Financial Genomics

By: Alpha Male 

Much has been reported about the Human Genome.  They say this monolithic set of codes holds the keys to life, the cure to devastating diseases and the promise of longevity.  They also say each person’s genetic profile is unique.  By testing one’s genes, doctors are able to provide personalized counseling and even targeting genetic therapy to modify defective genes. 

Each individual’s genetic profile contains the instructions on how their bodies will react to its environment.  Some will be more susceptible to certain forms of cancer.  Some will catch a lot of colds and some will develop allergies to certain foods.  Some will remain in thin and some will struggle with their weight for a lifetime. 

In essence, your body’s overall performance throughout life is correlated to a variety of factors and stimuli.  Your genetic profile can be thought of as a set of correlations to external variables (smoking, peanuts, cheesecake).  Some correlations are weak and some are strong.  Many are strong and negative (such as smoking).    

Every investment portfolio has a similar set of correlations to various external factors.  William Sharpe identified them in 1992 as manager style analysis.  He regressed a set of mutual funds against 11 factors to determine how the manager’s style correlated with them.  These factors were represented by 11 US Exchange Traded Funds (ETFs) ranging from bonds to stocks to more exotic assets such as mortgage-back securities. 

Every mutual fund, indeed every portfolio, has a genome.  And every investor should know theirs.  Not knowing your fund’s (and your portfolio’s) genetic make-up will leave you susceptible to genetic diseases that will eat away at your nest-egg (such as the Asian Contagion or a spike in oil prices.) 

The mutual fund genome is comprised of many genes, each with their own unique roles and behaviors.  Each gene is triggered by some external influence – be it interest rates, equity indices, or the skill of the particular fund manager.  There is no limit to the number of genes in a mutual fund genome.  But for our purposes, let’s assume a simple fund with  three genes.  Call it the fruit fly of our genetic analysis.

But before putting our fruit fly under the microscope, let’s step back and look at how the species evolved.

In the 1950’s and 1960’s, money managers were charged with simply making money – not with beating the index or being in the top quartile.   The proliferation of mutual funds over the past few decades and the relative lack of large-cap Canadian companies in which to invest has led to a great degree of correlation between most mutual funds and their benchmarks.  Now mutual fund managers must simply beat their peers (most of whom can’t even beat the market) in order to be heroes. 

Take the case of Canadian equity funds.  The average correlation between the largest 35 Canadian equity funds and the S&P/TSX Composite is approximately 75%.   In order to maintain a high degree of market correlation, the mutual fund’s holdings must look very much like the index weightings.  In fact, many mutual funds will begin with the index and make several small changes that reflect their own investment opinions and research.  They might underweight a few stocks they don’t like and overweight a few they like.  This process might propel the fund to beat the market by a little or it might propel the fund to under perform the market by a little.  This came to be known as index hugging. 

This process worked and the mutual fund industry we know today was built up around it.  Everyone made money in the 1980’s and 1990’s as markets rose.  But in the late 1990’s two important developments took place which will someday lead to the demise of the multi-billion dollar mutual fund industry. 

The first was the birth of Exchange Traded Funds.  ETFs provided investors an efficient means to invest in equity markets without the need for a mutual fund manager.  Early investors in ETFs were convinced that picking a money manager was a mug’s game.  They believed that the Efficient Market Hypotheses meant that it was impossible for active mutual fund managers to beat the market over the long run anyway – so why bother?  Why bother paying 2.5% for a mutual fund that will perform roughly in line with an ETF costing 20 basis points per annum?

With such a plentiful substitute, it quickly became apparent that a mutual fund (active) manager’s only value was in the amount they could add to the (passive) index.  The fee these managers charged for this value-add is effectively the additional MER above that of an ETF – 2.5% less 20 basis points, or 2.3% – a hefty fee for the small amount of underweighting and overweighting being performed to add value to the underlying ETF. 

And that’s where the second major threat to mutual funds comes in – hedge funds.   It turns out that the process of under- and over-weighting equities in a passive portfolio bears striking similarities to a long/short hedge fund. 

