我国P2P网络借贷信用风险影响因素研究——基于排序选择模型的实证分析

P2P(peer-to-peer)网络借贷是一种借助网络平台,由个人与个人间互为借贷双方的小额借贷交易。它作为互联网与民间借贷相结合的新兴金融模式,具有较高的信用风险。采用排序选择模型,基于 ex-celVBA 数据挖掘技术截取多个 P2P 网站数据,对平台信用风险的影响因素进行实证分析,结果表明：个人特征、信用变量、历史表现、借款信息分别对网络借贷信用风险存在正向影响,由此发现网站提供的信息对投资者避免信用风险没有起到实质作用。     

Credit rationing in P2P lending to SMEs: Do lender-borrower relationships matter?

This paper studies the role of individual P2P investors that are acquainted with the borrower in mitigating credit rationing in P2P lending to SMEs. I use proprietary data provided by one of the biggest Dutch P2P lending platforms, on which personal acquaintances of the borrower are able to invest before other P2P investors do. I find that P2P investors invest more in loans of borrowers to whom they are personally acquainted. More initial investment by investors acquainted with the borrower is subsequently associated with a higher likelihood of obtaining a second loan from the P2P lender, larger investments by other P2P investors and lower ex post defaults. These results are consistent with informal lenders having superior information or monitoring skills and rational herding following informal investors' investment decisions.     

Data-driven optimization of peer-to-peer lending portfolios based on the expected value framework

In recent years, peer-to-peer (P2P) lending has been gaining popularity amongst borrowers and individual investors. This can mainly be attributed to the easy and quick access to loans and the higher possible returns. However, the risk involved in these investments is considerable, and for most investors, being nonprofessionals, this increases the complexity and the importance of investment decisions. In this study, we focus on generating optimal investment decisions to lenders for selecting loans. We treat the loan selection process in P2P lending as a portfolio optimization problem, with the aim being to select a set of loans that provide a required return while minimizing risk. In the process, we use internal rate of return as the measure of return. As the starting point of the model, we use machine-learning algorithms to predict the default probabilities and calculate expected values for the loans based on historical data. Afterwards, we calculate the distance between loans using (i) default probabilities and, as a novel step, (ii) expected value. In the calculations, we utilize kernel functions to obtain similarity weights of loans as the input of the optimization models. Two optimization models are tested and compared on data from the popular P2P platform Lending Club. The results show that using the expected-value framework yields higher return.     

Institutional ownership and firm performance: The case of bidder returns

We employ corporate takeover decisions to investigate the impact of institutional ownership on corporate performance. The OLS regressions of bidder gains on institutional ownership indicate a positive relation between the two. However, we find institutional ownership to be significantly determined by firm size, insider ownership and the firm's presence in the S&P 500 index. Thus, when bidder gains are regressed on the predicted values of institutional ownership in two-stage regressions, the recursive estimates do not confirm the relationship shown by the OLS regressions. Furthermore, we do not find any evidence that active institutional investors (e.g., CalPERS) as a group enhance efficiency in the market for corporate control. These findings cast doubt on the superior selection/monitoring abilities of institutional investors.     

The information content of retail investors’ order flow

In this paper, we provide evidence that the trading activity of small retail investors carries significant genuine information that can be exploited for the short-term out-of-sample forecasting of foreign exchange rates. Our findings are based on a unique dataset of around 2000 retail investors from the OANDA FXTrade electronic trading platform. Our results are consistent with the view that in the foreign exchange market private information is highly dispersed, but can be extracted by observing customer order flow. Previous studies, however, focused on the information content of costumer order flow of dealers in the interbank market, whose clients are themselves large institutional and professional investors. Our study is the first that analyzes a crowd of small retail investors and shows that even the trading activity of these investors contains, on aggregate, important non-public information that can be exploited for short-term exchange rate forecasting. Our findings lead us to conjecture that retail investors (on aggregate) are not pure noise traders but process dispersed information at least partially in a similar way as large institutional investors and hence place their orders accordingly.     

