StreetEYE Blog

The StreetEYE manifesto

Rogue Trader (film)

Rogue Trader (film) (Photo credit: Wikipedia)

“Being good…is not good enough! Everyone must be connected to our strategy, or we will find you, and weed you out!

Information arbitrage is our business. If you don’t know what an information curve is, then find out!

Position yourself in an information curve. Dominate the curve!

Nick Leeson, who most of you know and all of you have heard of, runs our operation in Singapore, which l want all of you to try to emulate.”

— Ron Baker, in Rogue Trader (1999)

“It is this semi-sucker rather than the 100 percent article who is the real all-the-year-round support of the commission houses. He lasts about three and a half years on an average, as compared with a single season of from three to thirty weeks, which is the usual Wall Street life of a first offender. He knows all the don’ts that ever fell from the oracular lips of the old stagers-excepting the principal one, which is: Don’t be a sucker!” – Jesse Livermore

In 2005-2006, briefly, inexplicably, I had an information edge over most of Wall Street. I was reading the top financial blogs of the era, Calculated Risk and The Housing Bubble, marveling at flippers, NINJA borrowers (no income, no job or assets), negative-amortization. I asked friends in Wall Street mortgage departments how come, after Greenspan started raising rates, they weren’t shrinking profits and laying people off, what with a flat yield curve, shrinking net interest margin and all that. They said they were pushing ARMs, originating to sell, making it up on fees. I asked if they weren’t worried about defaults, was told the deals were overcollateralized to resist high defaults, and anyway it was the bondholders’ problem. I downloaded some applications for option ARMs and thought, ‘these guys are out of their cotton-picking minds.’ This was at a time when certain banks’ net profits consisted entirely of negative amortization, interest that was being tacked onto loan balances without any cash changing hands.

That was my formative experience in the power of crowd-sourced research. This is not a Monday morning quarterback, 20/20 hindsight claim. Those blogs saved my ass. And, in truth, a long list of people called the crisis1. The thing they had in common was, they thought for themselves, they did their homework, they were willing to bet against the crowd.

ANYTHING on talking head TV or put out by a bank, is a) selling something and b) conventional wisdom. There is just no money in anything else. At its best, it’s listening to awesome guys like Icahn or Druckenmiller, but who are primarily talking their book.2

OK, I didn’t really have an information edge. That was pretty much BS.

In the old days, you could say that the floor traders at the exchange had an information edge from being at the nexus of the flow, the upstairs trader had the customer flow.

I have the speculator’s edge, which is that I don’t have to do a thing. The market-maker has to provide a bid-ask, the institutional investor has to put his the clients’ money to work using the strategy he pitched. I just wait until the market does something that looks stupid and I try to apply calculated aggression, invest in meaningful size in an attractive risk-reward opportunity. Most of the time, I just try not to do something stupid. It’s not information arbitrage, it’s stupidity arbitrage.

If you’re like most people almost everybody, there is no information arbitrage, just old-fashioned hard work. I’m sure there were a bunch of suckers who thought they were geniuses being in Mike Milken’s orbit, or Bernie Madoff’s, or even just as Steve Jobs fans. Most of the time, there is exactly one guy in that information curve who is getting rich, and it’s not you. You’re their bitch, their greater fool, their sucker, their mark.

Even the floor traders and the upstairs traders don’t have the edge any more. They got information-arbitraged out of the loop by direct-access trading and HFT bots.

The real edge is, doing your homework, listening to a diversity of opinion, thinking out of the box. And not doing something dumb just because everyone else is doing it. And that’s all I had, a divergent opinion with an attractive risk-reward, and a healthy level of fear over what I was reading.

So, where does StreetEYE fit into this story? In the mid-2000s, I was fortunate to fall in love with blogs and find smart people with divergent opinions doing their homework. But it was a hard road for the early adopter, building a blogroll of dozens of blogs in FeedDemon, Google Reader. And (somewhat tragically) ultimately blogs never really achieved their full potential because there were too many, it was too hard for a lot of people to navigate, there was no central front page where great stuff filtered up, it was all ad-hoc blogrolls and hat-tips.

Then Twitter came along and it was a similar process… find some awesome people to follow, like these. Then say, hmmh… who do these guys follow, and find some more. Then say… I could probably write a script to do that.

So, at the first level StreetEYE is, let’s find the best people to follow, so you can leverage the twittersphere and blogosphere without being a total nerd.

And then at the next level it’s …. what are the stories everyone is talking about right now? I don’t want to come into the office and have everyone say, “did you see Soros’s Op-Ed in the FT?” I want to see everything as soon as it gets popular. Great news aggregation sites like Memeorandum and Hacker News and Reddit and Buzzfeed and Digg have cracked the code and become the ‘front page of the Internet,’ finding the most popular stuff in their respective fields. At some point I looked at what my filters were popping up and I said, to some extent I’ve cracked the code, somebody’s got to do the same thing for financial markets, and I might as well put this out and give it a shot.

