Relative Strength vs. Value – Performance Over Time

May 31, 2012

Thanks to the large amount of stock data available nowadays, we are able to compare the success of different strategies over very long time periods. The table below shows the performance of two investment strategies, relative strength (RS) and value, in relation to the performance of the market as a whole (CRSP) as well as to one another. It is organized in rolling return periods, showing the annualized average return for periods ranging from 1-10 years, using data all the way back to 1927.

The relative strength and value data came from the Ken French data library. The relative strength index is constructed monthly; it includes the top one-third of the universe in terms of relative strength.  (Ken French uses the standard academic definition of price momentum, which is 12-month trailing return minus the front-month return.)  The value index is constructed annually at the end of June.  This time, the top one-third of stocks are chosen based on book value divided by market cap.  In both cases, the universes were composed of stocks with market capitalizations above the market median.

Lastly, the CRSP database includes the total universe of stocks in the database as well as the risk-free rate, which is essentially the 3-month Treasury bill yield. The CRSP data serves as a benchmark representing the generic market return. It is also worthwhile to know that the S&P 500 and DJIA typically do worse than the CRSP total-market data, which makes CRSP a harder benchmark to beat.

 

Source:Dorsey Wright Money Management

The data supports our belief that relative strength is an extremely effective strategy. In rolling 10-year periods since 1927, relative strength outperforms the CRSP universe 100% of the time.  Even in 1-year periods it outperforms 78.6% of the time. As can be seen here, relative strength typically does better in longer periods. While it is obviously possible do poorly in an individual year, by continuing to implement a winning strategy time and time again, the more frequent and/or larger successful years outweigh the bad ones.

Even more importantly, relative strength typically outperforms value investment. Relative strength defeats value in over 57% of periods of all sizes, doing the best in 10-year periods with 69.3% of trials outperforming. While relative strength and value investment strategies have historically both generally beat the market, relative strength has been more consistent in doing so.

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How Safe Is Your Pension?

May 31, 2012

From “How Safe Is Your Pension?” in the July 2012 Consumer Reports, comes a stark assessment of the current pension landscape:

If you’re counting on a traditional defined-benefit pension, there’s reason to worry that you might not get everything you’ve earned.  About 80 percent of the 29,000 private-sector defined-benefit plans insured by the federal Pension Benefit Guaranty Corp. have been underfunded by $740 billion.  State and local public employee pensions were recently in a $1 trillion hole.

Instead of beefing up plan assets, many companies have cut benefits.  Employers can change their pension rules going forward using a variety of tactics, including tinkering with benefit formulas so that your eventual payout will be reduced, “freezing” the plan to stop further accruals, or terminating an underfunded plan.

“Vested” pension assets—those that legally become your property after a period of time—are generally safe thanks to federal law.  But if the plan is terminated, the PBGC, which itself is $26 billion in the red, is required to pay vested benefits only up to a certain amount, which varies by the employee’s age and the year in which the plan is terminated.

Pensions of government workers aren’t covered by the agency but are often protected by state constitutions or laws.  Still, 26 states have squeezed benefits for new hires, some other workers, and retirees.

Finding ways to back out of promised retirement benefits and/or reducing benefits for new hires is going to be a dominant theme in the pension world for many years to come.  For a flavor of current pension reform efforts consider the current proposal for public employees in Illinois:

Gov. Pat Quinn is proposing to raise the retirement age to 67 from 55; cap retirees’ annual cost-of-living increases at the lesser of 3% or half of the consumer price index; and increase workers’ pension contributions by three percentage points. But what makes these reforms bolder than most other states’ is that they would apply to current employees in addition to future hires.

As financial advisors, we are in a unique position to help people deal with these realities.  Right at the top of the list of things that we can do to truly help our clients is to help them come to terms with the pension reforms that are and will be taking place in the coming years and adopt an appropriate savings and investment plan that accounts for these changes. The pressures to scale back pension benefits will be like nothing seen by the last generation of retirees.

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From the Archives: The Math Behind Manager Selection

May 31, 2012

Hiring and firing money managers is a tricky business.  Institutions do it poorly (see background post here ), and retail investors do it horribly (see article on DALBAR  ).  Why is it so difficult?

This white paper on manager selection from Intech/Janus goes into the mathematics of manager selection.  Very quickly it becomes clear why it is so hard to do well.

