Title:
Post by: Rob L on January 11, 2014, 11:00:00 PM
Post by: Rob L on January 11, 2014, 11:00:00 PM
Been thinking of writing this a while and have an hour or so before football. Folio users may ignore this thread. That's an entirely different animal.
Before we see any loan LC has reviewed it in sufficient depth to its own satisfaction to assign an interest rate sub-grade. Not only do they have all data that is eventually available to us in the browsenotes but, given direct access to the borrower, they clearly have much more. They have a team of seasoned professionals that lead the underwriting process and they have been doing this a long time. They have developed a proprietary loan scoring model and every bit of historic loan data available to us is also available to them. They have access to expensive historic data from the credit bureaus likely not economic for the rest of us to obtain. Finally, history has shown LC has done a pretty good job of achieving its goal to "... provide higher risk-adjusted returns for each loan grade increment from A1 to G5." (actually not so well with D, F and G).
LC policy makers select an interest rate structure that they feel will maximize their fees at any point in time, and loans are uniformly underwritten to conform to that structure. All loans graded XX on any day are to the very best of LC's ability the same and no loans are improperly graded XX when they should be YY.
So why is it that we believe we can use a subset of the data available to LC, filter for zero inquiries, no business loans, income >$3k/mo., etc. and get the "best" notes? Typically when we filter we filter on grade, not sub-grade. That's pretty coarse risk bins. I guess it's possible to create a model better than LC's given the data we have or can get, but I don't see large obvious factors persisting over time.
Nonetheless filtering is widely practiced and by all accounts appears to work. What am I missing here? (Please avoid using the word heretic; also dummy if possible)
Before we see any loan LC has reviewed it in sufficient depth to its own satisfaction to assign an interest rate sub-grade. Not only do they have all data that is eventually available to us in the browsenotes but, given direct access to the borrower, they clearly have much more. They have a team of seasoned professionals that lead the underwriting process and they have been doing this a long time. They have developed a proprietary loan scoring model and every bit of historic loan data available to us is also available to them. They have access to expensive historic data from the credit bureaus likely not economic for the rest of us to obtain. Finally, history has shown LC has done a pretty good job of achieving its goal to "... provide higher risk-adjusted returns for each loan grade increment from A1 to G5." (actually not so well with D, F and G).
LC policy makers select an interest rate structure that they feel will maximize their fees at any point in time, and loans are uniformly underwritten to conform to that structure. All loans graded XX on any day are to the very best of LC's ability the same and no loans are improperly graded XX when they should be YY.
So why is it that we believe we can use a subset of the data available to LC, filter for zero inquiries, no business loans, income >$3k/mo., etc. and get the "best" notes? Typically when we filter we filter on grade, not sub-grade. That's pretty coarse risk bins. I guess it's possible to create a model better than LC's given the data we have or can get, but I don't see large obvious factors persisting over time.
Nonetheless filtering is widely practiced and by all accounts appears to work. What am I missing here? (Please avoid using the word heretic; also dummy if possible)
Title:
Post by: Randawl on January 11, 2014, 11:00:00 PM
Post by: Randawl on January 11, 2014, 11:00:00 PM
Title:
Post by: Lovinglifestyle on January 11, 2014, 11:00:00 PM
Post by: Lovinglifestyle on January 11, 2014, 11:00:00 PM
I hope somebody better qualified than I (who am not at all qualified) responds regarding the origination fee motivation factor discussed in other threads.
If LC comes out ahead on loans that go bad, but investors come out behind, that would be grounds for suspicion regarding whose best interests are in mind when grading loans. I believe in creating the highest good for all concerned (win-win), but the corporation has to look after its own bottom line first for anybody to win. To what extent it does that may be where filtering has an edge.
Sorry for stating some of the obvious here. Don't mean to waste your time.
If LC comes out ahead on loans that go bad, but investors come out behind, that would be grounds for suspicion regarding whose best interests are in mind when grading loans. I believe in creating the highest good for all concerned (win-win), but the corporation has to look after its own bottom line first for anybody to win. To what extent it does that may be where filtering has an edge.
Sorry for stating some of the obvious here. Don't mean to waste your time.
