The recent market activity and what we saw in September 08 and March 09 is why people should not listen to any ‘investment professional’ who tells you to invest based on how old you are. It’s ridiculous to apply the “average” growth rate of the market without considering where in the market cycle you are in. If you invested during any peak, you are almost guaranteed NEVER to see growth if your portfolio without a significant amount of dollar cost averaging just to get you back to even.
Here’s VanGuard’s model portfolio mix and my portfolio mix for my 401K. If my portfolio looked anything like their ‘model’ portfolio, I’d be, to put it meekly, royally screwed.
I’ve been in cash since the the 4Q of 2009 after being purely in individual stocks for most of the year (I was also completely in cash during 08-09 crash). Don’t get my wrong, I’m not advocating staying out of the market, but timing is important and my portfolio mix will look a bit more like the model mix when stocks become cheap, and hopefully irrationally cheap, again. Dow 8500 looks like a good place to start thinking about it. What do you think? Dow 8500 or Dow 11500 first?
So you are unemployed, with no job prospects in sight and a healthy mortgage and family to worry about. You are not alone as a staggering 17% of Americans are currently out of work (sorry, U.S. Bureau of Labor Statistics, this is a more realistic vision of the jobless rate, not the 10% that you are ‘reporting’ and will subsequently adjust a year from now when no one is paying attention). Here’s Mint.com’s spin on the real unemployment rate in America:
Now while many Americans are finding it hard to earn a wage, there is a strange thing going on with the employed folks at our country’s largest banks. They are getting bonuses, and not in a figurative sense of ‘hey, you have the bonus of keeping your job although your performance was a bit spotty abysmal over the past few years.’ No, they are paying themselves cold hard cash partly financed by the federal (read as American tax payer dollars) bank bail-out program. Now I understand that a few of these banks have begun to repay their financial debt to the country, but the ‘lifestyle debt’ of long-term unemployment, mortgage foreclosures, small business bankruptcies, and retirement saving losses that these banks helped create are nowhere close to being recovered. To turn a blind eye to anything beyond the reach of their balance sheets is just another example of the lack of fiscal and moral responsibility so prevalent in modern day banking.
In nearly any other industry, these poorly operated business would be purged in typical capitalist fashion during down business cycles; survival of the fittest, Darwinism in industry. The main reason these industry Dodos survived was because the government had to intervene and lend mega amounts of money to them. They were so big, and supported so many consumers and industries, that letting them fail would cause massive devastation to our economy (and the world’s economies). In fact, 15 of the top 21 recipients of bailout funds were banks or bank subsidiaries whose survival has been contingent purely on their size rather than their abilities to operate, a fact ignored when decisions were made to pay out bonuses this year.
Being “Too Big To Fail” creates an imbalanced risk/reward structure because it allows banks to engage in short-term highly profitable businesses (CMOs, proprietary trading, hedge funds) with limited consideration for the additional risk (thanks to 3rd party capital rescue), which tends to be a long-term compounding problem that grows unfettered over many years. It essentially allows them to share the risks over a larger capital base (theirs plus the American tax payer) during crisis, yet distribute profits accumulated from their activity that leads to the crisis to themselves (through short-term incentive structures like year-end bonuses on annual financial performance).
So what can you do (and how can Mint.com help) so that this does not happen again? Well, it’s pretty simple, fire your (big) bank. Firing your bank is basically saying “I will not allow you to get so big that you can act irresponsibly because you are not worried about going bankrupt.” To do this, all you need to do is move your savings accounts to a smaller, more responsible bank. This exponentially reduces the size of a big bank, because your deposits are significantly leveraged in the modern day banking system (see the section called “Effects on Money Supply“). Moving $1,000 dollars out of your large bank could potentially reduce the bank’s asset size by $9,000, so a lot if these jabs to shift the deposit base can amount to a staggering change.
Mint.com allows customers the opportunity to do this in their “Ways to Save” section. Mint could take this a step forward by giving higher visibility to responsible banks or discouraging consumers (think “Didn’t Screw the Economy” rating) from moving accounts to bailout banks. They could even flat out not allow irresponsible banks on their site, a move that would certainly be damaging to new customer acquisition for these banks. With information that is publicly available, along with the data they are collecting consumers relationships with banks, they could create a banking watchdog system that brings the same transparency to the banking industry as they have brought to personal finance. These types of ideas, although not necessarily beneficial in the short-term, could provide a larger pool of financially healthy individuals transacting in more stable and responsible banking industry.
