“ SEO valuation and insider manipulation of R & D ”

We examine a sample of 674 SEOs from 1999-2010 where reduced R&D spending is significantly associated with the lowering of insider ownership proportions. With this association established, we derive an R&D manipulation variable measuring underinvestment in R&D. We add to the SEO-R&D literature by examining the relation between R&D underinvestment and common stock valuation around SEOs. In contrast to the IPO research, we do not find that underinvestment in R&D leads to greater SEO stock valuations during the offer price setting process. Like the IPO research, we find that underinvestment in R&D leads to lower stock valuations for short-run post-offering tests. In contrast to the long-run IPO results, we find a significant association between R&D manipulation and stock valuation for long-run post-offering tests where underinvestment in R&D is associated with lower stock valuations. We also find the five % owner group for SEOs is important in explaining R&D manipulation and discover that underpricing for SEOs is not related to R&D manipulation. These latter two findings are different from IPOs. In conclusion, SEOs can be quite different from IPOs when examining the association between the insider manipulation of R&D and stock valuation.


Introduction ©
Even though efforts have been made on the research topic of how the manipulation of financial variables impacts valuation, unanswered queries remain including the question about the interrelation between insider manipulation of R&D and stock valuation. While researchers (Guo, Lev and Zhou, 2005;Guo, Lev and Shi, 2006; HKW 1 , 2013) have explored this question using initial public offerings (IPOs), there has not been much research for seasoned equity offerings (SEOs). This study fills this void by examining the interrelation of insider manipulation of R&D and SEO valuation.
According to the earnings manipulation hypothesis, earnings are the most important variable so that an increase in reported earnings produces the most favorable stock market valuation. If so, insiders are motivated to inflate stock prices by increasing reported earnings. A major means of achieving this increase is through lowering R&D expenditures. HKW (forthcoming) established that a bigger decrease in insider ownership due to an SEO is associated with a bigger decline in R&D spending, supporting the earnings manipulation hypothesis. In this paper, we build on this research by using their regression model on a sample of SEOs to get a manipulation variable that measures the manipulation of R&D by insiders. A second regression model is, then, used to test the impact of this manipulation variable on SEO stock valuation over time. This second model was developed by HKW (2013) who found that greater R&D underinvestment led to greater IPO stock offer price valuation with this underinvestment being associated with poorer valuations in the IPO aftermarket. Thus, while firms manipulating R&D downwards have higher IPO valuations based on the offer price, they have lower IPO valuations based on short-term closing stock prices.
Like IPOs, the pricing of SEOs is accompanied by conflicts of interest between insiders who would profit from an inflated offer price and new owners who would want to purchase at a fair price. Thus, SEO firms might behave similarly to IPO firms. On the other hand, unlike IPO firms, SEO firms' performance and behavior have already been scrutinized in a public fashion by market participants. Since SEO firms have already gone public, earnings manipulation efforts by SEO firms may be better detected, thus, frustrating insiders' desire to inflate offer prices. To properly assess the similarities and differences in the market's reaction to SEOs against IPOs, we formulated six research hypotheses based on the findings of HKW (2013). These hypotheses led to six tests generating six new SEO findings in which four of these findings indicate that SEOs behave differently from IPOs. Below we summarize these six new SEO findings.
First, unlike IPOs, we do not find a significant negative relation between insider R&D manipulation and stock valuations during the offer price setting process. On the contrary, we find a significant positive relation. Thus, any manipulation of R&D downwards by firms undergoing an SEO is a sign of weakness and deflates the SEO offer price. Second, our SEO results for short-run post-SEO valuation tests are similar to IPOs, as we find a significant positive relation between R&D manipulation and stock price valuation indicating downward manipulation of R&D renders poorer short-run valuations. Third, whereas long-run IPO tests were insignificant, all of our SEO long-run tests yield a significant positive relation between R&D manipulation and stock valuation. Thus, SEO firms perform poorer over time when they manipulate R&D downwards so as to inflate stock offer valuation, while those that increase R&D beyond what was expected perform better.
Fourth, like IPOs, we find the change in insider ownership proportions is significantly associated with SEO stock valuation from the offering price process to three years after the offering. Fifth, unlike IPOs, SEO tests reveal that thefive % ownership (FPO) group of insiders have an important influence on the relation between insider R&D manipulation and stock valuations. Sixth, we repeat the HKW "change" tests that measure underpricing. For our SEO tests, we find that our R&D manipulation variable is insignificant. This contrasts with IPO results that found a significant positive relation between greater R&D underinvestment (that implies greater negative values) and poorer price performance relative to the offer price. In conclusion, this paper offers new findings and, thus, fills a void in the SEO research in its examination of how unexpected changes in R&D investment influence SEO stock valuations.
We organize the remainder of our paper as follows. Section 1 provides background information, and gives our research hypotheses. In section 2, we describe our data and report descriptive statistics. In section 3, we explain the methodology used to derive our R&D manipulation variable and describe our regression tests. Section 4 presents our empirical findings, while the final section offers conclusions and future research possibilities.