Title:
Post by: TravelingPennies on January 11, 2014, 11:00:00 PM
Post by: TravelingPennies on January 11, 2014, 11:00:00 PM
Title:
Post by: core on January 11, 2014, 11:00:00 PM
Post by: core on January 11, 2014, 11:00:00 PM
Title:
Post by: Ran on January 11, 2014, 11:00:00 PM
Post by: Ran on January 11, 2014, 11:00:00 PM
Title:
Post by: bobeubanks on January 11, 2014, 11:00:00 PM
Post by: bobeubanks on January 11, 2014, 11:00:00 PM
I don't see how LC (or Prosper) has much motivation to truly assign interest rates in anything other than being more or less right. They get the origination fee up front, and then get about the same amount of extra income after that regardless of interest rate.
Title:
Post by: TravelingPennies on January 11, 2014, 11:00:00 PM
Post by: TravelingPennies on January 11, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on January 11, 2014, 11:00:00 PM
Post by: TravelingPennies on January 11, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on January 11, 2014, 11:00:00 PM
Post by: TravelingPennies on January 11, 2014, 11:00:00 PM
Title:
Post by: Fred on January 12, 2014, 11:00:00 PM
Post by: Fred on January 12, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on January 12, 2014, 11:00:00 PM
Post by: TravelingPennies on January 12, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on January 12, 2014, 11:00:00 PM
Post by: TravelingPennies on January 12, 2014, 11:00:00 PM
Title:
Post by: Emmanuel on January 12, 2014, 11:00:00 PM
Post by: Emmanuel on January 12, 2014, 11:00:00 PM
The loan issuer may want to optimize its model for consistency, not absolute returns. If LC goal is to lower the 'risk' (risk being defined as discrepancies in default rates), they may cluster together loans with different expected returns. For instance, if loans A, B, and C have the following properties: default risk 5% +/- 2%, 7% +/- 5%, 13% +/- 2%, Lending Club could cluster B and C together, and then investors focusing on returns only will consider B a bargain, because it has the same interest rate than C which a much lower (average) default risk.
Title:
Post by: rawraw on January 12, 2014, 11:00:00 PM
Post by: rawraw on January 12, 2014, 11:00:00 PM
Title:
Post by: Rob L on January 12, 2014, 11:00:00 PM
Post by: Rob L on January 12, 2014, 11:00:00 PM
Title:
Post by: Dennis on January 12, 2014, 11:00:00 PM
Post by: Dennis on January 12, 2014, 11:00:00 PM
Rob, you post a great question with this topic, one that has me really thinking. I haven't been on these boards for about 2 months now because the posts were becoming way too social and not very informative. But now you pose this great question....
I admit I'm one of those "best notes" people, in that I have my own criteria or "subset" as you put it, in choosing notes. The challenge then, for me, is to beat the averages which I have consistently done for 2 1/2 years now. This is a hobby for me, so I've mostly enjoyed the note selection process and I still do most of it manually. I have been using filters at LC somewhat the last several months though as it has become increasing difficult to get those "best" notes if you're not fast enough. But I use no filters at Prosper, and still hand pick (after using a general filter) all my notes at LC.
So personally I do think there is a subset within note grades (that's just my opinion) that can outperform the average in that grade. I have nowhere near the skill set of many here who can run numbers, accurately configure probabilities, or create API's or other software, but I still do okay.
I have 3 P2P accounts, and even with the many defaults I get because of my high risk exposure, my current combined weighted average return for those accounts is currently 14.72%. I've hovered around 15% for the last year and am currently at about the lowest return rate I've been at in 2 1/2 years. The first notes I purchased will start paying off in the next 6 months (for those that go full term), and my 3 year experiment with P2P should yield some interesting results. I am impressed with it so far.
Maybe I've been lucky, maybe I've stumbled on some skillset I don't know I have yet, or who knows what, but I will continue doing what I'm doing as long as I continue to get these results, which means that I believe there is a subset of notes that will outperform the averages.
All that said, I don't like all the defaults I get, I hate when borrowers make less than 10 payments, and in some cases they make only 1 or 2 payments and then pay off the note - those things waste my time and frustrate me, but I've learned that it's just part of the game and you have to accept it.
Again, great question........
