The Difficulty of Backtesting

One of the benefits of momentum based systems is that you don't have to deal with the vagaries of fundamental data. Leaving aside revisions to economic data, when they appear in real-time, and changes to their methodology, earnings data is difficult enough. You would think it would be a simple enough thing to just add up companies earnings to get a P/E or to use operating earnings instead of as reported earnings, but some even criticize those two points. Also, you have to deal with changes to index methodology.

Let me explain. In the late 1990s, MSCI changed the methodology by which they calculate indices. In the past, they used simple market-cap weights and tried to include 60% of the publicly traded firms (by market-cap) in the index. At the time, most other indices were float adjusted, where they only recognized shares available for trading in computing weights. Countries like Japan, which saw massively overvalued multiples in the late 80s and early 90s, also had companies that were not widely held as part of their indices. Since they weren't widely held, they should have had less weight in the index. Less weight in the index would have meant that prices wouldn't have gone as high and stocks wouldn't have been as overvalued in the early 90s.

The other main change was increasing the weight 60% limit to something like 80-85%. For instance, in the U.S. GM had 60% of the U.S. auto market so only GM was included, but Ford (despite being huge) was not.

How does this matter? Well, if the index methodology is changed to correct a massive overvaluation in Japanese stocks, it means that you cannot compare historical price or PE ratios to the current levels for most historical indices. For example, let's say I build a global tactical asset allocation model with a 50% weight on momentum and a 50% weight on value. I determine momentum for a country's equity index by how its momentum compares against the other countries and then I compare it to its history (using a z score). Then I take 5 pieces of valuation data (like P/E, P/Cash flow, P/B, RoE, RoA) for each country and compare each country relative to each other and again to the history for each series. In each case, comparing the multiplies to each other should have no bias. However, if I'm using an index that has changed its methodology and was severely biased upward, all my valuation and momentum data shouldn't mean much when I compare it historically. For instance, the standard deviation of P/Es would be much larger and the average value would be higher (due to Japan being overvalued, for instance). The z-scores could be telling me something that isn't true. So what might appear to be historically cheap now, after correcting the bias, actually may not be cheap. As a cross-check, it would make sense to also look at alternative indices that have not substantially changed their methodology over the period.

If Siegel is right (from WSJ article above), then even the earnings data should be biased.

My Solution to the AIG problem

TPM put up a timeline of the collapse of AIGFP. Most of the information is gleaned from the WaPo series from last year. One thing that is interesting to me is the video (I think this is the one, HT: Ritholtz) of Hank Greenburg when he comments about the extent of their CDS portfolio. He gives a bit of a different timeline than the WaPo piece. Since there are all these self-interested parties trying to shore up their reputations, I'm really not sure who's right. One thing I would note is that the CDS market was growing by like 100% a year at the time when they stopped writing CDS according to the timeline. From the time Greenberg began to be under investigation by Spitzer (when his influence probably began to wane), to the time they stopped writing CDS, their exposure could have doubled or more.

Anyway, to my solution. My reading of the problem is that AIG has suffered more from additional cash sent to counterparties as the result of ratings downgrades than it has from losses on their CDS portfolios. Now, I'm sure that they were aggressive in writing these and did face significant actual losses on these portfolios, but sending money to Goldman et al is the big source of their trouble. If AIG were AAA due to government backing (like Fannie or Freddie), then less capital would be required and AIG (and the government) could get their money back.

I see two main problems with this, first, AIG brought in people to wind down their contracts. If they've already taken a loss on the contracts, then they're SOL. The other problem is the political economy problem. Wall Street firms received most of this money and they would likely lobby Congress, the Fed, or Treasury department so that they don't have to give the money back. I think Spitzer was right saying those firms should have had some kind of haircut, but he usually isn't so it might just be an abberration.

My solution is really simple enough that I'm not quite sure why no one has brought it up. The firm is essentially backed by the government, so why not?

