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Are hospitality industry IPO stock returns predictable?
International Journal of Hospitality Management 44 (2015) 23–27
Contents lists available at ScienceDirect
International Journal of Hospitality Management journal homepage: www.elsevier.com/locate/ijhosman
Research note
Are hospitality industry IPO stock returns predictable? Richard Borghesi a,∗ , Katerina Annaraud b , Dipendra Singh c a
College of Business Administration, University of South Florida, United States College of Hospitality & Technology Leadership, University of South Florida, United States c University of Central Florida, Rosen College of Hospitality Management, United States b
a r t i c l e
i n f o
Keywords: Hospitality IPOs Predictability Overperformance Free cash flows Discretionary accruals Altman’s Z
a b s t r a c t We examine the post-IPO excess stock returns of hospitality firms from 1996 to 2012 and find underperformance relative to the market on average. However, there are large differences in returns and some firms significantly outperform. We demonstrate that a substantial portion of this variation can be reliably predicted by utilising pre-IPO financial measures such as firm size, free cash flows, discretionary accruals, and Altman’s Z. Our findings are potentially valuable to prospective hospitality IPO investors in selecting which stocks to buy and to hospitality firm managers in setting IPO issue prices. © 2014 Elsevier Ltd. All rights reserved.
1. Introduction The market for initial public offerings has recently experienced a striking increase in activity. In mid-2014, IPO issuances were on pace to match those in the dot-com era, and the market continues to expand with the number of issuances up 36% year-over-year (Renaissance Capital, 2014). The hospitality industry is keeping pace with an abundance of recent IPOs including Extended Stay America, which raised $565 million (Bloomberg, 2013a), and Hilton Hotels, which set the record for hotel IPOs by raising $2.35 billion and netting investors a 7% first-day return (Bloomberg, 2013b). Two of the top three IPO performers in 2013 were from the hospitality industry – Potbelly Corporation and Noodles & Company – each of which more than doubled in price on first-day trading. While the stocks of many corporations outperform the market in the days after issuance, research finds underperformance in the mid- and long-term. Ritter (1991) examined a sample of 1526 IPOs from 1975 to 1984 and determined that these companies significantly underperformed three years after their initial offering. Loughran (1993) investigated 3556 IPOs from 1967 to 1984 and found that after a period of six years the average return was only 17% while the return on the NASDAQ over the same period was 76%. Loughran and Ritter (1995) also examined IPOs from 1970 to 1990 and determined that the average return was 5% per year for the first
∗ Corresponding author at: College of Business, Department of Finance, University of South Florida, 8350 N. Tamiami Trail, Sarasota, FL 34243-2025, United States. Tel.: +1 9413594524. E-mail addresses: [email protected] (R. Borghesi), [email protected] (K. Annaraud), [email protected] (D. Singh). http://dx.doi.org/10.1016/j.ijhm.2014.09.007 0278-4319/© 2014 Elsevier Ltd. All rights reserved.
five years compared to a 12% annual return for companies of similar sizes. However, few studies have examined the stock returns of hospitality firms subsequent to their IPOs. Jang and Park (2010) analysed 113 hospitality financial articles and found that less than 2% of hospitality research was dedicated to IPOs, and these studies do not address long-term performance. An important motivation for focusing on this particular industry is that prior research identifies significant differences between hospitality and cross-industry IPO stock performance. For example, Atkinson and LeBruto (1995) and Canina (1996) found that first-day hospitality stock returns were greater than those of the overall IPO market. Likewise, examining the Chinese tourism industry from 1993 to 2006, Chen and Chen (2010) determined that mean 1day IPO returns were far greater than those of U.S. IPOs and that underpricing persisted for one year after the IPO date. One reason for disparities in observed performance between hospitality companies and firms in other types of industries may be the differences in the financial makeup between the former and latter. As noted in Olsen et al. (1983) conclusions based on analysis of traditional financial ratios involving manufacturing firms are not reliably transferable to hospitality firms. Therefore hospitality firms should be isolated and their financial makeup examined separately. Furthermore, what most IPO studies have in common is that they are descriptive in nature. In our study we examine whether it is possible to estimate the post-IPO excess stock returns of hospitality firms, and find evidence that excess returns are predictable to a significant degree. 2. Data We search the CRSP database for hospitality IPOs from 1996 to 2012 and record stock returns in the 36-month post-IPO
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R. Borghesi et al. / International Journal of Hospitality Management 44 (2015) 23–27
Table 1 Summary statistics. This table contains summary statistics for the 63 firms in the hospitality IPO sample. Accounting variables are obtained from each firm’s S-1, and are expressed in $MM.
