Efficiency of Constant Log Utility Market Makers
Researchers reveal that pricing securities in Constant Log Utility Market Makers (CLUM), used in prediction and decentralized finance, is #P-hard, making exact computation nearly impossible as outcomes increase. They introduce the first approximation algorithm, which works well if an oracle can quickly find the most-purchased outcomes. Notably, CLUMs have constant worst-case loss, unlike LMSR market makers. However, exact pricing remains intractable, and current approximations can be off by a factor of 64, posing risks for operators.
What it examines
This paper studies Constant Log Utility Market Makers (CLUMs), which help set prices in complex prediction markets and decentralized finance. It shows that pricing is computationally hard (#P-hard), and proposes an efficient approximation algorithm, especially for interval securities, making these markets more practical and scalable.
What it concludes
The research proves CLUM pricing is #P-hard but offers a practical approximation method, especially for interval securities using efficient data structures. Applications include prediction markets, options trading, and decentralized exchanges. Future work aims to extend efficient pricing to more security types and improve approximation accuracy for broader market use.
Evidence objects
Pricing securities in Constant Log Utility Market Makers (CLUM) is proven to be #P-hard, making exact price computation infeasible as outcome numbers growposing major challenges for prediction and DeFi markets.
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The authors introduce the first approximation algorithm for CLUMs, achieving high accuracy with a special oracle that identifies top-purchased outcomes, and use self-balancing binary search trees for practical interval security implementation.
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Despite these advances, exact pricing remains intractable for general cases, and alternative approximations can be off by a factor of 64, raising risks for market operators; efficient oracles only exist for certain securities.
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This paper establishes the \# P-hardness of pricing in Constant Log Utility Market Makers (CLUM), paralleling foundational LMSR results, and introduces a practical approximation algorithm for interval securities. By connecting CLUM with constant function market makers (CFMMs), it offers novel theoretical and practical insights, significantly advancing DeFi and computational finance literature.
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Raw abstract and provenance
- … We first establish the computational complexity of the … double auction mechanism ubiquitous in finance. AMMs … date (similar to how financial options traders predict the …
Source row: 675 · abstract type: snippet