Archive | Smart beta

Smart Beta ETFs: Summing it All Up

This is Part 10 in a series about smart beta ETFs. See below for links to other posts in the series. In this final installment, we review what we’ve learned and consider the pitfalls of embracing smart beta strategies.

 

At last we arrive at the final post in this series on smart beta ETFs. From the comments, tweets and few cancelled subscriptions, I know some readers didn’t make it this far. Even if you stuck it out, you’re probably asking why I’ve devoted so much space to this technical subject. After all, I’ve spent years arguing that investors should keep things simple with traditional index funds and ETFs, and let go of the dream there’s something better out there. Have I changed my tune and embraced a strategy that strives to beat the market by tilting toward value stocks, small caps, momentum, low volatility and high-quality companies?

Let’s be clear: I haven’t changed my position. I still recommend plain old cap-weighted ETFs for DIY investors, and our full-service clients. I still use them in my own portfolio.

So why devote so much space to smart beta?

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Is Your Smart Beta Strategy Doing Its Job?

This is Part 9 in a series about smart beta ETFs. See below for links to other posts in the series. In this installment, we look at how you can tell whether smart beta indexes will really perform as expected.

 

As we’ve worked through each of the factors targeted by smart beta ETFs—value, size, momentum, low volatility and quality—we’ve been careful to point out that no one really knows whether these premiums will persist in the future, especially once they’re on the radar of millions of investors.

However, we can look backward to see whether smart beta indexes have actually behaved as you would expect. Say you’re considering an ETF that targets value and small-cap stocks. If the index did well when value and small stocks outperformed, and did poorly when they lagged, that’s reassuring. But if you find the index’s performance had no correlation with value and size—or if it delivered outsized returns during periods when these stocks were dogs—that’s a red flag. It would be difficult to put a lot of faith in that ETF.

Think of a gardener who buys a system of rain barrels to capture the precipitation that falls on her roof.

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Exploring Multi-Factor Models

This is Part 8 in a series about smart beta ETFs. See below for links to other posts in the series. So far we’ve looked at ETFs that target specific factors. In this installment, we look at funds that offer exposure to more than one.

 

Over the last few weeks I’ve looked at the five factors most commonly associated with smart beta ETFs: value, size, momentum, low volatility and quality. The specific funds I’ve mentioned so far are designed to zero in on one of those factors. But what if you wanted to target more than one? Should you just use several funds, or are their ETFs that use “multi-factor” strategies?

Let’s first consider the reason for building a portfolio with exposure to more than one factor: better diversification. As we’ve seen, each factor is virtually guaranteed to see periods of underperformance, even if they deliver higher returns over the long haul. This can lead to tracking error regret, which is the frustration investors feel when they lag the broad market. Unless you believe strongly in your smart beta strategy,

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Understanding the Quality Factor

This is Part 7 in a series about smart beta ETFs. See below for links to other posts in the series. In this installment, we look at the quality factor: the idea that companies with strong balance sheets and profitable businesses tend to outperform.

 

So far in this series we’ve looked at value, size, momentum and low volatility as factors linked to higher returns over time. The final factor we’ll examine is the newest and the most nebulous. There are several definitions of the quality factor, though all of them are associated with durable and sustainable companies with competitive advantages, strong balance sheets, stable earnings and high margins.

While it might seem obvious that such companies would deliver higher returns, that’s not how efficient markets are supposed to work. A company’s higher quality should be reflected in a higher stock price, and expensive stocks aren’t supposed to outperform: that’s why there is a value premium, after all. As Larry Swedroe explains, this surprising factor “is based on quality characteristics irrespective of stock prices, while a value strategy is based on stock prices irrespective of quality.”

In 2012,

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Understanding the Low Volatility Factor

This is Part 6 in a series about smart beta ETFs. See below for links to other posts in the series. In this installment, we look at the low volatility anomaly: the surprising idea that stocks with lower risk have tended to outperform.

 

In our introduction to smart beta  a couple of weeks ago, we discussed the capital asset pricing model (CAPM), which is based on the idea that you should expect higher returns from stocks with higher risk. That’s an idea many investors now take for granted, since being rewarded for additional risk makes intuitive sense.