A friend in need is a friend indeed: Allocation and demand in IPO bookbuilding

This paper uses proprietary data on European IPOs with detailed information on the demand at different points of time and allocation for institutional and retail investors. The nature of the data allows us to analyze the reason of why institutional investors as a group get more allocations of underpriced issues than retail investors. By explicitly examining institutional and retail demand for different kinds of stocks, we find that this is due to institutional investors' superior ability to detect underpriced stocks rather than the underwriter's preferential treatment. At the same time, the subset of domestic institutional investors supports the underwriter in issues with weak demand and receives in turn favorable allocations in underpriced issues.     

Does the truth rest with the minority? Divergent views on nonfinancial firms' financial investments from the private equity market

We investigate how the seasoned equity market evaluates nonfinancial firms that recently bought wealth management products (WMPs). Using a sample of Chinese firms, we find that the stock market reacts less positively to private equity placements (PEPs) by firms that recently purchased WMPs (i.e., quasi-deposits) than to those that did not. Further analysis suggests that compared with retail investors, sophisticated (i.e., institutional and high-net-worth) investors pay a higher price for the shares of these WMP-buying firms. After PEPs, we find that the long-term operating performance and firm value of WMP-buying firms are higher than those of non-buying firms. Overall, the findings suggest that: (i) engaging in shadow banking activities (buying WMPs) does not mean a firm is distracted, and (ii) sophisticated investors are less concerned than retail investors about a firm's shadow banking activities.     

Global retail lending in the aftermath of the US financial crisis: Distinguishing between supply and demand effects

This paper examines the broader effects of the US financial crisis on global lending to retail customers. In particular we examine retail bank lending in Germany using a unique data set of German savings banks during the period 2006 through 2008 for which we have the universe of loan applications and loans granted. Our experimental setting allows us to distinguish between savings banks affected by the US financial crisis through their holdings in Landesbanken with substantial subprime exposure and unaffected savings banks. The data enable us to distinguish between demand and supply side effects of bank lending and find that the US financial crisis induced a contraction in the supply of retail lending in Germany. While demand for loans goes down, it is not substantially different for the affected and nonaffected banks. More important, we find evidence of a significant supply side effect in that the affected banks reject substantially more loan applications than nonaffected banks. This result is particularly strong for smaller and more liquidity-constrained banks as well as for mortgage as compared with consumer loans. We also find that bank-depositor relationships help mitigate these supply side effects.     

Incentivizing Calculated Risk‐Taking: Evidence from an Experiment with Commercial Bank Loan Officers

We conduct an experiment with commercial bank loan officers to test how performance compensation affects risk assessment and lending. High‐powered incentives lead to greater screening effort and more profitable lending decisions. This effect is muted, however, by deferred compensation and limited liability, two standard features of loan officer compensation contracts. We find that career concerns and personality traits affect loan officer behavior, but show that the response to incentives does not vary with traits such as risk‐aversion, optimism, or overconfidence. Finally, we present evidence that incentives distort the assessment of credit risk, even among professionals with many years of experience.     

Fund flows and performance: New evidence from retail and institutional SRI mutual funds

In this paper we provide a comprehensive analysis of the performance of US SRI mutual funds as well as its relation to the flow of new money that those funds experience in the context of investors sophistication. In particular, we compare the performance of SRI funds with their conventional peers, matched by both managers and characteristics criteria, using several performance measures. We investigate the role of investors sophistication and its influence on the flow-performance and performance-flow relations within the retail and institutional SRI fund shareclasses. For the analysis of the flow-performance relation we use portfolio approach along with monotonic relation test, while the shape of the flow-performance relation is studied using piecewise linear panel regressions. For the performance-flow relation, the flow and unexpected flow portfolios are formed and their risk-adjusted performance is evaluated. We find that SRI mutual fund sector earns positive abnormal returns before expenses and retail SRI funds outperform their institutional peers both, before and after fees. No differences in performance when we consider SRI and conventional funds run by the same management companies. Moreover, we find a positive flow-performance relation which is convex for retail SRI funds but no convexity is found for the institutional ones. We cannot confirm the smart money effect for retail SRI funds, instead we find a dumb money effect for SRI institutional funds. Our paper provides new insights into the role of the investors sophistication for those relations in the presence of sustainability preferences.     