Assets Book value of equity Current assets Current liabilities Discretionary accruals EBIT Free cash flow Liabilities Retained earnings
Mean
Standard deviation
Q1
Median
Q4
738.1 223.0 223.1 163.8 −49.8 40.1 −3.9 511.3 −37.2
2266.4 711.8 864.2 519.5 191.2 91.5 44.8 1933.3 432.9
37.2 9.1 4.5 5.4 −24.9 1.4 −20.8 16.6 −23.1
172.7 33.5 19.1 26.2 −10.5 8.1 −1.8 104.7 −3.6
536.6 183.1 70.5 77.9 −2.1 37.8 3.9 444.4 2.2
period.1 To obtain pre-IPO financial data we search the Securities and Exchange Commission’s Edgar website for the S-1 filings of identified firms. In the United States, companies planning an initial public offering are required to submit an S-1 to register their securities with the Securities and Exchange Commission. The S-1 form contains the balance sheet, income statement, statement of cash flows, and statement of retained earnings. Because the Edgar database does not contain S-1 forms for all IPOs, and because not all S-1 forms contain every financial variable that we require, our final data set consists of 63 hospitality IPOs. For firms that drop out of the sample due to bankruptcy or acquisition we utilise the CRSP delisting return as the final returns observation. Summary statistics for the identified accounting variables are presented in Table 1.
issuers with unusually high accruals in the IPO year experience poor stock return performance three years after. However, earnings manipulation via discretionary accruals need not be self-serving. For instance, Tucker and Zarowin (2006) argued that managers utilise discretionary accruals to improve the informativeness of current and past earnings. Likewise, Subramanyam (1996) found that managers use their discretion to improve the ability of earnings to reflect fundamental values. In the end, under either the manipulative or informativeness viewpoint, it is important to control for discretionary accruals. To impute discretionary accruals we utilise the method developed by Chan et al. (2008). In that study discretionary accruals are calculated based on total accruals and nondiscretionary accruals. Total accruals are calculated as:
3. Methods
Total Accruals = (CA − Cash)(CL − STD − TP) − DEP, (1)
In this study we utilise a series of regression models to examine the relationship between key financial variables and post-IPO cumulative excess stock returns in the hospitality industry. Our explanatory variables are firm size (total assets), free cash flows, discretionary accruals, Altman’s Z, and market returns. Controlling for assets is important because it has been demonstrated that IPO stock performance is partly determined by firm size, with underperformance occurring primarily among small firms (Brav et al., 2000). We include free cash flows because firms with high free cash flows may be more likely to suffer from agency problems that can destroy value.2 Jensen (1986) found that imperfect monitoring by shareholders over opportunistic managers creates the potential for managers to spend internally generated cash flows for their own benefit rather than for maximising firm value. Low free cash flows can be problematic as well. Olsen et al. (1983) determined that financial ratios that are indicative of cash flow problems are the best predictors of the failure of food service establishments. We also control for discretionary accruals, which represent firm earnings management policies, because high discretionary accruals may imply pre-IPO earnings manipulation. Using discretionary accruals, managers can either smooth earnings over longer periods of time or report earnings in excess of cash flows by taking large positive accruals. Such manipulation may mislead investors about future firm prospects. For example, Teoh et al. (1998) showed that
where CA is the change in current assets, Cash is the change in cash, CL is the change in current liabilities, STD is the change in short-term debt, TP is change in taxes payable, and DEP is depreciation and amortisation expense. S-1 forms do not typically list taxes payable, so we are forced to omit this variable from our analysis. The next step is to estimate the following model:
1 As in prior research (Canina, 1996; Canina et al., 2008) we use SIC codes to identify restaurants (5810, 5811, and 5812) and hotels and motels (7010 and 7011). We first identify new lists on the CRSP database and then eliminate ADRs and spinoffs from our sample. 2 Free cash flow is defined as cash flows from operations minus capital expenditures. Cash flows from operations are obtained from each firm’s statement of cash flows as net cash provided by operating activities. Capital expenditures are taken from consolidated statements of operations or from the text of the prospectus. Distributions are excluded.
