[HTML][HTML] Machine learning techniques and data for stock market forecasting: A literature review
In this literature review, we investigate machine learning techniques that are applied for
stock market prediction. A focus area in this literature review is the stock markets …
stock market prediction. A focus area in this literature review is the stock markets …
Applications of explainable artificial intelligence in finance—a systematic review of finance, information systems, and computer science literature
Digitalization and technologization affect numerous domains, promising advantages but
also entailing risks. Hence, when decision-makers in highly-regulated domains like Finance …
also entailing risks. Hence, when decision-makers in highly-regulated domains like Finance …
Interpretable stock price forecasting model using genetic algorithm-machine learning regressions and best feature subset selection
Recent stock market studies adopting machine learning and deep learning techniques have
achieved remarkable performances with convenient accessibility. However, machine …
achieved remarkable performances with convenient accessibility. However, machine …
Explainable artificial intelligence (XAI) in finance: a systematic literature review
J Černevičienė, A Kabašinskas - Artificial Intelligence Review, 2024 - Springer
As the range of decisions made by Artificial Intelligence (AI) expands, the need for
Explainable AI (XAI) becomes increasingly critical. The reasoning behind the specific …
Explainable AI (XAI) becomes increasingly critical. The reasoning behind the specific …
Application of artificial intelligence in stock market forecasting: a critique, review, and research agenda
The stock market is characterized by extreme fluctuations, non-linearity, and shifts in internal
and external environmental variables. Artificial intelligence (AI) techniques can detect such …
and external environmental variables. Artificial intelligence (AI) techniques can detect such …
A comprehensive review on financial explainable AI
The success of artificial intelligence (AI), and deep learning models in particular, has led to
their widespread adoption across various industries due to their ability to process huge …
their widespread adoption across various industries due to their ability to process huge …
Trust in AI: progress, challenges, and future directions
The increasing use of artificial intelligence (AI) systems in our daily lives through various
applications, services, and products highlights the significance of trust and distrust in AI from …
applications, services, and products highlights the significance of trust and distrust in AI from …
Stock portfolio optimization using a deep learning LSTM model
Predicting future stock prices and their movement patterns is a complex problem. Hence,
building a portfolio of capital assets using the predicted prices to achieve the optimization …
building a portfolio of capital assets using the predicted prices to achieve the optimization …
Learning to generate explainable stock predictions using self-reflective large language models
Explaining stock predictions is generally a difficult task for traditional non-generative deep
learning models, where explanations are limited to visualizing the attention weights on …
learning models, where explanations are limited to visualizing the attention weights on …
Portfolio optimization using reinforcement learning and hierarchical risk parity approach
J Sen - Data Analytics and Computational Intelligence: Novel …, 2023 - Springer
Portfolio Optimization deals with identifying a set of capital assets and their respective
weights of allocation, which optimizes the risk-return pairs. Optimizing a portfolio is a …
weights of allocation, which optimizes the risk-return pairs. Optimizing a portfolio is a …