I admit I'm one of those "best notes" people, in that I have my own criteria or "subset" as you put it, in choosing notes. The challenge then, for me, is to beat the averages which I have consistently done for 2 1/2 years now. This is a hobby for me, so I've mostly enjoyed the note selection process and I still do most of it manually. I have been using filters at LC somewhat the last several months though as it has become increasing difficult to get those "best" notes if you're not fast enough. But I use no filters at Prosper, and still hand pick (after using a general filter) all my notes at LC.
So personally I do think there is a subset within note grades (that's just my opinion) that can outperform the average in that grade. I have nowhere near the skill set of many here who can run numbers, accurately configure probabilities, or create API's or other software, but I still do okay.
I have 3 P2P accounts, and even with the many defaults I get because of my high risk exposure, my current combined weighted average return for those accounts is currently 14.72%. I've hovered around 15% for the last year and am currently at about the lowest return rate I've been at in 2 1/2 years. The first notes I purchased will start paying off in the next 6 months (for those that go full term), and my 3 year experiment with P2P should yield some interesting results. I am impressed with it so far.
Maybe I've been lucky, maybe I've stumbled on some skillset I don't know I have yet, or who knows what, but I will continue doing what I'm doing as long as I continue to get these results, which means that I believe there is a subset of notes that will outperform the averages.
All that said, I don't like all the defaults I get, I hate when borrowers make less than 10 payments, and in some cases they make only 1 or 2 payments and then pay off the note - those things waste my time and frustrate me, but I've learned that it's just part of the game and you have to accept it.
Again, great question........
Title:
Post by: TravelingPennies on January 12, 2014, 11:00:00 PM
Post by: TravelingPennies on January 12, 2014, 11:00:00 PM
Title:
Post by: Randawl on January 12, 2014, 11:00:00 PM
Post by: Randawl on January 12, 2014, 11:00:00 PM
Quote
Title:
Post by: TravelingPennies on January 12, 2014, 11:00:00 PM
Post by: TravelingPennies on January 12, 2014, 11:00:00 PM
Title:
Post by: Fred on January 12, 2014, 11:00:00 PM
Post by: Fred on January 12, 2014, 11:00:00 PM
Title:
Post by: rawraw on January 13, 2014, 11:00:00 PM
Post by: rawraw on January 13, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on January 13, 2014, 11:00:00 PM
Post by: TravelingPennies on January 13, 2014, 11:00:00 PM
Title:
Post by: dontvote on January 13, 2014, 11:00:00 PM
Post by: dontvote on January 13, 2014, 11:00:00 PM
Evaluating credit like this seems more like fundamental stock analysis than technical stock analysis. I would consider the analogy of picking notes out of LC's credit model being more like analyst rankings at your brokerage. With a bit of judgement and significant diligent effort, you can probably do better than the analyst (or at least pick a subset of his picks that fit with your investment scheme). You can apply two different models to the same data and get very different results. The model doesn't have to be filter results based or algorithmic, it's just a framework you can test.
Title:
Post by: Emmanuel on January 13, 2014, 11:00:00 PM
Post by: Emmanuel on January 13, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on January 13, 2014, 11:00:00 PM
Post by: TravelingPennies on January 13, 2014, 11:00:00 PM
Title:
Post by: Bohb Daishi on January 13, 2014, 11:00:00 PM
Post by: Bohb Daishi on January 13, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on January 13, 2014, 11:00:00 PM
Post by: TravelingPennies on January 13, 2014, 11:00:00 PM
Title:
Post by: Joleran on January 14, 2014, 11:00:00 PM
Post by: Joleran on January 14, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on January 14, 2014, 11:00:00 PM
Post by: TravelingPennies on January 14, 2014, 11:00:00 PM
Title:
Post by: dontvote on January 15, 2014, 11:00:00 PM
Post by: dontvote on January 15, 2014, 11:00:00 PM
Title:
Post by: Rob L on January 18, 2014, 11:00:00 PM
Post by: Rob L on January 18, 2014, 11:00:00 PM
Title:
Post by: Ran on January 18, 2014, 11:00:00 PM
Post by: Ran on January 18, 2014, 11:00:00 PM
Title:
Post by: Randawl on January 18, 2014, 11:00:00 PM
Post by: Randawl on January 18, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on January 18, 2014, 11:00:00 PM
Post by: TravelingPennies on January 18, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on January 18, 2014, 11:00:00 PM
Post by: TravelingPennies on January 18, 2014, 11:00:00 PM
Title:
Post by: neals384 on January 19, 2014, 11:00:00 PM
Post by: neals384 on January 19, 2014, 11:00:00 PM
LC has no incentive to price every loan at the "perfect" interest rate. Almost all of their listings fill anyway.
