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The Valuation Effect and Consequences of Clawback Adoption in Real Estate Investment Trusts

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Abstract

This study explored the valuation effect of clawback adoption in the REIT market and identified possible channels through which clawback may generate benefits to REITs. We first found that the stock market reacts positively to the announcement of clawback adoption, and that market response is more pronounced when the clawback policy is strong, based on a sample of initial clawback adoptions in REITs between 2007 and 2018. The valuation effect of clawback adoption is stronger among those REITs with higher likelihood of restatements and greater disclosure opacity prior to adoption, suggesting that REIT investors anticipate that the adopted clawbacks will reduce financial restatement risks and improve disclosure quality. Our further analysis found that clawback adoption reduces the chance that REITs will receive comment letters from the regulator, improve financial reporting readability and decrease investment aggressiveness in REITs. Compared with weak clawback adopters, strong adopters have lower incidences of financial restatements in the post-adoption period. Our findings indicate that clawback is a value-relevant corporate governance mechanism in REITs.

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Notes

  1. The percentage of REITs that had adopted clawback policy is 60.65% in 2017. This adoption rate is similar to the percentage of 61% of firms in other industries (Babenko et al., 2019).

  2. Our further analysis (untabulated) suggested that although clawback adoption is positively associated with subsequent market performance and operating performance in a REIT (Cashman et al., 2016), clawback adoption does not significantly mitigate the negative relationship between aggressive investment and REIT performance (Ling et al., 2019; Xu & Ooi, 2018). In other words, the investment conservatism arising from clawback adoption does not enhance firm performance.

  3. Cashman et al. (2016) is the only paper that studies clawback adoption in the REIT market. They investigate the determinants of clawback adoption and firm performance after a REIT has adopted a clawback policy. This study differs from theirs in several aspects: first, we explored the valuation effect of clawback adoption from stock market reactions to the adoption announcements; second, we differentiated clawback policies into strong and weak adoptions by the strengths; and third, we investigated the consequences of clawback adoption which directly test whether clawback policy can be used for internal governance control to curb managerial opportunism.

  4. See the details in: https://www.sec.gov/news/press/2007/2007-255.htm.

  5. See: https://www.wsj.com/articles/wells-fargo-board-actively-considering-executive-clawbacks-1474985652.

  6. A report by Reuters showed that CEOs in the banking industry became more cautious about their businesses after the clawback was enforced in Wells Fargo & Co. See the details in: https://www.reuters.com/article/us-wells-fargo-accounts-clawbacks/wells-fargos-ceo-pay-clawback-puts-wall-street-executives-on-notice-idUKKCN11Y358.

  7. The positive impacts of corporate governance structure of REITs are found in the IPO market and merge & acquisition market (Campbell et al., 2011; Hartzell et al., 2008). Baik et al. (2011) showed that an industry guidance to promote voluntary disclosure of funds from operations discourages the discretionary reporting of FFOs and the potential manipulations, which leads to incremental information content of FFOs to market investors.

  8. Previous studies (e.g., Babenko et al., 2019; Chen et al., 2015; Iskandar-Datta & Jia, 2013) have explored the valuation effect of clawback adoption in general firms; however, the heterogeneity of this effect across firms has not be investigated.

  9. Bauer et al. (2010) found that corporate governance enhances firm performance in REITs only when the dividend payout ratio is low (i.e., institutional constraint is weak).

  10. Babenko et al. (2019) showed that mitigating excessive risk-taking is ranked third by firms to justify the adoption of clawback provisions. They argue that if firms have aggressive investment and financing policies, employees may be encouraged to take inappropriate actions and trigger clawback by misconduct. Through risk reduction policies, the volatility of stock market and accounting performance in a firm would also be decreased, which in turn could lower the chance of the triggers of clawback.

  11. We used “aggressive investment” and “imprudent investment” interchangeably in this research to refer to large investment/asset growth in REITs, following Babenko et al. (2019). Ling et al. (2019) showed that large asset growth rate in the REITs is associated with poor subsequent firm performance in the REITs. Eichholtz and Yönder (2015) showed that corporate investments are larger in the REITs with overconfident CEO; and the investments are associated with poor returns.

