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IYR Outside Day

I’m not a technician, but I also don’t like to turn a blind eye. I’ve said it before, so my apologies for repeating: real estate is the beneficiary of excess profits. Fundamentally, it is still sick. Contrary to reports of rising rents, we are still at elevated pricing, ownership rates, etc.Viewing the remainder of this article requires a Subscription

Free money?

I often try to highlight trends and macro themes, but periodically, I feel compelled to share trading strategies, especially when one involves a structural arbitrage trade. In this case, the structures in question are ETFs. Specifically, levered ETFs. I have already highlighted that the daily compounding feature of these levered ETFs is detrimental to the health of longer-term investors. The basic strategy employed to take advantage of these ETFs is to short levered ETF’s and to short a comparable unlevered ETF in the opposite direction. For example, short the ultra short S&P 500 ETF (SDS or comparable), which then gives you a levered long exposure. At the same time, hedge that position with an equal short  exposure to SPY. Turns out that the challenge is actually finding the levered ETFs to short. For more specifics, read here: http://tiny.cc/9rt3b.

Then, yesterday I read this article (http://etfdb.com/2011/inverse-vix-etns-free-money-2/) and thought that much in the same light as the strategy above, there are structural trades based on ETFs. ETFs are derivative products, and by understanding the structure of the underlying, we can explore valid trading strategies. The recent one on the VIX is one I still have to research to fully understand the trading implications, but at first glance, I think there is potential here.

Relevant ETFs: VXX, VXZ, XIV

Looking for risk in all the wrong place

Sure the VIX is sitting at levels befitting the Pax Romana, when we’re in anything BUT. And yet, I can’t help thinking whether there are other signs of euphoria and risk taking that should concern us. Well, one of the areas I watch, and especially like at extremes, is closed-end funds. I won’t go into the mechanics of NAV, premium/discount, and leverage (70% of closed end funds use leverage), but I will mention that closed end funds tend to trade at a discount, and that discount tends to fluctuate/mean revert. When discounts get too big, closed end funds have allowed me to purchase assets at deep discounts, literally giving exposure to $1 of assets at a cost less than $0.80 (great than 20% discounts)!

So where are we today (actually as of 12/31/2010)? From the Closed-End Fund Association (CEFA) report:

…but discounts to NAV narrowed in 2010 to an average of 3.21% (according to Morningstar) on December 31, which is a narrower discount than the five-year average of 4.86% and smaller than the 3.97% average discount (as discounts were on January 1, 2010).

Read the full report here.

Closed end funds will be a great place in the future to pick up deals, but for now, they are a harbinger for the coming downward adjustment.

The Largest Arbitrage Ever Documented

This article showed up in the FT.com in mid-Sept. 2010, and referenced a working paper that took me a while to get to. That was a mistake. This is a very interesting study and might point to additional structural inefficiencies. From the paper:

In this paper, we study the relative pricing of TIPS and Treasury bonds. A simple no-arbitrage argument places a strong restriction on the relation between the prices of these securities. We show that this no-arbitrage relation is frequently violated in the markets. To our knowledge, this arbitrage, which can exceed $20 per $100 notional amount, represents the largest arbitrage ever documented in the literature. Furthermore, the sheer magnitude of this mispricing in markets as deep and actively traded as the Treasury bond and TIPS markets presents a serious challenge to conventional asset pricing theory.

titutional investors can take advantage of, but are not accessible to retail investors. The main idea here is that

Here’s the original FT.com article and here’s a link to the NBER working paper.

Slow news day, except this release (Q-ratio update)

Every quarter, I eagerly await the flow of funds report, so I can play around with the numbers on the back of an envelope – yup, I still use a pencil and scrap paper for quick calculations; call me old fashioned. Of course, long time readers will know that the Flow of Funds report contains 2 pieces of information that we use to calculate the q-ratio. A quick word on q-ratio for the un-initiated:

Q is a method of estimating the fair value of the stock market. It’s defined as the total price of the market divided by the replacement cost of all its companies.

The concept was originally developed by economist James Tobin. More recently, it’s been advocated by Andrew Smithers and Stephen Wright in their prescient book Valuing Wall Street.

…The data from which q is calculated are published in the “Flow of Funds Accounts of the United States Z1″, which is published quarterly by the Federal Reserve. This data source is available from 1952 onwards.

Smithers & Co.
http://www.smithers.co.uk/faqs.php

Benoit Mandelbrot dies at 85

When asked to look back on his career, Dr. Mandelbrot compared his own trajectory to the rough outlines of clouds and coastlines that drew him into the study of fractals in the 1950s.

“If you take the beginning and the end, I have had a conventional career,” he said, referring to his prestigious appointments in Paris and at Yale. “But it was not a straight line between the beginning and the end. It was a very crooked line.”

Read the NY Times piece here.

