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"Large market-cap and super high liquidity."

I work as a private consultant who develops quantitative investment models for various firms and when I ask for what they want in a model I usually am fed the following: big and liquid.

At first glance this seems like common sense. Stocks with massive market-capitalization are often household names, blue chip and have little risk of unexpectedly delisting or going bankrupt without warning. These highly traded titans may be able to buffer an economic downturn better than their competition. High liquidity will help keep trading costs down and will allow you to quickly exit all your positions without a large market impact.

It would appear that big and liquid stocks should logically lead to a reduction in risk ... or does it?

Measuring Risk

There are numerous ways to measure risk such as comparing historical return to volatility, statistical guesses as to the maximum you might lose over a certain time frame, comparing stock or portfolio price volatility to that of the market, the maximum portfolio loss from peak to trough, the length of time to recover from the max drawdown and so forth.

Depending on your objective, each of these measures will have meaning. Someone who needs a place to park $300,000 for 6 months before he buys a new home will describe risk differently than someone building up his retirement portfolio over the next 30 years. The former might be worried about short-term volatility and care very little about long-term gain while the latter may be concerned about exactly the opposite.

If you are investing for the long-term, you likely won't be happy with a low volatility portfolio and buttery smooth equity curve if the annual return ends up being well below the inflation rate. You are also likely to be unimpressed with a high maximum drawdown regardless of forecast returns since few can stomach a 75% investment loss - and there is a very good chance that you'll switch strategies or pull out of the market at exactly the wrong time when the pain is most excruciating - which leads to missing out on the subsequent rebound.

So with longer-range investing in mind, let us consider the return and max drawdown of the largest and most liquid names in the S&P 500 vs. the smaller and less liquid names in the same index. Is it less risky to follow the 'big money' or should we trade lesser known companies?

Setting Up Our Equal-Weight Benchmark

Before we start, we need to quickly discuss the testing setup.

  • All slippage (market impact) costs will be set at 0.15% for the S&P 500
  • We will use an equal-weight S&P 500 index as our benchmark since our target portfolio will also be equal-weight
  • Re-balancing will be every 6 months
  • We will use the Portfolio123 backtesting platform and run the test from 1999 to December 2013 (current)

The chart below shows the S&P 500 equal-weight index without dividends (blue line) and with dividends (red line). The equal-weight dividend benchmark uses the rebalancing and transaction cost parameters outlined above.

S&P 500 Equal-Weight Benchmark With and Without Dividends
(Click to enlarge)

If we held all 500 stocks in the S&P 500 with equal-dollar amounts, our annualized return since 1999 would be 8.73% with a max drawdown of 58.21%. This will be our benchmark for the tests going forward.

The Biggest Stocks

The next test is to take the highest 50% market-cap stocks of the index and track its performance. The performance of holding the largest 250 stocks is shown below:

Largest 250 stocks in the S&P 500
(Click to enlarge)

If you include dividends, we have already lost 3% annualized returns (compared to our equal-weight index including dividends) by trading the largest half of this index. True, the maximum drawdown has decreased by almost 3.5% - but this is a small consolation prize for the absent returns.

The Most Liquid Stocks

Our next test is to look at the most liquid stocks in the S&P 500. We will hold the 250 stocks with the highest average daily dollar-volume (share price x volume) over the past 200 days.

Highest Liquidity Stocks in the S&P 500
(Click to enlarge)

This too has reduced returns by more than 2% per year but with comparable draw-downs to holding all 500 stocks equal-weight.

2 Factor Model: Big and Liquid

The testing above is very broad. It is unlikely that we would hold 250 stocks equal-weight. To focus on the largest and most liquid names we will develop a simple ranking system. I will rank all the stocks in the S&P 500 on a relative scale based on market-cap and liquidity. I combine the two numbers and the highest ranking stocks will be held in our portfolio.

I will select the top 25 stocks using this 2 factor ranking system.

Top 25 Stocks

Top 25 Ranked Stocks With 2 Factor Model
(Click to enlarge)

The total return of such a system since 1999 is a paltry 1.57% with an annualized return of 0.10% and a max drawdown of over 72%!

What if we picked just the 'best' 5 names?

