Chinese Train Derailment

On July 23^rd, two-high speed trains collided in China causing 40 deaths and 192 injuries.  The crash is being blamed on government corruption and the ensuing cover-up has caused a public furor.  Many see the situation as a metaphor for the unchecked growth that the Chinese government is pursuing.

Let me be clear, I admire the Chinese for using the government to build up their infrastructure.  The 20^th century American investment in our highway system has returned untold dividends.  There are a few things that I believe are difficult to fund too much: preventive maintenance and upgrades to basic infrastructure, basic science, and education (this does not include the current debauchery of the student loan system, but that’s a post for another day).

So I don’t fault the Chinese for building up their infrastructure.  Where I do find blame is their rampant government corruption.  Reports have come out stating that the Chinese government has been taking people’s land without fair compensation.  The WSJ reports that there have been two recent firings of high ranking members of the Chinese railways ministry for graft.  I think in the coming weeks we’ll find that the Chinese trains and their tracks weren’t built to spec with the government officials pocketing the difference between public safety and peril.

Listen, I live in Chicago, so I know a corrupt government (Some attribute “Vote early and vote often.” to our own Al Capone, but it was used earlier).  Power corrupts, and that is certain.  But there are ways to check government power and therefore corruption.

It’s said that “Sunlight is the best disinfectant.”  A system of checks and balances for the government is needed, with an educated public and the media playing a vital role.  And on a positive note, the Chinese people are increasingly connected and working together to hold the government accountable.

As noted in this WSJ article, in 2000 there were 10 million Chinese on the Internet.  Now there are estimated to be 485 million.  The Chinese version of Twitter, Weibo, is an increasingly popular communication tool.  The WSJ notes

The government censors much content on the Internet, but it has allowed a surprising degree of openness on Weibo and other sites. In part, experts say, that's because it sees online commentary as a release valve for the public, but the government also fears the fury that would erupt if it took away those outlets.

I disagree with the experts here.  The Chinese government doesn’t censor Weibo to allow a release valve for the public.  Weibo isn’t censored because it’s not feasible to censor it and still allow it to exist.  How can you monitor, much less politically censor, a Twitter clone that has nearly eight times more users than Twitter?  To me there are two choices for the Chinese government in this situation: shut down Weibo outright or have a trivial amount of censorship of the site that amounts to nothing.

While the Internet is relatively open, China is cracking down on traditional state media reporting of the train accident, as reported by the New York Times today.

The sudden order from the Communist Party’s publicity department, handed down late Friday, forced newspaper editors to frantically tear up pages of their Saturday editions, replacing investigative articles and commentaries about the accident that killed 40 people in eastern China with cartoons or unrelated features.

Replacing tragic news with cartoons?  This would be funny if it weren’t so sad.  Because the Chinese people won’t be able to get news from the state-run media, they’ll increasingly look to the Internet.  I just hope that they’ll still be able to access it.

Cars That Drive Themselves!

I can’t wait until I can get in the driver’s seat of a car, input my destination into the GPS, and then take a nap as my car drives me there.  This sci-fi future, I believe, is closer than many people think.

Americans won’t just accept a fully autonomous self-driving car from out of nowhere.  It will have to happen piece by piece, where AI takes over one small thing then the next.  And indeed, this is what we find happening. 

In 2006, Lexus introduced a car that could parallel park itself.  That feature is becoming more and more common.  Intelligent braking if the driver is not acting is another recent example of cars driving themselves.  This feature has been extolled by the Insurance Institute for Highway Safety.  In a recently released report, IIHS announce that this intelligent braking outright prevents 25% of low-speed crashes in the vehicles in which it is installed.

It’s better that computers will eventually drive.  Humans just aren’t good at it.  The World Health Organization estimates that around 1.2 million people are killed in car crashes each year with around 45,000 of those deaths taking place in the US.  Almost all of these are due to human error, something that will be eliminated with self-driving cars. 

But how far away are cars that completely drive themselves?  Well, I can’t go out and buy one today.  But check out this TED talk.

Self-driving cars are already here!  Google’s self-driving cars have logged 140,000 miles across cities and highways through both day and night without an accident.  And if you think that Google accomplished this feat by relying on their cars driving meekly and 20 mph less than the speed limit, here’s one of their cars screeching tires and hauling ass through an obstacle course.

