Does equity market timing have a persistent impact on capital structure? Evidence from China

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Does equity market timing have a persistent impact on capital structure? Evidence from China* Yang Zhao a, Cheng-Few Lee b, Min-Teh Yu c, * a

School of Finance, Nankai University, Tianjin, China Department of Finance and Economics, School of Business, Rutgers University, Janice H. Levin Building, Rockefeller Rd., Piscataway, NJ, 08854, USA c Providence University and PAIR Labs, Taiwan b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 31 October 2018 Received in revised form 19 June 2019 Accepted 1 July 2019 Available online xxx

This paper uses the change in individual securities accounts as a measure of equity funding supply to examine whether the persistent timing effect on capital structure exists for the Chinese equity market. This new equity timing measure avoids previous criticisms over a timing measure not being independent of a firm's characteristics of capital structure. Our empirical results show that this new measure is an effective market timing variable for issuing equity in the Chinese equity market, and that a persistent effect of equity market timing on firm capital structure exists for more than 7 years. This paper offers evidence that the market conditions of equity funding supply play an important role in corporate financing decisions in China. © 2019 Published by Elsevier Ltd.

Keywords: Cost of equity Equity market timing Capital structure Equity funding supply Chinese IPO Securities accounts

1. Introduction Bond market timing and equity market timing are both important issues in financial research, with the corporate finance literature in recent decades generating a large strand of studies concerning the effect of equity market timing on capital €ter-Kant, & Warr, structure (Baker & Wurgler, 2002; Alti, 2006; Flannery & Rangan, 2006; Dittmar & Thakor, 2007; Elliott, Koe 2008; Mahajan & Tartaroglu, 2008; Huang & Ritter, 2009; Chang, Hilary, Shih, & Tam, 2010; Alti & Sulaeman, 2012; Yang, 2013; Huang, 2014, etc.). Equity market timing refers to firms timing the cost of equity so as to issue equity when their market values are high and to repurchase shares at low prices. Graham and Harvey (2001) use survey evidence to point out that equity market timing has a noticeable influence on financing decisions. Baker and Wurgler (2002, page 25) find equity market timing has a more than 10-year long-term effect on firm leverage in the U.S. and articulate the equity market timing theory, whereby “capital structure evolves as the cumulative outcome of past attempts to time the equity market”, to explain the persistent timing effect on capital structure. They conclude that the standard tradeoff and pecking order theories are both inappropriate in a dynamic financial decision process for most firms. The persistent impact of equity market timing on capital structure, implying loose leverage targets and a minimal role for traditional determinants of capital structure, has generated an active debate in the literature. Empirical evidence of persistent

* We thank session participants at the 2016 TFA Conference and the 24th PBFEAM Conference for helpful comments and suggestions. * Corresponding author. E-mail addresses: [email protected] (Y. Zhao), cfl[email protected] (C.-F. Lee), [email protected] (M.-T. Yu).

https://doi.org/10.1016/j.bar.2019.100838 0890-8389/© 2019 Published by Elsevier Ltd.

Please cite this article as: Zhao, Y et al., Does equity market timing have a persistent impact on capital structure? Evidence from China, The British Accounting Review, https://doi.org/10.1016/j.bar.2019.100838

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impacts are found by Baker and Wurgler (2002) and Huang and Ritter (2009). Nevertheless, many studies questioning the market timing interpretation and highlighting the persistent effect of equity market timing on finance policy are unable to rule out other determinants of capital structure, such as average market-to-book ratio, investment decision, etc. (Hennessy & Whited, 2005; Hovakimian, 2006; Kayhan & Titman, 2007; Leary & Roberts, 2005). Furthermore, Alti (2006) alters the measure of equity market timing and finds that the effect of market timing on capital structure is “not persistent”. Quantifying the market timing effect is very difficult, because it requires an equity market timing measure that is independent of other determinants of capital structure. Previous conclusions for the equity market timing effect on capital structure vary with different market timing measures (Alti, 2006; Baker & Wurgler, 2002; Huang & Ritter, 2009). Baker and Wurgler (2002) use the historical market-to-book ratio (a weighted average of a firm's past market-to-book ratios) to identify market timers with a history of raising capital at high market-to-book ratios and find persistent effects of equity market timing on firm leverage. In the IPO (initial public offering) context, Alti (2006) highlights two related implications of equity market timing: 1) firms are more inclined to go public when the equity market condition is favorable; and 2) a favorable market condition can trigger firms to sell more equity. Alti (2006) uses monthly IPO volumes in terms of the number of issuers as an equity market timing measure to identify the above favorable equity market condition. He shows that the impact of equity market timing on capital structure is short term and that the timing effect vanishes 2 years after the IPO. Various papers also employ some other measures for equity market timing and verify the short-term market timing effect on capital structure, but they fail to test whether the impact of equity market timing on capital structure is persistent (Dittmar & Thakor, 2007; Elliott et al., 2008; Chang et al., 2010; etc.). Appendix A summarizes the effect of equity market timing on capital structure under various market timing measures. This paper employs the supply funding condition of equity to identify market timing in the IPO context. Since market timing engages corporate financial transactions that take advantage of overvaluation in the market, proxies for market timing should capture non-fundamental market factors (e.g., investor sentiment). Baker and Wurgler (2006) indicate proxies that involve investor behavior (e.g., equity funding supply) are more prompt and effective at reflecting non-fundamental aspects in the market than proxies that are based on firm equity demand responses (e.g. IPO volumes). Based on China's unique securities account system in which Chinese investors can only have one securities account with an exchange, whereas an investor can own multiple securities accounts in the U.S. and elsewhere, we use the change in securities accounts, an equity funding supply condition variable, as a new market timing measure to test whether equity market timing has a persistent effect on capital structure. Under China's securities account system, the fluctuation of securities accounts registered with the government's central securities depository can more accurately reflect the market condition of equity funding supply faced by securities issuers. An increasing change of securities accounts implies a larger (irrational) equity funding supply of investors that results in a favorable equity funding supply market condition, thus lowering the cost of equity.1 If IPO firms identify the above favorable market condition of equity funding supply, then the market timing hypothesis indicates firms should make use of the low cost of equity to issue more equity than other forms of capital. Furthermore, to avoid previous criticisms of the timing measure in the literature, the change in securities accounts as a proxy for equity market conditions should be independent of a firm's fundamental characteristic determinants of equity issues. Therefore, we define market timers based on whether the IPO takes place in a month with an increase of securities accounts. Our empirical study builds upon Baker and Wurgler (2002) and Alti (2006) and has two major results. First, we find that the change of individual accounts (DINDACC) is a new and effective measure for equity market timing. An increase in DINDACC as a favorable market condition triggers more firms to go public and issue more equity, thus matching the two implications of the equity market timing hypothesis of Alti (2006) and revealing the extent of market timing. Second, our study shows the persistence impact of equity market timing on capital structure using the effective equity market timing measure of DINDACC. Controlling for firm characteristics and industry effects, our empirical results show DINDACC has more than 7-year-persistent effects on capital structure and firms cannot undo the leverage effect of market timing. Some papers challenge the persistent effect of equity market timing on capital structure, because the timing measure of Baker and Wurgler (2002) can be influenced by fundamental firm characteristic factors. Our market timing measure (DINDACC) is a proxy for equity funding supply conditions and isolates a firm's fundamental characteristic determinants of capital structure; thus, it is able to overcome the challenges of previous market timing measures that are related to demandside variables. Furthermore, we add the time measure of Baker and Wurgler (2002) into our regression analysis. The empirical results show that our timing measure, DINDACC, still maintains a significantly persistent effect on capital structure. Our study contributes to the literature in two ways. First, based on the regulation that investors can have only one securities account with an exchange in China, we propose and show that a market condition variable for equity funding supply (the change in individual accounts (DINDACC)) is a new and effective equity market timing measure. Leary and Roberts (2005) and Kayhan and Titman (2007), among others, criticize the timing measure of Baker and Wurgler (2002) in that its persistent effect on capital structure will be affected by a firm's fundamental characteristic factors. Our equity market timing measure, DINDACC, is a proxy for market conditions of equity funding supply, and empirical results show that our measure is independent of a firm's known fundamental characteristic factors of capital structure.

1 Chen, Chong, and She (2014) document that the fluctuation of securities accounts is a valid proxy for the (irrational) sentiment of investors in China's stock market.