Short sales,institutional investors and the cross-section of stock returns

Short-sale constraints are most likely to bind among stocks with low institutional ownership. Because of institutional constraints, most professional investors simply never sell short and hence cannot trade against overpricing of stocks they do not own. Furthermore, stock loan supply tends to be sparse and short selling more expensive when institutional ownership is low. Using institutional ownership as a proxy, I find that short-sale constraints help explain cross-sectional stock return anomalies. Specifically, holding size fixed, the under-performance of stocks with high market-to-book, analyst forecast dispersion, turnover, or volatility is most pronounced among stocks with low institutional ownership. Ownership by passive investors with large stock lending programs partly mitigates this under-performance, indicating some impact of stock loan supply. Prices of stocks with low institutional ownership also underreact to bad cash-flow news and overreact to good cash-flow news, consistent with the idea that short-sale constraints hold negative opinions off the market for these stocks.     

Institutional versus retail trades following financial restatements: The effect of Sarbanes-Oxley

Regulators have expressed concern that the growing number and size of financial restatements, especially since the passage of the Sarbanes-Oxley Act of 2002 (SOX), confuse investors and erode confidence in the capital markets. Given the investor protection goal of SOX, of particular concern is the relative accuracy with which retail investors process and trade on the information contained in restatement announcements. In this paper, we adopt a market based approach and examine the accuracy of the trading behavior of retail and institutional investors conditioned upon accounting restatement announcements, both prior- and subsequent to the passage of SOX. We find that retail investors engaged in more inaccurate (i.e., potentially loss-making) trades in the post-SOX period than in the pre-SOX period. In contrast, the accuracy (i.e., conditional profit potential) of institutional investors’ trades does not show any significant difference between the pre- and post-SOX regimes. Thus, we document that retail (but not institutional) investors processed post-SOX restatements more inaccurately than pre-SOX restatements.     

Institutional stock trading on loan market information

One of the most important developments in the corporate loan market over the past decade has been the growing participation of institutional investors. As lenders, institutional investors routinely receive private information about borrowers. However, most of these investors also trade in public securities. This leads to a controversial question: Do institutional investors use private information acquired in the loan market to trade in public securities? This paper examines the stock trading of institutional investors whose portfolios also hold loans. Using the Securities and Exchange Commission filings of loan amendments, we identify institutional investors with access to private information disclosed during loan amendments. We then look at abnormal returns on subsequent stock trades. We find that institutional participants in loan renegotiations subsequently trade in the stock of the same company and outperform trades by other managers and trades in other stocks by approximately 5.4% in annualized terms.     

Transparency in IPO mechanism: Retail investors’ participation,IPO pricing and returns

Using data from the transparent Indian IPO setting, the paper examines retail investors’ participation, their influence on IPO pricing and the returns they make on IPO investment. The transparency in the mechanism, which allows investors to observe prior investors’ participation, leads to demand which is concentrated at either one or two points of the offer price range. Analysis of investors’ demand during the offer period shows that the participation of retail investors is significantly influenced by the participation of institutional investors. We examine IPO pricing and find that favourable demand by retail investors is positively associated with a high IPO price even after controlling for demand by institutional investors. Further, we find that due to aggressive bidding by overconfident investors, retail investors are, on average, unlikely to make positive allocation weighted initial returns even in a setting where they do not have to compete with institutional investors. Retail investors, however, can earn significant positive allocation weighted initial returns if they limit their participation in IPOs with above average institutional investors’ demand.     

IPO auctions and private information

IPO auctions, which provide an impartial way of determining IPO pricing and share allocations, offer a natural setting for examining whether institutional investors possess private information, and for measuring how valuable their information is. Analyzing detailed bidding data from Taiwan’s discriminatory (pay-as-bid) auctions, we find that, relative to retail investors, institutional investors tend to bid higher in auctions when IPO shares are more valuable, and that underpricing is larger in auctions with relatively higher institutional bids. These results imply that institutional investors are better informed about IPO value, and that they obtain higher information rents when they bid higher relative to retail investors. We estimate the value of institutional investors’ private information to be worth about 8.68% of return, which is the extra rate of return they command on their informational advantages over retail investors.     