So are you going to move your accounts to smaller, more responsible banks? Would you like to see companies like Mint impact the banking industry for the better? Chime in on the comments section if it suits you … or don’t. I’ll be checking in to see if you did or didn’t while QAing Jeff’s latest build of Wixity. Fun.
Last week, I’ve started to learn Python through a peer-to-peer learning session set up through nextNY. The material that we’ve gone through has made learning programming very easy to wrap our heads around, and the environment of cooperative learning has been awesome. I’m looking forward to being a Python ninja* pretty soon.
With four and half chapters of Python at my disposal, I wanted to put my skills to the test. Since I’m a huge baseball fan, I thought I’d try my hand in simulating who would lose the World Series this year, a pillow-fight match-up between the New York Yankees and the Philadelphia Phillies.
The first thing to do was to crunch the numbers. Crunching the numbers means exactly that, figuring out the probabilities of events occurring over a seven game series. I incorporated things like Ryan Howard’s immense strike-out rate, Derek Jeter’s incredible lack of range at shortstop, and Brad Lidge’s ninth inning ERA. I also made sure to incorporate correlations, or how related each variable is to each other. Funny enough, the highest correlation I found was between having a runner on first base with less that two outs in the seventh inning onwards and Arod weakly grounding into a double-play. Numbers never lie.
Now this got me a pretty good picture of who would lose the World Series, but I hadn’t taken into consideration the qualitative variables, the intangibles, the “Cole Hamels’ is a play-off pitcher” and the “Mariano is unhittable in the World Series” bullshit bullshit. These are usually the ’statistics’ that overzealous fans throw out (with no meaningful data except their distorted memories) as their defense to a player’s immortality.
The classic intangible lies on the shoulders’ of the Yankee captain, Derek Jeter, a ball player that seems to find himself at the right place at the right time in the postseason. Yankee fans have constantly spouted his ‘greatness’, and refuse to admit that he was horribly out of position on the Jeremy Giambi play at the plate, and doesn’t even register as having the highest batting average in a World Series (that designation goes to Billy Hatcher who hit a sickening .750 for the Reds in 1990 in 12 ABs). Heck, Jeter doesn’t even deserve the nickname “Mr. November” for his play in the 2001 World Series. He had 1 HR, 1 RBI, and 2 runs scored in November, numbers that were almost matched by a pitcher for the Arizona Diamondbacks (1 RBI and 2 runs scored). Oh, and that pitcher also won two potentially series ending games in two days that November with a 2.22 ERA, .96 WHIP, 8Ks in 8.1 innings. Derek Jeter, I’d like you to meet the real “Mr. November,” Randy Johnson.
Okay, so I wrote my little Python program to capture all of this. The stats, the pseudo-stats, the Phillie Phanatic’s rants, and the countless times we’ll hear “26 World Series rings.” With so many probabilities and interactions, this program chugged along for two days, and finally, yesterday before the first pitch, I got the result: Value Error: Let’s Go Mets.
*Looking forward to the day when ninja is not used in start-up world employment searches and reverts back to its original awesomeness of stealthy nighttime assassin.
What is risk? When a lot of us hear this word, we automatically think that it has something to do with something bad happening. What is risk management? When a lot of us hear this phrase, we automatically think of “Along Came Polly.” Risk and risk management almost always equates to incredibly awful downsides whether it be in our drive to work (car crashes), our retirement accounts (stock market crashes), or our health (heart crashes).
When we consider risk this way, we are putting unfair weight on the downside of what risk really is. Risk is really the measure of the unexpected, and the unexpected can work in our favor as well as against us. That means that even a crazy unexpected positive outcome, like winning the lottery, is also as much of a reality of risk as a plane crash (on island which travels through time for all the LOST fans out there). Risk is saying that there is a range of outcomes that could happen and we don’t have a clue about what the hell is going to happen in the future. The wider that range of outcomes, both good and bad, the more risky something is.
Peter L. Berstein, author of Against the Gods: The Remarkable Story of Risk, explains it pretty well. He says by definition risk is a measure of the unknown, and because of that it is silly to presume and act as if we know what the future holds. Risk management really is understanding that the future is uncertain, and preparing ourselves and our institutions to deal with the times when things are different from our expectations:
I was particularly intrigued by his comments about using optionality models in corporations as a way to value the option of waiting as an alternative strategy to acting, primarily when making decisions that you can not go back and change. This is putting a value on the new information you can gain through the passage of time, simply by sitting back and waiting. Most people, especially in the start-up space, say there is no time for waiting, release early and release often, iterate iterate iterate. But what if the cost of this far out weighs the value of waiting?