Is the BoE reading the Money Illusion

I was reading the March BoE minutes when I came across the following statement:

"There was a high degree of uncertainty over the appropriate scale of purchases
necessary to keep inflation at target in the medium term. The Committee noted
that their February Inflation Report projections suggested that a significant
shortfall in nominal GDP was possible over the forecast period. Nominal GDP had
grown by, on average, around 5% since the inception of the MPC – a period over
which inflation had been close to the target on average. In contrast the
Committee’s February projections implied a small decline in nominal GDP in 2009,
with growth remaining below 5% in 2010. Therefore the projections suggested a
shortfall in nominal GDP of at least 5%."

The BoE is basically saying that they chose the amount of assets to purchase under the APF by figuring the gap of nominal GDP relative to the historical average. Since nominal GDP in 2009 will be close to 0% and the average is 5%, they should buy assets equal to 5% of GDP. Barclays has already noted (in MPC Watching) some of the problems with this methodology (like the money multiplier), but I was struck at how similar this is to Scott Sumner's nominal GDP targeting idea. You usually don't hear central banks talk about nominal GDP gaps.

Condor Options

I thought this post at Condor Options was very good. I think one thing that makes testing these types of option strategies difficult is that you really need some kind of software package to do it. I can test a simple mean-reversion strategy on S&P500 on excel that gets returns close to what they have, but it would be a pain to test bull and bear spread strategies without something at least like Trade Station and preferably something better. That being said, I think it will make a lot of sense to pursue this type of strategy further. Just like mean reversion strategies tend to work in spurts, so do trending strategies. Most strategies could use additional chances to earn some premiums.

50 shapers of Finance

The FT has released 50 shapers of finance, and not surprisingly KF is not among them. I count 31 of 50 (not counting Lou Jiwei, so it could be 32, but I don't really know what he does) as members of government organizations. Three are practicing private-sector economists: Krugman, Shiller, and Roubini. I think they are putting too much faith in private sector CEOs, you could probably narrow the list to ten. Bernanke, Obama, the three economists listed, and then five dead economists.

Greenspan is wrong

I don't think he's completely wrong here, but he's certainly wrong on the impact of low short-term rates impacting long-term rates. First, I could point him to the FRB at San Francisco that gives a little primer on the determinants of long-term mortgage rates. Mortgage yields tend to track government yields and long-term government yields are certainly affected by what happens to short-term government yields. Granted this relationship tends to break down, like it did when Greenspan notes, but historically the long-term decline in the discount rate has some effect on the mortgage rate.

Second and more importantly, the chart above of the effective interest rate charged on ARMs vs. FRMs says it all. Note the dramatic decline in ARM rates relative to FRM rates around the time when the bubble began to expode in 2002-3. Indeed, Greenspan himself said in a 2004 speech that, "recent research within the Federal Reserve suggests that many homeowners might have saved tens of thousands of dollars had they held adjustable-rate mortgages rather than fixed-rate mortgages during the past decade." The research from my (unpublished) M.A. thesis showed (among other things) that ARMs account for a significant portion of new subprime loans and the rise in delinquencies. Since the spread of subprime borrowing and ARMs were also a contributor to the growth in the housing boom, Greenspan's low interest rate policies (and speeches) fueled the interest in ARMs and hence the housing boom*.

I don't deny that increased investment from overseas was a factor, but it really doesn't make sense for the housing boom to be mostly caused by the global savings glut. They might have invested in MBS, but most of the decisions were first made by people buying homes in California, Nevada, and Florida and those who sold 'em to 'em.

* not a conclusion from my thesis, but it was a team project

Component TAA Update

Given its a start of a new year, I figured I would update my Component TAA model (for more see: here, here, and here) results to give an idea of how it performed out of sample.

For a brief refresher, I took Mebane Faber's Tactical Asset Allocation strategy and looked at adding risk-parity portfolio weights and then also at looking at within sector momentum strategies. So the basic strategy outlined by Faber (2007) is to go asset classes that are above their 10 month moving average and remain in cash the rest. Classes are equally weighted (and he uses US stocks, foreign stocks, commodities, real estate, and 10 year US bonds).

My addition is to weigh different asset classes so that each contributes equally to the risk. The idea is that bonds are much less risky than stock, so that the contribution to overall portfolio risk is dominated by stocks. If you leverage up the portfolio, then you're basically long US stocks and the other positions don't really matter so much in determining your returns.