Liquidity regulation and bank lending

Bank liquidity shortages during the global financial crisis of 2007–2009 led to the introduction of liquidity regulations, the impact of which has attracted the attention of academics and policymakers. In this paper, we investigate the impact of liquidity regulation on bank lending. As a setting, we use the Netherlands, where a Liquidity Balance Rule (LBR) was introduced in 2003. The LBR was imposed on Dutch banks only and did not apply to other banks operating elsewhere within the Eurozone. Using this differential regulatory treatment to overcome identification concerns and a difference-in-differences approach, we find that the LBR increased the volume of lending by Dutch banks relative to other banks located in the Eurozone. Increased equity, an inflow of retail deposits and subsequent increase in balance sheet size allowed Dutch banks to increase lending despite having to meet the LBR requirements. The LBR also affected the loan composition of Dutch banks (with corporate and retail lending increasing more than mortgage lending) and the maturity profile of loan portfolios. Our results have relevance for policymakers tasked with monitoring the impact of liquidity regulations on banks and the real economy.     

A Neural Network Approach for Analyzing Small Business Lending Decisions

In this paper, we apply the neural network method to small business lending decisions. We use the neural network to classify the loan applications into the groups of acceptance or rejection, and compare the model results with the actual decisions made by loan officers. Data were collected from a leading bank in Central New York. The sample contains important financial statement and business information of borrowers and the loan officers' decisions. We conduct the network training on the data sample and find that the neural network has a stronger discriminating power for classifying the acceptance and rejection groups than traditional parametric and nonparametric classifiers. The results show that the neural network model has a high predictive ability. Our findings suggest that neural networks can be a very useful tool for enhancing small-business lending decisions and reducing loan processing time and costs.     

Institutional investors and post-ICO performance: an empirical analysis of investor returns in initial coin offerings (ICOs)

We examine the role of institutional investors in initial coin offerings (ICOs). Taking a financial investor's perspective, we assess the determinants of post-ICO performance via buy-and-hold abnormal returns (BHAR) in a sample of 565 ICO ventures. Conceptually, we argue that institutional investors' superior screening (selection effect) and coaching abilities (treatment effect) enable them to partly overcome the information asymmetry of the ICO context and extract informational rents from their ICO investments. We find that institutional investor backing is indeed associated with higher post-ICO performance. Disentangling selection and treatment effects econometrically, we find that both of these effects explain the positive impact institutional investors have on post-ICO performance. Overall, our results highlight the importance of institutional investors in the ICO context.     

Auctioned IPOs: The US evidence

Between 1999 and 2007, WR Hambrecht completed 19 initial public offerings (IPOs) in the US using an auction mechanism. We analyze investor behavior and mechanism performance in these auctioned IPOs using detailed bidding data. The existence of some bids posted at high prices suggests that some investors (mostly retail) try to free-ride on the mechanism. But institutional demand in these auctions is very elastic, suggesting that institutional investors reveal information in the bidding process. Investor participation is largely predictable based on deal size, and demand is dominated by institutions. Flipping is at most as prevalent in auctions as in bookbuilt deals. But, unlike in bookbuilding, investors in auctions do not flip their shares more in “hot” deals. Finally, we find that institutional investors, who provide more information, are rewarded by obtaining a larger share of the deals that have higher 10-day underpricing. Our results therefore suggest that auctioned IPOs can be an effective alternative to traditional bookbuilding.     

Price Discovery and Dissemination of Private Information by Loan Syndicate Participants

We delineate key channels through which flows of confidential information to loan syndicate participants impact the dynamics of information arrival in prices. We isolate the timing of private information flows by estimating the speed of price discovery over quarterly earnings cycles in both secondary syndicated loan and equity markets. We identify borrowers disseminating private information to lenders relatively early in the cycle with firms exhibiting relatively early price discovery in the secondary loan market, documenting that price discovery is faster for loans subject to financial covenants, particularly earnings‐based covenants; for borrowers who experience covenant violations; for borrowers with high credit risk; and for loans syndicated by relationship‐based lenders or highly reputable lead arrangers. We then ask whether early access to private information in the loan market accelerates the speed of information arrival in stock prices. We document that the stock returns of firms identified with earlier private information dissemination to lenders indeed exhibit faster price discovery in the stock market, but only when institutional investors are involved in the firm's syndicated loans. Further, the positive relation between institutional lending and the speed of stock price discovery is more pronounced in relatively weak public disclosure environments. These results are consistent with institutional lenders systematically exploiting confidential syndicate information via trading in the equity market.