Lenders has a financial incentive to select notes with the most attractive interest rate given the known risks.
People and organizations with financial incentives, talent and the willingness to work hard (Lend Academy folks) will almost always outperform those with no incentive (LC).
Lenders has a financial incentive to select notes with the most attractive interest rate given the known risks.
People and organizations with financial incentives, talent and the willingness to work hard (Lend Academy folks) will almost always outperform those with no incentive (LC).
Title:
Post by: Emmanuel on January 19, 2014, 11:00:00 PM
Post by: Emmanuel on January 19, 2014, 11:00:00 PM
Title:
Post by: quantalcontent on January 21, 2014, 11:00:00 PM
Post by: quantalcontent on January 21, 2014, 11:00:00 PM
Title:
Post by: brycemason on January 22, 2014, 11:00:00 PM
Post by: brycemason on January 22, 2014, 11:00:00 PM
You are missing something. Incorporate the interest rate into your selection of notes. Then if LC changes it, so too will your selection of notes. If LC today made a stray "A" note with 40% interest, it would automatically be at the top of my profit max list.
The past is the absolute best thing we have to predict the future. I imagine LC set their initial risk model with personal loan data from some other market, and then has updated it as their own loans matured. How else are you imagining they set interest rates besides historically consistent relationships between borrower characteristics and the probability of default?
The past is the absolute best thing we have to predict the future. I imagine LC set their initial risk model with personal loan data from some other market, and then has updated it as their own loans matured. How else are you imagining they set interest rates besides historically consistent relationships between borrower characteristics and the probability of default?
Title:
Post by: TravelingPennies on January 22, 2014, 11:00:00 PM
Post by: TravelingPennies on January 22, 2014, 11:00:00 PM
I'm not sure what you mean by "incorporate interest rate". I did that by making a simple logistic regression model to predict default rates, using all the data for completed 36 month loans through 2013. I included a few parameters that seemed to contribute substantially to the variance; one of the best predictors was "inquiries in the last 6 months". I also included "interest rate" and a few others. So then I had a regression equation that let me determine the probability that any given loan would default. Of course it works well for the historical data (because that's what the model is based on). If LC recently bumped up the interest rate for borrowers with a high number of inquiries, the model might still work roughly OK because it includes interest rate as one of the predictors. (Although using "category", i.e., A1-G5, as a predictor actually works better than interest rate, presumably because it's not subject to the variability in interest rates that LC introduces by responding to market pressure.)
HOWEVER, this does not take into account the possibility that LC actually changed their underwriting for loans with higher inquiries. Suppose they rejected more of them unless the borrower met other requirements, such as having a higher income, lower DTI, or some other as yet non-transparent factor. Then the influence of inquiries on default rate would be fundamentally different today than it was in the last few years (because the population sampled by LC is now different: they are accepting and rejecting a different group of borrowers with different characteristics, among which "inquiries" is no longer strongly related to default rate), and the model would overpredict default rates. I'd be hurting myself by using "inquiries" as a criterion for rejecting notes.
So, while I completely agree with you that historical data is the best way to predict the future, those predictions assume some consistency between the past and the present. Because LC periodically changes its underwriting and rate-setting alrgorithms, there is less consistency - perhaps a lot less. So predictions are less valid, meaning that the risk of using them is greater.
HOWEVER, this does not take into account the possibility that LC actually changed their underwriting for loans with higher inquiries. Suppose they rejected more of them unless the borrower met other requirements, such as having a higher income, lower DTI, or some other as yet non-transparent factor. Then the influence of inquiries on default rate would be fundamentally different today than it was in the last few years (because the population sampled by LC is now different: they are accepting and rejecting a different group of borrowers with different characteristics, among which "inquiries" is no longer strongly related to default rate), and the model would overpredict default rates. I'd be hurting myself by using "inquiries" as a criterion for rejecting notes.
