How Do Firm’s Increases in R&D Affect Long-Run Performance of Intra-Industry

Competitors?

Weifeng HungWeifeng HungFeng Chia UniverstyFeng Chia Universty

Sheng-Syan ChenSheng-Syan ChenNational Taiwan UniversityNational Taiwan University

Yanzhi WangYanzhi WangYuan Ze UniversityYuan Ze University

R&D investment is a favorable strategy

• When a firm increases its R&D outlay, the firm earns positive abnormal return both in the short run (Chan, Martin and Kenisnger, 1990 JFE; Szewczyk, Tsetsekos and Zantout, 1996 FM) and in the long run (Chan, Lakonishok, and Sougiannis, 2001 JF; Eberhart, Maxwell and Siddique, 2004 JF; 2008 JAR).

R&D spillover effect

• According to Bernstein and Nadiri (1989),

• “A feature of R&D investment that distinguishes it from other forms of investment is that firms which do the investing are often not able to exclude others from freely obtaining the benefits from the R&D projects…”.

Short-run results are not consistent with the R&D spillover hypothesis

• Zantout and Tsetsekos (1994) document that the rivals of firms that make announcements of increases in R&D expenditures suffer a statistically significant negative abnormal return.

• Sundaram, John and John (1996) find that the market reaction to competitors varies depending on their competitive strategy measure.

Motivations• The long-term impact of R&D increase on intra-industry

competitors remains unknown. Particularly, no study explores the long-run market reactions to spillover effect.

• Lev and Sougiannis (1996) and Chan, Lakonishok and Sougiannis (2001) suggest that because the R&D valuation is hardly realized and not easily evaluated in a short horizon, long-term study is more adequate to capture the intangible information of the R&D investment.

• Managers seldom announce R&D increases formally. There might be a large time elapses between firm’s investment and market’s perception. As a result, the market might take time to fully reflect managers’ investment decision.

•

Motivations

• If the market underreacts to the direct future benefits of the R&D increases, it might also underreact to any indirect future benefits, if any, which a firm’s rivals might gain from that firm’s R&D increase.

• Fama (1998) argues that the abnormal returns might reflect normal random variations that occur in efficient markets, the long-term results can be viewed as an important challenge to the efficient market hypothesis.

The benefits of R&D spillover effect

• Bernstein and Nadiri (1988) have indicated that the R&D investment by a firm reduces its own production cost and, as a result of spillovers, costs of other firms are also reduced.

• If spillovers do lower rivals’ production cost, then we would expect this effect to show up in the operating performance of rivals.

• We use changes in operating performance and analyst forecast revisions to proxy for improvements in profitability.

Spillover Hypothesis

• Firms undertaking R&D investment are often not able to appropriate the R&D benefits; that is, the benefits from R&D investment may extend to other firms in the same industry and/or economy.

• It is possible for rival firms to abstain from R&D investment, and yet to take advantage of the knowledge generated by a firm that does invest in R&D. (Jeffe, 1986; Bernstein, 1989; Bernstein and Nadiri, 1988; Goto and Suzuki, 1989; Nadiri and Kim, 1996; Srinivasan, 1995).

• Intra-industry rivals earn positive long-term abnormal return and experience improvements in operating performance and analyst’s forecast revisions

Strategic Reaction Hypothesis• A firm that increases its R&D spending might gain

unfriendly attention from its rivals. • R&D increasing behaviors might be taken as that firm is

moving ahead in the race to be the first to innovate to exploit the future benefits.

• Sundaram, John, and John (1996) suggest that firms adopt R&D announcement as for a means of strategic interaction.

• Massa, Rehman, and Vermaelen (2007) suggest that a repurchase firm conveys a valuable signal about firm undervaluation, which threatens competitors. To undo this effect, the rival may mimic and repurchase shares.

• Massa, Rehman, and Vermaelen (2007) suggest that most strategic reacting behavior occurs in concentrated markets.

• Do managers use a non-announcement channel, such as R&D investment racing, to strategically react to their rivals?

Sample selection • Source:• The sample includes listed stocks in NYSE/AMEX/NASDAQ

during the period 1974 to 2006. Data on stock price and number of shares outstanding to compute market value of equity are obtained from the CRSP database.

