CHAPTER 5: MARKET TIMING AND PSEUDO MARKET TIMING: AN

EMPIRICAL EXAMINATION OF IPOs AND SEOs IN INDIA

5.1 Introduction

An important motive of firms is to raise capital to finance its investments. Firms raise

capital by issuing equity in the public market. If the equity prices are higher than the actual

fundamentals then issuing equity results in the transfer of wealth from new shareholders to the

firm or to the old shareholders. In other words, insiders may get the benefit at the expense of new

shareholders. In corporate finance, this phenomenon is called market timing. In particular,

market timing refers to selling equity when it is expensive and repurchasing equity when it is

cheap. The intention is to take advantage of temporary fluctuations in the value of equity until

the value converges to fundamentals. According to market timing, firms which issue equity have

the scope of timing the market because of two reasons: One, firms wait for the right time and

issue when the market valuations are high. Second, managers behave opportunistically and take

advantage of over-optimistic investors by selling equity at high prices and are too optimistic

about the future prospects of the company and believe that the firms have greater growth

opportunities/potential.

However, this view is not acceptable to those who believe that the markets are efficient

following equity issues. These researchers do not believe that managers possess market timing

ability and can sell overvalued equity to irrational or uninformed investors by using insider

information. According to them, firms issue equity when the economy is growing, markets are

rising and there is higher demand for capital because of greater growth opportunities. They refer

this phenomenon as market conditions hypothesis or pseudo market timing hypothesis. The

researchers till date, have not arrived at consensus regarding whether equity issuance decisions

are driven by equity market timing or market conditions. Hence, it is important to examine

whether equity issuance is driven by managers’ market timing or favorable economic conditions

or simply due to the genuine needs to finance investments.

An indirect test of market timing is the evidence of decline in the long-run stock

performance. If equity is sold at high price then in the long-run, stock prices converge to

fundamentals leading to underperformance of stock in the long-run. The prior literature has

documented the long-run1 underperformance of firms issuing equity through initial public

offerings (IPOs) and seasoned equity offerings (SEOs) which led to the conclusion that managers

time the market by selling overvalued equity. However, recently these studies are challenged on

two grounds. The first challenge comes from methodological ground that the previous studies

which have observed long-run stock underperformance of issuing firms have used event-time

approach to measure abnormal performance, which has serious flaws2. The use of calendar-time

approach over event-time to measure abnormal performance is advocated (Mitchell and Stafford,

2000 and Schultz, 2003). The second challenge comes from the fact that even in the absence of

managers possessing market timing abilities, the evidence consistent to successful market timing

can be observed (Schultz, 2003). The direct tests of market timing and market conditions

hypotheses are based on the positive relation of market timing variables and market conditions

variables with equity issuance (number of IPOs and SEOs). We intend to examine both direct

and indirect tests in our study.

The objective of the study is to examine the impact of market timing and pseudo market

timing on equity issuance decisions of IPO and SEO firms in an emerging economy India. First,

we carry out direct test to evaluate the impact of market timing and pseudo market timing

1 The performance in terms of stock returns over a period of 3-5 years is considered as long-run performance.

2 Event-study methodology assumes that any lag in the response of prices to an event is short term. As information

gets adjusted in prices slowly, one must examine returns over longer horizons which can give fair view of market

efficiency. To overcome this problem, calendar-time approach is suggested to examine long-run performance.

(market conditions) on the number of equity issues. Various proxies which reflect aggregate

market timing and firm-specific market timing are selected to examine the impact of market

timing on equity issues. When issuers take the advantage of market wide or sector wide over-

optimism then it is called aggregate market timing (Baker and Wurgler, 2002). When market

timing is driven by firm’s over-optimism (Ball, Hui Chiu and Smith, 2011) then it is called firm-

specific market timing. Also, various economy based proxies are used to examine the impact of

market conditions on equity issues. Second, we evaluate long-run performance of IPOs and

SEOs by calendar-time approach in order to test market timing against pseudo market timing.

This serves as indirect test of market timing and pseudo market timing.

