Social Networks and Supply and Demand on Online Lending Marketplaces

Allen, Linda; Peng, Lin; Shan, Yu

doi:10.2139/ssrn.3537714

Cited by 2 publications

(1 citation statement)

References 112 publications

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“…The biggest wins for machine learning appear to be in niche products, alternate channels, and serving the underbanked [80], as well as utilizing alternate data sources. A well-trained machine learning algorithm may preprocess deposit histories [81], corporate financial statements, Twitter posts [82], social media [83][84][85], or mobile phone use [86,87] to create input factors that eventually feed into deceptively simple methods like logistic regression models.…”

Section: Incorporating Alternate Datamentioning

confidence: 99%

An Age–Period–Cohort Framework for Profit and Profit Volatility Modeling

Breeden

2024

Mathematics

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The greatest source of failure in portfolio analytics is not individual models that perform poorly, but rather an inability to integrate models quantitatively across management functions. The separable components of age–period–cohort models provide a framework for integrated credit risk modeling across an organization. Using a panel data structure, credit risk scores can be integrated with an APC framework using either logistic regression or machine learning. Such APC scores for default, payoff, and other key rates fit naturally into forward-looking cash flow estimates. Given an economic scenario, every applicant at the time of origination can be assigned profit and profit volatility estimates so that underwriting can truly be account-level. This process optimizes the most fallible part of underwriting, which is setting cutoff scores and assigning loan pricing and terms. This article provides a summary of applications of APC models across portfolio management roles, with a description of how to create the models to be directly integrated. As a consequence, cash flow calculations are available for each account, and cutoff scores can be set directly from portfolio financial targets.

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Section: Incorporating Alternate Datamentioning

confidence: 99%

An Age–Period–Cohort Framework for Profit and Profit Volatility Modeling

Breeden

2024

Mathematics

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The Effects of COVID-19 on U.S. Small Businesses: Evidence from Owners, Managers, and Employees

et al. 2020

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We analyze a large-scale survey of owners, managers, and employees of small businesses in the United States to understand the effects of the early stages of the COVID-19 pandemic on those businesses. The survey was fielded in late April 2020 among Facebook business page administrators, frequent sellers on Facebook's e-commerce platform Marketplace, and the general Facebook user population. We observe more than 66,000 responses covering most sectors of the economy, including many businesses that had stopped operating due to the pandemic. The survey asks 136 questions covering topics such as changes in business operations and employment, changes in financing patterns, and the interaction of household and business responsibilities. We characterize the adjustments implemented to survive the pandemic and explore the key challenges to continue operating or to reopen. We show how these patterns differ across industry, firm size, owner gender, and other firm characteristics.

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Social Networks and Supply and Demand on Online Lending Marketplaces

Cited by 2 publications

References 112 publications

An Age–Period–Cohort Framework for Profit and Profit Volatility Modeling

An Age–Period–Cohort Framework for Profit and Profit Volatility Modeling

The Effects of COVID-19 on U.S. Small Businesses: Evidence from Owners, Managers, and Employees

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