Category Archives: Analyzing performance

institutional vs. individual investment decisions

August 1, 2014 By in Analyzing performance, How your broker gets paid, Investment firms, mutual fund holders and financial advisors, reasons analysts mis-estimate company earnings, sell-side analysts Tags: , , , , Leave a comment

This is a follow-up to my post from yesterday on perils of relying on an analyst’s investment recommendations.

The FINRA article I mentioned in that post comments that institutional investment decisions can be motivated by considerations that differ markedly from those we as individuals face.

What does this mean?  Here are some examples:

1.  for almost two decades, endowments (like those for universities) have made large investments in highly illiquid “alternative” assets.  They argue that their financial circumstances allow them to take liquidity risk in search of extra-high returns because they won’t need the money for, say, 25 years.

Such investments present several problems for you and me:

generally speaking, endowments haven’t cashed out of many of these investments, so it’s not clear how well they’ve done

we probably don’t have a 20-year+ investment time frame, and

we definitely will only be able to participate in alternatives on much less favorable terms than big institutions.  In retail-oriented projects, the organizers reap most of the rewards.

2.  An institution may try to offset the risk of “roll-the-dice” investments by being very conservative in other areas.  Without knowing its overall investment strategy, it’s hard to know how to evaluate any one part.  So when an institutional portfolio manager says he has a huge weighting in Treasury securities, it may be that he’s acting on instructions from his client, or it may be to offset the risk of holding a ton of risky emerging markets debt.

3.  Portfolio management is a craft skill that sometimes operates on less-than-obvious rules.  The IT sector, for instance, is the largest component of the S&P 500, making up almost 20% of the index.  The largest constituents are Apple, Microsoft, IBM, Google,  and Intel.

Let’s say I’m a PM and I don’t like the IT sector right now.  I probably won’t express my opinion by having no technology stocks.  To make up a number, I may elect to have 15% of my portfolio in IT.  If I’m right, I’ll make gains by having the “missing” 5% invested in a better=performing sector.  I may also decide that, because I want to be defensive in this area, I’ll shift my emphasis toward the biggest, lowest-multiple, most mature companies.

Boring!!

But that’s the point.  These will probably go down the least in a bad market.

As a result, I may end up having 3% of my portfolio in AAPL and another 3% in MSFT.  They may also be the largest holdings in my portfolio.  A cursory glance at my holding may give the impression that I like AAPL and MSFT.  I do, but only in the sense that I expect that they’d go down–they’ll lose less than smaller IT stocks, gaining me outperformance.   They’re my hedging alternative to making an all-or-nothing bet against IT.

Another situation:  let’s say I have no clue how AAPL will perform.  I may decide that I should concentrate my attention elsewhere in the portfolio, where (I hope) I can add value.  The easiest–and safest–thing I can do with AAPL is to neutralize it.  That is, I hold the market weight in the stock.  Yes, I won’t gain any outperformance this way, but I won’t lose any, either.  Because AAPL is the largest stock in the S&P 500, AAPL may end up being my largest position.  But, again, this doesn’t mean I like it.  It means I don’t want the stock to hurt me.

Will I explain any of this in an interview?  Yes, I’ll try.  But reporters’ eyes will glaze over.  What they’ll come away with is the idea they came in with–that my largest positions must be my favorites, and they’re AAPL and MSFT.

 

 

 

Sam Eisenstadt and Value Line

January 6, 2014 By in Academic Theories, Analyzing performance, Current Market Thoughts, Portfolio management, Value Line Tags: , , , , , , Leave a comment

About a week ago, the Wall Street Journal ran an article about the Value Line ranking system for stocks and its inventor, statistician Sam Eisenstadt.

I knew Sam in the late 1970s – early 1980s, during the heyday of Value Line.  At that time, the company was an incubator that launched the careers of a large number of successful investors.  The ranking system was also a formula for consistent outperformance.

Then the music began to stop.

What I find most interesting about the WSJ article is Sam’s belief that the Value Line ranking system will begin to work again.

how the VL ranking system works

The method, which was revolutionary when it was introduced in the middle of the last century, is taken straight out of a finance textbook.

It evaluates stocks by analyzing two main variables, cheapness and growth:

Cheapness is measured by taking the current price-earnings ratio for each stock and seeing where it stands relative to its PE over the prior ten years.  If the current PE is the lowest, the stock receives the highest score.  If the current PE is the highest, the stock gets the lowest score.

Growth is measured by taking the current per-share earnings growth rate and comparing it with the company’s earnings growth rate in each of the past ten years.  If the rate of growth is currently the highest, the stock gets the highest score.  If the growth rate is the currently the lowest, the stock gets the lowest score.

After scores for each stock are tallied, the totals are compared with those of all the other stocks in the VL universe of about 1,800 stocks–in the early days, this required a mainframe.  A couple of technical variables are mixed in.  The factors are weighted (this is the system’s secret sauce).  The end result is a ranking of the universe in order from 1 to 1,800.

The stocks are then grouped on a bell curve:

–the top 100 are ranked 1

–the next 300 are ranked 2

–the middle thousand are ranked 3

–the next 300 are ranked 4

–the bottom 100 are ranked 5.

Fresh rankings are published each week.

results

For over twenty years, the system worked like a charm.  1s consistently outperformed the market; 5s underperformed (in fact, academic research showed that 5s underperformed more deeply and for longer periods than 1s outperformed).  In most years, stocks performed precisely in line with their ranks.  That is, 1 outperformed 2s, which outperformed 3s, which outperformed 4s, which outperformed 5s.

Academics were flummoxed.  The system was statistically sound.  It used only publicly available historical data.  And yet, contrary to the “efficient markets hypothesis” (the academic assumption that, in simple terms, all publicly available information is immediately factored into stock prices), favorably ranked Value Line stocks outperformed as a group year after year after year.

Then, suddenly, the system didn’t work so well.  The WSJ article has a chart that shows the ten-year annualized performance of the VL 1s minus the performance of the 5s.  That outperformance figure peaks during the first half of the 1980s at a staggering 40% difference per year over the prior decade.  It then begins to fall pretty steadily through 2006, when the performance difference for the prior decade is close to zero.

(An aside:  one might question whether a 10-year time frame isn’t a bit too long or whether having the top 5% or so do better than the absolute bottom of the barrel is a high enough bar–but the author of the article, Mark Hulbert, didn’t go there and I won’t either.)

 

What happened?  Are the bad times over?  That’s for tomorrow’s post.