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Second, Baker and Wurgler (2002) and Huang and Ritter (2009) support the persistent effect of equity market timing on firm leverage. On the contrary, Alti (2006) alters the timing measure in the IPO context and finds that the persistent effect of equity market timing disappears. However, our empirical design reveals our market timing measure (DINDACC) has significant effects on firm leverage beyond 7 years, thus supporting that equity market timing has persistent impacts on capital structure. To our best knowledge, this is the first paper that uses a market condition timing measure, which avoids the above criticisms and finds a persistent impact of equity market timing on capital structure, to support the equity market timing theory. The traditional literature follows Modigliani and Miller (1958) and always assumes that capital supply is perfectly elastic, which means a firm's capital structure is determined solely by its demand for capital. This paper, however, provides evidence for equity funding supply effects on capital structure decisions. The remainder of the paper is organized as follows. Section 2 describes the data used in this paper and discusses China's securities accounts system. Section 3 presents the empirical design and results. Section 4 performs robustness testing, and the final section offers the conclusion. 2. Data and China's securities accounts system In this section, we present our data and China's securities accounts system. 2.1. Sample data and summary statistics Our data include all IPOs in China's A-share market over the time span from January 2003 to December 2013 in order to match the securities account data. Financial statements and IPO information come from the CSMAR Database. We remove missing data and samples without pre-IPO financial data. We exclude all financial institutions from our samples according to the industry code of China Securities Regulatory Commission (CSRC). The definitions of the research variables follow Baker and Wurgler (2002) and Alti (2006). Most variables are weighted by total assets (A); D/A is book leverage, where D is book debt and is defined as total liabilities and preferred stock minus deferred taxes and convertible debt; M/B is market-tobook ratio; d/A is the change in book debt (D); e/A is the change in book equity (E), which is defined as total assets minus book debt; EBITDA/A is earnings before interest, taxes, and depreciation; PPE/A is net plant, property, and equipment; INV/A denotes capital expenditures; CASH/A is the change in cash and short-term investments; SIZE is the logarithm of net sales; and DIV/E is dividends divided by year-end book equity (E). Except for M/B and SIZE, all of the above variables are in percentage terms. We add 3 variables to control for particular characteristics in China's capital market: state ownership (SOW), management ownership (MANAGE), and tradable shares (TRADE). The percentage of state ownership (SOW) aims at controlling the effect of state-owned shareholdings on firm leverage (Bhabra, Liu, & Tirtiroglu, 2008; Li, Yue, & Zhao, 2009). China's legal and financial systems as well as institutions are still in an under-developed stage (Allen, Qian, & Qian, 2005). With the lack of an effective financial system, the agency problem between managers and shareholders becomes severe. We thus add percentage of management ownership (MANAGE) in our analysis in order to comparatively control for the severe agency problem in China (Huang & Song, 2006). Finally, there are many non-tradable shares in the ownership structure of Chinese-listed firms. The split-share reform beginning from 2007 transforms non-tradable shares into tradable shares to reduce the agency problem between controlling and minority shareholders. Some studies demonstrate that the split-share reform has a significant impact on China's capital market (Chen, Chen, Schipper, Xu, & Xue, 2012; Hou, Kuo, & Lee, 2012; Liao, Liu, & Wang, 2014). This paper includes the percentage of tradable shares (TRADE) so that we can control for the effect of privatization. Table 1 presents the summary statistics. Appendix B provides detailed descriptions of the variables. 2.2. China's securities accounts system China's securities accounts system provides an advantageous condition to use supply-side funding variables to measure equity market timing. Equity market investors in the U.S., South Korea, Japan, and most other countries can own multiple securities accounts. However, China's regulatory authority, China Securities Depository & Clearing Corporation (CSDCC), requires each investor to register his/her real name and national identity card number to own a securities account and only one account is allowed for each exchange.2 Under this regulation, the level and change of securities accounts registered with China's central securities depository can be good measures for the equity funding supply faced by securities issuers. The source of securities accounts data is the RESSET database, and the period is from 2003 to 2013. The total securities accounts consist of two parts: individual securities accounts and institutional securities accounts. Institutional securities accounts are the sum of accounts owned by institutions, securities firms, securities investment funds, social security funds, and QFIIs (Qualified Foreign Institutional Investors). The change in securities accounts equals the difference between newly increased and canceled securities accounts divided by the total securities accounts of the previous period. We use DACC, DINDACC, and DINSACC to represent the percentage change of total, individual, and institutional securities accounts,

2

China's A-shares trade on either the Shanghai Stock Exchange (SSE) or the Shenzhen Stock Exchange (SZSE).

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Table 1 Summary Statistics. The table shows the means and the standard deviations of some financial variables. Most variables are normalized by year-end total book assets (A). D/A is book leverage, where D is book debt (D); M/B is market-to-book ratio; d/A is the change in book debt (D); e/A is the change in book equity (E); EBITDA/A is earnings before interest, taxes, and depreciation; SIZE is the logarithm of net sales; PPE/A is net plant, property, and equipment; INV/ A denotes capital expenditures; DIV/E is dividends divided by year-end book equity (E); cash/A is the change in cash and short-term investments. Except for M/B (market-to-book ratio) and SIZE (net sales), all of the other variables are in percentage terms. We add 3 ownership variables, state-owned shares (SOW), management ownership (MANAGE), and tradable shares (TRADE), to control for ownership characteristics in China's capital markets. Samples include all IPOs (Initial Public Offerings) in A-share markets from February 2003 to December 2013. Financial statements and related IPO data come from the CSMAR Database. We remove missing data and restrict samples with available pre-IPO financial data. We exclude all financial companies according to the industry code of CSRC (China Securities Regulatory Commission). t(year)

N

D/A

IPO-1

1009

IPO

1009

IPOþ1

1009

IPOþ2

1007

IPOþ3

979

IPOþ5

390

IPOþ7

229

48.43 (15.93) 25.42 (16.63) 29.29 (18.12) 32.82 (19.21) 36.25 (19.54) 43.93 (24.42) 50.39 (30.41)

M/B

2.65 (1.46) 2.31 (1.61) 2.10 (1.56) 2.33 (1.69) 2.37 (2.24) 1.97 (1.91)

d/A

0.86 (10.46) 7.19 (10.66) 7.42 (10.88) 8.04 (11.12) 5.85 (13.01) 6.52 (11.98)

e/A

51.44 (17.47) 4.30 (5.19) 4.85 (9.66) 6.39 (11.30) 6.87 (13.18) 6.04 (9.74)

EBITDA/A

SIZE

PPE/A

9.76 (3.41) 8.88 (4.81) 8.40 (7.85) 8.74 (7.83) 9.56 (8.37) 9.48 (6.62)

6.28 (1.24) 6.49 (1.16) 6.66 (1.18) 6.82 (1.19) 7.00 (1.23) 7.36 (1.36) 7.62 (1.36)

25.23 (16.34) 16.44 (13.76) 19.53 (14.70) 22.60 (15.29) 24.37 (15.55) 26.88 (17.04) 28.08 (17.58)

INV/A

7.46 (6.97) 9.53 (6.85) 8.66 (6.25) 7.27 (5.87) 6.35 (5.55) 6.25 (5.29)

DIV/E

cash/A

SOW

MANAGE

TRADE

0.11 (0.69) 0.10 (0.65) 0.09 (0.43) 0.10 (0.46) 0.15 (0.68) 0.33 (1.48)

34.67 (20.33) 34.67 (20.33) 7.36 (10.58) 3.60 (8.76) 1.34 (9.27) 1.14 (9.02) 0.79 (7.80)

12.69 (23.05) 12.69 (23.05) 11.14 (21.96) 10.58 (21.12) 5.32 (14.80) 6.38 (14.81) 4.88 (13.50)

22.03 (25.48) 22.03 (25.48) 20.06 (24.27) 19.70 (23.79) 15.74 (18.76) 8.09 (13.10) 3.16 (7.05)

25.19 (6.52) 25.19 (6.52) 38.77 (12.16) 41.09 (12.90) 68.53 (22.34) 77.61 (20.39) 88.12 (17.08)

respectively. The mean value and standard deviation of the percentage change of total securities accounts are about 0.47% and 0.4%, respectively. We remove outliers for changes of securities accounts exceeding 2% to eliminate the effect of any extreme value distribution. China's regulators have intervened in the initial public offering (IPO) markets through “lock-up” time periods in which no firms can go public in the domestic stock markets.3 There are four IPO “lock-up” periods in our sample time span that together exceed 36 months. We thus drop the four IPO “lock-up” periods in our sample.

3. Empirical designs and results 3.1. Measures of equity market timing and issuance activities This section explores whether the change in securities accounts can meet the two equity market timing implications: 1) firms are more willing to go public; and 2) issue more equity when the equity market condition is favorable. More specifically, we want to see which of the three (total, individual, and institutional) securities accounts can more accurately reflect market timing attempts. Second, we test whether the effect of our timing measures (changes of securities accounts) on capital structure can be independent of a firm's characteristic determinants of capital structure. Given the first implication, we sort the monthly changes of securities accounts from low to high and divide them equally into three groups. The results of Panel A in Table 2 show that changes in all three securities accounts have a positive relationship with the number of IPO firms. For example, the number of IPO firms in the low, middle, and high groups for the percentage changes in total securities accounts (DACC) are 209, 333, and 467, respectively. It suggests that firms perceive increases in securities accounts as a favorable market timing condition to go public. Therefore, changes in all three securities accounts meet the first implication of the equity market timing measure that more firms are willing to go public when securities accounts increase. For the second implication, we test the relation between the change in securities accounts and the amount of equity issued by the IPOs. We use (Proceeds/A)t and (Procceds/A)t-1 to measure the amount of equity issued. The variable (Proceeds/A)t represents total IPO proceeds divided by IPO yearend total assets, and (Procceds/A)t-1 represents the proceeds divided by total assets at the beginning of the IPO year. To control the effect of firm characteristics, we perform the following regression (1):

Yt ¼ co þ c1 XIPO þ c2

 



 M EBITDA PPE D þ c3 þ c4 SIZEt1 þ c5 þ c6 þ c7 SOWt þ c8 MANAGEt B t A A A t1 t t1

(1)

þ c9 TRADEt þ εt :

3 Chinese regulators use “lock-up” to forbid all IPOs in a certain time period from changing rules or as a mechanism to control stock market fluctuations. The first “lock-up” period covers July 1994 through December 1994. There are four “lock-ups” in our sample period: 2004/08e2005/01; 2005/05e2006/06; 2008/12e2009/06; 2012/11e2014/01.

