Are Quants victims of their own success?

Are there too many quants? In the past few months I have repeatedly heard similar versions of the same complaint:

You guys are all using Compustat, IBES, First Call, Barra, etc. and building the same models and coming to the same solutions….

In August 2007, equity quant fund performance blew up to what were then called 10 and 20 sigma (standard deviation) events. I call it being in a crowded trade. Andy Lo wrote a paper suggesting that it was:

...initiated by the rapid unwind of one or more sizable quantitative equity market-neutral portfolios…likely the result of a forced liquidation by a multi-strategy fund or proprietary-trading desk.

in other words, they were was in a crowded trade and tried to get out at the same time.


Be an Architect, not an Engineer
The easy availability tools such as MarketQA, Barra and Matlab, just to name a few, have vastly brought down the cost of entry into quantitative investing. The price of that low cost of entry is that many quants are framing the problem of alpha generation and risk control in similar ways. Given the large allocation of funds to quantitative equity investing, the events of August 2007 were inevitable.

Recently a career ad for a quant asked for an “architect, not an engineer”. I have referred to this in the past as combining quantitative skills with market knowledge and experience, others have called it “domain knowledge”.

The advantage of quantitative investing is the ability of a computer to systematically process a large amount of information. Your advantage as a human being and an experienced investor is your knowledge of the markets. A smart way of being a good quant is to combine those two elements by using the computer to model the way fundamental investors think about the markets.

As an example, the chart below shows the returns of an alternate quantitatively driven US equity market neutral portfolio during August 2007. The underlying model is not the Holy Grail and has its limitations, but it is still possible to build quant models that don’t put you in a crowded trade.

Is the hedge fund industry façade cracking?

Last week I wrote about that high correlations of hedge fund returns to the S&P 500 was a bad sign for the hedge fund industry here. This weekend the Sunday Times reports that Crisis grips European hedge funds, that:

Up to 10 European hedge funds have suspended redemptions after investors clamoured for their cash when the managers made severe losses.

A London prime broker told The Sunday Times that even before last week’s extreme gyrations, nearly two-thirds of London-based hedge funds had lost between 4% and 10% of their value. A “significant number” had lost much more, he said.

The manager of one of Britain’s biggest hedge funds said: “It’s been an extraordinary week. Even in the crash of 1987 I don’t remember so much carnage.”

I believe in the "cockroach theory" of trouble in financial markets. When you see one cockroach, there are usually more.

High hedge fund/S&P 500 correlation = Bad News for the hedge fund industry?

Hedge fund returns remain highly correlated to the S&P 500, as I have pointed out before and this is a negative development for the hedge fund industry longer term. The latest available figures to Jan 18th show that the S&P 500 was down 9.8% YTD, while the HFRX Global Hedge Fund Index was down 3.0%


When the Tech Bubble burst in 2000 and equities went down in the ensuing bear market, hedge fund returns were uncorrelated to equity returns. Thus, they appeared attractive as an alternative investment because of their alpha and their low correlation to equities and other asset classes. Given this recent persistent high level of equity correlation, investors will no doubt begin to question the role of hedge funds in a diversified portfolio.


Even Equity Market Neutral Funds are correlated
The accompanying chart shows the returns of the HFRX Equity Market Neutral Index (-3.0% YTD) versus the S&P 500 (-9.8% YTD). Returns started becoming more correlated in late 2005 and early 2006 and have more or less continued to this day. For a group of funds that is supposed to be non-directional to the market their returns are exhibiting a very high market beta.






Addendum: Information Arbitrage has a similar view in his post Ratchet down your expectations for hedge funds and private equity funds.

Sentiment Models Going to More Bearish Extremes

Since my recent post on Sentiment Models Pointing to a Rally in US Equities, the S&P 500 has descended 6.4%. Investor sentiment has gotten even more bearish, which is bullish from a short-term viewpoint.

A check in with AAII shows that individual investor sentiment has become bearish and readings are virtually off the charts. ISEE, which calculates a call/put ratio that only uses opening long customer transactions to calculate bullish/bearish market direction, shows similar extreme levels of investor bearishness.

