Abstract

Based on the big data of Chinese listed manufacturing firms, we examine the relationship between research and development (R&D) investment and earnings quality. We construct a regression model and find that corporate R&D investment has a positive effect on earnings quality. R&D investment is useful to repress earnings management motives and thus improve earnings quality in listed manufacturing firms in China. And this positive correlation is more significant in state-owned firms than that in non-state-owned firms.

Highlights

  • Is research and development (R&D) investment a tool for earnings management or for improving the quality of earnings? The relationship between R&D investment, corporate value and corporate performance has been widely concerned

  • The impact of R&D investment on the development of enterprises starts from the performance level and profitability

  • Tang (2015) found that even in the economic downturn, increasing R&D investment intensity is conducive to improving enterprise performance, especially for manufacturing enterprises [8]

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Summary

Introduction

Is R&D investment a tool for earnings management or for improving the quality of earnings? The relationship between R&D investment, corporate value and corporate performance has been widely concerned. Others believe that accounting treatment of R&D expenditure provides the space for earnings management, and the management may increase earnings noise by manipulating the capitalization of R&D expenditure, and damage the value relevance of accounting information (Cazavan-Jeny and Jean, 2006 [14]; Markarian et al, 2008 [15]) Another way is to balance the amount of R&D investment to affect the quality of earnings. We study the impact of R&D expenditure on earnings quality, and find that R&D investment in China’s manufacturing listed companies is not completely a tool for earnings management, but can effectively improve corporate earnings quality. R&D investment has a more significant impact on earnings quality in state-owned firms

Theoretical analysis and research hypothesis
Model design
Sample selection and data sources
Descriptive statistics
Regression analysis
Conclusions
Full Text
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