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Keywords:

  • convertibles bond;
  • earnings management;
  • accounting accruals;
  • long-run performance

Abstract

  1. Top of page
  2. Abstract
  3. 1. INTRODUCTION
  4. 2. SAMPLE SELECTION AND METHODOLOGY
  5. 3. ACCRUALS AND OFFER/POST-OFFER PERFORMANCE
  6. 4. PREDICTION OF POST-OFFER STOCK RETURNS
  7. 5. CONCLUSIONS
  8. REFERENCES

Abstract:  This paper examines whether the long-run underperformance of convertible bond issuers can be explained by earnings management, as reflected in discretionary current accruals around the time of the offer. Consistent with the earnings management hypothesis, we find that convertible issuers who adjust their discretionary current accruals to report higher net income in the issue year will generally experience inferior operating and stock return performance over the five-year post-issue period. Our findings indicate that there is some temporary overvaluation of convertible issuers by the stock market, but that the resultant disappointed investors will subsequently correct their valuation errors. The similarity of our results to those reported within the prior literature on initial public offers (IPOs) and seasoned equity offers (SEOs) suggests that the earnings management hypothesis is not unique to stock offers, but that it actually extends to convertible bond offers.


1. INTRODUCTION

  1. Top of page
  2. Abstract
  3. 1. INTRODUCTION
  4. 2. SAMPLE SELECTION AND METHODOLOGY
  5. 3. ACCRUALS AND OFFER/POST-OFFER PERFORMANCE
  6. 4. PREDICTION OF POST-OFFER STOCK RETURNS
  7. 5. CONCLUSIONS
  8. REFERENCES

It is noted in several of the prior studies that firms experience poor long-run operating and stock price performance following their convertible bond offers, with the average annual stock return of convertible bond issuing firms being about 4 to 8% less than that of non-issuing matched firms over the five-year post-offer period.1 Many of these prior studies also show that, as compared to matched firms, convertible bond issuers have much greater declines in cash flow and investment-related operating performance over the four-year post-offer period (McLaughlin et al., 1998; Lee and Loughran, 1998; and Lewis et al., 2001).

In this study, we set out to examine whether aggressive earnings management by income-increasing accounting adjustments around the time of convertible bond offers can explain the subsequent inferior operating and stock price performance of the issuing firms. Legoria et al. (1999) find that firms use discretionary accruals to create a pattern of improved financial performance leading up to the year of the debt issue. Similarly, firm managers also have incentives to manage reported earnings prior to the issuing of convertible bonds in order to minimize the company's risk assessment by creditors.

In addition, theory predicts that for some firms, convertible bonds can simultaneously mitigate both debt- and equity-related financing problems. However, Lewis et al. (2001) find no support for any prediction of such investment behavior by firms; indeed, they conclude that firms rationed out of the equity market will respond either by immediately issuing convertible bonds, or by subsequently issuing equity. Munro (1996) provides some preliminary evidence concerning the nature of convertible bonds issuers in the UK, but his study still would not be able to rationalize the use of such issues.

From a standpoint of the conversion mechanism embedded in convertible bonds, it is implicit that some element of the nature of issuing convertible bonds is similar to that of issuing common equity; hence, Lewis et al. (1999) provide a methodology for separating issuers into groups, with the securities design suggesting that issuers are debt-like, equity-like or hedgers; that is, convertible bonds can be regarded as straight debt with an embedded call option. It is therefore clear that the economic substance of convertible bonds is an issue of significant importance to investors (Casson, 1998; and Mehran and Homaifar, 1993).

If the stock market were to interpret the offer of convertible bonds as being more akin to equity than debt, then the increased likelihood of dilution of existing shareholder wealth would send out a strong negative signal to the market (Purdy, 1977). Furthermore, investors might expect to observe unusual phenomenon during season equity offers; managers will, for example, often engage in upward earnings management in the pre-issuance period to fool the market into setting a better offer price (Marquardt and Wiedman, 2004). We therefore expect to find a negative relationship between conversion probability and long-term stock price performance.

Very little empirical evidence has thus far been presented to indicate whether the poor long-term operating and stock price performance of issuers following convertible and non-convertible security offers may simply be attributable to common factors. Thus, similarities in operating performance following the issue of both convertible bonds and common equity motivate us to examine whether the earnings management associated with equity offers may also occur in the case of the issuance of convertible bonds.2

Rangan (1998) and Teoh et al. (1998a and 1998b) note that stock issuers are found to have both unusually high income-increasing accounting adjustments around the time of the offer, and unusually poor operating and stock return performance in the subsequent post-offer period; similarly, stock issuers are also found to exhibit inferior performance when they make unusually large income-increasing accounting adjustments around the time of the offer.

This suggests that stock issuers will generally engage in the upward management of earnings in order to increase the offer proceeds, and that investors can often misinterpret the transitory increases in earnings reported at the time of stock offers, resulting in their substantial overvaluation of such issues. In the post-offer period, when high earnings cannot be sustained, the market is invariably disappointed and subsequently corrects the balance by lowering its valuation.

However, this earnings management hypothesis may not be unique to stock offers, since it may be that it also extends to convertible bond offers. Firms which are overvalued are likely to issue securities of any type; thus, it is likely that convertible offers, like stock offers, provide a signal of the overvaluation of the firm. The inability of investors to unravel earnings management surrounding convertible offers is therefore a potential source of the issuers' subsequent poor performance.

In this study, we use discretionary current accruals to measure the manipulation of earnings determined under management discretion, adopting the methods developed in Teoh et al. (1998a and 1998b). We report evidence consistent with the earnings management hypothesis on a sample of convertible bond offers which took place between 1981 and 1998, and find a discernible increase in discretionary current accruals prior to the issue date, a peak during the issue year, and a subsequent decline thereafter.

Our results indicate that in the five-year period following convertible bond offers, issuers experienced declines in asset-scaled operating income before depreciation plus interest income (OIBD/assets) and return on assets (ROA). This post-issue decline in OIBD/assets and ROA is particularly pronounced for those issuers who adopted aggressive earnings management in the issue year. Our evidence suggests that managers advance their accruals to increase their reported net income during the convertible bond issue period.

We also find that discretionary current accruals around the offer can predict the underperformance of post-issue stock returns. We find that during the five-year post-issue period, the stock performance of those convertible issuers who adopted aggressive earnings management in the issue year was inferior to that of issuers who adopted conservative earnings management; these results hold for various measures of abnormal returns, benchmarks and accumulation periods. We further show that post-issue abnormal stock returns have a significantly negative correlation with discretionary current accruals in the issue year.

Although our evidence suggests that discretionary current accruals have significant influences on subsequent stock returns for convertible bond issuers, we are unable to determine any robust relationship between conversion probability and post-issue stock performance, indicating that the issues may not be classified by the market on the basis of their economic substance. The similarity of our results to those on seasoned equity offers reported in Rangan (1998) and Teoh et al. (1998b) provide support for the earnings management hypothesis. If equity financing is substituted by convertible financing, the managers of the issuing firms still have incentives to manipulate their reported earnings to increase the offer proceeds, and investors have a continuing tendency to overvalue such new issues. Therefore, income-increasing accounting adjustments around the time of convertible offers can explain the subsequent inferior performance of the issuers.

The remainder of this paper is organized as follows. Section 2 describes our sample selection procedure and methodology, followed in Section 3 by our examination of whether discretionary current accruals surrounding convertible bond offers can predict operating underperformance in the post-offer period. In Section 4, we investigate the predictability of post-offer stock return underperformance with discretionary current accruals around the time of the offer. The final section presents the conclusions drawn from this study.