Background, goals and research hypotheses
In this section, we provide background information on prior research. We also discuss our six research hypotheses.

Background.
Scholars provide bipolar hypotheses to cover the two contrasting schools of thought related to which variable, R&D or earnings, should be inflated to impact valuation. First, the signaling hypothesis (Trueman, 1986;Aoki and Reitman, 1992) argues increased R&D and other investment expenditures signal optimistic information. This hypothesis, applied to our study, advocates that increasing R&D around the time of a security offering would enhance the offer price and, thus, lead to maximum stock valuation. This theory also suggests that the signaling from increased investment expenditures will lead to strong positive stock valuations over time. Second, the earnings manipulation hypothesis (Stein, 1989;Baber, Fairfield and Haggard, 1991;Sloan, 1996) posits that investors believe earnings are the most important valuation factor. Thus, insiders have an incentive to inflate stock prices by reducing any planned R&D expenditures. R&D is a perfect candidate for reduction to increase earnings as R&D is expensed (and not depreciated) so that the pre-tax reported earnings increase by the amount of the decrease in R&D. Bange and De Bondt (1998) and Osma and Young (2009) examine situations for which companies will adjust R&D to manage accounting earnings and stock valuation.
The IPO-R&D research supports the earnings manipulation hypothesis by showing that the offer price setting process produced greater stock valuation when R&D was managed downwards. Darrough and Rangan (2005) offered proof for this hypothesis using a sample of 243 IPOs from 1986 to 1990. HKW (2014) strengthened this support, by not only using a larger sample of 447 IPOs, but also covering both a bubble period of high IPO intensity (1997)(1998)(1999)(2000) and also a non-bubble period of low IPO intensity (2001)(2002)(2003)(2004)(2005). Together Darrough and Rangan and HKW have: (1) verified bigger downward R&D manipulation for greater decreases in insider ownership around IPOs; and (2) demonstrated which insider variables can best be associated with this R&D manipulation. Most recently, the SEO-R&D research of HKW (forthcoming) offers support for both the earnings manipulationand signaling hypotheses. The support for the earnings manipulation hypothesis is found more for those insiders who are in the directors and officers (DandO) group, while support for the signaling hypothesis is found more in the five % ownership (FPO) group consisting of large owners who are not in the DandO group.
There have been several studies on SEO firms' earnings management and market valuation. Rangan (1998) considers discretionary accruals, and finds that the market temporarily overvalues SEO firms and is subsequently disappointed by predictable declines in earnings caused by earnings management. Qian et al. (2012), one of the most closely related to our paper, find that investors respond more favorably to the SEO announcements of high-tech issuers with positive discretionary R&D, thereby supporting the signaling hypothesis. In spite of similarities in methodology, our paper is distinct from Qian et al. (2012) in that (1) our focus is on the market's reaction to insider ownership and R&D underinvestment, while their focus is on the market's reaction to R&D overinvestment of high-tech and low-tech firms, (2) we offer more detailed analysis of market valuation for various short-run and long-run periods.
As pointed out by HKW (2013), there are two incentives governing why insiders are motivated to achieve a maximum offer price. First, they can be selling their own shares if there is a secondary component to the offering. For SEOs, this reason may be more prevalent, because secondary selling as a proportion of outstanding shares for our sample of SEOs averages 0.056 compared to 0.030, as reported by HKW for IPOs. Second, insiders control large ownership % ages after the offerings (42.6% for SEOs and 63.2% for IPOs) and, thus, has a vested interest in their companies and would want a maximum offer price to raise as much funds as possible for future investments. In conclusion, both incentives can simultaneously occur such that insiders who are selling shares can also be maintaining large control.