Total Accrualsi 1 Salesi PPEi = ˛0 + ˛1 + ˛2 + εi , TAi TAi TAi TAi
(2)
where TA is the average of total assets one year prior to the IPO and total assets at the date of filing of the S-1, Sales is the change in sales from the year before the IPO to the IPO year, and PPE is the value of property, plant, and equipment. We then use the resulting coefficient estimates to predict each firm’s nondiscretionary accruals based on its change in sales and the value of its property, plant and equipment. Nondiscretionary accruals are estimated from the fitted values of the expression: NDAi =
ˆ 1 Salesi + ˛ ˆ 2 PPEi ˛ ˆ0 + ˛ . TAi
(3)
Next, we calculate discretionary accruals as the difference between total accruals and nondiscretionary accruals: DAi =
Total Accrualsi − NDAi TAi
(4)
Finally, we examine the effect of Altman’s Z which is used to measure firm financial health (Altman, 1968). The higher the Z score, the less financially distressed the firm is. The Z score is computed based on four financial ratios: (current assets − current liabilities)/total assets (T1 ), retained earnings/total assets (T2 ), EBIT/total assets (T3 ), and book value of equity/total liabilities (T4 ). Altman’s Z is expressed as: Z = 6.56 T1 + 3.26 T2 + 6.72 T3 + 1.05 T4 .
(5)
R. Borghesi et al. / International Journal of Hospitality Management 44 (2015) 23–27
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Fig. 1. Post-IPO cumulative stock returns. This figure illustrates post-IPO cumulative returns for hospitality firms from 1996 to 2012. The solid line represents cumulative buy-and-hold returns while the dashed lines represent cumulative excess returns (ER) relative to CRSP value-weighted and equal-weighted indexes.
Table 2 Determinants of post-IPO cumulative excess stock returns. This table presents the results of an OLS regression in which the dependent variable is post-IPO cumulative excess stock returns. IPO month 1 < Month ≤ 6
Month = 1
Intercept Log assets Free cash flows/assets Discretionary accruals/assets Altman’s Z Cumulative market returns N R-square
6 < Month ≤ 12
12 < Month ≤ 36
Estimate
p-Value
Estimate
p-Value
Estimate
p-Value
Estimate
p-Value
−0.165 0.008 0.080 0.055 0.000 −0.552
0.246 0.265 0.216 0.618 0.852 0.152
−1.285 0.066 0.187 0.222 0.003 −0.264
0.000 0.000 0.005 0.054 0.021 0.453
−2.240 0.115 0.170 0.505 0.006 −0.449
0.000 0.000 0.092 0.004 0.001 0.359
−3.184 0.162 0.024 0.727 0.000 −0.181
0.000 0.000 0.804 0.000 0.992 0.676
63 0.095
Because of the predictive power of Z scores (Borghesi and Pencek, 2013), we expect them to give important information regarding post-IPO viability and stock performance.
382 0.219
377 0.270
In Table 2 we utilise a set of OLS regressions in which the dependent variable is post-IPO cumulative excess stock returns4 : CumERi = ˇ0 + ˇ1 LogAssetsi + ˇ2
4. Results We begin by plotting post-IPO cumulative stock returns and post-IPO cumulative excess stock returns. We, calculate excess returns as stock returns minus the returns on a value-weighted or equal-weighted market portfolio.3 As illustrated in Fig. 1 our data suggest that, consistent with IPO literature, hospitality firms on average underperform relative to the market for at least the first three years following their IPOs. However, as Fig. 2 demonstrates, there is great variability in returns among hospitality firms. This raises the question of whether future strong and weak performers can be identified prior to their IPOs. If so, this information would be valuable to IPO investors and would also help signal to managers when they are in a good position to publicly offer shares.
3
It should be noted that since stock returns following IPOs are more volatile than otherwise, comparing post-IPO stock returns to a value-weighted index may overstate excess returns.
1257 0.154
FCF Assets
i
+ ˇ3
+ ˇ4 Altman’s Zi + ˇ5 CumMktReti + ei .
DA Assets
i
(6)
Between months 2 and 12 following the IPO date, the explanatory variables are generally positive and significant. Estimates for assets are consistent with findings of Brav et al. (2000) in that smaller firms underperform relative to larger firms. Consistent with Chathoth and Olsen (2007) we find that in the hospitality industry free cash flows, which are positively related to liquidity and therefore to sales growth and growth potential, are critically important. Discretionary accruals estimates support the hypothesis that managers use discretionary accruals to produce more reliable and timelier measures of firm performance (Guay et al., 1996) and/or indicate that managers are using their discretion to communicate higher future earnings expectations (Tucker and Zarowin, 2006). Estimates
4 To test for multicollinearity we calculate variance inflation factors (VIFs), each of which is below 1.1. We also test the joint hypothesis that errors are homoscedastic and independent. The resulting p-value of 0.24 indicates that we cannot reject this hypothesis.
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R. Borghesi et al. / International Journal of Hospitality Management 44 (2015) 23–27
Fig. 2. Cumulative excess returns. This plot shows post-IPO cumulative excess stock returns of hospitality firms divided into quartiles. Excess returns are calculated as cumulative returns minus cumulative CRSP value-weighted market portfolio returns.