The accompanying chart shows the large speculator, or fast money, position in the NASDAQ 100 futures, a high-beta instrument that they often use to make directional bets. The fast money crowd has raised their shorts the in the NASDAQ 100 since the last update and readings are definitely in the crowded short zone from which the market has rallied in the past.

However, the recent break of the S&P 500 through the long-term trend line is a worrying technical sign. To technicians, this is an indication that the uptrend in the stock market is broken and we may be in a bear market or at least a sideways consolidation pattern.

My conclusion: The market will likely rally hard but don’t count on the bull market of the last few years to continue.

What do you after you've made your picks (part 3)

I have had a number of discussions over the years with investment professionals, most of whom are in the brokerage community, who believe that the investment management process is straightforward. You just need to pick the right things: the right stocks, the right sectors, countries, themes, etc. The rest is just the “messy” business of implementation.

In practice, I have found that as a portfolio manager I only spent about one-third to one-half of my time figuring out my picks. All that other “messy” stuff, if improperly managed, can lead to distressing results. Some examples are:

- Our diagnostics show that our selection process worked, but why did we underperform?
- We got fired over a misunderstanding???
- I’d hate to tell you this but John the portfolio manager and Mary the trader are continuously at each others’ throats…

This is one in a series of posts on all that "messy" stuff: What do you do after you’ve made your picks. I would emphasize that there is no one size fits all answer. Your mileage will vary. (See part 1 on Reading your client and part 2 on Portfolio Construction).


Trading: Not an afterthought
A lot of investors spend so much time on selection that trading is treated as an afterthought. In some shops the responsibility for trading and execution is relegated to the most junior person on the team. This is an enormous mistake.

Portfolio management can be a game of inches. In many of the surveys that I have seen over the years, the difference in ten-year returns between the first quartile and the median manager for a US large cap S&P 500-like mandate has varied between 0.8% to 1.5%. You can make all the right picks, get your portfolio construction and risk control right and easily lose it all in trading. (Admittedly this example is somewhat extreme as the spread between median and first quartile managers tend to be much higher in other kinds of mandates but I am just trying to prove a point here.)


How do you measure trading costs?
There are several popular ways of measuring trading costs:

- Commission (which is what many brokers focus on when I talk to them)
- Commission + Execution shortfall against a benchmark (usually VWAP, or Volume Weighted Average Price)
- Commission + Execution shortfall + Opportunity costs (or the cost of not trading)
- Implementation shortfall vs. a paper portfolio

Once upon a time, execution benchmarks such as VWAP weren’t prevalent that we had to explain the concept to a lot of brokerage firms that we dealt with. Today this is a commonly accepted benchmark to measure execution. While it is a valid concept there are limitations to the measure as a trading cost measure:

- The size of trade may be too big, in which case you become VWAP
- The stock that you are trading may be too thin for a VWAP benchmark as it may only trade in blocks
- Volume is migrating away from the floor of the NYSE and NASDAQ to the upstairs market and dark pools and therefore VWAP does not accurately measure the actual trades done
- There is an arms race going on out there: With the prevalence of VWAP as a benchmark, many brokers now have VWAP matching trading algorithms, where they slice and dice a block trade into smaller orders to feed into the market. Others have also developed algorithms to spot these types of orders.

What about the costs of not trading? Many years ago one institution used to base the bonus of the trading desk on the difference between the execution price and VWAP. As a result, the traders tried very hard to buy only on the bid and sell only on the ask. The executed prices against VWAP looked great, but very little of the order got done. In the case of the said institution, friction developed between the portfolio managers and the trading desk as a result of this mis-aligned incentive system.

If you add in opportunity costs you have a more complete picture. This approach was suggested by Wayne Wagner, who co-founded the Plexus Group to do execution cost measurement, now part of ITG. Supposing that a trade didn’t executed, then opportunity cost is the difference between the decision price (the price at the time you decide to trade) and the ending price for the measurement period.

