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Predicting Operating Income via a Generalized Operating-Leverage Model

Author

Listed:
  • Sherwood Lane Lambert

    (College of Business, University of West Florida, Pensacola, FL 32514, USA)

  • Kevin Krieger

    (College of Business, University of West Florida, Pensacola, FL 32514, USA)

  • Nathan Mauck

    (Henry W. Bloch School of Management, University of Missouri-Kansas City, Kansas, MO 64108, USA)

Abstract

We propose a generalized, practitioner-oriented operating-leverage model for predicting operating income using net sales, cost of sales, depreciation, and SG&A. Prior research links operating income directly to these items; hence, our model includes all aggregate revenues and expenses that comprise operating income. Prior research finds that the cost of sales is “much less” sticky than depreciation and SG&A; hence, we use the cost of sales as a proxy for the total variable costs and depreciation and SG&A as proxies for the sticky fixed costs. We introduce a new adjustment to the textbook operating-leverage model so that the ratio of sales to the cost of sales remains constant for the reference and forecast periods. Inspired by prior research, we adjust depreciation and SG&A for cost stickiness. We find that using our generalized operating-leverage model improves the forecast accuracy of next-quarter and next-year operating income predictions compared to predictions made using textbook operating leverage, which is a special case of our model.

Suggested Citation

  • Sherwood Lane Lambert & Kevin Krieger & Nathan Mauck, 2024. "Predicting Operating Income via a Generalized Operating-Leverage Model," IJFS, MDPI, vol. 12(1), pages 1-19, January.
  • Handle: RePEc:gam:jijfss:v:12:y:2024:i:1:p:11-:d:1325018
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    References listed on IDEAS

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    5. Jagjeevan Kanoujiya & Pooja Jain & Souvik Banerjee & Rameesha Kalra & Shailesh Rastogi & Venkata Mrudula Bhimavarapu, 2023. "Impact of Leverage on Valuation of Non-Financial Firms in India under Profitability’s Moderating Effect: Evidence in Scenarios Applying Quantile Regression," JRFM, MDPI, vol. 16(8), pages 1-20, August.
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