This paper introduces stylized facts and agent-based modeling in finance.
problem Replicating stylized facts in financial markets.
method Agent-based computational economic market models.
result Introduction of universal building blocks for agent-based models.
Develops methods to create consistent surrogate models for agent-based simulators.
problem High computational costs and misjudgment of interventions in agent-based models.
method Causal abstractions to learn interventionally consistent surrogate models.
result Surrogates trained for interventional consistency closely mimic the agent-based model's behavior under interventions.
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…
Reduced models derived from agent-based systems using Koopman theory.
problem Time-consuming simulations of large agent-based systems.
method Koopman operator theory applied to simulation data.
result Derived reduced models match known analytical results.
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…
Agent-based model helps design financial markets.
problem Designing financial markets that work well is complex and risky.
method Agent-based model to simulate and analyze financial markets.
result Tick size reduction impacts market efficiency.
Study develops time-continuous models and probabilistic descriptions for agent-based economic market models.
problem Formulating and describing agent-based economic market models in a time-continuous and probabilistic manner.
method Derived time-continuous formulations, discussed impact of time-scaling, proved stability, presented probabilistic descriptions using kinetic theory.
result Time-continuous formulations and probabilistic descriptions for agent-based economic market models.
XGB-Chiarella model generates realistic intra-day financial price data using agent-based models.
problem Generating accurate intra-day financial price data for research and risk management.
method Agent-based financial market simulation with XGBoost machine learning calibration.
result XGB-Chiarella model accurately reflects real market behaviours and generates realistic price time series.
New game model improves financial stylized facts reproduction.
problem Difficulty in reproducing financial stylized facts.
method Agent-based speculation game with unique features.
result Successfully reproduces 10 out of 11 stylized facts.
Study stylized facts in ABCEM models using a new simulator.
problem Understanding stylized facts in economic market models.
method Used SABCEMM tool to simulate and recombine existing ABCEM models.
result Identified and recombined stylized facts in ABCEM models.
We extend Kirman's model by introducing variable event time scale. The proposed flexible time scale is equivalent to the variable trading activity observed in financial markets. Stochastic version of the extended Kirman's agent based model is compared to the non-linear stochastic models of long-range memory in financia…
New methods help calibrate complex ABMs more efficiently.
problem Calibrating parameters in complex ABMs is challenging.
method Integrates different sampling methods and surrogate models.
result Surrogate assisted methods perform better than standard methods.
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…
Real world markets display power-law features in variables such as price fluctuations in stocks. To further understand market behavior, we have conducted a series of market experiments on our web-based prediction market platform which allows us to reconstruct transaction networks among traders. From these networks, we …
Paper uses agent-based simulation to identify investor types in financial markets.
problem Identifying investor types in real financial markets.
method Computational adaptation of PCA with agent-based simulation.
result A reduced set of investor models can approximate financial time series.
We present examples of agent-based and stochastic models of competition and business processes in economics and finance. We start from as simple as possible models, which have microscopic, agent-based, versions and macroscopic treatment in behavior. Microscopic and macroscopic versions of herding model proposed by Kirm…
The paper analyzes optimal dealer strategies in agent-based market models.
problem Optimal dealer strategies in market models.
method Agent-based simulations extended from Chiarella's model to include liquidity providers.
result Dealers with greater risk aversion tend to perform better, but quote size effects are mixed.
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 …
Agent-based model simulates market dynamics with real-time order matching.
problem Realistic simulation of market dynamics with realistic price impact.
method Agent-based model with asynchronous, event-time order matching.
result Realistic price impact curves and stylized facts presented.
We present an overview of some representative Agent-Based Models in Economics. We discuss why and how agent-based models represent an important step in order to explain the dynamics and the statistical properties of financial markets beyond the Classical Theory of Economics. We perform a schematic analysis of several m…
A simple learning agent learns to trade in an agent-based market model.
problem Optimal execution of trades in an agent-based financial market model.
method Asynchronous trading through a matching engine, varying initial order sizes and state spaces, calibration of empirical stylized facts and price impact curves.
result Smaller state space agents converge faster in learning and can trade intuitively using spread and volume states.
Some agent-based models for growth and allocation of resources are described. The first class considered consists of conservative models, where the number of agents and the size of resources are constant during time evolution. The second class is made up of multiplicative noise models and some of their extensions to co…
Agent-based model simulates speculative electronic market with price bubbles.
problem Understanding speculative behavior and price bubbles in electronic markets.
method Agent-based model with two types of traders: mean reverting and speculative.
result Speculative traders lead to increased volatility and price deviations from fundamental value.
ElecSim models long-term electricity planning with agent-based Monte-Carlo simulations.
problem Transitioning to zero-carbon energy systems requires careful policy decisions.
method Agent-based Monte-Carlo model for long-term electricity investment decisions.
result Monte-Carlo simulation improves model performance by 52.5%.
Evology models US equity mutual funds interactions for investment strategies.
problem Understanding complex interactions in financial markets.
method Agent-based model (ABM) of US stock market participants and their strategies.
result Trading strategies interact with other market participants and conditions.
