Home Business Factor Timing Doesn’t Like Value Stocks With Momentum: Morgan Stanley

Factor Timing Doesn’t Like Value Stocks With Momentum: Morgan Stanley

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The art of factor timing is extremely difficult to master—if it can be mastered at all. For the average investor, it’s not only a topic of interest, but the understanding of it can be elusive—except of course (well, maybe) for those who spend all their time studying the markets’ movements across all asset classes all the time. The extreme volatility in the markets has made timing even more difficult as even the best of the best minds on Wall Street are having time predicting what will happen.

What is factor timing?

Morgan Stanley analysts provided an excellent, thorough explanation of it and examples in their March 8 U.S. Quant Research report titled “Timing is Everything.” Analyst Adam S. Parker, Ph.D. and team define it as “tactically varying exposures to equity cohorts and styles to improve performance,” to put it simply, although it is anything but simple.

In putting together the report, they attempt to forecast relative monthly returns for a month out for 38 popular long equity positions and long/ short spreads. They warn, however, that the models they came up with to highlight the topic of factor timing aren’t viable “as stand-alone strategies, but can aid entry/exit points, particularly in junk, high beta, value and momentum cohorts.”

In making their predictions about what will happen to these stocks in a month, they used a number of predictive variables, like macro factors and the stocks’ recent performances, changes in valuation, returns dispersions within their cohort and others. Among the macro factors they used were interest rates, currency moves and commodity prices.

When factor timing doesn’t work well

The Morgan Stanley team noted several trends through their research and sampling of factor timing. For example, their methods suggested that value stocks that posted strong performance over the last year may not do well in the near term.

factor timing

They also found that zero-yield stocks and those with poor momentum may make attractive targets to move overweight on.

factor timing

Additionally, they found that factor timing appears to point best to when to purchase low quality and high beta stocks but not when buying defensives and other cohorts.

factor timing

Further, they said factor timing as a method for stock picking performs better in terms of predicting relative returns rather than beta-adjusted returns because “noise” in estimating beta decreases the method’s performance, especially when dealing with momentum stocks. They also observed that the method seems to work better when looking on an out-of-sample basis in zero yield, junk, high beta, value and momentum cohorts, which they say could be a novel finding because they haven’t heard anyone else say that factor timing works better in some cohorts than it does in others.

factor timing

The best factors to use in determining timing

The Morgan Stanley team listed several variables that appear most helpful in factor timing, which are the trailing one-month return for the S&P 500, the trailing three-month percent change in the U.S. dollar, the trailing 12-month cohort relative performance and dispersion of stock returns within each cohort.

factor timing

They added that the factor types that appear in each cohort’s model don’t look to be connected with out-of-sample performance.

Potential overweights and underweights based on Morgan Stanley’s factor timing models

They conclude that Salesforce rules among stocks “with exposures consistent with those of the index: large-cap stocks (but not mega cap stocks), with a growth bios (i.e., not value or neither), a low-quality bias (bottom two quartiles) and a momentum bias) top three quintiles of price momentum,” noting that Discretionary, Airline and Technology stocks dominate their “Overweight-Consistent List.”

factor timing

ExxonMobil comes out on top for “stocks with the opposite factor sensitivities to equity hedge fund indexes, drawn from either the mega-cap and large-cap cohorts,” noting that they have a “value bias (not growth or neither), a high quality bias (top two quartiles in our quality model), and a negative momentum bias (bottom three quintiles of price momentum).”

factor timing

Financials and Energy stocks dominate their list of possible underweights:

factor timing

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