Advances in Pacific Basin Business, Economics and Finance

Cheng-Few Lee, Min-Teh Yu, Cheng-Few Lee, Min-Teh Yu

• Publisher: Emerald Publishing Limited
• Year: 2022
• Print ISBN: 9781801173131
• eBook ISBN: 9781801173148
• Topic: Economics
• Subtopic: Corporate Governance
• Index: Economics

Advances in Pacific Basin Business, Economics and Finance (APBBEF) is an annual series designed to focus on interdisciplinary research in finance, economics, and management among Pacific Rim countries. All articles published are reviewed and recommended by at least two members of the editorial board. Topics of interest include, but are not limited to, the following:

1. Policy and management on financial markets and financial institutions;
2. Options, futures, and other derivatives markets;
3. Corporate finance and investment decisions;
4. Insurance and risk management;
5. Accounting, auditing, and taxation;
6. Marketing, supply chain management, and business policies;
7. Artificial intelligence and new technology in finance;
8. Monetary and foreign exchange policy;
9. Income, employment, and education;
10. Other economic policies among the Pacific Rim countries.

APBBEF is indexed in ABI/INFORM, EconLit, EBSCO, ProQuest, ResearchGate, and Google Scholar. Manuscript submission: [email protected].

Does the P2P Credit Spread Predict Economic Activity? Evidence from LendingClub

Amy Yueh-Fang Ho^a, Wen-Chang Lin^a and Hung-Yuan Yu^b

Abstract

Peer-to-Peer (P2P) lending, which makes borrowers and investors meet directly through online platforms bypassing traditional financial institutions, is an emerging financing market after the traditional financial institutions crushed during the global financial crisis from 2007 to 2009. P2P lending platforms meet the credit demand more efficiently and play a vital role for the credit market and economic activity. This study sheds light on whether the credit spread of P2P lending is well predictive of economic activity compared to the bond credit spread which has been fully investigated in prior studies. Our findings show that the P2P credit spread performs similarly in predicting the economic activity as bond credit spread only during the financial crisis. However, the predictive power of P2P credit spread becomes inverse during the noncrisis periods since P2P lending platforms provide an alternative and easier financing channel to individuals who hardly borrow money for refinancing from traditional financial institutions. This study highlights the alternative role of P2P lending platform in financing and provides the evidence of different predictive powers of P2P credit spread on economic activity in different time periods.

Keywords: Credit spread; P2P lending; peer-to-peer lending; LendingClub; economic activity; financial technology

JEL classification: G12; G23; G24

1. Introduction

Peer-to-peer (P2P) lending has grown rapidly since 2010, driven by financial technology (FinTech). P2P lending platforms not only provide an alternative channel for borrowers to easily obtain loans versus utilizing traditional lending channels where many borrowers are unable to receive financing but also attract lenders with higher rates of return, lower transaction costs, and loose regulations.¹ Gabor and Brooks (2017) point out that the digital revolution of P2P lending can broaden financing access and promote financial inclusion by lowering interest rates, reducing information asymmetry, and increasing transparency. Moreover, Maskara, Kuvvet, and Chen (2021) demonstrate that P2P lending enhances the financial inclusion in rural areas and inner cities with fewer bank branches. Zhong and Jiang (2021) also find that P2P lending can alleviate financial exclusion which refers to the phenomenon that traditional financial institutions discriminate against poorer and disadvantaged borrowers.² As a result, the P2P lending platforms meet the credit demand more efficiently and play a vital role for the credit market and economic activity.

With advanced digital technology and network channels, P2P lending platforms are able to adopt nontraditional soft information to qualify borrowers who would be rated as subprime based on traditional credit assessment criteria to be classified with higher loan grades, thus allowing them to obtain lower cost financing. To reduce default risk and information asymmetry on the side of borrowers, P2P lending platforms assess the creditability of borrowers by collecting diverse and soft information of borrowers such as photos, posting texts, online shopping history, or nonstandard data through online channels and social networks. Chen, Jin, Zhang, and Yang (2016) indicate that both hard inform...

Table of contents

• Cover
• Series Editior
• Editorial Advisory Board
• Title
• Copyright
• Contents
• Market Competition, Managerial Overconfidence, and Corporate Social Responsibility
• Static Hedging Methods for Pricing Double Barrier Options
• Does the P2P Credit Spread Predict Economic Activity? Evidence from LendingClub
• ESG Momentum Strategies: A Comparison between Taiwanese and Japanese Markets
• A Direct Test of Attention Theory through an Examination of Retail Investor Behavior: Evidence from Taiwanese IPO Subscriptions
• Impact of the Federal Reserve's Corporate Credit Facility on S&P 500 during COVID-19 Pandemic
• Market Sentiments and Artificial Intelligence Neural Network Algorithms in Taiwan Derivatives Markets
• The Effect of Cyber Risk Management Services in Insurance Policies
• The Role of Real Exchange Rates in the Relationship between Tourist Arrival and Economic Growth: An Asymmetric Perspective from Malaysia
• Measuring Yield Curve Movements: A Principal Component Analysis of Spot Rate Changes in the JPY, USD, GBP, and CHF Interest Rate Swap Markets
• Effect of Fair Value Accounting and Corporate Governance on Stock Prices: Evidence of Information Efficiency in Thailand