Six research hypotheses.
This paper will test six research hypotheses. Each research hypothesis is formulated based on prior IPO results. Thus, if we reject a hypothesis, we have evidence that SEOs perform differently from IPOs. Thus, by testing these hypotheses, we will be able to offer new findings on the similarities and differences between SEOs and IPOs. Our first research hypothesis is: H−1: SEOs will successfully manipulate the offer price setting process so that the manipulation of R&D downward to inflate earnings will lead to setting higher offer prices.
H−1 predicts a negative relation, because when R&D downward manipulation intensifies (greater negative values), then, expected offer price valuations and the actual offer price valuation increase. Rejection of H−1 can occur, because SEOs, unlike IPOs, are publicly traded prior to the offer date so that the offer price setting process avoids the opaqueness found in the IPO price setting process.
Our second research hypothesis is: H−2: SEOs will have greater negative short-run stock valuations when there is greater downward manipulation of R&D.
H−2 predicts a positive association, because greater unexpected decreases in R&D will be associated with more negative SEO stock valuations for short-run post-SEO periods. Not only did IPOs have a positive relation on the first day of trading, but also this positive relation got stronger, as the short-run period increased. Rejection of H−2 can occur if SEOs have already fully reacted in a positive manner during the offer price setting process which is possible for SEOs since, unlike IPOs, they are already publicly traded prior to the offering. Thus, any positive association found for IPOs may be diluted yielding a non-positive association for SEOs.
Our third research hypothesis is: H−3: In the long-run post-SEO market, stock valuation will be neutral in the sense that correct and fair prices have already been efficiently incorporated during short-run post-SEO periods.
H−3 predicts a neutral SEO response as occurred for IPOs. Rejection of H−3 is consistent with signaling theory in that those firms that underinvest in R&D (greater negative values) convey that their future is poor with greater negative stock valuations occurring in the long-run. Similarly, a positive relation would hold for firms that overinvest in R&D, as they signal their future is bright and, thus, should reap greater stock valuations.
Our fourth research hypothesis is: H−6 predicts a positive relation between R&D manipulation and underpricing as computed not only from the offer price to the closing price on day 0 (where day 0 is the day the offer price is first revealed in the final registration statement), but also up to one year after the offering, as found for IPOs. Rejection of H−6 is consistent with SEOs having much less underpricing than IPOs such that any change based on the offer price may be too small to have any significant impact.

Data and descriptive statistics
Our sample of 674 SEOs is supplied by HKW (forthcoming) where SEOs were identified from the Investment Dealers' Digest (IDD) for the period of 1999-2010. Observations were expunged if the required data were not found in the prospectus, CRSP and Compustat. Two prevalent causes for a deletion were absence of prospectus data for insider ownership and lack of Compustat data for R&D. Table 1 gives summary statistics. Panel A provides R&D statistics. This panel reveals the median R&D as a proportion of total assets for the second fiscal year ending before the offer date is 0.106. This median falls to 0.092 for the next fiscal year and further declines to 0.064 for the fiscal year ending after the offer date. R&D as a fraction of net sales has medians of 0.192, 0.205 and 0.212, respectively, for the two years before, one year before, and one year after the offer date. Panel B of Table 1 gives price statistics. We compute two expected offer prices as described in Table 1 In Panel A, Year -2 and Year -1 refer to the second and first fiscal year ending before the offer date, while Year 0 refers to the fiscal year containing the offer date. In Panel B, the 1 st Low is the minimum closing price between days -20 to -6 and the 1 st High is the maximum for this period. The 2 nd Low is the minimum closing price between day -5 and -1 and the 2 nd High is the maximum for this period. Day 0 is the date of the final registration statement when the offer price is first revealed. In Panel C, DandO refers to the group of insiders who are directors and officers, while FPO refers to the group of insiders who are not in the DandO group and includes all individuals, institutional owners, and venture capitalists who control at least 5% of the pre-SEO outstanding shares. In Panel D, we use CRSP's exchange-based, equal-weighted monthly index when computing compounded index returns. In Panel E, the industry PE and BM ratios for each SEO is computed (at the time of the offer date) based on the SEO's three-digits SIC code with medians used. The number of observations (n) for each statistic is 674 unless noted otherwise.  Finally, the industry price-to-earnings ratio and book equity-to-market equity ratio average 41.51 and 3.74, respectively.

