Chapter 36: Further Reading

DeFi Integration and Liquidity Mining

DeFi Foundations

  1. Schär, F. (2021). Decentralized Finance: On Blockchain- and Smart Contract-Based Financial Markets. Federal Reserve Bank of St. Louis Review, 103(2), 153-174. The most cited academic overview of DeFi. Schär provides a layered model of DeFi architecture (settlement, asset, protocol, application, aggregation layers) and analyzes the composability properties that enable prediction market integration. The section on risk factors is directly applicable to understanding the hazards of stacking yield across multiple protocols.

  2. Adams, H., Zinsmeister, N., & Robinson, D. (2020). Uniswap v2 Core. The technical specification of the constant product AMM that inspired most prediction market liquidity pools. Understanding $x \times y = k$ is essential for analyzing impermanent loss, fee accrual, and arbitrage in prediction market AMMs. The paper also covers flash swaps, which have direct parallels to flash loan arbitrage in prediction markets. Available at uniswap.org/whitepaper.pdf.

  3. Adams, H., Zinsmeister, N., Salem, M., Keefer, R., & Robinson, D. (2021). Uniswap v3 Core. Concentrated liquidity represents the state of the art in AMM design. The ability to concentrate liquidity within specific price ranges (e.g., [0.3, 0.7] for a prediction market) dramatically improves capital efficiency. Several prediction market protocols have adapted concentrated liquidity mechanisms for outcome token pools.

Impermanent Loss

  1. Pintail (2019). Uniswap: A Good Deal for Liquidity Providers? Medium. The first rigorous analysis of impermanent loss for AMM LPs. Derives the IL formula and shows under what conditions fees compensate for IL. While focused on standard token pairs, the mathematical framework directly applies to prediction market pools. The key insight --- that IL is always negative and only offset by fee income --- is essential for evaluating prediction market LP strategies.

  2. Loesch, S., Hindman, N., Richardson, M. B., & Welch, N. (2021). Impermanent Loss in Uniswap v3. arXiv:2111.09192. Extends IL analysis to concentrated liquidity positions, showing that concentrated positions have higher IL per unit of capital but also higher fee income. For prediction market LPs who concentrate liquidity around the current probability estimate, this paper provides the framework for computing expected returns.

Yield Farming and Liquidity Mining

  1. Xu, J., Paruch, K., Cousaert, S., & Feng, Y. (2021). SoK: Decentralized Finance (DeFi). arXiv:2101.08778. A systematic review of DeFi protocols covering lending (Aave, Compound), DEXes (Uniswap, Curve), derivatives, and yield aggregators. The taxonomy of DeFi risks (smart contract, oracle, governance, liquidity) provides a framework for evaluating the risks of prediction market DeFi integrations. The section on yield farming economics is particularly relevant.

  2. Cousaert, S., Xu, J., & Livshits, B. (2022). SoK: Yield Aggregators in DeFi. IEEE S&P. Analyzes yield aggregators (Yearn, Harvest, Beefy) that auto-compound LP positions. For prediction market LPs who deposit LP tokens into aggregators, this paper explains how compounding works, what fees are charged, and what risks are introduced. The framework for comparing aggregator strategies is directly applicable.

MEV and Front-Running

  1. Daian, P., Goldfeder, S., Kell, T., et al. (2020). Flash Boys 2.0: Frontrunning in Decentralized Exchanges, Miner Extractable Value, and Consensus Instability. IEEE S&P 2020. The foundational paper on MEV. Demonstrates how transaction ordering manipulation enables profitable front-running and sandwich attacks on DEX trades. For prediction market traders, this paper explains why on-chain AMM trades are vulnerable and motivates the move to off-chain order matching (as used by Polymarket).

  2. Qin, K., Zhou, L., & Gervais, A. (2022). Quantifying Blockchain Extractable Value: How dark is the forest? IEEE S&P. Empirically measures MEV extraction across DeFi protocols. The paper finds that sandwich attacks account for a significant fraction of all MEV, with DEX trades being the primary target. The quantitative framework can be applied to estimate MEV risk for prediction market AMM traders.

  3. Flashbots. Documentation and Research. Flashbots provides infrastructure for MEV mitigation, including private transaction pools and MEV-Share (which returns MEV to users). For prediction market traders, using Flashbots' Protect endpoint prevents sandwich attacks on AMM trades. The documentation explains how to integrate Flashbots into Python trading bots. Available at docs.flashbots.net.

Flash Loans

  1. Qin, K., Zhou, L., Livshits, B., & Gervais, A. (2021). Attacking the DeFi Ecosystem with Flash Loans for Fun and Profit. Financial Cryptography. Catalogs real-world flash loan attacks and develops a framework for modeling flash loan profitability. The paper shows how flash loans can be used to manipulate oracle prices, which is directly relevant to prediction market security. The defense mechanisms discussed (TWAP oracles, multi-block verification) are applicable to oracle design for prediction markets.

Prediction Market-Specific DeFi

  1. Gnosis. Omen Documentation. Omen is a prediction market platform built on the Gnosis conditional token framework with Uniswap-style AMM pools. The documentation covers LP mechanics specific to prediction markets, including how the constant product formula interacts with binary outcome tokens and how fees are distributed. Available at omen.eth.limo.

  2. Angeris, G., Kao, H. T., Chiang, R., Noyes, C., & Chitra, T. (2019). An Analysis of Uniswap Markets. Cryptoeconomic Systems. Formal analysis proving that rational arbitrageurs will keep AMM prices aligned with external markets. For prediction market AMMs, this implies that as new information arrives, arbitrageurs update the pool price to reflect the new probability estimate. The paper's framework for analyzing arbitrageur-LP dynamics is directly applicable.

  3. Othman, A., Pennock, D. M., Reeves, D. M., & Sandholm, T. (2013). A Practical Liquidity-Sensitive Automated Market Maker. ACM EC. Introduces the LS-LMSR (Liquidity-Sensitive Logarithmic Market Scoring Rule), which adjusts the market maker's sensitivity based on trading volume. This is an alternative to constant product AMMs for prediction markets and offers better capital efficiency for low-liquidity markets.

  4. Harvey, C. R., Ramachandran, A., & Santoro, J. (2021). DeFi and the Future of Finance. Wiley. A comprehensive textbook treatment of DeFi with chapters on lending, DEXes, derivatives, and insurance. The chapter on composability risks is particularly relevant, explaining how systemic risks can propagate through interconnected DeFi protocols. This broader perspective helps predict market participants understand the ecosystem they are operating in.