Top 5 Stocks

Top 5 Ranked Stocks With 2 Factor Model
(Click to enlarge)

The top 5 names would have actually lost us money since 1999 with a staggering drawdown of 81%. What are the current names that top our big and liquid list?

  1. Apple (AAPL)
  2. Google (GOOG)
  3. Microsoft Corp (MSFT)
  4. Exxon Mobil Corp (XOM)
  5. General Electric (GE)

These are some of the most well-known and household names that, with the exception of Google, pay dividends. The PE ratios do not look terrible when comparing them to the industry group and the average analyst recommendation is in the buy-hold range. At a quick glance, the big and liquid names appear good. But remember, although good companies are the backbone of investing - we are buying shares which only make money when they increase in value. To make money we must consider more than the company - we must determine whether the price of the shares are fairly valued. It is likely that the more well-known and traded a stock is - the more efficient it becomes leading to less alpha. In the stock market, you do not necessarily want to buy a stock simply because everyone else is doing it.

So what happened to our theory that these large and liquid names would offer downside protection in a bear market? How much did we really save by trading these well-known stocks? When you consider return and max drawdown - it would appear that we have greatly increased our risk by trading large and liquid stocks.

Smaller Is Often Better

It is my opinion that liquidity risk is highly over-rated, as is size or market-cap risk. Holding these large and liquid names would have resulted in a drawdown of 81%. Granted, the more active you become as an investor the more you need to consider market impact. If you are turning your portfolio over every month with $500 million dollars - then you'll need to stick to the bigger names and hope your strategy is robust enough to consistently overcome the high cost of trading.

But for the average investor - does big or liquid really equate to lower risk? Micro-cap or highly illiquid penny stocks are one thing, but what about the smaller names in the S&P 500 index? Let us quickly consider the performance of the least liquid and smallest market cap stocks (2 factor ranking system).

The bottom 25 stocks in the S&P 500:

Bottom 25 Ranked Stocks Using 2 Factor Model
(Click to enlarge)

If all we did was invest in the smaller S&P 500 names that were not as highly traded as their counterparts - we would have significant outperformance since 1999.

The bottom 5 names:

  1. Integrys Energy Group (TEG)
  2. Bemis (BMS)
  3. Patterson Companies (PDCO)
  4. Graham Holdings (GHC)
  5. Apartment Investment and Management (AIV)

What is Your Biggest Fear?

What are investors most worried about?

Are they worried about the risk of stocks delisting or companies going bankrupt? What if 5% of our picks went belly up? If the remaining picks had 6% annual out-performance (assuming we have a high-risk strategy), we would still be ahead in most scenarios. And it is unlikely that our bankruptcy risk will increase by 5% per year if trading the S&P 500.

Is the investor worried about max drawdown? Then I encourage them to run some simple simulations to see the correlation between factor and drawdown. The worst offenders were the big and liquid names. True, buying smaller names may result in a slightly higher drawdown of a few percent in a bear market (as opposed to the equal-weighted index). If that is your biggest fear, consider off-setting some of this risk by using 1 - 2 % of your capital to buy out of the money Put options in a broad market ETF such as the SPY about a year out. If you do this when the market is slowly trending upwards and the Volatility Index (VIX) is low - this will be an economical way to hedge. As the market begins to experience a drawdown of 20% or more, progressively sell off your options and buy back into the market. There are other ways to hedge such as shorting an ETF - which is more economical than buying Put options when implied volatility is high.

Is the greatest fear that you will invest for 10 or 20 years and discover too late that you have vastly underperformed the market? It is ironic that the fear of underperformance could actually be the reason many investors buy stocks that historically lag the market.

The Bottom Line

All of this is not to say that investors should avoid large and liquid stocks like the plague. There may be a compelling reason to buy on a stock-by-stock basis. Or perhaps you feel you have developed a strategy that can outsmart the big funds and traders competing with you for the same alpha on these well-known names. I have developed numerous systems based or the Russell 1000, S&P 500 and Dow Jones indices, so I am not categorically saying that big is bad.

However, if you are long-term investor looking for a basket of stocks to trade infrequently and are selecting from among one of these large indices, and are looking at long-term performance and max drawdown as your gauge for a successful portfolio ... definitely look beyond the big and well-known names and you'll probably increase your odds of stumbling across alpha.

Source: Are Large And Liquid Stocks Lower Risk?