Mutual Fund Sales Charges and Your Fund Performance

Its no secret that many mutual funds are a bit of a ripoff.  There's so many fees hidden and buried in many of the less reputable ones (thankfully that's starting to change) that are completely unjustifiable.

One such fee in my opinion is sales charges.  There's several ways they can work but the most common are front end sales charges and are usually in the 5-6% range.  What happens is that when you invest in that fund, your contribution (lets say $100) will have the sales charge be deducted (lets say 5.5%) so you'll end up investing only $94.5 instead.  That's a horrendous drop in return to begin with.

Other variations of sales charges are back end, deferred, etc etc.  But with so many sales charge free (or no load) funds nowadays, why bother?  Some justification is that sales charges can help pay for better managers which allow the funds to outperform.  Does it?

In my MarketWatch article being published soon, I did a study on large actively managed value funds and took a quick look at this exact question.  What did I find?

Looking at only the 10 largest value funds by asset, I plotted their 10 year return vs total fees.  The funds with sales charges do actually have a slightly better return than the no sales charge ones so there may be something to it.

However!  If you do the return after accounting for the loss from the sales charge, you'll find the performance benefit virtually disappears and you end up at basically the same performance as the non-sales charge.  So yes, some funds with sales charges do outperform but very few do and even the ones that do end up being useless for the investor because the extra return is lost anyway.


Moral of the story?  Don't bother with sales charge funds to begin with.

Gini Coefficient & Singapore

Did some reading the other today about income inequality and its general propensity as an indicator of social unrest.  I found a neat table from CIA World Factbook listing where countries rank.

Very interesting to note that the top developed countries (is South Africa considered developed? Iffy in my opinion) on the list would be:

1 - Hong Kong (53.7)
2 - Singapore (47.8)
3 - USA (45.0)
4 - South Korea (41.9)
5 - UK (40.0)

Its a bit sad that these countries (esp #1 & 2) are on the same level as places like Guatemala, Zimbabwe, Madagascar, and China.  I'm not exactly a socialist or communist but large distributions of wealth and poverty is never a good thing.  Having traveled to many of the countries here, its really appalling to leave the nice snazzy hotel and see tarps/tents on the road median just in front with families huddled together beneath it.

For Hong Kong & Singapore, I suspect the inequality may be worse than listed because the coefficient doesn't take into account several factors like housing costs (its insane to buy a decent home in Singapore & HK without being rich).  For the USA, I'm surprised its so high considering there is still a decently sized middle class there (though shrinking every year).  Below, I also show historical Gini coefficient, notice how many countries are increasing in income inequality over time.

RANK COUNTRY DISTRIBUTION OF FAMILY INCOME - GINI INDEX DATE OF INFORMATION

1 Lesotho 63.2 1995 2 South Africa 63.1 2005 3 Botswana 63.0 1993 4 Sierra Leone 62.9 1989 5 Central African Republic 61.3 1993 6 Namibia 59.7 2010 7 Haiti 59.2 2001 8 Colombia 58.5 2011 9 Honduras 57.7 2007 10 Guatemala 55.1 2007 11 Hong Kong 53.7 2011 12 Thailand 53.6 2009 13 Paraguay 53.2 2009 14 Bolivia 53.0 2010 15 Chile 52.1 2009 16 Panama 51.9 2010 est. 17 Brazil 51.9 2012 18 Papua New Guinea 50.9 1996 19 Zambia 50.8 2004 20 Swaziland 50.4 2001 21 Costa Rica 50.3 2009 22 Gambia, The 50.2 1998 23 Zimbabwe 50.1 2006 24 Sri Lanka 49.0 2010 25 Mexico 48.3 2008 26 Singapore 47.8 2012 27 Ecuador 47.7 December 2012 28 Madagascar 47.5 2001 29 China 47.4 2012 30 Dominican Republic 47.2 2010 est. 31 El Salvador 46.9 2007 32 Rwanda 46.8 2000 33 Malaysia 46.2 2009 34 Peru 46.0 2010 35 Georgia 46.0 2011 36 Argentina 45.8 2009 37 Mozambique 45.6 2008 38 Jamaica 45.5 2004 39 Bulgaria 45.3 2007 40 Uruguay 45.3 2010 41 United States 45.0 2007 42 Philippines 44.8 2009 43 Cameroon 44.6 2001 44 Guyana 44.6 2007 45 Iran 44.5 2006 46 Uganda 44.3 2009 47 Nigeria 43.7 2003 48 Macedonia 43.2 2009 49 Kenya 42.5 2008 est. 50 Burundi 42.4 1998

Easy come, easy go!

