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Decomposing the market, industry, and firm components of profitability: implications for forecasts of profitability


Academics and practitioners frequently highlight that overall market and industry performance is an important aspect of a firm’s profitability. However, few studies allow for the decomposition of a firm’s profitability into market, industry, and idiosyncratic components, and those that do often assume that the market and industry components are cross-sectional constants. In this study, we allow for variation in firm-specific sensitivities to market, industry, and idiosyncratic economic shocks, and then assess whether and when this decomposition results in improved forecasts of profitability. For the overall sample, we find significant improvements in terms of the magnitude of forecast errors and the frequency with which forecasts based on the decomposed values are superior versus forecasts using only total profitability. Across the sample as a whole, decomposing profitability in the forecasting process results in more accurate forecasts greater than two-thirds of the time (increasing to almost 80% within certain subsamples). Our results provide strong support for the role that firm-specific measures of market and industry profitability play in predicting a firm’s future performance, as well as highlighting settings where the decomposition provides the greatest benefit in terms of predicting future changes in profitability.

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  1. 1.

    The terms earnings and profitability are used interchangeably throughout the paper.

  2. 2.

    For example, Macquarie Research provides a “Quantamenals” report that directly addresses industry-level and macroeconomic determinants of corporate earnings (October 6, 2009). Similarly, in its August 4, 2017 report UBS Financial Services refers to “macro” and industry (e.g., consumer-focused banks and diversified financials) in its discussion of US Financials. In its February 1, 2008 equity research report for Chevron, Credit Suisse states “like much of the rest of Big Oil” when discussing Chevron’s potential for growth. Similarly, in its February 21, 2012 “Flash Update” for Chesapeake Energy, Canaccord highlights macroeconomic investment risks related to this company by noting “Global crude oil prices are affected by overall supply and demand, political developments…seasonal weather patterns.” Morgan Keegan & Co., Inc. provides support for a poor forecast for BigBand Networks, Inc. stating “in light of seemingly weak cable TV spending”, which is an overall industry effect.

  3. 3.

    For example, in Signet Jewelers’ first quarter earnings announcement on May 25, 2017 the company references the retail environment “we had a very slow start to the year as continued headwinds in the overall retail environment were exacerbated by a slowdown in jewelry spending.” In its 4th quarter 2008 10-Q, Home Depot discusses construction and home improvement markets “There were a number of factors that contributed to our comparable store sales decline. The residential construction and home improvement markets continued to be soft…”, and in J.P. Morgan Chase’s 2008 earnings announcement the CEO, Jamie Dimon references the general economic environment “If the economic environment deteriorates further…it is reasonable to expect additional negative impact.”

  4. 4.

    In untabulated analysis, we extend the analysis from Bhojraj et al. (2003) that examines the superiority of GICS industry classifications relative to SIC industry classifications to our sample period and continue to find GICS classifications to be superior as defined in Bhojraj et al. (2003).

  5. 5.

    Bernard (1995) highlights the importance of profitability predictions, stating that profitability prediction “is tantamount to the ability to approximate current value” (p. 736). This view is echoed in Nissan and Penman (2001) who state “the analysis of current financial statements should be guided by the ‘predictive ability’ criterion: any enhancement that improves forecasts is an innovation” (p. 124).

  6. 6.

    Any common information between the industry and market will be attributed to the market factor in this case. We use 20 quarters of information for all industry-level regressions (2a).

  7. 7.

    By construction, the residual from the first stage is independent of RNOAM.

  8. 8.

    Our approach to estimating the market, industry, and firm-specific profitability components differs from previous studies. Hui et al. (2015), for example, estimate equal-weighted industry earnings, where the difference between total earnings and the industry earnings is the firm-specific component. This approach, however, does not allow for differential sensitivities to industry-level or market-level earnings.

  9. 9.

    Restricting the sample to calendar quarters assures that firms are aligned across time to calculate market and industry-level information on a contemporaneous basis.

  10. 10.

    Our results are robust to deleting observations where either the mRNOAbeta or iRNOAbeta are not significant at less than a ten-percent level. This approach, however, means there are some very large beta estimates which impact the computation of MktRNOA, IndRNOA, and hence, IdiosRNOA.