• Sample selection criterion:• (1) R&D intensity (measured by the ratio of R&D-to-assets

(RDA, data46/data6) and R&D-to-sales (RDS, data46/data12) of at least 5%,

• (2) increases in dollar R&D by at least 5% (R&D growth rate, or RDI),

• (3) increases in ratio of R&D-to-assets (RDAI) by at least 5%.

Sample selection criterion:

• Further, we exclude the non-common stock ADRs, SBIs, unit trusts, closed-end funds, REITs, and financial firms, as the work done by Fama and French (1992, 1993).

• Sample stocks are also excluded if they have the following conditions:

• (1) non-positive book equity, (2) without sales, operating income before depreciation (data13), earnings before interest and taxes (data178), total assets, or market value (3) without industry concentration measures (4) a firm have not appeared in COMPUSTAT for more than two years (Banz and Breen, 1986).

• The final sample consists of 10,452 firm-year observations, in which the sample includes 3,646 R&D increasing firms.

Definition of industry rivals

• Throughout the paper, we use CRSP four-digit SIC classification to define industry membership.

• We measure the industry concentration using Herfindahl-Hirschman Index (HHI).

• HHI is a commonly accepted as the measure of the product market concentration. HHI is the sum of squared market share of each firm in the industry.

• Each year, we classy three groups based on HHI, where low concentration portfolio corresponds to the 30% of industries with the lowest concentration, while high concentration portfolio corresponds to the 30% of industries with the highest concentration.

Definition of industry rivals cont.

• For each sample firm, we construct its corresponding industry portfolio as all stocks, except the sample firm itself, in the same four-digit SIC industry as the sample stock.

• The returns on industry portfolio are equally and value weighted.

• That is, if we have 10,452 firm-year observations, then 10,452 industry portfolios will be obtained.

Methodologies

• Calendar time abnormal returns

• For each calendar month t in our sample period, we form a portfolio of all sample firms that have significantly increased their R&D investment in the previous five years (60 months).

• We then run the Fama and French three-factor model and Carhart four-factor model for long-term abnormal stock returns shown in the following equation:

• Both equally- and value-weighted portfolio returns are calculated.

• Rolling-over method:

• A firm’s risk may change in response to its R&D change (Berk, Green, and Naik, 1999; Chan, Lakonishok, and Sougiannis, 2001)

• We use the first 60 monthly returns (e.g., from April 1975 to March 1980) of the portfolio to estimate its factor loadings, and calculate the expected portfolio return in month 61 (e.g., April 1980) based on these factor loadings estimated over the previous 60 months multiplied by their corresponding factor returns in month 61.

• The abnormal return in month 61 is the difference between the actual portfolio return and expected portfolio return.

• Delisted-adjusted returns: • To mitigate survival-ship bias in returns for firms delisted from

CRSP for performance reasons, we follow the procedure of Shumway (1997) and Shumway and Warther (1999).

• Specifically, for firms delisted for performance reasons, we substitute -30% as the last return for NYSE and Amex stocks and -55% for Nsadaq firms.

• Cumulative benchmark adjusted returns:• Our procedure for calculating benchmark-adjusted returns

follows the methodology outlined in the Daniel, Grinblatt, Titman, and Wermers (1997, JF) study that developed benchmarks to evaluate mutual fund performance.

• Specifically, we form 25 benchmark portfolios that capture three stock characteristics namely book-to-market equity and size which are significantly related to the cross-sectional variation in returns.

• Each stock, in each year, is assigned to a benchmark portfolio according to its rank based on SZ and BM. Excess monthly returns of a particular stock are then calculated by subtracting the stock’s corresponding benchmark portfolio’s returns from the stock’s returns. Specifically, the characteristics-adjusted return is defined as:

• where and are the return on security i and the return on a SZ-BM-matched portfolio in month t, respectively.

• Each month, we use characteristics-adjusted return to calculate portfolio’s abnormal returns, then the abnormal monthly returns after formation period are cumulated as cumulative abnormal returns.

itR iCHtR

• RATS approach : • Sock excess returns are regressed on the Carhart (1997) four-

factor for each month in event time, and the estimated intercept represents the monthly average abnormal return for each event month.

• The long-run abnormal returns between 1 month and 60 months (j) after a large increase in R&D at a sample firm are adopted.