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Table 2 The Effect of Equity Market Timing on Issuance Activity. Panel A shows the mean value of monthly numbers of IPOs in low, middle, and high groups for


  M EBITDA PPE changes in securities accounts. Panel B is the result of the following regression: Yt ¼ co þ c1 XIPO þ c2 þ c3 þ c4 SIZEt1 þ c5 þ B t A A t1 t
 D þ c7 SOWt þ c8 MANAGEt þ c9 TRADEt þ εt . The time subscript t is the IPO year. Yt includes (Proceeds/A)t, (Proceeds/A)t-1, and (d/A)t. (Proceeds/A)t c6 A t1 is total IPO proceeds divided by IPO year-end total assets, and (Proceeds/A)t-1 is the total IPO proceeds divided by total assets at the beginning of the IPO year. d/A is the net debt issue and equals the change in book debt (D). XIPO refers to changes in total securities accounts (DACC) in the IPO month, changes in individual securities accounts (DINDACC), and/or changes in institutional securities accounts (DINSACC) in the IPO month. The numbers in parentheses are robust t-statistics. All regressions are estimated with an industry fixed-effect based on the industry code of CSRC (China Securities Regulatory Commission) to control the effect of industry. a, b, and c denote statistical significance at the 1%, 5%, and 10% levels. Panel A: Mean values of monthly numbers of IPOs Account Type/Account Change

Number of IPO firms Low Group

Middle Group

High Group

Total/DACC Individual/DINDACC Institutional/DINSACC

209 209 246

333 345 331

467 455 432

Panel B: Regression Analysis

DACCIPO

(Proceeds/A)t

(Proceeds/A)t-1

d/At

6.76a (4.89)

29.93a (3.62)

1.86c (1.73)

DINDACCIPO DINSACCIPO (M/B)t (EBITDA/A)t SIZEt-1 (PPE/A)t-1 (D/A)t-1 SOWt MANAGEt TRADEt Constant R2 N

3.27a (7.93) 0.43a (3.31) 2.95a (3.35) 0.03 (0.84) 0.34a (9.29) 0.11a (5.42) 0.07a (4.48) 0.12 (1.25) 56.93 (7.72) 0.63 979

10.46a (6.22) 5.97a (3.75) 3.40a (8.16) 0.47a (3.57) 3.00a (3.35) 0.02 (0.75) 0.33a (8.92) 0.10a (4.91) 0.07a (4.19) 0.11 (1.19) 58.51 (7.83) 0.63 979

6.21a (4.61)

3.35a (8.11) 0.46a (3.52) 2.93a (3.33) 0.02 (0.82) 0.34a (9.17) 0.11a (5.40) 0.07a (4.46) 0.11 (1.17) 57.16 (7.77) 0.63 979

22.78a (7.38) 4.80a (5.85) 7.87a (3.29) 0.31c (1.70) 2.08a (10.64) 0.35a (3.85) 0.47a (4.09) 0.58 (1.5) 190.6 (6.39) 0.51 979

39.35a (3.58) 19.82c (1.76) 23.52a (7.59) 4.98a (6.09) 7.87a (3.24) 0.29 (1.64) 2.04a (10.34) 0.31a (3.41) 0.46a (3.95) 0.53 (1.36) 197.39 (6.71) 0.51 979

25.25a (3.16)

23.34a (7.55) 4.95a (6.06) 7.62a (3.20) 0.30c (1.67) 2.07a (10.53) 0.34a (3.83) 0.48a (4.11) 0.52 (1.34) 192.90 (6.49) 0.50 979

0.78a (2.81) 0.59a (4.98) 0.43 (0.94) 0.10a (3.89) 0.01 (0.52) 0.02 (1.19) 0.03b (2.55) 0.00 (0.04) 7.13 (1.40) 0.18 981

5.25a (3.75) 4.81a (3.86) 0.83a (2.97) 0.58a (4.95) 0.37 (0.8) 0.10a (3.96) 0.01 (0.27) 0.02 (0.81) 0.03b (2.23) 0.00 (0.01) 7.62 (1.45) 0.19 981

1.84c (1.73)

0.79a (2.84) 0.58a (4.98) 0.43 (0.94) 0.10a (3.88) 0.01 (0.49) 0.02 (1.18) 0.03b (2.53) 0.00 (0.05) 7.17 (1.40) 0.16 981

In equation (1), t is the IPO year, the dependent variable Yt is (Proceeds/A)t or (Proceeds/A)t-1, while XIPO refers to changes in three securities accounts: 1) DACC denotes changes in total securities accounts in the IPO month; 2) DINDACC denotes changes in individual securities accounts in the IPO month; 3) DINSACC denotes changes in institutional securities accounts in the IPO month. We add market-to-book ratio, profitability, size, tangibility of assets, and lagged book leverage as control variables that are the main determinants of capital structure (Bhabra et al., 2008; Huang & Song, 2006; Li et al., 2009; Rajan & Zingales, 1995; Titman & Wessels, 1988; Zou & Xiao, 2006). The equation does not include a research and development variable, because Chinese-listed firms usually do not disclose research and development data in their financial statements. We add three ownership variables, including percentages of state-owned shares (SOW), management ownership (MANAGE), and tradable shares (TRADE), to control for particular characteristics in China's capital markets. Market-to-book ratio (M/B), profitability (EBITDA/A), and three Chinese ownership characteristic variables (SOW, MANAGE, and TRADE) take on current year data, while other variables are lagged one year. In this study, all regressions adopt an industry fixed effect based on the industry code of CSRC (China Securities Regulatory Commission) to control for the industry effect. Columns 1 and 4 of Panel B in Table 2 show that the percentage change of total securities accounts (DACC) is positively and significantly related to both equity issue measures (Proceeds/A)t and (Proceeds/A)t-1). This indicates that IPO firms regard increases in total securities accounts as a favorable equity market condition and more equity is issued in IPOs when total securities accounts increase. Based on the above results, the change in total securities accounts (DACC) matches the two equity market timing implications.

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Equity market timers also try to identify the market condition when it is more favorable to issue equity than to issue debt, and so we test the changes in securities accounts on net debt issues. Column 7 of Panel B in Table 2 reports a negative and significant relationship between changes in total securities accounts (DACC) and net debt issues (d/A), confirming that increases in total securities accounts (DACC) come with less debt issued. In order to further look into the effect of equity market timing on capital structure, we subdivide total securities accounts (DACC) into individual accounts (DINDACC) and institutional accounts (DINSACC). We then replace DACC by DINDACC and DINSACC in the regressions and report the results in columns 2 and 5 of Panel B in Table 2. We find that the coefficient of DINDACC is significantly positive, but DINSACC has a negative and significant impact on equity issues during the IPO. A possible explanation for this negative relation is that increases in institutional accounts may have a co-movement effect on equity and debt markets and reflect a more favorable condition for debt financing relative to equity financing.4 To confirm the above conjecture, we use changes in individual accounts (DINDACC) and institutional accounts (DINSACC) and regress them on net debt issues (d/A). Column 8 of Panel B shows that the coefficients of individual accounts (DINDACC) and institutional accounts (DINSACC) are 5.25 and 4.81, respectively, and both are significant at the 1% level. The increases of DINSACC lower the equity amount, but raise net issues of debt. This is consistent with our conjecture that the co-movement effect of increases in institutional accounts (DINSACC) indicates a more favorable market condition of debt relative to equity, and thus an increase of institutional accounts (DINSACC) has a negative impact on equity issues. In contrast, a rise in individual accounts (DINDACC) still reflects equity market timing attempts. Adopting only the change of individual accounts (DINDACC) in regressions, columns 3, 6, and 9 of Panel B in Table 2 show a positive and significant relation between changes of individual accounts (DINDACC) and IPO proceeds ((Proceeds/A)t and (Proceeds/A)t-1). This implies that DINDACC is an effective equity market timing measure, and its effect on financing decisions is also consistent with the equity market timing behavior. Previous studies indicate that firm fundamental characteristics, such as growth opportunities and payout policy, also influence the effect of equity market timing on capital structure (Li, Jiao, Yu, & Zhao, 2019). We consider two aspects of these demand factors of firm characteristics. First, firm growth may impact the equity market timing effect on financing decisions. High-growth firms have a higher amount of investment in current and subsequent IPO years, which can lead firms to issue more equity in IPOs to meet investment demand rather than for any equity market timing consideration. From columns 1 to 6 in Table 3, all coefficients of DACC and DINDACC are not significant in IPO and subsequent years, and so market timers issuing more equity in IPOs are not influenced by firm investment activities. Second, a firm's dividend policy may be another possible factor for more equity issued in IPOs. If dividend payouts of firms are at higher rates, then firms are likely to finance part of this dividend policy by raising equity capital. Results from columns 7 to 9 in Table 3 show that both coefficients of DACC and DINDACC are not significant. This demonstrates that the impact of equity market timing on equity issue activity is unrelated with dividend policy. The literature also suggests that low profitability firms are more opportunistic at identifying favorable equity market timing conditions (Baker, Stein, & Wurgler, 2003), as they tend to regard equity market timing as an important determinant of their financing decision, because it is difficult for less profitable firms to go public. Supporting the above view of equity market timing, our results in the last 6 columns of Table 3 show that both DACC and DINDACC are negatively related to EBITDA in current and subsequent IPO years. Our results show that firms with lower profits place greater value in increases of total securities account (DACC) and individual accounts (DINDACC) and are more likely to be market timers. The above results confirm that the effects of both equity market condition variables, changes in total securities account (DACC) and individual accounts (DINDACC), on financing decisions are not driven by the demand-side factors of firm characteristics. Previous studies use demand-side factors of firms, such as historical market-to-book ratio (Baker & Wurgler, 2002), as equity market timing measures, but receive more criticisms for not being independent of the firm characteristics of capital structure (Hovakimian, 2006; Kayhan & Titman, 2007). Our equity market timing measure (DINDACC) and its effect come from the supply-side behavior of investors, and so it avoids the criticisms of using demand-side factors of firm characteristics to measure equity market timing. The change in total securities accounts (DACC) meets the two implications of the equity market timing measure as shown in Table 2. However, when we subdivide total securities accounts (DACC) into individual accounts (DINDACC) and institutional accounts (DINSACC), the latter actually mislead equity market timing. An increase in institutional accounts (DINSACC) may signal a lower cost of debt relative to that of equity, as well as a greater chance of market timing for debt rather than equity. We propose that the change in individual accounts (DINDACC) is a more effective measure of equity market timing. Moreover, Alit (2006) uses IPO volume as the measure for equity market timing, but his measure is invalid due to “lock-up” periods in China.5 Our proposed measure, change of individual accounts (DINDACC), has practical significance for serving as a measure of equity market timing in China.