But at the ultimate level, it’s about you, dear reader, and it’s about us. My hunch is, if we get a smart bunch of investors to come to the site every day, read, share, and especially upvote the stuff everyone needs to know, together, using the wisdom of crowds, the power of technology, and a light touch from a humble editor/curator, we can find the best journalism, blogging, research, and analysis, and make the best goddamn front page for investors on the Internet.

And that, for now, is the StreetEYE manifesto. Together, we can find the best content on the Internet, be better informed, elevate the quality of the conversation, and if we don’t all get rich, maybe we can at least avoid the next Really Stupid Thing.

I welcome any and all suggestions, issues, complaints. Please email me at druce@streeteye.com. The most important thing I need now is feedback on how to make it better.

And keep reading, doing the hard work of staying informed, and upvoting/retweeting to pass it along to your fellow investors.

Your humble fellow investor and curator -
Druce

1Even excluding talking heads like Roubini, Zelman, … Paulson, Ackman, Einhorn, Falcone, Eisman… I could go on for a while. (As an aside, for anyone jumping on the anti-Ackman bandwagon and mocking his HLF and JCP plays… go back and look at what he said about MBIA before the crisis.)

2It’s not just that there’s no skepticism and thinking outside the box. My pet peeve is the way every other word is invested with heavy emotional weight, fraught with meaning, an incantation to soothe the faithful. It’s all so tribal and inflammatory, when the essence of good investing is to be independent, dispassionate and unemotional. And then, anyone who shows up and says the emperor has no clothes is mocked mercilessly. Anybody willing to stake their reputation and go against the crowd is worth a respectful listen… and even better to find the ones too crazy to even be mentioned.

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Obama goes all-in on Inspector Clouseau for the Fed

Peter Sellers as Chief Inspector Clouseau in t...

Peter Sellers as Chief Inspector Clouseau in the The Pink Panther (Photo credit: Wikipedia)

So, the Obama Administration is ‘all-in’ on Summers, despite nearly everyone who hasn’t worked for him (and a number who have) thinking he’s not the best candidate.

The argument: ‘crisis experience,’ and the need for a ‘steady hand.’

Summers’s crisis experience is like Inspector Clouseau’s, the master detective who always seems to be at the scene of the crime.
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Risk arbitrage – Investing and poker

When I was young people called me a gambler. As the scale of my operations grew, I became known as a speculator. Now I am called a banker. But I have been doing the same thing all the time. – Ernest Cassel

To win, you must understand the game, you must understand the players, and above all you must understand yourself. – Source unknown

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“Cat Food” revisited – final thoughts – part 4

Here is the long-awaited conclusion to the wonky 4-part discussion of safe retirement spending. We went pretty far down the rabbit hole, and I think the conclusions are useful.
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‘Cat Food’ revisited – testing dynamic spending rules – Part 3

In the last part of our look at dynamic rules for spending in retirement, we discussed how changing the allocation between stocks and bonds affects the maximum sustainable spending rate. We can summarize this relationship by plotting the highest feasible initial spending rate for any acceptable shortfall level.1
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‘Cat Food’ revisited – testing dynamic spending rules – Part 2

The last post discussed a framework for evaluating simple dynamic spending rules.

  • We defined a spending factor s as spending each year at a rate of s/(remaining life expectancy); and lifetime spending expectancy as the total amount you could expect to spend over your lifetime.
  • We showed how, as you increase the spending factor, lifetime spend expectancy initially increases rapidly, but the curve flattens out as spending rate increases.
  • We showed how, as you increase the spending factor, shortfall risk initially increases slowly, but the curve steepens as spending rate increases.

We found a simple dynamic spending rule could increase lifetime spending vs. a traditional fixed 4% rule, while keeping shortfall risk relatively low (arguably reducing risk by making the worst case more benign, at the cost of increased volatility, lowered starting spending, higher probability of modest shortfalls).

In this post we’ll look at how smoothing spending can improve outcomes, and how changing the equity/bond mix over time affects outcomes.
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‘Cat Food’ revisited – testing dynamic spending rules – Part 1

How much can you safely spend out of a portfolio in retirement? Spend conservatively and you may be unnecessarily curbing the lifestyle and aspirations of you and your loved ones. Overspend and risk a shortfall and painful adjustment – in the extreme, the (hopefully apocryphal) “cat food” diet.

A traditional rule of thumb is a fixed 4% per year of your starting portfolio, adjusted each year for inflation. A previous post discussed why this rule may not be safe:

  • Low bond yields – 1.8% for 10-year Treasurys and negative TIPS out to 10 years – mean historical bond returns are mathematically unobtainable.1
  • 2.2% real returns since 2000 on a 60/40 blended portfolio suggest that long-run return expectations need to be revisited. Low long-term interest rates are a forecast of low future returns, ie low growth and inflation expectations. To the extent equity risk premiums haven’t widened, they forecast lower than normal equity returns.
  • Taxes and investment expenses must be included. Work supporting 4% tends to ignore them.
  • US demographics are not very positive for growth, inflation, tax rates, and hence, real after-tax investment returns (which is reflected in the US fiscal position). The US dependency ratio is forecast to rise by 15 points over the next 20 years.

