Hazardous to your wealth? Investing in ASX firms making rights issues and private placements

Transcription

1 Hazardous to your wealth? Investing in ASX firms making rights issues and private placements By Dr Raymond da Silva Rosa* Dr Robert Durand* Ms Olivia Ker* SIRCA & UWA Business School US studies indicate that companies that issue private placements and rights issues subsequently under-perform. For instance, Loughran and Ritter (1995) find that the average raw return to US firms issuing 3,702 SEOs is only 7% per year in the five years following the offering while matched non-issuing firms earned 15% per year, i.e., an investor would have to invest 44% more money in issuers than in non-issuers of the same size to have the same wealth five years after the offering. Loughran and Ritter observe that these findings suggest investing in firms making rights issues and private placements is hazardous to your wealth. Spiess and Affleck-Graves (1995) report similar results and conclude they are consistent with managers issuing equity when their stock is overvalued. The importance of investigating whether the US results apply in Australia may be appreciated by considering that rights issues and private placements accounted for 40.5% of the $ billion in total equity capital raisings (excluding proceeds from the Telstra float) on ASX over the ten year period from January 2002 (ASX Fact Book 2003, see Figure 1). Further, it is an open question whether the US findings apply in Australia. In the U.S. most SEOs are undertaken via a public offer of shares. In contrast, SEOs in Australia are typically rights issues or private placements. The difference is relevant because public offers trigger a negative share-market reaction whilst announcements of private placements and rights issues are better received. One question of interest is whether the short-term market reaction is an accurate pointer to long-term performance. Corresponding author: ray.dasilvarosa@uwa.edu.au Tel: (08)

2 We extend the US research to the Australian market by analyzing the short and long run share market performance of 932 ASX-listed firms that made 3,643 SEOs over the period January 1993 through to 31 December The length of period covered and large sample size makes our study the most comprehensive Australian study to date (other Australian studies include (Soucik and Allen 1999, and Goh and How 2001). We also incorporate the latest research techniques to deal with the now well known difficulties in measuring long-run share market performance. Sample description We source our sample companies from Thomson Financial s SDC Platinum Securities database. Over the seven year period from January 1993 to December 1999, 932 ASX firms raised $27.1 billion from private placements and rights issues. The 3,061 private placements raised $17.49 billion, and the 582 rights issues raised $9.63 billion. On a proceeds per issue basis, the private placements raised $5.7 million on average, whilst the average raised by the rights issues was $16.5 million. However, the average proceeds as percentage of market value was 23.33% for the private placements and just 2.91% for the rights issues. These comparisons indicate firms making rights issues are substantially larger than those making private placements. Further, the standard deviation (221.17%) and range (0.001% to 9,685.25%) of the private placement sample is substantially greater than that of the rights issue sample (standard deviation = 1.17%, range of 0.217% to 4.63%). Across all SEOs, 20% of firms account for less than 93% of the total proceeds raised. These figures suggest it is hazardous to rely solely on simple averages when summarizing the performance of SEO firms. We are likely to be mislead about the economic significance of findings if we rely on the simple average because small SEOs will drive its value. The distribution of SEOs across industries is illustrated in Figure 2. The natural resource industry 1 accounts for 2,117 SEOs (58%) of the sample. Interestingly, just 383 firms were 1 The natural resources sector comprises ASX industry classification codes 01 to 05, namely the Gold, Other Metals, Diversified Resources and Energy industries. 2