A more holistic way of approaching the trading cost measurement is to run a parallel paper model portfolio. Put in the changes to the portfolio when you decide to buy or sell and measure the returns of the paper portfolio against the actual portfolio. The difference is implementation cost. The problem with this approach is that it does not disaggregate costs.


What to do?
There are vendors and brokerage firms with trading cost estimate models. These models work on average but actual results can vary greatly from the estimate. I am a proponent of customizing the way you trade to the speed of the idea that you are trying to trade.

A deep-value investor (and deep-value investors are usually early in the timing of their trades) should probably be patient and buy only on or below the bid price and sell on or above the ask price. On the other hand, if you have fast breaking information (a mining company had a big strike or a biotech’s has just announced results on one of their drugs) buying on the bid and selling on the ask is the wrong thing to do.

My suggestion: Undertake a study to understand the reasons behind your trades and their short-term price momentum. Are the trades chasing momentum or are they showing negative momentum? Is post-trade momentum positive or negative?

In conclusion, there is no one-size-fits-all solution for the same reason that trading cost estimate models only work well on average. Trade lists with consistent positive price momentum call for an aggressive style of trading, while negative momentum trade lists call for a more patient style.

What do you do after you’ve made your picks? (Part 2)

I have had a number of discussions over the years with investment professionals, most of whom are in the brokerage community, who believe that the investment management process is straightforward. You just need to pick the right things: the right stocks, the right sectors, countries, themes, etc. The rest is just the “messy” business of implementation.

In practice, I have found that as a portfolio manager I only spent about one-third to one-half of my time figuring out my picks. All that other “messy” stuff, if improperly managed, can lead to distressing results. Some examples are:

- Our diagnostics show that our selection process worked, but why did we underperform?
- We got fired over a misunderstanding???
- I’d hate to tell you this but John the portfolio manager and Mary the trader are continuously at each others’ throats…

This is one in a series of posts on all that "messy" stuff: What do you do after you’ve made your picks. I would emphasize that there is no one size fits all answer. Your mileage will vary. (See part 1 on Reading your client here).


Portfolio Construction: how much to buy and sell
If the selection process is about deciding on what to buy and sell, portfolio construction is about deciding on how much to buy and sell. I would break down this process into the following steps:

- Deciding on your benchmark
- Deciding on what your bets are: minimizing your un-intended bets and properly sizing your intended bets

What are your bets?
You should only make bets only when you have an edge. What is the essence, or the underlying themes, of your selections and how confident you are about them?

Risk models can help and I am a big fan of them. A portfolio manager with a risk model can see more easily see his bets and therefore eliminate or minimize his un-intended bets and properly size his intended bets. Size the intended bets according to Grinold’s principles: a manager’s value-added (Information Ratio) is a function of his selection skill (Information Coefficient) and the number opportunities (N) he has.

There is no one size fits all solution in choosing risk models. It depends on your selection process. A top-down manager should probably use a risk model that focuses mainly on macro-economic risk factors to analyze his portfolio. A traditional bottom-up stock selector or sector rotator might want to use a fundamental factor model, such as the one pioneered by Barra. Traders with shorter term time horizons may be better served by principal component models, as offered by firms such as APT and Northfield.

Should you optimize your portfolio?
Some managers use risk models just to analyze and understand their risk exposures. Others take the additional step of asking the risk model to construct the portfolio for them through an optimization process. This quantitative technique may not be suitable for investors with fundamentally driven processes as this group often have trouble numerically specifying many of the inputs to the optimizer.

Portfolio Optimization: What kind of painter do you want to be?
Managers who use optimization need to understand the nuances of the optimizer and how it interacts with the forecast alphas. I would use the analogy of being a painter and knowing what you want to paint. A quant with an index-plus, or a low tracking error active mandate, will keep the risk aversion parameter high with fairly low forecast alphas. This would be the equivalent of painting a series of subtle colors with smooth transitions between colors.

