Search Results
Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management
Deep or reinforcement learning (RL) approaches have been adapted as reactive agents to quickly learn and respond with new investment strategies for portfolio management under the highly turbulent financial market environments in recent years.
DAM: A Universal Dual Attention Mechanism for Multimodal Timeseries Cryptocurrency Trend Forecasting
In the distributed systems landscape, Blockchain has catalyzed the rise of cryptocurrencies, merging enhanced security and decentralization with significant investment opportunities.
AlphaSharpe: LLM-Driven Discovery of Robust Risk-Adjusted Metrics
Financial metrics like the Sharpe ratio are pivotal in evaluating investment performance by balancing risk and return.
Mack-Net model: Blending Mack's model with Recurrent Neural Networks
In general insurance companies, a correct estimation of liabilities plays a key role due to its impact on management and investing decisions.
The impact of big winners on passive and active equity investment strategies
We investigate the impact of big winner stocks on the performance of active and passive investment strategies using a combination of numerical and analytical techniques.
Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation
Modeling and managing portfolio risk is perhaps the most important step to achieve growing and preserving investment performance.
Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States
Our framework aims to address two unique challenges in financial PM: (1) data heterogeneity -- the collected information for each asset is usually diverse, noisy and imbalanced (e. g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
FLOWViZ: Framework for Phylogenetic Processing
But most of these scientific workflow systems cannot be easily installed and configured, are available as centralized services, and, usually, it is not easy to integrate tools and processing steps available in phylogenetic frameworks.
Social and Information Networks
Portfolio Optimisation within a Wasserstein Ball
We study the problem of active portfolio management where an investor aims to outperform a benchmark strategy's risk profile while not deviating too far from it.
Predicting Survival of Tongue Cancer Patients by Machine Learning Models
The prognostic factors identified by our method are consistent with previous clinical studies.
