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1. Stability Effects of Arbitrage in Exchange Traded Funds: An Agent-Based Model

   Shearer, Megan; Byrd, David; Balch, Tucker H; Wellman, Michael P (January 2021, ACM International Conference on Artificial Intelligence in Finance)

   An index-based exchange traded fund (ETF) with underlying securities that trade on the same market creates potential opportunities for arbitrage between price deviations in the ETF and the corresponding index. We examine whether ETF arbitrage transmits small volatility events, termed mini flash crashes, from one of its underlying symbols to another. We address this question in an agent-based, simulated market where agents can trade an ETF and its two underlying symbols. We explore multiple market configurations with active and inactive ETF arbitrageurs. Through empirical game-theoretic analysis, we find that when arbitrageurs actively trade, background traders’ surplus increases because of the increased liquidity. Arbitrage helps the ETF more accurately track the index. We also observe that when one symbol experiences a mini flash crash, arbitrage transmits a price change in the opposite direction to the other symbol. The size of the mini flash crash depends more on the market configuration than the arbitrageurs, but the recovery of the mini flash crash is faster when arbitrageurs are present. 

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2. Adaptive Curves for Optimally Efficient Market Making

   https://doi.org/10.4230/LIPIcs.AFT.2024.25  

   Nadkarni, Viraj; Kulkarni, Sanjeev; Viswanath, Pramod (January 2024, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Böhme, Rainer; Kiffer, Lucianna (Ed.)

   Automated Market Makers (AMMs) are essential in Decentralized Finance (DeFi) as they match liquidity supply with demand. They function through liquidity providers (LPs) who deposit assets into liquidity pools. However, the asset trading prices in these pools often trail behind those in more dynamic, centralized exchanges, leading to potential arbitrage losses for LPs. This issue is tackled by adapting market maker bonding curves to trader behavior, based on the classical market microstructure model of Glosten and Milgrom. Our approach ensures a zero-profit condition for the market maker’s prices. We derive the differential equation that an optimal adaptive curve should follow to minimize arbitrage losses while remaining competitive. Solutions to this optimality equation are obtained for standard Gaussian and Lognormal price models using Kalman filtering. A key feature of our method is its ability to estimate the external market price without relying on price or loss oracles. We also provide an equivalent differential equation for the implied dynamics of canonical static bonding curves and establish conditions for their optimality. Our algorithms demonstrate robustness to changing market conditions and adversarial perturbations, and we offer an on-chain implementation using Uniswap v4 alongside off-chain AI co-processors. 

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3. Real-time energy market arbitrage via aerodynamic energy storage in wind farms

   https://doi.org/10.23919/ACC45564.2020.9147730  

   Shapiro, Carl R.; Ji, Chengda; Gayme, Dennice F. (July 2020, American Control Conference)

   Energy storage can generate significant revenue by taking advantage of fluctuations in real-time energy market prices. In this paper, we investigate the real-time price arbitrage potential of aerodynamic energy storage in wind farms. This under-explored source of energy storage can be realized by deferring energy extraction by turbines toward the front of a farm for later extraction by downstream turbines. In large wind farms, this kinetic energy can be stored for minutes to tens of minutes, depending on the inter-turbine travel distance and the incoming wind speed. This storage mechanism requires minimal capital costs for implementation and potentially could provide additional revenue to wind farm operators. We demonstrate that the potential for revenue generation depends on the energy arbitrage (storage) efficiency and the wind travel time between turbines. We then characterize how price volatility and arbitrage efficiency affect real-time energy market revenue potential. Simulation results show that when price volatility is low, which is the historic norm, noticeably increased revenue is only achieved with high arbitrage efficiencies. However, as price volatility increases, which is expected in the future as the composition of the power system evolves, revenues increase by several percent. 

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4. Tangent-V: Math Formula Image Search Using Line-of-Sight Graphs

   https://doi.org/10.1007/978-3-030-15712-8_44  

   Davila, Kenny; Joshi, Ritvik; Setlur, Srirangaraj; Govindaraju, Venu; Zanibbi, Richard (January 2019, Proceedings of the European Conference on Information Retrieval (ECIR))

   We present a visual search engine for graphics such as math, chemical diagrams, and figures. Graphics are represented using Line-of- Sight (LOS) graphs, with symbols connected only when they can ‘see’ each other along an unobstructed line. Symbol identities may be provided (e.g., in PDF) or taken from Optical Character Recognition applied to images. Graphics are indexed by pairs of symbols that ‘see’ each other using their labels, spatial displacement, and size ratio. Retrieval has two layers: the first matches query symbol pairs in an inverted index, while the second aligns candidates with the query and scores the resulting matches using the identity and relative position of symbols. For PDFs, we also introduce a new tool that quickly extracts characters and their lo- cations. We have applied our model to the NTCIR-12 Wikipedia Formula Browsing Task, and found that the method can locate relevant matches without unification of symbols or using a math expression grammar. In the future, one might index LOS graphs for entire pages and search for text and graphics. Our source code has been made publicly available. 

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5. Market making under a weakly consistent limit order book model

   https://doi.org/10.1002/hf2.10050  

   Law, Baron; Viens, Frederi (January 2020, High Frequency)

   Abstract We develop a new market‐making model, from the ground up, which is tailored toward high‐frequency trading under a limit order book (LOB), based on the well‐known classification of order types in market microstructure. Our flexible framework allows arbitrary order volume, price jump, and bid‐ask spread distributions as well as the use of market orders. It also honors the consistency of price movements upon arrivals of different order types. For example, it is apparent that prices should never go down on buy market orders. In addition, it respects the price‐time priority of LOB. In contrast to the approach of regular control on diffusion as in the classical Avellaneda and Stoikov (Quantitative Finance, 8, 217, 2008) market‐making framework, we exploit the techniques of optimal switching and impulse control on marked point processes, which have proven to be very effective in modeling the order book features. The Hamilton‐Jacobi‐Bellman quasi‐variational inequality (HJBQVI) associated with the control problem can be solved numerically via finite‐difference method. We illustrate our optimal trading strategy with a full numerical analysis, calibrated to the order book statistics of a popular exchanged‐traded fund (ETF). Our simulation shows that the profit of market‐making can be severely overstated under LOBs with inconsistent price movements. 

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