The second part is to look within the asset class instead of as a whole. Given the research by Jegadeesh and Titman, my decision for choosing what sectors to invest was determined by momentum. For the purposes of this study, I have been concentrating on the past four months worth of momentum and holding at least for two months. The top 25% of individual securities are used. As an example, instead of going long only Commodities, I might be only long oil and gold.

So the information I am going to present are first the 2008 returns of each strategy (and leveraged 100%), followed by tables with the historical returns, and their historical charts (scaled by natural logs). I'll follow up at the end with a summary of the performance of the CTAA and what it was holding at the end of the year.




The following are the historical charts, investing $1 in each strategy (and scaled by the natural log).





So, at the end of 2008, you would want to be in bonds (though you would be losing money in them now, suggesting TAA is off to a bad start), and that's about it. Risk Parity Weights are approximately at 37% bonds, 18% Commodities, and close to 15% in the rest of the asset classes. The Component TAA is invested in SHY, IEF, AGG, TLT, and MBB equally weighted in bonds (avoiding corporate, international, municipal, and TIPS ETFs).
Obviously, I don't think the Component TAA is something that should be blindly followed, the way that most people could blindly follow the TAA. It is designed to perform best when momentum is a factor (like during booms) and is expected to underperform at times when the TAA adds most value (by being in cash). It was particularly hurt in 2008 due to the collapsing energy prices. Risk-parity weights as a tool to reduce risk, however, worked well in 2008.

Kaizen ECB quotes

In a speech titled, "(Under-)pricing of risks in the financial sector" by Jean-Claude Trichet

"The periods of crisis bring to light the major shortcomings of the
underlying mathematical [risk] models. In those periods the behaviour of
amrkets and prices does not appear to follow any probabilistic model ex
ante but rather reflects a more fundamental Knightian uncertainty in which
even probabilities are unknown."

Buy and Hold (Part 2.5)

This post will eventually get merged into a Part 3. I've had some programming difficulties with the final part of the project and I've spent too much time watching Lost to resolve them before the Christmas break.

Continuing on with the Buy and Hold series (part 1, part 2) I've been writing. I was first curious to look at a long-term history of what a Markowitz Mean-Variance portfolio would look like over the years. Originally I planned on using about 90 years worth of data, but it seems really unstable for that period, so I only used the past twenty years (to get the weights, I used more data than that). I wanted to use this as a benchmark to compare strategies using similar Markowitz-type weights.

About three months ago, I did some research into interest rate environments similar to what has been done at MarketSci). After seeing their posts, I wanted to see if a long-term investor who solely identifies what interest rate environment they are in to determine their portfolio weights would outperform the typical Markowitz portfolio. I love what they do at MarketSci, but there is also value at creating rules that are simple enough for your Grandma to follow (like Mebane Faber's 200 day MA rule that I love so much).

Back to brass tacks, I have to concede that I couldn't operate the Matlab Mean-Variance optimizer. I could generate the portfolios, but then when I used those portfolios that I created in the optimizer it never worked. I'm still not sure why I was getting errors, but I decided that a simple approximation was to choose weights that maximize the Sharpe ratio, since that could replicate the optimizer's results. Unfortunately, this didn't let me use risk aversion to be able to change anything, but all I want to do is to compare one strategy vs. a benchmark stock/bond mean-variance-like strategy. I don't need things to get too crazy.

To identify periods of interest rates rising/falling/neutral, I looked at how much interest rates had changed over the past 6,12,18 months and if the difference was greater than some standard deviation multiples.

Then, I identified the returns in each period and separated them into different portfolios. As though they were investing in three separate strategies (ie expected returns and covariances for the positive interest rates were separate from the , I calculated weights using my Sharpe ratio optimizer. Where I'm stuck now is in error checking my lines of codes to combine them together (I might have the solution (pretty easy, just haven't gotten around to it), and will update after the holidays. Sometimes writing facilitates thinking.)

Again my hypothesis is that long-term investors could benefit just by investing differently depending on what interest rate environment it is.

Re: Hulbert

Mark Hulbert wrote an interesting piece in Barron's about a week ago.

He notes that the 39 week moving average on the DJIA underperforms the buy and hold strategy since 1990. I wasn't quite sure why he used 39week instead of 40 week or 10 month or 200 day. But it's interesting how right he is.