4 Baker and Wurgler (2012) also note that there are some cross-section co-movement patterns between specific equity and debt markets after controlling for timing-varying aggregate correlations. 5 Regulators have stopped initial public offerings in China four times from 2003 to 2013, and these four “lock-up” periods cover over 36 months. In the case of China, Alti's timing measure, monthly IPO volume, is not continuous; and monthly IPO volume is substantially disturbed by such “lock-ups” and cannot fully reflect the market timing behavior of firms. Therefore, IPO volume cannot be an effective timing measure in China's IPO markets.

Please cite this article as: Zhao, Y et al., Does equity market timing have a persistent impact on capital structure? Evidence from China, The British Accounting Review, https://doi.org/10.1016/j.bar.2019.100838

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7

Table 3 Market Timing and Equity Demand Factors. Table 3 reports the results of the following regression:


  M EBITDA PPE Yt ¼ co þ c1 XIPO þ c2 þ c3 þ c4 SIZEt1 þ c5 þ þc6 SOWt þ c7 MANAGEt þ c8 TRADEt þ εt Here, subscript t refers to the IPO year. B t A A t1 t IPO þ N represents N years after IPO year (t). Yt represents the investment rates ((INV/A)t) for years IPO and IPOþ1, the IPO-year dividend payout ratio (DIV/ EIPO), and profitability ((EBITDA/A)t) for years IPO and IPOþ1. XIPO refers to changes in total securities accounts (DACC) in the IPO month, changes in individual securities accounts (DINDACC), and/or changes in institutional securities accounts (DINSACC) in the IPO month. The numbers in parentheses are robust t-statistics. All regressions are estimated with an industry fixed-effect based on the industry code of CSRC. Superscripts a, b, and c denote statistical significance at the 1%, 5%, and 10% levels. (INV/A)t

DACCIPO

IPOþ1

DIV/EIPO

IPO

IPOþ1

0.10 (0.13)

0.94 (1.34)

0.07 (1.18)

2.61a (7.69)

1.58a (3.43)

DINDACCIPO DINSACCIPO (M/B)t (EBITDA/A)t SIZEt-1 (PPE/A)t-1 SOWt MANAGEt TRADEt Constant R2 N

(EBITDA/A)t

IPO

0.52a (2.82) 0.16b (2.17) 0.07 (0.37) 0.10a (4.85) 0.02c (1.72) 0.01 (0.62) 0.18a (3.81) 1.41 (0.29) 0.21 981

0.79 (0.85) 1.32c (1.69) 0.50a (2.80) 0.16b (2.14) 0.08 (0.40) 0.10a (4.87) 0.02 (1.50) 0.01 (0.61) 0.18a (3.81) 1.45 (0.31) 0.21 981

0.16 (0.23)

0.51a (2.82) 0.16b (2.17) 0.07 (0.35) 0.10a (4.86) 0.02c (1.72) 0.00 (0.54) 0.18a (4.82) 1.41 (0.29) 0.21 981

0.36c (1.74) 0.27a (3.87) 0.48b (2.55) 0.14a (5.96) 0.02b (2.04) 0.00 (0.54) 0.05 (1.19) 1.31 (0.56) 0.15 1007

0.64 (0.70) 0.25 (0.29) 0.37c (1.79) 0.26a (3.84) 0.46b (2.50) 0.14a (5.93) 0.02b (2.09) 0.01 (0.57) 0.04 (1.15) 1.30 (0.56) 0.15 1007

0.83 (1.20)

0.37c (1.81) 0.26a (3.83) 0.47b (2.51) 0.14a (5.96) 0.02b (2.04) 0.00 (0.55) 0.04 (1.16) 1.37 (0.59) 0.15 1007

0.01 (1.00) 0.00 (0.17) 0.02 (0.91) 0.00 (0.19) 0.00 (0.80) 0.00b (2.51) 0.01b (1.98) 0.21 (0.77) 0.04 959

0.07 (0.72) 0.01 (0.12) 0.01 (1.10) 0.00 (0.22) 0.01 (0.87) 0.00 (0.17) 0.00 (0.80) 0.00b (2.54) 0.01b (2.00) 0.22 (0.82) 0.04 959

0.74a (7.73)

2.23a (4.58) 0.28 (0.63) 0.72a (7.54)

0.72a (7.53)

0.64a (6.11) 0.07a (8.54) 0.01b (2.32) 0.01a (3.45) 0.10a (4.93) 1.35 (0.41) 0.27 982

0.63a (6.05) 0.07a (8.56) 0.01b (2.29) 0.01a (3.47) 0.10a (4.84) 1.36 (0.42) 0.27 982

0.63a (6.04) 0.07a (8.56) 0.01b (2.37) 0.01a (3.45) 0.10a (4.85) 1.37 (0.42) 0.27 982

0.06 (1.00)

0.01 (1.10) 0.00 (0.22) 0.01 (0.86) 0.00 (0.17) 0.00 (0.80) 0.00b (2.53) 0.01b (2.01) 0.22 (0.80) 0.04 959

2.43a (7.26)

1.22a (8.60)

1.94a (3.07) 0.55 (1.09) 1.21a (8.53)

1.52a (3.35)

1.21a (8.55)

0.62a (3.61) 0.11a (7.02) 0.01 (1.26) 0.02a (2.77) 0.04 (1.52) 2.87 (0.67) 0.20 1008

0.62a (3.61) 0.11a (7.00) 0.01 (1.40) 0.02a (2.70) 0.04 (1.52) 2.82 (0.67) 0.10 1008

0.61a (3.58) 0.11a (7.03) 0.01 (1.28) 0.02a (2.76) 0.04 (1.51) 2.84 (0.67) 0.20 1008

3.2. Short-run effect of market timing on capital structure The above empirical results show that the change in individual accounts (DINDACC) is an effective timing measure and that equity market timers can identify an increase in them as a favorable equity market condition to issue more equity. This section examines the short-run effect of equity market timing on firm leverage and quantifies the effect of equity market timing on the balance sheets of IPO firms. We first use the change in book leverage in the IPO year to measure the short-run change of firm leverage. We expect a decrease in firm leverage when securities accounts increase. In the following regression, t is the year of IPO, and the dependent variable on the left side of equation (2) is the change of firm leverage (D/A) between the pre-IPO(t-1) year and the IPO(t) year.

 





 D D M EBITDA PPE D  ¼ co þ c1 XIPO þ c2 þ c3 þ c4 SIZEt1 þ c5 þ c6 þ c7 SOWt A t A t1 B t A A A t1 t t1 þ c8 MANAGEt þ c9 TRADEt þ εt

(2)

The independent variable XIPO refers to changes in total securities accounts (DACC) in the IPO month, changes in individual securities accounts (DINDACC), and/or changes in institutional securities accounts (DINSACC) in the IPO month. The first column in Table 4 shows that DACC has a significant negative influence on the change of firm leverage. The second column in Table 4 shows that the coefficients of both individual accounts (DINDACC) and institutional accounts (DINSACC) are significant at the 1% level, but they have opposite effects on the change of firm leverage. Consistent with the results in section 3.1, an increase of DINSACC may lower the cost of debt relative to that of equity. The significant coefficient of changes to individual accounts (DINDACC) reflects a negative short-run impact of equity market timing on the change of firm leverage between preIPO and IPO years. We follow Alti (2006) and decompose the change in leverage between the pre-IPO(t-1) and IPO(t) years into three parts: net equity issues, change of assets, and retained earnings:

Please cite this article as: Zhao, Y et al., Does equity market timing have a persistent impact on capital structure? Evidence from China, The British Accounting Review, https://doi.org/10.1016/j.bar.2019.100838

8

Y. Zhao et al. / The British Accounting Review xxx (xxxx) xxx

Table 4 Short-Term Effect of Equity Market Timing and Capital Structure. Table 4 is the results of the following regression:



   M EBITDA PPE D Yt ¼ co þ c1 XIPO þ c2 þ c3 þ c4 SIZEt1 þ c5 þ c6 þ c7 SOWt þ c8 MANAGEt þ c9 TRADEt þ εt Here, subscript t refers to the B t A A t1 A t1 t IPO year. IPO þ N represents N years after IPO year (t). Yt is the change of leverage between IPO year and pre-IPO year ((D/A)t-(D/A)t-1), net equity issues ((e/ A)t), the change of cash ((cash/A)t), the change in other assets ((DOther Assets/A)t), and the level of leverage ((D/A)t). XIPO refers to changes in total securities accounts (DACC) in the IPO month, changes in individual securities accounts (DINDACC), and/or changes in institutional securities accounts (DINSACC) in the IPO month. The numbers in parentheses are robust t-statistics. All regressions are estimated with an industry fixed-effect based on the industry code of CSRC to control the effect of industry. Superscripts a, b, and c denote statistical significance at the 1%, 5%, and 10% levels.

