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The Challenge of Calculating Industry Momentum for Diversified Firms

Companies and Markets

By Hiroki Miyahara  |  February 18, 2020

A core component throughout the investment process is the concept of a company’s industry classification. Most classification systems map companies to single sectors based on their primary lines of business; even highly diversified companies operating across multiple industries are classified as conglomerates or something comparable. This lack of detail makes it very difficult (next to impossible) to understand/quantify a firm’s exposure to the industries in which it operates. If single sector classifications do not fully represent firm exposures, how does this impact the ability of the market to incorporate new information?

Industry Momentum and Diversified Firms

In a 2011 paper, Cohen and Lou demonstrated that compared with their simpler peers, the market takes more time to digest industry trends for more diverse firms, i.e., those with quantified exposure to multiple industries, resulting in greater return predictability for the set of diverse firms. In order to test these findings, we needed a classification system that provided more industry detail than just “conglomerate.”

FactSet RBICS with Revenue is a multi-sector classification system that standardizes a company’s line of business into a detailed taxonomy containing over 1,500 distinct sectors at its most granular sixth level. The table below shows the sector exposures of the German industrial conglomerate Siemens using RBICS. Deconstructing this company based on revenue, exposures within finance, healthcare, and industrials are exposed.

Siemens Revenue Exposure by Sector and Industry

For the U.S. Russell 3000 universe, approximately a quarter of its constituents do not attribute more than 75% of their revenue to a single RBICS Industry group (L4). To replicate the findings of Cohen and Lou, we used RBICS with Revenue to divide the Russell 3000, FTSE LSE All Share, and the TOPIX into “pure-play” companies, those with over 75% revenue from a single L4, and “conglomerate” companies, those that do not generate 75% revenue from any L4 The pure-play company daily returns were then used to calculate industry benchmark returns for each L4; for benchmarks comprised of fewer than three companies, we used the average market return instead. Next, we calculated daily industry momentum factors for each conglomerate firm using the sum of their weighted L4 revenue exposures and the relevant industry benchmark returns. We then ranked the conglomerates by the industry momentum factor, divided them into quintile portfolios, and calculated the daily returns of each quintile.

Portfolio construction process

We performed this analysis on the constituents of our three country indices using a daily rebalance from January 2015 through October 2019. The chart below shows the average return of each quintile portfolio and the spread return between quintile 5 and quintile 1 for each universe. As hypothesized, the top ranked quintile (5) portfolio generated the highest cumulative return while the lowest quintile (1) performed the worst across each universe. The cumulative spread returns between quintile 5 and quintile 1 portfolios for the Russell 3000, FTSE LSE All Share, and TOPIX were 77.2%, 37.6%, and 86.8% respectively.

Russell 3000

FTSE LSE All Share


Analyzing Global Markets with a Longer Investment Horizon

We extended this analysis using a global universe—the MSCI World Investable Market Index (IMI)—and a longer holding period, rebalancing quarterly instead of daily from December 2014 to October 2019. The cumulative spread return between the quintile 5 and quintile 1 portfolios was 18.7%. As shown in the chart below, we observed a smaller cumulative spread with our global index but the same trend is present as with our single-country indices.


Accounting for Firm Size

Firm size is another important consideration when constructing industry benchmarks and can be accounted for using weighted-average returns. We calculated the industry benchmark return using the weighted average of pure-play company returns where the weights were based on market cap ($US) and market share (revenue multiplied by L4 exposure). We compared these weighted-average returns to equal-weighted returns.

As shown in the chart below, the incorporation of size into the process improves performance of our sector momentum factor for the global universe, with market-share weighting performing the best. The cumulative returns for market share, market cap, and equal-weighted industry benchmarks were 29.4%, 24.9%, and 17.8% respectively.

MSCI World IMI Spread Returns


Our analysis supports the hypothesis that sector performance effects are incorporated into the stock prices of diversified conglomerate firms with a lag. Our investigation, focusing on the Russell 3000, FTSE LSE All Share, TOPIX, and MSCI World IMI universes required a granular industry classification system in order to capture the complex industry exposures of conglomerates.

Daniel Grundig, Vice President, Principal Product Manager for CTS Data and Solutions, also contributed to this article.

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Hiroki Miyahara

Senior Product Manager, Japan

Mr. Hiroki Miyahara is a Senior Product Manager at FactSet, based on Tokyo, Japan. In this role, he covers the Asia Pacific region for FactSet proprietary content sets including supply chain, RBICS, GeoRev, shipping, and FactSet Data Management Solution. Mr. Miyahara joined FactSet in 2011 and previously held roles as an account executive and product developer. He earned an MSc in economics from the University of Essex.


The information contained in this article is not investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.