Table 3 Excess return predictability. This table shows the realised post-IPO cumulative excess stock returns for firms predicted to be in the top and bottom excess returns quartiles. To obtain a prediction we first regress on data from 1996 to 2000 and then use the resulting parameters to estimate the excess returns of firms in the 2001 to 2012 time period. The p-values indicate results of a test for differences in realised mean excess returns between the predicted top (Q4) and bottom (Q1) quartiles.
Firm-years regressed Firm-years predicted Realised mean Q4 excess returns Realised mean Q1 excess returns Difference p-Value
Month = 1
1 < Month ≤ 6
6 < Month ≤ 12
12 < Month ≤ 36
29 34
198 184
184 193
461 796
2.56% 3.29%
4.79% −9.74%
6.21% −34.28%
24.51% −31.05%
−0.73% 0.876
14.53% 0.113
40.49% 0.001
55.56% 0.001
for Altman’s Z indicate that pre-IPO financial health is an important determinant of post-IPO cumulative excess stock returns. However, our explanatory variables are insignificant determinants of firstmonth post-IPO excess stock returns. It is likely that variability in initial IPO returns is largely driven by difficulties in the valuation of private firms (Lowry et al., 2010). We next explore whether we can reliably predict which hospitality firms will outperform the market. We divide the sample into two time periods (1996–2000 and 2001–2012) such that each subset contains roughly the same number of IPOs. We then re-estimate the models in Table 2 utilising the earlier time period and use these estimates to predict excess returns in the later period. Finally, we divide firms into quartiles based on their predicted excess returns and test whether there are reliable performance differences between the top and bottom groups. Results in Table 3 suggest that one-month excess returns are difficult to predict based on our variables, however the stocks of firms in the highest quartile of each variable experience significantly greater excess returns from 7 to 36 months following their IPOs. 5. Conclusions Examining hospitality IPOs, we demonstrate that pre-IPO firm size, free cash flows, discretionary accruals, and Altman’s Z are important determinants of post-IPO excess stock returns. Consistent with prior cross-industry literature, we find that post-IPO
hospitality stock returns are significantly lower than those of the market three years after the IPO date. However, some stocks outperform the market and we offer evidence that the post-IPO excess stock returns of hospitality firms are predictable to a significant degree. Our findings are potentially meaningful for at least two reasons. First, this knowledge could steer hospitality IPO investors towards stocks that are more likely to overperform. Pre-IPO financial data is publicly available so the resulting trading strategy would be implementable. Additionally, one need not be an initial IPO subscriber to implement the methods described. It would be possible to buy stocks up to seven months after the IPO date and still benefit. Second, findings here could be used by hospitality firm managers as an indicator in setting IPO issue price, the goal being to mitigate the severe underpricing described in Canina et al. (2008) and Chen and Chen (2010). One limitation of this study is that it is specifically designed to examine the hospitality industry. There are important financial differences between firms in the hospitality and, for instance, those in the manufacturing industry which would necessitate a re-estimation of parameters and require utilisation of a different Altman’s Z model.
References Altman, E., 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Financ. 23, 589–609.
R. Borghesi et al. / International Journal of Hospitality Management 44 (2015) 23–27 Atkinson, S., LeBruto, 1995. Initial public offerings in the gaming industry: an empirical study. Int. J. Hosp. Manage. 14, 285–292. Bloomberg, 2013a. Retrieved from: http://www.bloomberg.com/news/2013-11-12/ extended-stay-raises-565-million-in-initial-public-offering.html Bloomberg, 2013b. Retrieved from: http://www.bloomberg.com/news/2013-12-11/ blackstone-s-hilton-raises-2-34-billion-in-largest-hotel-ipo.html Borghesi, R., Pencek, T., 2013. Predicting first-year returns of health care IPOs. J. Appl. Bus. Res. 29, 877–884. Brav, A., Geczy, C., Gompers, P., 2000. Is the abnormal return following equity issuances anomalous? J. Financ. Econ. 56, 209–249. Canina, L., 1996. Initial public offerings in the hospitality industry – underpricing and overperformance. Cornell Hotel Restaur. Admin. Q. 37, 18–25. Canina, L., Chang, C., Gibson, S., 2008. IPO underpricing in the hospitality industry: a necessary evil? J. Hosp. Financ. Manage. 16, 33–54. Chan, K., Cooney, J., Kim, J., Singh, A., 2008. The IPO derby: are there consistent losers and winners on this track? Financ. Manage. 37, 45–79. Chathoth, P., Olsen, M., 2007. Does corporate growth really matter in the restaurant industry? Int. J. Hosp. Manage. 26, 66–80. Chen, S.-J., Chen, M.-H., 2010. The underpricing of initial public offerings in the chinese tourism industry. Tour. Econ. 16, 647–663. Guay, W., Kothari, S.P., Watts, R., 1996. A market-based evaluation of discretionary accrual models. J. Acc. Res. 34, 83–105.
