Geoff Considine

Dividend Aristocrats Will Continue to Outperform

Typo alert:

Article says:
"For the two years through March of 2007, DVY had average annual returns of -35.8% per year..."

It should read:
"For the two years since March of 2007, DVY had average annual returns of -35.8% per year"

Dividend Aristocrats Will Continue to Outperform

DKCO:

Ahh--but you have neglected a crucial factor. Let's say that a model predicted two years ago that bonds would out-perform stocks over the future years. Then the market crashed. And your point would be that any bond portfolio would have out-performed in a market crash, so big deal. But, the model--when it predicted this--did not know that the market would crash. The model was implying something important. Same here. Anyone can say that a low Beta portfolio has and should out-perform in a down market, but we did not know there would be a down market when I wrote the original article. Make sense?


On May 04 07:26 PM dkco wrote:

> Geoff
> A quick skim of some of the characteristics of your simulation, beta
> of about 0.60 and equal weighted positions of the dividend aristocrats
> would indeed predict outperformance of the cap-weighted S&P 500
> over the period when it fell 25%. Equal wighted stocks significantly
> outperformed cap-weighted during the time, and a portfolio with a
> beta of 0.6 should roughly fall 60% of the amount of the market (and
> rise only 60%). 60% of -25% is -15%. So, the combination of the portfolio
> beta and equal weightedness is the primary explanation for the dividend
> portfolio's performance, not what stocks they are, or, even that
> they pay dividends. A comparable analysis would hold for any basket
> of stocks, dividend paying or not, with a portfolio beta of 0.60.
> I know, most low beta stocks pay dividends, but not all, and not
> all div-payers are financials.

Dividend Aristocrats Will Continue to Outperform

Yes, but there is little evidence that investors as a whole are rational. Witness bubbles and other speculative extremes. There are a range of data points that suggest that investors are not rational. One of my favorites is that low Beta strategies tend to return more than they should (Fama and French 2004 showed this). In a perfect market CAPM would be correct--and you would be correct--but Fama and French 2004 shows that this is clearly not the case, even over long periods.


On May 03 08:54 PM Flav wrote:

> Geoff:
> If agents are rational and forward looking, and past information
> of returns and risk are at their disposal; knowing that a montecarlo
> simulation which uses this information shows that a basket of stocks
> will outperform the S&P, shouldn't create an arbitrage opportunity
> that will soon erase the return diferential, so that the future expected
> return of this basket is the same of that of the S&P?

Dividend Aristocrats Will Continue to Outperform

Alan:

Trust me--I am well aware that -18% is not a happy outcome. For the people who are net short or who have otherwise sustained no losses in the last couple of years, I am duly impressed. For the institutional investors and retail investors who maintain net long positions, relative out-performance matters a great deal. The Dividend Aristocrats have turned a REALLY bad year into far less substantially bad year. You seem to be suggesting that the only good strategy is one that never has losses. If you believe that this is possible, I wish you luck.

If price appreciation is anemic, I will be very grateful for the dividends for my net long positions.

With regard to comments from donzelion and Whidbey:

The theory etc. behind why companies pay dividends and why investors care is long and interesting. Dividends are a signal to investors. Some companies have tried to exploit this with leveraged dividend strategies or simply payouts that were simply too high to be sustained. Dividends that are raised or maintained over long periods can signal managements approach to growth, etc. Yes, companies can and do cut their dividends and they may do so in the future. Do you think that the out-performance of the Aristocrats in 2008 was just coincidence? Time will tell.

Regards,

Geoff

Is There Good News in This Market?

Larry:

Bravo! Amidst all the fear that the sky is falling, it is worth looking at the longer landscape of investing. Investing is risky--and it is precisely the ability to bear that risk that equity investors are paid for! If the markets couldn't drop severely, there would be no equity risk premium. We never know what the next crisis will be, but there will always be a next crisis and it will always be something else--some other confluence of events.

Geoff

The Do-It-Yourself Market-Neutral Portfolio

Steve:

Over the last 12 months, I get about the same result--depending on whether you drop OMM when it was delisted due to acquisition or substitute its acquirer. Either way, the portfolio is down by an amount close to the S&P500, as you suggest. That said, note that the R^2 of 20% or less over long periods of time means that it is not really a good idea to benchmark against something like the S&P500--most of the volatility in the portfolio is not due to moves in the S&P500. A "market neutral" apporoach that has fairly high volatility (like this one) may not drop when the market drops--or it may--the market direction is not the driver. In this case, the credit crisis has been a big driver because of the exposure to banks here.