Benoit Mandelbrot was the father of fractal theory. His work was influential in fields from animation to finance. In animation, his fractals and fractal geometry help artists create realistic landscapes that mimic nature. In finance, his theories helped shape non-normal distribution models. In science, he influenced chaos theory. And much more.

‘Fractals are easy to explain, it’s like a romanesco cauliflower, which is to say that each small part of it is exactly the same as the entire cauliflower itself,’ Catherine Hill, a statistician at the Gustave Roussy Institute, told AFP.

‘It’s a curve that reproduces itself to infinity. Every time you zoom in further, you find the same curve,’ she said.

He was a great mind, constantly challenging the established academic community, and using his position to teach others. I would encourage everyone to check out The (Mis)Behavior of Markets.

For quants and trendfollowers alike

What happens when one of the worlds largest hedge funds starts a new fund focused on computer-driven trend-following? Well, it probably means there are fewer opportunities left for the little guys. On the other hand, it also means that it might drive the market itself and provide some opportunities to take the other side. That is, if everyone is becoming a trend follower, maybe it’s time to revert to the mean.

Brevan Howard — Europe’s largest hedge fund manager, the world’s 4th — is launching a new fund, the FT reports.

A new computer-driven fund.

Which is, in itself, unusual.

After all, Brevan is a firm where almost all trading is done by people. Very few people, at that. (Alan Howard alone trades as much as 40 per cent of the $25bn Brevan Howard Master Fund portfolio. Apocryphal tales suggest more, but whichever way you cut it, he’s the most powerful trader in the world)

The new fund will be Brevan’s first foray into the world of trend following: an hedge fund investment strategy hitherto utterly dominated by three firms: Man (AHL), Winton Capital (Winton Futures) and BlueCrest (BlueTrend).

Brevan has already thrown $300m at the strategy as seed money. Since March the fund is up 9.3 per cent.

While that doesn’t quite eclipse industry-leader AHL, which is up 10.07 per cent so far this year, the whole move doesn’t bode well.

Read the full article from the FT here.

Capital Structure Mispricings

I expect capital structure arbitrage/mispricings to be more prevalent with ZIRP. As investors try to gauge how to price risk-free return in this environment, many models of efficient markets become skewed by market realities and provide the nimble with opportunities. This one is courtesy of ZeroHedge:

the mispricing of tail risk as represented by equity and credit derivatives in BP at the time when the company’s bankruptcy seemed like a sure thing. Due to a major skew resulting from a huge imbalance in implied vol, a perfectly hedged trade which saw the selling of equity vol through near terms puts, coupled with the purchase of default protection via 6 month CDS, would have yielded a 158% annualized return at trade unwind 3 months later. In other words, which it is difficult to generalize, it appears that in times of dramatic risk, equity derivatives tend to overprice fat tail risk, while default protection is underpriced. Such capital structure arbitrage trades will become increasingly more profitable as the Fed-created drift between equity and credit accelerates, and as vol pricing allows phenomenal arbitrage opportunities.

Read the full article here.

The challenge, of course, is that these can only be implemented by sophisticated institutions and not available for anyone else.

Dangers of Indexing – This is important for money managers

There has been a lot of debate recently about the death of stock pickers, usually in conjunction with some discussion on high correlation, lower factor predictive abilities, etc. and at the same time, there has been an obvious growth in the use of index investing styles. I have tried to show different reasons why notifications of our death are premature, but a recent paper from the NBER has made me more optimistic about stock-pickers chances of success than I have recently been. For starters, a simple comparison of RSP (equal-weighted index) and SPY (market-cap-weighted index) shows that the average stock in the S&P 500 tends to outperform the index, leaving room for stock pickers over the index. For a quick chart, click here.

If that doesn’t satisfy you – because it didn’t satisfy me either – here’s a more thorough and interesting report (from FT.com):

A new paper from the National Bureau of Economic Research by Jeffrey Wurgler, Nomura professor of finance at New York University, discusses the potentially overlooked perils of indexing…

In terms of the effects, he finds:

On average, stocks that have been added to the S&P between 1990 and 2005 have increased almost nine percent around the event, with the effect generally growing over time with Index fund assets. 6 Stocks deleted from the Index have tumbled by even more. Given that mechanical indexers must trade 8.7% of shares outstanding in short order, and an even higher percentage in terms of the free float, not to mention the significant buying associated with benchmarked active management—this price jump is easy to understand and, perhaps, impressively modest.

——

If a one-time inclusion effect of a few percentage points were the end of the story, then the overall impact of indexing on prices would be modest. But the inclusion effect is just the beginning. The return pattern of the newly-included S&P 500 member changes magically and quickly. It begins to move more closely with its 499 new neighbors and less closely with the rest of the market. It is as if it has joined a new school of fish.