One of the frustrations of the optimizer output from index-plus style optimizations is that the optimizer will often replace one stock ranked "hold" with another that is ranked "hold" for risk control reasons. If the intent is a to build a "pedal-to-the-metal" portfolio, then the manager needs to take steps to emphasize the tails, or extremes, of the forecast alpha score distributions. In other words, only buy stocks ranked "buy" and sell stocks ranked "sell". This would be the equivalent of painting a bright colorful mosaic, compared to the dull but subtle colors of the index-plus mandate.

Sentiment Models Pointing to a Rally for US Equities

Both Fast-Money and Individual Investor Sentiment at Bearish Extremes (Contrarian Bullish)
The US equity market’s fundamental background has been deteriorating as analysts have been drastically taking down their estimates (analysis here). The latest employment report on Friday was also a shocker to the market, which suggested a weakening economy. Sentiment data, however, shows that expectations are very low as we head into Earnings Season and any positive surprises are likely to spark a rally.

The accompanying chart shows the position of large speculators (mostly fast-money hedge funds) in NASDAQ 100 futures. I use the NASDAQ 100 instead of the S&P 500 as the fast money seem to prefer to use the NASDAQ 100 as a vehicle for its directional exposure because of its high-beta characteristics. Readings are in the crowded short area from which rallies have occurred in the past. The latest update from the American Association of Individual Investors (AAII) Sentiment Survey also shows excessive bearishness from individual investors.

All this doesn’t mean that the market can’t go even lower. However, the odds given this sentiment backdrop favor a rally from current levels. Traders positioning for a rally could buy high-beta ETFs such as QQQQ or IWM. Even more aggressive traders can consider double long exposure ETFs such as SSO and QLD.

What do you do after you’ve made your picks? (Part 1)

I have had a number of discussions over the years with investment professionals, most of whom are in the brokerage community, who believe that the investment management process is straightforward. You just need to pick the right things: the right stocks, the right sectors, countries, themes, etc. The rest is just the “messy” business of implementation.

In practice, I have found that as a portfolio manager I only spent about one-third to one-half of my time figuring out my picks. All that other “messy” stuff, if improperly managed, can lead to distressing results. Some examples are:

- Our diagnostics show that our selection process worked, but why did we underperform?
- We got fired over a misunderstanding???
- I’d hate to tell you this but John the portfolio manager and Mary the trader are continuously at each others’ throats…

This is one in a series of posts on all that "messy" stuff: What do you do after you’ve made your picks I would emphasize that there is no one size fits all answer. Your mileage will vary.

Portfolio Construction: how much to buy and sell
If the selection process is about deciding on what to buy and sell, portfolio construction is about deciding on how much to buy and sell. I would break down this process into the following steps:

- Deciding on your benchmark
- Deciding on what your bets are: minimizing your un-intended bets and properly sizing your intended bets

Reading your client, or What's the Real benchmark?
Benchmarks can vary greatly from one client to another. Here are some sample answers of what you might get when you ask the client “what is the benchmark” (with translations in parentheses):

(1) We’ve given this question a lot of thought and have done very careful studies, your benchmark is ___. (The benchmark is the stated benchmark).

(2) Make me money. Just don’t lose any. (The benchmark is the better of cash or the market)

(3) We selected you/your firm because of its history of adding value; or we are committing funds to this asset class by diversifying our exposure between three managers. (The benchmark is some combination of the returns of your competitors and the stated benchmark.)

These are just some common examples. In my experience (1) is rare. One simple example of this would be an index fund. If it's an active mandate and the client has already done a lot of work, this may be a highly customized benchmark.

Individual investors give (2) as an answer a lot. It might also be the pension plan or deferred compensation plan of a small group of executives in a company. Ideally, you should build some sort of timing model to understand when the asset class or your selection process gets into trouble and minimize risk during those environments. If you don’t have a timing model, figure out how much tolerance for loss the client has and then position your benchmark between cash and the market. Translate your risk tolerance estimate into weights of the relative importance of these two components.

