A new signed network framework links asset co-movements and higher-order return moments to graph motifs, enabling effective dimensionality reduction for ...
Incorporating time-varying GARCH volatility into prospect theory portfolio decisions boosts investor certainty equivalents by more than twenty percent over three-year ...
Researchers have introduced carbon at risk, a probabilistic framework that standardizes permanence evaluation and optimizes multi-technology carbon removal portfolios across ...
Researchers have built quantum and quantum-inspired physics-informed neural networks that solve the Hamilton–Jacobi–Bellman equation from the Merton portfolio optimization problem ...
The DAC-CPPO reinforcement learning model reported a 1.91 Sharpe ratio and 0.14 volatility, outperforming comparison models in the study’s portfolio ...
A slacks-based data envelopment analysis framework screened 34 Tehran-listed ETFs, identified seven efficient funds and converted super-efficiency scores into exact ...
A double deep Q-network combining news and social-media sentiment with price, volume and momentum signals outperformed four machine-learning approaches in ...
Researchers have fused an improved particle swarm optimization algorithm with simulated annealing to build a hybrid portfolio optimization model that ...
A new study shows that large language models can dynamically reallocate portfolio risk across 55 U.S. equities, delivering a 46.4% ...
Researchers in Tehran have developed Confidence-MPT, a hybrid framework that embeds LSTM neural network forecasts and a measure of predictive ...
Embark on a thrilling journey of discovery with Scienmag.com—your ultimate source for cutting-edge breakthroughs. Immerse yourself in a world where curiosity knows no limits and tomorrow’s possibilities become today’s reality!