DACCIPO

(D/A)t-(D/A)t-1

(e/A)t

(cash/A)t

(DOther Assets/A)t

(D/A)t

4.18a (4.30)

6.73a (4.66)

8.62a (4.81)

3.75a (2.58)

1.78 (1.36)

DINDACCIPO

a

DINSACCIPO (M/B)t (EBITDA/A)t SIZEt-1 (PPE/A)t-1 (D/A)t-1 SOWt MANAGEt TRADEt Constant R2 N

1.96a (6.25) 0.40a (3.91) 1.58b (2.43) 0.01 (0.46) 0.38a (13.75) 0.07a (3.66) 0.04a (3.38) 0.04 (0.63) 8.05 (1.34) 0.34 981

7.90 (6.30) 5.44a (4.71) 2.04a (6.40) 0.38a (3.78) 1.63b (2.46) 0.01 (0.56) 0.39a (13.94) 0.06a (3.16) 0.04a (3.00) 0.04 (0.64) 7.44 (1.27) 0.36 981

a

3.45a (8.36) 0.06 (0.45) 2.73a (3.48) 0.06c (1.87) 0.33a (9.46) 0.10a (4.97) 0.07a (4.47) 0.10 (1.11) 73.35 (10.64) 0.62 981

10.87 (6.31) 6.47a (4.14) 3.58a (8.59) 0.03 (0.24) 2.78a (3.48) 0.05c (1.79) 0.31a (9.12) 0.09a (4.41) 0.07a (4.14) 0.10 (1.08) 72.58 (10.65) 0.63 981

c

3.42a (5.95) 0.42b (2.41) 3.30a (3.53) 0.08b (2.18) 0.31a (6.33) 0.02 (0.80) 0.05b (2.11) 0.08 (0.82) 66.54 (7.60) 0.46 981

4.44 (1.93) 4.79b (2.21) 3.48a (6.11) 0.44a (2.58) 3.20a (3.48) 0.08b (2.20) 0.31a (6.33) 0.03 (1.06) 0.05b (2.27) 0.09 (0.97) 66.77 (7.73) 0.46 981




 e D D E DCash þ DOther Assets  ¼ þ *  DRE=At A t A t1 A t A t1 At

0.75 (1.52) 0.10 (0.73) 0.14 (0.22) 0.07b (2.22) 0.00 (0.03) 0.06a (2.72) 0.01 (0.44) 0.18b (2.26) 13.94 (2.13) 0.07 981

1.18 (0.61) 6.46a (3.86) 0.73 (1.52) 0.10 (0.71) 0.05 (0.08) 0.07b (2.16) 0.00 (0.11) 0.05b (2.23) 0.01 (0.70) 0.19b (2.37) 13.43 (2.10) 0.08 981

4.03a (7.71) 0.49a (3.75) 4.22a (3.84) 0.06b (2.22)

7.41a (4.30) 7.56a (5.10) 4.05a (7.71) 0.48a (3.67) 4.29a (3.87) 0.06b (2.10)

0.08a (3.08) 0.08a (4.73) 0.04 (0.42) 20.46 (1.93) 0.53 982

0.07a (2.58) 0.07a (4.29) 0.03 (0.33) 20.71 (1.92) 0.54 982

(3)

On the right side of equation (3), the first term is negative net equity issues. If all equity financing is used to repay debt, then there is a one-for-one relation between net equity issues and the change of firm leverage, which means the left side equals the first term on the right side of the equation. In practice, new equity capital that reduces firm leverage is always less than one-for-one, which means a part of new equity adds to the part of total assets. The second term on the right side of the equation is the effect of new equity issues on asset growth, and we can further decompose it into cash and other assets. According to the equity market timing hypothesis, a favorable equity market condition makes market timers issue more equity than they need. Because the fluctuations of long-term assets are always due to demand-side factors of firm characteristics, excessive equity capital should add to cash assets and temporary investment rather than the part of other assets. This means equity market timing triggers more equity issues, which should just impact the cash part of total assets on the balance sheet. The final term of equation (4) is change in retained earnings (DRE). As shown in the columns under (e/A)t in Table 4, the change in individual accounts (DINDACC) has a significant positive effect on equity issues. In the assets part, the significant positive coefficient of DINDACC on cash assets indicates market timers issue excessive equity capital and add them into the cash part of total assets. On the other hand, the effect of DINDACC on other assets is insignificant, meaning the change in long-term assets is not affected by market timing behavior. Because retained earnings are not available in pre-IPO years, we cannot estimate the impact of individual accounts on the changes of retained earnings. Overall, the effect of equity market timing on the balance sheet of IPO firms is consistent with the equity market timing hypothesis - that is, market timers value favorable market conditions more than actual capital requirements and may issue excessive equity capital that only adds into the cash balance of total assets. We finally test the short-term effect of equity market timing on firm leverage in years of IPOs. We perform the following regression (4), and the results are in the (D/A)t columns of Table 4.

Please cite this article as: Zhao, Y et al., Does equity market timing have a persistent impact on capital structure? Evidence from China, The British Accounting Review, https://doi.org/10.1016/j.bar.2019.100838

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9

 



D M EBITDA PPE ¼ co þ c1 XIPO þ c2 þ c3 þ c4 SIZEt1 þ c5 þ c6 SOWt þ c7 MANAGEt þ c8 TRADEt þ εt A t B t A A t1 t (4) The last column of Table 4 shows the significantly negative impact of individual accounts (DINDACC) on leverage, which is consistent with our expectations whereby equity market timing plays an important role on the determination of capital structure in the short run. 3.3. Persistence in the impact of market timing on capital structure The above analysis demonstrates that the change of individual accounts (DINDACC) is an effective measure of equity market timing and timing behavior has only a short-term impact on firm leverage. This section focuses on whether the effect of equity market timing on capital structure can be persistent. The cumulative change of leverage over tþ1 years, ((D/A)t -(D/ A)PREIPO), measures the persistent effect of market timing on capital structure. If market timing has a persistent effect on firm leverage, then the cumulative change in leverage from its pre-IPO level should continue to reflect the effect of the market timing measure (DINDCC) in the years after the IPO year. We use varying firm leverage between pre-IPO years and subsequent years (t) to measure the cumulative change of firm leverage and conduct the following regression (5).

 





 D D M EBITDA PPE D  ¼ co þ c1 XIPO þ c2 þ c3 þ c4 SIZEt1 þ c5 þ c6 þ c7 SOWt1 A t A PREIPO B t1 A A A t1 t1 t1 þ c8 MANAGEt1 þ c9 TRADEt1 þ εt (5) In equation (5), t is the subsequent IPO year and t ¼ 0 means the IPO year. The left side of equation (5) is the cumulative change of firm leverage over tþ1 years, and XIPO refers to changes in three securities accounts: 1) DACC denotes changes in total securities accounts (DACC) in the IPO month; 2) DINDACC denotes changes in individual securities accounts (DINDACC) in the IPO month; 3) DINSACC denotes changes in institutional securities accounts (DINSACC) in the IPO month. The regression results of Panel A in Table 5 indicate that the coefficients for DINDACC remain significant and negative from the current IPO year through five subsequent years. The market-to-book ratio is considered to be influenced by many factors related to market timing behavior. In order to rule out the effect of the market-to-book ratio, we replicate regression (5) without the M/B ratio variable. The unreported results show that persistence in the impact from the change in individual accounts (DINDACC) on capital structure is still significant, and so the long-term effect of equity market timing on the leverage of cumulative changes is not affected by the market-to-book ratio. We next provide a direct test of the persistent effect of equity market timing on firm leverage. In Panel B of Table 5, the coefficients for the change of individual accounts (DINDACC) on capital structure remain significantly negative for 6 years. Consistent with the effect on cumulative change leverage, an obvious long-term effect of equity market timing on firm leverage exists. The long-term negative effect of equity market timing is robust when excluding the market-to-book ratio in the regression. Therefore, this paper uses a proxy from the equity funding supply market condition as a measure for equity market timing. Our measure can avoid the demand-side influences of firm characteristics on equity financing. The empirical results show the persistent impact of equity market timing on capital structure and support the equity market timing theory.6 3.4. Comparison of different timing measures There are two major types of market timing measures developed in the literature: the historical market-to-book ratio (M/ Befwa) of Baker and Wurgler (2002) and the IPO volume of Alti (2006). Baker and Wurgler (2002) use the historical market-to-book ratio (M/Befwa) as the equity market timing measure and find that equity market timing has a persistent impact over 10 years on firm leverage in the U.S.7 Following Baker and Wurgler (2002), we use the historical market-to-book ratio and examine its effect on capital structure in China. In the first six columns of Table 6, we find that there is a significant negative relationship between the historical market-to-book ratio (M/Befwa) and firm leverage, and this relation persists for over five years. Therefore, consistent with the results of Baker and Wurgler (2002) using U.S. data, our historical market-to-book ratio (M/Befwa) also has a long-term effect on capital structure in China. Since both the change in individual accounts (DINDACC) and the historical market-to-book ratio (M/Befwa) exhibit persistent impacts of market timing on capital structure, we put these two timing measures in the regression at the same time

6 We use Alti's (2006) approach to define a dummy variable of large and small changes in individual securities accounts as the equity market timing measure. The dummy variable equals one if the firm goes public in a month with large changes in individual securities accounts and zero otherwise. The unreported results show that both Alti's (2006) dummy variable approach and our approach have consistent findings in that equity market timing has a long-term effect on capital structure.
 P 7 es þds M =Befwa; t1 ¼ t1 * M , where e and d denote net equity and net debt issues, respectively. s¼0 Pt1 B

e þdr r¼0 r

s

Please cite this article as: Zhao, Y et al., Does equity market timing have a persistent impact on capital structure? Evidence from China, The British Accounting Review, https://doi.org/10.1016/j.bar.2019.100838

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Y. Zhao et al. / The British Accounting Review xxx (xxxx) xxx