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Jang, S.C., Park, K., 2010. Hospitality finance research during recent two decades: subjects, methodologies, and citations. Int. J. Contemp. Hosp. Manage. 23, 479– 497. Jensen, M., 1986. Agency costs of free cash flows. Am. Econ. Rev. 76, 323–329. Loughran, T., 1993. NYSE vs NASDAQ returns: market microstructure or the poor performance of initial public offerings. J. Financ. Econ. 33, 241–260. Loughran, T., Ritter, J., 1995. The new issues puzzle. J. Financ. 50, 23–51. Lowry, M., Officer, M., Schwert, W., 2010. The variability of IPO initial returns. J. Finance 65, 425–465. Olsen, M., Bellas, C., Kish, L.V., 1983. Improving the prediction of restaurant failure through ratio analysis. Int. J. Hosp. Manage. 2, 187–193. Renaissance Capital, 2014. Retrieved from: http://www.nasdaq.com/article/ renaissance-capitals-2q-2014-us-ipo-market-review-cm366922 Ritter, J., 1991. The long-run performance of initial public offerings. J. Financ. 46, 3–27. Subramanyam, K.R., 1996. The pricing of discretionary accruals. J. Acc. Econ. 22, 249–281. Teoh, S.H., Welch, I., Wong, T.J., 1998. Earnings management and the long-run market performance of initial public offerings. J. Financ. 53, 1935–1974. Tucker, J., Zarowin, P., 2006. Does income smoothing improve earnings informativeness? Acc. Rev. 81, 251–270.
Contents lists available at ScienceDirect
International Journal of Hospitality Management journal homepage: www.elsevier.com/locate/ijhosman
Research note
Are hospitality industry IPO stock returns predictable? Richard Borghesi a,∗ , Katerina Annaraud b , Dipendra Singh c a
College of Business Administration, University of South Florida, United States College of Hospitality & Technology Leadership, University of South Florida, United States c University of Central Florida, Rosen College of Hospitality Management, United States b
a r t i c l e
i n f o
Keywords: Hospitality IPOs Predictability Overperformance Free cash flows Discretionary accruals Altman’s Z
a b s t r a c t We examine the post-IPO excess stock returns of hospitality firms from 1996 to 2012 and find underperformance relative to the market on average. However, there are large differences in returns and some firms significantly outperform. We demonstrate that a substantial portion of this variation can be reliably predicted by utilising pre-IPO financial measures such as firm size, free cash flows, discretionary accruals, and Altman’s Z. Our findings are potentially valuable to prospective hospitality IPO investors in selecting which stocks to buy and to hospitality firm managers in setting IPO issue prices. © 2014 Elsevier Ltd. All rights reserved.
1. Introduction The market for initial public offerings has recently experienced a striking increase in activity. In mid-2014, IPO issuances were on pace to match those in the dot-com era, and the market continues to expand with the number of issuances up 36% year-over-year (Renaissance Capital, 2014). The hospitality industry is keeping pace with an abundance of recent IPOs including Extended Stay America, which raised $565 million (Bloomberg, 2013a), and Hilton Hotels, which set the record for hotel IPOs by raising $2.35 billion and netting investors a 7% first-day return (Bloomberg, 2013b). Two of the top three IPO performers in 2013 were from the hospitality industry – Potbelly Corporation and Noodles & Company – each of which more than doubled in price on first-day trading. While the stocks of many corporations outperform the market in the days after issuance, research finds underperformance in the mid- and long-term. Ritter (1991) examined a sample of 1526 IPOs from 1975 to 1984 and determined that these companies significantly underperformed three years after their initial offering. Loughran (1993) investigated 3556 IPOs from 1967 to 1984 and found that after a period of six years the average return was only 17% while the return on the NASDAQ over the same period was 76%. Loughran and Ritter (1995) also examined IPOs from 1970 to 1990 and determined that the average return was 5% per year for the first
∗ Corresponding author at: College of Business, Department of Finance, University of South Florida, 8350 N. Tamiami Trail, Sarasota, FL 34243-2025, United States. Tel.: +1 9413594524. E-mail addresses: [email protected] (R. Borghesi), [email protected] (K. Annaraud), [email protected] (D. Singh). http://dx.doi.org/10.1016/j.ijhm.2014.09.007 0278-4319/© 2014 Elsevier Ltd. All rights reserved.
