The fair value of investment property and stock price crash risk

ABSTRACT

This study examines whether recognizing fair value of investment property is more associated with stock price crash risk than recognizing historical cost of investment property. Using a sample of publicly traded firms that held investment property from 2007 through 2011 in China, we find that firms that recognize investment property at fair value in China experience an increase in crash risk. The findings suggest that fair value reporting for investment property in China does not convey private managerial information regarding firm value and could be a channel for concealing information. In additional analysis, we also find evidence that the association between fair value reporting and increased crash risk is mitigated in firms with strong corporate governance.

KEYWORDS:

• Crash risk
• investment property
• fair value
• historical cost

Acknowledgments

We thank participants at Accounting Theory and Practice Conference & Asian Accounting Association Conference 2017 and participants at the American Accounting Association Conference 2017. We also would like to thank Ministry of Science and Technology grants for funding the project.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. In IAS 40, investment property is defined as property (land and (or) buildings) held (by the owner or by the lessee under a finance lease) to earn rentals or for capital appreciation, or both [IAS 40.5].

2. In IAS 40, investment property is defined as property (land and (or) buildings) held (by the owner or by the lessee under a finance lease) to earn rentals or for capital appreciation, or both [IAS 40.5]. In 2008, the IASB also included properties under construction or development for future use as investment properties.

3. We require at least 26 weeks in each year estimation.

4. We also exclude current, lagged, and lead value-weighted weekly industry returns of financial sectors in Equation (2) for a robustness check.

5. We also estimate the measures of crash risk based on raw residual returns for a robustness check.

6. Following Kim, Li, and Zhang (2011a, 2011b), we also use 3.20 standard deviation as the cut-off to identify CRASH indicator for a firm in each year and the results (untabulated) are robust.

7. Following Chen, Hong, and Stein (2001), we first measure TURNOVER_jt as the average monthly share turnover in stock i; defined as shares traded divided by shares outstanding over period t. DTURN, detrended turnover, is calculated by subtracting from the TURNOVER variable a moving average of its value over the prior12 months.

8. Piotroski and Roulstone (2004) suggest that the prevalence of institutional investors and analysts following improves the information environment, related to price synchronicity; we also include variables to control for this situation and the results are robust.

9. The mean of CRASH indicator in this study is 0.085, less than that in the U.S. market (e.g. 0.172 in Kim and Zhang 2016)” because since 16 December 1996, the daily stock price fluctuation of A stocks in China is limited to 10% and −10%.

Additional information

Funding

This work was supported by the Ministry of Science and Technology [107-2410-H-002 −040].