{"title":"自动做市商实现使用kelly标准的发票贴现","authors":"Peplluis R. Esteva, Alberto Ballesteros Rodríguez","doi":"arxiv-2302.09009","DOIUrl":null,"url":null,"abstract":"There is a persistent lack of funding, especially for SMEs, that cyclically\nworsens. The factoring and invoice discounting market appears to address delays\nin paying commercial invoices: sellers bring still-to-be-paid invoices to\nfinancial organizations, intermediaries, typically banks that provide an\nadvance payment. This article contains research on novel decentralized\napproaches to said lending services without intermediaries by using liquidity\npools and its associated heuristics, creating an Automated Market Maker. In our\napproach, the contributed collateral and the invoice trades with risk is\nmeasured with a formula: The Kelly criterion is used to calculate the optimal\npremium to be contributed to a liquidity pool in the funding of the said\ninvoices. The behavior of the algorithm is studied in several scenarios of\nstreams of invoices with representative amounts, collaterals, payment delays,\nand nonpayments rates or mora. We completed the study with hack scenarios with\nbogus, nonpayable invoices. As a result, we have created a resilient solution\nthat performs the best with partially collateralized invoices. The outcome is\ndecentralized market developed with the Kelly criterion that is reasonably\nresilient to a wide variety of the invoicing cases that provides sound profit\nto liquidity providers, and several premium distribution policies were checked\nthat contributed with extra resilience to the performance of the algorithm.","PeriodicalId":501310,"journal":{"name":"arXiv - CS - Other Computer Science","volume":"48 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Invoice discounting using kelly criterion by automated market makers-like implementations\",\"authors\":\"Peplluis R. Esteva, Alberto Ballesteros Rodríguez\",\"doi\":\"arxiv-2302.09009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There is a persistent lack of funding, especially for SMEs, that cyclically\\nworsens. The factoring and invoice discounting market appears to address delays\\nin paying commercial invoices: sellers bring still-to-be-paid invoices to\\nfinancial organizations, intermediaries, typically banks that provide an\\nadvance payment. This article contains research on novel decentralized\\napproaches to said lending services without intermediaries by using liquidity\\npools and its associated heuristics, creating an Automated Market Maker. In our\\napproach, the contributed collateral and the invoice trades with risk is\\nmeasured with a formula: The Kelly criterion is used to calculate the optimal\\npremium to be contributed to a liquidity pool in the funding of the said\\ninvoices. The behavior of the algorithm is studied in several scenarios of\\nstreams of invoices with representative amounts, collaterals, payment delays,\\nand nonpayments rates or mora. We completed the study with hack scenarios with\\nbogus, nonpayable invoices. As a result, we have created a resilient solution\\nthat performs the best with partially collateralized invoices. The outcome is\\ndecentralized market developed with the Kelly criterion that is reasonably\\nresilient to a wide variety of the invoicing cases that provides sound profit\\nto liquidity providers, and several premium distribution policies were checked\\nthat contributed with extra resilience to the performance of the algorithm.\",\"PeriodicalId\":501310,\"journal\":{\"name\":\"arXiv - CS - Other Computer Science\",\"volume\":\"48 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Other Computer Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2302.09009\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Other Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2302.09009","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Invoice discounting using kelly criterion by automated market makers-like implementations
There is a persistent lack of funding, especially for SMEs, that cyclically
worsens. The factoring and invoice discounting market appears to address delays
in paying commercial invoices: sellers bring still-to-be-paid invoices to
financial organizations, intermediaries, typically banks that provide an
advance payment. This article contains research on novel decentralized
approaches to said lending services without intermediaries by using liquidity
pools and its associated heuristics, creating an Automated Market Maker. In our
approach, the contributed collateral and the invoice trades with risk is
measured with a formula: The Kelly criterion is used to calculate the optimal
premium to be contributed to a liquidity pool in the funding of the said
invoices. The behavior of the algorithm is studied in several scenarios of
streams of invoices with representative amounts, collaterals, payment delays,
and nonpayments rates or mora. We completed the study with hack scenarios with
bogus, nonpayable invoices. As a result, we have created a resilient solution
that performs the best with partially collateralized invoices. The outcome is
decentralized market developed with the Kelly criterion that is reasonably
resilient to a wide variety of the invoicing cases that provides sound profit
to liquidity providers, and several premium distribution policies were checked
that contributed with extra resilience to the performance of the algorithm.