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Accruals, Investment, and Future Performance

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  • Jenny Chu

Abstract

It is well documented that accounting measures of investment, such as working capital and capital expenditures, negatively predict future stock returns. The earnings fixation hypothesis suggests that investors overestimate and overvalue the persistence of the accrual component of earnings. Another stream of the literature argues that since accruals capture growth, the accruals anomaly can be explained by the investment anomaly, which finds that firms that grow their assets tend to have lower future returns. As empirical proxies for accruals and investment are either positively correlated or interchangeably used, it is difficult to distinguish between the competing hypotheses in empirical tests. This study contributes to the debate by identifying two special economic settings in which the two explanations offer diverging predictions. First, investment in research and development (R&D) represents an investment expenditure that reduces earnings but is not subject to accrual accounting. Thus, the earnings fixation hypothesis predicts a positive relation between increases in R&D investments and future returns, whereas the investment anomaly predicts a negative relation. Second, firms operating with negative working capital have working capital accruals that are negatively correlated with other forms of investment and growth. Therefore, while the earnings fixation hypothesis still predicts a negative relation between accruals and future returns in this setting, the investment explanation predicts a positive relation. For both sets of tests, the empirical evidence supports the earnings fixation hypothesis for the accruals anomaly and is inconsistent with the notion that the investment anomaly subsumes earnings fixation in explaining future stock returns.

Suggested Citation

  • Jenny Chu, 2019. "Accruals, Investment, and Future Performance," Abacus, Accounting Foundation, University of Sydney, vol. 55(4), pages 783-809, December.
  • Handle: RePEc:bla:abacus:v:55:y:2019:i:4:p:783-809
    DOI: 10.1111/abac.12177
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    Cited by:

    1. Alam, Nurul & Gao, Junbin & Jones, Stewart, 2021. "Corporate failure prediction: An evaluation of deep learning vs discrete hazard models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).

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