So, while I completely agree with you that historical data is the best way to predict the future, those predictions assume some consistency between the past and the present. Because LC periodically changes its underwriting and rate-setting alrgorithms, there is less consistency - perhaps a lot less. So predictions are less valid, meaning that the risk of using them is greater.
Title:
Post by: TravelingPennies on January 22, 2014, 11:00:00 PM
Post by: TravelingPennies on January 22, 2014, 11:00:00 PM
It's a tautology - the only data is the best data. The past is the only information we have to predict the future but while statistical methods can help shed light on some possible fundamental relationships, they are not able to show them explicitly.
I don't think anyone would disagree that the limited fields we have avail are mere proxies for what is actually going on in people's lives.
dontlietomedamnrobot
I don't think anyone would disagree that the limited fields we have avail are mere proxies for what is actually going on in people's lives.
dontlietomedamnrobot
Title:
Post by: TravelingPennies on January 22, 2014, 11:00:00 PM
Post by: TravelingPennies on January 22, 2014, 11:00:00 PM
Title:
Post by: edward on January 22, 2014, 11:00:00 PM
Post by: edward on January 22, 2014, 11:00:00 PM
For those of you with a credit granting background, does LC see the FICO history when they run an inquiry or just the snapshot at that moment in time? In your experience, would it make any difference if they did--that is, how predictive is past FICO of future repayment likelihood?
Title:
Post by: TravelingPennies on January 22, 2014, 11:00:00 PM
Post by: TravelingPennies on January 22, 2014, 11:00:00 PM
Title:
Post by: Fred on January 23, 2014, 11:00:00 PM
Post by: Fred on January 23, 2014, 11:00:00 PM
Title:
Post by: Rob L on January 27, 2014, 11:00:00 PM
Post by: Rob L on January 27, 2014, 11:00:00 PM
In the interest of continuity I'm repeating this post from another thread which was in response to Simon's blog article. It is very on topic here.
http://www.lendingmemo.com/redlining-florida-lending-club-prosper/
http://www.lendacademy.com/forum/index.php?topic=2000.0
Making loans to Florida is only a bad bet if LC has set the interest rate too low.
From the discussion in this thread it appears that, early on, the LC model did not weight # inquires heavily enough and lenders were under compensated. Over time it appears LC recognized this and updated their model. I wondered if the same had happened for the state of Florida parameter.
Using Interest Radar I found the following (only 36 month term loans included):
All Grade Loans A,B Only Loans
Year # Loans Int Rate FL Int Rate # Loans Int Rate FL Int Rate
2009 4716 12.2% 12.2% 2543 10.3% 10.2%
2010 8466 11.1% 11.1% 5217 9.0% 9.0%
2011 14101 10.6% 10.5% 10301 8.9% 8.9%
2012 43470 12.6% 12.6% 27558 10.4% 10.4%
2013 97776 13.4% 13.6% 55971 10.6% 10.7%
Each year FL loans were between 6.7% and 7.3% of total originations.
It seems pretty clear that, unlike # inquires, LC has not learned from history and has not increased interest rates for Florida borrowers. Simon is right. By all appearances filtering by state works and Florida likely remains a bad bet.
Could be that the law (or the possibility of negative publicity) precludes consideration of such redlining or hot button factors by LC in their model. There's a story I read about one lender that used the number of vowels in the borrower's last name to filter for ethnicity. The word got out and there were very serious repercussions. If memory serves it cost them their business.
http://www.lendingmemo.com/redlining-florida-lending-club-prosper/
http://www.lendacademy.com/forum/index.php?topic=2000.0
Making loans to Florida is only a bad bet if LC has set the interest rate too low.
From the discussion in this thread it appears that, early on, the LC model did not weight # inquires heavily enough and lenders were under compensated. Over time it appears LC recognized this and updated their model. I wondered if the same had happened for the state of Florida parameter.