I looked at weekly returns (using his 39 week, which is close enough to 40 week, but the data also does not include dividends) and I also looked at monthly returns. I then used rolling periods of close to 19 years (from 1990 to now) to check how average returns and Sharpe ratios looked. On weekly data, buy and hold average returns outperform the TAA strategy in only 28.8% of weeks, but Sharpe ratios are also higher in TAA than buy and hold in 76.6% of weeks. The general story is that in the early years of the strategy (until 1980), 19 year ahead arithmetic returns and Sharpe ratios are greater for the TAA strategy than for the buy and hold. After 1980, not 1990, things begin to reverse.

Looking at monthly results, average returns are greater in 46% of TAA 19 year(ish) rolling periods than buy and hold as well as 64% in the case for Sharpe Ratios. Monthly also pushes the reversal period back further, to 1974. I also looked at rolling 5 year periods for the monthly data. In 46.8% of rolling periods, the TAA outperforms the buy and hold on Sharpe Ratio, 40% for returns.

I freely admit that the 200 day strategy is not the most profitable and won't even outperform the buy and hold. However, it's key benefit (beyond simplicity that anyone can understand) is that it reduces risk. If you looked just since 1990, the monthly return on the 10 month DJIA strategy (ex dividends) is 5.75% with 10.8 stdev where the buy and hold is 6.5% with 14.5 stdev. Using a 4% risk-free rate, the buy and hold has a Sharpe of .17 while the TAA is .16. However, when you look at geometric returns, the TAA return declines to 5.3% while the b&h falls to 5.5% so that the TAA nudges out the b&h on a Sharpe ratio basis.

Overall, this does confirm what Thornton is saying when he notes that it underperforms recently. However, it's not necessarily as simple as he makes it. Yes, it underperformed recently, but on a risk-adjusted basis it doesn't. The 200day MA still provides a useful indication of when major markets trends have begun or end. They aren't great indicators for short-term traders, but if Grandma paid a bit more attention, then she would be able to reduce some risk.

Though it is obvious to me, I should also note that the 200 day average on just DJIA is not, by itself, what advocates of these TAA systems would use. It is TAA b/c you look at multiple asset classes that should perform well as others do not.

So as an additional treat, I looked at the 10 month TAA strategy using weights of 60/40 on stocks and bonds as represented by both the S&P500 and the DJIA (including dividends) since 1950. The TAA strategy is applied to both stocks and bonds. For reference, the S&P500 TAA strategy performs the best, with a Sharpe of .52, followed by .44 for the TAA DJIA, lastly the buy and holds were the weakest at about .39 each. Since 1990, both the DJIA and the TAA DJIA strategies including dividends and a 60/40 allocation have been roughly the same (Sharpes ~.56). However, the S&P500 TAA strategy has a Sharpe of .72 while the S&P500 version of the 60/40 is only .47. Over the whole period, using the roughly 19 year rolling average methodology from above, the buy and hold strategies outperform the TAA is roughly 72% of the months, but the TAA strategy has a higher Sharpe ratio in 72% of months as well.

So in general, the TAA strategy will likely reduce your returns. Know that when using it. However, it will also improve your risk adjusted returns, but reducing the volatility of your strategy. It also makes most sense to use the TAA strategy on a proper asset allocation strategy and not just looking at it as market timing one index. There is still value at looking at long-term trends when it comes to investing.

Kaizen BoE quotes

I'm pretty much done writing the program for my next buy and hold post, but there's been some setbacks and it has taken longer than expected. I should be able to finish it the weekend after I get back from Thanksgiving holidays. In the mean time, enjoy some Kaizen BoE quotes. I love it when Central Bankers admit mistakes.

"Because a number of countries, most obviously China, chose to peg their currencies either to the dollar or to a basket in which the dollar featured heavily, the FOMC had to cut rates more aggressively to maintain domestic activity than would have been the case if the dollar had been free to depreciate against them. Moreover, by virtue of the currency pegs, this monetary looseness in the United States was transmitted overseas, despite attempts at sterilisation. Now the primary driver behind the surge in commodity prices over the past three years or so has been the rapid development of the emerging market economies and the consequent growth in commodity demand running up against relatively inelastic supply. But the general pickup in inflation worldwide, together with the appreciation of a range of asset prices, suggests that accommodative monetary policies may have also played a part.

