Advances in artificial intelligence (AI) and deep learning are transforming quantitative finance, offering new ways to model complex market dynamics. This study explores how deep learning models can enhance portfolio management and asset allocation, addressing the limitations of traditional models. The rapid advancement of Artificial Intelligence (AI)
Gabriel Olanrewaju, Ayobami +2 more
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Quantum computing in finance: a literature review and future directions for trustworthy financial AI. [PDF]
Muzzioli S +4 more
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Entropy-Driven Volatility Prediction for ETF Quantitative Investment in the Chinese Stock Market: A Machine Learning Framework for Barbell Strategy Optimization. [PDF]
Yan K, Yue Z, He Q, Li Y.
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Explainable AI augmented retailer segmentation using knowledge graph. [PDF]
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Investor sentiment and stock portfolio construction: A study based on the black-litterman model. [PDF]
Sun L, Fu B, Liu W.
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Wavelet-Decoupled Spatiotemporal Network for Stock Return Prediction. [PDF]
Liao L, Wang C, Wang J, Liao Y, Lai Y.
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A multi-agent equilibrium model in an incomplete market with discrete dividends: Applications to long-term discount curves. [PDF]
Saito T, Takahashi A.
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Implementing innovative financing mechanisms in global health - perspectives of stakeholders from global health organisations: a qualitative study. [PDF]
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An Entropy-Regularised AI Framework for Multi-Asset Volatility Spillover Forecasting and CVaR-Constrained Portfolio Allocation in Financial Markets. [PDF]
Yu J, Wang L, Sun X.
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A multi-objective portfolio optimization model incorporating sentiment analysis of quarterly reports and LSTM-based price prediction. [PDF]
Taheripour E, Sadjadi SJ, Amiri B.
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