3 responsible for the 1,810 private placements in the natural resources sector, which equates to an average of almost five private placements per firm over the 7 year period. Of the 932 SEO firms, 259 (28%) of them undertook just one SEO, while 673 (72%) of firms had multiple offerings. Private placement firms are more likely to make multiple offerings with 72% of our sample making more than one placement within our sample period, one firm making 32! In contrast, just 28% of rights issuers made multiple issues within our sample period, the most prolific firm making six. Results Table 1 reports mean buy-and-hold abnormal returns (Panel A) and cumulative abnormal returns (Panel B) for the six month period leading up to the SEO announcement month and for holding periods of up to five years after the announcement month. Consistent with the US results Australian firms making SEOs significantly underperform in the long-run. Figure 3 provides a graphical representation of this underperformance. In the six months leading up to the SEO announcement issuing firms outperform all benchmarks. Using BHARs to measure performance, Panel A of Table 1 indicates that the average (median) SEO firm outperforms the market index (AOAI) by 33.58% (3.80%) in the six months prior to the issue. A similar pattern is also found using CARs (Panel B) with issuers beating the market on average (median) by 28.54% (8.13%). This finding is consistent with the window of opportunity hypothesis that firms announce equity issues when their stock is overvalued. In dramatic contrast to their pre-announcement performance, Figure 1 shows that the post-issue performance of SEO firms is poor. On average the SDC Platinum firms under perform the value weighted market index (equal-weighted market index) in 50 (56) of the 60 months following the issue 2. Over a three year holding period, Table 1 Panel A 2 Our finding of a short-term positive impact following the SEO is consistent with the findings of Spiess and Affleck-Graves (1995) who reports significant positive abnormal returns in the first months following the announcement. Furthermore Perzi (2002) document the presence of positive post-announcement drift following private placements in Australia. 3

4 indicates that the average buy-and-hold return to SEO firms relative to the value weighted market index (VwMkt), the equally weighted market index (EwMkt), a size and industry matched non-issuing firm and a comparable size decile portfolio is %, %, % and % respectively. This poor performance is robust to alternative methods of measuring long-run performance. Table 1 Panel B reports negative three year cumulative abnormal returns relative to the VwMkt, EwMkt and a matched industry and size non-issuing firm of -0.11%, % and %. Buy-and-hold abnormal returns are positively skewed due to the compounding of single period returns (see Figure 4 3 ), and so we test the significance of our results using. the bootstrapped skewness adjusted t-statistic (t sa -statistic). Private placements of equity, particularly high value placements by large firms, are generally to sophisticated institutional investors and are assumed to have a positive certification effect (Hertzel and Smith, 1993). Table 2 summarizes the buy-and-hold performance of our sample of private placements and rights issues. The results indicate that private placements and rights issues significantly under-perform in the long-run. These results are consistent with US and UK findings (see Hertzel, Linck and Rees 1999, Levis 1995, and Suzuki 2000). The significant pre-announcement increase in share prices associated with private placements lends support to Myers and Majluf s (1984) prediction that firms sell equity after a period of share price increases. On the other hand rights issues are not associated with any significant pre-announcement share price activity. Our event-time results illustrate several points about the influence of research method. We find (results not tabulated) that BHARs adjusted by the value-weighted market are significantly negative four and five years after the offer when conventional t-statistic and z-statistic are used to assess significance. However this conclusion changes when the skewness adjusted t-statistic is used to assess significance. We also see that there is more noticeable difference between the conventional t-statistic and bootstrapped skewness 3 Figure 4 illustrates the distribution of 3 year market adjusted buy-and-hold and cumulative abnormal returns of our sample of SEOs. Panel A highlights the positive skewness in the distribution of buy-and-hold returns, warranting the use of our bootstrap skewness adjusted t-statistic. In comparison cumulative abnormal returns exhibit less severe skewness and kurtosis. 4

5 adjusted t sa -statistic for BHAR s as opposed to CAR s. As expected, the effect is greater for BHARs given the severe positive skewness in their underlying distribution (for more discussion, see Lyon, Barber and Tsai 1999, and Mitchell and Stafford 2000). Multivariate analysis We undertake a multivariate regression analysis to investigate what factors affect the long-run performance of SEOs. The regressions are run on our full sample of SEOs using three, four and five year buy-and-hold and cumulative abnormal returns. To preserve concision, we report just our principal findings. More detail on method and specific findings are available on request. Curiously, while there is a positive relationship between firm size and BHARs when size is investigated in isolation the relationship becomes insignificant in multivariate analysis. Further, older firms experience more severe post-offer under-performance. We know that established firms generally finance new investment via retained earnings so the issue of equity by older firms might reflect their poor profitability per se. Miller and Rock (1985) develop a model which predicts that larger issues send a more negative signal to the market. We find evidence consistent with this prediction. Given the strong correlation between firm and issue size, we also investigate the explanatory power of offer size controlling for the size of the firm. Curiously, we do not find a statistically significant relationship. This is consistent with US findings by Mikkelson and Partch (1986) and Hansen and Crutchely (1990). In univariate analysis, we find a statistically significant relationship between post-offer performance and number of SEOs undertaken by the firm, however, our multivariate regressions indicate that in the presence of other explanatory variables it is not able to significantly explain variation in post-offer performance. Loughran and Ritter s (1995) windows of opportunity hypothesis states that SEO longrun under performance is driven by firms who act opportunistically and issue equity 5