Figure 2 (right) illustrates the phenomenon. It is worth repeating that this pattern is occurring in some of the largest and most liquid stocks in the world.7

In essence, he argues that the liquidity and market capitalisation of the stock becomes increasingly irrelevant once it becomes a member of a major index…

As he explains:

The net flows into index-linked products are both large and not perfectly correlated with other investors’ trades. Indexers and index-product users are by definition pursuing different strategies from those of the more active investor. They are less interested in keeping close track of the relative valuations of index and non-index shares. Some are index arbitrageurs or basis traders who care only about price parity between index derivatives and the underlying stock portfolio. The upshot is that over time, the index members can slowly drift away from the rest of the market, a phenomenon I will call “detachment.”

Consequently, Wurgler says this detachment may lead to a significant price premium for S&P 500 Index members.

He also cites a paper by Morck and Yang from 2001 which matched stocks within indices as closely as possible to a stock outside the index, with compatability defined in terms of size and industry.

The comparative valuations showed that S&P membership drove up a price premium in the order of 40 per cent.

Which leads him to conclude:

…the evidence is that stock prices are increasingly a function not just of fundamentals but also of the happenstance of index membership. This drives many of the negative consequences noted below.

As for those negative consequences, those are:

1) Bubbles and crashes.

The S&P 500 Index’s visibility and the easy access to ETFs and Index funds facilitate a high sensitivity of flows to returns.

—

Index membership also affects high-frequency risks, and may encourage trading activity that exacerbates those risks. Dramatic examples include the crash of October 19, 1987 and the intraday “flash crash” of May 6, 2010. SEC investigations have centered on S&P 500 derivatives in both cases.

2) A confused risk-return relationship.

Whereas the basic proposition of asset pricing theory is the positive relationship between risk and expected return, he says in stock markets, the proposition is incorrect:

High risk stocks have, on average, delivered lower returns than low risk stocks in both U.S. markets and those around the world.

—-

A $1 investment in a low beta portfolio in 1968 grows to $60.46 by 2008, while the same investment in a high beta portfolio yields $3.77. The high beta portfolio actually has a negative real return; the 2008 portfolio adjusted for inflation is worth 64 cents. Restricting to larger cap stocks doesn’t significantly change the qualitative picture.

And this he says is because the basic problem is that managers benchmarked against a simple index will tend to favour high beta stocks – because the index is actually already outperforming versus the fundamentals.

In other words, for a manager benchmarked against the market portfolio, a stock with an alpha of 2% can be a candidate for underweighting. A similar argument shows that such a manager is also incentivized to overweight a low or negative alpha, high beta stock, unless the alpha is extremely negative.

For the full article, click here.

This should come as good news for stock pickers, but also might provide some interesting fodder for new tests, particularly low-latency equity long-short strategies.

Update on q-ratio

As readers already know, I use the q-ratio (aka Tobin’s q) for insight into the general market levels: The signs ain’t good. With the Flow of Funds report coming out June 10th (for the 1st quarter), the q-ratio was standing at >1. It’s historical average is 0.65. That implies a >35% decrease from here, which implies an S&P 500 level of 724. That’s just to approach fair value; however, we all know that these mean reverting series tend to overshoot, in the case to the downside.

For the full Flow of Funds data, click here. The applicable table is B.102 on page 105.

Non-financial companies, including both quoted and unquoted, were 62% overvalued according to q at 31st March 2010, when the S&P 500 index was 1169. Adjusting for the subsequent decline to 1087 (10th June, 2010), the overvaluation had fallen to 50%. Revisions to data had little impact on q, with downward revision to net worth for Q4 2009 of 2.9% being offset by a downward revision to the market value of non-financial equities of 2.1%. Net worth for Q1 2010 fell slightly as equity buy-backs exceeded profit retentions.

The listed companies in the S&P 500 index, which include financials, were 58% overvalued at 31st March 2010, according to our calculations for CAPE, based on the data from Professor Robert Shiller’s website. Adjusting for the subsequent decline to 1087 (10th June, 2010), the overvaluation had fallen 46%. (It should be noted that we use geometric rather than arithmetic means in our calculations.)

For a bit further discussion (and the source of the chart and quote) click here.

Caution before laying blame

As everyone searches for a culprit, the fingers are easy to point: quants, fat fingers, or whatever. Could quants have exacerbated the move? Perhaps, and time will tell. But for those out there who are quick to come to conclusions, I’d like to express some caution. Quants take on risk in one way, shape, or form, and in many markets provide liquidity on the scale never before experienced, including compressed bid/ask spreads, execution liquidity, etc. Additionally, not all quants are the same (with some programs being purely momentum driven, others going into action on extended moves and being contrarian, others placing orders on both sides of the market). Lastly, speaking to some quants around the street, executions were problematic for everyone, so while a lucky few were able to get hit on insane bids, most faced the same liquidity and execution issues discussed on this forum and others.