2. SAMPLE SELECTION AND METHODOLOGY

  1. Top of page
  2. Abstract
  3. 1. INTRODUCTION
  4. 2. SAMPLE SELECTION AND METHODOLOGY
  5. 3. ACCRUALS AND OFFER/POST-OFFER PERFORMANCE
  6. 4. PREDICTION OF POST-OFFER STOCK RETURNS
  7. 5. CONCLUSIONS
  8. REFERENCES

(i) Sample Design and Characteristics

The sample adopted for this study is based upon US convertible bond offers which took place between 1981 and 1998, as recorded in the Securities Data Company's New Issues Database. In order to qualify for the final sample, the issues had to meet the following criteria:

  • (a) 
    The common stock of the issuing firm must have been listed on the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX) or the Nasdaq, and must have securities returns available from the Center for Research in the Securities Prices (CRSP) tapes;
  • (b) 
    The issuing firm must not be a regulated utilities or financial institution;
  • (c) 
    The issuing firm must have financial information available from Compustat; and
  • (d) 
    The issuing firm must not have engaged in any other convertible bond offers in the five-year period prior to the current issue date.3 Our final sample comprised of 312 convertible bond offers.

Sample distribution details are provided in Table 1, by calendar year and by industry. As Panel A shows, the largest number of convertible bond offers in one year was 1986, when there were 57 offers (18.3%), followed by 1987 with 34 offers (10.9%) and 1985 with 25 offers (8%). The high level of issue activity in the years 1985 to 1987 reflects the convertible offers reported in Lee and Loughran (1998), McLaughlin et al. (1998) and Lewis et al. (2001).

Table 1.  Sample Distributions of Convertible Bond Offers, by Year and Industry
Panel A: Sample Distribution by Year
YearNumberPercent of Sample
198118 5.8
198215 4.8
198317 5.4
198413 4.2
198525 8.0
19865718.3
19873410.9
1988 8 2.6
198915 4.8
199010 3.2
199112 3.8
199220 6.4
199322 7.1
1994 6 1.9
199510 3.2
199613 4.2
199712 3.8
1998 5 1.6
Total312 100.0 
Panel B: Sample Distribution by Industry
Industry GroupTwo-digit SIC CodeNumberPercent of Sample
  1. Notes: This table summarizes the sample distributions of 312 convertible bond offers recorded in Securities Data Company's New Issues Database during the period from 1981 to 1998. The sample selection criteria used are: (i) the common stock of the issuing firm is listed on the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), or the Nasdaq, and has security returns available from the Center for Research in Securities Prices (CRSP) tape; (ii) the issuing firm is not a regulated utility or a financial institution; (iii) the issuing firm must have financial information available from Compustat; and (iv) the issuing firm must have had no other convertible bond offers in the five years prior to the issue date. The industries (defined by Compustat two-digit SIC codes) listed in Panel B each have 10 or more convertible bond offers.

Computer equipment and services35,736420.5
Wholesale and retail50,51,52,53,54,56,57,594113.1
Electric and electronic equipment3628 9.0
Manufacturing30,32,33,3424 7.7
Transportation37,39,40,41,42,4524 7.7
Chemicals and allied products2821 6.7
Eating and drinking establishments5814 4.5
Scientific instruments3813 4.2
Health services8012 3.8
Oil and gas extraction1312 3.8
Paper and paper products24,25,26,2711 3.5
Other4815.4

Panel B of Table 1 shows that the issuers represent a broad cross-section of industries, thereby indicating that convertible bond financing is not specific to a small group of industries. The top three industries, in terms of the number of convertible offers, were the computer equipment and services industries (20.5%), wholesale and retail industries (13.1%) and electric and electronic equipment industries (9%).

(ii) Measuring Earnings Management

If earnings management is used to increase earnings, the increase can be accomplished through the acceleration of the recognition of revenue, or a delay in the recognition of expenses, relative to cash flows.4 The differences between recognized revenue and cash received, or between recognized expenses and cash expenditure, result in ‘accruals’ or ‘deferrals’. Since the basis of earnings management lies in the difference between cash flows and earnings, the analysis of accruals, which is the difference between cash flows and earnings, provides insights into earnings management practices.

It should be noted, however, that accrual items are not subject to equal manipulation or management. Long-term accrual and deferral items relate to accounting adjustments to long-term assets or liabilities (such as depreciation), and these are quite difficult to manage or adjust, essentially because the accounting choices relating to long-term assets remain constant over several years. Conversely, short-term accruals, which are accounting adjustments to short term assets (such as changes in accounts receivable), are much easier to manage, simply because accounting choices are made over a much shorter time horizon. Since short-term accruals are more readily subject to management, the focus of this study, as in many of the recent studies, is on short-term accruals.5

The computation of accruals in this study is based upon the definition of accruals provided by Perry and Williams (1994), in which total accruals are computed as the change in non-cash working capital (excluding current maturities of long-term debt, less total depreciation expenses for the current period).6 Their definition is similar to that of Jones (1991), differing only by the exclusion of the adjustment made for income tax. Perry and Williams (1994) elected to include income tax in their model essentially because income tax accruals could well represent an important component of an earnings management strategy.

Earnings management is revealed by an abnormal level of accruals relative to a firm's business activities. Since a regression model can be used to estimate expected accruals, any deviations from the expected accruals would suggest that they were subjected to management discretion, which might well be attributable to earnings management. We follow the methodology of Teoh et al. (1998a and 1998b) to estimate the expected current accruals from a modification of the Jones (1991) model. Expected accruals are estimated from a cross-sectional regression of current accruals on the change in sales in a given year using an estimation sample which includes all firms with the same two-digit SIC code as the convertible issuer, whilst excluding the actual issuer and other convertible issuers.

At least 30 firms are required with exactly the same two-digit SIC code in order to ensure the preciseness of the estimated coefficients obtained from the regression. In addition to the adoption of the appropriate SIC code filter, all non-ordinary common stocks, such as ADRs, closed-end funds and REITs are also excluded from the estimation sample. A cross-sectional regression is then performed for each fiscal year, with all of the variables being scaled by lagged total assets in order to reduce the potential heteroskedasticity within the data. The fitted current accruals of the issuer are then calculated for a specific year using the estimated coefficients from the corresponding regression and the change in sales, net of the issuer's change in trade receivables, for that year.7

The fitted current accruals, which are then referred to as non-discretionary current accruals (NDCA), are the asset-scaled proxies for non-manipulated accruals dictated by business conditions. It is presumed that the remaining portion of the current accruals, referred to as discretionary current accruals (DCA), are not dictated by either firm or industry conditions; thus, they provide the proxies for the manipulation of earnings determined at the discretion of managers. Using the estimation sample, we run the following cross-sectional regression:

  • image(1)

where TA is total current accruals; AT is total assets; ΔSales is the change in sales; j indicates the firms in the estimation sample; and t indicates the year. Non-discretionary (or expected) current accruals for firm i are estimated by:

  • image(2)

where inline image is the estimated intercept; inline image is the slope coefficient for convertible bond issuing firm i; and ΔTRi,t is the change in trade receivables for year t for convertible bond issuing firm i. The increase in trade receivables is then subtracted from the change in sales to allow for the possibility of credit sales manipulation by the issuer.

Discretionary current accruals, DCAi,t, for convertible bond issuing firm i for year t, are then estimated as:

  • image(3)

These discretionary current accruals are used as the measure of abnormal accruals, which in this study, is also the proxy for earnings management.

(iii) Measuring Conversion Probability

We use standard Black-Scholes assumptions and follow the model of Lewis et al. (1999) to estimate conversion probability; in their model, the decision on the choice of security issues is treated as a financing problem as opposed to a dichotomous debt-only or equity-only financing choice. Managers will usually endogenously determine the amount of debt and equity to be included in their incremental capital sourcing decisions. We assume the underlying common stock follows a diffusion process described by geometric Brownian motion. This probability is then estimated as N(d2) where N(·) is the cumulative probability under a standard normal distribution function. The probability is denoted as CP. The model is as follows:

  • image(4)

where S is the current price of the underlying common stock; X is the conversion price; r is the continuously compounded yield estimated from a 10-year US Treasury bond on the issue date; div is the issuing firm's continuously compounded dividend yield for the fiscal year-end immediately preceding the offer date; σ is the standard deviation of the continuously compounded common equity return estimated over the 240 to 40 trading-day period prior to the issue date; and T is the number of years until the maturity of the bond. The left-hand-side variable CP can take a continuous value between 0 and 1; therefore, as CP approaches 1, the more likely the issuer is to issue equity-like securities.