  12. The Ziman database is widely used in REIT studies; see Ro and Ziobrowski (2011), Glascock and Lu-Andrews (2015). Ling and Naranjo (2015), Ling et al. (2020), Shen (2021), Shen et al. (2021a, 2021b), etc.

  13. Following Babenko et al. (2019), we search keywords in each DEF 14A proxy statement (and 10-K filing), including clawback, claw back, claw-back, compensation recover, compensation recoup, recoup provision, recoup policy, recoup award, and recover award.

  14. The number of adopters (152) initial clawback adoptions is less than the number of adopters (167) in the panel data, because the cumulative abnormal returns requires at least 240 trading days before initial adoptions to estimate the market reactions to the announcements of clawback adoptions. The 15 REITs adopted clawback policies before IPOs or shortly after IPOs, and hence does not have sufficient trading data to calculate CARs for initial adoptions.

  15. A clawback policy is with more strengths if it covers different types of compensations (e.g., incentive compensation, stock-related compensation, cash compensation), if the employee coverage is comprehensive, if a REIT is obligated to recoup executives’ compensation, if the look-back period that the policy pertains is long, and if both financial and non-financial events can trigger the recoupment. The detailed criteria to measure the strength in a clawback policy are shown in Appendix 1.

  16. The following method was used to normalize sub-index into the interval between 0 and 1:

    $$\mathit{\operatorname{norm}}\_{subindex}_{ij}=\frac{raw\_{subindex}_{ij}-\min \left( raw\_ subinde{\mathrm{x}}_j\right)}{\max \left( raw\_{subindex}_j\right)-\min \left( raw\_{subindex}_j\right)}$$

    where raw _ subindexij is the raw value of clawback strength subindex j for REIT i; min(raw _ subindexj) and max(raw _ subindexj) are the minimum value and maximum value for the sub-index j in the sample respectively, and norm _ subindexij is the value of clawback strength subindex j for REIT i after normalization.

  17. Section 408 of the Sarbanes-Oxley Act (SOX408) requires the SEC to review the financial reports of public firms no less than every three years. If there are any questions raised by the SEC, it will issue a comment letter to express concern about financial reporting practices. If the responses of a firm are satisfactory, there is no further action by SEC. However, if the questions are substantial and cannot be answered appropriately, firms may be required to restate their financial reports (SEC, 2019).

  18. Normal FFO in a REIT is calculated using income before extraordinary items, minority interest, the depreciation and amortization of real estate property, and the gain or loss on sales of real estate property. Normal FFO was calculated from relevant Compustat items (Zhu et al., 2010). The data of reported FFO in a REIT were extracted from IBES, supplemented by data from annual reports. In the unreported analysis, we also measured the earnings management in the REITs by discretionary accruals (Jones, 1991; Dechow et al., 1995). The results remain similar.

  19. The Fog index is developed by Gunning (1952) and is widely used when evaluating the reading capacity of high school seniors. Gunning Fog index is also applied to measure financial reporting quality (Biddle et al., 2009; Lawrence, 2013; Li, 2008; Li, 2010; Loughran & McDonald, 2014). In the unreported analysis, we also calculated financial reporting readability by the Flesch–Kincaid Grade Level index (Dempsey et al., 2012; Kincaid et al., 1975; Subramanian et al., 1993). The results are similar.

  20. According to PCAOB, a material weakness is “a deficiency, or a combination of deficiencies, in internal control over financial reporting, such that there is a reasonable possibility that a material misstatement of the company’s annual or interim financial statements will not be prevented or detected on a timely basis” (PCAOB, 2004).

  21. Audit fee is a function of firm characteristics of REITs, property type and REIT type (Danielsen et al., 2009). The abnormal audit fee is the residual from the regression. Appendix 2 provides the details to calculate this variable.

  22. We require at least 90 days to calculate the cumulative returns (RET) and standard deviation of daily returns (STD) over a fiscal year.

  23. The average number of internal control weakness in REITs is much smaller than 0.49 in common stocks (Lu et al., 2011). This is consistent to Chen and Keung (2018) which find that the disclosure of internal control weakness varies extremely across different industries. For instance, there are 64.29% of firms in electrical industry with internal control weakness while only 4.87% of firms in building material and construction industries are reported with internal control weakness.

  24. Chen et al. (2015) showed that the average cumulative abnormal return from the market model in a three-day event window in 388 new clawback adoptions by general firms is 0.36%.