Using Interest Radar I found the following (only 36 month term loans included):
All Grade Loans A,B Only Loans
Year # Loans Int Rate FL Int Rate # Loans Int Rate FL Int Rate
2009 4716 12.2% 12.2% 2543 10.3% 10.2%
2010 8466 11.1% 11.1% 5217 9.0% 9.0%
2011 14101 10.6% 10.5% 10301 8.9% 8.9%
2012 43470 12.6% 12.6% 27558 10.4% 10.4%
2013 97776 13.4% 13.6% 55971 10.6% 10.7%
Each year FL loans were between 6.7% and 7.3% of total originations.
It seems pretty clear that, unlike # inquires, LC has not learned from history and has not increased interest rates for Florida borrowers. Simon is right. By all appearances filtering by state works and Florida likely remains a bad bet.
Could be that the law (or the possibility of negative publicity) precludes consideration of such redlining or hot button factors by LC in their model. There's a story I read about one lender that used the number of vowels in the borrower's last name to filter for ethnicity. The word got out and there were very serious repercussions. If memory serves it cost them their business.
Title:
Post by: RaymondG on January 27, 2014, 11:00:00 PM
Post by: RaymondG on January 27, 2014, 11:00:00 PM
I just realized that I have a large amount of loans from CA, FL and NV. But the lose from these loans are average or better which might be because I use a little more restrict criteria to pick loans from these three states.
[attachment deleted by admin]
[attachment deleted by admin]
Title:
Post by: Bohb Daishi on January 28, 2014, 11:00:00 PM
Post by: Bohb Daishi on January 28, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on February 01, 2014, 11:00:00 PM
Post by: TravelingPennies on February 01, 2014, 11:00:00 PM
A natural question to ask is "How accurate has LC's model been over the years".
For each loan in the browsenotes file LC provides its sub-grade which is defined by "interest rate" and "expected default rate" (both provided).
Terrific; all I have to do is go back to the LoanStats files, pull the expected default rates over the periods used (LC updates these along with interest rates periodically), and see how well LC did.
As I'm sure it's well known to folks that have been here much longer than I, LC doesn't record in LoanStats the expected default rate they provided at the time of loan issuance. See:
http://www.lendacademy.com/forum/index.php?topic=560.msg1916#msg1916
Very convenient. Looks like LC must not want folks going back to see how well their predictions held up.
The only way to have the required data is to have recorded and preserved one or more browsenotes files during each of the periods of sub grade use.
My browsenotes records only go back to 05/13. Not much help. Any old timers out there have the long record?
Superbowl time!
For each loan in the browsenotes file LC provides its sub-grade which is defined by "interest rate" and "expected default rate" (both provided).
Terrific; all I have to do is go back to the LoanStats files, pull the expected default rates over the periods used (LC updates these along with interest rates periodically), and see how well LC did.
As I'm sure it's well known to folks that have been here much longer than I, LC doesn't record in LoanStats the expected default rate they provided at the time of loan issuance. See:
http://www.lendacademy.com/forum/index.php?topic=560.msg1916#msg1916
Very convenient. Looks like LC must not want folks going back to see how well their predictions held up.
The only way to have the required data is to have recorded and preserved one or more browsenotes files during each of the periods of sub grade use.
My browsenotes records only go back to 05/13. Not much help. Any old timers out there have the long record?
Superbowl time!
Title:
Post by: Emmanuel on February 02, 2014, 11:00:00 PM
Post by: Emmanuel on February 02, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on February 02, 2014, 11:00:00 PM
Post by: TravelingPennies on February 02, 2014, 11:00:00 PM
Title:
Post by: dontvote on February 03, 2014, 11:00:00 PM
Post by: dontvote on February 03, 2014, 11:00:00 PM
The best indication that their credit model is working accurately is a sameness between captured returns across the grades. b-g should basically give you the same return if you buy all the notes offered. This is the fully diversified naive portfolio that shows how well they are pricing risk to give an after default economic return for the platform. I think they've been doing a pretty darn good job. B-G are basically giving you about the same.
