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Decentralized finance (DeFi) is an integral component of the blockchain ecosystem, enabling a range of financial activities through smart-contract-based protocols.
C. Spearman, “The Proof and Measurement of Association between Two Things,” The American Journal of Psychology , vol. 15, no. 1, p. 72, 1 1904. [Online]. Available: https://www.jstor.org/stable/1412159?origin=crossref
1904
Earlier work this paper cites.
depressedape, “ARC: Increase AAVE Liquidation Threshold,” 2021. [Online]. Available: https://governance.aave.com/t/arc-increase-aave-liquidation-threshold/1909
1909
Earlier work this paper cites.
M. F. M. Osborne, “Periodic Structure in the Brownian Motion of Stock Prices,” Operations Research , vol. 10, no. 3, pp. 345–379, 6 1962. [Online]. Available: https://pubsonline.informs.org/doi/10.1287/opre.10.3.345
1962
Earlier work this paper cites.
J. Geweke, “Macroeconometric modeling and the theory of the representative agent,” American Economic Review , vol. 75, no. 2, pp. 206–210, 6 1985
1985
Earlier work this paper cites.
T. Bollerslev, “A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return,” The Review of Economics and Statistics , vol. 69, no. 3, p. 542, 8 1987. [Online]. Available: https://about.jstor.org/termshttps://www.jstor.org/stable/1925546?origin=crossref
1987
Earlier work this paper cites.
C. J. C. H. Watkins and P. Dayan, “Q-learning,” Machine learning , vol. 8, pp. 279–292, 1992
1992
Earlier work this paper cites.
M. Barreno, B. Nelson, R. Sears, A. D. Joseph, and J. D. Tygar, “Can machine learning be secure?” in ACM Symposium on Information, Computer and Communications Security , vol. 2006. New York, NY, USA: ACM, 3 2006, pp. 16–25. [Online]. Available: https://dl.acm.org/doi/10.1145/1128817.1128824
2006
Earlier work this paper cites.
L. Huang, A. D. Joseph, B. Nelson, B. I. Rubinstein, and J. D. Tygar, “Adversarial machine learning,” in Proceedings of the 4th ACM workshop on Security and artificial intelligence . New York, NY, USA: ACM, 10 2011, pp. 43–58. [Online]. Available: https://dl.acm.org/doi/10.1145/2046684.2046692
2011
Earlier work this paper cites.
P. Treleaven, M. Galas, and V. Lalchand, “Algorithmic trading review,” Communications of the ACM , vol. 56, no. 11, pp. 76–85, 11 2013. [Online]. Available: https://dl.acm.org/doi/10.1145/2500117
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
A. P. Chaboud, B. Chiquoine, E. Hjalmarsson, and C. Vega, “Rise of the machines: Algorithmic trading in the foreign exchange market,” Journal of Finance , vol. 69, no. 5, pp. 2045–2084, 10 2014. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1111/jofi.12186
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
H. Van Hasselt, A. Guez, and D. Silver, “Deep reinforcement learning with double q-learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 30, no. 1, 2016
2016
Earlier work this paper cites.
P. McDaniel, N. Papernot, and Z. B. Celik, “Machine Learning in Adversarial Settings,” IEEE Security & Privacy , vol. 14, no. 3, pp. 68–72, 5 2016. [Online]. Available: http://ieeexplore.ieee.org/document/7478523/
2016
Earlier work this paper cites.
Z. Jiang, D. Xu, and J. Liang, “A deep reinforcement learning framework for the financial portfolio management problem,” 2017
2017
Earlier work this paper cites.
B. M. Weller, “Does Algorithmic Trading Reduce Information Acquisition?” The Review of Financial Studies , vol. 31, no. 6, pp. 2184–2226, 6 2018. [Online]. Available: https://dx.doi.org/10.1093/rfs/hhx137https://academic.oup.com/rfs/article/31/6/2184/4708266
2018
Earlier work this paper cites.
J. De Spiegeleer, D. B. Madan, S. Reyners, and W. Schoutens, “Machine learning for quantitative finance: fast derivative pricing, hedging and fitting,” Quantitative Finance , vol. 18, no. 10, pp. 1635–1643, 10 2018. [Online]. Available: https://www.tandfonline.com/doi/full/10.1080/14697688.2018.1495335
2018
Cited alongside, same era.