(iv) Measuring Operating Performance

We measure the operating performance of convertible bond issuers over the period from year −5 to year +5 using the two accounting ratios adopted in Lewis et al. (2001), including operating income before depreciation plus interest income scaled by total assets (OIBD/assets), and return on assets (ROA). We measure the abnormal operating performance of the issuing firms as compared with the performance of our control samples, following the procedure suggested by Barber and Lyon (1996) to construct the performance-matched samples to control for industry and economic conditions, as well as the mean-reversion tendency following abnormal pre-event performance.

(v) Measuring Long-run Stock Price Performance

The debate goes on within the asset pricing literature with regard to the appropriate method of measurement for long-run stock returns, with contrasting viewpoints having been proposed on the topic in various studies.8 We use four methods in this study, buy-and-hold returns (BHAR), cumulative abnormal returns (CAR), the three-factor model of Fama and French (1993) and a four-factor model which includes the three factors of Fama-French in conjunction with a one-year momentum factor, as proposed in Carhart (1997).

Fama (1998) suggests that the adoption of the buy-and-hold abnormal returns methodology can be problematic because the distribution of long-term buy-and-hold returns is skewed. Although it is subsequently suggested by Lyon et al. (1999) that cumulative abnormal returns are less skewed than buy-and-hold abnormal returns, they nevertheless note that the skewness problem still remains.

In order to address this skewness problem, in our test of the statistical significance of the cumulative abnormal returns, in addition to the conventional cross-sectional t-statistic, we also use a skewness-adjusted t-statistic, as derived by Hall (1992) and similar to the procedure described in Lyon et al. (1999). We calculate the cumulative abnormal returns relative to three benchmarks, the CRSP equally-weighted market index, the CRSP value-weighted market index, and a size and book-to-market matched control sample; the procedure for measuring the last of these follows that of Lyon et al. (1999). We first identify all of the firms within the CRSP database with an equity market value between 70% and 130% of the equity market value of a sample firm, and then select from this set of firms the firm with the book-to-market ratio which is closest to that of the sample firm. Firm size is the firm's total common equity market value measured on the first day of the issue month. Book-to-market ratio is the firm's equity book value divided by its equity market value, measured at the fiscal year end prior to the issue.

Fama (1998) and Lyon et al. (1999) note that the stock returns of firms announcing a specific corporate event are usually correlated, whilst Lyon et al. (1999) also show that cumulative abnormal returns are affected by a problem of cross- sectional dependence. In order to solve this problem, we also use the monthly calendar-time portfolio approach, as suggested by Fama (1998) and Mitchell and Stafford (2000), to estimate the three- and four-factor models.

3. ACCRUALS AND OFFER/POST-OFFER PERFORMANCE

  1. Top of page
  2. Abstract
  3. 1. INTRODUCTION
  4. 2. SAMPLE SELECTION AND METHODOLOGY
  5. 3. ACCRUALS AND OFFER/POST-OFFER PERFORMANCE
  6. 4. PREDICTION OF POST-OFFER STOCK RETURNS
  7. 5. CONCLUSIONS
  8. REFERENCES

(i) Total and Discretionary Current Accruals Around Convertible Bond Offers

Panel A of Table 2 divides our sample of convertible issuers into four quartiles, by their DCA in the issue year, with each quartile containing 78 firms. Following Teoh et al. (1998a and 1998b), we label the quartile of issuers with the highest DCA as ‘aggressive issuers’ and the quartile of issuers with the lowest DCA as ‘conservative issuers’. The ‘aggressive’ quartile has a DCA of at least 8.8%, the second quartile has a DCA of 1.4% to 8.8%, the third quartile has a DCA of −2.7% to 1.4%, and the ‘conservative’ quartile has a DCA below −2.7%.

Table 2.  Summary Statistics of Convertible Bond Issuers
Panel A: Discretionary Current Accruals
  Whole SampleAggressive Q1 (DCA ≥ 8.8%)Q2 (8.8% > DCA ≥ 1.4%)Q3 (1.4% > DCA ≥−2.7%)Conservative Q4 (DCA < −2.7%)
  1. Notes: This table presents summary statistics for discretionary current accruals (DCA), market value of common equity (MVE), book-to-market ratio (BM), net income (NI), and total assets (AT), classified by DCA at the year of convertible bond offers. We follow the methodology of Teoh et al. (1998a and 1998b) to estimate DCA. The aggressive and conservative quartiles contain the firms with the highest and smallest discretionary current accruals (DCA) at year 0, where year 0 is the fiscal year of the convertible offering. P-values are reported in parentheses.

DCA (%)Mean5.7030.374.49−0.64−11.41
  (<0.01) (<0.01)     (<0.01)      (<0.01)   (<0.01)
Median1.3818.934.07−0.61−6.79
  (<0.01) (<0.01)     (<0.01)      (<0.01)   (<0.01)
 
Panel B: Firm Characteristics
  Whole SampleAggressive Q1Q2Q3Conservative Q4
 
MVE ($ million)Mean4,228.61   2,046.27   4,362.01   4,222.14   4,252.83  
Median219.34123.34193.63296.31251.40
BM (%)Mean49.6037.6849.3962.6449.24
Median41.5032.3743.9956.4138.88
NI ($ million)Mean42.8015.5843.0688.9720.60
Median9.657.358.5613.4712.11
AT ($ million)Mean1,435.61   627.02  895.26  2,570.79   1,649.36  
Median241.18129.90208.33398.35289.52

The mean and median values of the market value of common equity (MVE), book-to-market ratio (BM), net income (NI), and total assets (AT) in the issue year within each quartile are reported in Panel B of Table 2. The two quartiles with the highest DCA comprise of smaller firms than the two more conservative quartiles, although this relationship is not monotonic across the four quartiles. In contrast, there is no systematic pattern between DCA and the book-to-market ratio.

The mean value of DCA is 0.057 (5.7% of the total assets), which is statistically significant at the 0.01 level (t= 4.31). The mean value can be interpreted as discretionary current accruals comprising of 5.7% of the total assets for the year of the convertible bond offer. This value, as a percentage of total assets, is similar to the results in Teoh et al. (1998a) on a sample of seasoned equity offering (SEO) firms, in which they report that discretionary current accruals comprise of an average of 5.59% of the total assets.

The mean DCA (0.057) comprises of a larger percentage of total assets than net income, indicating that without the inclusion of discretionary accruals, the average net income could well be negative. The median DCA (0.0138, which is 1.38% of total assets) is also significant at the 0.01 level. With the median assets for the sample being $241.18 million, the median accruals amount to $3.33 million, which is about one-third of the median net income (Table 2).

Using the aggressive quartile to present another example, with the median assets being $129.9 million and DCA being 0.1893, the median accruals amount to $24.42 million, which is three times as much as the median net income. Not only is the median DCA statistically significant, but its effect as a proportion of the total median net income is economically significant. Following Teoh et al. (1998a), we take this evidence as an indication that managers are engaging in the upward management of earnings prior to the convertible bond offer.

Table 3 presents the mean total current accruals (TA) and DCA over the 11-year period surrounding the convertible bond offers (years −5 to +5) for the whole sample and for the aggressive and conservative quartiles. Both the mean TA and DCA values, measured as the percentage of total assets at the end of year −1, tend to increase prior to the issuing year, and then decrease following the issuing year, with the highest mean for TA being 9.75% and the highest for DCA being 5.7%. Both the mean TA and DCA values for years −1 to +1 are significantly positive at the 5% level or better. After adjusting for control firms, the mean values of TA and DCA still exhibit similar patterns, with the values in years −1 to +1 being significantly positive at the 5% level or better.