The Fed taper surprise last week really caught the markets off guard and led to a big rally.  Oh how short lived that rally was!

Just 4 short days later, we're now below where the S&P 500 was when the taper delay rally started.  A bit surprising.  Did the market already priced in a delay (I don't think so), did the market realize that the delay doesn't mean that much (likely, taper will happen in Dec or so likely now).

Much ado about nothing after all?

Why the Declining Unemployment Rate is Deceptive

Guest Post - Today I'll like to introduce a guest post from Daniel Noon, a Ph.D. student at UCLA, courtesy of Otavio Dalarossa, an Los Angeles investor.  He digs a bit into the true meaning of the job situation.

Reading between the lines and spotting uneasy trends

Is the job situation really recovering? The unemployment rate seems to indicate that it is, having declined from 10% to 7.6% since October of 2009. While most economists take this statistic to be a credible measure of broad economic conditions, an alternate unemployment measure shows a starkly different picture.

The employment-to-population ratio measures the number of people who have jobs relative to the number of people of eligible working age as its base. This metric, as well as the unemployment rate, are both maintained by the Bureau of Labor Statistics. The key difference between the two is that the unemployment rate uses the much more restrictive labor forceas its base. The labor force excludes those who have given up looking for employment, yet who say they "want a job now"- this results in a rosier unemployment rate figure. (Source: http://www.bls.gov/cps/cpsaat35.htm).

The chart below compares the unemployment rate to unemployment measured by the employment-to-population ratio with data from 1948. Both unemployment metrics appear to generally move in tandem together during each job boom and bust cycle. The two metrics have been undeniably consistent with each other with respect to their movements over much of US history.

However, this does not hold true for the most recent timeframe. After the 2008 crash, the unemployment rate rose, peaked out on October of 2009 and has been on a steady decline ever since. Unemployment measured by the employment-to-population ratio similarly rose with the unemployment rate after the crash, but has remained flat during the supposed recovery instead of declining (see plot below).

Why is this happening? This can be explained by a stagnant or contracting labor force relative to the country's growing working population. The labor force, central to the unemployment rate's definition, excludes individuals who have essentially given up looking for work. In 2007, the BLS reported that 4.7 million working eligible individuals were "not in the labor force but wanted a job now." This figure has risen each year since 2007 and hit 6.6 million in 2012, a trend that contrasts a job recovery notion. These figures help explain some of the recent discrepancy between the two employment metrics. 

Those who are considered unemployed by the alternate metric are effectively categorized by BLS as "not in labor force, do not want job now." When this category was adjusted for population growth, it experienced an unprecedented surge concomitant with the onset and aftermath of the Great Recession: a rate of increase about substantially worse compared to the early 2000s when the dot-com bubble burst and 9-11 occurred (see table below).

Timeframe	Average Annual Growth Rate in “not in labor force, do not want job now” normalized to 1994 population of working-age individuals
1994-2008	0.4%
2000-2003	0.8%
2008-2012	1.3%

While it is difficult to figure out exactly why this has happened, it’s easy to be skeptical and wonder how many do want a job and are unwilling to admit to it, or are subject to flawed survey procedures. In fact, BLS adds a footnote to some of its “not in the labor force” tabulations admitting that some individuals are not even asked if they want a job (see below). 

With the labor force as a persistent point of debate between supporters and opponents of the unemployment rate, these charts suggest that the employment-to-population ratio could function as a more honest measure of jobs in the economy. For investors seeking to look into real job figures for additional data on the economic picture, this metric may prove to be very useful.