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Firm Leverage and Stock Price Crash Risk: The Chinese Real Estate Market and Three-Red-Lines Policy

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Abstract

This study examines the relationship between firm leverage and stock price crash risk, where extant literature shows mixed findings on the impact of leverage on stock price crashes. Utilizing the Chinese real estate industry setting with high debt financing, we show that leverage is significantly positively associated with stock price crash risk. We further examine how the relationship between leverage and stock price crash risk is attributed to the unique Chinese institutional and economic environment. We show that the effect of leverage on stock price crash risk is concentrated in regions of low social trust, low marketization, and low economic growth. We next consider China’s new guidance of the “three red lines” that governs firms’ debt financing policies, and we find that, among the three-red-line debt measures, the liability-to-asset ratio is the most significant determinant of crash risk. We also examine the implementation of the guidance in August 2020 with a difference-in-difference research design, and we show that the new guidance has significantly reduced stock price crash risk for high-leverage firms. Overall, our results show consistent evidence of stock price crash risk increases for Chinese real estate firms that have shown overreliance on debt financing. Our study highlights the vital role of firm leverage on stock price crash risk that pertains specifically to highly-levered firms.

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Notes

  1. Most literature adopts the perspective of defining stock price crash risk as firm-specific risk related to the higher moments of the stock return distribution that could potentially result in large stock price drops or crashes. For a detailed review and discussion of crash risk literature, see Habib et al. (2018).

  2. Under the new guidance, real estate firms must maintain a liability-to-asset ratio (excluding advance receipts) of less than 70%, a net gearing ratio of less than 100%, and a cash-to-short-term debt ratio of more than one (UBS, 2021).

  3. Source: Jing (2021). https://www.wenweipo.com/a/202101/18/AP60049f74e4b0093c7f26d024.html.

  4. The new guidance limited Evergrande’s borrowing capacity, forcing it to pay down its loans by liquidating assets in a fire-sale condition. The resulting losses further worsened its reported debt ratios, pressuring for its further cut-back. As a result, it created a downward leverage-induced spiral for the firm.

  5. The average net debt-to-equity ratio of Chinese mainland-listed real estate developers is estimated at 90.9% in the third quarter of 2020 (Guo & Wang, 2020), while the average leverage measured by liability-to-asset ratio is at 78.5% by June 2021 (Li, 2021). As a comparison, the average adjusted debt-to-equity ratio of Chinese corporates stands at about 70.3% in 2021 (Chan & Tan, 2021), and prior academic studies commonly report an average leverage ratio for general Chinese firms at about 50% (e.g., Cao et al., 2016; Chen et al., 2018; Ji et al., 2021; Xu et al., 2014; Yuan et al., 2016).

  6. Anecdotally, we witness the meltdown of Evergrande, one of the largest real estate developers in China and in the world, and the most indebted company globally with more than $300 billion in debt (Ni, 2021). The real estate developer had its credit ratings downgraded to the junk bond category in 2021 (Tan, 2021a), and it was on the edge of default for missing $84 million of interest payments on its loans, and, coincidentally, Evergrande’s share price tumbled by more than 75% by the fall of 2021 (Farrer, 2021).

  7. According to UBS (2021), the objectives of the three-red-lines guidance include controlling housing prices, managing land markets, rationing credit to the real estate sector, lowering cyclicality, introducing more stability in the real estate capital market, and ensuring a more sustainable future for the real estate sector.

  8. Source: Wang (2020). https://www.reuters.com/article/chinese-property-developers-funding-rule-idCNKBS25P01Y.

  9. For instance, China’s Shanghai and Shenzhen stock exchanges suffered some of the biggest crashes in 2015 after their benchmark composite indices had risen by 150% and 191% respectively in the previous year. By July 2015, the two exchanges had lost over $3 trillion, close to one-third of China’s GDP (Dun & Bradstreet, 2015).

  10. For instance, Ott et al. (2005) document that investment in U.S. REITs was financed by 91% equity, retained earnings, and long-term debt, while short-term debt and preferred stock made up only 9% of the financing.

  11. We further consider endogeneity issue by adopting an instrumental variable (IV) approach and by conducting a difference-in-difference (DID) analysis discussed in later sections.

  12. While we do expect firms change their leverage strategies after the policy was introduced, we do not expect it would create significant bias in our difference-in-difference design. Since leverage policies are typically negotiated beforehand and prior research (e.g., Devos et al., 2017) has shown it takes time for firms to adjust their leverage, we suppose firms could not alter their leverage policies immediately. In our difference-in-difference analysis, we therefore define POST as a short period of the fourth quarter of 2020. In so doing, we can focus solely on the market expectation effect of the guidance, holding firms’ existing leverage policy (i.e., our partitioning variable) relatively constant. In fact, we test the difference of leverage in the pre- and post-periods, and it is statistically insignificant. As the three-red-lines guidance was announced amid the third quarter of 2020, we also exclude this quarter in our analysis to have a clean experimental setting.

  13. We use the pre-period from the first quarter of the year 2019 to the second quarter of the year 2020 (i.e., six quarters) to calculate an average RED_LINE ratio for each firm, which we then use this measure to partition the firms into treatment and control firms.

  14. The exclusion restriction assumption for exogeneity requires that the IV in the first-stage regression is not correlated with the error term in the second-stage regression. Unfortunately, similar to many corporate finance studies, we acknowledge it is difficult to conduct statistical test on the validity of this condition, as it is hard to find another instrument to test for the validity of exclusion restriction (Fahlenbrach et al., 2010).

  15. Since our initial sample excludes firms with special treatment (ST) status, we find that most of our sample firms have not experienced these negative firm events. On the other hand, in unreported analysis, we find that most firms that have experienced negative firm events are also ST firms that have been excluded from our sample.

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Acknowledgements

We thank Spencer Couts, our discussant, as well as the organizers and participants of the Real Estate Finance & Investment Symposium 2022, for their comments and suggestions. We also thank the editor (Brent Ambrose) and an anonymous reviewer for their helpful comments and suggestions

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  1. CUHK Business School, Chinese University of Hong Kong, Hong Kong SAR, China

    Xiaoling Chu & Desmond Tsang

  2. Wisconsin School of Business, University of Wisconsin-Madison, Madison, WI, USA

    Yongheng Deng

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Chu, X., Deng, Y. & Tsang, D. Firm Leverage and Stock Price Crash Risk: The Chinese Real Estate Market and Three-Red-Lines Policy. J Real Estate Finan Econ (2023). https://doi.org/10.1007/s11146-023-09953-0

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