A bit more courtesy would have been welcome. You sound very condescending. And I hope you talk to your clients in a different way!
Let me address your methodology comments nonetheless, which are for the most part unfounded.
* I don't have any finding about annual income. I don't think it is mentionned anywhere in my conclusions.
* "delinquencies and public records has a big impact on returns as newer loans are not aged enough": because I average across vintage, and because I don't average based on volume on the platform, I account for the aging biais.
* "Time is not risk [..] kurtosis etc.": I don't say that time is risk. I suggest the reader to look at the return series through time. Essentially to look at the volatility of the returns ( without pronouncing the word volatility to keep the content accessible to a novice reader). I essentially encourage the reader to visually assess his Sharpe ratio. Which is a good universal risk measure.
* "reconsider average across vintage": averaging across vintage is a first approximation. I acknowledge the fact that a better methodology would be to take a weighted average that matches the amortization profile of a loan.
* I maintain that any statistics you compute in 2006, 2007 or 2008 is less reliable (statistically). Yes it is an important period to have because of the crisis. And this is why I put on the chart. However, you can't compute very reliable returns when you have a dozen of loans to average across.
Anyway, I happy to exchange with you in PM on methodology if you would like to continue the discussion
There are many pearls to find. It depends on your 'set of preferences'. You want return? you want low-risk ? you care to deploy a lot of money, or not that much?
But I'd say that for starter, anything in the 8%-9% range is a very good deal these days in this environment
Look for the Pearl!
you can definitely get 8% interest over the long run with a good liquidity on your cash deployed. To me, it's totally worth it for money that you don't need in the really short term
The code to process the lending club data is done in python. I got the full history of all payments made on the platform and pre-process it to have something that's light enough to be explored with a good user experience.
For the viz', yes I used DC.js, I can open source the .js if you guys want it.
I have tried to research whether Steve Jobs was 'suffering' from narcissistic personality disorder http://en.wikipedia.org/wiki/Narcissistic_personality_disord... without finding much more than speculations. But if you look at the 'symptoms' list, and compare to the various reports from people having worked with him, it looks like it. Lack of empathy is a typical symptom.
"Those looking to sell themselves will have to do so on results, fad-of-the-day, or personal relationships"
100% agree.
Actually I tend to think more and more that personal relationship is really a big big one.
"Many of the jobs most at risk are lower down the ladder (logistics, haulage), whereas the skills that are least vulnerable to automation (creativity, managerial expertise) tend to be higher up"
Your job vulnerability, I think, is not only a matter of automation. It also a question of "commoditization". If you become a commodity, meaning that, if
* what you do can be well described
* your workflow can be well described
* the tools you use are becoming standard
* there is no real barrier for entering your field.
Then, you'll lose very quickly any bargaining power and your 'salary' or 'margin' will decrease.
Technology, as it progresses, tends to commoditize 'producers', whereas usually 'distributors' are less vulnerable.
Interesting to see that France which is supposed to be the most visited country in the world is not the brightest area in Europe...at all.
Could there be a bias in the data due to the fact that all the countries don't have the same usage rate of social medias ? Or just population density ?
Or official statistics are the ones that are biaised...