Table 5 Long-Term Effect of Equity Market Timing on Capital Structure. Panels A and B are results of the following regression:



   M EBITDA PPE D þ c3 þ c4 SIZEt1 þ c5 þ c6 þ c7 SOWt1 þ c8 MANAGEt1 þ c9 TRADEt1 þ εt Here, subscript t Yt ¼ c0 þ c1 XIPO þ c2 B t1 A A t1 A t1 t1 refers to the IPO year. IPO þ N represents N years after IPO year (t). Yt is the cumulative change of leverage ((D/A)t -(D/A)PREIPO) or the level of leverage ((D/ A)t) from IPOþ1 year to IPOþ6 year, respectively. XIPO refers to changes in total securities accounts (DACC) in the IPO month, changes in individual securities accounts (DINDACC), and/or changes in institutional securities accounts (DINSACC) in the IPO month. The numbers in parentheses are robust t-statistics. All regressions are estimated with an industry fixed-effect based on the industry code of CSRC (China Securities Regulatory Commission). Superscripts a, b, and c denote statistical significance at the 1%, 5%, and 10% levels. Panel A: Regression Analysis of the Cumulative Change of Firm Leverage from Year t-1 to Year t t

DACCIPO

(D/A)t -(D/A)

IPOþ2

IPOþ3

IPOþ4

IPOþ5

IPOþ6

6.29a (5.66)

8.27a (6.86)

6.60a (5.05)

5.13a (2.67)

4.35b (1.85)

2.89 (0.61)

DINDACCIPO DINSACCIPO (M/B)t-1 (EBITDA/A)t-1 SIZEt-1 (PPE/A)t-1 (D/A)t-1 SOWt-1 MANAGEt-1 TRADEt-1 Constant R2 N

PREIPO

IPOþ1

0.79b (2.42) 0.64a (5.05) 2.99a (8.05) 0.11b (2.57) 0.42a (15.22) 0.04b (2.03) 0.03c (1.92) 0.11c (1.75) 8.36 (1.48) 0.29 1000

8.63a (5.45) 3.50b (2.42) 0.85a (2.61) 0.62a (4.94) 3.03a (8.20) 0.11b (2.51) 0.42a (15.40) 0.03c (1.74) 0.03c (1.70) 0.11c (1.79) 7.50 (1.32) 0.29 1000

0.41 (0.85) 0.77b (2.39) 3.99a (9.16) 0.13a (3.08) 0.42a (13.38) 0.04a (1.68) 0.03c (1.68) 0.07c (1.92) 6.34 (1.08) 0.26 997

10.62a (6.25) 3.39b (2.15) 0.32 (0.67) 0.76b (2.37) 4.02a (9.27) 0.13a (3.12) 0.43a (13.55) 0.03 (1.42) 0.03 (1.41) 0.07b (2.00) 5.74 (0.95) 0.26 997

0.60c (1.77) 0.51a (5.48) 4.61a (9.89) 0.13a (3.27) 0.50a (14.36) 0.07a (2.78) 0.00 (0.21) 0.14a (4.10) 1.65 (0.30) 0.29 861

7.62a (4.11) 1.65 (0.97) 0.53 (1.53) 0.51a (5.38) 4.64a (9.94) 0.13a (3.33) 0.50a (14.32) 0.07a (2.68) 0.01 (0.37) 0.15a (4.20) 0.82 (0.14) 0.29 861

1.82a (3.17) 0.01 (0.03) 2.83b (2.34) 0.05 (1.08) 0.50a (8.22) 0.12a (2.72) 0.01 (0.29) 0.01 (0.21) 15.69 (2.26) 0.25 652

5.66b (2.14) 0.72 (0.32) 1.82a (3.17) 0.01 (0.03) 2.84b (2.34) 0.05 (1.05) 0.50a (8.21) 0.12a (2.70) 0.01 (0.24) 0.01 (0.21) 15.70 (2.25) 0.25 652

0.88b (2.57) 0.86a (22.29) 3.08b (2.22) 0.00 (0.04) 0.55a (6.34) 0.07 (1.00) 0.13 (1.44) 0.09 (1.32) 23.00 (1.59) 0.62 378

6.84c (1.90) 2.75 (0.99) 0.91a (2.62) 0.86a (22.36) 3.08b (2.22) 0.00 (0.04) 0.55a (6.32) 0.07 (0.98) 0.12 (1.30) 0.09 (1.31) 23.66 (1.66) 0.62 378

3.68b (1.70) 0.02 (0.02) 0.70 (0.31) 0.41b (2.11) 0.19 (1.00) 0.12 (1.27) 0.28b (2.03) 0.12 (1.34) 15.05 (0.85) 0.27 282

6.98 (1.24) 7.71 (1.30) 3.80b (1.72) 0.04 (0.05) 0.74 (0.32) 0.40b (2.09) 0.20 (1.04) 0.12 (1.25) 0.26c (1.91) 0.13 (1.43) 14.62 (0.82) 0.27 282

Panel B: Regression Analysis of Firm Leverage at Year t t

DACCIPO

(D/A)t IPOþ1

IPOþ2

IPOþ3

IPOþ4

IPOþ5

IPOþ6

5.05a (3.67)

9.14a (6.78)

9.09a (6.50)

8.98a (4.70)

7.95a (3.97)

6.39 (1.29)

DINDACCIPO DINSACCIPO (M/B)t-1 (EBITDA/A)t-1 SIZEt-1 (PPE/A)t-1 SOWt-1 MANAGEt-1 TRADEt-1 Constant R2 N

2.32a (6.30) 0.84a (5.76) 6.12a (15.87) 0.24a (5.19) 0.04 (1.60) 0.06a (3.19) 0.16b (2.04) 6.27 (1.23) 0.51 1008

8.26a (4.37) 4.22a (2.61) 2.34a (6.36) 0.82a (5.62) 6.15a (16.07) 0.24a (5.08) 0.03 (1.29) 0.05a (2.96) 0.15b (2.01) 6.48 (1.25) 0.52 1008

0.32 (0.62) 1.01a (2.92) 7.40a (16.47) 0.27a (6.05) 0.04 (1.61) 0.07a (3.07) 0.09b (2.14) 16.82 (2.32) 0.47 1005

12.57a (6.44) 4.50b (2.54) 0.38 (0.73) 1.00a (2.92) 7.40a (16.65) 0.27a (6.06) 0.03 (1.26) 0.06a (2.76) 0.09b (2.24) 15.92 (2.12) 0.47 1005

1.36a (3.76) 0.63a (5.72) 7.53a (16.43) 0.21a (4.76) 0.07b (2.50) 0.03 (1.17) 0.17a (4.27) 10.91 (1.62) 0.49 869

10.01a (4.94) 1.23 (0.67) 1.28a (3.42) 0.64a (5.61) 7.56a (16.43) 0.21a (4.81) 0.07b (2.38) 0.02 (1.02) 0.17a (4.38) 10.23 (1.49) 0.49 869

2.87a (4.08) 0.02 (0.04) 5.05a (4.08) 0.13b (2.34) 0.08c (1.78) 0.07 (1.56) 0.01 (0.16) 4.88 (0.53) 0.40 658

10.60a (3.93) 2.10 (0.96) 2.86a (4.06) 0.02 (0.04) 5.06a (4.08) 0.13b (2.28) 0.08c (1.74) 0.06 (1.40) 0.01 (0.18) 4.80 (0.52) 0.40 658

1.47a (4.32) 0.89a (19.13) 5.39a (4.72) 0.05 (0.75) 0.04 (0.56) 0.13 (1.38) 0.09 (1.24) 10.54 (0.76) 0.63 385

11.10a (3.61) 3.34 (1.46) 1.51a (4.41) 0.90a (19.21) 5.36a (4.69) 0.04 (0.66) 0.04 (0.53) 0.11 (1.18) 0.09 (1.19) 32.70 (2.20) 0.63 385

3.44 (1.40) 0.15 (0.19) 3.02c (1.66) 0.49b (2.11) 0.07 (0.66) 0.26c (1.89) 0.12 (1.29) 4.14 (0.21) 0.24 289

10.93c (1.89) 7.50 (1.46) 3.58 (1.43) 0.18 (0.22) 2.94 (1.61) 0.48b (2.10) 0.06 (0.63) 0.22 (1.62) 0.12 (1.34) 1.77 (0.09) 0.24 289

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 Table 6 M Comparison of Different Equity Market Timing Measures. Table 6 is the results of the following regression: Yt ¼ co þ c1 XIPO þ c2 þ


  B efwa;t1 M EBITDA PPE þ c4 þ c5 SIZEt1 þ c6 þ c7 SOWt1 þ c8 MANAGEt1 þ c9 TRADEt1 þ εt . Here, subscript t refers to the IPO year. IPO þ N c3 B t1 A A t1 t1 represents N years after IPO year (t). Yt represents the level of leverage ((D/A) t) from IPOþ1 year to IPOþ6 year, respectively. (M/Befwa)t-1 is the historical M/ B ratio. XIPO refers to changes in total securities accounts (DACC) in the IPO month, changes in individual securities accounts (DINDACC), and/or changes in institutional securities accounts (DINSACC) in the IPO month. The numbers in parentheses are robust t-statistics. All regressions are estimated with an industry fixed-effect based on the industry code of CSRC (China Securities Regulatory Commission) to control the effect of industry. Superscripts a, b, and c denote statistical significance at the 1%, 5%, and 10% levels. t

(D/A)t IPOþ1

IPOþ2

IPOþ3

IPOþ4

IPOþ5

IPOþ6

DINDACCt

IPOþ2

DINSACCt (M/Befwa)t-1 (M/B)t-1 (EBITDA/A)t-1 SIZEt-1 (PPE/A)t-1 SOWt-1 MANAGEt-1 TRADEt-1 Constant R2 N

