A Not so Random Swagger

Burton's Malkiel's 1973 book, "A Random Walk Down Wall Street," popularized the notion of random walk, now one of the most contested theories in finance. The theory states that markets are efficient because equity price movements follow random walks (rather than trends), which can't be forecasted. In a random walk world, all information that is learned by market participants is disseminated and priced almost instantaneously. Therefore, any effort to glean valuable information from public sources is fruitless.

The random walk theory attempts to explain an extreme case of market efficiency. At this juncture in financial history, however, efficient markets are less of an expectation and more of a wish. One only has to look at the dramatic events of the last five years to see that market participants have a tendency to overreact and misinterpret information, especially when it is complex. The good news for investors is that there are alternative funds out there that can profit from these inefficiencies.

The Adaptive Market Hypothesis
First, let's take a look the notion of market efficiency, and why inefficiencies exist. At the forefront of the efficient market/random walk debate was Andrew Lo in his 1999 book, "A Non-Random Walk Down Wall Street." Lo argued that markets don't need to follow a random walk to be considered efficient, because efficiency is not an absolute scale but a relative one. To illustrate, Lo uses a car engine analogy. Engines are not 100% efficient. That is, some fuel is wasted on byproducts such as excess heat or rattling rather than powering the car. But just because an engine isn't 100% efficient, doesn't mean it's 100% inefficient--there are plenty of cars on the road.

Furthermore, markets need inefficiencies. Grossman (1976) and Grossman and Stiglitz (1980) postulated that if market participants can't generate a profit, they have little incentive to trade, ultimately leading to the market's demise. Certainly, there are less-efficient markets, such as the market for old collectables, and more-efficient ones, like publicly traded stocks or bonds. But in most markets, there is room for inefficiencies due to irrationality and human behavioral biases. Investors tend to underreact and overreact to news as well as anchor old price estimates when making new forecasts (the latter may mean markets tend to react slowly to new information). That could be why momentum or managed-futures strategies, which have been around since the 1970s, have worked in the past. The S&P Diversified Trends Indicator, a momentum-based index, returned 5.63% annually from 1985 to 2003 (performance was calculated based on Standard & Poor's back test); in 2009 and 2011, managed-futures strategies fell substantially. Why has post-2008 been a bad environment for many of these strategies?

Lo's adaptive markets hypothesis (Lo 2004) may offer an explanation. The theory says that certain arbitrage opportunities or market inefficiencies are not persistent--that is, they come and go based on which participants are in the market. An ideal example is merger arbitrage. When merger deals pick up, arbitrage strategies can be quite lucrative, but when the activity subsides, so do the opportunities, because there are too many arbitrageurs chasing too few opportunities. For managed futures, the adaptive market hypothesis means that managers must use different ways to identify price trends caused by market inefficiencies. For example, the simple seven-month exponential moving-average trend identifier used in funds such as Guggenheim Managed Futures Strategy RYMTX has really lagged since 2008, as the strategy has increased in popularity and as the macroeconomic environment has shifted to a shorter-term "risk-on" "risk-off" mentality. If management applied the adaptive market hypothesis, it would rethink its investment process.

The final insight that the adaptive market hypothesis provides is that one doesn’t necessarily need to take on more risk to achieve return. Managed-futures strategies can be less volatile than the stock market yet still achieve stock market-like returns. The Morningstar MSCI Systematic Trading Hedge Fund Index, an index of momentum-tracking hedge funds in Morningstar's database, has gained an annualized 6.2%, compared with 5.3% for the S&P 500, over the last 10 years. It has also exhibited superior risk-adjusted returns.

Investing in an Adaptive Market
Natixis ASG Managed Futures Strategy AMFAX, which is managed by Andrew Lo, partially bases its investment process on the adaptive market hypothesis. The fund attempts to spot short- and medium-term price trends by looking at tens of thousands of historical trends and pinpointing the ones that have worked most recently. Other managed-futures mutual funds may rely on a single or static set of trend identifiers or trading processes that management believes will work for the foreseeable future. Though the Natixis ASG Managed Futures Strategy fund still generally relies on momentum, which can fall in and out of favor, its dynamic process means it adapts to changes when market participants and investor preferences change. During 2011, for instance, managed-futures funds were whipsawed by extreme price reversals. This fund performed significantly better than its peers (it went up 0.25% compared with a loss of 6.9% for the category for 2011), as the fund bounced back better than other futures funds when the market suddenly changed directions.

How Investors Can Adapt
Many studies have been written about market anomalies and inefficiencies, such as with small-cap stocks, or even value investing. Many alternative funds, such as arbitrage funds or managed-futures funds, take advantage of these market inefficiencies, but they may not work in every environment. The best solution is to diversify among alternative strategies and select the managers with the most forward and innovative thinking.