Lending Market Research Articles

The role of narrative style in a peer-to-peer lending market: an empirical investigation

ABSTRACT Decision-making behavior can be capricious and may vary from individual to individual. This paper provides a straightforward method for the empirical identification of the narrative style affecting financial decisions under time pressure. Punctuation, Chinese characters and sentence length constitute different narrative styles. Using data from Renrendai, we find that as the number and type of punctuation marks in narratives increase, the loan funding and repayment performance increase. Also, as the number of characters and long sentences in narratives increases, loan funding increases, whereas loan performance suffers. These conclusions remain valid after robustness checks.

Digital redlining and the fintech marketplace: Evidence from US zip codes

AbstractThe rise of digital technologies enables new manifestations of racialization in financial services with marketplace implications. Akin to redlining in the lending market, racialization in the spatial availability of digital technologies—including financial technologies or “fintech”—may raise the costs of banking in black and brown communities. This paper investigates associations between communities' racial makeup and rates of fintech by leveraging 2015 Esri Business Analyst Market Potential data from the universe of high‐poverty zip codes. Poor black and brown communities experience a form of digital redlining by having the lowest fintech rates. Every percentage increase in a community's black population was associated with an 18% decrease in their rate of high‐speed internet access, 1% decrease in smartphone ownership, 12% decrease in online banking, and 3% decrease in mobile banking. Relationships were opposite for communities with increasing white populations where whiteness attracts higher rates of fintech, even amidst high poverty.

Shorting Activity and Stock Return Predictability: Evidence From a Mandatory Disclosure Shock

We study the effect of a mandatory improvement in public disclosure due to the adoption of International Financial Reporting Standards (IFRS) on the stock return predictability of shorting activity. To assess the impact of the disclosure shock, we measure monthly changes in the demand for and supply of stocks for shorting and whether those changes predict negative returns in the following month. We provide international evidence that the ability of increases in shorting demand and supply to predict negative returns declines after the shock. The predictive ability of shorting in the month before a negative earnings surprise and news of a firm’s questionable M&A transaction also declines after the shock. These findings imply that the shock of the mandatory accounting change crowds out some of short-sellers’ value-relevant information in the equity lending market. Thus, while the democratization of information from a structured accounting change may make sophisticated investors worse off by reducing their ability to predict future returns, this change may also benefit all investors through timely stock price discovery.

Interbank relationship lending revisited: Are the funds available at a similar price?

Under the market discipline hypothesis, monitoring by interbank lenders may induce changes in either the price or availability of new interbank funds to borrower banks. However, the presence of interbank relationship lending has been evaluated based on the availability of funds only—disregarding their price. We revisit relationship lending in unsecured interbank lending markets by simultaneously evaluating the availability and price of funds. We calculate the survival ratio of networks containing the price of daily interbank lending in Colombia from 2014 to 2020. Under this framework, an interbank relation survives from one day to the next if the funds are available at a price that does not increase too much; that is, either a halt in interbank funding or a sizeable increase in the price of interbank funding mark a break in the relation between two banks. We find that about 38 percent of relations in the Colombian unsecured interbank lending market survive from one day to the next. Therefore, from a comprehensive market discipline perspective, we find evidence of interbank relationship lending in Colombia.

Price transparency in OTC equity lending markets: Evidence from a loan fee benchmark

We study the effects of a price transparency shock in the Brazilian equity lending market, an over-the-counter market. Previously, the available loan fee benchmark was the mean loan fee of the past 15 trading days. On March 1, 2011, this interval was reduced to three days, significantly improving short-sellers’ ability to predict current loan fees. We find that after the benchmark change, loan fees fell, lending volume increased, total lending revenue remained stable, high-cost lenders lost market share, and price efficiency increased. Our results suggest implementing price benchmarks in OTC markets can improve market quality.

The crowding-out effect of formal finance on the P2P lending market: An explanation for the failure of China's P2P lending industry

Using the credit records of the borrowers from a peer-to-peer (P2P) lending platform in China, this paper finds that P2P borrowers from cities with broader coverage of formal finance are more likely to default. Moreover, as formal finance develops, high-quality borrowers become less likely to turn to P2P lending, which suggests that formal finance exerts a crowding-out effect on the P2P lending market. This paper provides an explanation for the failure of China's P2P lending industry, implying that Fintech intended to promote financial coverage, if not reducing information asymmetry, does not necessarily lead to desirable economic and social outcomes.