Many investors believe that a ten-year performance record for a group of managers is sufficiently long to make it easy to spot the good managers. In fact, it is unlikely that the good managers will stand out.  Posit a good manager whose true average relative return is 200 basis points (bps) annually and true tracking error (standard deviation of relative return) is 800 bps annually. This manager’s information ratio is 0.25. To put this in perspective, an information ratio of 0.25 typically puts a manager near or into the top quartile of managers in popular manager universes.

Posit twenty bad managers with true average relative returns of 0 bps annually, true tracking error of 1000 bps annually, hence an information ratio of 0.00.

There is a dramatic difference between the good manager and the bad managers.

The probability that the good manager beats all twenty bad managers over a ten-year period is only about 9.6%.  This implies that chasing performance leaves the investor with the good manager only about 9.6% of the time and with a bad manager about 90.4% of the time.

In other words, 90% of the time the manager with the top 10-year track record in the group will be a bad manager!  Maybe a longer track record would help?

A practical approach is to ask how long a historical performance record is necessary to be 75% sure that the good manager will beat all the bad managers, i.e., have the highest historical relative return. Assuming the same good manager as before and twenty of the same bad managers as before, a 157 year historical performance record is required to achieve a 75% probability that the good manager will beat all the bad managers.

It turns out that it would help, but since none of the manager databases have 150-year track records, in practice it is useless.  The required disclaimer that past performance is no guarantee of future results turns out to be true.

There is still an important practical problem to be solved here.  Assuming that bad managers outnumber good ones and assuming that we don’t have 150 years to wait around for better odds, how can we increase our probability of identifying one of the good money managers?

The researchers show mathematically how combining an examination of the investment process with historical returns makes the decision much simpler.  If the investor can make a reasonable assumption about a manager’s investment process leading to outperformance, the math is straightforward and can be done using Bayes’ Theorem to combine probabilities.

…the answer changes based on the investor’s assessment of the a priori credibility of the manager’s investment process.

It turns out that the big swing factor in the answer is the credibility of the underlying investment process.  What are the odds that an investment process using Fibonacci retracements and phases of the moon will generate outperformance over time?  What are the odds that relative strength or deep value will generate outperformance over time?

The research paper concludes with the following words of wisdom:

A careful examination of almost any investor’s investment manager hiring and firing process is likely to reveal that there is a substantial component of performance chasing. Sometimes it is obvious, e.g., when there is a policy of firing a manager if he has negative performance after three years. Other times it is subtle, e.g., when the initial phase of the manager search process strongly weights attractive historical performance. No matter the form that performance chasing takes, it tends to produce future relative returns that are disappointing compared to expectations.

Historical performance alone is not an effective basis for identifying a good manager among a group of bad managers. This does not mean that historical performance is useless. Rather, it means that it must be combined efficiently with other information. The correct use of historical performance relegates it to a secondary role. The primary focus in manager choice should be an analysis of the investment process.  [emphasis added]

This research paper is eye-opening in several respects.

1) It shows pretty clearly that historical performance alone–despite what our intuition tells us–is not sufficient to select managers.  This probably accounts for a great deal of the poor manager selection, the subsequent disappointment, and rapid manager turnover that goes on.

2) It is very clear from the math that only credible investment processes are likely to generate long-term outperformance.  Fortunately, lots of substantive academic and practitioner research has been done on factor analysis leading to outperformance.  The only two broadly robust factors discovered so far have been relative strength and value, both in various formulations–and, obviously, they have to be implemented in a disciplined and systematic fashion.  If your investment process is based on something else, there’s a decent chance you’re going to be disappointed.

3) Significant time is required for the best managers to stand out from the much larger pack of mediocre managers.

This is a demanding process for consultants and clients.  They have to willfully reduce their focus on even 10-year track records, limit their selection to rigorous managers using proven factors for outperformance, and then exercise a great deal of patience to allow enough time for the cream to rise to the top.  The rewards for doing so, however, might be quite large–especially since almost all of your competition will ignore the correct process and and simply chase performance.

—-this article originally appeared 1/28/2010.  I have seen no evidence since then that most consultants have improved their manager selection process, which is a shame.

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Fund Flows

May 31, 2012

The Investment Company Institute is the national association of U.S. investment companies, including mutual funds, closed-end funds, exchange-traded funds (ETFs), and unit investment trusts (UITs).  Members of ICI manage total assets of $11.82 trillion and serve nearly 90 million shareholders.  Flow estimates are derived from data collected covering more than 95 percent of industry assets and are adjusted to represent industry totals.

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