MarS simulates financial markets using generative models.
problem Simulating realistic financial market effects.
method Order-level generative foundation model (LMM) for realistic, interactive, and controllable order generation.
result Strong scalability and robust realism in MarS.
Simulation reveals relationships in stock market pyramid schemes.
problem Understanding pyramid scheme behavior in stock markets.
method Agent-based simulation with four investor types and parameters.
result Relationships between main fund's rate of return and trend investors' proportion.
New RL framework simulates financial market dynamics.
problem Complex financial market dynamics under various scenarios.
method Two RL families learn simultaneously, using Deep RL and parametrized reward.
result Agents learn a shared policy for diverse behaviors.
FinRL-Meta creates diverse market environments for DRL in finance.
problem Inaccurate financial data and diverse market environments challenge DRL in finance.
method Open-source data processing tools, hundreds of market environments, and multiprocessing.
result FinRL-Meta improves DRL accuracy and speed in financial simulations.
LLMs simulate financial markets, revealing consistent trading strategies and market dynamics.
problem Testing financial theories with AI trading agents.
method Simulated stock market with LLMs using a persistent order book and varied strategies.
result LLMs can simulate different trading strategies and market dynamics.
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.
SHIFT simulates realistic financial markets for research and industry.
problem Creating a realistic simulation platform for financial market research.
method Developed a highly realistic financial market simulator with multiple traders and assets.
result Demonstrated that automated agents can produce price processes similar to real markets.
Financial Wind Tunnel generates versatile market data for model testing.
problem Inconsistent market dynamics across different scales and sources.
method Retrieval-augmented diffusion-based simulator integrating macro and micro patterns.
result Enhanced performance and adaptability of downstream models in complex markets.
Proposes ABIDES-Gym for financial markets simulation.
problem Training multi-agent systems in financial markets.
method Wrapping DEMAS into OpenAI Gym framework.
result Developed two financial markets OpenAI Gym environments.
Study models opaque financial markets using multi-agent simulation.
problem Challenges in financial markets with obscured data availability.
method Multi-agent simulation with small-scale meta-heuristic methods.
result Captures bilateral market dynamics of OTC trading.
Generative Adversarial Networks simulate realistic market interactions.
problem Lack of agent-level historical data limits market simulation realism.
method Conditional Generative Adversarial Networks (CGANs) trained on real data.
result CGAN-based synthetic market generator outperforms previous methods in market responsiveness and realism.
A new neural network model simulates financial markets without assuming underlying dynamics.
problem Modeling financial time series without assuming underlying dynamics.
method Neural network based generative model using a parsimonious Variational Autoencoder framework.
result Works reliably in small data environments, providing a new performance evaluation metric.
New simulation model predicts financial market dynamics with high accuracy.
problem Extreme difficulty in financial market projections due to human behavioural complexity.
method Agent-based modeling with a hierarchical knowledge architecture to simulate diverse human groups.
result Simulator achieves 13.29% deviation in crisis scenarios and lower mean square error under normal conditions.
In this paper we propose an Ising model which simulates multiple financial time series. Our model introduces the interaction which couples to spins of other systems. Simulations from our model show that time series exhibit the volatility clustering that is often observed in the real financial markets. Furthermore we al…
Novel quantum algorithm for financial market modeling.
problem Accurate quantum state preparation for financial simulation.
method Multi-Split-Steps Quantum Walk (multi-SSQW) with PQC and variational solver.
result Highly accurate modeling of complex financial distributions.
Market-GAN adds context control to financial market data generation.
problem Lack of context labels and precision in generating context-aligned financial data.
method Proposes Contextual Market Dataset and Market-GAN architecture integrating GAN, autoencoder, and supervisors.
result Market-GAN outperforms state-of-the-art models in Dow Jones data generation.
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.
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.
TRADES generates realistic market simulations for financial modeling.
problem Generating realistic and responsive market simulations for financial tasks.
method TRADES uses a transformer-based denoising diffusion probabilistic engine to generate time series order flows conditioned on market state.
result TRADES improves market simulation metrics by 3.27-3.48 over state-of-the-art (SoTA) methods.
Developed scalable ABM for complex financial markets.
problem Simulating large-scale agent-based financial markets.
method Agent-based modeling, distributed computing, continuous double auction.
result Captures statistical properties of real financial markets.
PRIME models cryptocurrency exchange market impact.
problem Understanding and predicting market impact in cryptocurrency exchanges.
method Developed a multi-agent simulation to model market impact.
result Allows better estimation of market slippage and knock-on consequences.
Study examines if LLMs' trading styles match real market behavior.
problem Lack of behavioral consistency in LLMs' trading strategies.
method Year-long simulations with LLMs, operationalizing behavioral finance drivers, and comparing with financial theory.
result LLMs' strategy switching is only partially consistent with behavioral finance theories.
A new method uses diffusion models to simulate financial markets accurately.
problem Generating realistic synthetic financial market data.
method Diffusion model approach with efficient numerical integration.
result Synthetic data closely matches observed market data in key aspects.
New method calibrates financial market simulators using neural networks.
problem Calibrating market simulators to specific trading periods.
method Neural density estimators and embedding networks.
result Approach accurately identifies high-probability parameter sets.
Study complexity in financial market using Shannon entropy.
problem Measuring complexity in financial market information traffic.
method Reconstructing financial dynamics from share prices, calculating Shannon entropy.
result Shannon entropy quantifies complexity in financial market information.
This paper tackles non-identifiability in financial market simulations using multivariate time series data.
problem Non-identifiability issue in social simulation models, leading to indistinguishable simulated time series data.
method Proposes a maximization-based aggregation function to form a new calibration objective function using multiple time series features.
result Significant improvements in alleviating non-identifiability and achieving higher simulation fidelity.