As an aside, my formulation of a benchmark as being the better return of X and Y is not exactly fair, but whoever said that life was fair?

The answer (3) is very typical of an institutional mandate. It is a sad truth in life but in general, only top-quartile managers get new assets and bottom-quartile get dropped. As an example of the importance of the competitor positions, during the 1990s most international equity managers were vastly underweight Japan compared to the EAFE index. As a result most managers handily outperformed the stated benchmark of EAFE as Japan had been a laggard during that period. It was therefore important to know the median manager weight in Japan was during that period for EAFE-mandate managers.

I also knew of one manager who picked two “smart” competitors, top performing managers in his asset class, and estimated these competitors’ exposures. He then pegged the benchmark and portfolio to the average macro exposure of these two competitors (see the sidebar entitled Reverse Engineering a Manager's Macro Exposure for an example of how to estimate competitor position weights).

In future posts I will address other issues such as risk models, portfolio optimization, minimizing trading costs, etc.

A Value Opportunity in the Oil Patch?

Further to my recent post on energy stocks resuming a relative uptrend, conditions remain largely unchanged since that observation and there remains a healthy dose of skepticism on energy. Oil prices and energy equities have been in a long multi-year uptrend, aided and abetted by a softening US Dollar. If you believe that the long-term secular trend for this sector is still up, then there are some laggards that you could look at, such as real estate plays in the Oil Patch.

Divergence = Opportunity?
The accompanying chart shows the indexed US$ values of the XLE (Energy Select SPDR ETF) and a couple of smaller cap Canadian property developers, Melcor Developments (MRD.TO) and Gendis Land Development (GDC.TO), both listed in Toronto. These are developers who are mainly focused in Alberta, the heart of the Canadian Oil Patch. While the XLE has been on a steady uptrend for the past year, the Canadian developers have been on a roller coaster ride. Over time, there is still good physical demand for property from wage and employment gains in that part of the country and property prices should move in line with the region’s underlying economy.

The Canadian residential property market has gone through a cycle somewhat similar to the US, albeit more muted. Lending standards did not get as wild as they did south of the border. The most aggressive lending products were zero-down mortgages and the worst of the US excesses such as no-doc and negative amortization loans did not migrate to Canada.

Key risks: These are smaller capitalization stocks and their prices could be volatile. In addition, the group does face a headwind from Canadian lenders starting to tighten up on lending standards which would restrain demand.

Energy stocks ready for another upleg?

LT Uptrend + Breakout + Neutral Sentiment = Bullish

Energy stocks may be ready for another upleg for three reasons.

Long term uptrend: the first chart shows the relative ratio of XLE (Energy Select SPDR ETF) to SPY (S&P 500 SPDR ETF). As you can see the Energy sector has been in a long term relative uptrend against the market, as defined by the S&P 500. As oil prices approached $100 and pulled back, so did the Energy relative to the market.

Relative strength breakout: the sector broke out to an all-time relative high against the S&P 500 in mid-December.



Neutral mutual fund sentiment: Using the technique shown in the sidebar (titled Reverse Engineering a Manager's Macro Exposure) I imputed the average Energy sector exposure of 22 US large cap blend equity mutual funds. These 22 funds can be thought of as a composite of the S&P 500-like mandate funds from the largest mutual fund complexes. As you can see from the chart, mutual funds moved from a significant overweight to a neutral/underweight position in the Energy sector.

In future posts I will highlight other divergences and opportunities within the Energy space.

No Skill and No Opportunity = No Value-Added.

Richard Grinold once showed that:

He meant that a manager’s value-added (Information Ratio) was a function of his selection skill (Information Coefficient) and the number opportunities (N) he had. In other words, no skill = no value-add and no opportunity = no value-added.

During the holiday season the markets are thin and small trades can create a lot of price movements. In this environment I have no skill and little opportunity to add value. Blogging will therefore be very light and I will back in the New Year.

Happy Holidays and Happy 2008!