Imagine a stock market with two stocks: Star Corp. (ticker: STR) and Dog Group (ticker: DOG).  STR and DOG are the same size and each represent half of the stock market index.  The biggest mutual fund in this market is the Index-hugger Opportunities – Class A fund with $100 million of assets under management.  The manager of this fund likes Star’s growth potential, but believes Dog will soon lower its guidance.  So instead of investing $50 million in each company (to match the index weightings), she invests $55 in Star and $45 in Dog.  (You’d be excused for asking why she would invest anything in Dog if she really didn’t like it.  But Index-hugger has a policy of, well, index-hugging). 

Notice that the over-weighting and under-weighting performed by the fund manager is essentially the same as going long STR and short DOG.  So taken as a whole, the Index-Hugger Opportunities Fund is nothing but a combination of an ETF and a long/short fund.
This simple example can be expanded to include 3 stocks, 30 stocks or 300 stocks.  The same rule applies: an equity mutual fund is not much more than an ETF plus a long/short equity hedge fund. 

Naturally, some equity funds closely resemble the index (as in our example above) and some funds stray far from the index (say, 20% DOG and 80% STR, or even 0% DOG and 100% STR).  So naturally, we would expect those funds that resemble the index to be highly correlated to the overall market while those that stray from index will have a low market correlation.  (As we shall see, this is not always the case, but can be managed around regardless).

But the story is not quite complete.  If equity funds can be described simply in terms of measured deviations from market weightings, then what explains the fact that nearly all equity mutual funds have a volatility much low than the overall market?  The answer can be found in holdings in cash or cash equivalents.  By design or out of necessity, equity fund much hold a certain amount cash.  If we assume that the assets puts to work in the market using an index-hugging strategy should have volatility equal to that of the market, then it follows that we should be able to back-out the proportion of cash in the fund simply by looking at its volatility vs. the market. 

By definition, cash has a zero volatility and a zero market correlation.  So the volatility of a portfolio containing cash and a risky asset is simply equal to the proportion invested in the risky asset times the volatility of the risky asset.  For example, the Index-hugger Opportunities Fund made small (5%) bets for and against certain stocks.  Let’s say it had a volatility that was half that of the overall market.  Knowing these two facts, we can safely assume that roughly half of the fund’s assets must have been in cash.  Further, if the fund’s volatility was only 10% of the market’s volatility, then we could assume roughly 90% of the fund was actually in cash.

Of course, I’m not alleging that mutual funds with a volatility half the market’s volatility have only invested half their cash (and deserve only half the fees).  There are other ways to achieve half-market volatility – like picking a portfolio of stocks that are extremely stable.  But even if that were the case, we would still be able to replicate that fund with 50% cash and 50% ETF.  Besides, low beta, stable stocks may in fact be that way becuase they carry excess cash on their balance sheets.  So the mutual fund manager may still be invested in excessive amounts of cash!        

So now we have identified three primary components or “genes” of any mutual fund: embedded cash, an embedded ETF, and an embedded hedge fund.  These are the three primary genes in any mutual fund.  (note: The genome for a US equity funds contains a fourth gene – the US dollar gene.)

Obviously, the number of genes in any fund or security is limitless.  But, like the human genome, a few genes are responsible for a large portion of the organism’s behavior. (witness that humans and pigs differ by only a small portion of their genes).

Hedge fund managers play the role of geneticists every day.  They identify a security with an attractive gene, remove the genes they don’t want via short-selling and derivatives, then they harvest the gene they want (whether it is a cheap way to buy volatility or credit risk or it is simply a return that just can’t be replicated at all). 

But the potential for financial advisors to use financial genomics is truly vast.  Financial advisors can now conduct genetic testing on a client’s portfolio, fund or even group of funds.  Having identified weaknesses, they can provide critical genetic counseling to lay out the options available to their patient.  But most importantly, they can also undertake a form of financial genetic therapy whereby they use new technologies to modify portfolios so they contain only the genes (risk factors) the patient wants. 

But an entirely new risk arises in this brave new world of financial genomics: the risk of unintended consequences.  As financial product manufacturers become genetic engineers, they will undoubtedly create genetically modified mutual funds with entirely new and unknown risk factors.  To borrow from the lexicon used by critics of genetically modified foods, the financial genomics field must avoid creating Franken-funds.

– Alpha Male

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