Purpose: This paper investigates whether China's R&D tax deduction policy triggers firms to manipulate their R&D expenditures upward.Design/methodology/approach: This paper employs the ratio of actual tax savings as a proxy for the benefits of the R&D tax deduction policy based on manually collected and systematically cross-checked data. The relationship between tax benefits and abnormal R&D spending is estimated in a sample of Chinese A-share listed companies for the period 2007–2018.Findings: The findings suggest that tax deductions lead to positive abnormal R&D spending and that this deviation in R&D spending may be attributed to firms' upward R&D manipulation for tax avoidance. The results also indicate that this behavior is more significant for the period after the policy revision, in non-HNTEs (high and new technology enterprises), and in firms with a high ratio of R&D expenses.Research limitations/implications: It is difficult to establish a sophisticated and unified model to identify the specific strategy of upward R&D manipulation that firms use to obtain tax benefits.Practical implications: Managers should take into account upward R&D manipulation when designing governance mechanisms. Policymakers in developing countries may further pursue preferential tax policies that cover every stage of innovation activities gradually; the local provincial governments need to leverage their proximity and flexibility advantages to develop a tax collection and administration system.Originality/value: This study contributes to the understanding of the complex effect of R&D tax incentives and helps more fully illuminate firms' upward R&D manipulation behavior from the perspective of tax planning strategies, which are underexplored in previous research.

Existing theoretical investment literature shows that Tobin’s q is a crucial factor of investment, while empirical studies find that cash flow exhibits a more significant effect on investment compared with Tobin’s q. Using up-to-date empirical data, we distill innovative investment from total investment to investigate this issue using a panel of 335 innovative companies in China. We unveil that innovative investment is more sensitive to growth opportunity (q) than cash flow. Furthermore, we discover that high investor sentiment can facilitate financing activities of privately owned enterprises, which may impel their innovative investments. On the other hand, state-owned enterprises will raise their tangible investments rather than innovative investment during periods of economic expansion. Lastly, we reveal that external financing cost is the channel in the external financing environment that could affect corporate innovative investment.

Against the backdrop of frequent global trade conflicts and accelerated competition in the digital economy, we investigated how trade friction shocks affect firms’ total factor productivity (TFP), with a focus on the role of digital transformation. We employed a statistical analysis method based on the multi-period difference-in-differences model with anti-dumping and countervailing measures as quasi-natural experiments to analyze firm-level panel data for Chinese exporters from 2003 to 2016. The results indicate that trade frictions compel export firms to enhance their TFP, while digital transformation further promotes this growth. This main conclusion still holds after a series of tests such as parallel trend checks, placebo tests, and variations in primary variables. The mechanism analysis shows that, in response to trade friction shocks, firms aiming to boost productivity have strategically reallocated internal resources by increasing exports of alternative products and expanding into overseas markets rather than solely relying on innovation. Additionally, digital transformation mitigates the negative effects of trade friction on firm innovation and facilitates faster internal resource reallocation during such challenges. The heterogeneity analysis reveals that digital transformation proves more beneficial for firms that are multi-product, non-state-owned, engaged in general trade, and operating in highly competitive industries. This paper contributes to the understanding of trade friction and productivity at the micro level, offering valuable insights into digital transformation strategies for firms navigating these challenges.

Purpose: – The purpose of this paper is to explore the price implication of a newly developed estimator of the bid-ask spread by Corwin and Schultz (2012). The paper focusses on whether the new measure as a liquidity proxy commands a significant premium. The research helps the understanding on the validity of the Corwin-Schultz estimate as a liquidity measure. Design/methodology/approach: – The authors carry out their examination based on the portfolio approach, cross-sectional regressions, and time-series regressions. For comparison, the authors also adopt other three liquidity proxies and mainly rely on the Fama-French three-factor model as the benchmark. The sample includes NYSE/AMEX/ARCA/NASDAQ ordinary common stocks over 1926-2010. Findings: – The paper finds that Corwin-Schultz spread lacks significant power to predict returns either in the pre- or post-1963 period. In contrast, other liquidity measures such as the price impact of Amihud (2002), trading discontinuity of Liu (2006), and turnover show stronger return predictability than the Corwin-Schultz spread estimate. Research limitations/implications: – The evidence indicates the limited ability of the Corwin-Schultz spread estimate to describe liquidity. Practical implications: – The comparison of the Corwin-Schultz spread with other liquidity measures helps practitioners and academic researchers to identify the appropriate proxy. Originality/value: – This paper, for the first time, provides a thorough assessment of the Corwin-Schultz spread estimate as a liquidity proxy, which distinguish from Corwin and Schultz (2012) who focus on whether their spread estimate measures transaction costs. Our study not only helps practitioners and academic researchers to select an adequate liquidity measure and an asset pricing model to use, but it also sheds light on the current debate about whether transaction costs have the first order importance in asset pricing.

This paper assesses the explanatory power of the liquidity-risk-based pricing models relative to the Fama–French three-factor model (FF3) and the extensions to the FF3. We find that the liquidity-augmented capital asset pricing model (LCAPM) performs no worse but generally better than other models considered in describing liquidity risk and a variety of anomaly portfolios. Our finding remains intact relative to the troublesome portfolios related to small, value, and aggressive investment.. This study highlights that liquidity risk is not negligible, which is in contrast to some recent findings that the price-impact-based liquidity risk factor contributes little to explain average returns.