More on surviving as a quant

Here is another post in the series of surviving and prospering as a quant. John Maudlin, writing in the 14 Dec 2007 edition of his newsletter Thoughts from the Frontline, commented about how the Fed seems to have mis-handled its FOMC statement. The US equity market lurched downwards after the FOMC statement came out at about 2:15pm ET and rallied furiously the next morning on the news of the coordinated central action. John commented that:

By and large, this Fed is a room full of academics that have never "run money," with the exception of Richard Fisher of Dallas who ran a hedge fund at one point in his career. We are in the middle innings of what will be seen by history as the single biggest credit crunch since the 1930's. With the exception of Fed governor Donald Kohn, they have never been in a crisis when they were in the driver's seat.

The Fed is full of people who are far smarter than I am, but there is a difference between book smart and market savvy and a good quant should be both.

Stat Arb + Economic Stress = Trouble?

As part of a continuing series on surviving as a quant , I would like to focus on how investors need to know the economic rationale behind a quant strategy.

The statistical arbitrage hedge fund strategy, or “stat arb”, is a case in point. Classic stat arb can be simplified as buying oversold stocks and shorting overbought stocks, along with some risk control layered on top of the stock selection process.

The economic rationale behind this type of strategy is that the stat arb practitioner is being paid to provide liquidity to the market. In normal times, this approach can be quite profitable but it can backfire badly during periods of economic stress. If you use a short-term investment strategy of buying oversold stocks and shorting overbought stocks during a recession, you will ride the big losers (e.g. Adelphia, Enron, etc.) all the down to the bottom.

The accompanying chart shows the investment results of an overbought/oversold model. It ranks US large cap stocks on a short-term overbought/oversold measure and buys the bottom 20% most oversold and shorts the top 20% most overbought stocks. I do not pretend for the moment that this is an actual stat arb strategy as it has no risk control. However, it does serve as a proxy for the performance for these types of strategies as I have discussed elsewhere. This model had a drawdown of over 20% in 2001, as many stat arb strategies did at the time, and has been having some difficulty currently.

The signs of economic stress in are everywhere, particularly in the US. Investors should be wary of too much exposure to stat arb strategies under these economic conditions.

An interesting Oil and NatGas divergence

Natural gas hasn’t followed the rally of crude oil. Even as crude oil approached $100 natural gas languished in the $7-8 range, compared to the highs of $14-16 seen in late 2005. The accompanying change shows the ratio of the price of natural gas to crude oil futures. I have used the 12-month strip as the reference prices (1/12th the front month + 1/12th the 2nd month + … + 1/12th the 12 month future) as natural gas prices can be seasonal. The chart shows that natural gas prices are probing new lows against oil prices.

A look at the Commitment of Traders data from the CFTC shows a very different kind of story. Commercial traders, who are usually thought of as the “smart money”, are excessively long natural gas and giving a bullish signal. On the other hand, the signal from the COT data for crude oil can be best described as neutral.

As a former trader I can attest that all these fundamental and sentiment signals don’t matter until they matter. Others have traded successfully on COT data but I have found them problematical as a timing tool. These conditions have persisted for several weeks. Just because these conditions are at extremes doesn’t mean that they can’t get stretched further.

In future posts I will examine other interesting divergences in the energy and energy related markets.

Financials or Tech: Which would you prefer?

S&P 500 Financial Index vs. S&P 500
No Capitulation in Financials
The above chart shows the relative performance of the S&P 500 Financials vs. the S&P 500. Given the well-publicized subprime problems, the sector is not surprisingly oversold.

Using the technique shown in the sidebar (titled Reversing Engineering a Manager's Macro Exposure) I imputed the average financial sector exposure of 22 US large cap blend equity mutual funds. These 22 funds can be thought of as a composite of the S&P 500-like mandate funds from the largest mutual fund complexes. As you can see from the chart, mutual funds have been overweight the sector and appear to be increasing their weight.



Large cap blend mutual fund managers, as a group, are finding value in Financials and there is no sign of panic over the subprime meltdown in their behavior.