  25. Babenko et al. (2019) showed that the mean CARs in firms with high clawback strength are 0.297% and 0.259% in three-day and seven-day event windows.

  26. The results are similar if two-day and five-day CARs are used.

  27. The results remain similar if these variables are constructed by three-year average values before first clawback adoptions in the REITs.

  28. The findings remain robust in the tests based on CARs from the Fama-French model or from two-day/five-day CARs. All control variables are included in the regressions. The coefficients on control variables and their t-statistics are also unreported. These results are available upon request.

  29. Anglin et al. (2013) showed that internal corporate governance through director board and auditing can effectively reduce real activities-based earnings manipulation in REITs. In our unreported results, we found that the valuation effect of clawback adoption is also unrelated to prior real activity manipulation in the REIT market.

  30. The magnitudes are calculated using standard deviations of Fog index and its coefficients, i.e., 0.363% * 1.104 = 0.403%.

  31. Schrand et al. (2021) show that the number of SEC comment letters received by REITs increase significantly from 164 in 2006 to 559 in 2010, and then decrease subsequently. So the increase of SEC comment letters in the post-adoption period may be coincident with the overall trend of SEC comment letters received in the REIT market.

  32. We appreciate the recommendation of instrumental variable approach from a referee. Previous studies also apply a propensity score matching approach to mitigate the endogeneity concern (e.g., Chan et al., 2015). Due to the small size of REIT sample, a sufficient number of REIT adopters and matched non-adopter cannot be obtained after the matching for regression analysis. As an alternative option, an entropy balancing approach was used following previous studies (Chapman et al., 2019; Hainmueller, 2012; McMullin et al., 2019). The entropy approach assigns weights to observations in the control group in a continuous scale and achieves almost identical covariate balance between treated observations and weighted control observations. This approach is similar to a matching approach but allows to preserve the full sample. The results show that the inferences are similar to the findings from IV approach.

  33. The director network data of the REITs and Russell 3000 stocks were collected from BoardEx database.

  34. In the results of second stage regressions, the financial restatement and earnings manipulation variables in the Panel B are negatively associated with returns on equity and the volatility of stock returns. The financial readability variable in the Panel C is negatively associated total assets, Tobin’s Q ratio, returns on equity and the volatility of stock returns. Two investment variables in the Panel D are negatively related to the Tobin’s Q ratio, returns on equity, leverage ratio and the volatility of stock returns.

  35. We also test whether the reduction in the financial restatement risk and the improvement in financial disclosure quality are associated with enhanced performance after clawback adoption based on a similar model. The results show that the coefficients on the interaction term of clawback adoption dummy and restatement dummy, clawback adoption dummy and the number of SEC comment letters, clawback adoption dummy and Fog index, and clawback adoption dummy and abnormal auditing fee are all significantly negative in the regressions of Tobin’s Q in the year t + 1, indicating that clawback provisions benefit REITs through the channels of the reductions of financial restatement risk, disclosure opacity and audit risk in financial reports. For the sake of brevity, these results are not reported but available upon the request.

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Funding

Shen acknowledges a research grant (P0030199) from the Hong Kong Polytechnic University. The authors thank the editor, Professor C. F. Sirmans, and one referee for their insightful comments and Professor Michael Anson for his editorial work.

This study was supported by a Start-up grant from the Hong Kong Polytechnic University (Grant numbers [P0030199]).

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Authors and Affiliations

  1. International School of Economics and Management, Capital University of Economics and Business, Beijing, China

    Daoju Peng

  2. Department of Building and Real Estate, The Hong Kong Polytechnic University, Hum Hong, Hong Kong, China

    Jianfu Shen, Eddie C. M. Hui & Kwokyuen Fan

  3. Department of Accounting, Deakin University, Melbourne, Australia

    Simon Yu Kit Fung

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  1. Daoju Peng
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  2. Jianfu Shen
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Contributions

Conceptualization: Daoju Peng, Jianfu Shen, Simon Yu Kit Fung, Eddie C.M. Hui; Methodology: Jianfu Shen, Simon Yu Kit Fung, Kwok Yuen Fan; Formal analysis and investigation: Kwok Yuen Fan, Jianfu Shen; Writing - original draft preparation: Jianfu Shen, Kwok Yuen Fan, Daoju Peng; Writing - review and editing: Simon Yu Kit Fung, Eddie C.M. Hui; Funding acquisition: Jianfu Shen; Supervision: Jianfu Shen, Eddie C.M. Hui.