Title:
Post by: Beekaycee on February 03, 2014, 11:00:00 PM
Post by: Beekaycee on February 03, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on February 03, 2014, 11:00:00 PM
Post by: TravelingPennies on February 03, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on February 04, 2014, 11:00:00 PM
Post by: TravelingPennies on February 04, 2014, 11:00:00 PM
What I'm calling the 'economic return' is the return net of defaults. That means that you will have a higher rate of defaults on higher interest rate loans. So yes investors get higher rates on riskier loans with higher default rates. If the system works well, your rate of return will be the same after all is said and done, irregardless of the loan grade you invest in.
this is describing the same principal that evens asset class returns. higher return assets get bid up until all returns from risky money are basically equivalent. It doesn't always work cleanly, but it is a basic market force that contributes to asset class prices.
in the p2p space we saw this early when people took a look at the 14% average returns from higher yield notes and said anyone investing in higher grade notes earning 11% after defaults was leaving money on the table.
this is describing the same principal that evens asset class returns. higher return assets get bid up until all returns from risky money are basically equivalent. It doesn't always work cleanly, but it is a basic market force that contributes to asset class prices.
in the p2p space we saw this early when people took a look at the 14% average returns from higher yield notes and said anyone investing in higher grade notes earning 11% after defaults was leaving money on the table.
Title:
Post by: TravelingPennies on February 04, 2014, 11:00:00 PM
Post by: TravelingPennies on February 04, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on February 04, 2014, 11:00:00 PM
Post by: TravelingPennies on February 04, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on February 05, 2014, 11:00:00 PM
Post by: TravelingPennies on February 05, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on February 06, 2014, 11:00:00 PM
Post by: TravelingPennies on February 06, 2014, 11:00:00 PM
Title:
Post by: hoggy1 on July 22, 2014, 11:00:00 PM
Post by: hoggy1 on July 22, 2014, 11:00:00 PM
You beat me to it trevor!
Title:
Post by: trevor on July 22, 2014, 11:00:00 PM
Post by: trevor on July 22, 2014, 11:00:00 PM
Title:
Post by: rawraw on July 22, 2014, 11:00:00 PM
Post by: rawraw on July 22, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on July 22, 2014, 11:00:00 PM
Post by: TravelingPennies on July 22, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on July 23, 2014, 11:00:00 PM
Post by: TravelingPennies on July 23, 2014, 11:00:00 PM
Title:
Post by: TravelingPennies on July 23, 2014, 11:00:00 PM
Post by: TravelingPennies on July 23, 2014, 11:00:00 PM
Title:
Post by: cfb on July 23, 2014, 11:00:00 PM
Post by: cfb on July 23, 2014, 11:00:00 PM
20,000 researchers examined thousands of terabytes of data gathered since January of 2009. In every analysis Obama was president, he was alive, and the stock market went up on an annualized basis.
Researchers concluded that based on the long trend of evidence, Obama would always be president, he would live forever, and as long as he was president the stock market would always go up. An alternate theory was developed that the rising stock market caused Obama to be president, and also gave him immortality.
Researchers concluded that based on the long trend of evidence, Obama would always be president, he would live forever, and as long as he was president the stock market would always go up. An alternate theory was developed that the rising stock market caused Obama to be president, and also gave him immortality.
Title:
Post by: TravelingPennies on July 23, 2014, 11:00:00 PM
Post by: TravelingPennies on July 23, 2014, 11:00:00 PM
Oh, you are good CFB.
Title:
Post by: Fred93 on July 23, 2014, 11:00:00 PM
Post by: Fred93 on July 23, 2014, 11:00:00 PM
Thank you CFB.
Title:
Post by: Randawl on July 23, 2014, 11:00:00 PM
Post by: Randawl on July 23, 2014, 11:00:00 PM
I was in the process of typing something similar. Changed my mind and saw hours later that rawraw and cfb said it better anyway. 
Title:
Post by: TravelingPennies on July 24, 2014, 11:00:00 PM
Post by: TravelingPennies on July 24, 2014, 11:00:00 PM
Its an old one I just updated a little.
The other comment that falls to mind during moments of intense data and trend analysis is what Isaac Newton said after losing his shirt in the South Sea bubble. "I can calculate the movement of the stars, but not the madness of men".
The other comment that falls to mind during moments of intense data and trend analysis is what Isaac Newton said after losing his shirt in the South Sea bubble. "I can calculate the movement of the stars, but not the madness of men".