 1, 2003 to December 31, 2012) to explore some possible …

The accelerated progress in artificial intelligence encourages sophisticated deep learning
methods in predicting stock prices. In the meantime, easy accessibility of the stock market in …

Intelligent forecasters are now being considered in the stock market, providing essential
insights and strategic guidance to investors and traders by presenting analytical tools and …

Incorporating ground-breaking technologies such as deep learning (DL) has revolutionized
predictive modelling in the rapidly evolving landscape of the finance sector. DL approaches …

The consideration of environmental, social, and governance (ESG) aspects has become an
integral part of investment decisions for individual and institutional investors. Most recently …

Deep-SDM is a unified layer framework built on TensorFlow/Keras and written in Python
3.12. The framework aligns with the modular engineering principles for the design and …

LSTM-SDM is a python-based integrated computational framework built on the top of
Tensorflow/Keras and written in the Jupyter notebook. It provides several object-oriented …

The autism dataset is studied to identify the differences between autistic and healthy groups.
For this, the resting-state functional magnetic resonance imaging data of the two groups are …