"The pattern of global imbalances that resulted from this mix of policies has vexed policymakers for some time. We knew they were unsustainable and worried that the unwinding might be disorderly, though I don’t think anyone could have guessed the course that events would actually take. But we did see that there were vulnerabilities present. However, nothing very much was done about these imbalances. Why was that?"
...
"Indeed, a central bank seeking to stabilize inflation over a sufficiently long time horizon should necessarily recognize the possible adverse longterm consequences of a credit-driven asset-price boom in its policy deliberations."

All from Charles Bean - Deputy Governor for monetary policy of the BoE -
‘Some Lessons for Monetary Policy from the Recent Financial Turmoil’ -
Remarks at Conference on Globalisation, Inflation and Monetary Policy -
Istanbul, 22 November 2008

Kaizen Fed Quotes

"In short, we still do not fully know what caused the run-up in house prices and over-building. Short-term rates were low in 2002-04 as the Federal Reserve countered the risks it saw to good economic performance, and these low rates probably had some effect on housing markets at the time. But the problems largely built up after policy rates were well on their way to neutral, and other factors appear to have played major roles. We have learned little about the likely effect that a somewhat higher funds rate would have had on the speculative element of prices. Of course, it is important to keep an open mind about the relationship of short-term interest rates and speculative activity. If it becomes clear that monetary policy can predictably influence the evolution of bubbles, central banks should take that ability into account when crafting policies intended to keep output rising in line with its potential and inflation low and stable." - Vice Chairman Donald L. Kohn At the Cato Institute's Twenty-Sixth Annual Monetary Policy Conference, Washington, D.C., November 19, 2008, "Monetary Policy and Asset Prices Revisited"

AQR and Leverage

Damian over at Skill Analytics wrote a post on the AQR article from Allaboutalpha.

I agree with his sentiments regarding the way they determine their leverage. I would guess they don't use that formula to determine their leverage but it could be a simplification of something they do use. Nevertheless I would strongly advise not using it.

Let's use l as leverage. They have two portfolios A and B with correlation p and standard deviations stdev(A) and stdev(B). The standard deviation of the portfolio is
stdev(p)=[(stdev(A)/2)^2+(stdev(B)/2)^2+.5*stdev(A)*stdev(B)*p]^2

They set (stdev(A)+stdev(B))/2=l*stdev(p) or l=(stdev(A)+stdev(B))/(2*stdev(p))
Now, if I were to assume that stdev(B)=x*stdev(A) just for mathematical simplification
that would mean l=(1+x)*stdev(a)/(2*[(stdev(A)^2*(1+x^2))/4+.5*x*p*stdev(A)^2]^.5
and: l=(1+x)/[(1+2*x*p+x^2]^.5

So what we have from this little mathematical porn is that if there's no correlation then l=(1+x)/[1+x^2]^.5. In other words if the standard deviations of each asset are the same (x=1) and correlation is 0, then you'd use leverage l=2^.5 which is the maximum leverage you would use. Strangely, as the ratio of the two variances goes from something like x=.75 to 1.25, the peak is when x=1 and declines on either side. The same is generally true for other correlations except that the closer the correlation is to 1, the lower the leverage.

So why does this matter. Basically, if you were to use a system like this to determine your leverage, it is based on two things, the correlation between the two assets and the difference between the variances. In other words, the levels of variance do not matter in this framework, only the difference between the two assets' variances. The correlation part makes sense, but this seems a little too simplistic.

Buy and Hold (Part 2)

This is the second part in a three part series. The first is here.

To look into why buy and hold doesn't work, I wanted to compare a relatively simple asset allocation strategy with the typical 60/40 stock/bond allocation. I obtained data from the Global Financial Database for the S&P500 and 10 year treasuries going back to 1921. Now the S&P500 wasn't actually published before 1950 or so, they use the methodology going back farther. Also there really wasn't a way to invest in the indices until the 70s or later. As with most things in finance, this isn't perfect by a long shot and is just showing what could happen.