MarketGPT models financial time series with realistic order flow data.
problem Creating accurate financial market simulations.
method Generative pre-trained transformer (GPT) for long sequence generation.
result Model reproduces key features of real financial markets and stylized facts.
A three-state model based on the Potts model is proposed to simulate financial markets. The three states are assigned to "buy", "sell" and "inactive" states. The model shows the main stylized facts observed in the financial market: fat-tailed distributions of returns and long time correlations in the absolute returns. …
Study uses OT to simulate markets, revealing power-law returns are driven by informational effect.
problem Reproduce power-law returns in financial markets using realistic simulations.
method Constructed artificial markets, used optimal transport (OT) to measure similarity, incrementally introduced behavioral components.
result Informational effect of prices is dominant in reproducing power-law returns, and multiple components interact synergistically.
Hybrid model simulates market dynamics using neural stochastic background traders.
problem Lack of realistic LOB simulations that combine historical data and dynamic interactions.
method Neural stochastic background trader trained on historical LOB data, embedded in multi-agent simulation.
result Hybrid model recreates stylised market facts and financial herding behaviors.
A new Python-C++ framework for agent-based simulation.
problem Understanding market dynamics and effects of delays.
method User-friendly Python API with efficient C++ implementation, message-driven architecture.
result Investigated the role of order processing delay in financial markets.
A new RL framework tackles asset allocation problems using Monte Carlo simulation.
problem Existing asset allocation methods fail to consider portfolio management and financial market characteristics.
method Proposes a new reinforcement learning framework that considers portfolio state and uses Monte Carlo simulation to prevent overfitting.
result The proposed method outperforms benchmarks in various test intervals.
Study proposes a new approach for deep hedging using artificial market simulations.
problem Challenges in selecting the best model for underlying asset simulations in deep hedging.
method Proposes artificial market simulations to replicate financial market stylized facts.
result Achieves similar performance to traditional approaches without mathematical finance models.
This work models market regimes using CTMSTOU and simulates trading policies.
problem Defining and understanding market regimes in finance.
method Discrete event time multi-agent market simulation with CTMSTOU model.
result Illustrates the importance of regime-awareness in trading policies.
To investigate the universal structure of interactions in financial dynamics, we analyze the cross-correlation matrix C of price returns of the Chinese stock market, in comparison with those of the American and Indian stock markets. As an important emerging market, the Chinese market exhibits much stronger correlations…
This tutorial introduces quantum computing for financial portfolio optimization.
problem Combinatorial portfolio optimization in financial markets.
method Application of Quantum Approximate Optimization Algorithm (QAOA) to portfolio optimization.
result Quality of combinatorial portfolio optimization solutions using QAOA on quantum simulator.
Designing a financial market that works well is very important for developing and maintaining an advanced economy, but is not easy because changing detailed rules, even ones that seem trivial, sometimes causes unexpected large impacts and side effects. A computer simulation using an agent-based model can directly treat…
New simulation shows trading algorithms' performance varies with parallelism.
problem Validation of trading algorithms' performance in parallel markets.
method Used TBSE, a threaded market simulator, to compare algorithms' performance.
result Trading algorithms' performance differs in parallel vs. sequential markets.
The paper uses machine learning to simulate financial markets and improve trading strategy backtesting.
problem Improving risk management of quantitative investment strategies.
method Simulates financial markets using Boltzmann Machines and Generative Adversarial Networks to preserve asset return distributions and dependencies.
result Developed a framework to estimate backtest statistics more accurately.
We perform a large-scale simulation of an Ising-based financial market model that includes 300 asset time series. The financial system simulated by the model shows a fat-tailed return distribution and volatility clustering and exhibits unstable periods indicated by the volatility index measured as the average of absolu…
TraderTalk uses LLMs to simulate human trading interactions in financial markets.
problem Simulating realistic human trading interactions in financial markets.
method Hybrid ABM with LLM-generated behaviors for detailed conversations.
result Successfully replicates trade-to-order volume ratios in financial markets.
Study evaluates discretized arbitrage strategies in fractional financial markets.
problem Serial correlation in financial markets with fractional Brownian motion.
method Revisit and transfer Shiryaev and Salopek's strategies to a real-world setting, distretizing dynamics and introducing transaction costs.
result Both strategies are promising with respect to terminal portfolio values and loss probabilities.
We introduce a new Self-Organized Criticality (SOC) model for simulating price evolution in an artificial financial market, based on a multilayer network of traders. The model also implements, in a quite realistic way with respect to previous studies, the order book dy- namics, by considering two assets with variable f…
We study the emergence of instabilities in a stylized model of a financial market, when different market actors calculate prices according to different (local) market measures. We derive typical properties for ensembles of large random markets using techniques borrowed from statistical mechanics of disordered systems. …
ABIDES-MARL uses MARL to study market behavior in a realistic financial simulation.
problem Understanding equilibrium behavior in complex financial market games.
method Combines MARL with a realistic LOB simulation to study market behavior.
result Validated approach by solving an extended Kyle model and showing how execution strategies shape market dynamics.
Proposes a neural LOB model for market-making.
problem Capturing dynamic LOB events in financial markets.
method Neural Hawkes process for modeling LOB events.
result Model captures real market price fluctuations.
DeepTraderX learns from other strategies to place market orders.
problem Creating efficient trading strategies in multi-threaded market simulations.
method Deep Learning model trained on historical market data to predict optimal market orders.
result DeepTraderX outperforms existing strategies in multi-threaded market simulations.
Simulates financial market orders using anomalous diffusion models.
problem Anomalous diffusion in financial market order dynamics.
method Discrete Time Random Walk with Sibuya waiting times, non-uniform sampling, and cubic spline interpolation.
result Demonstrates price impact for different forcing functions and model parameters.