This kind of portfolio is a good one to monitor over the long haul--thanks for the reminder :)

Geoff

Using Default Risk to Limit Downside in Individual Stock Investing

Quaazy1:

Any Monte Carlo model that does not account for correlation between positions is essentially pointless. QPP has a sophisticated tool for capturing and managing the correlations between all positions. QPP is actually considerably more sophisticated that a tools based on Style Analysis in this regard because it captures all correlation effects and not just correlations due to mutual correlation to indexes.

I have performed long-term out-of-sample tests on portfolios of assets and have shown that QPP does a solid job of generating expected risk and return in total portfolios of correlated assets.

Using Default Risk to Limit Downside in Individual Stock Investing

Hi Quaazy1:

The current version of QPP assumes no serial correlation in return in the simulation. This is measured historically, however. This is okay for investors with time horizons beyond about a year because the dcay time scale for autocorrelation is about a year in most studies. QPP is not intended to be a momentum investing tool.

Using Default Risk to Limit Downside in Individual Stock Investing

Bear Stearns Part 2: The comment above is especially notable given that Moody's had BSC rated at A2 until 3/14/08, at which time Moody's downgraded BSC to Baa1. QPP's projected 1% / 1 year risk as calculated at the end of February is in line with credit ratings right on the edge of junk (see chart in this article).

Using Default Risk to Limit Downside in Individual Stock Investing

FYI: Bear Stearns had a 1-year, 1% ile projected return in QPP of -75% for the period ending 2/29. In the article above, I suggested that individual investors avoid stocks with 1% ile projected return worse than -50% to -60%. BSC would have been avoided on this basis.

Using Default Risk to Limit Downside in Individual Stock Investing

Hi Manhapf:

The idea of using QPP as a leading projection of upgrades or downgrades is an interesting one--and you can look for future articles on this. I have been doing some testing and it appears that QPP has shown very high tail risk prior to some recent downgrades.

Black Swans, Real Estate and Financial Stocks

Mithrandir is correct. A lot of the problem with dmnieren's focus on five multi-month swings is that it misses the fact that when you even look to an annual basis, things get much better. Then there is the related issue of 'timing bias.' A good model like QPP estimates the risk over a specific future period (12 months in this article). To note that there have been some really bad events misses the more important statistic of the likelihood of such bad events over the next N months.

I cannot help but feel that some portfolio of the popularity of black swan thinking (as expressed here) has to do with a lack of understanding of statistics.

Black Swans, Real Estate and Financial Stocks

FH: Okay, don't stretch the metaphor too far. First, you should not lose all your savings on one position even if the firm goes belly up--this is the first core precept of portfolio thinking: be able to live through a low percentile event. If your primary point is the risk of false confidence, here is the question. If a model has been shown over a range of market conditions to provide plausible warnings of the potential for extreme events, are you better off using it or not? If you place bets such that you will only thrive if a model is near perfect (like LTCM), you will blow eventually. On the other hand, if you use a model prudently to manage overall portfolio risk, the model can help you to design a better portfolio. This is a major issue in much of quantitative finance and it is a thriving area of study. Investors will be missing some tools that are standard in professional portfolio management if they simply believe Mr. Taleb that the world is so fundamentally unpredictable that models are useless.

Black Swans, Real Estate and Financial Stocks

To VAMC:

While testing models after the fact it is always a good idea to be careful. The model I used QPP was run with default inputs used in my articles for years. This is not a case of over-fitting. There is no reverse engineering issue here.

The bottom line here is that you and everyone else are free to disregard available models because you feel they have the potential for 'black swan blindness.' You may as well argue that weather forecasts are pointless--and you are free to do so. Just carry your umbrella and slicker every day.

Black Swans, Real Estate and Financial Stocks

To dmnieren:

I appreciate your enthusiasm, but you are not grasping the numbers. First of all, the probability of a cumulative 12-month decline is different than the various partial years, etc. Just to prove a point, I have gotten the 25-year history of C and analyzed the 2.5th percentile in returns for all 12-month periods. Guess what--its -36%! So, my analysis was predicting a substantially higher probability of the observed loss than history.