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Sector Rotation: A Sound Investment Approach or an Exercise in Futility?

Gallery:Step 6|Step 6: Style Drifters

Introduction

Recently, many investors have succumbed to one of the siren songs of active investing, sector-picking. They believe that they can predict which industrial sectors will outperform the total market either for the short-term or the long-term. Their views are often formed from predictions about the economy. For example, if they believe that we are going into a recession, they might sell manufacturers and buy consumer staples. These views are often not well thought out because it is difficult to know the exact state of the economy today, much less where it is going in the next six to twelve months. Unfortunately, their options for implementing these views are many and are increasing like kudzu. Sector funds can be anything from very general categories like financials to ultra-specific fields like nanotechnology. Most of the newer sector funds are exchange-traded funds from providers like iShares which currently counts over sixty sector funds in its stable.

IFA has completed a study of historical returns of major industry sectors based on data from Ken French’s Website. The question explored can be phrased as follows: Does industry sector explain returns beyond the three factors of market, size, and value to the point where an investor should consider either a tactical (short-term) or a strategic (long-term) concentration in certain sectors beyond their weight in the overall market?

Background

The Fama/French three-factor model says the expected return of a broadly diversified stock portfolio in excess of a risk-free rate is a function of that portfolio’s sensitivity or exposure to three common risk factors: (1) a market factor, as measured by the excess return of a broad equity market portfolio relative to a risk-free rate; (2) a size factor, as measured by the difference between the returns of a portfolio of small stocks and the returns of a portfolio of large stocks; and (3) a relative price factor, as measured by the difference between the returns of a portfolio of high book-to-market (or value) stocks and the returns of a portfolio of low book-to-market (or growth) stocks. The underlying premise of this model is that small cap and value stocks are riskier than large cap and growth stocks and thus carry higher expected returns. Given a time series of monthly returns of any diversified portfolio, the exposure to the three factors can be calculated via regression, as well as deductions from returns such as a constant monthly expense ratio. In general, when a domestic equity portfolio is analyzed with a 3-factor regression of monthly returns, any previously attributed alpha either disappears or is found to be statistically not significant. While repeated empirical testing has found that the three-factor model captures about 90 to 96% of the variation of returns in a diversified portfolio, none of the portfolios examined here can be considered diversified because they are all concentrated in one general sector.

 

Analysis

The data series of monthly returns for each sector starts on July 1st, 1926 and ends on December 31st, 2011. At first glance, there appears to be a wide variation in the returns of various sectors, as seen in the table below.

Sector

Annualized Return

Annualized Std. Deviation

Retail

10.11%

20.54%

Financials

9.34%

24.06%

Consumer Staples

10.91%

16.21%

Consumer Discretionary

9.75%

27.15%

Manufacturing

10.00%

23.67%

Energy

11.10%

20.89%

Technology

10.07%

25.68%

Healthcare

11.39%

19.86%

Utilities

9.05%

19.54%

It would appear that one would be a fool to invest in consumer discretionary companies given that consumer staples have produced a higher return at a lower risk. Correct? Not so fast. Before ascribing higher risk-adjusted returns to particular sectors, we need to determine the portion of returns that are attributable to the three risk factors. It may well be that the average consumer discretionary company is larger and more growth-oriented than the average consumer staple company and therefore has a lower expected return according to the Fama/French 3-Factor model. This does not mean that investors should favor consumer staples companies at the expense of consumer discretionary, unless we can show that a substantial portion of the difference in returns is explained by factors beyond market, size, and value.

A 3-factor regression was run for each series for the whole time period. The primary number of interest is R-squared, the percentage of the variation in returns that is explained by the three factors. They are as follows:

Sector

R-Squared (% of Returns Explained by Risk Factor Exposure)

Retail

74%

Financials

86%

Consumer Staples

78%

Consumer Discretionary

76%

Manufacturing

93%

Energy

63%

Technology

85%

Healthcare

66%

Utilities

62%

Exposure to the three risk factors explains no less than 62% of the returns for each of the categories. When Fama and French conducted their original research in 1992, they tested industry sector and determined that it was not a significant determinant of long-term returns. Nothing that we have seen in the updated data contradicts this assertion.

Regarding the question of whether there is a pattern in the short-term returns of industry sectors that could be successfully exploited by investors, the chart below tells us the answer. Returns are random, and there is no predictable “rotation” of sectors we can use to predict next year’s (or quarter or month) winner.

Sector Rotation: A Sound Investment Approach or an Exercise in Futility?

 

To summarize, industrial sector is not a factor that drives long-term returns, and investors who concentrate in one or a few sectors take on uncompensated risk. Advisors who advocate sector rotation as a strategy are possibly doing a disservice to their clients.