6 when their shares are overvalued by the market. Our results indicate that those firms which are most overvalued on the announcement date, which we interpret as those firms with largest pre-announcement increases in share-price, experience greater negative longrun abnormal performance. Finally, we find that mining firms perform significantly worse than non-mining firms. This finding is driven by the large number of small mining firms in our sample. In sum, our multivariate results suggest that information asymmetries help explain SEO long-run performance. Smaller firms and issuers who experience greater increases in preannouncement share price perform worse in the long-run. When the market is well informed about the firm and its operations, it is better equipped to interpret the circumstance of the SEO and will more rapidly punish issues which fail to create value for shareholders. As such we expect that large firms (and consequently high-value SEOs) will perform better and show little abnormal performance after the issue. Our calendar time results, discussed next, support this interpretation. Calendar-time performance results Our calendar time performance analysis estimates the monthly abnormal returns that could have been earned by staying invested in SEO firms as they made their issues for a period of up to three months subsequent to each issue. The sample firms returns in each calendar month are regressed against the return to factors assumed to be priced by the market. In our case, we use the Fama-French (1993) three-factor model As in Fama and French s work, we interpret the intercept (α) from the time-series regressions as an indicator of SEO abnormal performance. Table 3 reports the value (Panels A & B) and equal-weighted (Panels C & D) calendar time portfolio abnormal returns. On an equal-weighted basis Panel C of Table 3 confirms that the SEOs significantly underperforms in the long-run. For the equal-weighted portfolio, the regression intercept (α) indicates that private placements and rights exhibit 6

7 average abnormal returns of -1.8% and -1.0% per month respectively for the 3 year period following the issue, statistically significant at the 5% level in both instances. Although the three factor model has difficultly in pricing the equal-weighted SEO portfolio returns, once returns are value weighted (Panel A) the intercepts become statistically insignificant - the result one would expect of an efficient market. Given the prevalence of small firms in the sample (39% of SEOs are conducted by firms in the bottom 3 size deciles), weighting each firm equally clearly overstates the extent of SEO long-run underperformance. Mitchell and Stafford (2000) reach a similar conclusion. Specifically, their equal-weighted SEO portfolio displays significantly negative abnormal returns in the three years following the issue, however when they value weight the portfolio there is virtually no evidence of underperformance. It appears that it is the smallest firms that drive the poor long-run performance of SEOs. Conclusion Consistent with the existing SEO literature, we find that Australian firms undertaking SEOs substantially underperform in the long-run. This finding is based on the commonly used technique for measuring SEO long-run performance, namely and event-time strategy with each SEO weighted equally. We show that despite the more favourable announcement price reaction experienced by private placements, both private placements and rights issues significantly underperform in the long-run. Our cross-sectional analysis confirms the importance of information asymmetry in determining long-run performance. Smaller firms and those most overvalued at the issue date display the worst long-run performance. Partitioning the sample based on firm size, we see that larger firms perform better than smaller firms, however long-run performance is not monotonic with respect to firm size. Further, firms conducting several SEOs and issuers from the natural resource industry perform worse in the long-run. Our calendar time analysis supports this result. It appears that small firms drive the ostensible long-run underperformance of SEOs. Although both private placements and rights issues significantly underperform in the long-run on an equal-weighted basis, they exhibit 7