Two regression models
In this section, we use two regression models. Model 1 is utilized to derive a variable that measures the insider manipulation of R&D. Model 2 uses this manipulation variable to test its impact on SEO valuation. First, using Model 1 3 , ∆RD is regressed against its eleven independent variables. From 2 this regression, the coefficients are identified. Second, excluding the two coefficients for INS and RIN so as to be free from the influence of insider ownership, we use the coefficients for the nine non-insider variables to get a point estimate of the predicted change in R&D in the absence of insiders' motivation to manipulate R&D. Our predicted ∆RD equation with only the nine non-insider variables is: We next subtract the predicted ∆RD for each observation from its actual ∆RD to estimate R&D manipulation without the influence of insider ownership. This subtraction yields a "difference in R&D" variable named DRD. In equation form, we have: where DRD is the measure of R&D manipulation 4 . A negative DRD value implies downward manipulation or underinvestment in R&D where the actual ΔRD that includes the influence of insider behavior is less than the predicted ΔRD. A negative DRD value is associated with the notion that R&D will be cut so as to inflate earnings, and thus, stock valuation. While a positive DRD value indicates overinvestment, this rarely occurs, as 95% of the SEOs have negative values for DRD. 3 Table 2, this model is:

Model 2. Model 2 is our valuation regression model to determine if there is a significant relation between our R&D manipulation variable (DRD) and stock valuation. As given in
Valuation = a 0 CON + a 1 DRD + a 2 INS + a 3 RIN + + a 4 RDB + a 5 IPE + a 6 ORW + a 7 MCV + a 8 IBR + + a 9 SIZ + a 10 TIM + ε, where Valuation = Price × (post-SEO shares outstanding) / SAA 5 , DRD = R&D manipulation variable: 4 (actual ∆RDpredicted ∆RD) 6 , INS = (post-SEO fraction of shares owned by insiders) 5 -(pre-SEO fraction of shares owned by insiders), RIN = shares retained by insiders after SEO / Shares outstanding after SEO. RDB = R&D for the third plus fourth fiscal years ending before the offer date / SAA 6 , IPE = industry price-to-earnings ratio as defined in Table 1, ORW = average of the two expected 6 offer price range widths given in Table 1, MCV = market condition variable: compounded monthly index return for one year before SEO, IBR = investment bankers rankings from 1 to 9 with nine the highest (normalized by 9), SIZ = size variable given by minus one times 4 In their normalized form, the means (medians) for actual ∆RD, predicted ∆RD, and DRD are 0.864 (0.397), 3.423 (3.190), and -2.560 (-2.662), respectively. 5 SAA, as defined in Table 3, is the square root of the average assets where average assets consider the total assets for the fiscal year before and after the offer date. 6 DRD is already normalized by SAA, as the actual ∆RD and predicted ∆RD are normalized by SAA. 7 The RDB was judged the best fit, as it was not significantly correlated with DRD like other RDB values we tried. The choice of our RDB variables is also consistent with the fact DRD for SEOs was derived using a three-year R&D value instead of a two-year R&D value as used by HKW for IPOs. Each valuation measure is based on one of eight prices multiplied by the number of post-SEO shares outstanding and then normalized by SAA. As shown in Panel A of Table 2, these eight prices cover three stages during the offer price setting process: first expected offer price, second expected offer price and the actual offer price. We also cover five post-SEO prices; closing prices on days 0 and 50 (where day 0 is the day the offer price is revealed in the final registration statement) and closing monthly prices for months 12, 24, and 36 (where month 0 is the month of the offering). For the five post-SEO prices, our valuation variables are calculated after adjusting the prices for a market index return as described in Table 2. Table 2 describes the ten independent variables that take into consideration related research ( , and gives two "prediction" columns for independent variables for each of the eight valuation tests. The first column gives the predicted sign for the first three valuation tests (VA1 − VA3) that occur during the offer price setting process. The second column provides predictions for the five "post-SEO" tests (VA4 − VA8). For the pre-SEO R&D variable (RDB) and the bubble period variable (TIM), the 2 nd column has two predictions with the explanations for the two predictions given below. We will now explain the predictions for each independent variable.

Predicted coefficient signs. Panel B of
The earnings manipulation hypothesis suggests that insiders will manage earnings upwards by reducing R&D to increase stock valuation. Thus, this theory predicts negative coefficients for DRD for the first three valuation tests (VA1 − VA3), as the two expected offer prices and the final offer price should be inflated in proportion to negative values for DRD that epitomize degrees of R&D underinvestment. If the market is successfully fooled by the R&D manipulation, we would expect stock valuation to fall once market participants realize the manipulation. If the market does not detect this manipulation until after the offer price is announced, then, we expect a positive coefficient for DRD for the VA4 test. If the realization of manipulation is gradual, we anticipate greater positive coefficients for DRD for the VA5 − VA8 tests.