Once cars are completely driving themselves, even more opportunities open up.  Scientists in Italy are working on software that allows cars to talk to each other, with the end goal of connecting every single car on the road.  What would this system be used for? 

When a car in an accident experiences a sudden change in acceleration, this change would be captured by the sensor and alert cars and drivers approaching the same spot.

In addition to preventing accidents in the first place, self-driving cars will better react to accidents when they do occur, preventing even more accidents.  This double whammy, I believe, will have an incredibly dramatic effect on the number of deaths and injuries from auto accidents each year.  Once self-driving cars are widespread a 95% reduction of traffic deaths is within the realm of possibility.

One last bit of good news: traffic will also be lessened once self-driving cars are the norm.  Every time I sit in a traffic jam, I dream that self-driving cars are already here.  There aren’t any idiots switching lanes to try to get ahead only to end up slowing everyone down.  There are no rubberneckers braking to stare at the gore of an auto accident.  There is no one suddenly accelerating and then stopping, disrupting the flow of traffic.

Instead, there are thousands of self-driving cars proceeding at a quick and steady clip on the highway.  People now get to their destination much sooner and rush hours have been greatly alleviated, in some places entirely eliminated.  Self-driving cars are following each other closer than would be possible if driven by humans.  No one brakes for no reason.  There is no road rage.  When a two-lane highway merges into one lane, the cars evenly space themselves and merge while barely losing any speed, like a zipper being zipped.  Everything is beautiful.  Nothing hurts.

So how will this paradise come about?  I expect cabs and buses might be the first early adopters, for economic reasons.  Maintaining a mass-produced AI will be cheaper than paying individual drivers.  There would be some differences, sure.  Video surveillance would have to be used to prevent and prosecute hooligans.  But in the end, we’d be much better off with self-driving cars.  And I believe (or at least want to believe) that I will ride in my first self-driving car before 2020.

My Complete Lending Club Strategy

For future reference, I’m going to chronicle my initial Lending Club posts into one article.  Without further ado:

Part 1: a quick introduction to the concept of P2P lending

Part 2: why I decided to use Lending Club over Prosper

Part 3: the final filters that I apply to find the best loans to fund

Part 4, Part 5, Part 6, and Part 7: the primary filters that I apply to weed out borrowers first

I will also continue my monthly updates on my Lending Club return, to see if these filters really work.

The information available at Michael Grabowski is for your general information only and is not intended to address your particular requirements.  This information is not any form of advice by Michael Grabowski and is not intended to be relied upon by users in making any investment decisions.  Michael Grabowski is not liable for any loss or damage which may arise directly or indirectly from use of or reliance on such information.

A Club for Lending (Part 7): Advanced Strategery 4

This will be my final post on Lending Club filters (except for the summary immediately following this)!  As in all the other posts, items in bold and red are filters that the Lending Club website itself cannot perform, but can be performed with downloaded loan data from the website.

13.  Remove loans not yet approved by Lending Club

Interestingly enough, you can invest in loans that are not yet approved by the Lending Club credit department.  If these loans end up not being approved by Lending Club, the money that you have invested is returned to your account.  I don’t want to bother with this, so I just avoid loans that have yet to be approved.

14.  Remove loans of borrowers with income less than $30,000

The vast majority of Lending Club loans are for over $6,000.  If you make $30,000 and take out a Lending Club loan, your debt-to-income (explained in one of my previous posts) is 20% just from your Lending Club loan.  I use this income filter in addition to my debt-to-income filter to be consistent.

But that’s not to say I just want ultra-high income borrowers.  Basically, I just want to understand the borrower’s financial situation.  If I see a borrower that makes $60,000 and has $10,000 in credit card debt at 23% interest, it makes sense to me if he wants to take out a Lending Club loan for $10,000 at 15% interest.  He’ll come out ahead of the game with the Lending Club loan and I can understand that.

On the other hand, I recently saw a borrower asking for a $25,000 loan to build a pool.  The borrower had a verified income of more than $25,000 per month (more than $300,000 per year).  This situation I didn’t get.  Couldn’t this borrower just tighten his belt for a few months to pay for the pool with cash?  Why does he want to take out a 5-year loan if he has this type of cash flow?  It didn’t add up to me so I didn’t fund the loan.