Table 3.  Mean Total Current Accruals and Discretionary Current Accruals Around Convertible Bond Offers
 Year Relative to the Issue Year
543  −2  −1  01 2 345
  1. Notes: This table reports the total current accruals (TA) and discretionary current accruals (DCA) (in percentage) over the 11-year period surrounding convertible bond offers (years −5 to years +5). We follow the methodology of Teoh et al. (1998a and 1998b) to estimate TA and DCA. Using the estimation sample, we run the following cross-sectional regression:

    • image

    where TA is total current accruals, AT is total assets, ΔSales is the change in sales, j indicates the firms in the estimation sample, and t indicates the year. Nondiscretionary (or expected) current accruals for firm i, is estimated as:

    • image

    where inline image is the estimated intercept, inline image is the slope coefficient for convertible bond issuing firm i, and ΔTRi,t is the change in trade receivables for year t for convertible bond issuing firm i. ΔTRi,t is subtracted from the change in sales to allow for the possibility of credit sales manipulation by the issuer. Discretionary current accruals, DCAi,t, for convertible bond issuing firm i for year t is then estimated as:

    • image

    Aggressive issuers are the quartile of issuers with the highest DCA and conservative issuers are the quartile of issuers with the lowest DCA. T-tests are used to test the hypotheses that the means of TA and DCA will be equal to zero. T-statistics are reported in parentheses. ‘a’, ‘b’, and ‘c’ represent 1%, 5% and 10% significance levels, respectively.

Panel A: Full Sample
TA/assets1.443.183.504.778.529.754.922.451.951.600.33
(0.89)(3.20)a(3.14)a(4.32)a(4.85)a(7.39)a(6.28)a(4.04)a(2.02)b(3.56)a(0.53)
Control-firm- adjusted TA−2.921.43−3.761.516.686.083.921.290.423.33−4.76
(−0.99)(0.75)(−1.05)(1.00)(2.98)a(3.58)a(3.79)a(1.29)(0.29)(1.14)(−1.69)c
DCA/assets0.940.17−0.391.093.705.702.790.32−0.140.66−0.97
(0.52)(0.17)(−0.31)(1.10)(2.18)b(4.47)a(3.62)a(0.59)(−0.15)(0.94)(−1.68)c
Control-firm- adjusted DCA−0.730.27−4.851.505.154.772.66−0.19−0.393.07−6.33
(−0.21)(0.13)(−1.64)c(0.79)(2.32)b(2.99)a(2.45)b(−0.22)(−0.28)(1.00)(−1.93)c
 
Panel B: Aggressive Quartile
TA/assets6.812.843.0611.1912.2632.088.462.544.602.242.49
(1.89)c(0.87)(0.89)(4.09)a(4.09)a(8.17)a(3.60)a(2.14)b(1.66)c(2.29)b(1.54)
Control-firm- adjusted TA−0.55−1.07−0.646.6312.3324.525.11−1.635.155.33−9.89
(−0.05)(−0.26)(−0.10)(1.66)c(3.51)a(4.73)a(1.96)b(−0.68)(1.26)(1.53)(−0.86)
DCA/assets8.92−2.54−2.285.742.6225.366.700.691.722.990.22
(2.44)b(−0.75)(−0.50)(2.55)b(0.76)(8.30)a(3.05)a(0.63)(0.58)(1.25)(0.19)
Control-firm- adjusted DCA−1.66−4.61−2.108.145.8023.414.55−1.911.618.08−12.31
(−0.13)(−0.99)(−0.44)(1.40)(1.34)(5.29)a(1.73)c(−0.81)(0.37)(1.64)(−0.91)
 
Panel C: Conservative Quartile
TA/assets−2.654.246.034.6413.31−1.234.385.182.49−2.654.24
(−0.70)(2.04)b(2.55)b(1.73)c(2.44)b(−0.71)(2.98)a(3.41)a(1.99)b(−0.70)(2.04)b
Control-firm- adjusted TA−10.702.410.24−3.4010.49−4.093.895.770.07−10.702.41
(−1.38)(0.60)(0.06)(−1.43)(1.45)(−1.79)c(1.90)c(2.33)b(0.03)(−1.38)(0.60)
DCA/assets−2.262.423.221.577.60−7.940.131.50−0.87−2.262.42
(−0.67)(1.02)(1.64)(0.60)(1.46)(−4.16)a(0.08)(1.19)(−0.68)(−0.67)(1.02)
Control-firm- adjusted DCA−13.211.63−3.39−3.347.91−7.180.072.92−1.11−13.211.63
(−1.37)(0.38)(−0.88)(−1.27)(1.15)(−3.23)a(0.03)(1.54)(−0.49)(−1.37)(0.38)

The results of the ‘aggressive’ quartile in Table 3 show that both the mean TA and DCA values are even higher, with the highest mean for TA being 32.08% and the highest for DCA being 25.36%. After adjusting for control firms, almost all the mean TA and DCA values from years −1 to +1 remain significantly positive at the 10% level or better, with the highest mean for TA being 24.52% and the highest for DCA being 23.41%.

The ‘conservative’ quartile in Table 3 reveals quite different results for TA and DCA, with no clear patterns being discernible for either the mean TA or control-firm adjusted TA; there is also no obvious pattern for DCA, although surprisingly, the mean DCA and control-firm adjusted DCA are significantly negative at the 1% level in year 0. Our results are consistent with the effects of managers advancing their accruals to increase reported net income in the convertible issuance period, similar to the findings for IPOs and seasoned equity offers (SEOs) as documented by Rangan (1998) and Teoh et al. (1998a and 1998b).

(ii) Prediction of Post-offer Underperformance with Discretionary Current Accruals Around the Offer

The median operating performance for our sample firms over the 11-year period surrounding the convertible bond offers (years −5 to +5) are reported in Table 4, with operating performance measured by OIBD/assets reported in Panel A, and operating performance measured by ROA reported in Panel B. As the table shows, convertible bond issuers experience deterioration in their operating performance surrounding the convertible bond offers; for example, there is a decline in the median OIBD/assets, from 14.98% in year −5, to 13.24% in year 0, with a further decline to 12.93% in year +5. A similar pattern is also displayed by the median ROA around the issuing year.

Table 4.  Operating Performance of Convertible Bond Issuers
  Fiscal Year Relative to the Issue Year
54321012345
  1. Notes: This table presents median operating performance of 312 convertible bond issuers between 1981 to 1998. Panels A and B report OIBD/assets and ROA for the whole sample, the aggressive quartile, and the conservative quartile, respectively. OIBD/assets is operating income before depreciation plus interest income deflated by fiscal year-end total assets. ROA is net income divided by fiscal year-end total assets. The number of observations (N) varies because of data unavailability. The aggressive and conservative quartiles contain firms with the highest and smallest discretionary current accruals (DCA) at year 0, where year 0 is the fiscal year of the convertible offering. T-statistics are reported in parentheses. ‘a’, ‘b’, and ‘c’ represent 1%, 5% and 10% significance levels, respectively.

Panel A: OIBD/assets
Whole sampleSample Medians (%)14.9814.6914.6915.3614.4913.2413.0612.6313.0313.1512.93
Benchmark Medians (%)13.8314.0814.0814.2814.4213.3112.9512.8814.4813.1212.84
Z-Stat. (Sample – Benchmark)(1.37)(1.17)(1.17)(1.64)(0.02)(−0.22)(−0.44)(−0.43)(−1.11)(−0.14)(−0.58)
N134219219283302279247212176152137
Aggressive quartileSample Medians (%)15.6514.6914.7015.6714.6712.1611.078.7911.3911.6811.78
Benchmark Medians (%)11.5914.0814.0813.6114.5713.1711.8413.6914.5116.4016.24
Z-Stat. (Sample – Benchmark)(1.16)(1.17)(0.42)(1.93)c(0.11)(−0.87)(−1.00)(−2.69)a(−1.91)c(−1.58)(−1.44)
N2649497176675949393231
Conservative quartileSample Medians (%)13.4014.7014.0014.5514.0613.2513.1512.7313.1812.5112.56
Benchmark Medians (%)11.7714.0812.4214.1914.0613.0614.0811.9513.5811.5212.14
Z-Stat. (Sample – Benchmark)(0.47)(0.42)(0.57)(−0.33)(−0.05)(0.05)(−0.03)(−0.27)(−0.07)(−0.19)(−0.58)
N2949497175685950423933
 