five years compared to a 12% annual return for companies of similar sizes. However, few studies have examined the stock returns of hospitality firms subsequent to their IPOs. Jang and Park (2010) analysed 113 hospitality financial articles and found that less than 2% of hospitality research was dedicated to IPOs, and these studies do not address long-term performance. An important motivation for focusing on this particular industry is that prior research identifies significant differences between hospitality and cross-industry IPO stock performance. For example, Atkinson and LeBruto (1995) and Canina (1996) found that first-day hospitality stock returns were greater than those of the overall IPO market. Likewise, examining the Chinese tourism industry from 1993 to 2006, Chen and Chen (2010) determined that mean 1day IPO returns were far greater than those of U.S. IPOs and that underpricing persisted for one year after the IPO date. One reason for disparities in observed performance between hospitality companies and firms in other types of industries may be the differences in the financial makeup between the former and latter. As noted in Olsen et al. (1983) conclusions based on analysis of traditional financial ratios involving manufacturing firms are not reliably transferable to hospitality firms. Therefore hospitality firms should be isolated and their financial makeup examined separately. Furthermore, what most IPO studies have in common is that they are descriptive in nature. In our study we examine whether it is possible to estimate the post-IPO excess stock returns of hospitality firms, and find evidence that excess returns are predictable to a significant degree. 2. Data We search the CRSP database for hospitality IPOs from 1996 to 2012 and record stock returns in the 36-month post-IPO
24
R. Borghesi et al. / International Journal of Hospitality Management 44 (2015) 23–27
Table 1 Summary statistics. This table contains summary statistics for the 63 firms in the hospitality IPO sample. Accounting variables are obtained from each firm’s S-1, and are expressed in $MM.
Assets Book value of equity Current assets Current liabilities Discretionary accruals EBIT Free cash flow Liabilities Retained earnings
Mean
Standard deviation
Q1
Median
Q4
738.1 223.0 223.1 163.8 −49.8 40.1 −3.9 511.3 −37.2
2266.4 711.8 864.2 519.5 191.2 91.5 44.8 1933.3 432.9
37.2 9.1 4.5 5.4 −24.9 1.4 −20.8 16.6 −23.1
172.7 33.5 19.1 26.2 −10.5 8.1 −1.8 104.7 −3.6
536.6 183.1 70.5 77.9 −2.1 37.8 3.9 444.4 2.2
period.1 To obtain pre-IPO financial data we search the Securities and Exchange Commission’s Edgar website for the S-1 filings of identified firms. In the United States, companies planning an initial public offering are required to submit an S-1 to register their securities with the Securities and Exchange Commission. The S-1 form contains the balance sheet, income statement, statement of cash flows, and statement of retained earnings. Because the Edgar database does not contain S-1 forms for all IPOs, and because not all S-1 forms contain every financial variable that we require, our final data set consists of 63 hospitality IPOs. For firms that drop out of the sample due to bankruptcy or acquisition we utilise the CRSP delisting return as the final returns observation. Summary statistics for the identified accounting variables are presented in Table 1.
issuers with unusually high accruals in the IPO year experience poor stock return performance three years after. However, earnings manipulation via discretionary accruals need not be self-serving. For instance, Tucker and Zarowin (2006) argued that managers utilise discretionary accruals to improve the informativeness of current and past earnings. Likewise, Subramanyam (1996) found that managers use their discretion to improve the ability of earnings to reflect fundamental values. In the end, under either the manipulative or informativeness viewpoint, it is important to control for discretionary accruals. To impute discretionary accruals we utilise the method developed by Chan et al. (2008). In that study discretionary accruals are calculated based on total accruals and nondiscretionary accruals. Total accruals are calculated as:
3. Methods
Total Accruals = (CA − Cash)(CL − STD − TP) − DEP, (1)
In this study we utilise a series of regression models to examine the relationship between key financial variables and post-IPO cumulative excess stock returns in the hospitality industry. Our explanatory variables are firm size (total assets), free cash flows, discretionary accruals, Altman’s Z, and market returns. Controlling for assets is important because it has been demonstrated that IPO stock performance is partly determined by firm size, with underperformance occurring primarily among small firms (Brav et al., 2000). We include free cash flows because firms with high free cash flows may be more likely to suffer from agency problems that can destroy value.2 Jensen (1986) found that imperfect monitoring by shareholders over opportunistic managers creates the potential for managers to spend internally generated cash flows for their own benefit rather than for maximising firm value. Low free cash flows can be problematic as well. Olsen et al. (1983) determined that financial ratios that are indicative of cash flow problems are the best predictors of the failure of food service establishments. We also control for discretionary accruals, which represent firm earnings management policies, because high discretionary accruals may imply pre-IPO earnings manipulation. Using discretionary accruals, managers can either smooth earnings over longer periods of time or report earnings in excess of cash flows by taking large positive accruals. Such manipulation may mislead investors about future firm prospects. For example, Teoh et al. (1998) showed that
where CA is the change in current assets, Cash is the change in cash, CL is the change in current liabilities, STD is the change in short-term debt, TP is change in taxes payable, and DEP is depreciation and amortisation expense. S-1 forms do not typically list taxes payable, so we are forced to omit this variable from our analysis. The next step is to estimate the following model:
1 As in prior research (Canina, 1996; Canina et al., 2008) we use SIC codes to identify restaurants (5810, 5811, and 5812) and hotels and motels (7010 and 7011). We first identify new lists on the CRSP database and then eliminate ADRs and spinoffs from our sample. 2 Free cash flow is defined as cash flows from operations minus capital expenditures. Cash flows from operations are obtained from each firm’s statement of cash flows as net cash provided by operating activities. Capital expenditures are taken from consolidated statements of operations or from the text of the prospectus. Distributions are excluded.
