A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Cryptocurrencies evolve through survival of the fittest, modeled with evolutionary finance.
MadEvolve optimizes trading algorithms using LLMs, achieving significant improvements in feature generation and trading strategy optimization.
EFS uses LLMs to optimize sparse portfolios by evolving alpha factors.
This paper reviews digital transformation research from 2011-2024, focusing on corporate finance.
FTX's failure linked to Terra-Luna collapse and Binance's influence.
Evolutionary methods improve understanding of LLMs and their relationships.
In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being ro…
evo-RL combines evolutionary computation with reinforcement learning for better adaptability.
Momentum speeds up evolutionary processes in machine learning.
We define Lie algebroids over infinite jet spaces and establish their equivalent representation through homological evolutionary vector fields.
EAP clusters evolving data, promoting temporal smoothness and automatic cluster tracking.
In this note, we extend an evolutionary stochastic portfolio optimization framework to include probabilistic constraints. Both the stochastic programming-based modeling environment as well as the evolutionary optimization environment are ideally suited for an integration of various types of probabilistic constraints. W…
Many biological characteristics of evolutionary interest are not scalar variables but continuous functions. Here we use phylogenetic Gaussian process regression to model the evolution of simulated function-valued traits. Given function-valued data only from the tips of an evolutionary tree and utilising independent pri…
A co-evolutionary approach for Heston model calibration reduces overfitting with diverse datasets.
The paper improves evolutionary computation by optimizing selection rates.
Novel evolutionary strategy solves stochastic constrained optimization problems.
CoNES optimizes blackbox functions using convex optimization and information geometry.
Proves FR-NGD optimally approximates evolutionary dynamics and continuous Bayesian inference.
Neural Architecture Search has shown potential to automate the design of neural networks. Deep Reinforcement Learning based agents can learn complex architectural patterns, as well as explore a vast and compositional search space. On the other hand, evolutionary algorithms offer higher sample efficiency, which is criti…
Evolutionary algorithms improve neural network performance by discovering better activation functions.
EPNE models evolving network patterns for better predictions.
A method to control results of gradient descent unsupervised learning in a deep neural network by using evolutionary algorithm is proposed. To process crossover of unsupervisedly trained models, the algorithm evaluates pointwise fitness of individual nodes in neural network. Labeled training data is randomly sampled an…
Paper withdrawn; AI game behavior needs diversity.
New algorithm improves game learning with randomised optimism.
In many applications of finance, biology and sociology, complex systems involve entities interacting with each other. These processes have the peculiarity of evolving over time and of comprising latent factors, which influence the system without being explicitly measured. In this work we present latent variable time-va…
Combines variational and evolutionary optimization for generative models.
Evolutionary algorithms improve decision tree ensembles.
New research connects evolutionary dynamics to Bayesian learning.
An evolutionary game model analyzes e-commerce and traditional retail trends during the pandemic.
Evolutionary Strategies optimize hyper-parameters for off-policy learning.
This research proposes the econophysics kinetic market model as an evolutionary algorithm's instance. The immediate results from this proposal is a new replacement rule for family competition genetic algorithms. It also represents a starting point to adding evolvable entities to kinetic market models.
The last financial and economic crisis demonstrated the dysfunctional long-term effects of aggressive behaviour in financial markets. Yet, evolutionary game theory predicts that under the condition of strategic dependence a certain degree of aggressive behaviour remains within a given population of agents. However, as …
Enhanced evolutionary algorithms solve NP-hard portfolio optimization with cardinality constraints.
Many cooperative multiagent reinforcement learning environments provide agents with a sparse team-based reward, as well as a dense agent-specific reward that incentivizes learning basic skills. Training policies solely on the team-based reward is often difficult due to its sparsity. Furthermore, relying solely on the a…
Proposes Genetic Thompson Sampling for multi-armed bandits, improving performance in nonstationary settings.
Evolutionary clustering aims at capturing the temporal evolution of clusters. This issue is particularly important in the context of social media data that are naturally temporally driven. In this paper, we propose a new probabilistic model-based evolutionary clustering technique. The Temporal Multinomial Mixture (TMM)…
The accurate and interpretable prediction of future events in time-series data often requires the capturing of representative patterns (or referred to as states) underpinning the observed data. To this end, most existing studies focus on the representation and recognition of states, but ignore the changing transitional…
I consider the existence and structure of conservation laws for the general class of evolutionary scalar second-order differential equations with parabolic symbol. First I calculate the linearized characteristic cohomology for such equations. This provides an auxiliary differential equation satisfied by the conservatio…
I consider the geometry of the general class of scalar 2nd-order differential equations with parabolic symbol, including non-linear and non-evolutionary parabolic equations. After defining the appropriate -structure to model parabolic equations, I apply Cartan techniques to determine local geometric invariants (quan…
Unified method for analyzing evolving manifolds using de Rham-Hodge theory.
Time series prediction with deep learning methods, especially long short-term memory neural networks (LSTMs), have scored significant achievements in recent years. Despite the fact that the LSTMs can help to capture long-term dependencies, its ability to pay different degree of attention on sub-window feature within mu…
Many applications in machine learning require optimizing a function whose true gradient is unknown, but where surrogate gradient information (directions that may be correlated with, but not necessarily identical to, the true gradient) is available instead. This arises when an approximate gradient is easier to compute t…
Evolution and learning are two of the fundamental mechanisms by which life adapts in order to survive and to transcend limitations. These biological phenomena inspired successful computational methods such as evolutionary algorithms and deep learning. Evolution relies on random mutations and on random genetic recombina…
A family of replicator-like dynamics, called the escort replicator equation, is constructed using information-geometric concepts and generalized information entropies and diverenges from statistical thermodynamics. Lyapunov functions and escort generalizations of basic concepts and constructions in evolutionary game th…
Simplicial learning improves classification by generating compact sparse representations.
VisEvol uses evolutionary optimization to find optimal hyperparameters for machine learning models.
The paper analyzes cryptocurrency and equity markets using advanced statistical methods.
Paper proposes deep learning model for dynamic stock repurchase forecasting.