This is not to say that quants shouldn’t be held responsible if there was indeed some foul play. However, to blame the entire group, which is pretty loosely defined anyway, seems irresponsible and reactionary. Michael Davis noted that to say we don’t have a quant problem “is ignorance”. I think to say that we do without any evidence that “quants” are the problem is emotional (at best) and dangerous.

High Risk = Low Return

When I go shopping, I like a good deal. Who doesn’t?

Yet when shoppers enter the big mall of stock investing, they switch gears, often shunning good deals for the marked up items instead. Why? So many reasons we won’t even go there. Academia has proposed some theories about buyers only taking on risk if they are compensated for it, and measuring risk against a basket or market or securities. In fact, the data leads to a different conclusion altogether.

A recent paper by Baker, Bradley, and Wurgler continues brings further evidence to support our contention that risk doesn’t pay off:

Abstract
Over the past 41 years, high volatility and high beta stocks have substantially underperformed low volatility and low beta stocks in U.S. markets. We propose an explanation that combines the average investor’s preference for risk and the typical institutional investor’s mandate to maximize the ratio of excess returns and tracking error relative to a fixed benchmark (the information ratio) without resorting to leverage. Models of delegated asset management show that such mandates discourage arbitrage activity in both high alpha, low beta stocks and low alpha, high beta stocks. This explanation is consistent with several aspects of the low volatility anomaly including why it has strengthened in recent years even as institutional investors have become more dominant.

For the full article, click here.

And for those who haven’t read it yet, please read Josef Lakonishok’s work on how behavioral biases lead to value opportunities. This is an oldie but a goodie from Lakonishok, Vishny, and Shleifer. Here’s the abstract:

For many years, stock market analysts have argued that value strategies outperform the market. These value strategies call for buying stocks that have low prices relative to earnings, dividends, book assets, or other measures of fundamental value. While there is some agreement that value strategies produce higher returns, the interpretation of why they do so is more controversial. This paper provides evidence that value strategies yield higher returns because these strategies exploit the mistakes of the typical investor and not because these strategies are fundamentally riskier.

For the full article, click here.

Equity analysts: Still too bullish

As earnings season is under way, McKinsey put out an interesting study. The information isn’t new and we’ve even discussed it here before, but I thought the presentation was really compelling and it reinforced some tenets of investing: don’t trust analyst estimates.

…This pattern confirms our earlier findings that analysts typically lag behind events in revising their forecasts to reflect new economic conditions. When economic growth accelerates, the size of the forecast error declines; when economic growth slows, it increases.³ So as economic growth cycles up and down, the actual earnings S&P 500 companies report occasionally coincide with the analysts’ forecasts, as they did, for example, in 1988, from 1994 to 1997, and from 2003 to 2006.

The article continues to show how analysts have been consistently overoptimistic in their estimates for the past 25 years (with only a couple of exceptions). And the news doesn’t suggest that this time it’s different.

To read the complete article, click here.

this is

Inflation Adjusted DJIA vs. Demographic of 45-54 Year Olds

We often discuss the BIG THEMES, such as wealth transfer from baby boomers to younger generations, wealth transfer from west to east, etc. Then, on the other side, we delve into valuations, expected returns, and what factors the markets have already priced in. One indicator that we have discussed in the past deserves an update.

Harry Dent discussed looking at spending patterns of different age groups, then mapping those to the equity markets, which would make sense since company earnings, being driven by consumer spending, should be impacted. Then, a couple of years ago Daniel Arnold came out with a book that had a sensationalist title (The Great Bust Ahead), and built on Dent’s ideas. So I decided to revisit some of their work.

To start, let’s look at spending patterns by different age groups:

(Source: HS Dent Foundation)

Then, Dent goes on to map the highest spending group (46-50 year olds vs. the inflation adjusted Dow):

(Source: HS Dent Foundation)

As you can see, we were off-course in 2009, as the Dow “should have been” at 11K – guess where we are now?

So where do we go from here? Well, Arnold shows us some of the same information, with some estimates going forward:

(source: http://www.thegreatbustahead.com/)

There are a few differences between the series used (such as Arnold using 45-54, not just 46-50) but the idea is the same: we recover into 2010-2012 and then go down on an inflation adjusted basis something on the order of 50%.

If you visit the site above, you’ll also see the statistics for Japan, which imply a sharp rebound (although I think US based investors will lose on the currency, but for those able to hedge out the currency, the Japanese markets have some potential).

There are two ways to achieve the performance above: the markets could go down or inflation could rise significantly (or a combo). We often discuss longer term indicators that are not great for market timers, such as q-ratios, CAPE, and now this one. Yet, they all point to similar fingers of instability that we’ve discussed in the past, and pose serious dangers for the longer term investor.