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Found 9 papers, 3 papers with code
Topological Portfolio Selection and Optimization
Following the seminal work of Markowitz, optimal asset allocation can be computed using a constrained optimization model based on empirical covariance.
Homological Convolutional Neural Networks
Deep learning methods have demonstrated outstanding performances on classification and regression tasks on homogeneous data types (e. g., image, audio, and text data).
Homological Neural Networks: A Sparse Architecture for Multivariate Complexity
The rapid progress of Artificial Intelligence research came with the development of increasingly complex deep learning models, leading to growing challenges in terms of computational complexity, energy efficiency and interpretability.
Domain-adapted Learning and Imitation: DRL for Power Arbitrage
In this paper, we discuss the Dutch power market, which is comprised of a day-ahead market and an intraday balancing market that operates like an auction.
Domain-adapted Learning and Interpretability: DRL for Gas Trading
Deep Reinforcement Learning (Deep RL) has been explored for a number of applications in finance and stock trading.
Regime-based Implied Stochastic Volatility Model for Crypto Option Pricing
The increasing adoption of Digital Assets (DAs), such as Bitcoin (BTC), rises the need for accurate option pricing models.
Anatomy of a Stablecoin's failure: the Terra-Luna case
We quantitatively describe the main events that led to the Terra project's failure in May 2022.
Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series
We propose an end-to-end architecture for multivariate time-series prediction that integrates a spatial-temporal graph neural network with a matrix filtering module.
Dynamic Portfolio Optimization with Inverse Covariance Clustering
Market conditions change continuously.
