TL;DR
A multi-stage machine learning framework for portfolio selection and weight optimization in high-frequency crypto-asset pairs trading that decomposes the allocation problem into economically meaningful stages and allows each decision layer to be modeled according to its own statistical structure is proposed.
A portfolio optimization framework that combines machine learning-based stock price prediction with Modern Portfolio Theory (MPT) and provides a systematic decision-support approach for combining predictive analytics with portfolio optimization is developed.
Cryptocurrency holdings in conventional portfolios challenge the empirical adequacy of standard tail-risk estimators. This study identifies a calibration mechanism that brings feature-based machine learning to supervisory-grade value at risk (VaR) coverage, improves its joint VaR and expected shortfall (ES) record rela...
Effective portfolio diversification remains a central challenge in quantitative asset management. In this study, we propose a data-driven framework based on the predict-then-optimize (PO) paradigm, which combines return forecasting with portfolio allocation in a sequential manner. The forecasting module employs DLinear...
The hybrid approach between machine learning and mixed-integer quadratic programming (MIQP) is proposed in this study to optimize a multi-objective portfolio that trades off between return rate and downside risk. This research is inspired by traditional approaches such as Markowitz variance and fuzzy-clustering, which...
Artificial intelligence is increasingly used in asset management, although evidence that greater model complexity consistently improves investment performance remains limited. This study examines whether machine-learning methods generate incremental value in European equity selection beyond transparent investment rules...
This study examines whether integrating machine learning-based return forecasting and dynamic covariance estimation into a mean-variance portfolio framework produces measurable improvements over a conventional benchmark. Three strategies are constructed and evaluated over a five-year out-of-sample window from January 2...
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