Say your company is launching a new product, and you have to decide how to spend a $1 million dollar budget to advertise it’s awesomeness to the world. Your marketing division comes to you with a proposal allocating dollars to buying Google Ad Words, a full-page ad in your industry’s top trade magazine, and a viral video campaign. In passing they mention that the behavioral study of your existing customer base is going well, and the results should be ready in threesix nine months, in time for the industry trade show.
We usually get a lot of information about how search engine marketing has the highest brand recall and video has the best consumer retention rate and the top ten sites that have the exact demographic that we are targeting. However this information doesn’t guarantee success; the future is completely unknown and its outcomes could range from the greatest advertising campaign of all time to the the most colossal failure destined to be top business school study material (Advertising Mismanagement: A Case on (Insert Your Company Name Here). But what is the value of waiting for more information to launch our advertising campaign, specifically our behavioral study? What if spending $50,000 to finish up the study tells us exactly who to target, and we only need to spend $500,000 to reach them? Wouldn’t that trade-off be awesome information to have? This is possible by modeling the value of waiting to act on future information! This would certainly help in trying to avoid “being too early,” something that venture capital firms often express concerns about.
So we know understand that risk is more than just danger, and really a representation of ranges (positive and negative) of what an outcome can be. Risk management is really preparing ourselves for the range of outcomes that could happen, and better risk management would also involve valuing what a “wait and see” approach would be. We do not know what the future holds, so it’s okay to make mistakes, and the sooner we realize that we can’t do anything about uncertainty (that’s not to say we can’t do anything to mitigate the impact of adverse situations), then the sooner we can be happy as a hippo.
Berkshire Hathaway had their annual shareholders’ meeting last Saturday (May 2), and Warren Buffett and Charlie Munger totally hated on “higher-order” mathematics used in finance. Come on guys, what did little ol’ math do to you? Math and modern portfolio theory were picked on by these investment gurus more than Arnold was picked on by the Gooch! Don’t worry math, I got your back.
The truth of the matter is while Mr. Buffet and Mr. Munger are right about Wall Street’s reliance on complex math, the real blame should be focused on the consultants and investment managers who hawked these models as the end-all, be-all, best thing since sliced bread. This is one case where it is totally fine to shoot the messenger in the face, however, we shouldn’t abandon using math to help us make better decisions. We just need to find a better translator, because the message has some very valuable insights.
The reason why we build financial models, or really any models, is to keep track of numerous and complex relationships, something that is very difficult to do in our heads. The world does not move in simple, predictable ways and the real value in modeling frameworks is to find the best representation of how the world actually behaves. Sometimes a simple relationship just doesn’t make sense; Mr. Buffet would surely agree that modeling investment growth as a simple linear change is not nearly a good as modeling it as an exponential change (there are a number of high school curriculum that consider this “higher math”).
The key is to fully understand and make transparent that as we increase complexities in models, we increase the number of things that can go wrong, and therefore decrease our certainty of performance. Think back to our first calculator, which for a lot of us often doubled as our first watch (wicked). Simple, easy, and reliable. Now add in a 2.66Ghz Intel Processor, 8GB RAM, 320GB of Storage, and a super-fly, aluminum cased, glow in the dark keyboard. We have a kick-ass laptop that let’s us do all sorts of things a whole lot better, but it’s not surprising that its average lifespan is somewhere around 2 – 4 years. And when it goes, we lose everything (yes, even that awesome illegally downloaded music collection that was the envy of our less tech savvy and risk adverse friends). The funny thing is that Casio can still multiply two five-digit numbers, even after 20+ years! But that doesn’t make it better.
Unfortunately, the certainty of performance only really bothers us in the worst of times, like when our computers crash and the stock market collapses. Now, just like backing up our hard-drives, there are ways that we can create more security around financial modeling. A few things that come to mind are good stress testing frameworks (if your models can’t do this easily for you, then be very cautious with its results), putting good translators (i.e., people who get how the model works AND understand its limitations) in front of decision makers early and often, and moving to a risk-based incentive compensation model (a discussion for another time).
Modeling frameworks are very useful, but they shouldn’t be used as a reason to stop thinking about what we are doing. The human element in analyzing data can never be replaced by a pure modeling framework. We shouldn’t site blantent disregard of rational thought by high-paid consultants and star investment analysts as failures in mathematical modeling. Because remember, when you point your finger at your model, there are three fingers pointing back at you … wait for it …. wait for it … okay, you got it, cool.
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