Panel B: ROA
Whole sampleSample Medians (%)5.255.145.145.325.504.513.743.233.493.813.17
Benchmark Medians (%)4.745.075.074.704.893.773.623.554.193.764.00
Z-Stat. (Sample – Benchmark)(0.94)(1.03)(1.03)(1.96)b(1.89)c(1.43)(−0.61)(−0.73)(−1.58)(0.07)(−1.37)
N134219219283302279247212176152137
Aggressive quartileSample Medians (%)5.875.146.647.276.365.093.352.083.493.842.42
Benchmark Medians (%)2.805.074.994.585.914.293.373.483.124.604.52
Z-Stat. (Sample – Benchmark)(1.71)c(1.03)(1.25)(3.18)a(1.18)(0.68)(0.52)(−1.61)(0.34)(−0.37)(−2.27)b
N2649497176675949393231
Conservative quartileSample Medians (%)5.346.643.684.725.114.253.754.013.743.602.54
Benchmark Medians (%)5.634.992.623.974.163.584.064.164.543.753.25
Z-Stat. (Sample – Benchmark)(−0.23)(1.25)(0.68)(0.12)(0.98)(0.15)(−0.74)(−0.84)(−1.10)(−0.23)(−0.06)
N2949497175685950423933

Barber and Lyon (1997) and Lie (2005) suggest the use of a matched sample of firms, by pre-event performance levels, as a means of controlling for the mean- reversion tendency in performance measures, particularly where firms report either abnormally good or bad pre-event performance. As compared to control firms matched by industry and performance, both the median OIBD/assets and ROA for our sample firms are better in the pre-issue periods and worse in the post-issue periods, similar to the findings for SEOs and convertible bond offers reported by Loughran and Ritter (1997) and McLaughlin et al. (1998). However, the differences in performance between our sample firms and the matched firms are statistically significant in some years only for ROA.

There are, however, differences between the aggressive and conservative quartiles in the patterns of matched-firm adjusted operating performance. In the aggressive quartile, OIBD/assets reaches its peak of 15.67% in year −2, and then declines to 11.78% in year +5. In years −2, +2 and +3, the differences in OIBD/assets between the sample firms and the matched firms are statistically significant at the 10% level or better. In contrast, in the conservative quartile, as compared to the control firms, no significant differences are found in OIBD/assets surrounding the offer year. This suggests that managers may be engaging in the upward manipulation of earnings prior to their convertible bond offers.

A similar pattern of operating performance is found from our examination of ROA. In the aggressive quartile, ROA again reaches its peak of 7.27% in year −2, and then declines to 2.42% in year +5. In years −2 and +5, the differences in ROA between the sample firms and the matched firms are statistically significant at the 5% level or better. This pattern stands in sharp contrast to the performance of the conservative quartile, where, as compared to the control firms, no significant differences in ROA are found.

4. PREDICTION OF POST-OFFER STOCK RETURNS

  1. Top of page
  2. Abstract
  3. 1. INTRODUCTION
  4. 2. SAMPLE SELECTION AND METHODOLOGY
  5. 3. ACCRUALS AND OFFER/POST-OFFER PERFORMANCE
  6. 4. PREDICTION OF POST-OFFER STOCK RETURNS
  7. 5. CONCLUSIONS
  8. REFERENCES

(i) Abnormal Stock Returns

The abnormal post-issue stock return performance levels for the whole sample, as well as those for the aggressive and conservative quartiles, are reported in Table 5 for various holding periods. These are measured by buy-and-hold abnormal returns (BHAR) and cumulative abnormal returns (CAR) using size and book-to-market matched firms as the benchmarks.

Table 5.  Long-Run Mean Abnormal Stock Returns Subsequent to Convertible Bond Offers
Holding Period Buy-and-Hold (BHAR)Cumulative Abnormal (CAR)
Whole SampleAggressive QuartileConservative QuartileWhole SampleAggressive QuartileConservative Quartile
  1. Notes: This table presents both matched-firm-adjusted buy-and-hold returns (BHAR) and cumulative abnormal returns (CAR) following convertible bond offers for various holding periods, where matching firms are chosen on the basis of size and the book-to-market ratio. The long-run mean buy-and-hold abnormal returns on security i over the holding period T are defined as:

    • image

    where Ri,t is the monthly raw return for firm i in month t, and Rbenchmark,t is the monthly raw return for the benchmark in month t. Holding-period stock returns are calculated for different lengths of post-offering periods. The cumulative abnormal return through month T, CART, is defined as the sum of monthly abnormal returns over T months. Cumulative mean abnormal returns (CAR) are defined as:

    • image

    where Ri,t and Rbenchmark,t are as defined previously. The sample consists of 312 convertible bond offers during the period from 1981 to 1998. We use cross-sectional t-statistics and skewness-adjusted t-statistics to test the significance of the mean values of BHAR and CAR. Aggressive issuers are the quartile of issuers with the highest DCA and conservative issuers are the quartile of issuers with the lowest DCA. ‘a’, ‘b’, and ‘c’ represent 1%, 5% and 10% significance levels, respectively.

+1Mean Abnormal Return−3.10−7.171.01−2.62−6.910.75
Cross-sectional t-stat−1.68c−1.70c0.23−1.42−1.640.19
Skewness-adjusted t-stat−1.63−1.630.24−1.43−1.65c0.19
+2Mean Abnormal Return−0.86−13.446.640.42−13.457.49
Cross-sectional t-stat−0.30−2.18b1.010.15−2.15b1.24
Skewness-adjusted t-stat−0.30−1.80c1.100.15−2.20b1.22
+3Mean Abnormal Return−0.68−13.4911.020.02−16.318.03
Cross-sectional t-stat−0.18−1.71c1.260.01−1.83c0.98
Skewness-adjusted t-stat−0.18−1.531.370.01−1.84 c0.97
+4Mean Abnormal Return−2.91−22.2111.15−1.41−13.5811.43
Cross-sectional t-stat−0.69−2.71a1.23−0.33−2.36b1.34
Skewness-adjusted t-stat−0.67−2.21b1.34−0.33−2.44b1.34
+5Mean Abnormal Return−2.94−27.2116.91−0.10−27.2615.18
Cross-sectional t-stat−0.63−2.60a1.73 c−0.02−2.58a1.48
Skewness-adjusted t-stat−0.62−2.12b1.85 c−0.02−2.53 b1.44

The long-run mean buy-and-hold abnormal returns on security i over the holding period T are defined as:

  • image(5)

where Ri,t is the monthly raw return for firm i in month t, and Rbenchmark,t is the monthly raw return for the benchmark in month t.

Holding-period stock returns are calculated for different post-offer period lengths, ranging between one and five years, with our computation of the returns being based upon the CRSP monthly returns file. CART, the cumulative mean abnormal return at month T, is defined as the sum of monthly abnormal returns over T months. The cumulative mean abnormal returns (CAR) are defined as:

  • image(6)

where Ri,t and Rbenchmark,t are as previously defined.

As Table 5 shows, abnormal returns reveal a strong pattern of underperformance for the aggressive quartile vis-à-vis the conservative quartile. Measured by BHAR, this underperformance ranges between 8% (−7.17% vs. 1.01%) for the one-year holding period and 44% (−27.21% vs. 16.91%) for the five-year holding period.

Measured by CAR, the underperformance similarly ranges between 8% for the one-year holding period and 42% for the five-year holding period. The differences between the aggressive and conservative quartiles in the five-year holding period returns of both BHAR and CAR are statistically significant at the 1% level.9 Our results suggest that more aggressive earnings management in the issue year is an effective predictor of inferior post-issue stock return performance.

The OLS regressions on post-issue stock return performance on DCA in the issue year are presented in Table 6, for various holding periods. The model is stated as:

  • image(7)

where AR represents abnormal returns as measured by BHAR or CAR; DCA are the discretionary current accruals in the issue year; ln(MVE) is the issuer's logged market value of equity, ln(BM) is the logged book-to-market ratio, CP is the conversion probability in the issue year, and ɛi,t is the error term.

Table 6.  Ordinary Least Squares Regressions of Abnormal Stock Returns Subsequent to Convertible Bond Offers
Holding PeriodIndependent VariablesAdj-R2F-value
InterceptDCAln(MVE)ln(BM)CP
  1. Notes: This table presents ordinary least squares regressions of post-issue stock return performance on discretionary current accruals (DCA) over various holding periods. The sample comprises of 312 convertible bond offers between 1981 to 1998. The regression model used is as follows:

    • image

    where AR is abnormal return as measured by BHAR or CAR, DCA is discretionary current accruals in the issue year, ln(MVE) is issuer's logged market value of equity, ln(BM) is logged book-to-market ratio, CP is conversion probability in the issue year, and ɛi,t is the error term. The dependent variable in Panel A is the matched-firm-adjusted buy-and-hold mean abnormal return (BHAR), where matching firms are chosen on the basis of size and the book-to-market ratio. The dependent variable in Panel B is the matched-firm-adjusted cumulative mean abnormal return (CAR). T-statistics are reported in parentheses. ‘a’, ‘b’, and ‘c’ represent 1%, 5% and 10% significance levels, respectively.

