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.
Agent-based model simulates market dynamics with real-time order matching.
Agent-based modeling is a powerful simulation technique to understand the collective behavior and microscopic interaction in complex financial systems. Recently, the concept for determining the key parameters of the agent-based models from empirical data instead of setting them artificially was suggested. We first revi…
TraderTalk uses LLMs to simulate human trading interactions in financial markets.
A multi-agent simulator evaluates trading strategies using Market Replay and Interactive Agent-Based Simulation.
Develops methods to create consistent surrogate models for agent-based simulators.
Flocking refers to collective behavior of a large number of interacting entities, where the interactions between discrete individuals produce collective motion on the large scale. We employ an agent-based model to describe the microscopic dynamics of each individual in a flock, and use a fractional PDE to model the evo…
Reduced models derived from agent-based systems using Koopman theory.
Agent Based Modeling (ABM) has become a widespread approach to model complex interactions. In this chapter after briefly summarizing some features of ABM the different approaches in modeling spatial interactions are discussed. It is stressed that agents can interact either indirectly through a shared environment and/or…
Agent-based simulation assesses tradable credit schemes for congestion reduction.
XGB-Chiarella model generates realistic intra-day financial price data using agent-based models.
Agent-based models, particularly those applied to financial markets, demonstrate the ability to produce realistic, simulated system dynamics, comparable to those observed in empirical investigations. Despite this, they remain fairly difficult to calibrate due to their tendency to be computationally expensive, even with…
INTAGS uses interactive simulation to improve realism in multi-agent systems.
Hybrid model simulates market dynamics using neural stochastic background traders.
ESOP uses Bayesian optimization to find optimal lock-down schedules.
Agent-based model helps design financial markets.
DEPLOYERS models multi-country economic systems using ABM.
Evology models US equity mutual funds interactions for investment strategies.
This paper describes an agent-based model of interacting firms, in which interacting firm agents rationally invest capital and labor in order to maximize payoff. Both transactions and production are taken into account in this model. First, the performance of individual firms on a real transaction network was simulated.…
Model shows how traders' interactions can create market patterns.
We propose a class of Markovian agent based models for the time evolution of a share price in an interactive market. The models rely on a microscopic description of a market of buyers and sellers who change their opinion about the stock value in a stochastic way. The actual price is determined in realistic way by match…
Paper uses agent-based simulation to identify investor types in financial markets.
An agent-based model for firms' dynamics is developed. The model consists of firm agents with identical characteristic parameters and a bank agent. Dynamics of those agents is described by their balance sheets. Each firm tries to maximize its expected profit with possible risks in market. Infinite growth of a firm dire…
Agent-based model compares different COVID-19 testing policies and their effectiveness.
Many learning agents impact a financial market model, showing complex dynamics.
Study shows how diverse investors' learning and preferences shape financial markets.
Study Figgie card game strategies using agent-based simulation.
We describe a new model to simulate the dynamic interactions between market price and the decisions of two different kind of traders. They possess spatial mobility allowing to group together to form coalitions. Each coalition follows a strategy chosen from a proportional voting ``dominated'' by a leader's decision. The…
Estimates interaction kernels from agent-based dynamics data.
We are looking for the agent-based treatment of the financial markets considering necessity to build bridges between microscopic, agent based, and macroscopic, phenomenological modeling. The acknowledgment that agent-based modeling framework, which may provide qualitative and quantitative understanding of the financial…
Agent-based model simulates financial market crashes and identifies key factors.
The current economic crisis has provoked an active response from the interdisciplinary scientific community. As a result many papers suggesting what can be improved in understanding of the complex socio-economics systems were published. Some of the most prominent papers on the topic include (Bouchaud, 2009; Farmer and …
Study shows how high-budget agents can manipulate prediction markets.
We have conducted an agent-based simulation of chain bankruptcy. The propagation of credit risk on a network, i.e., chain bankruptcy, is the key to nderstanding largesized bankruptcies. In our model, decrease of revenue by the loss of accounts payable is modeled by an interaction term, and bankruptcy is defined as a ca…
A new Python-C++ framework for agent-based simulation.
In complex financial systems, the sector structure and volatility clustering are respectively important features of the spatial and temporal correlations. However, the microscopic generation mechanism of the sector structure is not yet understood. Especially, how to produce these two features in one model remains chall…
We study the qualitative and quantitative appearance of stylized facts in several agent-based computational economic market (ABCEM) models. We perform our simulations with the SABCEMM (Simulator for Agent-Based Computational Economic Market Models) tool recently introduced by the authors (Trimborn et al. 2019). Further…
Study uses agent-based simulation to analyze impact of OBI strategy on financial markets.
fintech-kMC simulates financial platforms for AI/ML model validation.
Developing an Agent-Based Model to Mitigate Adverse Selection in Uniswap v3 Liquidity Providers
We present a linear agent based model on brand competition. Each agent belongs to one of the two brands and interacts with its nearest neighbors. In the process the agent can decide to change to the other brand if the move is beneficial. The numerical simulations show that the systems always condenses into a state when…
A simple learning agent learns to trade in an agent-based market model.
Study uses RL to simulate realistic market behavior.
New method calibrates ABMs using graph neural networks for microdata.
We propose a Markov jump process with the three-state herding interaction. We see our approach as an agent-based model for the financial markets. Under certain assumptions this agent-based model can be related to the stochastic description exhibiting sophisticated statistical features. Along with power-law probability …
GeomHerd predicts herding behavior before market prices move, using Ricci curvature of agent interaction graphs.
This review deals with several microscopic (``agent-based'') models of financial markets which have been studied by economists and physicists over the last decade: Kim-Markowitz, Levy-Levy-Solomon, Cont-Bouchaud, Solomon-Weisbuch, Lux-Marchesi, Donangelo-Sneppen and Solomon-Levy-Huang. After an overview of simulation a…
The paper analyzes optimal dealer strategies in agent-based market models.
The dual crises of the sub-prime mortgage crisis and the global financial crisis has prompted a call for explanations of non-equilibrium market dynamics. Recently a promising approach has been the use of agent based models (ABMs) to simulate aggregate market dynamics. A key aspect of these models is the endogenous emer…