What is your opinion on the fund mixes mentioned in the link below? Over simplistic or not too bad?
MJG suggested looking into these mixes in one of my previous posts and considering my inexperience it seems I may be better off with one of these rather than paying a financial advisor or worse yet rolling dice by selecting funds myself.
I'm not sure why they are all Vanguard funds but the expense ratios are very low and Vanguard seems to have a decent reputation.
Any feedback will be greatly appreciated.
Thanks in advance!
http://www.marketwatch.com/lazyportfolio
Comments
Regards,
Ted
http://assetbuilder.com/lazy_portfolios/
I don't understand the difference between 5 year (gains) and 5 year standard variation.
I'll give this a try:
5 year gains = Your average return over a 5 year period (June 2008 - June 2013)
5 year standard deviation = The maximum dispersion away from that average return over a 5 year time frame.
"The road to (average return) is full of ups and downs (standard devaition)"
Your question is about the statistical characterization of a data set. Bee posted an on-target brief explanation. Let me expend on his answer.
The standard deviation is indeed a measure of the dispersion of the assembly of data points around an average value. Standard deviation is often called Sigma in texts. There is a simple equation that allows calculation of that dispersion, statistically known as the standard deviation of that data.
The five year qualifier statement indicates that the data was collected for a 5-year period, probably on a monthly basis. The 5-year timeframe and 3-year collection times are very typical for marketplace data. The average (or mean) statistic and its standard deviation are usually reported as annual values.
By using the average and the standard deviation to characterize the data statistically, it is assumed that the distribution has the shape of a symmetrical Bell curve. Of course, that is an approximation of reality, but it is commonly used and reasonably accurate.
Remember that a Bell curve is heavy (dense) with data points near the average value with a point population density that tapers away from the central tendency. The population is very sparse far away from the average value. Hence its called a Bell curve (or a Normal curve).
The average value and its standard deviation are very useful parameters when assessing a candidate investment holding. I’ll summarize three practical interpretations of those parameters.
Obviously, the average return allows you to compare that return against a benchmark and/or the return of a risk free investment. A low standard deviation means that most of the returns are clustered near the average value. A large standard deviation means you are in for a wild ride since returns will vary considerably on an annual basis.
If you add and then subtract one standard deviation from the average value, a range will be defined. Statistically, 67 % of the annual returns will fall within that band. If you add and subtract two standard deviations from the average value, the resultant band will be broader and will statistically include 95 % of the expected returns. The lower the standard deviation number, the more concentrated (stable) will be the annual returns.
You can use the average return and its standard deviation to estimate how often the investment is likely to generate a negative annual return. For example, if your data computes to a 10 % average annual return with a 10 % standard deviation, the likelihood of a negative return is about 17 %.
How do we get 17 %? We know that 67 % of the returns are contained within the plus and minus one standard deviation band. That leaves 33 % of the returns outside the band. Since the Bell curve is symmetrical, half that number are on the low negative side and the other half are on the high positive return side. You will need to consult a Normal statistical distribution table for non-whole number subtractions.
A high standard deviation erodes final end wealth. To get final cumulative end wealth you can not simply multiply annual average returns together. You need to compute an annual compound (geometric) return to properly determine an end wealth. Average annual return and its standard deviation allows you to make that conversion.
Simply put, annual compound return is equal to the annual average return minus the standard deviation times standard deviation divide by two term. The compound return is always less than the average return.
Notice that the larger that the standard deviation is, the more that compound return is reduced. Returns volatility (dispersion, standard deviation) has a wealth destroying impact. Given the same expected annual average return, the lower standard deviation investment is a superior choice.
I recognize that this could be very confusing if you have not been exposed to statistical methods. If you invest, you must become familiar with these methods to fully understand investment risks and odds. You can acquire this skill with just a little effort.
I hope this is helpful in getting you off the starting line.
Best Wishes.
The portfolios change infrequently, but the changes have been helpful in avoiding, for example, the major downdraft in 2008.
In my rush to respond, I omitted two further illustrations of the significance of controlling standard deviation in the downward direction.
I’ll correct my neglect immediately. These comments should be considered as an extension of my earlier posting. Sorry about the need for a few equations.
When I commented on the probability of a negative return year, I used a 10 % return with a 10 % volatility model assumption to calculate a 17 % likelihood of a zero annual return. To further demonstrate the power of standard deviation impacts, suppose you manage to design a portfolio with the unlikely characteristics of a 10 % return with a mere 5 % standard deviation.
Know that a 5 % standard deviation portfolio is next to impossible if it contains a healthy dose of equity positions. But if you were skillful enough to accomplish that task, the likelihood of a negative year is reduced from 17 % down to the 2.5 % level.
How? The analysis is the same as the previous one except that the portfolio is now 2 standard deviations removed from a zero percent annual reward. Remember that two standard deviations encompasses 95 % of all the data points used in the modeling data sets. You do the rest of the calculation.
Lastly, I wanted to close with the Harry Markowitz story.
Markowitz was awarded a Nobel prize in economics after he discovered and developed his Modern Portfolio Theory in the mid-1950s.
For our present purpose, he described a hooked line that has been termed the Efficient Frontier. In a graphic format, the line is plotted with annual returns on the vertical axis and the ubiquitous standard deviation on the horizontal axis. In general, the line is fish-hooked at its lower standard deviation end and becomes a bent line with an upward slope as standard deviation increases.
That’s why a lot of folks use standard deviation as a partial and imperfect measure of risk. More risk, more return. Or alternately, no return without risk.
Markowitz’s main goal was to find a method to maximize portfolio return at a given, acceptable level of risk, as measured by standard deviation. Now, at the portfolio level, standard deviation is a dominant factor.
So, portfolios are almost always designed to control and minimize standard deviation, while simultaneously delivering an acceptable annual rate of return.
How that’s done is a much more challenging question with no clear single answer. Methods and procedures exist, but are controversial. Also, the analysis requires not only annual average return with its standard deviation as input, it also requires correlation coefficients for all pairs of assets in the portfolio.
That’s a topic that demands a lot more than a MFO letter length exchange. I’m finally finished.
Best Wishes.