Table 2. Valuation regression model
Our regression model to test the impact of R&D manipulation on SEO valuation is: Panel A describes the eight dependent valuation variables used to capture stock valuation based on the two expected offer prices, the offer price, and the five post-SEO closing prices. Day 0 is the date of the final registration statement when the offer price is first revealed. Month 0 is the offer month. Thus, months 12, 24, and 36 represent one-year, two-year, and three-year periods. Post-SEO prices are adjusted by multiplying by one minus the compounded market index return for the period being considered. The market index uses CRSP's exchange-based, equal-weighted index. We use daily index returns for short-run periods and monthly index returns for long-run periods when computing the market's compounded return. "Post-SEO shares outstanding" refers to the number of shares outstanding after the SEO is completed. 1 st EOP and 2 nd EOP refer to the first and second expected offer prices, respectively. Panel B describes the ten independent variables. MCV uses CRSP's exchange-based, equal-weighted monthly index. The last column offers two predictions for the valuation variables. The first prediction is for the first three valuation variables (VA1 − VA3), while the second prediction is for the last five valuation variables (VA4−VA8). Two exceptions are seen in the last column for RDB and TIM given by "−/+" which indicates we predict negative coefficients for VA4 and VA5 and positive coefficients for VA6−VA8. SAA stands for the square root of average assets where average assets are the average of total assets for the fiscal years ending before and after the offer date. The average assets are expressed in millions of dollars before we take the square root. To overcome heteroscedasticity while maintaining a greater value for SIZ as firm value increases, we compute SIZ as minus one times the log of the inverse of firm value where firm value is expressed in millions of dollars. Firm value includes market value of common stock, liquidation value of preferred stock, and book value of total liabilities. Firm value is adjusted using inflation as given by http://www.usinflationcalculator.com/inflation/historical-inflation-rates/. Since stock values can be positively skewed, we winsorize all dependent variables at the ½% level on each side. DRD is already normalized by SAA as the Actual ∆RD and Predicted ∆RD are normalized by SAA.    Hanley (1993) writes that the offer price range is an ex ante measure of risk. If greater risk renders greater stock values, then, we expect a positive coefficient for ORW. Lerner (1994) and Brau and Fawcett (2006) suggest that a positive market condition is an important determinant of issuing new securities implying that more favorable stock valuations will result during stronger markets. Consequently, we anticipate positive coefficients for MCV.
Brau and Fawcett (2006) suggest a positive coefficient for IBR, since investment bankers with greater reputations will signal higher quality and thus greater stock valuations. Larger firms should be better equipped to time their market offering to take advantage of superior market conditions, should be older and more experienced, and should have larger offerings. Ritter (1991) finds that firms with these attributes perform better after an offering. Thus, we expect a positive coefficient for SIZ. We expect a negative coefficient for TIM for the VA1 − VA5 tests, because observations that occur during the internet-technology bubble period (TIM = 0) would be expected to have more optimism and higher valuations for the offer price setting tests and short-run post-SEO tests. For longer time frames, many bubble period SEOs will undergo the bursting of the bubble and achieve poorer stock valuations from one to three years after their offerings. Consequently, we predict positive coefficients for the VA6 − VA8 tests.

