In their paper “Momentum Crashes”, Kent Daniel and Tobias J. Moskowitz set out to build a better momentum strategy. After reviewing the research on momentum strategies for equities of the years they dive in and create a strategy that aims to avoid the periods when momentum strategies dramatically under-perform the markets:
We show that the low ex-ante expected returns in panic states are consistent with a conditionally high premium attached to the option-like payoffs of past losers. An implementable dynamic momentum strategy based on forecasts of momentum’s mean and variance approximately doubles the alpha and Sharpe Ratio of a static momentum strategy, and is not explained by other factors.
Their primary finding is that stocks that have done very poorly in a market downturn, often outperform when the markets turn around. They go on to explain this in a very mathematical fashion and suggest that:
in bear market states, and in particular when market volatility is high, the down-market betas of the past-losers are low, but the up-market betas are very large. This optionality does not appear to generally be reflected in the prices of the past losers. Consequently, the expected returns of the past losers are very high, and the momentum effect is reversed during these times.
To put in plainly, without the math, stocks that have been punished during a downturn often go on to out perform the market when the recovery starts. The junk rally in 2009 is a great example, the stocks most beat up in 2008 were some of the highest gainers in 2009.
In the end it is a pretty common sense finding, since much of what causes stocks to crater has more to do with psychological panic than real market fundamentals. It stands to reason that irrationally punished stocks will outperform when the market regains a modicum of rationality. We see this phenomenon all the time, particularly when high ranking individuals leave companies or their is a extra salacious headline out about a firm. These events make big news and move stock prices, but often don’t have the slightest effect on the true underlying value of a company.
What does it mean for investors? Not a whole lot unless you are looking to build a complex momentum model, but it does serve as a nice reminder that even in the midst of market chaos there are good opportunities to be had.
Full paper here
There has been tons of research as to the existence of true skill among mutual fund managers. Often researchers will look at advanced statistics and in depth breakdowns of holdings. While this research is interesting, I think it is simpler than that…. Do mutual fund managers actually make me or you more money than owning an index ETF? To me, if a manager can add value over 5 or 10 years then they are skilled in my mind. This proves quite difficult for most managers.
To start, let us look at Large Cap Value. Using Morningstar’s categories, deleting out all funds with fewer than 10 years of performance, and removing all share classes except for the lowest fee class (in an attempt to give mutual funds the best shot at looking at least decent), I come to a database of 163 Large Cap Value funds. Their benchmark should be the Russell 1000 Value. I use IWD, an ETF that tracks the Russell 1000 Value, because let’s be honest, you other option aside from the mutual fund is not owning the index, it is owning the index ETF.
Below is a chart showing the percentage of funds outperforming the benchmark during 1, 3 and 5 year periods.
As you can see from the chart, consistently beating the benchmark is hard… really hard. As of the end of May, fewer than 40% of Large Cap Value funds are beating their index over the last 3 and 5 years, and keep in mind I am using the lowest fee class (investment minimums in these are often $1,000,000 plus).
Whats more, it is not only the act of out-performance that is rare, being able to do so consistently is even rarer. When we look at 3 year periods of performance, we find that funds doing well for one 3 year period will tend to do poorly over the next 3 years. Below is a chart looking at the best 20% of performers over the previous 3 years at each date listed on the right. The line represents the rolling 3 year active return over time. As you can see below, Alpha (return generated over benchmark) tends to bleed lower, going negative in almost all cases meaning that funds producing positive return over the benchmark in one period will tend to drift towards negative return in a future period.
What is the take away of this really brief look? Doing a good job in active management is really tough. Next post I will take out fees to show the deleterious effect fees have on the performance of these funds.
We all see what happens to the companies that supply Apple with components the moment Apple’s newest product is opened up and it’s guts are cataloged, delight for those small firms included and disaster for the firms that have been passed by. Cohen and Frazzini dig deeper, beyond the obvious linkages you here about day in and day out (auto suppliers, tech suppliers) and capture a broad range of customer-supplier linkages. As with the research on risk sentiment in annual filings this is a fairly intuitive idea; companies that are linked financially are likely linked in their stock returns as well.
They use 11,484 customer-supplier relationships where the customer makes up more than 10% of the suppliers total sales, dated from 1980 t0 2004. They predict that a large shock to the customer’s stock price will take time to be reflected in the supplier’s price, thus creating a trading opportunity in the supplier’s stock. In their paper, the construct a long-short strategy that took long positions in 20% of customer stocks with the highest returns in month t-1, and short positions in the 20% of customer stocks with the lowest returns in t-1. They find
The customer momentum strategy that is long the top 20% good customer news stocks and short the bottom 20% bad customer news stocks delivers Fama and French (1993) abnormal returns of 1.45% per month ( t-statistic = 3.61), or approximately 18.4% per year.
At a previous firm we did the work to collect the customer-supplier relationship data, which was maddening and slow work. We found our backtest results were similar to Cohen and Frazzini’s but the effect did not work so well with larger supplier companies (the relationships were already known we surmised) and in certain sectors like financials.
It seems like a relatively straightforward idea that statements by a firms management would contain information outside of what the firms financials say. Now, while reading up on the corporate filings (10Qs and 10Ks) of your favorite five companies is pretty easy, what about analyzing the statements of say the largest 3000 companies in the country? This would be a daunting task, and Feng Li from University of Michigan set out to figure out a way to gather all this data and analyze it. He looked at the language in annual filings for for around 3,000 firms over a ten year time span. Using a text algorithm Li assessed the incidence of language associated with risk in companies annual filings (1oKs). Li finds that:
an increase in risk sentiment is associated with lower future earnings: Firms with a larger increase in risk sentiment have more negative earnings changes in the next year. Risk sentiment of annual reports can predict future returns in a cross-sectional setting: Firms with a large increase in risk sentiment experience significantly negative returns relative to those firms with little increase in risk sentiment in the twelve months after the annual report filing date.
To explain plainly, and again this is pretty straight forward, companies that suddenly begin to worry about risk a whole lot more are more likely to be exposed to more actual risk. This should be reflected in lower annual returns in companies with heightened risk concerns. To that end, Li contends that:
A hedge portfolio based on buying firms with a minor increase in risk sentiment of annual reports and shorting firms with a large increase in risk sentiment generates an annual Alpha of more than 10% measured using the four-factor model including the Fama-French three factors and the momentum factor.
While I take past returns and back-tested returns with a grain of salt, the idea makes intuitive sense and implementing Li’s strategy is an interesting way to take a broad look at how companies are thinking about the risks they are exposed to.
Here is the paper, for those interested in reading all about risk sentiment in annual reports -> ssrn-id898181
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