Lending Club – 操作逻辑回归预测在网贷平台上的投资收益

#配景:<br /> Lending Club是美国最大的P2P网贷生意业务平台,操作网络技能直接毗连了小我私家投资者和小我私家借贷者,缩短资金畅通的细节,绕过传统的大银行等金融机构,使得投资者和借贷者都能获得更多实惠。对付投资者来说,可以得到更好的回报;对付借贷者来说,则可以得到相对较低的贷款利率。于此,我们就用一个简朴的例子去领略如何操作逻辑回归预测在P2P网贷的投资回报。</p> <p>![](http://img.shujuren.org/pictures/FL/5870fdf49bd1c.png)</p> <p>#预测目标:<br /> 在借贷行业中,投资者向借贷者提供贷款以获取利钱。假如借贷者顺利送还贷款,投资者则得到利钱收益。假如借贷者无法送还贷款,投资者则损失贷款本金。因此，对付投资者来说,需要预测借贷者无法送还贷款的风险，最洪流平地制止投资损失，最洪流平地实现投资回报。</p> <p>#数据来历:<br /> 我们将利用Lending Club网站的果真数据,数据集为自2007年5月起至2010年2月LendingClub平台发放的9578条3年期贷款。</p> <p>**因变量"not.fully.paid"**暗示不完全付出,要预测这个因变量,如下自变量可供投资者选择是否投资此项贷款:<br /> **Credit Policy**:客户是否满意Lending Club的授信尺度,1为是,0为否;<br /> **Purpose**:贷款的目标;(例:信用卡还款,债务处理惩罚,教诲,购置大件,中小企业策划等等);<br /> **int.rate**:贷款利率;较高的贷款利率意味着较高的风险;<br /> **installement**:每月分期的金额;<br /> **log.annual.inc**:借贷者的年收入的自然对数;<br /> **dti**:借贷者的债务收入比;<br /> **fico**:借贷者的FICO信用评分;<br /> **days.with.cr.line**:借贷者有信用额度的天数;<br /> **revol.bal**:借贷者的账户余额(尚未结清的金额);<br /> **revol.util**:借贷者的信用账户操作率（利用的金额/授信的金额);<br /> **inq.last.6mths**:借贷者在已往6个月被借钱者咨询的次数;<br /> **delinq.2yrs**:借贷者在已往2年过时还款高出30天的次数;<br /> **pub.rec**:借贷者民众事业记录差评的次数;</p> <p>####第一步: PREPARING THE DATASET<br /> 重要处理惩罚数据的语法-**填充缺少的调查值 imputation**</p> <p>源数据共有13个自变量,每1个变量都有呈现缺少调查值的环境,所以我们要对源数据举办处理惩罚,填充缺少的调查值;通过如下代码可以有所调查:<br /> “`{r}<br /> missing = subset(loans, is.na(log.annual.inc) | is.na(days.with.cr.line) | is.na(revol.util) | is.na(inq.last.6mths) | is.na(delinq.2yrs) | is.na(pub.rec))<br /> “`</p> <p>填充缺少的调查值:<br /> “`{r}<br /> library(mice)<br /> set.seed(144)<br /> vars.for.imputation = setdiff(names(loans), "not.fully.paid")<br /> imputed = complete(mice(loans[vars.for.imputation]))<br /> loans[vars.for.imputation] = imputed<br /> “`</p> <p>####第二步: PREDICTION MODEL<br /> 回收逻辑回归算法成立模子:<br /> “`{r}<br /> set.seed(144)<br /> library(caTools)<br /> split = sample.split(loans$not.fully.paid, SplitRatio = 0.7)<br /> train = subset(loans, split == TRUE)<br /> test = subset(loans, split == FALSE)<br /> LoansLog = glm(not.fully.paid ~.,data=train,family=binomial)<br /> summary(LoansLog)<br /> predicted.risk=predict(LoansLog,type="response",newdata=test)<br /> test$predicted.risk=predicted.risk<br /> table(test$not.fully.paid, predicted.risk > 0.5)<br /> library(ROCR)<br /> ROCRpred = prediction(predicted.risk, test$not.fully.paid)<br /> as.numeric(performance(ROCRpred, "auc")@y.values)<br /> “`</p> <p>视察在逻辑回归模子下:<br /> 逻辑回归模子正确率:0.8364<br /> Baseline模子正确率:0.8399<br /> 模子AUC:0.672<br /> 综上所述,可以视察在对所有变量举办逻辑回归的进程中,模子自己对预测的表示并不抱负;</p> <p>####第三步: A SMART BASELINE<br /> Lending Club会按照对贷款风险的预测来分派贷款利率,接下来我们进一步研究int.rate变量对模子预测的影响; 成立bivariate模子,<br /> “`{r}<br /> bivariate = glm(not.fully.paid~int.rate, data=train, family="binomial")<br /> summary(bivariate)<br /> “`</p> <p>与全变量的逻辑回归模子对比,int.rate变量的重要性产生了显著的变革;<br /> 接下来，举办变量相关性调查:<br /> “`{r}<br /> cor(train$int.rate, train$fico)<br /> “`</p> <p>视察功效显示变量int.rate和变量fico存在相关性,全变量模子存在multicollinearity多重共线性;贷款利率和借贷者的信用积分存在相关性;</p> <p>针对测试集运用bivariate模子:<br /> “`{r}<br /> pred.bivariate = predict(bivariate, newdata=test, type="response")<br /> summary(pred.bivariate)<br /> table(test$not.fully.paid, pred.bivariate > 0.5)<br /> “`</p> <p>测试功效显示,测试会合最大大概的贷款违约率约为0.4266。<br /> “`{r}<br /> ROCRpred = prediction(pred.bivariate, test$not.fully.paid)<br /> as.numeric(performance(ROCRpred, "auc")@y.values)<br /> “`</p> <p>模子AUC:0.624</p> <p>####第四步: COMPUTING THE PROFITABILITY OF AN INVESTMENT<br /> “`{r}<br /> test$profit = exp(test$int.rate*3) – 1<br /> test$profit[test$not.fully.paid == 1] = -1<br /> test[which.max(test$profit),] “`</p> <p>####第五步：A SIMPLE INVESTMENT STRATEGY<br /> 在此处先容一个简朴的投资思想:投资者选择投资网贷P2P的时候,会在风险和收益之前寻求均衡; 所以我们的方针等于投资收益较高但风险较低的贷款;<br /> “`{r}<br /> highInterest=subset(test, "int.rate" >= 0.15 )<br /> “`<br /> 分出int.rate在15%以上的贷款,<br /> “`{r}<br /> mean(highInterest$profit)<br /> table(highInterest$int.rate>=0.15,highInterest$not.fully.paid)<br /> “`</p> <p>这部门int.rate较高的贷款平均收益或许在0.23,这部门int.rate较高的贷款违约率或许在0.25;<br /> 此刻我们在这些highInterest的贷款中选择风险最低的100只贷款；<br /> “`{r}<br /> cutoff = sort(highInterest$predicted.risk, decreasing=FALSE)[100] selectedLoans = subset(highInterest, predicted.risk <= cutoff)<br /> sum(selectedLoans$profit)<br /> table(selectedLoans$not.fully.paid)<br /> 0.23 0.31<br /> 0.25 0.19<br /> “`</p> <p>通过成立投资模子,发此刻违约率仅仅提高了一点(0.19)的环境下,网贷的投资收益回报有了显著地提高;</p> <p>需要提醒的是:模子预测的时间相对较短;在实际的投资中,投资者还需要思量econcomic shock等外部情况因素。</p> <p>### 扩展阅读:<br /> 《来认识一下即将上市的全球最大P2P网贷公司Lending Club》，虎嗅网<br /> http://www.huxiu.com/article/41472/1.html<br /> 《理会网贷龙头Lending Club的盈利模式》<br /> http://news.loan.cngold.org/c/2014-12-18/c2944728.html<br /> 书籍推荐:<br /> 《逾越金融》,作者:乔治索罗斯<br /> http://book.douban.com/subject/4244859/</p> </div> </article> <div class="tagcloud"><ul class='wp-tag-cloud' role='list'><h3>关键字：</h3> </ul> </div> <div class="col-sm-6 col-box bg-gray-light-2 contact-form"> <div role="form" class="wpcf7" id="wpcf7-f17097-o1" lang="zh-CN" dir="ltr"> <div class="screen-reader-response"></div> <form 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