IPOþ3

6.34a (4.56)

2.82a (7.98) 0.71a (4.97) 5.78a (14.88) 0.23a (5.05) 0.04c (1.71) 0.06a (3.57) 0.22a (2.93) 6.31 (1.07) 0.51 1008

3.02a (6.82) 0.47 (0.88) 0.87b (2.54) 6.31a (13.73) 0.23a (5.22) 0.04c (1.69) 0.07a (3.20) 0.09b (2.13) 22.71 (3,23) 0.47 997

0.93a (2.89) 1.22a (3.29) 0.57a (5.26) 6.99a (14.55) 0.24a (5.33) 0.09a (3.09) 0.04c (1.79) 0.18a (4.37) 2.79 (0.31) 0.48 860

1.04b (2.20) 2.18a (3.70) 0.00 (0.01) 5.02a (4.53) 0.14b (2.55) 0.11b (2.29) 0.11b (2.50) 0.03 (0.83) 30.21 (3,13) 0.39 649

0.98b (2.07) 0.03 (0.05) 1.29a (7.05) 5.76a (6.19) 0.10 (1.49) 0.00 (0.06) 0.22a (2.91) 0.14b (2.12) 15.71 (1.16) 0.50 370

0.57 (0.94) 0.38 (0.22) 1.37a (4.09) 5.07a (5.76) 0.07 (0.83) 0.07 (0.87) 0.23a (2.11) 0.04 (0.53) 12.95 (1.30) 0.46 273

2.34a (5.15) 0.49 (0.91) 0.93a (2.72) 6.84a (14.97) 0.23a (5.06) 0.03 (1.31) 0.07a (2.86) 0.10b (2.34) 22.33 (3.17) 0.48 997

1.79 (1.43) 2.89a (6.39) 0.51 (0.95) 0.88b (2.57) 6.41a (13.92) 0.23a (5.19) 0.04c (1.71) 0.08a (3.21) 0.09b (2.17) 23.04 (3.30) 0.47 997

10.01a (5.08) 5.04a (2.82) 2.30a (5.06) 0.38 (0.72) 0.92a (2.72) 6.85a (15.11) 0.22a (5.04) 0.03 (1.02) 0.06b (2.57) 0.10b (2.35) 21.18 (2.91) 0.48 997

8.52a (5.68)

0.40c (1.66) 1.22a (3.30) 0.61a (5.31) 7.49a (15.94) 0.21a (4.64) 0.07b (2.43) 0.02 (1.08) 0.17a (4.31) 4.60 (0.47) 0.49 860

4.60a (3.30) 0.67b (2.01) 1.35a (3.66) 0.58a (5.43) 7.13a (14.95) 0.23a (5.04) 0.08a (3.07) 0.04 (1.80) 0.17a (4.30) 3.21 (0.34) 0.48 860

9.83a (4.72) 1.86 (0.96) 0.42 (1.21) 1.17a (3.07) 0.61a (5.24) 7.51a (15.98) 0.21a (4.63) 0.06b (2.33) 0.02 (0.96) 0.17a (4.32) 4.71 (0.48) 0.50 860

to compare their timing effects. The last six columns of Table 6 indicate that when we add individual accounts (DINDACC) into regressions, the persistent effect of the historical market-to-book ratio (M/Befwa) disappears in the third year after the IPO, but the significant effect of the historical market-to-book ratio (M/Befwa) does not change when we add institutional accounts (DINSACC) into the regressions. When we add both individual accounts (DINDACC) and institutional accounts (DINSACC) into the regressions, we find that our timing measure of individual accounts (DINDACC) still has a significant negative impact on capital structure. Therefore, the persistence of DINDACC on firm leverage is not influenced by the historical market-to-book ratio (M/Befwa). Our results present evidence that the impact of our equity market timing measure (DINDACC) on capital structure is not affected by firm characteristic factors. Thus, we put forward that equity funding supply is a key source of the persistent timing effect on capital structure. With a favorable equity supply market condition, IPO firms face relatively lower costs of equity and will issue more equity; such timing behavior creates a long-term effect on capital structure. Alti (2006) uses IPO volume as the equity market timing measure and argues that equity market timing only has a shortterm effect on firm leverage. We do not directly compare our equity market timing measure with Alti (2006), because there are IPO “lock-up” periods in China. During these periods, IPOs are not allowed and no data of IPO volumes are available.8 4. Robustness tests Table 7 shows some robustness test results. We report the coefficients of the change of individual accounts (DINDACC) in regressions (1), (4), and (5) for each test. The objective of regression (1) is to analyze the effect of our timing measure on IPO proceeds. The leverage regression (4) and cumulative leverage change regression (5) from the IPO year to the IPOþ3 year aim to test the short- and long-run relations between equity market timing and capital structure. We first drop three control variables for China's capital markets: state ownership (SOW); management ownership (MANAGE); and tradable shares (TRADE); and then we re-do the regressions. The first panel in Table 7 shows that the

8 We exclude “locking-up” periods and follow Alti (2006) to construct a dummy variable for a hot issue market (HOTIPO) as an equity market timing measure. The unreported empirical results show that this dummy variable has a long-term effect on firm leverage in China, but it does have an effect on firm leverage that lasts for only 5 years, whereas our timing measure (DINDACC) has an effect of more than 7 years. Thus, our timing measure (DINDACC) can more effectively reveal the impact of equity market timing on capital structure in China than Alti's hot issue market measure.

Please cite this article as: Zhao, Y et al., Does equity market timing have a persistent impact on capital structure? Evidence from China, The British Accounting Review, https://doi.org/10.1016/j.bar.2019.100838

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 Table 7 M Robustness Tests. Table 7 shows the coefficient of the change in individual accounts (DINDACC) in the following regression: Yt ¼ co þ c1 X þ c2 þ


   B t EBITDA PPE D þ c4 SIZEt1 þ c5 þ c6 þ c7 SOWt1 þ c8 MANAGEt1 þ c9 TRADEt1 þ εt . Here, Yt includes the total IPO proceeds divided c3 A A t1 A PREIPO t by year-end total assets, the cumulative change of leverage ((D/A)t -(D/A)PREIPO), and the level of leverage((D/A)t) from years IPO (t ¼ 0) to IPOþ3, respectively. Panel 1 provides results without Chinese control variables. Panel 2 is for subsamples of two different time spans. Panel 3 is for firm size subsamples of small and big. Panel 4 provides results excluding the first month observations after lock-up periods. The numbers in parentheses are robust tstatistics. All regressions are estimated with an industry fixed-effect based on the industry code of CSRC (China Securities Regulatory Commission). Superscripts a, b, and c denote statistical significance at the 1%, 5%, and 10% levels. (D/A)t

DINDACCIPO Without Chinese control variables Subsample1 01/2003e05/2010

Subsample2 06/2010e12/2013

pre-IPO small sales

pre-IPO big sales

Excluding the first month obs. after lock-up periods

t-value R2 N DINDACCIPO t-value R2 N DINDACCIPO t-value R2 N DINDACCIPO t-value R2 N DINDACCIPO t-value R2 N DINDACCIPO t-value R2 N

(D/A)t -(D/A)PREIPO

(Procceds/A)t

IPO

IPOþ1

IPOþ2

IPOþ3

IPO

IPOþ1

IPOþ2

IPOþ3

13.14a (7.95) 0.61 979 6.46b (2.53) 0.68 436 12.85a (6.50) 0.63 543 11.36a (4.92) 0.55 523 9.31a (4.04) 0.57 456 10.38a (6.08) 0.63 972

9.96a (6.48) 0.59 1008 9.77a (3.53) 0.58 437 4.05b (2.05) 0.56 545 8.88a (3.85) 0.38 524 6.37a (2.76) 0.52 458 7.05a (4.06) 0.54 975

10.65a (5.76) 0.51 1008 11.67a (4.44) 0.58 455 4.68b (1.96) 0.49 553 9.36a (3.57) 0.42 534 7.11a (2.59) 0.47 474 7.78a (4.09) 0.51 1001

14.31a (7.29) 0.46 1005 15.34a (5.10) 0.53 453 6.61a (2.66) 0.47 552 14.5a (5.69) 0.42 533 10.43a (3.60) 0.43 472 12.02a (6.11) 0.47 998

12.07a (6.08) 0.48 869 7.49a (2.65) 0.55 451 7.49b (2.37) 0.44 418 10.83a (4.11) 0.45 457 8.94a (3.10) 0.49 412 9.96a (4.82) 0.49 862

9.47a (7.61) 0.33 981 6.77a (3.29) 0.33 437 7.04a (4.46) 0.49 544 8.54a (5.24) 0.42 523 7.50a (4.20) 0.36 458 7.89a (6.18) 0.36 974

10.37a (6.67) 0.29 1000 8.10a (3.42) 0.30 448 7.35a (3.53) 0.37 552 8.98a (3.89) 0.33 530 8.17a (3.56) 0.30 470 8.41a (5.26) 0.30 993

11.67a (6.89) 0.25 997 11.18a (4.05) 0.28 446 6.08a (2.72) 0.33 551 11.84a (5.37) 0.31 529 8.84a (3.54) 0.29 468 10.47a (6.10) 0.27 990