We present a simple, yet realistic, agent-based model of an electricity market. The proposed model combines the spot and balancing markets with a resolution of one minute, which enables a more accurate depiction of the physical properties of the power grid. As a test, we compare the results obtained from our simulation…
Study compares ABM calibration methods, finds Bayesian estimation superior.
problem Criticism of ABM rigour, particularly in calibration practices.
method Comparison of Bayesian and frequentist ABM calibration methods through computational experiments.
result Bayesian estimation outperforms frequentist methods in producing reasonable parameter estimates.
fintech-kMC simulates financial platforms for AI/ML model validation.
problem Validation of AI/ML models in real-world financial applications.
method Agent-based model with kinetic Monte Carlo engine.
result Generates realistic synthetic data for testing AI/ML models.
Agent-based model simulates financial market crashes and identifies key factors.
problem Analyzing and understanding flash crashes in financial markets.
method Agent-based modelling approach with calibrated high-frequency financial simulator.
result Model accurately reproduces historical flash crash events and identifies key factors.
New method learns fractional order of PDEs from flocking particle simulations.
problem Deriving effective nonlocal influence functions from discrete agent-based models.
method Agent-based model, fractional PDEs, Gaussian process regression, Bayesian optimization.
result Learned Euler equations accurately predict flocking behavior.
We investigate an inhomogeneous Ising model in the context of tax evasion dynamics where different types of agents are parametrized via local temperatures and magnetic fields. In particular, we analyse the impact of backauditing and endogenously determined penalty rates on tax compliance. Both features contribute to a …
SABCEMM simulates economic market models with millions of agents.
problem Efficiently simulating large-scale economic market models.
method Object-oriented C++ implementation, XML configuration, abstract model class.
result Different random number generators significantly impact ABCEM model run time.
Develops a generic two-layer framework for adaptive ABMs.
problem Bi-level adaptation problem in ABMs: agents adapt to environment, and environment adapts to agents.
method Formalizes bi-level problem as a Stackelberg game with conditional policies, solving coupled non-linear equations.
result Unified framework for adaptive ABMs, addressing traditional ABM limitations.
Based on criteria of mathematical simplicity and consistency with empirical market data, a model with volatility driven by fractional noise has been constructed which provides a fairly accurate mathematical parametrization of the data. Here, some features of the model are discussed and, using agent-based models, one tr…
Model shows how traders' interactions can create market patterns.
problem Explaining stylized facts in high-frequency trading markets.
method Agent-based model of limit order book trading with zero-intelligence agents.
result Scale-free connectivity between traders reproduces market patterns, while no interaction does not.
The three-state agent-based 2D model of financial markets as proposed by Giulia Iori has been extended by introducing increasing trust in the correctly predicting agents, a more realistic consultation procedure as well as a formal validation mechanism. This paper shows that such a model correctly reproduces the three f…
Improved ABFMs capture market complexities, aiding policy decisions.
problem Limited usefulness of current ABFMs due to missing microstructure and agent behaviors.
method Developed ABMMS with realistic market structure, communication, and auction mechanisms; populated with adaptive agents.
result Generated data from ABMMS more accurately reflects real market phenomena.
This work develops an agent-based model for the study of how the leverage through the use of repurchase agreements can function as a mechanism for the propagation and amplification of financial shocks in a financial system. Based on the analysis of financial intermediaries in the repo and interbank lending markets duri…
The paper models cryptocurrency market bubbles using agent-based models.
problem Understanding speculative bubbles in cryptocurrency markets.
method Agent-based models and ODE models to estimate return rates and market values.
result Formulated formula for total system risk.
Agent-based model uses SAM to create realistic economic system.
problem Lack of tools to understand and predict economic crises.
method Agent-based modeling (ABM) with Social Accounting Matrix (SAM) calibration.
result ABM can produce economic systems close to real-world data.
Neural networks improve scalability for agent-based modeling demonstrations.
problem Scalability issues in training models of dynamic systems from demonstrations.
method Use of neural networks to reduce the search space for agent-level parameters.
result More scalable architecture for reproducing emergent behavior from demonstrations.
Model financial markets with social media influences using hierarchical networks.
problem Understanding social media's impact on financial markets.
method Agent-based model with hierarchical influence network.
result Model accurately simulates real-world financial market behaviors.
Model shows how social norms and individual ethics affect tax evasion.
problem Effects of social norms and individual ethics on tax evasion.
method Agent-based model with simulations of different tax compliance behaviors.
result Threshold levels in society composition explain tax evasion extent.
Researchers develop methods to calibrate ABMs using Bayesian techniques.
problem Challenges in calibrating ABMs due to likelihood intractability and non-differentiability.
method Generalised variational inference for differentiable ABMs.
result Accurate Bayesian parameter inferences for differentiable ABMs demonstrated.
Proposes a new agent-based model for deep hedging that outperforms existing models.
problem Improving effectiveness of deep hedging strategies.
method Agent-based model with momentum, fundamental, and volatility traders following Heston volatility signal.
result Deep hedging agent trained with Chiarella-Heston model data outperforms baseline models in various transaction cost levels.
Paper validates ABM using stylized financial facts.
problem Validate ABM-generated financial data against real-world data.
method Compare ABM results with stylized financial facts.
result Model successfully replicates stylized financial facts.
Luck is crucial for success, talent alone isn't enough.
problem The role of luck and talent in achieving success.
method Agent-based model to simulate the role of luck and talent.
result Luck is more important than talent for success.
Hybrid model combines deep learning and agent-based methods for synthetic LOB generation.
problem Generating realistic financial time series data for model training.
method Combining TABL model with Chiarella model for intraday trading activity simulation.
result Hybrid model generates realistic price dynamics but fails to accurately recreate market microstructure.