A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting
From exchanging budgetary instruments to tracking individual spending plans to detail a
business's profit, money-related organisations utilise computational innovation day by day …
business's profit, money-related organisations utilise computational innovation day by day …
Deep learning for financial applications: A survey
Computational intelligence in finance has been a very popular topic for both academia and
financial industry in the last few decades. Numerous studies have been published resulting …
financial industry in the last few decades. Numerous studies have been published resulting …
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 …
[图书][B] Machine learning for factor investing: R version
G Coqueret, T Guida - 2020 - taylorfrancis.com
Machine learning (ML) is progressively reshaping the fields of quantitative finance and
algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers …
algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers …
A new appraisal model of second-hand housing prices in China's first-tier cities based on machine learning algorithms
L Xu, Z Li - Computational Economics, 2021 - Springer
The accurate appraisal of second-hand housing prices plays an important role in second-
hand housing transactions, mortgages and risk assessment. Machine learning technology …
hand housing transactions, mortgages and risk assessment. Machine learning technology …
Optimization of investment strategies through machine learning
The main objective of this research is to develop a sustainable stock quantitative investing
model based on Machine Learning and Economic Value-Added techniques for optimizing …
model based on Machine Learning and Economic Value-Added techniques for optimizing …
Stock portfolio management by using fuzzy ensemble deep reinforcement learning algorithm
Z Hao, H Zhang, Y Zhang - Journal of Risk and Financial Management, 2023 - mdpi.com
The research objective of this article is to train a computer (agent) with market information
data so it can learn trading strategies and beat the market index in stock trading without …
data so it can learn trading strategies and beat the market index in stock trading without …
Machine learning and manager selection: evidence from South Africa
Purpose This study investigates whether the skilled minority of active equity managers in
emerging markets can be identified using a machine learning (ML) framework that …
emerging markets can be identified using a machine learning (ML) framework that …
[PDF][PDF] MEAN-VARIANCE PORTFOLIO OPTIMIZATION WITH STOCK RETURN PREDICTION USING XGBOOST.
KIM Hongjoong - … Computation & Economic Cybernetics Studies & …, 2021 - ecocyb.ase.ro
Portfolio optimization is one of the most concerning issues in finance and its success relies
on accurate prediction of future stock market, which is challenging due to its dynamic, non …
on accurate prediction of future stock market, which is challenging due to its dynamic, non …
State-dependent stock selection in index tracking: a machine learning approach
R Bradrania, D Pirayesh Neghab… - Financial Markets and …, 2022 - Springer
We focus on the stock selection step of the index tracking problem in passive investment
management and incorporate constant changes in the dynamics of markets into the …
management and incorporate constant changes in the dynamics of markets into the …