Quantum machine learning on near-term quantum devices: Current state of supervised and unsupervised techniques for real-world applications
The past decade has witnessed significant advancements in quantum hardware,
encompassing improvements in speed, qubit quantity, and quantum volume—a metric …
encompassing improvements in speed, qubit quantity, and quantum volume—a metric …
[HTML][HTML] Prediction of realized volatility and implied volatility indices using AI and machine learning: A review
ES Gunnarsson, HR Isern, A Kaloudis… - International Review of …, 2024 - Elsevier
In this systematic literature review, we examine the existing studies predicting realized
volatility and implied volatility indices using artificial intelligence and machine learning. We …
volatility and implied volatility indices using artificial intelligence and machine learning. We …
Quantitative stock portfolio optimization by multi-task learning risk and return
Selecting profitable stocks for investments is a challenging task. Recent research has made
significant progress on stock ranking prediction to select top-ranked stocks for portfolio …
significant progress on stock ranking prediction to select top-ranked stocks for portfolio …
[HTML][HTML] The impact of oil and global markets on Saudi stock market predictability: A machine learning approach
This study investigates the predictability power of oil prices and six international stock
markets namely, China, France, UK, Germany, Japan, and the USA, on the Saudi stock …
markets namely, China, France, UK, Germany, Japan, and the USA, on the Saudi stock …
Improving flight delays prediction by developing attention-based bidirectional LSTM network
Recently, the significance of accurate aircraft delay forecasting has grown in the aviation
sector, which caused multi-billion-dollar losses faced by airlines and airports and passenger …
sector, which caused multi-billion-dollar losses faced by airlines and airports and passenger …
Lob-based deep learning models for stock price trend prediction: a benchmark study
The recent advancements in Deep Learning (DL) research have notably influenced the
finance sector. We examine the robustness and generalizability of fifteen state-of-the-art DL …
finance sector. We examine the robustness and generalizability of fifteen state-of-the-art DL …
A Hybrid MCDM Approach Using the BWM and the TOPSIS for a Financial Performance-Based Evaluation of Saudi Stocks
AT Alsanousi, AY Alqahtani, AA Makki, MA Baghdadi - Information, 2024 - mdpi.com
This study presents a hybrid multicriteria decision-making approach for evaluating stocks in
the Saudi Stock Market. The objective is to provide investors and stakeholders with a robust …
the Saudi Stock Market. The objective is to provide investors and stakeholders with a robust …
Comparative Analysis of NLP-Based Models for Company Classification
M Rizinski, A Jankov, V Sankaradas, E Pinsky… - Information, 2024 - mdpi.com
The task of company classification is traditionally performed using established standards,
such as the Global Industry Classification Standard (GICS). However, these approaches …
such as the Global Industry Classification Standard (GICS). However, these approaches …
Robust hybrid learning approach for adaptive neuro-fuzzy inference systems
Abstract The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a regression model that
uses fuzzy logic and neural networks, making it suitable for modeling the uncertainty of …
uses fuzzy logic and neural networks, making it suitable for modeling the uncertainty of …
Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting
The present study makes two significant contributions to the extended body of literature in
the context of International Finance. First, it forecasts the inflation in an emerging economy …
the context of International Finance. First, it forecasts the inflation in an emerging economy …