  11. 11.

    Using the quarterly data, annualized mean (median) RNOA is 13.44% (11.88%).

  12. 12.

    In untabulated results we repeat the analysis using seasonal data (we regress the change in RNOA from four quarters ahead to the current quarter on current quarter RNOA and the change in RNOA from the current quarter to four quarters lagged). The mean reversion in profitability is qualitatively unchanged using a seasonal model, but the coefficients and explanatory power of the reported models are smaller. Inclusion of both one-quarter lags and seasonal lags do not add much in terms of explanatory power; thus for the sake of parsimony we report results using only one-quarter lags.

  13. 13.

    We do not tabulate the results from the 73 different GICS industries that satisfy data requirements given the difficulty in providing parsimonious results that are easily interpretable. Results are available from the authors upon request.

  14. 14.

    Our results are qualitatively the same using ten years (40 quarters) or three years (12 quarters) of prior observations. Using a shorter time series is akin to setting the weights in later years to one and earlier years to zero. Early research in time-series forecasting implies that later years should be weighted more heavily, although the approach has not been applied to panel data. The robustness of our findings to alternative estimation periods indicates that weighting more recent observations does not alter the superiority of the various forecasting models.

  15. 15.

    Following Call et al. (2016), in unreported analyses we also estimate each regression on a firm-specific basis instead of using pooled data. Under the firm-specific approach there are significantly fewer observations available for estimation, and as a result forecast accuracy is significantly worse.

  16. 16.

    In untabulated analysis we also use a naïve model where the forecast of ΔRNOA = 0 without any change in inferences.

  17. 17.

    Bradshaw et al. (2012) also find that analyst forecasts generally do not outperform random-walk forecasts. This, along with a lack of analyst forecasts of RNOA and the complexity of backing out an analyst RNOA forecast from the I/B/E/S files, keeps us from using analyst forecasts as a benchmark.

  18. 18.

    For the components approach, in untabulated results we estimate a model that uses all observations to forecast ΔMktRNOA, only industry data to forecast ΔIndRNOA, and estimate ΔIdiosRNOA on a firm-specific basis. This allows for an explicit relaxation of the assumption that profitability and growth in profitability of all firms revert to a common benchmark at the same rate (Fairfield et al. 2009), while still allowing for industry and firm-specific reversions. As with the firm-specific estimations for the total and components approach, this method is significantly worse than the CP method.

  19. 19.

    Industry 452,040 (Office Electronics) drops out from the sample due to insufficient data in the out-of-sample tests. We do not tabulate the results, although they are available upon request from the authors.

  20. 20.

    We also analysed cross-sectional differences in forecast superiority based on both industry concentration and the number of business segments disclosed by firms. When we sort firms into terciles based on industry concentration (measured based on the number of firms included in an industry) we find that the forecast improvement and superiority is the greatest when there are more firms in the industry, although the difference is economically small (65.5 (68.9) percent superiority for the lowest (highest) tercile). We also document cross-sectional differences in the forecast superiority based on the number of segments the firms report. We find that our method of decomposition performs best for single segment firms (about 11.6% of our sample). However, in untabulated analyses our methodology provides significant forecasting superiority relative to using total RNOA for every sub-category of business segments including firms with up to and greater than 10 segments (forecasting superiority ranges from a low of 63.5% for firms with 8 segments to a high of 67.9% for firms with 4 segments, excluding single segment firms which experience superior forecasts 71.9% of the time using the decomposition methodology).


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We appreciate comments from two anonymous reviewers, Steve Penman (editor), Philip Brown, Peter Clarkson, Ilia Dichev, Doug Foster, Steve Hillegeist, Stijn Masschelein, Jim Ohlson, Cathy Shakespeare, Cameron Truong, Marvin Wee, and seminar participants at the 2016 AAA Annual Meeting, 2016 AFAANZ Annual Conference, 2017 MEAFA Research Meeting, Monash University, UNSW Sydney, University of Otago, University of Technology Sydney, and University of Western Australia. All errors remain our own responsibility.