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Correspondence to Jianfu Shen.

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Appendices

Appendix 1: Clawback strength index construction

We construct the clawback strength index following Erkens et al. (2018). We perform linguistic analysis on clawback provisions in the definitive proxy statements (Form DEF 14A) filed by REITs in the US from 2007 to 2018. The strength of clawback provisions is evaluated based on five dimensions: (1) compensation coverage, (2) employee coverage, (3) enforcement, (4) time period, and (5) trigger. For each dimension, we construct a sub-index to quantify the strength of a clawback policy.

Compensation coverage

The sub-index of compensation coverage is calculated based on the types of compensation that are subject to forfeiture, including incentive compensation, indirect profits such as stock sale gains, deferred compensation, stock and option compensation, cash compensation and other compensation. One point is awarded for each of the compensation categories if it is explicitly stated in the clawback provisions. The sub-index is the sum of the points.

Employee coverage

The sub-index of employee coverage is constructed based on employees covered in the clawback provisions. If the provision mentions CEO/CFO or chief officers of other functions explicitly, 0.2 points are awarded. One point is added for the clawback policy that explicitly mentions executives and NEOs (named executive officers). Two points are added if the provisions cover employees in general. One further point is added if the provisions cover all employees/executives and former employees/executives respectively.

Enforcement

The enforcement sub-index measures the level of discretion that a REIT can activate a clawback adoption. A higher score is assigned in the enforcement sub-index if clawback provisions contain less discretion. One point is awarded if the provisions explicitly state that the board has an obligation to claw back excess compensations. If the provisions state that the board has the right or option to claw back excess compensations, 0,75 points are added. If the provisions state that the board has the discretion to claw back excess compensations, 0.25 points are subtracted. If the provision mentions that additional actions (e.g. termination of employment) will be taken, 0.25 points are added.

Time Period

The time period sub-index is constructed based on the look-back period of clawback provisions. The longer the look-back period, the higher the score assigned in the time period sub-index. One point is awarded if the look-back period is equal to or shorter than six months. An extra point is added if the period is increased by six months until the look-back period is longer than 36 months.

Trigger

The trigger sub-index captures financial and non-financial events that can trigger a clawback adoption. The trigger events are categorized into financial triggers (including financial restatement, misstatement, poor performance, etc.) and non-financial triggers (including violating employment agreements, criminal behaviours, early departure, etc.). One point is added if clawback provisions mention any financial trigger event, and one point is added for any non-financial trigger event. 0.25 points are subtracted if clawback provisions set some hurdles for a clawback, including materiality hurdle, misbehavior hurdle, and deliberateness hurdle.

The strength of clawback provisions

The sub-index of each dimension is standardized within a range of [0–1] interval using the following algorithm:

$$\mathit{\operatorname{norm}}\_{subindex}_{ij}=\frac{raw\_{subindex}_{ij}-\min \left( raw\_{subindex}_j\right)}{\max \left( raw\_{subindex}_j\right)-\min \left( raw\_{subindex}_j\right)}$$

where raw _ subindexij is the raw value of clawback strength subindex j for REIT i, min(raw _ subindexj) and max(raw _ subindexj) are the minimum value and maximum value for the sub-index j in the sample, respectively, and norm _ subindexij is the value of clawback strength subindex j for REIT i after normalization.

The sum of all standardized sub-indexes gives the strength of a clawback provision.

Appendix 2: Estimating variables on managerial opportunistic behaviors

The absolute value of discretionary funds from operations

The measure of discretionary funds from operations (DFFO) is estimated using the absolute value of the difference between reported FFO and normal FFO. Following Zhu et al. (2010), normal FFO is calculated with the following equation:

$${FFO}_{i,t}={IB}_{i,t}+{MII}_{i,t}+{DPRET}_{i,t}-{SRET}_{i,t}$$

where IBi, t is income before extraordinary items, MIIi, t is minority interest, DPRETi, t is depreciation and amortization of real estate property, and SRETi, t is the gain or loss on sales of real estate property.

