[HTML][HTML] Machine learning techniques and data for stock market forecasting: A literature review

MM Kumbure, C Lohrmann, P Luukka… - Expert Systems with …, 2022 - Elsevier
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 …

Financial applications of machine learning: A literature review

N Nazareth, YVR Reddy - Expert Systems with Applications, 2023 - Elsevier
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 …

Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process

KK Yun, SW Yoon, D Won - Expert Systems with Applications, 2021 - Elsevier
The stock market has performed one of the most important functions in a laissez-faire
economic system by gathering people, companies, and flows of money for several centuries …

Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis

M Nabipour, P Nayyeri, H Jabani, S Shahab… - Ieee …, 2020 - ieeexplore.ieee.org
The nature of stock market movement has always been ambiguous for investors because of
various influential factors. This study aims to significantly reduce the risk of trend prediction …

CatBoost model and artificial intelligence techniques for corporate failure prediction

SB Jabeur, C Gharib, S Mefteh-Wali, WB Arfi - … Forecasting and Social …, 2021 - Elsevier
Financial distress prediction provides an effective warning system for banks and investors to
correctly guide decisions on granting credit. Ensemble methods have demonstrated their …

Mean–variance portfolio optimization using machine learning-based stock price prediction

W Chen, H Zhang, MK Mehlawat, L Jia - Applied Soft Computing, 2021 - Elsevier
The success of portfolio construction depends primarily on the future performance of stock
markets. Recent developments in machine learning have brought significant opportunities to …

Forecasting gold price with the XGBoost algorithm and SHAP interaction values

SB Jabeur, S Mefteh-Wali, JL Viviani - Annals of Operations Research, 2024 - Springer
Financial institutions, investors, mining companies and related firms need an effective
accurate forecasting model to examine gold price fluctuations in order to make correct …

[HTML][HTML] Bitcoin price prediction using machine learning: An approach to sample dimension engineering

Z Chen, C Li, W Sun - Journal of Computational and Applied Mathematics, 2020 - Elsevier
After the boom and bust of cryptocurrencies' prices in recent years, Bitcoin has been
increasingly regarded as an investment asset. Because of its highly volatile nature, there is a …

Deep learning for stock market prediction

M Nabipour, P Nayyeri, H Jabani, A Mosavi, E Salwana - Entropy, 2020 - mdpi.com
The prediction of stock groups values has always been attractive and challenging for
shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper …

[HTML][HTML] Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?

SA Basher, P Sadorsky - Machine Learning with Applications, 2022 - Elsevier
Bitcoin has grown in popularity and has now attracted the attention of individual and
institutional investors. Accurate Bitcoin price direction forecasts are important for determining …