8 insignificant abnormal performance on a value-weighted basis. In sum, our results suggest that SEO long-run underperformance is not as pervasive as first thought, instead we report results more inline with rational investor behaviour in a competitive capital market. Finally, our results clearly illustrate that assessments of long-run abnormal share price performance are highly sensitive to the research method employed. References Goh, A., and J. How, (2001), The long-run performance of Seasoned Equity Offerings: Australian Evidence, Working Paper, University of Western Australia. Hansen, R.S., and C. Crutchley, (1990), Corporate earnings and financings: an empirical analysis, The Journal of Business 63, Hertzel, M., and R. Smith, (1993), Market discounts and shareholder gains for placing equity privately, The Journal of Finance 48, Hertzel, M., Lemmon, M., Linck, J.S., and L. Rees, (2002), Long-run performance following private placements of equity, The Journal of Finance 57, Levis, M., (1995), Seasoned equity offerings and the short and long-run performance of initial public offerings in the UK, European Financial Management 1, Loughran, T., and J.R. Ritter, (1995), The new issues puzzle, The Journal of Finance 50, Loughran, T., and J.R. Ritter, (1997), The operating performance of firms conducting seasoned equity offerings, The Journal of Finance 52, Miller, M., and K. Rock, (1985), Dividend policy under asymmetric information, Journal of Financial Economics 15, Mitchell, M. L., and E. Stafford, (2000), Managerial decisions and long term stock price performance, The Journal of Business 73, Perzi, P., (2002), Private placements and renounceable issues: Empty certification?, Unpublished Honours Thesis, University of Western Australia. Soucik, V., and D.E. Allen, (1999), Long run underperformance of seasoned equity offerings: fact or illusion?, Working Paper, Edith Cowan University Spiess, D.K., and J. Affleck-Graves, (1995), Underperformance in long-run stock returns following seasoned equity offerings, Journal of Financial Economics 38, Spiess, D.K., and J. Affleck-Graves, (1999) The long-run performance of stock returns following debt offerings, Journal of Financial Economics 54, Summary of Findings 1) On average, SEO firms in Australia significantly under-perform in the long-run. 2) Underperformance, on average, is true for both private placements and rights issues 3) Larger SEO firms perform significantly better than smaller SEO firms in the longrun. 4) Older firms do not perform significantly better than younger firms in the long-run. 5) Firms with greater pre-announcement run-up in share prices perform worse postissue 6) SEO long-run performance is not significantly related to the size of the issue. 7) Value-weighting returns eliminates underperformance 8

9 Figure 1 Equity Capital Raisings by ASX Firms ( ) Figure 1 displays the approximate market value of equity raised through (1) private placements, (2) rights issues, (3) dividend reinvestment plans, (4) calls, exercise of options and employee share schemes and (5) initial public offerings in Australia over the period according to the ASX Fact Book The figures exclude the $30.9 billion raised as consequence of the float of Telstra Ltd Equity Raised ($ millions) Year Private Placements Rights Issues Reinvested Dividends Calls,Options, ESP New Floats Figure 2 Distribution across industries of Australian SEOs ( ) 1400 Industrial Distribution of SDC Platinum SEO Sample Number of SEOs Gold Other Metals Diversified Resources Energy Infrastructure & Utilities Developers & Contractors Building Materials Alcohol & Tobacco Food & Household Chemicals Engineering Paper & Packaging Retail Transport Media Banks & Finance Insurance Telecommunications Investment & Financial Services Property Trusts Health Care & Biotechnology Miscellaneous Industries Diversified Industrials Tourism & Leisure Private Placem ents Rights Issues 9

10 Figure 3 Abnormal returns to ASX firms making SEOs (Returns expressed in event-time, i.e., relative to month of offering) % Return Months Since Issue VW BHAR EW BHAR VW CAR EW CAR 10