Panel A: Matched-Firm-Adjusted BHAR
1-year−0.09−0.23−0.01−0.010.140.011.67
(−0.59)(−2.21)b(−0.70)(−0.39)(1.10)  
2-year0.01−0.23−0.010.030.060.001.00
(0.05)(−1.54)(−0.32)(0.67)(0.31)  
3-year0.47−0.32−0.010.07−0.410.011.70
(1.52)(−1.60)(−0.09)(1.27)(−1.65)  
4-year0.27−0.54−0.010.07−0.220.022.21
(0.80)(−2.47)b(−0.05)(1.19)(−0.81)  
5-year0.49−0.670.010.10−0.460.043.57
(1.36)(−2.89)a(0.27)(1.64)(−1.63)  
 
Panel B: Matched-Firm-Adjusted CAR
1-year−0.05−0.19−0.01−0.010.110.001.25
(−0.34)(−1.89)c(−0.73)(−0.22)(0.85)  
2-year0.13−0.25−0.010.04−0.060.001.14
(0.53)(−1.63)(−0.04)(1.08)(−0.31)  
3-year0.58−0.42−0.020.03−0.400.021.98
(1.92)c(−2.12)b(−0.77)(0.69)(−1.68)c  
4-year0.63−0.63−0.020.04−0.460.032.85
(1.83)c(−2.84)a(−0.63)(0.68)(−1.70)c  
5-year0.90−0.67−0.020.05−0.710.043.49
(2.41)b(−2.78)a(−0.54)(0.86)(−2.43)b  

The dependent variable in Panel A is the matched-firm-adjusted BHAR. We include the issuer's logged equity market value, ln(MVE), and logged book-to-market ratio, ln(BM), as control variables in the regression, since underperformance following convertible bond offers may be attributable to a particular subset of firms. Furthermore, the conversion probability (CP) is also included to test how the convertible bond conversion mechanism affects post-issue stock performance.

The results in Panel A indicate that the coefficients of DCA are all negative, with significantly negative coefficients at the 5% level or better for the one-, four- and five-year holding periods.10 Panel A also shows that the coefficients of CP are positive for the one- and two-year holding periods, but negative for the other three holding periods. Furthermore, CP has a statistically insignificant relationship with post-issue stock performance for all five holding periods. In other words, it is apparent that the market cannot clearly discern whether the economic substance of convertible bond issues is debt-like or equity-like around the issue date. The results in Panel A suggest that convertible issuers with aggressive DCA levels in the issue year have significantly inferior performance after the offer, consistent with the results reported in Table 5.

We also use the matched-firm-adjusted CAR as the dependent variable in order to check the robustness of our results; our findings are reported in Panel B. Similar to the results in Panel A, post-issue stock return performance is significantly poorer for convertible issuers with more aggressive earnings management in the issue year. The results hold, irrespective of the length of the holding period; therefore, our findings are robust to alternative measures of computation of the abnormal returns.11

(ii) Abnormal Returns Based on the Fama-French and Carhart Models

We estimate abnormal stock returns for the aggressive and conservative quartile issuers based upon the three-factor model of Fama and French (1993); the results are reported in Table 7. We adopt the calendar-time portfolio approach, as suggested by Fama (1998) and Mitchell and Stafford (2000), using value-weighted monthly abnormal stock returns.12 Specifically, monthly convertible bond offer portfolios are formed in calendar time using the following regression model:

  • image(8)

where Rp,t is the return on portfolio p in month t; Rft is the return on one-month Treasury bills in month t; Rmt is the return on a market index in month t; SMBt is the difference in the returns for a portfolio of small and big stocks in month t; HMLt is the difference in returns for portfolios of high and low book-to-market stocks in month t; and ɛp,t is the error term for portfolio p in month t. The estimation of the intercept coefficient (αp) provides a test of the null hypothesis of zero average abnormal returns.

Table 7.  Post-Offering Abnormal Stock Returns Based on the Calendar Time Fama-French Three-Factor Model by Aggressive and Conservative Quartiles of Convertible Issuers
Holding PeriodAggressive QuartileConservative Quartile
αβshAdj-R2αβshAdj-R2
  1. Notes: This table presents abnormal stock returns for the aggressive and conservative quartiles of convertible issuers based upon the three-factor model of Fama and French (1993). Time-series regression coefficients are estimated in calendar time using the following model:

    • image

    where Rp,t is the return on portfolio p in month t, Rft is the return on one-month Treasury bills in month t, Rmt is the return on a market index in month t, SMBt is the difference in the returns of a portfolio of small and big stocks in month t, and HMLt is the difference in the returns of a portfolio of high book-to-market stocks and low book-to-market stocks in month t, and ɛp,t is the error term for portfolio p in month t. Portfolio returns are value-weighted. The estimate of the intercept coefficient (αp) provides a test of the null hypothesis of zero average abnormal return. The aggressive (conservative) quartiles comprise of firms with the highest (lowest) discretionary current accruals (DCA) at year 0, where year 0 is the fiscal year of the convertible offering. The regression coefficients are estimated using weighted least squares to correct for heteroskedasticity. T-statistics are reported in parentheses. ‘a’ and ‘c’ represent 1% and 10% significance levels, respectively.

1-year−0.021.200.65−0.320.510.011.130.96−0.300.45
(−3.04)a(10.56)a(3.01)a(−1.32) (0.66)(9.14)a(4.44)a(−1.31) 
2-year−0.021.310.76−0.070.65−0.011.180.70−0.350.56
(−4.28)a(15.62)a(4.99)a(−0.42) (−0.31)(12.00)a(4.33)a(−1.89)c 
3-year−0.011.220.82−0.120.66−0.011.190.58−0.100.61
(−3.82)a(15.75)a(6.39)a(−0.76) (−1.17)(14.83)a(4.56)a(−0.68) 
4-year−0.011.240.71−0.030.65−0.011.150.490.010.61
(−3.35)a(16.61)a(5.95)a(−0.21) (−1.33)(15.70)a(4.36)a(0.15) 
5-year−0.011.290.470.040.64−0.011.140.470.070.63
(−3.79)a(17.45)a(4.12)a(0.28) (−1.20)(17.34)a(4.76)a(0.65) 

The estimated alpha coefficients for the aggressive quartile issuers in Panel A are significantly negative at the 1% level for various holding periods, ranging between one and five years. The monthly abnormal returns range between −1.09% and −1.68%, thereby implying annualized abnormal returns of between −12.32% and −18.40%. In contrast, the alpha coefficients for the conservative quartile issuers in Panel B are insignificantly different from zero for all five holding periods.

The monthly abnormal returns range between −0.39% and 0.34%, thereby implying annualized abnormal returns of between −4.58% and 4.16%. Consistent with the results presented in Table 5, the results reported in Table 7 indicate that convertible issuers who adopt aggressive earnings management practices in the issue year experience inferior post-issue stock performance than those who adopt conservative earnings management practices.

We go on to use the calendar-time portfolio approach to estimate value-weighted abnormal stock returns for the aggressive and conservative quartile issuers based on a four-factor model; the model comprises of the three factors of the Fama and French (1993) model in conjunction with the momentum factor proposed in Carhart (1997); the results are reported in Table 8. The regression model is:

  • image(9)

where Rp,t is the return on portfolio p in month t; Rft is the return on one-month Treasury bills in month t; Rmt is the return on a market index in month t; SMBt is the difference in the returns for a portfolio of small and big stocks in month t; HMLt is the difference in returns for portfolios of high and low book-to-market stocks in month t; PR1YRt is the difference in the returns of portfolios of previous-year high and low stock returns in month t; and ɛp,t is the error term for portfolio p in month t. The portfolio returns are value-weighted, with the estimation of the intercept coefficient (αp) providing a test of the null hypothesis of zero abnormal returns.