Total Accrualsi 1 Salesi PPEi = ˛0 + ˛1 + ˛2 + εi , TAi TAi TAi TAi
(2)
where TA is the average of total assets one year prior to the IPO and total assets at the date of filing of the S-1, Sales is the change in sales from the year before the IPO to the IPO year, and PPE is the value of property, plant, and equipment. We then use the resulting coefficient estimates to predict each firm’s nondiscretionary accruals based on its change in sales and the value of its property, plant and equipment. Nondiscretionary accruals are estimated from the fitted values of the expression: NDAi =
ˆ 1 Salesi + ˛ ˆ 2 PPEi ˛ ˆ0 + ˛ . TAi
(3)
Next, we calculate discretionary accruals as the difference between total accruals and nondiscretionary accruals: DAi =
Total Accrualsi − NDAi TAi
(4)
Finally, we examine the effect of Altman’s Z which is used to measure firm financial health (Altman, 1968). The higher the Z score, the less financially distressed the firm is. The Z score is computed based on four financial ratios: (current assets − current liabilities)/total assets (T1 ), retained earnings/total assets (T2 ), EBIT/total assets (T3 ), and book value of equity/total liabilities (T4 ). Altman’s Z is expressed as: Z = 6.56 T1 + 3.26 T2 + 6.72 T3 + 1.05 T4 .
(5)
R. Borghesi et al. / International Journal of Hospitality Management 44 (2015) 23–27
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Fig. 1. Post-IPO cumulative stock returns. This figure illustrates post-IPO cumulative returns for hospitality firms from 1996 to 2012. The solid line represents cumulative buy-and-hold returns while the dashed lines represent cumulative excess returns (ER) relative to CRSP value-weighted and equal-weighted indexes.
Table 2 Determinants of post-IPO cumulative excess stock returns. This table presents the results of an OLS regression in which the dependent variable is post-IPO cumulative excess stock returns. IPO month 1 < Month ≤ 6
Month = 1
Intercept Log assets Free cash flows/assets Discretionary accruals/assets Altman’s Z Cumulative market returns N R-square
6 < Month ≤ 12
12 < Month ≤ 36
Estimate
p-Value
Estimate
p-Value
Estimate
p-Value
Estimate
p-Value
−0.165 0.008 0.080 0.055 0.000 −0.552
0.246 0.265 0.216 0.618 0.852 0.152
−1.285 0.066 0.187 0.222 0.003 −0.264
0.000 0.000 0.005 0.054 0.021 0.453
−2.240 0.115 0.170 0.505 0.006 −0.449
0.000 0.000 0.092 0.004 0.001 0.359
−3.184 0.162 0.024 0.727 0.000 −0.181
0.000 0.000 0.804 0.000 0.992 0.676
63 0.095
Because of the predictive power of Z scores (Borghesi and Pencek, 2013), we expect them to give important information regarding post-IPO viability and stock performance.
382 0.219
377 0.270
In Table 2 we utilise a set of OLS regressions in which the dependent variable is post-IPO cumulative excess stock returns4 : CumERi = ˇ0 + ˇ1 LogAssetsi + ˇ2
4. Results We begin by plotting post-IPO cumulative stock returns and post-IPO cumulative excess stock returns. We, calculate excess returns as stock returns minus the returns on a value-weighted or equal-weighted market portfolio.3 As illustrated in Fig. 1 our data suggest that, consistent with IPO literature, hospitality firms on average underperform relative to the market for at least the first three years following their IPOs. However, as Fig. 2 demonstrates, there is great variability in returns among hospitality firms. This raises the question of whether future strong and weak performers can be identified prior to their IPOs. If so, this information would be valuable to IPO investors and would also help signal to managers when they are in a good position to publicly offer shares.
