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Found 15 papers, 8 papers with code
Automated Risk Management Mechanisms in DeFi Lending Protocols: A Crosschain Comparative Analysis of Aave and Compound
In contrast, liquidations in v2 have an insignificant impact, which indicates that the most recent v3 protocols have better risk management than the earlier v2 protocols.
Risk-aware black-box portfolio construction using Bayesian optimization with adaptive weighted Lagrangian estimator
Existing portfolio management approaches are often black-box models due to safety and commercial issues in the industry.
Cross-Modal Temporal Fusion for Financial Market Forecasting
Accurate financial market forecasting requires diverse data sources, including historical price trends, macroeconomic indicators, and financial news, each contributing unique predictive signals.
Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static Relations
To address this issue, we propose the Dynamic Graph Representation with Contrastive Learning (DGRCL) framework, which integrates dynamic and static graph relations to improve the accuracy of stock trend prediction.
Artificial Intelligence for Collective Intelligence: A National-Scale Research Strategy
Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or trans-national scale.
Simulation of Social Media-Driven Bubble Formation in Financial Markets using an Agent-Based Model with Hierarchical Influence Network
We propose that a tree-like hierarchical structure represents a simple and effective way to model the emergent behaviour of financial markets, especially markets where there exists a pronounced intersection between social media influences and investor behaviour.
Multi-relational Graph Diffusion Neural Network with Parallel Retention for Stock Trends Classification
Stock trend classification remains a fundamental yet challenging task, owing to the intricate time-evolving dynamics between and within stocks.
DGDNN: Decoupled Graph Diffusion Neural Network for Stock Movement Prediction
Moreover, the ablation study and sensitivity study further illustrate the effectiveness of the proposed method in modeling the time-evolving inter-stock and intra-stock dynamics.
Neural Stochastic Agent-Based Limit Order Book Simulation: A Hybrid Methodology
We show that the stylised facts remain and we demonstrate order flow impact and financial herding behaviours that are in accordance with empirical observations of real markets.
Using coevolution and substitution of the fittest for health and well-being recommender systems
We demonstrate that SF is able to maintain engagement better than other techniques in the literature, and the resultant recommendations using SF are higher quality and more diverse than those produced by EvoRecSys.
Nonstationary Continuum-Armed Bandit Strategies for Automated Trading in a Simulated Financial Market
We approach the problem of designing an automated trading strategy that can consistently profit by adapting to changing market conditions.
Substitution of the Fittest: A Novel Approach for Mitigating Disengagement in Coevolutionary Genetic Algorithms
We propose substitution of the fittest (SF), a novel technique designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms.
The Limit Order Book Recreation Model (LOBRM): An Extended Analysis
The limit order book (LOB) depicts the fine-grained demand and supply relationship for financial assets and is widely used in market microstructure studies.
The LOB Recreation Model: Predicting the Limit Order Book from TAQ History Using an Ordinary Differential Equation Recurrent Neural Network
In an order-driven financial market, the price of a financial asset is discovered through the interaction of orders - requests to buy or sell at a particular price - that are posted to the public limit order book (LOB).
Fools Rush In: Competitive Effects of Reaction Time in Automated Trading
In real-world financial markets, speed is known to heavily influence the design of automated trading algorithms, with the generally accepted wisdom that faster is better.