• The following regression is run each event month j:

• ri,t are the equally- and value-weighted portfolio returns on industry portfolios in calendar month t that corresponds to the event month j, with j = 0 being the month of the beginning of the fourth month following fiscal year-end in which there is a large increase in R&D at a sample firm.

Summary statistics

• The statistics reported in Table 1 are very similar to the those reported in EMS.

• The average (median) HHI is 0.245 (0.176), suggesting that the most of sample firms are within less concentrated industries.

• The average (median) number of rival firms in each industry portfolio is around 91 (58).

Table 1 Summary statistics

Spillover effect

• Consistent with EMS, Panel A of Table 2 shows that both equally and value-weighted long-run abnormal returns on sample stocks are significantly positive. The abnormal returns are 0.86% and 0.34% for equally- and value-weighted method. The results are quantitatively similar to EMS.

• There are significantly positive abnormal returns for the rival portfolio.

Table 2 Long-Term Abnormal Return for Large R&D-Increase Firms and Rival Portfolios

The influence of strategic reaction

• Table 3 shows that the coefficient of the Concentration Concentration x R&D increase wavex R&D increase wave term is 0.261 (Model 3), which is significant at 1% confident level.

• This indicates that the higher the concentration of the industry and the higher total number of firms that largely increase R&D expense over past five years in the industry, the more likely that the firms located in that industry will increase their R&D expense.

Table 3. Probit Regression of Indicator for Large Increases in R&D

The influence of strategic reaction

• Table 4 shows significant positive abnormal returns for the rival portfolio in less concentrated industries.

• Instead, in the concentrated industries, the abnormal returns for the rival portfolios are not significant, and some rival portfolios even earn negative abnormal returns.

Table 4 Long-Term Abnormal Return for Rival Portfolio Sorted by Industry Concentrations

The influence of strategic reaction

• Fig. 1 shows that the long-term return of the rival portfolio in low concentration industry experiences high return. In particular, the rival portfolio in high concentration industry earns negative BHARs.

• Table 5 demonstrates that over 12 (24, 36, 48, 60) months, for the full sample, the cumulative equally-weighted average abnormal returns of 10.05% (22.28%, 34.93%, 46.54%, 58.50%), are all significant at the 1% level. The results of the subsample indicate that for the low industry concentration group, the CARs are all significant at the 1% level.

• Therefore, these results further provide supports for the strategic reaction hypothesis

Figure 1 Cumulative Abnormal Return for Rival Portfolios

Table 5. Long-Term Cumulative Abnormal for Rival Portfolios

Cross-sectional regression analysis

• The dependent variable is 60-month buy-and-hold abnormal returns (BHAR) of each industry portfolio, in which the buy-and-hold abnormal return is controlled for the size, B/M matching portfolio return.

Further spillover evidence

• In Model 1 and 2, the results show that the the BHAR of BHAR of sample firmsample firm term is positive and highly significantly across all the models indicating that the higher the buy-and-hold abnormal returns to sample firms occur following the R&D increases, the greater the buy-and-hold abnormal returns to rival portfolios will earn.

• The long-run abnormal returns of industry portfolio are also positively associated with the level of R&D increases by largely R&D-increase firm.

• This clearly suggests that the R&D increases has spillover effect on rival firms, and is consistent with the spillover hypothesis.

Further strategic reaction evidence

• The coefficient estimate of ConcentrationConcentration is significantly negative. Thus, the higher the concentration of the industry, the lower the long-run abnormal returns to industry portfolios will be.

• The coefficients of the interacting terms are negative.

Table 6 Cross-Sectional Analysis of Long-Run Abnormal Returns to Rival Portfolios

Changes in operating performance

• First, the operating performance of the rival portfolios deteriorates prior to the event year and increased subsequent to the event year.

• Second, the improvements in post-event operating performance are the higher for rivals with low concentration and lower for rivals with high concentration.

Figure 2 Changes in Return on Assets (ROA) of Rival Portfolios

Figure 3 Changes in Profit Margins (PM) of Rival Portfolios

Cross-sectional regression analysis

• The dependent variable is five-year average post-event changes in operating performance (ROA and PM) of each industry portfolio.

• First, for all models, the intercept indicates that industry portfolio experiences positive changes in ROA (PM) post to the R&D increasing year.

• The long-run post-event changes in ROA of industry portfolio are positively associated with the level of R&D increases by sample firm.