If the 4% rule hasn’t been decisively breached, forward-looking indicators are a bit worrisome. Could a more flexible rule not only be safer, but in favorable circumstances allow a higher level of spending? In this 3-part post, we test dynamic rules that vary withdrawal rates based on age and the size of the portfolio, and vary the composition of the portfolio over time.
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What’s the worst that could happen?

It’s not whether you get knocked down, it’s whether you get up. – Vince Lombardi

Playing around with DataNitro, an add-in that lets you run Python in Excel1.

What is the worst that could happen to someone who owns stocks, bonds, bills, over a 1-, 5-, 10-year time-frame? Here are the worst rolling periods for each asset class for 1928-2010, adjusted for inflation.

Stocks Bonds Bills
1-year -38.2% 1937, 1974 -15.5% 1980 -17.8% 1946
2-year -52.5% 1972-1974 -26.2% 1978-1980 -24.6% 1945-1947
5-year -44.7% 1936-1941 -37.5% 1976-1981 -27.3% 1945-1950
10-year -37.5% 1964-1974 -43.2% 1971-1981 -43.9% 1940-1950
20-year 10.7% 1961-1981 -40.8% 1961-1981 -48.9% 1932-1952
30-year 243.5% 1964-1994 -39.3% 1950-1980 -43.4% 1932-1962

 

Worst case real returns for rolling periods from 1 to 30 years, 1928-2010

Worst rolling returns

Over short timeframes, stocks can do quite a bit worse. The worst 2-year period is -52.5% for stocks, v. -26% for bonds and -25% for bills. Around year 8, stocks cross over. The worst 20-year period for stocks sees you up 10.7%, and the worst 30-year period sees you up 243%! When bonds and bills get hurt by inflation, they stay down for very long periods.

Rerunning the analysis for the post-war era doesn’t change much. Most of the worst-case periods for stocks and bonds were after 1946, but bills did worst around the war and better afterwards.

Worst case real returns for rolling periods from 1 to 30 years, 1946-2010

Worst Case real returns, 1946-2010

Spreadsheet here.

?View Code PYTHON
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import numpy as np # not used in this example, but works!
 
def rolling_return(series, n):
    "given a series of m returns, compute m-n rolling n-period returns"
    m = len(series)
    retarray = []
    for i in range(m-n+1):
        rr = 1.0
        for j in range(n):
            rr = rr * (1+ series[i+j])
        retarray.append(rr-1)
    return retarray
 
active_sheet("Returns")
stocks = CellRange("Equities").value
bonds = CellRange("Bonds").value
bills = CellRange("Bills").value
cpi = CellRange("CPI").value
 
realbonds = [bonds[i]-cpi[i] for i in range(len(cpi))]
realbills = [bills[i]-cpi[i] for i in range(len(cpi))]
realstocks = [stocks[i]-cpi[i] for i in range(len(cpi))]
 
active_sheet("Data_1928")
for i in range(1,31):
    tempbonds = rolling_return(realbonds,i)
    tempbills = rolling_return(realbills,i)
    tempstocks = rolling_return(realstocks,i)
    Cell(i+1,2).value = min(tempstocks)
    Cell(i+1,3).value = min(tempbonds)
    Cell(i+1,4).value = min(tempbills)
 
active_sheet("Data_1946")
 
realbonds46=realbonds[18:]
realbills46=realbills[18:]
realstocks46=realstocks[18:]
 
for i in range(1,31):
    tempbonds = rolling_return(realbonds46,i)
    tempbills = rolling_return(realbills46,i)
    tempstocks = rolling_return(realstocks46,i)
    Cell(i+1,2).value = min(tempstocks)
    Cell(i+1,3).value = min(tempbonds)
    Cell(i+1,4).value = min(tempbills)

 

1Why is Python a good thing? Lots of very powerful packages for data manipulation, optimization, statistical analysis, machine learning are available in Python. Also, Python is a powerful, expressive, readable language that makes it easy to manipulate complex data structures.

‘Big Data’

If ‘The Graduate’ were made today, Benjamin Braddock might hear a well-meaning uncle stage-whisper ‘Big Data’ instead of ‘Plastics.’ (Runners-up: ‘The Cloud’, ‘Social Discovery’, ‘Gamification’, the list goes on.) ‘Big data’ is a buzzword that people throw around a lot. What does it mean? Large data sets are not new. The IRS, the Census, Walmart, money center banks have always had big data sets.

What’s changed?
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What I Learned

I didn’t really post as much as I would have liked this year. I envy people whose thoughts come out in a more or less coherent, finished form. When I post something, I always think of what I really wanted to say after hitting ‘publish’.

Today, I’m going to just try to write for an hour and post what comes out, hopefully resisting the temptation to ninja-edit.

My buddy Josh does a post with quotes where a bunch of people say what they learned over the last year. So what did I learn?
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All this will not be finished in the first one hundred days, nor will it be finished in the first one thousand days, nor in the life of this administration, nor even perhaps in our lifetime on this planet. But let us begin. - John Fitzgerald Kennedy

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