The strategy I looked into compares stocks and bonds. I looked at whether bonds have outperformed stocks in the past 12 and 6 months. I gave a weight of 2/3rds to the 12 month ratio and 1/3 to the 6 month ratio. So if stocks outperform bonds in 12 months and 6 months, they get a value of 1, and bonds get a value of -1. If stocks outperform in 12 months, but bonds outperform over 6 months, stocks get a value of 1/3 and bonds get a value of -1/3.

Since I am comparing a strategy against 60/40 allocations, I decided that my starting point would be the 60/40. I use a base value of 60% for the stock allocation and the bond allocation is always 100%-stock. There is no leverage so stocks and bonds are capped at 0% and 100%. Finally, there is a multiplier against each of these values, so if stocks start at 60% with a multiplier of 20%, then if stocks have a value of +1, their allocation is 80% (and 20% bonds). A fairly simple, straightforward strategy.


Since there are caps, the efficient frontier is truncated at the top (as you increase the multiplier the stock level just goes to 100% or 0% immediately). However, the clear result is that you can improve returns by increasing allocations when different asset classes are outperforming relative to each other. The best Sharpe ratio I reported was actually with a multiplier of .6, indicating that if stocks are outperforming on both a 12 month and 6 month basis, you should be in 100% stocks and vice-versa for bonds. If over the next 6 month period stocks outperform (but bonds have outperformed over the 12 month period), then you should increase your stock position to 40% (according to this strategy). Since 1995 this strategy has outperformed the buy and hold by 50%, or an alpha of 3.2%. Since 1970, it would have lost money in 73, 81, and 87 (it was entirely in stocks in October 87, if you were wondering), but 73 and 81 were quite mild.

Another strategy to come in part 3, hopefully by next weekend.

Buy and Hold (Part 1)

I can get behind the argument that the average investor should index. Security selection is difficult and most don't want to spend the time to attempt to outperform the index. Time spent trying to outperform the index might be better spent doing other things (esp. based on the size of their holdings).

However, the decision to index or not index is one part of the equation. The investor chooses not only the securities to invest in, but the relative proportions of different asset classes, or holdings in different ETFs/Index funds. If you believe in buy and hold, you might keep your asset allocation constant over time, changing them only as your risk aversion increases as you age (to hold more bonds). Given the cyclical nature of our economic system, this strategy is incredibly misguided. Different asset classes perform different over different time periods, suggesting that an investor should change their allocation as economic conditions change. Put more emphasis on stocks when the economy is doing well and pare back when it slows.

The mutual fund industry is interested in selling Beta, but due to the cyclical nature of the economy, many investors sell their funds as the market falls. In effect, the mutual fund companies receive more volatile, cyclical earnings as their AUM flucuates. However, if they were to focus on products taking advantage of cycles rather than just offering Beta, they would see less liquidation as markets fail, and investors would be less likely to sell their funds. Earnings would be less cyclical. Further, I would argue that this focus could result in a much more successful fund manager than normal. If people view their products as safer, not only would they be more willing to hold their assets with that firm in the long-term, but they would also want to hold more assets with them.

One concern you could have is that if all funds were structured as broad asset allocation funds that take advantage of the cycle, economic cycles would moderate. While I think returns to the strategy would be competed away in such a situation, I think there are three criticisms to that argument. First, not all funds would want to manage funds in that way. At present, most people are happy believing buy and hold is the best way to manage money or they believe their own method is more succesful, it would hard to convince everyone. Second, not everyone would structure their funds the same way. Some would focus on economic data, some might focus on valuations, some on technicals and momentum, while others could use a combination. Not all of the signals would come at the same time. Finally, for that argument to be true, the lack of participation of major investors would be a sufficient condition to smooth the business cycle. Personally, I am of the view that economic cycles are the fault of the Federal Reserve and they appear in specific sectors due to primarily technological change but also government regulations. The strategies I will look at don't try to time these changes, but use momentum data to figure out when others think it has changed. So if everyone were following this strategy, surely the momentum data would no longer be viable, but I doubt everyone would follow it.

I plan on following this post up with two more posts detailing two strategies I have looked at. One is simple enough that anyone could implement, but the second is more complex.

