A Network Simulation of OTC Markets with Multiple Agents

• URL: http://arxiv.org/abs/2405.02480v1
• Date: Fri, 3 May 2024 20:45:00 GMT
• Title: A Network Simulation of OTC Markets with Multiple Agents
• Authors: James T. Wilkinson, Jacob Kelter, John Chen, Uri Wilensky,
• Abstract summary: We present a novel approach to simulating an over-the-counter (OTC) financial market in which trades are intermediated solely by market makers. We show that our network-based model can lend insights into the effect of market-structure on price-action.
• Score: 3.8944986367855963
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel agent-based approach to simulating an over-the-counter (OTC) financial market in which trades are intermediated solely by market makers and agent visibility is constrained to a network topology. Dynamics, such as changes in price, result from agent-level interactions that ubiquitously occur via market maker agents acting as liquidity providers. Two additional agents are considered: trend investors use a deep convolutional neural network paired with a deep Q-learning framework to inform trading decisions by analysing price history; and value investors use a static price-target to determine their trade directions and sizes. We demonstrate that our novel inclusion of a network topology with market makers facilitates explorations into various market structures. First, we present the model and an overview of its mechanics. Second, we validate our findings via comparison to the real-world: we demonstrate a fat-tailed distribution of price changes, auto-correlated volatility, a skew negatively correlated to market maker positioning, predictable price-history patterns and more. Finally, we demonstrate that our network-based model can lend insights into the effect of market-structure on price-action. For example, we show that markets with sparsely connected intermediaries can have a critical point of fragmentation, beyond which the market forms distinct clusters and arbitrage becomes rapidly possible between the prices of different market makers. A discussion is provided on future work that would be beneficial.

Related papers

• Neuro-Symbolic Traders: Assessing the Wisdom of AI Crowds in Markets [0.0]
  We develop a form of virtual traders that use deep generative models to make buy/sell decisions. Under our framework, neuro-symbolic traders are agents that use vision-language models to discover a model of the fundamental value of an asset. We find that this leads to price suppression compared to the historical data, highlighting a future risk to market stability.
  arXiv  Detail & Related papers  (2024-10-18T16:37:52Z)
• A Multi-agent Market Model Can Explain the Impact of AI Traders in Financial Markets -- A New Microfoundations of GARCH model [3.655221783356311]
  We propose a multi-agent market model to derive the microfoundations of the GARCH model, incorporating three types of agents: noise traders, fundamental traders, and AI traders. We validate this model through multi-agent simulations, confirming its ability to reproduce the stylized facts of financial markets.
  arXiv  Detail & Related papers  (2024-09-19T07:14:13Z)
• When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments [55.19252983108372]
  We have developed a multi-agent AI system called StockAgent, driven by LLMs. The StockAgent allows users to evaluate the impact of different external factors on investor trading. It avoids the test set leakage issue present in existing trading simulation systems based on AI Agents.
  arXiv  Detail & Related papers  (2024-07-15T06:49:30Z)
• Modelling crypto markets by multi-agent reinforcement learning [0.0]
  This study introduces a multi-agent reinforcement learning (MARL) model simulating crypto markets. It is calibrated to the crypto's daily closing prices of $153$ cryptocurrencies that were continuously traded between 2018 and 2022.
  arXiv  Detail & Related papers  (2024-02-16T16:28:58Z)
• An Auction-based Marketplace for Model Trading in Federated Learning [54.79736037670377]
  Federated learning (FL) is increasingly recognized for its efficacy in training models using locally distributed data. We frame FL as a marketplace of models, where clients act as both buyers and sellers. We propose an auction-based solution to ensure proper pricing based on performance gain.
  arXiv  Detail & Related papers  (2024-02-02T07:25:53Z)
• Price Interpretability of Prediction Markets: A Convergence Analysis [6.623653707600985]
  We show that the limiting price converges to the geometric mean of agent beliefs in exponential utility-based markets. We also show that the limiting price can be characterized by systems of equations that encapsulate agent beliefs, risk parameters, and wealth.
  arXiv  Detail & Related papers  (2022-05-18T13:15:11Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• Macroscopic properties of buyer-seller networks in online marketplaces [55.41644538483948]
  We analyze two datasets containing 245M transactions that took place on online marketplaces between 2010 and 2021. We show that transactions in online marketplaces exhibit strikingly similar patterns despite significant differences in language, lifetimes, products, regulation, and technology.
  arXiv  Detail & Related papers  (2021-12-16T18:00:47Z)
• Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market [58.720142291102135]
  This paper focuses precisely on the study of these markets makers strategies from an agent-based perspective. We propose the application of Reinforcement Learning (RL) for the creation of intelligent market markers in simulated stock markets.
  arXiv  Detail & Related papers  (2021-12-08T14:55:21Z)
• Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders [47.32228513808444]
  We present a realistic scenario in which an attacker influences algorithmic trading systems by using adversarial learning techniques. We show that when added to the input stream, our perturbation can fool the trading algorithms at future unseen data points.
  arXiv  Detail & Related papers  (2020-10-19T06:28:05Z)
• Deep Probabilistic Modelling of Price Movements for High-Frequency Trading [0.0]
  We propose a deep recurrent architecture for the probabilistic modelling of high-frequency market prices. The resulting deep mixture models simultaneously address several practical challenges important in the development of automated high-frequency trading strategies. We show that our model outperforms the benchmark models in both a metric-based test and in a simulated trading scenario.
  arXiv  Detail & Related papers  (2020-03-31T19:25:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.