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An Empirical Evaluation of Investment Strategies Based on Core Performance Measures

  • Asset Management Review
  • Abbr : AMR
  • 2023, 11(1), pp.1~32
  • DOI : 10.23007/amr.2023.11.1.1
  • Publisher : Institute of Management Research, SungKyunKwan University
  • Research Area : Social Science > Business Management > Finance
  • Received : November 15, 2022
  • Accepted : February 8, 2023
  • Published : June 30, 2023

Hee-Yeon Sunwoo 1,  Wonsuk Ha 2,  Duri Park 3,  Woo-Jong Lee ORD ID 4

1세종대학교
2중앙대학교
3서강대학교
4서울대학교

Accredited

ABSTRACT

Separating transitory components from recurring components of earnings is a primary task in financial statement analysis. A recent study by Rouen et al. (2021) uses a proprietary database compiled by New Constructs and evaluate whether their earnings measure after excluding transitory components from GAAP earnings has incremental explanatory power for future earnings. They show that their “core” earnings measure (as referred to in Rouen et al. (2021)) is more persistent than Compustat-defined operating income, suggesting that a core earnings measure effectively excludes transitory (i.e., less persistent) components of GAAP earnings. They also find that investors and financial analysts do not immediately incorporate the differential implications of the recurring and non-recurring components of earnings into stock prices, and that a trading strategy based on their non-core earnings components yields annual abnormal return of 8%. Given the findings of Rouen et al. (2021), it is an empirical question whether a core earnings measure distinguishes between recurring and transitory components of earnings more effectively than do other earnings measures in countries outside the U.S. In this study, we follow the procedure outlined in Rouen et al. (2021) and attempt to exclude the non-recurring components from net income of Korean listed firms. Specifically, we identify the non-recurring components of earnings from the DataGuide database and estimate a core earnings measure by excluding from net income (1) currency fluctuations, (2) discontinued operations, and (3) gains and losses labeled as “other” on the income statement. We then compare the persistence and return predictability of core earnings and various commonly used adjusted income measures such as operating income, gross margin, income before income taxes, and income from continuing operations of Korean firms listed in KOSPI and KOSDAQ from 2012 to 2020. The results suggest that operating income is most persistent, followed by core earnings, income from continuing operations, gross margin, and income before income taxes when predicting one-year ahead net income. We further report that both non-operating income (i.e., net income minus operating income) and non-core earnings (i.e., net income minus core earnings) have some information contents in that they are predictive of one-year ahead net income with smaller magnitudes compared to their counterparts. Portfolio analyses based on operating income and core earnings show that both measures are predictive of future stock returns, implying that investors in the Korean stock market act as if they fail to fully incorporate the information contained in the transitory and permanent components of current earnings into stock prices, similar to the findings of Rouen et al. (2021). Our results confirm that 1) operating income and core earnings are comparably superior to other adjusted earnings measures as key performance indicators of a firm and 2) a trading strategy based on these measures yields profitable returns in the Korean stock market. Our results should be interpreted with caution. While Rouen et al. (2021) indicate that New Constructs uses both fundamental analysis and machine learning to extract transitory components of earnings, the core earnings measure used in this study only utilizes the income statement items readily available from the DataGuide database for Korean listed firms. A procedure that closely replicates Rouen et al. (2021) (including machine learning) may yield a core earnings proxy with improved persistence and future earnings predictability by distinguishing between transitory and permanent components of earnings more effectively. Despite the differences in the measurement, our results suggest that operating income is comparable to core earnings in terms of earnings and return predictability without considering a machine-learning algorithm for estimating core earnings. Given that implementation of a machine-learning algorithm could be costly, operating income could be an effective alternative to core earnings. Future research is warranted to further establish a cost-effective measurement of permanent components of earnings.

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