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如何构建在线借贷投资组合

构建线上借贷的高效组合的方式有很多,我们在之前已经介绍了其中的一些要素,他们是包括:

  1. 适当的多样化-在大量的贷款投资中正确选择每一笔投资的数量可以分散损失风险。投资组合中的贷款数量越多,违约的方差也就越小,这样负投资收益的可能性就越小。
  2. 变量筛选-我们在prosper和lending club的案例分析中列举了不同的变量,有些是直接相关的(例如调查),另一些是不明显而需要计算的(例如信贷的稳健性和偿付能力)。

三种常见的线上借贷投资策略是:指数方式,变量筛选和统计模型。每一个这样的策略都会得出借贷通过或者不能满足某一特定投资标准的结论。

  • 指数方式会导致所有的投资通过,这种策略在拥有大量资本或那些没有时间和能力去获得详细信用策略的投资者手中颇受欢迎,
  • 变量筛选剔除了大量那些不满足投资者需求的贷款。例如,如果一个投资者分析借款人的历史借款信息,它可能表明, 以“婚礼”为筹款目的的借款人目的不符合利率、违约率、或者组合要求的收益率。在这个例子中,投资者将排除所有目的为“婚礼”贷款,而不管这笔贷款的其他属性。
  • 统计模型将每一笔贷款独立的打分或者是排名,然后投资者创造一种策略来决定哪种评分或者排名可以被接受。

每一种投资筛选策略都有其优缺点。指数方式投资要求当量很大并且易于投资。然而,它完全依赖于发放平台的承保能力。筛选易于开发和实施,但是它会大大缩小“好“贷款的数量,特别是筛选中包括了大量的变量。统计模型是最精准的投资方式,但是如果没有编程基础对于投资者来说就会实施起来太困难。模型同样要求有着统计的知识并且为了保证稳定性需要定期的测试和验证。

抛开这些选择策略,投资者必须首先决定在特定的时间那些具体的贷款是可以投资的,如果不考虑利率的分布。如果贷款数量比投资者持有的投资现金还要多的话,这样的贷款可以随机选择投资。另一个对于投资者的选择是确定某些贷款比之其他有着优先级。投资所有条件都满足的贷款是最开始的选择,要么投资者简单的设置投资策略并且投资于一切,或者如果贷款数量超过持有现金的请款下随机的选择。这个选项的问题是投资者将很大程度上依赖于借款人的发起者,优先级选项使投资者能够更紧密地管理投资组合的回报。

为了证明这一点,假设投资者使用被动投资方式,即指数方法。如果投资者简单的投资于每一笔prosper开放状态的部分贷款,那么投资组合的结果会有着以下的收益和信用等级分布(数据来源于2012年,并微调以满足今天的利率环境)。

portblog1 第1张

这会在信用分布的权重下导出一个6.4%的总体回报。如果投资者根据信用排序来投资,并且优先投资于信用等级的贷款,那么结果又将不一样:

portblog2 第2张

运用上表的分布,投资者将会获得7.3%的收益,然而违约率也会同步升高,这会被利率的升高所抵消。这种分布符合高风险偏好投资者的需求。考虑到这种分布状况,很少有贷款可以满足除第一种方案之外的投资方式,所以投资者需要将未来贷款的可能性和预期投资现金数量相比较,得出每一笔贷款应该投资多少以获得相对多元化的投资。

结论:线上借贷组合的投资管理有着好几种方法,无论投资者适合哪种方式,优先排序方法都会让投资策略更加精确。这在贷款数量大于投资者可购买数量时特别有用,或者在当下的情况中,当大量贷款很快就会融资完毕时,优先排序是需要的。投资者需要面对的一个难题是使用图形化界面来实施这样的策略。为了高效通过一种有效投资策略来管理组合,编程与发放贷款平台是首选。

There are several ways to build a high performing portfolio of Online Lending loans. We have written blog posts on some of the factors that are involved in making a portfolio successful. They include:

  1. Proper diversification – Selecting the correct investment amount per loan in order to invest in a large enough number of loans to mitigate risk of loss. The more loans that are in a portfolio, the lower the variance in default rate, which makes the likelihood of a negative return less likely as well.
  2. Variable Filters – we’ve done many posts on different variables on both Lending Club and Prosper. Some are straightforward (e.g. inquiries), others are less obvious and necessitate some calculation (e.g. credit robustness or ability to pay).

The 3 most common investment strategies for Online Lending are index-style, variable filters, and statistical models. Each of these strategies results in a loan either passing or failing to meet a specified set of investment criteria.

  • Index-Style investing results in all loans passing. This strategy tends to be popular among investors with large amounts of capital, or those who do not have the time or ability to maintain a detailed credit strategy.
  • Filters remove swaths of loans that do not meet an investor’s requirements. For example, if an investor analyzes historical loans by loan purpose, it may show that loans to borrowers stating “wedding” as the purpose do not meet the interest rate, default rate or return requirements for the portfolio. In this case, the investor would exclude all loans with “wedding” as the loan purpose, regardless of the other attributes of the loan.
  • A statistical model scores or ranks each loan individually, then the investor creates a strategy to determine the levels of these scores/rankings that are acceptable.

Each investment selection strategy has merits and drawbacks. Index style investing results in the largest volume and is easiest to implement. However, it is completely reliant on the origination platform’s underwriting capabilities. Filters are easy to develop and implement, but they can cut out a lot of “good” loans, especially if a filter contains a large number of variables. Statistical models are the most precise way to invest, but implementation is difficult without a programmatic connection to the originator. Models also require a knowledge of statistics and need to be maintained with periodic testing and validation.

Regardless of the selection strategy, the investor must decide which specific loans to invest in at any given time. The investor may choose to invest in every loan that qualifies under the selected strategy, regardless of the distribution of interest rates. If there are more loans available than the investor has cash to invest, then the loans would be randomly selected for investment. The other option for the investor is to prioritize certain loans over others for investment. Investing in every qualifying loan is the easiest option, as the investor simply sets the strategy and then invests in everything or randomly selects if more loans are available than cash. The issue with this option is that the investor will be relying heavily on the borrower mix of the originators. The prioritization option enables the investor to more closely manage portfolio returns.

To illustrate this, assume an investor uses the passive-style, index approach. If the investor simply invested in every Prosper fractional loan that became available, the resulting portfolio (numbers below are from 2012, adjusted slightly to today’s interest rate environment) would have the following returns and distribution by credit grade
portblog1 第1张
This would result in an overall return, weighted by the distribution of credit grades, of 6.4%.

If the investor were to adjust the portfolio based on the credit ratings, and prioritize lower credit grade loans, the return could be different:
portblog2 第2张
Using the distribution in the above table, an investor would achieve a 7.3% return. While the default rates are higher as well, it should be offset by the increased interest rates. This distribution would make sense for an investor with a higher risk appetite. Given the distribution, fewer loans would qualify than the first approach, so the investor would need to compare the likely future inventory available with the anticipated amount of cash to invest and determine how much to invest in each loan to reach a relatively diversified portfolio.

In conclusion, there are several different approaches to manage an online lending portfolio. Regardless of which approach is right for an investor, prioritizing investments is helpful in making the strategy even more precise. This is especially useful when there are more loans available than an investor can actually buy, or in the current situation, when many loans become fully-invested quickly, and prioritization is required. The difficulty an investor will encounter is that it is very difficult to actually implement such a strategy using graphical interfaces. In order to effectively and efficiently manage this portion of an investment strategy, a programmatic interface with the origination platform is preferable.


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