C. Hou, M. Zhou, Y. Ji, P. Daian, F. Tramèr, G. Fanti, and A. Juels, “SquirRL: Automating attack discovery on blockchain incentive mechanisms with deep reinforcement learning,” 2019
2019
Cited alongside, same era.
L. Gudgeon, S. Werner, D. Perez, and W. J. Knottenbelt, “DeFi Protocols for Loanable Funds: Interest Rates, Liquidity and Market Efficiency,” in The 2nd ACM Conference on Advances in Financial Technologies , 2020, pp. 92–112. [Online]. Available: https://doi.org/10.1145/3419614.3423254
2020
Cited alongside, same era.
H.-T. Kao, T. Chitra, R. Chiang, and J. Morrow, “An analysis of the market risk to participants in the compound protocol,” in Third International Symposium on Foundations and Applications of Blockchains , 2020. [Online]. Available: https://scfab.github.io/2020/FAB2020_p5.pdf
J.-Z. Huang and Z. Shi, “Machine-Learning-Based Return Predictors and the Spanning Controversy in Macro-Finance,” Management Science , vol. 69, no. 3, pp. 1780–1804, 3 2023. [Online]. Available: https://pubsonline.informs.org/doi/10.1287/mnsc.2022.4386
2022
Later among the works it cites.
nagaking, “Evaluating Proposed Parameter Changes for FRAXBP (Vote #267),” 2022. [Online]. Available: https://gov.curve.fi/t/evaluating-proposed-parameter-changes-for-fraxbp-vote-267/4317
2022
Later among the works it cites.
J. Xu and Y. Feng, “Reap the Harvest on Blockchain: A Survey of Yield Farming Protocols,” IEEE Transactions on Network and Service Management , vol. 20, no. 1, pp. 858–869, 3 2023. [Online]. Available: https://ieeexplore.ieee.org/document/9953979/
2023
Closest in time.
snapshot, “Voting types,” 2023. [Online]. Available: https://docs.snapshot.org/user-guides/proposals/voting-types
2023
Closest in time.
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alphaXiv is searching for related work…
2020
Cited alongside, same era.
J. Fan, Z. Wang, Y. Xie, and Z. Yang, “A theoretical analysis of deep Q-learning,” in Learning for Dynamics and Control . PMLR, 2020, pp. 486–489
2020
Cited alongside, same era.
2021
Cited alongside, same era.
M. Bartoletti, J. H.-y. Chiang, and A. L. Lafuente, “SoK: Lending Pools in Decentralized Finance,” in Workshop Proceedings of Financial Cryptography and Data Security . Springer, Berlin, Heidelberg, 3 2021, pp. 553–578. [Online]. Available: https://link.springer.com/10.1007/978-3-662-63958-0_40
2021
Cited alongside, same era.
D. Perez, S. M. Werner, J. Xu, and B. Livshits, “Liquidations: DeFi on a Knife-Edge,” in International Conference on Financial Cryptography and Data Security (FC) , vol. 12675 LNCS, 2021, pp. 457–476. [Online]. Available: https://link.springer.com/10.1007/978-3-662-64331-0_24
2021
Cited alongside, same era.
K. Qin, L. Zhou, B. Livshits, and A. Gervais, “Attacking the DeFi Ecosystem with Flash Loans for Fun and Profit,” in Financial Cryptography and Data Security (FC) , vol. 12674 LNCS, 2021, pp. 3–32. [Online]. Available: https://link.springer.com/10.1007/978-3-662-64322-8_1
2021
Cited alongside, same era.
S. Werner, D. Perez, L. Gudgeon, A. Klages-Mundt, D. Harz, and W. Knottenbelt, “SoK: Decentralized Finance (DeFi),” in The 4th ACM Conference on Advances in Financial Technologies . New York, NY, USA: ACM, 9 2022, pp. 30–46. [Online]. Available: https://dl.acm.org/doi/10.1145/3558535.3559780
2022
Cited alongside, same era.
H. Hamid Ekal and S. N. Abdul-wahab, “DeFi Governance and Decision-Making on Blockchain,” Mesopotamian Journal of Computer Science , vol. 2022, pp. 9–16, 8 2022. [Online]. Available: https://mesopotamian.press/journals/index.php/cs/article/view/64
2022
Cited alongside, same era.