Reduction of the key rate as a factor in the development of the Russian mortgage lending market

Reduction of the key rate as a factor in the development of the Russian mortgage lending market

Analysis of the mortgage lending market. Problems and prospects for development

Analysis of the mortgage lending market. Problems and prospects for development

Competition and Distortion: A Theory of Information Bias on the Peer-to-Peer Lending Market

Despite the popular emergence of peer-to-peer (P2P) lending platforms, relevant research investigating the role of these platforms on P2P markets still lags. In this paper, we present a model to study the market incentives of P2P lending platforms' optimal information-reporting strategies when the following exist: (i) uncertainty on the return of loans and (ii) competition from entrants. We focus on the information bias of platforms driven by demand-side actors—investors’ optimism/pessimism about risk—while we keep the platforms being rational. We characterize platforms' equilibrium reporting strategies under different market conditions. Surprisingly, we find that when uncertainty is significant, and the threat of entry is strong but not detrimental, the platform has incentives to bias information toward investors' biased beliefs. This result demonstrates a case where competition and uncertainty may jointly lead to information bias. However, a properly designed uncertainty-resolution mechanism could reduce the incentive. Our findings contribute to the literature on the P2P lending market by analyzing platform decisions and offer policy implications for regulating P2P lending market.

A Long and a Short Leg Make for a Wobbly Equilibrium

We provide evidence that the online discussion on the WSB subreddit had a substantial negative impact on the profitability of shorting strategies across a number of stocks — even those that were neither heavily discussed on the subreddit, nor experienced an unusual increase in retail buying volume. We provide a model to explain how fears among short sellers can become self-fulfilling. In the model, the market for shares and the lending market must clear jointly. Despite standard assumptions, the model features multiple equilibria and run-type'' behavior by shorting agents. More broadly, the model provides a tractable framework to interpret several empirical observations on the relation between short interest, lending fees, and expected returns. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

PROBLEMS OF MODELING THE MORTGAGE MARKET OF UKRAINE

Abstract. The evolution of mortgage market modeling in the context of implementing the strategy of technological development of the national economy is analyzed, that allowed to take into account the disadvantages and advantages of all models, modern socio-economic problems and implement the mortgage market as a mechanism for achieving structural changes in the national economy. Approaches to solving modern problems of mortgage lending in Ukraine through the introduction of specialized mortgage-industrial centers are reasoned: 1) diver­sification of primary sources of financing in order to maximize them and reduce risks; 2) the presence of an industrial partner company that ensures high production efficiency and has a permanent demand for goods or services; 3) finding the management of the model in the same business hands, in particular the bank, which ensures coordination and optimal distribution of profits between the participants; 4) the need for permanent control over the operation of the model in order to reduce technical, financial and moral risks for mortgage market participants. The universality of the proposed model of mortgage lending for industry (industrialization: renewal of the fleet of equipment and facilities, introduction of technological innovations), agro-industrial complex (modernization of agricultural machinery, development of agricultural science, introduction of world technologies in crop and livestock production, raising food production standards), housing construction and united territorial communities (replacement of the fleet of public transport equipment — buses, trolleybuses, trams) to address the issue of investing current tasks. Keywords: mortgage lending market, mortgage risks, national economy, development strategy, mortgage-industrial center. JEL Classification A13, E17, E44, E69 Formulas: 1; fig.: 2; tabl.: 0; bibl.: 10.

Macroeconomic Determinants of Loan Delinquencies: Evidence from the US Peer-to-Peer Lending Market

The study documented in this paper utilises a probit regression analysis to empirically investigate the key macroeconomic factors that influence credit risk in the peer-to-peer (P2P) lending market. By aggregating the United States (US) state-level data with LendingClub’s loan book covering the period from 2008–2019, this study examines multiple factors related to default risks of loans issued by P2P lending platforms. Our results show that a higher interest rate and inflation increase the probability of default in the P2P lending market. We also find that the impact of interest rate on the probability of default is significantly higher for loans with lower ratings. The study’s outcomes, by paving the way to future market best practices, are applicable to P2P lending platforms and investors in their default estimation of loans.

P2p lending platform evaluation of operating peculiarities

Rapidly evolving financial technologies (FinTech) are changing established, time-tested financial services delivery and competition strategies. The growing diversity of financial services is evolving entrepreneurial ecosystems, making it a challenge for all financial market participants. One of the largest Fintech markets in Lithuania is the lending market. This market acts as an alternative to traditional financial institutions’ credit and distinguish by its relatively new and developing. Therefore, the research aims to evaluate the activities and peculiarities of operating lending platforms in Lithuania. The analysis uses P2P lending market development indicators, return and risk identified and analyzed in the research. The study revealed that the portfolio of consumer loans provided through operators of P2P lending platforms is growing steadily every year. AB NEO Finance maintains the same tendencies and secures a leading position in the Lithuanian P2P lending market. AB NEO Finance stands out as the only one of all P2P lending platforms, because it is listed on Lithuania’s stock exchange. It covers more than half of the P2P lending market regarding the amount and number of disbursed consumer loans. Were found that the remaining loan repayment is proliferating in the P2P lending market. However, AB NEO Finance is experiencing a lower number of overdue payment days than the P2P lending market, which indicates a lower risk.