Table 8.  Post-Offering Abnormal Stock Returns Based on the Calendar Time Carhart Four-Factor Model by Aggressive and Conservative Quartiles of Convertible Issuers
Holding PeriodAggressive QuartileConservative Quartile
αβshpAdj-R2αβshpAdj-R2
  1. Notes: This table presents abnormal stock returns based on a four-factor model comprising of three factors of the Fama and French (1993) model and the momentum factor of Carhart (1997), for the aggressive and conservative quartiles of convertible issuers. Time-series regression coefficients are estimated in calendar time using the following model:

    • image

    where Rp,t is the return on portfolio p in month t, Rft is the return on one-month Treasury bills in month t, Rmt is the return on a market index in month t, SMBt is the difference in the returns of a portfolio of small and big stocks in month t, and HMLt is the difference in the returns of a portfolio of high book-to-market stocks and low book-to-market stocks in month t, PR1YRt is the difference in the returns of a portfolio of prior-year high return stocks and prior-year low return stocks in month t, and ɛp,t is the error term for portfolio p in month t. Portfolio returns are value-weighted. The estimate of the intercept coefficient (αp) provides a test of the null hypothesis of no abnormal performance. The aggressive (conservative) quartiles comprise of firms with the highest (lowest) discretionary current accruals (DCA) at year 0, where year 0 is the fiscal year of the convertible offering. The regression coefficients are estimated using weighted least squares to correct for heteroskedasticity. T-statistics are reported in parentheses. ‘a’, ‘b’, and ‘c’ represent 1%, 5% and 10% significance levels, respectively.

1-year−0.021.220.64−0.34−0.130.510.011.140.94−0.32−0.110.46
(−2.92)a(10.50)a(2.93)a(−1.39)(−0.74) (0.77)(9.14)a(4.37)a(−1.37)(−0.60) 
2-year−0.021.300.76−0.06−0.040.65−0.011.190.69−0.41−0.250.56
(−4.27)a(15.40)a(4.98)a(−0.35)(−0.32) (−0.11)(12.15)a(4.26)a(−2.18)b(−1.90)c 
3-year−0.011.230.82−0.14−0.060.66−0.011.200.56−0.16−0.280.62
(−3.65)a(15.70)a(6.39)a(−0.85)(−0.53) (−0.55)(15.16)a(4.51)a(−1.12)(−2.72)a 
4-year−0.011.250.71−0.09−0.200.66−0.011.150.47−0.08−0.260.62
(−2.87)a(16.80)a(6.03)a(−0.62)(−2.15)b (−0.64)(16.01)a(4.24)a(−0.59)(−2.88)a 
5-year−0.011.290.46−0.05−0.300.66−0.011.140.460.04−0.160.64
(−3.03)a(17.86)a(4.16)a(−0.36)(−3.53)a (−0.69)(17.46)a(4.72)a(0.32)(−2.00)b 

The abnormal return estimations using the four-factor model for the aggressive and conservative quartile issuers are very similar to those shown in Table 7. Again, evidence of underperformance is revealed for firms in the aggressive quartile, but not for those in the conservative quartile. The monthly abnormal returns, as measured by the alpha coefficients, are negative and statistically significant at the 1% level for firms in the aggressive quartile for all five holding periods. In contrast, the alpha coefficients for the conservative quartile issuers are insignificantly different from zero for all holding periods. The results reported in Table 8 are consistent with those in Table 7, with the aggressive convertible issuers demonstrating inferior post-offer stock performance to that of the conservative issuers.

(iii) Robustness Checks – Fama-French and Carhart Models with DCA Dummy Variable

In the previous section, we examined the long-run stock performance for both the aggressive and conservative quartiles; in this section, further analysis is undertaken involving the inclusion of a dummy variable into the three- and four-factor models. This dummy variable will capture the underperformance effect caused by the aggressive quartile. The models are stated as:

  • image(10)
  • image(11)

where all of the variables have the same definitions as those described in equations (8) and (9), with the exception of the dummy variable DCA_dmyp, which takes the value of 1 for the aggressive quartile; and 0 for the conservative quartile.

The results with dummy variables are presented in Table 9, with Panel A reporting those for the Fama-French three-factor model and Panel B reporting those for the Carhart four-factor model. As Panel A shows, the estimated coefficients for abnormal stock returns are not significant for all five holding periods, implying that the conservative quartile has no abnormal returns; however, the estimated coefficients of DCA_dmyp are significantly negative at the 10% level or better for most of the holding periods. Similar results are also revealed in Panel B. Our evidence therefore strongly suggests that the aggressive quartile underperforms the conservative quartile.

Table 9.  Post-Offering Abnormal Stock Returns Based on the Calendar Time Fama Three-Factor and Carhart Four-Factor Model with DCA Dummy Variable
Holding PeriodPanel A: Three-Factor Model with DCA Dummy VariablePanel B: Four-Factor Model with DCA Dummy Variable
αqβshAdj-R2αqβshpAdj-R2
  1. Notes: This table presents abnormal stock returns based on a four-factor model comprising of the three factors of the Fama and French (1993) model and the momentum factor of Carhart (1997), for the aggressive and conservative quartiles of convertible issuers. Time-series regression coefficients are estimated in calendar time using the following models:

    • image
    • image

    where Rp,t is the return on portfolio p in month t, Rft is the return on one-month Treasury bills in month t, DCA_dmyp is a dummy variable with the value of one for the aggressive quartile and zero for the conservative quartile, Rmt is the return on a market index in month t, SMBt is the difference in the returns of a portfolio of small and big stocks in month t, and HMLt is the difference in the returns of a portfolio of high book-to-market stocks and low book-to-market stocks in month t, PR1YRt is the difference in the returns of a portfolio of prior-year high return stocks and prior-year low return stocks in month t, and ɛp,t is the error term for portfolio p in month t. Portfolio returns are value-weighted. The estimate of the intercept coefficient (αp) provides a test of the null hypothesis of no abnormal performance. The aggressive (conservative) quartiles comprise of firms with the highest (lowest) discretionary current accruals (DCA) at year 0, where year 0 is the fiscal year of the convertible offering. The regression coefficients are estimated using weighted least squares to correct for heteroskedasticity. T-statistics are reported in parentheses. ‘a’, ‘b’ and ‘c’ represent 1%, 5% and 10% significance levels, respectively.

1-year0.01−0.041.020.74−0.180.590.01−0.041.010.73−0.170.050.59
(0.70)(−2.10)b(5.88)a(1.97)b(−0.37) (0.65)(−2.08)b(5.40)a(1.88)c(−0.35)(0.16) 
2-year0.00−0.021.190.36−0.310.680.00−0.021.210.42−0.29−0.150.68
(0.22)(−3.03)a(12.09)a(1.87)c(−1.31) (0.39)(−2.98)a(12.11)a(2.08)b(−1.24)(−1.04) 
3-year−0.01−0.021.110.42−0.230.76−0.00−0.021.160.43−0.33−0.300.77
(−1.15)(−2.39)b(11.68)a(2.16)b(−1.02) (−0.75)(−2.43)b(11.97)a(2.28)b(−1.41)(−1.97)b 
4-year−0.00−0.001.130.67−0.210.570.00−0.001.140.68−0.27−0.170.57
(−0.24)(−0.63)(13.19)a(4.88)a(−1.33) (0.17)(−0.58)(13.27)a(4.93)a(−1.67)c(−1.59) 
5-year0.00−0.011.200.42−0.010.480.01−0.011.200.41−0.06−0.130.48
(0.83)(−1.67)c(13.95)a(3.15)a(−0.04) (1.12)(−1.66)c(13.91)a(3.10)a(−0.38)(−1.25) 

5. CONCLUSIONS

  1. Top of page
  2. Abstract
  3. 1. INTRODUCTION
  4. 2. SAMPLE SELECTION AND METHODOLOGY
  5. 3. ACCRUALS AND OFFER/POST-OFFER PERFORMANCE
  6. 4. PREDICTION OF POST-OFFER STOCK RETURNS
  7. 5. CONCLUSIONS
  8. REFERENCES

This paper examines whether earnings management, as reflected in discretionary current accruals around the time of the offer, can explain the long-run underperformance of convertible bond issuers. We find that there is an increase in discretionary current accruals prior to the offer, a peak in the offer year, and a subsequent decline in the post-offer period. We also show that issuers experience declines in both OIBD/assets and ROA following convertible debt offers, with these post-issue declines being particularly pronounced for issuers with aggressive earnings management in the issue year. We further document a significantly negative relationship between discretionary current accruals in the issue year and post-issue stock returns. This negative relationship with stock returns holds for various measures of abnormal returns, benchmarks and accumulation periods.