OLS regression results for valuation tests using
Model 2 Table 3 provides OLS regression results for our SEO valuation tests using Model 2. We found no evidence of multicollinearity, as variance inflation factors and condition index values are well below cut-off levels. Additionally, we performed clustered regression tests to adjust the standard errors for the fact we have multiple SEOs in a month that would all have the same return data. We also performed clustered regression tests using various schemes of SIC code classifications, because firms with R&D spending are clustered within certain industries. These clustering tests did not change our findings.
We also conducted tests that corrected for heteroscedasticity and the statistics remained significant. Finally, our results are the same with winsorizing using standard cut-off levels. Table 3 gives the largest adjusted r-square and F values of 0.53 and 75.6 for the VA2 test with values falling with longer periods tested. Signs for coefficients for independent variables are generally as predicted. Due to space constraints, we will focus our analysis results pertaining to our six research hypotheses that revolve around our manipulation variable (DRD) and our insider ownership change variable (INS).  We conducted the change (or "underpricing") tests given by HKW (2013) who found that less underpricing occurs when insiders are manipulating earnings upwards by lowering R&D. This occurred not only during the offer price process, but also as much as one year after the offer date. Unlike IPOs, we found that DRD was not significant for any of the SEO tests. Thus, we reject H−6 that states: Greater underinvestment in R&D leads to less underpricing and poorer post-SEO stock prices relative to the offer price up to one year after the SEO. The insignificant SEO results reflect the differences in underpricing between IPOs and SEOs as the change tests focus on the change in the stock price over time relative to the offer price as opposed to just stock valuation at points in time.
In conclusion, we find major differences between IPOs and SEOs as we rejected four of our six hypotheses, thus, demonstrating that SEOs can be quite different from IPOs when examining the influence of insider manipulation of R&D. Besides those tests already mentioned, we conducted other robustness checks. For example, we repeated our tests using firm value as the normalization variable. We also performed tests with only those 574 observations without missing prices and returns. We also recomputed "Predicted ∆RD" by including the RIN coefficient in the equation and only omitting the INS coefficient. Additionally, we repeated our regression tests by deleting INS to see if this affected the results for DRD and, then, by deleting DRD from the tests to see if the results for INS were affected. Next we tested SIZ using other substitutes. Our SIZ results are invariant to whether or not we adjust for monthly inflation. For all these robustness checks, we found nothing to change our findings. Finally, we divided our sample based on the variable TIM and also a division based on small versus large firms. We found our results for DRD are driven by observations located after the internet-technology bubble period (TIM = 1) and by larger firms.

Conclusions and future research
We examine a sample of 674 SEOs from 1999-2010 where reduced R&D spending is significantly associated with the lowering of insider ownership proportions. With this positive association established, we derive an insider R&D manipulation variable to test its influence on SEO stock valuation. The results of these tests generate six new SEO findings that are summarized below.
In contrast to the IPO research, we do not find that underinvestment in R&D leads to greater SEO stock valuations during the offer price setting process. Like the IPO research, we find that underinvestment in R&D leads to lower stock valuations for short-run post-offering tests. Contrary to IPOs, we find a significant association between R&D manipulation and SEO stock valuation for long-run post-offering tests where underinvestment in R&D is associated with lower stock valuations. Like IPOs, the change in insider ownership proportions is significantly associated with SEO stock valuation from the offering price process to three years after the offering. We find the five % owner group for SEOs is important in explaining R&D manipulation and also discover that underpricing for SEOs is not related to R&D manipulation. These latter two findings are different from IPOs. In conclusion, SEOs can be quite different from IPOs when examining the association between the insider manipulation of R&D and stock valuation.
This study fills a void in the SEO research by examining the effect of R&D manipulation on SEO stock valuation. We pattern our research after the recent IPO research, so comparisons between SEOs and IPOs could be better performed. There is a limitation of patterning our SEO study on IPO research, because IPOs have no stock values to analyze in the pre-IPO market, as IPO shares have yet to be traded publicly. Thus, future SEO and R&D research should look at the relation between R&D manipulation and stock valuation in the pre-SEO market. Research issues that can be explored when analyzing the pre-SEO market include whether there is manipulation at some pre-SEO point in time. In other words, at what point in time, if such a point in time exists, might we find evidence consistent with the earnings manipulation? Relatedly, can we establish a general time frame when the market begins to recognize this manipulation? One to two years before the SEO? Two to three years before? Finally, given the strong relation we found between R&D manipulation and SEO stock valuation, future research can study the interrelation between R&D manipulation and stock valuation for other applicable corporate events such asmergers, restructurings, repurchases, stock splits and dividend changes. In closing, there are major implications for practicing managers and investors concerned with understanding R&D spending and stock price behavior around SEOs. For example, insiders for SEOs cannot inflate the offer price setting process by manipulating R&D. Furthermore, underinvesting in R&D is associated with poor stock performance in the SEO aftermarket. These practical implications have a societal impact, as they influence the activities of the business and investment communities in regards to how they perceive SEOs compared to IPOs. For example, unlike IPOs, decreases in R&D to inflate earnings cannot increase the offer price. Additionally, unlike IPOs, the direction of the unexpected change in R&D for SEOs is positively related to future stock price behavior. These latter differences reveal to the investment community that, whereas it may be possible to manipulate IPO prices, it is difficult to manipulate SEO prices and those that do are foretelling poor future performances.