V. Mohan, P. Khezr, and C. Berg, “Voting with time commitment for decentralized governance: Bond voting as a Sybil-resistant mechanism,” Available at SSRN , 2022
2022
Cited alongside, same era.
2023
Closest in time.
A. P. John, J. Devaraj, L. Gandhimaruthian, and J. A. Liakath, “Swarm learning based credit scoring for P2P lending in block chain,” Peer-to-Peer Networking and Applications , vol. 16, no. 5, pp. 2113–2130, 9 2023. [Online]. Available: https://link.springer.com/article/10.1007/s12083-023-01526-5
2023
Closest in time.
L. Cao, “AI in Finance: Challenges, Techniques, and Opportunities,” ACM Computing Surveys , vol. 55, no. 3, pp. 1–38, 3 2023. [Online]. Available: https://dl.acm.org/doi/10.1145/3502289
2023
Closest in time.
T. A. Xu and J. Xu, “A Short Survey on Business Models of Decentralized Finance (DeFi) Protocols,” in International Conference on Financial Cryptography and Data Security (FC) . Springer, 2023, pp. 197–206. [Online]. Available: https://link.springer.com/10.1007/978-3-031-32415-4_13
2023
Closest in time.
J. Xu, K. Paruch, S. Cousaert, and Y. Feng, “SoK: Decentralized Exchanges (DEX) with Automated Market Maker (AMM) Protocols,” ACM Computing Surveys , vol. 55, no. 11, pp. 1–50, 11 2023. [Online]. Available: https://dl.acm.org/doi/10.1145/3570639
2023
Closest in time.
T. Phan-Minh, F. Howington, T. S. Chu, M. S. Tomov, R. E. Beaudoin, S. U. Lee, N. Li, C. Dicle, S. Findler, F. Suarez-Ruiz, B. Yang, S. Omari, and E. M. Wolff, “DriveIRL: Drive in Real Life with Inverse Reinforcement Learning,” in International Conference on Robotics and Automation , vol. 2023-May. IEEE, 5 2023, pp. 1544–1550. [Online]. Available: https://ieeexplore.ieee.org/document/10160449/
2023
Closest in time.
M. Bastankhah, V. Nadkarni, C. Jin, S. Kulkarni, P. Viswanath, X. Wang, C. Jin, S. Kulkarni, and P. Viswanath, “Thinking Fast and Slow: Data-Driven Adaptive DeFi Borrow-Lending Protocol,” in 6th Conference on Advances in Financial Technologies , vol. 316. Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 7 2024
2024
Closest in time.
C. V. Babu, M. Sudhir, L. George Kishore, and V. Sanjay Kumar, “Application of support vector machine algorithm in automated lending protocols for decentralized finance platforms,” Decentralized Finance and Tokenization in FinTech , pp. 1–20, 6 2024
2024
Closest in time.
G. Palaiokrassas, S. Scherrers, E. Makri, and L. Tassiulas, “Machine Learning in DeFi: Credit Risk Assessment and Liquidation Prediction,” in International Conference on Blockchain and Cryptocurrency (ICBC) . IEEE, 5 2024, pp. 650–654. [Online]. Available: https://ieeexplore.ieee.org/document/10634435/
2024
Closest in time.
S. Arora, Y. Li, Y. Feng, and J. Xu, “SecPLF: Secure Protocols for Loanable Funds against Oracle Manipulation Attacks,” in Asia Conference on Computer and Communications Security , vol. 1. New York, NY, USA: ACM, 7 2024, pp. 1394–1405. [Online]. Available: https://dl.acm.org/doi/10.1145/3634737.3637681
2024
Closest in time.
Y. Luo, Y. Feng, J. Xu, and P. Tasca, “Piercing the Veil of TVL: DeFi Reappraised,” in International Conference on Financial Cryptography and Data Security (FC) , 4 2025. [Online]. Available: https://fc25.ifca.ai/preproceedings/94.pdf
2025
Closest in time.
T. A. Xu, J. Xu, and K. Lommers, “DeFi versus TradFi: Valuation Using Multiples and Discounted Cash Flows,” in Digital Assets: Pricing, Allocation and Regulation , R. Aggarwal and P. Tasca, Eds. Cambridge University Press, 2025, ch. 3, pp. 44–68. [Online]. Available: https://doi.org/10.1017/9781009362290.004
2025
Closest in time.