Shorting activity and stock return predictability: Evidence from a mandatory disclosure shock

AbstractWe study the effect of a mandatory improvement in public disclosure due to the adoption of International Financial Reporting Standards (IFRS) on the stock return predictability of shorting activity. To assess the impact of the disclosure shock, we measure monthly changes in the demand for and supply of stocks for shorting and whether those changes predict negative returns in the following month. We provide international evidence that the ability of increases in shorting demand and supply to predict negative returns declines after the shock. The predictive ability of shorting in the month before a negative earnings surprise and news of a firm's questionable merger and acquisitions transaction also declines after the shock. These findings imply that the shock of the mandatory accounting change crowds out some of short‐sellers’ value‐relevant information in the equity lending market. Thus, although the democratization of information from a structured accounting change may make sophisticated investors worse off by reducing their ability to predict future returns, this change may also benefit all investors through timely stock price discovery.

Current Trends in the Development of the Housing Mortgage Lending Market in the Russian Federation

Current Trends in the Development of the Housing Mortgage Lending Market in the Russian Federation

Private Information, Securities Lending, and Asset Prices

Abstract We study the role of private information in the equity lending market in a rational expectations model with endogenous loan fees. When all investors are privately informed, an increase in information precision reduces the fee by increasing trade aggressiveness and decreasing demand dispersion. However, when some investors are uninformed, the information asymmetry tends to increase the fee, and, thus, the overall effect of an increase in precision is ambiguous. We show that the fee can be incrementally informative given the stock price and that fee opaqueness tends to increase the fee but has an ambiguous effect on the stock price.

Противоречия, конфликты и кризисы в развитии функциональной инфраструктуры потребительского кредитования

When characterizing the infrastructure of the consumer lending market, it should be assessed not only by isolated structural elements, but also in general in their interaction. It is worth noting that statistical material on the infrastructure of the consumer lending market is often rather scarce and cannot give an exhaustive picture of the state of the consumer lending infrastructure, therefore, for the development of the consumer lending infrastructure, it is necessary to consider not only the infrastructure elements, but also need to penetrate into their problems.

The Herd Behavior on Peer-To-Peer Online Lending Markets: Evidence from China

Based on the transaction data and related borrowers’ characteristics of Renrendai.com, this study conducts an empirical study on the influencing factors of the investor’s herd behavior and rationality of herd behavior on a Chinese online lending platform. We mainly find that investors’ herd behavior exists significantly on Renrendai.com; there is an “inverted U-shaped” relationship between the number of bids and the herd behavior of investors. When the number of bids exceeds a certain amount, the time required for the order to obtain another bid will be prolonged, and the investors’ herd behavior will be slowed down; herd behavior on Renrendai.com in Chinese market is a partly rational pursuit, but irrational in general.

Innovation, Institutions, and Social Change in Peer-to-Peer Lending: Evidence from China

In this chapter, we focus on the development of the peer-to-peer (“P2P”) lending industry in China. As a modern borrowing platform, peer-to-peer lending allows clients to obtain funding from peer lenders for a multitude of loan purposes, including credit consolidation, personal purchases, and the development of business ventures. However, the speed at which this industry has grown has created numerous problems for regulatory agencies, particularly in China, the largest peer-to-peer lending market in the world. Our chapter examines how lenders in the Chinese context continue to function as formal institutions regulating this sector continue to grow following a series of highly-publicized illegal lending activities in recent years. Additionally, we determine whether implemented regulatory measures are providing an overall benefit or detriment to the Chinese P2P lending industry. Finally, we highlight the potential for positive social change and social entrepreneurship arising from P2P lending, particularly in terms of the empowerment of traditionally disadvantaged groups by providing access to capital. We use the peer-to-peer lending industry in the United States, currently the second-largest in the world and one operating in a highly-regulated financial industry, as a comparison for the Chinese case.

Application of XGBoost in P2P Default Prediction

P2P network lending is a “ peer-to-peer ” loan through a third-party Internet platform built by P2P companies. The application of machine learning algorithms to the field of P2P loan default prediction will improve the operating capabilities of the platform, and also effectively regulate the lending market. In this paper, we use Lending Club’s loan data and feature engineering technology to apply the XGBoost algorithm to construct a P2P loan default prediction model, and we choose five performances: accuracy, AUC value, error rate, model robustness, and model run time to compare it with Logistic Regression and Decision Trees. The result shows that the prediction accuracy rate of the XGBoost algorithm is 97.705%, which fits the actual results better, and can effectively control the loss cost caused by model errors. In addition, we also select 10 features that have the greatest impact on loan default rates based on the XGBoost algorithm, and provide a reference for P2P lending platforms.