Presidential Inaugurals

Following Obama's Victory Speech earlier this week, the news media informed us that it was one of his best speeches yet. I don't dispute that, despite my belief that ideas matter and his aren't that good. But I wanted to see how Obama's speech compares to other speeches in American history. Now it isn't always easy to get your hands on victory speeches, b/c American President-elects didn't always give them. I compared his speech to Bush's speeches and Clinton's victory speeches using a tool that calculates a bunch of statistics of how complex your language is. One statistic, more commonly quoted is apparently called the Flesch-Kincaid grade level. According to this statistic, Obama's speech wasn't that much different from the more recent Victory Speeches.

However, I decided to go further back, mainly out of an interest in finding old Presidential speeches/addresses to see how politicians used to talk to Americans. So I found all of the inaugural speeches for Presidents since 1896 (since McKinley bridges both centuries I included both of his) and ran them through this tool. The tool provides many different statistics of how complex different texts are and I don't really know enough to tell which ones are best. So I created a Z-Statistic for one and then averaged them all to create one single value for each President(the negative of Flesch reading ease tests were used). Z-statistics are a little unrealistic, but I'm just using them as the first-best method of simplification. I couldn't find Eisenhower's 1956 speech, so I just assumed they had the same values (not realistic, but only used for creating the statistic).

While his speech is not an inaugural address and this method isn't perfect, Obama falls in at 27 of 29. For a speech listed as his best, it fails the complexity of language test. Surprisingly, Bush's second inaugural used quite complex language, as well as Nixon's inaugural speech. Clinton's first speech was not as "good" as either of Reagan's (by this standard), but his second was.

A final problem I didn't note is that since we have had television, these speeches have definitely changed. Earlier speeches mostly ran in the newspapers, are longer and could be thought of as like a State of the Union Address that we would see today. I took the time to read Coolidge and Taft's speeches to get a feel for them and they lay out all sorts of policies in much further detail than current ones do. Compare that to Bush I's speech where he talks in generalities and a Thousand Points of Light, but nothing specific. Here's another surprising fact, Bush II's second inaugural was the most complex inaugural since television began.

A well-received speech doesn't necessarily mean it was well-written or at a high grade level. It is as much true that you need to deliver the speech properly. Based on my analysis, I think the MSM is thinking more of the delivery of his speech rather than necessarily the content or eloquence of his speech. I did my best to quantify the eloquence, but the content is left to you.

Or you could think that the media is just completely biased for this guy (mi amigo, my compatriot, that one, my friend).

Note: For reference, if you add in MLK's "I have a dream" speech, it is close to Reagan's second inaugural. This post would fall between T. Roosevelt and Bush II.

Corzine idea

I was just watching Jon Corzine on the Daily Show and I got to thinking about an idea for a research paper. It would be interesting to trace the major Cabinet secretaries (like Defense, Treasury, etc) back to Wall Street. It would be interesting to look at their political ideologies and see how Wall Street has or hasn't influenced them generally over time and was there any bias to a certain political party (or ideology, since the parties have changed)?

Willing to admit it

Arnold Kling posted today about how Economists as a whole do not know what is going on and that their textbook models are wrong. I couldn't agree more. However, I haven't spoken with anyone who has said, "wow this Rational Exepectations model really helped me forecast this crisis."

Two of my colleagues and I spent some time with Johnny Walker this afternoon... wait I mean John Walker of Oxford Economics. He seemed perfectly willing to admit that his workhorse economic model doesn't work well during this time period. I'm not sure how true this is for academic economists who build models, but I would think that most people who spend their time forecasting are perfectly willing to admit that they use them as a tool to think about the economy rather than something absolute.

In principle Kling is right, it is better to admit pseudo-knowledge than not admit it. I just think that professionals are more willing to admit it than he gives them credit for.

How the financial collapse killed libertarianism by partisan hack

I love these death of articles by people ignorant of not just the political philosophy that is their subject, but also the conditions leading to its collapse.