11 Figure 4 Distribution of 3yr Market Adjusted Abnormal Returns Panel A: Buy-and-Hold Abnormal Returns Series: 3yr Vw BHAR Observations 627 Mean Median Maximum Minimum Std. Dev Skewness Kurtosis Jarque-Bera Probability Panel B: Cumulative Abnormal Returns Series: 3yr Vw CAR Observations 627 Mean Median Maximum Minimum Std. Dev Skewness Kurtosis Jarque-Bera Probability This table highlights the distribution of market adjusted buy-and-hold (Panel A) and cumulative (Panel) abnormal returns for a holding period of 3 years following the last SEO made by the 932 firms in the SDC Platinum Sample of SEOs. 11

12 Table 1 Event-time Abnormal Returns Table reports average buy-and-hold abnormal returns (Panel A) and cumulative abnormal returns (Panel B). Taking the last SEO made by the firm, the data cover 932 SEOs (comprising both private placements and rights issues) made between the 1 st January 1993 to 31 st December SEO firms raw returns are adjusted by the return on (i) the SPPR value-weighted market index (VwMkt); (ii) the SPPR equalweighted market index (EwMkt); (iii) a matched industry and size non-issuer ; and (iv) a corresponding size-decile. The matched industry and size non-issuing firms comprise ASX listed firms who have not issued equity within three years of the SEOs announcement date. The bootstrapped skewness-adjusted t- statistic (tsa-statistic) is used to test for significance test with p-values reported in parentheses. Panel A: Buy-and-Hold Abnormal Returns [-6,0] [+1,+12] [+1,+24] [+1,+36] [+1,+48] [+1,+60] n=892 n=767 n=669 n=606 n=263 n=164 (i) Value Weighted Market Adjusted Mean (%) tsa-statistic p-value (0.000) (0.618) (0.010) (0.003) (0.210) (0.030) Median (%) (ii) Equal Weighted Market Adjusted Mean (%) tsa-statistic p-value (0.000) (0.010) (0.003) (0.000) (0.219) (0.030) Median (%) (iii) Size and Industry Matched Non-Issuer Adjusted n=594 n=489 n=408 n=173 n=98 Mean (%) tsa-statistic p-value (0.057) (0.071) (0.153) (0.020) (0.132) Median (%) (iv) Size-Decile adjusted Mean (%) tsa-statistic p-value (0.000) (0.021) (0.000) (0.002) (0.003) (0.201) Median (%) Panel B: Cumulative Abnormal Returns [-6,0] [+1,+12] [+1,+24] [+1,+36] [+1,+48] [+1,+60] (i) Value Weighted Market Adjusted Mean (%) p-value (ii) Equal Weighted Market Adjusted Mean (%) p-value (iii) Size and Industry Matched Non-Issuer Adjusted n=594 n=489 n=408 n=173 n=98 Mean (%) tsa-statistic p-value

13 Table 2 Buy-and-hold returns to private placements and rights issues [-6,0] [+1,+12] [+1,+24] [+1,+36] [+1,+48] [+1,+60] Panel A: Value Weighted Market Adjusted Private Placements n=698 n=597 n=522 n=480 n=200 n=130 Mean (%) tsa-statistic p-value Median (%) Rights Issues n=194 n=170 n=147 n=126 n=63 n=34 Mean (%) tsa-statistic p-value Median (%) Mann Whitney z-statistic p-value Panel B: Size and Industry Matched Non-Issuer Adjusted Private Placements n=456 n=378 n=316 n=129 n=73 Mean (%) tsa-statistic p-value Median (%) Rights Issues n=138 n=111 n=92 n=44 n=25 Mean (%) tsa-statistic p-value Median (%) Mann Whitney z-statistic p-value Panel C: Size-Decile Adjusted Private Placements n=698 n=597 n=522 n=480 n=200 n=130 Mean (%) tsa-statistic p-value Median (%) Rights Issues n=194 n=170 n=147 n=126 n=63 n=34 Mean (%) tsa-statistic p-value Median (%) Mann Whitney z-statistic p-value