8.93a (4.91) 0.27 861 4.74c (1.65) 0.35 444 4.75c (1.64) 0.29 417 7.29a (3.04) 0.32 453 7.32a (2.75) 0.36 408 7.89a (4.17) 0.29 854

coefficients for the change of individual accounts (DINDACC) are positive and significant. In addition, changes in both leverage and cumulative leverage present that equity market timing has a long-term effect on capital structure. In unreported results, the persistence of equity market timing on firm leverage lasts for more than 7 years. The second panel concerns equity market timing at different periods in our sample. We split our sample at June 2010 to redo our regressions for two subsamples. We choose June 2010 as the split point for the following two reasons: 1) It is about the middle point of our sample, and thus we can avoid statistical error due to a small sample size in the two subsamples; 2) China Securities Regulatory Commission (CSRC) revised “Issuance of Securities and Underwriting Regulations” in June 2010. In the third panel we conduct robustness tests on samples of different firm sizes. We use 5 billion RMB of pre-IPO sales as the dividing point to subdivide our sample into small and large firm subsamples. All empirical results of the four subsamples in panels 2 and 3 are similar to our previous results. The equity market timing effect lasts for over four years on capital structure, and so robustness tests of the subsamples support that the effect of equity market timing on capital structure is persistent. Due to the existence of lock-up periods in Chinese IPOs, the magnitude of IPOs may significantly increase right after the end of lock-up periods. If investors are inclined to set up accounts right after the end of lock-up periods to subscribe to IPO shares after a certain period, then there should be an automatic positive relation between the variables. If that is the case, then the positive relation between IPO proceeds and the change in accounts neither serves as evidence of the market timing hypothesis nor provides validity for securities accounts as a market timing variable. In order to eliminate possible effects from lock-up periods, we drop the first month observations after lock-up periods and conduct robustness tests in Panel 4. The empirical results still show that the change of individual accounts (DINDACC) is positively and significantly related to IPO proceeds and has a persistent effect on capital structure. Thus, our conclusions are not influenced by the existence of lock-up periods in Chinese IPOs.

5. Conclusion This paper investigates the effect of equity market timing on capital structure. We use the individual securities accounts (DINDACC) of China's securities account system to construct a new equity market timing measure. The empirical results indicate that the percentage change of individual securities accounts is an effective measure that can ensure a lower cost of equity for firms issuing shares and also reveals a better extent of market timing. Please cite this article as: Zhao, Y et al., Does equity market timing have a persistent impact on capital structure? Evidence from China, The British Accounting Review, https://doi.org/10.1016/j.bar.2019.100838

Y. Zhao et al. / The British Accounting Review xxx (xxxx) xxx

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Market timers can thus identify the above favorable market condition to issue more equity. Using our market timing measure (DINDACC), we find a long-term effect of equity market timing on capital structure, which supports the equity market timing theory (Baker & Wurgler, 2002; Huang & Ritter, 2009). Such timing behavior is independent of firm characteristics such as investment behaviors and dividend payout policies. Therefore, our equity market timing measure can avoid the criticism that the persistent timing impact on firm leverage can be influenced by some firm characteristic determinants of capital structure (Alti, 2006; Hovakimian, 2006; Kayhan & Titman, 2007; Leary & Roberts, 2005). The existing literature on the determinants of capital structure mostly follows Modigliani and Miller (1958) by assuming a perfectly elastic capital supply and focuses on the effect of firm characteristics on capital demand. Our study shows that the persistent timing effect on capital structure is in fact due to imperfect elasticity of capital supply and that the source of the persistent timing effect is driven by the market condition of equity funding supply. This paper provides a link between the market condition of capital supply and firm financial decisions and offers evidence that the supply-side condition of equity funding plays an important role in corporate financing behaviors. Funding Funding support from Taiwan MOST project 104-2410-H-009-004 is appreciated. Appendix A The Effect of Equity Market Timing on Capital Structure with Various Market Timing Measures. Equity Market Timing Measures

Data

The Effect of Equity Market Timing on Capital Structure

Publication

Short-term Long-term effect effect Baker and Wurgler (2002) Leary and Roberts (2005) Alti (2006) Flannery and Rangan (2006) Hovakimian (2006)

External finance weighted average market-to-book ratio (M/Befwa) M/Befwa (Baker & Wurgler, 2002)

U.S. firms during 1968e1999

YES

YES

Journal of Finance

U.S. firms during 1984e2001

YES

NO

Journal of Finance

Monthly IPO volumes M/Befwa (Baker & Wurgler, 2002)

U.S. firms during 1979e1999 U.S. firms during 1965e2001

YES YES

NO No Test

M/Befwa (Baker & Wurgler, 2002)

U.S. firms during 1983e2002

YES

NO

U.S. firms during 1993e2002

YES

No Test

Journal of Finance Journal of Financial Economics Journal of Financial and Quantitative Analysis Journal of Finance

U.S. firms during 1960e2003

YES

NO

Journal of Finance

U.S. firms during 1980e1999

YES

No Test

Journal of Financial Intermediation

G-7 countries (Canada, France, Germany, Italy, Japan, UK, and U.S.) during 1993e2005 U.S. firms during 1963e2001

YES

NO

Journal of Banking and Finance

YES

YES

Journal of Financial and Quantitative Analysis

Dittmar and Thakor 1) Post-Issue EPS Change: difference (2007) between a firm's EPS following the issue and its EPS the quarter prior to the issue divided by the prior EPS; 2) Post-Issue EPS Abnormal Return: the cumulative abnormal return at the EPS announcement; 3) △Breadth: the change in the number of mutual funds holding a stock; 4) The dispersion of analyst forecasts and stock prices; 5) Turnover: volume Kayhan and Titman Yearly timing measure (YT): Splitting (2007) M/Befwa (Baker & Wurgler, 2002) into yearly (YT) and long-term timing measures. YT is the covariance between external financing and the market-tobook ratio scaled by the average external financing. Elliott et al. (2008) VP (value to price ratio): VP is the mispricing component of book-tomarket ratios M/Befwa (Baker & Wurgler, 2002) Mahajan and Tartaroglu (2008) Huang and Ritter Equity Risk Premium (ERP): estimated (2009) using analyst earnings forecasts at the end of the previous calendar year for the 30 stocks in the Dow Jones Industrial Average

(continued on next page)

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Appendix A (continued ) Equity Market Timing Measures

Data

The Effect of Equity Market Timing on Capital Structure

Publication

Short-term Long-term effect effect Chang et al. (2010)

1) M/Befwa (Baker & Wurgler, 2002); Japanese firms during 1977 2) Yearly timing measure (YT) of e2004 Kayhan and Titman (2007); 3) External finance weighted average firm-specific pricing error (BWFSE)

YES

No Test

Financial Management

Appendix B Definition of Variables. Variables

Definition

Dependent Variables 1) Issuance Activity Variables (Proceeds/A)t Total IPO proceeds divided by IPO year-end total assets (Proceeds/A)t-1 Total IPO proceeds divided by total assets at the beginning of the IPO year d/A Changes in book debt (D) divided by total assets (A) 2) Equity Demand Factors D/APRE-IPO Pre-IPO book leverage INV/A Investment rates; capital expenditures (INV) divided by total assets (A) DIV/E Dividends divided by year-end book equity (E) EBITDA/A Earnings before interest, taxes, and depreciation (EBITDA) divided by total assets (A) 3) Capital Structure Variables D/A Book debt (D) divided by total assets (A) (D/A)t-(D/A)t-1 Changes in book leverage between IPO year and pre-IPO year (D/A)t-(D/A)PRE-IPO Cumulative change leverage. The difference in firm leverage between pre-IPO and subsequent years (t) 4) Other Dependent Variables e/A Changes in equity (E) divided by total assets (A) cash/A Changes in cash and short-term investments divided by total assets (A) DOther Assets/A Changes in other assets divided by total assets (A) Equity Market Timing Measure Variables DACC Changes in total securities accounts are the net increase of total securities accounts divided by the previous total securities accounts DINDACC Changes in individual securities accounts are the net increase of individual securities accounts divided by the previous individual securities accounts DINSACC Changes in institutional securities accounts are the net increase of institutional securities accounts divided by
 the previous institutional securities accounts P es þ ds M M/Befwa Historical market-to-book ratio, and M =Befwa; t1 ¼ t1 * , where e and d denote net equity s¼0 Pt1 B r¼0 er þ dr and net debt issues, respectively Other Determinant Variables of Capital Structure M/B Book debt plus the market value of equity divided by total assets (A) SIZE Logarithm of net sales PPE/A Net plant, property, and equipment divided by total assets (A) Control Variables for China's Capital Markets SOW Percentage of state-owned shares MANAGE Percentage of management ownership TRADE Percentage of tradable shares

References Allen, F., Qian, J., & Qian, M. (2005). Law, finance, and economic growth in China. Journal of Financial Economics, 77(1), 57e116. Alti, A. (2006). How persistent is the impact of market timing on capital structure? The Journal of Finance, 61(4), 1681e1710. Alti, A., & Sulaeman, J. (2012). When do high stock returns trigger equity issues? Journal of Financial Economics, 103(1), 61e87. Baker, M., Stein, J. C., & Wurgler, J. (2003). When does the market matter? Stock prices and the investment of equity-dependent firms. Quarterly Journal of Economics, 118(3), 969e1005. Baker, M., & Wurgler, J. (2002). Market timing and capital structure. The Journal of Finance, 57(1), 1e32. Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. The Journal of Finance, 61(4), 1645e1680. Baker, M., & Wurgler, J. (2012). Comovement and predictability relationships between bonds and the cross-section of stocks. The Review of Asset Pricing Studies, 2(1), 57e87. Bhabra, H. S., Liu, T., & Tirtiroglu, D. (2008). Capital structure choice in a nascent market: Evidence from listed firms in China. Financial Management, 37(2), 341e364.

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Please cite this article as: Zhao, Y et al., Does equity market timing have a persistent impact on capital structure? Evidence from China, The British Accounting Review, https://doi.org/10.1016/j.bar.2019.100838