But what if it’s not true?

As early as 1972, Robert Haugen and James Heins found the exact opposite in the data on US stocks between 1926 and 1971. The researchers uncovered a negative relationship between risk and return: high volatility stocks actually tended to deliver lower returns, while low-vol stocks outperformed. In the decades since, many researchers have demonstrated that this low volatility anomaly exists in stock markets around the world, and even in many bond markets.

As with all of the smart beta factors,

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Understanding the Momentum Factor

This is Part 5 in a series about smart beta ETFs. See below for links to other posts in the series. In this installment, we look at the momentum factor: the idea that stocks that have recently risen or fallen in price will continue that trend over the medium term.

 

Like value, momentum in the stock market is an old idea, but the academic evidence goes back only to the early 1990s. It was first documented in a 1993 paper suggesting you could generate excess returns by buying US stocks that performed well over the previous three to 12 months and selling those that performed poorly over the same period. Later studies found that momentum also exists in international markets.

In 1999, Mark Carhart published a now famous paper arguing that outperforming mutual funds cannot be reliably identified in advance. Carhart suggested fund managers who seem to have a “hot hand” are often just the lucky beneficiaries of momentum in stock returns. He eventually added momentum to the factors identified by Fama and French to create the Carhart Four-Factor Model.

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Understanding the Size Factor

This is Part 4 in a series about smart beta ETFs. See below for links to other posts in the series. In this installment, we look at the size factor: the idea that smaller companies should deliver higher returns than large-cap companies.

 

The idea that small-cap stocks have higher expected returns has a relatively long pedigree. The pioneering paper was authored in 1981 by Rolf Banz of the University of Chicago, who looked at US stocks from 1936 to 1975 and found that smaller firms, on average, enjoyed higher returns than larger companies. It found that smaller firms had higher returns, on average, than larger firms. That finding was confirmed in a ground-breaking 1992 paper by Kenneth French and Eugene Fama, the same one I discussed in my previous post on the value factor.

Over long periods, size has mattered a lot: depending how you define them, small caps in the US have outperformed large caps by about 2% to 4% per year since 1927 (the year the reliable data begin). And while most of the research on the small-cap premium has focused on US stocks,

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Understanding the Value Factor

This is Part 3 in a series about smart beta ETFs. See below for links to other posts in the series. In this installment, we look at the value factor: the idea that stocks whose prices are low relative to their fundamentals should deliver superior returns.

 

The roots of value investing go back to 1934, the year Columbia finance professors Benjamin Graham and David Dodd published Security Analysis, a biblical volume that is still studied today. Graham and Dodd outlined a strategy for identifying stocks trading for less than their intrinsic value, though they did not frame their work in the context of market-beating returns. That notion came much later, when academics began testing ideas about market efficiency.

In the 1970s and 1980s, a growing body of evidence began to show stocks classified as “cheap” delivered higher returns, regardless of their beta—that is, even if they were not more volatile than the overall market. Sanjoy Basu published a 1977 paper arguing that the performance of stocks was consistently related to their price-to-earnings (P/E) ratios. Another important paper in 1985 found a similar relationship between stocks with low price-to-book (P/B) values and argued this was “pervasive evidence of market inefficiency.”

Many subsequent studies supported the idea that it is possible to identify undervalued stocks by comparing market prices to the company’s fundamentals.

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A Brief History of Smart Beta

This post is Part 2 in a series about smart beta ETFs. See below for links to other posts in the series.

 

Smart beta is a relatively new term, but its roots stretch back several decades. Let’s look at the history of how the idea developed.

We’ll start with a simple question that has long been asked by students of the financial markets: what explains the difference in returns among stocks?

Back in the 1960s, economists and finance professors developed the capital asset pricing model (CAPM), which suggested that returns were directly related to risk. The formula began with a risk-free rate of return—for example, Treasury bills—plus an additional return for the stock market as a whole, called the equity risk premium. Then the model considered how sensitive a given stock is to the volatility of the overall market using a measure called beta. If a stock had a low beta—making it relatively less risky than the market—it should have a lower expected return. Stocks with higher beta should theoretically reward investors with a greater return.

CAPM is an elegant formula,

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