14 Table 3 Time-series Regressions of Monthly SEO Portfolio Returns on Fama & French s (1993) Three- Factor Model This table gives the regression estimates of monthly returns on the Fama & French (1993) three factor model: R pt R = α + b ( R R ) + s SMB + h HML + ε ft p p mt ft p The sample includes 3,641 SEOs (3,011 private placements and 630 rights issues) made between 1/1/1993 to 31/12/1999. R pt is the price relative (P t /P t-1 ) minus the risk-free rate (Rf f ) for the monthly portfolio t (t=1,2,,108) which is formed on either a value or equal-weighted basis. R mt -R ft is the t month return on the SPPR Value- Weighted Market Index (R mt ) in excess of the risk-free rate (R ft ). SMB t is the difference between the return on a portfolio of small firms minus big firms in month t. HML t is the t month difference between the return on a portfolio of high book-to-market stocks and the return on a portfolio of low book-to-market stocks. In Panel A (Panel B) we regress the value weighted return of our SEO portfolio on the Fama & French (1993) factors formed on a value (equal) weighted basis. In Panel C (Panel D) the equal-weighted return of our SEO portfolio is regressed on the equal-weighted (value-weighted) Fama & French factors. The regressions are estimated using OLS and the t- statistics (in square brackets) are reported using White s heteroskedasticty corrected standard errors. Significance is indicated by ***,**,* at the 1%, 5% & 10% levels respectively. All R-squared values are reported as percentages and each regression uses 108 months. Average Parameter Values α b s h R 2 (Adj R 2 ) F-stat (pvalue) Panel A: Value-Weighted Portfolio Returns on Value-Weighted Factors All SEOs *** Priv Placements *** Rights issues *** 0.153*** Panel B:Value-Weighted Portfolio Returns on Equal-Weighted Factors All SEOs 0.021*** 0.708*** *** Priv Placements 0.015** 0.589*** *** Rights issues 0.021*** 0.78*** *** Panel C: Equal-Weighted Portfolio Returns on Equal-Weighted Factors All SEOs *** 1.248*** Priv Placements ** 1.246*** 0.236* Rights issues -0.01** 1.138*** Panel D: Equal-Weighted Portfolio Returns on Value Weighted Factors All SEOs *** 1.352*** 0.871*** Priv Placements *** 1.257*** 0.913*** Rights issues *** 1.283*** 0.76*** t p t pt 14

15 Appendix A: Definition of abnormal return measures Buy-and-hold abnormal return The average buy-and-hold abnormal return BHAR, across all firms from month t=j to month t=k is calculated as follows: 1 BHAR = N k j N i= 1 k j 1 BHRi N where BHR i 4 is the buy-and-hold return for firm i from month t=j to month t=k. BHR B 5 is the buy-and-hold return of our expected return benchmark from month t=j to month t=k and N= the number of SEOs in the sample. Four return benchmarks are used: the rate of return on (1) a value-weighted market index (AOAI), (2) a equal weighted market index (AOAI ew ), (3) a corresponding size decile portfolio (R size ) and (4) a size and industry matched non-issuing firm (R indsize ). Cumulative abnormal returns The cumulative abnormal return for firm i over a τ month period is calculated via the following formula: CAR τ i, τ = ( Ri, t RB, t ) i= 1 where CAR iτ is the τ month period cumulative abnormal return (summed monthly abnormal returns) for firm i, R it is the t month rate of return for firm i, R Bt is the t month expected return for security i, τ is the number of months over which the CARs are calculated and N is the number of SEOs in the sample. We employ the same expected return proxies for both our BHARs and CARs. As with the BHARs, the average CAR of the sample is calculated as the equally weighted average of cumulative abnormal returns across all firms: CAR τ 1 = N N i= 1 CAR i, τ N i= 1 k j BHR B 4 BHR i is calculated by the following formula: k k ( 1, ) j BHR i = + 1 Ri t t = j where R i,t is the return of stock i for month t. 5 BHR B is calculated by the following formula: k k ( 1, ) j BHR B = + 1 RB t t= j where R B,t is either (1) R AOAI the rate of return on the All Ordinaries Accumulation Index (AOAI) in month t (2) R AOAIew the rate of return on the equally weighted AOAI (AOAI EW ) in month t. (3) R Size the rate of return of a corresponding AOAI size-decile portfolio for size i firms in month t (4) R IndSize the rate of return of a size and industry matched non-issuing firm in month t. 15