3
It should be noted that since stock returns following IPOs are more volatile than otherwise, comparing post-IPO stock returns to a value-weighted index may overstate excess returns.
1257 0.154
FCF Assets
i
+ ˇ3
+ ˇ4 Altman’s Zi + ˇ5 CumMktReti + ei .
DA Assets
i
(6)
Between months 2 and 12 following the IPO date, the explanatory variables are generally positive and significant. Estimates for assets are consistent with findings of Brav et al. (2000) in that smaller firms underperform relative to larger firms. Consistent with Chathoth and Olsen (2007) we find that in the hospitality industry free cash flows, which are positively related to liquidity and therefore to sales growth and growth potential, are critically important. Discretionary accruals estimates support the hypothesis that managers use discretionary accruals to produce more reliable and timelier measures of firm performance (Guay et al., 1996) and/or indicate that managers are using their discretion to communicate higher future earnings expectations (Tucker and Zarowin, 2006). Estimates
4 To test for multicollinearity we calculate variance inflation factors (VIFs), each of which is below 1.1. We also test the joint hypothesis that errors are homoscedastic and independent. The resulting p-value of 0.24 indicates that we cannot reject this hypothesis.
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R. Borghesi et al. / International Journal of Hospitality Management 44 (2015) 23–27
Fig. 2. Cumulative excess returns. This plot shows post-IPO cumulative excess stock returns of hospitality firms divided into quartiles. Excess returns are calculated as cumulative returns minus cumulative CRSP value-weighted market portfolio returns.
Table 3 Excess return predictability. This table shows the realised post-IPO cumulative excess stock returns for firms predicted to be in the top and bottom excess returns quartiles. To obtain a prediction we first regress on data from 1996 to 2000 and then use the resulting parameters to estimate the excess returns of firms in the 2001 to 2012 time period. The p-values indicate results of a test for differences in realised mean excess returns between the predicted top (Q4) and bottom (Q1) quartiles.
Firm-years regressed Firm-years predicted Realised mean Q4 excess returns Realised mean Q1 excess returns Difference p-Value
Month = 1
1 < Month ≤ 6
6 < Month ≤ 12
12 < Month ≤ 36
29 34
198 184
184 193
461 796
2.56% 3.29%
4.79% −9.74%
6.21% −34.28%
24.51% −31.05%
−0.73% 0.876
14.53% 0.113
40.49% 0.001
55.56% 0.001
for Altman’s Z indicate that pre-IPO financial health is an important determinant of post-IPO cumulative excess stock returns. However, our explanatory variables are insignificant determinants of firstmonth post-IPO excess stock returns. It is likely that variability in initial IPO returns is largely driven by difficulties in the valuation of private firms (Lowry et al., 2010). We next explore whether we can reliably predict which hospitality firms will outperform the market. We divide the sample into two time periods (1996–2000 and 2001–2012) such that each subset contains roughly the same number of IPOs. We then re-estimate the models in Table 2 utilising the earlier time period and use these estimates to predict excess returns in the later period. Finally, we divide firms into quartiles based on their predicted excess returns and test whether there are reliable performance differences between the top and bottom groups. Results in Table 3 suggest that one-month excess returns are difficult to predict based on our variables, however the stocks of firms in the highest quartile of each variable experience significantly greater excess returns from 7 to 36 months following their IPOs. 5. Conclusions Examining hospitality IPOs, we demonstrate that pre-IPO firm size, free cash flows, discretionary accruals, and Altman’s Z are important determinants of post-IPO excess stock returns. Consistent with prior cross-industry literature, we find that post-IPO
hospitality stock returns are significantly lower than those of the market three years after the IPO date. However, some stocks outperform the market and we offer evidence that the post-IPO excess stock returns of hospitality firms are predictable to a significant degree. Our findings are potentially meaningful for at least two reasons. First, this knowledge could steer hospitality IPO investors towards stocks that are more likely to overperform. Pre-IPO financial data is publicly available so the resulting trading strategy would be implementable. Additionally, one need not be an initial IPO subscriber to implement the methods described. It would be possible to buy stocks up to seven months after the IPO date and still benefit. Second, findings here could be used by hospitality firm managers as an indicator in setting IPO issue price, the goal being to mitigate the severe underpricing described in Canina et al. (2008) and Chen and Chen (2010). One limitation of this study is that it is specifically designed to examine the hospitality industry. There are important financial differences between firms in the hospitality and, for instance, those in the manufacturing industry which would necessitate a re-estimation of parameters and require utilisation of a different Altman’s Z model.
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