15.  Remove loans already invested in

Pretty simple filter here: I invest in loans that pass all my tests, but I don’t want to double down on the same loans again and again.  So I remove loans from my search that I’ve already invested in.

The information available at Michael Grabowski is for your general information only and is not intended to address your particular requirements.  This information is not any form of advice by Michael Grabowski and is not intended to be relied upon by users in making any investment decisions.  Michael Grabowski is not liable for any loss or damage which may arise directly or indirectly from use of or reliance on such information.

June Lending Club Income Update

Lending Club update time!  I’m going to show two different ways of calculating returns.  The first is already done for me, by the Lending Club website. 

Sweet, a 15.52% return!  How do I stack up against other Lending Club lenders?

Alright!  My rate of return is better than 91% of Lending Club lenders.  However, this should be taken with a handful of salt.  These Lending Club loans are a fixed income instrument.  That means that my return of 15.52% on these loans will almost certainly go down over time as some of my loans default (hopefully a small percentage, loans on the site average less than a 3% default rate since inception).

Furthermore, as mentioned in my May update, my returns are primarily so great because I received a $100 bonus for being referred to the site by a friend.  So I won’t go patting myself too hard on the back yet.

Lending Club calculates their net annualized return using a fairly complicated formula, as explained here.  It makes since to me, but has some drawbacks.  On Social Lending Network, it is pointed out that the Lending Club formula assumes that all your money is 100% invested in notes all the time. 

Obviously this is not the case.  And even if the Lending Club formula was perfect, I would still perform my own calculations to verify that each month.  The second way to calculate my returns uses the monthly statements generated by Lending Club.

So, as of 6/30/2011, I have $5123.19 total in my Lending Club account.  Note: this amount is smaller than the one reported at the top of this post.  This is because the June monthly statement was generated a few weeks ago, so the payments I’ve received since then are not reported.

I then simply use the XIRR function in Excel as explained on Social Lending.  I put the amounts I’ve deposited into Lending Club and the current total to find

So my calculated NAR is 15.10%, slightly different than the Lending Club results.

The information available at Michael Grabowski is for your general information only and is not intended to address your particular requirements.  This information is not any form of advice by Michael Grabowski and is not intended to be relied upon by users in making any investment decisions.  Michael Grabowski is not liable for any loss or damage which may arise directly or indirectly from use of or reliance on such information.

A Club for Lending (Part 6): Advanced Strategery 3

This should be next to last post on my Lending Club filters.  As in the last post, items in bold and red are filters that the Lending Club website itself cannot perform, but can be performed with downloaded loan data from the website.  Onto the filters:

9.  Remove loans of borrowers with more than 5 credit inquiries in the last 6 months more than 1 credit inquiry in the last 6 months (8/14/2011)

Whenever any borrower asks for a loan, the lender performs a credit check by requesting (AKA inquiring) for your credit history from one of the three credit bureaus (Experian, TransUnion, or Equifax).  So if a borrower has a bunch of credit inquiries in the past 6 months, it means they are requesting a bunch of lines of credit.  This is a big red flag to me.  It signals that the borrower is frequently looking to borrow money and they don’t have a good handle on their personal finance situation and should be passed up.

10.  Remove loans of borrowers with revolving credit utilization above 90%

Credit utilization is a pretty easy concept.  Say I have a credit limit of $10,000.  If I currently have $1,000 in outstanding loans, I have a credit utilization of $1,000 divided by $10,000 or 10%.  If I have $9,500 in outstanding loans with the same credit limit, my credit utilization is 95%.

In a similar vein to my discussion of a high debt-to-income ratio in my last post, high credit utilization is an indication that the borrower is close to being “maxed out”.  One minor crisis and they will be pushed over the edge and will be likely to default, so I avoid them.

11.  Removing loans of borrowers with revolving credit balance above $50,000

This is just a lot of money to owe, no matter what your income is. 

12.  Remove loans of borrowers with monthly payments on their Lending Club loan above 20% of gross income

Here’s another filter that is not currently available on the Lending Club website (and also related to debt-to-income).  I don’t want the borrower’s Lending Club loan to be a massive portion of their monthly cash flow.  If it is, the borrower is more likely to default from the very start of their loan as the large cost immediately overwhelms them, plain and simple.

Next week I’ll finally wrap up my Lending Club filters!