Let us start with his claim that, "after LTCM's collapse, it became abundantly clear to anyone paying attention to this unfortunately esoteric issue that unregulated credit market derivatives posed risks to the global financial system, and that supervision and limits of some kind were advisable." First, credit default swaps as we know them today were still in their infancy in 1998 so it would be difficult to say they were as important to LTCM's collapse as Myron Scholes' shoes were. Second, he's attacking the wrong problem, to me, one of the biggest lessons from LTCM is that risk-models and excessive leverage are a dangerous combination. Those problems were never fixed, but it is hard to say that libertarianism is or isn't the culprit. Libertarians would say that banks who lend money to institutions who use excessive leverage might fail if the bets go wrong, and they should be allowed to fail. Harping on, the author notes that "the Washington Post ran an excellent piece this week on how one such attempt to regulate credit derivatives got derailed." Again, the author fails to distinguish between a credit derivative and a derivative. That article is as much about regulating currency and bond derivatives as it is about CDS.

So here again we are faced with the theory that conservatives, liberals, and a central banker who control the government, conspired together to halt attempts to regulate derivatives. The reader is left to his or her imagination to determine how regulating derivatives would have made a difference. I agree with Ritholtz that the decision to allow investment banks to lever up to more than 30x from their original 15x was a mistake. However, I'm not quite sure what else would have or could have been done. Much of the trade in CREDIT derivatives was to get bad assets or the impact of said assets off their balance sheet, a form of regulatory arbitrage. If they threw up some more regulations, I have little doubt that the industry would have tried to find new, exciting, and complex ways around it.

The author notes that consistent libertarians, as opposed to conservatives like Gramm that he is confusing with libertarians, opposed the bail-out and then he invokes the Great Depression that many could be employed in soup-kitchens. Implicitly he is tying the libertarians with the liquidationist view of the Great Depression. L. White has done a great job explaining how Mellon wasn't a liquidationist and Hayek and Robbins weren't liquidationists.

Finally he argues, "libertarians react to the world's failing to conform to their model by asking where the world went wrong. Their heroic view of capitalism makes it difficult for them to accept that markets can be irrational, misunderstand risk, and misallocate resources or that financial systems without vigorous government oversight and the capacity for pragmatic intervention constitute a recipe for disaster."
First, there are libertarians who believe the market is efficient and there are libertarians who do not believe that. I would say that there are many many more in the latter category. I'm perfectly willing to say that markets can be irrational, misunderstand risk, and misallocate resources. However, I would also be willing to say that almost all of the times when they do this, you can point to a government regulation or a government program that is leading to this. The ABCT doesn't really describe the depth of our current situation on its own, but it sure does a good job explaining how the government encouraged the market to misallocate resources into the housing boom. The difference between the author and I is that I want to see market oversight and market regulation where he only is looking to the government for the solution. Well, I think there are plenty of cases where you can point to the government being the problem.

What's interesting to me, is that the death of socialism was predicted by Hayek and the Austrians several decades before it happened. In all reality, I'll admit that what the Soviets had and Chinese (before Deng) had wasn't really socialism. It was only really tried in the WW1 War Economy in Russia and it failed miserably, as predicted. The system that grew out of it, at least in Russia, was more of a market socialism, mostly socialism, but a little markets and freedom thrown in. Libertarians, mostly Hayekians, have predicted that the global financial system is unsustainable in its current form. Many predicted that the housing boom would lead to a situation like what we're currently experiencing. That's because what we don't have is capitalism and anyone with a brain should realize that. Even before the bail-out bill, we were on our third-way, though not as far to the socialist side as Europe. It's not that this doesn't fit with our model, but when you take our government and say we live in a capitalist country. People like me need and have stood up and said we do not live in a capitalist country. Our theories aren't to blame, our theories told us we would end up in this mess.

Malkiel's Wambulance

"It is very tempting to try to time the market. We all have 20/20 hindsight. It is clear that selling stocks a year ago would have been an excellent strategy. But neither individuals nor investment professionals can consistently time the market." - Burton Malkiel

My problem with this statement is that it is not specific. I would agree with him that investment professionals can't time the market on a short-term or medium-term basis, for the most part. However, pretty much everyone knew without 20/20 hindsight that there were big problems in the financial sector, more than a year ago. Some people, using insights from a variety of schools of thought or just plain, old common sense, got out of the market. You don't need to time the market when it goes up, you just need to know that business cycles happen and it pays to get out of the market when the downturn is coming. The regular investor can index away in the good times, but that doesn't mean that always indexing is the proper course of action.