• Second, the coefficient estimate of ConcentrationConcentration is significantly negative.

Table 7 Cross-Sectional Analysis of Changes in Long-run Operating Performance of Rivals

Analysts forecast revisions

• The dependent variable is the post-event 60-month average of abnormal analysts’ EPS forecast revisions of industry portfolios.

• The evidence indicates that the long-run averages of abnormal analysts’ forecast revisions of industry portfolio are positively associated with the level of R&D increases by largely R&D-increase firm.

• On the other hand, the coefficient estimate of ConcentrationConcentration is significantly negative.

Table 8 Cross-Sectional Analysis of Changes in Analysts’ EPS Forecast Revisions of Rivals

Institutional Trading Surrounding Share Repurchase Announcements (SRA)

Weifeng Hung (洪偉峰 )Department of Finance, Feng Chia University, Taiwan

Agenda

• Motivations• Contributions• Data and methodologies• Empirical Results• Conclusions

Motivation: SRA attracts institutions?

• Allen, Bernardo, and Welch (2000) argue that undervalued firms who want to signal their worth would like to attract institutions because institutions are better at assessing the firm’s true worth.

• Several studies indicate that SRA attracts institutions (Grinstein and Michaely, 2005; Shleifer and Vishny, 1986; Allen, Bernardo, and Welch, 2000).

• On the other hand, unlike individual investors, institutions are expected to be less prone to attention-driven trading behavior (Barber and Odean, 2008).

Motivation: Institutional response• Institutional investors are expected to have ability to move

prices directly through their own trading, as well as indirectly, by influencing the trading decisions of other market participants who may follow their actions.

• Institutional trading affects stock returns(Bannet et al., 2003; Gompers and Metrick, 2001).

• Understanding of whether SRA attracts institutions is of great importance for firms announcing share repurchases.

Motivation: Superior information?

• Institutions would be expected as sophisticated investors in processing information to arbitrage the repurchases anomaly to earn superior returns.

• Prior studies indicate that institutional investors are able to correctly identify corporate events, such as IPO and SEO.

• Why SRA?• The buyback anomaly has persisted for 25 years in U.S. stock

market (Peyer and Vermalen, 2008).

Motivation: Superior information cont.

• SRA in Taiwan:• On average, firms announcing share buyback earn significantly

positive abnormal returns.• However, about 45% of events in Taiwan experience negative

cumulative abnormal returns in the 30 days following SRA.

• If institutional investors do have informational advantages in processing corporate activities, it is intuitively credible that individual investors can profit from the buy-sell information by imitating institutional trades surrounding the corporate announcements.

Motivation: Unique datasets • Since daily institutional trading data is not easily assessed,

most empirical studies of institutional trading have focused on quarterly or annual data, such as 13(F) database.

• Few studies have explored the relationship between institutional daily trading behavior and SRA.

• Puckett and Yan (2010) suggest that trading performance shown by prior studies using quarterly data are biased downwards because of inability of publicly accessing interim trades.

Motivation: Unique datasets, cont.

• We argue that the quarterly holdings data cannot capture the intra-quarter institutional trading, such as the exact timing of institutional trading surrounding the share repurchases announcements.

• Particularly, we show that institutional trading occurs very near to the SRA date, about 10 days before SRA and a month after.

• Daily institutional trading data in Taiwan allows us, for the first time, to contribute to the literature by examining the daily institutional trading behavior in response to SRA.

Contributions

• 1. SRA significantly attracts institutions, switching their trading behavior from net selling to net buying. This finding is consistent with the argument that SRA attracts institutions (Grinstein and Michaely, 2005; Shleifer and Vishny, 1986; Allen, Bernardo, and Welch, 2000).

• 2. There is an institutional price impact before and after SRA.• 3. Institutional trading seems to have predictive ability for the

post-SRA stock performance. • 4. However, this trading skill disappears after controlling for

their post-SRA price impact.

Data

• We obtain daily data from Taiwan Economic Journal (TEJ), including stock repurchases announcement events (for the interests of shareholders), market index returns (including dividends), and institutional trading volumes.

• Annual accounting data, such as book equity, are also retrieved from TEJ. This paper includes 610 repurchasing samples from October 13, 2000 through December 31, 2006.

• We exclude events without institutional trading, stock returns, market value, and accounting variables at announcement date. The stocks with less than 130 trading days prior to the share repurchase announcement are also dropped.