The information available at Michael Grabowski is for your general information only and is not intended to address your particular requirements.  This information is not any form of advice by Michael Grabowski and is not intended to be relied upon by users in making any investment decisions.  Michael Grabowski is not liable for any loss or damage which may arise directly or indirectly from use of or reliance on such information.

June Blog Income Update

June update time!  In the month of June, my earnings were

 

I held steady as my May earnings were $7.11.  My past earnings in graphical form:

I'm pretty happy with the results.  With just a little bit more earnings, my blogging will be able to pay for my Internet bill.

I did want to include an update on my Lending Club loans, but I will have to do this next week.  This is because I want to use monthly statements for my analysis, and those are made available one week after the end of each calendar month.  I will compare my analysis of my results to those generated by the Lending Club website.

I guess that will be my formula from now on.  In addition to my regular posts, I will post a monthly blog income update on or around the first Sunday of the month.  On the following Sunday I will update my Lending Club results.

A Club for Lending (Part 5): Advanced Strategery 2

Let’s hop right into more of my filters.  As in the last post, items in bold and red are filters that the Lending Club website itself cannot perform, but can be performed with downloaded loan data from the website.

5.  Remove loans if they failed income verification

This is a more nuanced filter.  There are four possible results for income verification.  The first is “verified”, wherein Lending Club asks the borrower to verify his income and he succeeds.  The second possible result is “requested”.  Here the borrower has been asked to verify his income but has not currently done so.  The third is “failed”, pretty self-explanatory.  The final result is the kicker, though.  It is “not required”.

And as explained here, the “loss rate for loans where Lending Club has verified the borrower’s income is 2.8%; the loss rate for loans where Lending Club hasn’t verified the borrower’s income is lower, at 2.7%.”  Why is this so?  It’s because for the most creditworthy borrowers, Lending Club doesn’t want to bother them to complete the somewhat cumbersome task of income verification.  These borrowers are creditworthy enough to seek loans elsewhere if they have to jump through a bunch of hoops.

So on the Lending Club website, you can filter out loans that haven’t passed income verification, but you’d be doing yourself a disservice because the very best borrowers aren’t asked to complete income verification.  So instead of filtering out “requested”, “failed”, and “not required”, and only keeping loans that have income “verified” (as is done on the Lending Club website filter), I only filter out loans that have  “failed” income verification.

6.  Remove loans if borrower has debt-to-income (DTI) greater than 20%

DTI is a pretty simple concept.  If I make $100,000 per year and I have outstanding loans for $25,000, then my DTI is $25,000 divided by $100,000 or 25%.  The higher the borrower’s DTI, the more likely they are to default.  This is because higher DTI borrowers are closer to being “maxed out”. 

If I make $2,500 per month after taxes and I use every cent of it to pay for living expenses and to pay my debt obligations, once a crisis occurs (car breaks down, sickness, etc.) I will be pushed over the edge and will be likely to default. 

7.  Remove loans if borrower has had any delinquency

This is quite similar and most likely redundant with #2 “Remove loans if borrower has had any delinquencies in the last 2 years” in my last post.  However, there is a fine distinction.  In the downloaded data from the Lending Club website, there is a column entitled Delinquencies2Yrs and one entitled MnthsSinceLastDelinquency.  As expected, the first column lists number of delinquencies in the last 2 years (0, 1, 2, etc.) while the second lists the months passed since last delinquency.  Because I don’t want to lend money to borrowers with any prior delinquencies, I could probably get away with just filtering out loans with any nonzero month since last delinquency.  However, as a failsafe, I perform both filters.

8.  Remove loans with monthly payments greater than $750

$750 per month or $9,000 per year is a lot of money to most people.  If they have any crisis, most people will have a tough time finding this money to pay their Lending Club loan obligation.  So I filter out the higher monthly payment loans.

Next week I’ll get into a few more of my primary filters.  I have 15 total, so I should be wrapped up in a few weeks.  Later today or possibly tomorrow I’ll post my June monthly update, showing my June earnings for both my blog and Lending Club.

The information available at Michael Grabowski is for your general information only and is not intended to address your particular requirements.  This information is not any form of advice by Michael Grabowski and is not intended to be relied upon by users in making any investment decisions.  Michael Grabowski is not liable for any loss or damage which may arise directly or indirectly from use of or reliance on such information.