Our results indicate that aggressive earnings management through income- increasing accounting adjustments around the time of convertible debt offers can explain the subsequent inferior performance of issuers; however, whether convertible bond issues are more like equity or debt, as measured by conversion probability, does not appear to play any significant role in explaining post-issue stock performance. We cannot conclude from our analysis whether the market is able to clearly discern convertible bond offers based on their economic substance.

Overall, the evidence provided by this study is consistent with the earnings management hypothesis. Firms issuing convertible bonds will engage in the upward management of earnings in order to increase their offer proceeds, and investors will invariably misinterpret the transitory increases in earnings reported at the time of offers; as a result, they will tend to overvalue the issues. Subsequently, when earnings management is reversed, and convertible issuers record declines in earnings in the post-offer period, the market becomes disappointed resulting in a downward revision of its earlier valuation. The similarity of our results to those reported in the literature on IPOs and SEOs suggests that the earnings management hypothesis is not unique to stock offers, and indeed, that it actually extends to convertible bond offers.

Footnotes

REFERENCES

  1. Top of page
  2. Abstract
  3. 1. INTRODUCTION
  4. 2. SAMPLE SELECTION AND METHODOLOGY
  5. 3. ACCRUALS AND OFFER/POST-OFFER PERFORMANCE
  6. 4. PREDICTION OF POST-OFFER STOCK RETURNS
  7. 5. CONCLUSIONS
  8. REFERENCES
  • Barber, B. M. and J. D. Lyon (1997), ‘Detecting Long-Run Abnormal Stock Returns: The Empirical Power and Specification of Test Statistics’, Journal of Financial Economics, Vol. 43, No. 3, pp. 34172.
  • Carhart, M. M. (1997), ‘On Persistence in Mutual Fund Performance’, Journal of Finance, Vol. 52, No. 1, pp. 5782.
  • Casson, P. (1998), ‘A Re-Examination of the Case for Accounting Separately for the Debt and Equity Features of Convertible Debt’, Journal of Business Finance & Accounting, Vol. 25, No. 5&6 (June/July), pp. 595612.
  • Fama, E. F. (1998), ‘Market Efficiency, Long-Term Returns, and Behavioral Finance’, Journal of Financial Economics, Vol. 49, No. 3, pp. 283306.
  • Fama, E. F. and K. R. French (1993), ‘Common Risk Factors in the Returns on Stocks and Bonds’, Journal of Financial Economics, Vol. 33, No. 1, pp. 356.
  • Garcia, L., M. Juan, B. G. Osma and A. Mora (2005), ‘The Effect of Earnings Management on the Asymmetric Timeliness of Earnings,’ Journal of Business Finance & Accounting, Vol. 32, No. 3&4 (April/May), pp. 691726.
  • Hall, P. (1992), ‘On the Removal of Skewness by Transformation’, Journal of the Royal Statistical Society, Series B, Vol. 54, No. 1, pp. 22128.
  • Jones, J. J. (1991), ‘Earnings Management During Import Relief Investigation’, Journal of Accounting Research, Vol. 29, No. 2, pp. 193228.
  • Lee, I. and T. Loughran (1998), ‘Performance Following Convertible Bond Issuance’, Journal of Corporate Finance, Vol. 4, No. 2, pp. 185207.
  • Legoria, J., D. Cagwin and K. F. Sellers (1999), ‘Earnings Management in Anticipation of Debt Financing’, Accounting Enquiries, Vol. 9, No. 1, pp. 4653.
  • Lewis, C. M., R. J. Rogalski and J. K. Seward (1999), ‘Is Convertible Bond a Substitute for Straight Debt or for Common Equity?’, Financial Management, Vol. 28, No. 3, pp. 527.
  • Lewis, C. M., R. J. Rogalski and J. K. Seward (2001), ‘The Long-Run Performance of Firms that Issue Convertible Bond: An Empirical Analysis of Operating Characteristics and Analyst Forecasts’, Journal of Corporate Finance, Vol. 7, No. 4, pp. 44774.
  • Lie, E. (2005), ‘Operating Performance Following Open Market Share Repurchase Announcements’, Journal of Accounting and Economics, Vol. 39, No. 3, pp. 41136.
  • Loughran, T. and J. R. Ritter (1997), ‘The Operating Performance of Firms Conducting Seasoned Equity Offerings’, Journal of Finance, Vol. 52, No. 5, pp. 182350.
  • Loughran, T. and J. R. Ritter (2000), ‘Uniformly Least Powerful Tests of Market Efficiency’, Journal of Financial Economics, Vol. 55, No. 3, pp. 36189.
  • Lyon, J. D., B. M. Barber and C.-L. Tsai (1999), ‘Improved Methodology for Tests of Long-Run Abnormal Stock Returns’, Journal of Finance, Vol. 54, No. 1, pp. 165201.
  • Marquardt, C. A. and C. I. Wiedman (2004), ‘The Effect of Earnings Management on the Value Relevance of Accounting Information’, Journal of Business Finance & Accounting, Vol. 31, No. 3&4 (April/May), pp. 297332.
  • Mehran, J. and G. Homaifar (1993), ‘Analytics of Duration and Convexity for Bonds with Embedded Options: The Case of Convertibles’, Journal of Business Finance & Accounting, Vol. 20, No. 1 (January), pp. 10713.
  • McLaughlin, R., A. Safieddine and G. K. Vasudevan (1998), ‘The Long-Run Performance of Convertible Bond Issuers’, Journal of Financial Research, Vol. 21, No. 4, pp. 37388.
  • Mitchell, M. L. and E. Stafford (2000), ‘Managerial Decisions and Long-Run Stock Price Performance’, Journal of Business, Vol. 73, No. 3, pp. 289329.
  • Munro, J. W. (1996), ‘Convertible Debt Financing: An Empirical Analysis’, Journal of Business Finance & Accounting, Vol. 23, No. 2 (March), pp. 31934.
  • Peasnell, K. V., P. F. Pope and S. Young (2005), ‘Board Monitoring and Earnings Management: Do Outside Directors Influence Abnormal Accruals?’, Journal of Business Finance & Accounting, Vol. 32, No. 7&8 (September/October), pp. 131146.
  • Perry, S. E. and T. H. Williams (1994), ‘Earnings Management Preceding Management Buyout Offers’, Journal of Accounting and Economics, Vol. 18, No. 2, pp. 15779.
  • Purdy, D. E. (1977), ‘Accounting for Convertible Debt’, Journal of Business Finance & Accounting, Vol. 4, No. 1 (March), pp. 99114.
  • Ragan, S. (1998), ‘Earnings Management and the Performance of Seasoned Equity Offerings’, Journal of Financial Economics, Vol. 50, No. 1, pp. 10122.
  • Spiess, D. K. and J. Affleck-Graves (1999), ‘The Long-Run Performance of Stock Returns Following Debt Offerings’, Journal of Financial Economics, Vol. 54, No. 1, pp. 4573.
  • Teoh, S. H., I. Welch and T. J. Wong (1998a) ‘Earnings Management and the Long-Run Market Performance of Initial Public Offerings’, Journal of Finance, Vol. 53, No. 6, pp. 193574.
  • Teoh, S. H., I. Welch and T. J. Wong (1998b) ‘Earnings Management and the Underperformance of Seasoned Equity Offerings’, Journal of Financial Economics, Vol. 50, No. 1, pp. 6399.
  • White, H. (1980), ‘A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity’, Econometrica, Vol. 48, No. 4, pp. 81738.