Institutional trade imbalance

A positive (negative) institutional trade imbalance for a stock stems from institutional net buying (net selling) activities and increases (decreases) in institutional ownership for the stock.

We use the mean institutional trade imbalance of period from day -130 through day -31 (relative to the initial announcement day 0) to estimate the expected institutional trade imbalance.

The daily abnormal institutional trade imbalance is calculated as the difference between the actual trade imbalance and expected trade imbalance across stocks for each day.

Operating performance

• We define the unexpected change in performance as the change in performance of the repurchasing firm minus the change in performance of a matching firm.

• Empirical results

Favorable information

• The mean (median) CAR for the announcement period (-2,+2) of 1.20% (1.18%) is positive and significant at the 1% level.

• The positive mean (median) CAR of 2.96% (2.23%) for the post-announcement (+3,+30) period indicates a significant reversal for firms announcing stock repurchases.

Table 1 Summary Statistics

S.R.A. attracts institutions

• SRA attracts institutions:• The SRA significantly affects institutional trading behavior,

i.e., from net selling behavior to net buying behavior.

• Price impact: • There is a positive concurrent relationship between

institutional trading and stock returns around SRA window.

Table 1 Summary Statistics

Table 2 Short-Run Price Reactions and Institutional Reactions of Each Year

Table 3 Summary Statistics of Each Industry

Predictive ability

• Institutional trading has predictive ability for the stock performance following SRA.

• Specifically, it appears that institutional investors are able to identify stocks with good (underpriced) or bad (overpriced) SRA.

Table 5 Abnormal Institutional Trade Imbalances and Price Behavior Surrounding S.R.A.

Institutional trading following S.R.A.

• Institutional investors are not feedback traders following SRA, i.e., their post-SRA trading behavior is not driven by prior returns.

• The decisions of institutional trading following share repurchase announcements seem to be consistent with the institutional herding hypothesis, i.e., they trade by following themselves or others’ trades.

Price impact

• Post-event institutional trading indeed impacts stock price. • For the net sell and net buy groups, the CARs(+3,+30) are

significantly negative and positive at the 1% level, respectively.

• However, the impact accounts for partial market reactions to SRA.

• The CAR(+3,+30) of the neutral group, 4.15%, is significant at the 1% level.

Table 10 Portfolios based on CATI(+3,+30)

Short-run predictive ability V.S. price impact

It thus seems natural to ask whether the short-run trading skill of institutional trading surrounding SRA is due to the price impact caused by their trading persistence.

If institutional trading skill mainly results from the price impact caused by their persistent trading, we should see an insignificant cumulative abnormal return following SRA.

For groups without persistent trading, their market reactions are not significantly different from zero, implying that the predictive ability of institutional trading surrounding SRA mainly results from their post-eve nt price impact.

Table 11 Independent Double Classifications based on CATI(-2,+2) and CATI(+3,+30)

Conclusions• We use daily data to study institutions in response to buyback

announcements.

• Buyback announcement attracts institutions. Institutions are net sellers before buyback announcement and net buyers after.

• Institution’s trading skill is driven by their post-event price impact.

• The evidence does not support institutional informed trading.

Can Institutional and Individual Trading Drive Value and Size

Premiums in Japan?

Weifeng HungAssociate Professor, Department of Finance,

Feng Chia University, Taichung, Taiwan

Agenda

• Motivations• Data descriptions and variable definitions• Empirical results• Conclusions

The possible explanations of value and size premiums

• 1. Rational: compensation for risks (Fama and French, 1993; 1996)

• 2. Behavioral bias: overreaction (Lakonishok, Shleifer, and Vishny, 1994)

• 3. Data snooping (Lo and MacKinlay, 1990)

Price impact by institutional trading

• Institutional trading has a dynamic relation with stock returns .

• Two possible effects: • 1. Destabilization: If trading results from fads, reputational

concerns, or preference for certain firm characteristics, such trading may drive asset prices away from fundamental values and create return reversals in the subsequent period. (DeLong et al., 1990; Choe et al., 1999; Wermers, 1999; Sias, 2004)

• 2. Stabilization: Institutional buying (selling) may stabilize the stock market when prices are undervalued (overvalued).

• Which one does institutional trading have?