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Correspondence to Andrew B. Jackson.


Appendix 1

Appendix 1 Variable definitions

Appendix 2

Appendix 2 Forecasting classification

This appendix describes the methods used to forecast ΔRNOA. All reported forecasting models are estimated using pooled data.

The TOTAL (TP) method uses RNOA and Δ RNOA over the prior 20 quarters to estimate the persistence coefficients which are then used to create out-of-sample forecasts for quarter q + 1 Δ RNOA:

$$ \Delta {RNOA}_{i,q}={\delta}_0+{\delta}_1{RNOA}_{i,q-1}+{\delta}_2\Delta {RNOA}_{i,q-1}+\varepsilon $$

Forecasts of future changes in profitability (Δ RNOAi,q + 1) are based on in-sample coefficients applied out-of-sample, as in equation (A2):

$$ \Delta {RNOA}_{i,q+1}={\widehat{\delta}}_1\ast {RNOA}_{i,q}+{\widehat{\delta}}_2\ast \Delta {RNOA}_{i,q} $$

The AGGREGATE (AP) method allows for differential persistence parameters across the three components of profitability in terms of the relation with ΔRNOA, as well as cross-sectional variation in the ultimate forecast of ΔRNOAi,q + 1 given the components of profitability (MktRNOA, IndRN OA, IdiosRNOA, and their respective changes) vary cross-sectionally

$$ {\displaystyle \begin{array}{l}\Delta {RNOA}_{i,q}=\\ {}{\gamma}_0+{\gamma}_1{MktRNOA}_{i,q-1}+{\gamma}_2{IndRNOA}_{i,q-1}+{\gamma}_3{IdiosRNOA}_{i,q-1}+{\gamma}_4\Delta {MktRNOA}_{i,q-1}+\\ {}{\gamma}_5\Delta {IndRNOA}_{i,q-1}+{\gamma}_6\Delta {IdiosRNOA}_{i,q-1}+\varepsilon \end{array}} $$

In the COMPONENT (CP) method, we separately estimate the persistence of each component in terms of its relation with future realizations of the same component. This differs from the AP approach, which simultaneously estimates the persistence relations of all components to overall ΔRNOA

$$ \Delta {MktRNOA}_{i,q}={\theta}_0+{\theta}_1 Mkt{RNOA}_{i,q-1}+{\theta}_2\Delta {MktRNOA}_{i,q-1}+\varepsilon $$
$$ \Delta {IndRNOA}_{i,q}={\varphi}_0+{\varphi}_1 Ind{RNOA}_{i,q-1}+{\varphi}_2\Delta {IndRNOA}_{i,q-1}+\varepsilon $$
$$ \Delta {IdiosRNOA}_{i,q}={\omega}_0+{\omega}_1 Idios{RNOA}_{i,q-1}+{\omega}_2\Delta {IdiosRNOA}_{i,q-1}+\varepsilon $$

After calculating the separate components of earnings, we forecast the individual components and combine the forecasts to arrive at our forecast of changes in profitability for quarter q + 1 (Δ RNOAi,q + 1), as in equation (A5)

$$ \Delta {RNOA}_{i,q+1}={\widehat{\theta}}_1\ast {MktRNOA}_{i,q}+{\widehat{\theta}}_2\ast \Delta {MktRNOA}_{i,q}+{\widehat{\varphi}}_1\ast {IndRNOA}_{i,q}+{\widehat{\varphi}}_2\ast \Delta {IndRNOA}_{i,q}+{\widehat{\omega}}_1\ast {IdiosRNOA}_{i,q}+{\widehat{\omega}}_2\ast \Delta {IdiosRNOA}_{i,q} $$

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Jackson, A.B., Plumlee, M.A. & Rountree, B.R. Decomposing the market, industry, and firm components of profitability: implications for forecasts of profitability. Rev Account Stud 23, 1071–1095 (2018).

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  • Macroeconomy
  • Market
  • Industry
  • Profitability
  • Forecasting
  • Firm-specific estimates

JEL classification

  • M41