The difference between the reported FFO and the normal FFO is scaled by total assets in a REIT in the previous year. As Anglin et al. (2013) argue that managers have an incentive to manipulate the FFOs upwards and downwards to fulfill financial targets, we use the absolute value of the difference (DFFO) as the performance manipulation proxy. For ease of interpretation, we multiply the final value by 100.

Abnormal audit fees

Abnormal audit fees are estimated following the approach of Whisenant et al. (2003) and Danielsen et al. (2009). The audit fees can be estimated from the following equation in each fiscal year:

$$\ln \left[{AUDITFEE}_i\right]={\beta}_0+{\beta}_1\ln \left[{ASSETS}_i\right]+{\beta}_2\ln \left[{EMPLOYEE}_i\right]+{\beta}_3{LEV}_i+{\beta}_4{LIQID}_i+{\beta}_5{INVREC}_i+{\beta}_6{ROA}_i+{\beta}_7{INITIAL}_i+{\beta}_8{FOROPS}_i+{\beta}_9{LOSS}_i+{\beta}_{10}{SALEGROW}_i+{\beta}_{11}{OPINION}_i+{\beta}_{12}{EMPLOYPLAN}_i+{\beta}_{13}{BM}_i+{\beta}_{14}{DISCOP}_i+{\beta}_{15}{RESTATE}_i+{PropertyType}_i+{ReitType}_i+{\varepsilon}_i$$

where AUDITFEEi is the audit fees paid by REIT i in the current fiscal year; ASSETS is the total assets; EMPLOYEE is the number of employees; LEV is the leverage ratio which is calculated by the total debt divided by total asset; LIQID is the liquidity ratio which is calculated by current assets divided by current liability; INVREC is the inventory plus account receivable divided by total asset; ROA is the returns on asset ratio; INITIAL is a dummy variable equal to one if auditor is in the first or second year of the audit engagement of a REIT and zero otherwise; FOROPS is a dummy variable equal to one if a REIT has foreign operations and zero otherwise; LOSS is a dummy variable equal to one if a REIT earns a negative income in either of the prior two years and zero otherwise; SALEGROW is the sales growth ratio over the previous year; OPINION is a dummy variable equal to one if a REIT does not receive a unqualified opinion in the current or prior fiscal year, and zero otherwise; EMPLOYPLAN is a dummy variable equal to one if a REIT has a pension or post-retirement plan, and zero otherwise; BM is the book-to-market ratio; DISCOP is a dummy variable equal to one if a REIT has extraordinary items or discontinued operations, and zero otherwise; RESTATE is a dummy variable equal to one if a REIT restates its financial statement and zero otherwise; PropertyType is the property type fixed effect; and ReitType is the REIT type fixed effect.

The abnormal audit fees is of REIT i is εi, which is the residual from the regression.

Investment

Investment is measured by the amounts financed by REIT i in year t. Following Fama and French (1999) and Ott et al. (2005), the components of investment can be expressed as:

$${INVESTMENT}_{i,t}={REC}_{i,t}+\triangle {S}_{i,t}+\triangle {LTD}_{i,t}+\triangle {STD}_{i,t}$$

where RECi, t is retained capital earnings, △Si, t is the net issuance of equity, △LTDi, t is the change in long-term debt, and △STDi, t is the change in short-term debt.

Specifically, retained capital earnings RECi, t can be estimated as:

$${REC}_{i,t}={IB}_{i,t}+{XIDO}_{i,t}+{DP}_{i,t}+{TXDI}_{i,t}-\left({DVC}_{i,t}+{DVP}_{i,t}\right)$$

where IBi, t is the income before extraordinary items, XIDOi, t is the extraordinary items and discontinued operations, DPi, t is the depreciation expense, TXDIi, t is the deferred taxes, DVCi, t and DVPi, t are the dividends paid on common stocks and preferred stocks, respectively.

The variable INV is calculated using the investment amount scaled by total assets in the previous year.

Appendix 3

Table 8 Variables definitions
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Appendix 4

Table 9 Additional results
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Peng, D., Shen, J., Fung, S.Y.K. et al. The Valuation Effect and Consequences of Clawback Adoption in Real Estate Investment Trusts. J Real Estate Finan Econ 68, 274–317 (2024). https://doi.org/10.1007/s11146-022-09909-w

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