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.