S&P 500 Technology Index vs. S&P 500

Tech looks constructive
By contrast, this chart shows that the relative returns of the S&P 500 Technology Index vs. the S&P 500. The Tech sector rebounded in the Summer of 2006 and has been in a relative uptrend since the the Summer of 2007. The sector has pulled back but the relative uptrend remains intact.

The same mutual fund analysis shows that the average US large cap equity blend mutual fund is roughly market weight the sector. From a sentiment analysis viewpoint, this gives the Tech sector room to resume its leadership trend that it began a few months ago.

Surviving and prospering as a quant

I have learned working over the years that the key to surviving and prospering as a quantitative investor is having the combination of quantitative skills with market knowledge and experience. It’s critical to understand the context and limitations of your models. Here is a quick quant quiz:

What is less risky (this not a trick question)?
1 To jump out of an airplane at 30,000 feet without a parachute
2 To stay in the plane while someone else jumps out (yes, there is a pilot flying the plane)

The correct quant answer is 1. Jumping out of the airplane is less risky because the standard deviation of the outcome is zero.

This illustrates the importance of reality checks for models. Mrs. Humble Student of the Markets, who is a pilot, calls it “raising your head up from the instrument panel and looking out the cockpit window once in a while”. Bob Park of FINCAD, a vendor of derivatives software, calls it “domain knowledge”.

Quantitative systems work beautifully most of the time. When they fail they can fail spectacularly, particularly if the failure is due to faulty assumptions. The greatest quantitative failure in the last fifty years was neither the recent sub-prime meltdown nor the Long Term Capital Management collapse.

The greatest quant failure occurred in the 1960s and it was caused by Robert McNamara and the “whiz kids” in their conduct of the Vietnam War. They incorrectly framed the problem and focused on the wrong metrics. The results scarred an entire generation and altered American foreign policy ever since. As an example, you can find an analysis of differing analysis of a battle of the Vietnam war here at Fabius Maximus' blog.

Is Bill Miller becoming a (gasp) Value manager?

Bill Miller’s Legg Mason Value Trust (LMVTX) has had a great long-term track record that would be the envy of most managers. Unfortunately the fund lagged the S&P 500 in 2006 and it looks like it will lag again in 2007, barring a last minute recovery.

Using the techniques shown in the sidebar titled Reverse engineering a manager's macro exposures, I estimated his style (Value/Growth) exposures. Despite the “value” label in LMVTX, Miller has long been thought of as tilting towards the Growth style (remember his big holdings in AOL in the fund?) However the fund has tilted more towards the Value style and has tended to outperform when Value outperforms Growth and underperformed when Growth outperforms Value.

Using the same form of analysis, his other macro exposures are:

- Long market beta
- Short oil and USD
- Long emerging markets vs. the US market

In future posts I will use the same technique to examine what other investors (hedge funds, mutual funds, etc.) are doing in the market.

What exactly are hedge funds hedging?

Real alpha is hard to find
Can someone remind me why investors pay 2% and 20% fees?

This chart shows the weekly returns of the HFRX Global Hedge Fund Index and the S&P 500. The HFRX Index is an investable index of hedge funds and returns are reported daily, net of fees.

Yes - you can choose other flavors of hedge fund indices but the results are going to be roughly the same. The correlation of most diversified hedge fund indices to the S&P 500 is 0.8 and up.

Bridgewater Associates did a study in 2004, updated in 2006, called Hedge Funds Selling Beta as Alpha showing that you can replicate the return patterns of many strategies with simple instruments. Some sample quotes:

emerging market hedge funds are over 80% correlated to a simple 50/50 mix of emerging market equities and bonds...

and

M&A arb funds...do no better than simply buying the top 10 announced targets and selling the top 10 acquirers.

A few years ago CALPERS noted that hedge funds fees were too high - a comment that the press seized on. They went on to say, however, something to the effect that they were not averse to paying for alpha, a comment that the press did not trumpet at the time.

Real alpha is hard to find. There are a lot of crowded trades and strategies out there. The real trick for a hedge fund investor is to find a differentiated alpha.