A trader's view of fundamental indexing

As fundamental indexing has gained a foothold in the consciousness of investors, there have been critiques of the theoretical underpinnings of the concept. Today, I would like to offer a trader’s view of fundamental indexing.


How fundamental indexing works
Here is how fundamental indexing works. Instead of weighting a portfolio by market capitalization, you weight it by some fundamental measure (e.g. sales, book value, etc.). In practice, fundamental indices are weighted by a combination of fundamental factors, rather than a single factor, in order to increase stability.

There is an important difference between cap weighted indices and fundamentally weighted indices. Cap weighted indices are far more passive. In the absence of membership changes and changes in shares outstanding because of buybacks or new issues, a cap weighted index portfolio requires no trading or portfolio rebalancing, other than the periodic re-investment of dividends.

By contrast, fundamental indices need to be periodically re-weighted and rebalanced. As stock prices move over time, the actual weight in a fundamentally weighted portfolio will deviate from the target fundamental weight. In practice, the rebalancing occurs annually.


The perils of rebalancing
Years ago, I was involved in the management of an international equity portfolio benchmarked to a GDP weighted EAFE index. The GDP weighting was conceptually appealing to investors at the time because Japan was such a large weight in the cap-weighted EAFE index. Virtually no manager was at cap weight in the EAFE portfolio because it would leave the portfolio with too much country specific risk. In practice, problems occurred on an annual basis when MSCI rebalanced the GDP-weighted EAFE index to its GDP weight. Japan’s weight in the index would typically move overnight by 5-10%. Such huge swings in the benchmark made it very difficult for an active, never mind passive, manager to run the portfolio.


An invitation for front-running
Any trader will tell you that the worst place to be as a trader is when the rest of the world knows what you have to do – and you have to do it despite that knowledge. This foreknowledge exacerbated the effects of the market crash of 1987 as market makers knew the portfolio insurers/program traders needed to sell more stocks as the market moved down. It also played a part in the sinking of Long Term Capital Management. The Street knew LTCM’s book. They knew the firm was in trouble. Arbs were front-running the firm’s positions, which worsened their losses.

As fundamental indexing gains in popularity, arbitrageurs, hedge funds and position traders can estimate the amount of rebalancing that will need to be done by the fundamental indexers – and front run them. This form of front running is not illegal, just smart trading, and it will serve to reduce the returns to fundamental indexing.

Investing in semi-passive investment strategies such as fundamental indexing is a game of inches. As fundamental indexing becomes more popular, rebalancing and implementation costs could easily take away any gains from the underlying investment concept.

Algo trading – too much of a good thing?

Back in the Olden Times when I started watching the markets, 30-40 million shares on the NYSE were considered to be big volume days. Institutional execution was done in the upstairs market by brokerage block traders calling around their accounts: “we have 100,000 XYZ for sale, do you have any interest?”

That has all changed.

Today, there are automated trading bots, or algos (short for algorithm), everywhere. It began with algos trading VWAP (volume weighted average price) orders. With the advent of day trading automation inevitably followed and today you can find specialized trading bots on the internet and even floor brokers are now using algos (see report). There are even some blogs, such as Skill Analytics and Zen Trader to name a few, devoted to this topic.


Flying on autopilot is not always a good thing
With so much automation around, some “reality check” questions come to mind:

• Is anyone doing a reality check on these algorithms?
• Can someone game these algos?

For example, there are brokers offering VWAP algos and guaranteed VWAP trading for individual investors for very low cost. Given the widespread adoption of these algos, don’t you think that someone could write a pattern recognition program to watch for a VWAP algo buying or selling a stock? This information is worth something to someone. As a trader once told me, portfolio insurance program trading (that exacerbated the Crash of 1987) was a huge boon to him because you knew that once the portfolio insurer had started the trading program he had more orders behind it.

If you must use algos and go on autopilot, do it intelligently. As I wrote in a previous post:

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”.

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.