James T. Wilkinson, Jacob Kelter, John Chen, Uri Wilensky
arXiv 3 May 2024 · Econometrics
arXiv:2405.02480 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Kyungsik Kim, S. M. Yoon, and K. H. Chang (2004) Power law distributions for stock prices in financial markets, 2004 | 0.928 | 4 | 3 | 100% |
| 2 | Missaka Warusawitharana (2016) Time-varying volatility and the power law distribution of stock returns, 2016 | 0.928 | 4 | 3 | 100% |
| 3 | Andrei Kirilenko, Albert S Kyle, Mehrdad Samadi, and Tugkan Tuzun (2017) The flash crash: High-frequency trading in an electronic market | 0.737 | 3 | 2 | 100% |
| 4 | Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioanni… (2013) Playing atari with deep reinforcement learning | 0.737 | 3 | 2 | 100% |
| 5 | Mark Paddrik, Roy Hayes, William Scherer, and Peter Beling (2017) Effects of limit order book information level on market stability metrics | 0.644 | 2 | 2 | 100% |
| 6 | Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joe… (2015) Human-level control through deep reinforcement learning | 0.585 | 3 | 1 | 100% |
| 7 | P. Jefferies, M.L. Hart, P.M. Hui, and N.F. Johnson (2001) From market games to real-world markets | 0.511 | 2 | 1 | 100% |
| 8 | Gew-rae Kim and Harry M. Markowitz (1989) Investment rules, margin, and market volatility | 0.511 | 2 | 1 | 100% |
| 9 | Moshe Levy (2008) Stock market crashes as social phase transitions | 0.511 | 2 | 1 | 100% |
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