Trading preference by institutional investor

• Frazzini and Lamont (2008) and Sharma, Hur, and Lee (2006) indicate that institutions tend to buy growth stocks and sell value stocks.

• However, seldom studies explicitly show evidence that trading preference by institutional investor drives or mitigates the value and size premiums.

Trading preference by individual investor

• Kaniel, Saar, and Titman (2008) examine NYSE trading data and find that individual investor tend to be contrarian traders in the short-run, i.e., they buy stocks after prices decrease and sell stocks after prices increases.

• However, there is less agreement about the long-run trading preference by individual investors.

• Particularly, the long-run relation between individual trading and future stock returns has received little attention.

Purposes

• 1. Do institutional (individual) investors buy (sell) growth stocks and sell (buy) value stock in Japan?

• 2. What is the dynamic relation between institutional (individual) trading and stock returns?

• 3. Does institutional trading and/or individual trading drive value and size premiums?

• 4. Can the strategy based on trading preferences by institutional investors or individual investors enhance value and/or size strategy?

Data

• From Pacific Basin Capital Market Research Center (PACAP)

• 2. The sample period from 1975 to 2005• 3. The risk-free interest rate: 30-day Gensaki

rate • 4. 36,233 firm-year observations

Variables

• We compute BE/ME as the ratio of book value of equity (as Fama and French, 1992) at the end of March (the end of the fiscal year) divided by the market value of equity at the end of March from 1975 to 2005.

• 2. We compute market capitalization (ME) using market equity at the end of June in the calendar year t.

Variables

• 3.We calculate institutional trading (DITH) as changes in institutional ownership between fiscal year end t-2 and fiscal year end t-1.

• To control for systematic component, we compute industry adjusted change in institutional ownership (AdjDITH) as DITH subtracts median value of industry DITH, where industry DITH is measured by two-digits SIC industry.

• Adjusted change in individual ownership (AdjDIND) is defined similarly.

Relations among BE/ME, size, AdjDITH, and AdjDIND

• BE/ME is negatively associated with institutional trading and positively associated with individual trading.

• Size is negatively associated with individual trading, however, unrelated with institutional trading.

Table 3. Characteristics for quintile portfolios formed on book-to-market equity ratio or size

Table 4. Average parameter values from cross-sectional regressions of annual book-to-market ratio and size on

changes in institutional and individual ownership

AdjDITH, AdjDIND, and the cross-section of average stock returns

• The institutional trading is significantly and negatively related to future stock returns.

• Individual trading has no significant influence on current price. Particularly, their trading is also unrelated to future stock returns.

Table 5. Average monthly percent returns and characteristics for decile portfolios formed on AdjDITH

Table 6. Average monthly percent returns and characteristics for decile portfolios formed on the

AdjDIND

Table 7. Average parameter values from cross-sectional regressions of monthly returns on size, book-to-market

ratio, and AdjDITH and AdjDIND

Institutional and individual trading behavior and BE/ME and size premiums

• The relation between AdjDITH (or AdjDIND) and BE/ME (or size) seems to be weak.

• After purging the premiums associated with AdjDITH and AdjDIND, the BE/ME and size premiums are still significantly positive.

Table 8. Portfolio returns based on two-way independent sorts

Table 9. Portfolio returns based on dependent double sorts

Table 10. Average returns of BE/ME decile portfolios

Table 11. Average returns of size decile portfolios

Size strategies with institutional and individual trading preference

• Neither the institutional trading nor individual trading can significantly improve the size strategy!

Table 12. Investing strategies based on independent double sorts

Value strategies with institutional and individual trading preference

• The strategy (3) is the highest profits among strategies (1) to (4).

• This suggests that by including the information about institutional trading preference, i.e., buy growth stock and sell value stock, one can improve the profitability of the value strategy.

• Information about individual trading preference has limited ability in improving value strategy.

Table 12. Investing strategies based on independent double sorts

Conclusions

• A significantly and economically negative relation between institutional trading and future stocks returns exists.

• There is a negative association between institutional trading and book-to-market ratio (BE/ME). However, insignificant relation between institutional trading and size has been found.

• Although institutional and individual trading seem to be associated with BE/ME and size, their impacts appear to be limited on BE/ME and size premiums.

• Incorporating information about the institutional trading preference can significantly enhance the value strategy.

The EndThanks