In this study, we find the evidence of both market timing and pseudo market timing in the

context of Indian IPOs and SEOs. In other words, our results show that in India, firms issue

equity not just to time the market but market conditions also play an important role in the equity

issuance decision of the firms. Our results of firm-specific market timing and aggregate market

timing are also supported by negative long-run performance of IPOs and SEOs. However, we

find that the evidence of market timing is strong in hot issue markets as compared to cold issue

markets. Further, market timing of IPOs is stronger than that of SEOs.

The rest of the chapter is organized as follows. Section 5.2 provides review of literature

on market timing and pseudo market timing and development of hypotheses. Section 5.3

describes the data, variable definitions and methodology. Empirical results are discussed in

Section 5.4 and Section 5.5 concludes the chapter.

5.2 Prior Evidence and Hypotheses Development

The question, “Why do firms issue equity?” has attracted the attention of many

researchers all over the world but still we fail to find a full-proof answer in the existing literature.

On one hand, there are studies which show that firms issuing equity are able to time the market

and behave opportunistically by selling their equity when it is overvalued in the market. In the

literature, this view is known as ‘Market Timing Hypothesis’. On the other hand, there are few

other studies which find evidence that market conditions play an important role in the decision

equity issuance of a firm which is totally unrelated to the idea of market timing. In the literature,

this view is known as ‘Pseudo Market Timing/Market Conditions Hypothesis’. There are two

ways in which both types of studies (hypotheses) are carried out (tested) in literature: indirect

tests and direct tests. In indirect tests, market timing hypothesis is tested indirectly by examining

long-run performance of IPOs and SEOs for three or five years after the equity issuance. The

negative (neutral/positive) long-run performance of IPOs and SEOs has been considered as an

evidence of market timing (pseudo market timing or market conditions) hypothesis. In direct

tests, the impact of variables reflecting market and market conditions is studied on the equity

issuance of IPOs and SEOs. Both strands of literature (market timing and pseudo market

timing/market conditions) are reviewed below:

5.2.1 Market Timing

By using indirect test Ritter (1991) shows that the issuers are able to time the market to

take advantage of “windows of opportunity”. He analyzes 1,526 U.S. IPO firms which issued

equity during 1975-1984. The performance of IPOs is analyzed by using cumulative average

adjusted returns (CAARs) and three years buy and hold returns (BHRs) from various angles:

firm-wise, industry-wise, year-wise, gross proceeds-wise and age-wise. The results show that

there is a variation in the degree of underperformance but it persists in all cases. Small size offer

IPO firms which have the highest initial returns performed worst in the long-run relative to big

size offer IPO firms. Industry level analysis indicates that long-run underperformance also varies

across different industries. Out of all 14 industries considered, underperformance is found in 11

industries. Financial firms outperformed substantially whereas oil and gas firms underperformed

substantially. When the performance is categorized by the year of issuance of equity, it is seen

that underperformance is more in the period of heavy equity issuance. Supporting Ritter (1991),

Loughran and Ritter (1995) also use indirect test and find the evidence of long–run under

performance for the companies issuing equity in both IPO as well as SEO. They examine the

long-run performance of U.S. 4,753 IPOs and 3702 U.S. SEOs which made equity offerings

during 1970 to 1990. The authors use BHRs to examine long-run performance for two intervals –

three year and five year. The results show that both IPOs and SEOs under-perform significantly

relative to non-issuing firms and other alternative benchmarks. The authors state that many firms

which go public are high growth firms and many firms which conduct SEOs have had high

valuations (high market-to-book ratio). Since the benchmark firms are matched on the basis of

size (market capitalization), the authors also analyze the long-run performance by running cross-

sectional and time series regressions using monthly returns after controlling for size as well as

market-to-book effects. In cross-sectional regressions, the dependent variable is monthly returns

of listed stocks and independent variables are market value, market-to-book ratio and “Issue

dummy” which is 1 when the firm issued equity in the preceding five years and 0 otherwise. The

coefficient of dummy variable “Issue” is found negative and significant in all cases indicating

significant under-performance. The time series regressions are run by using Fama and French

(1993), three factor time-series regression model of monthly returns for two portfolios of issuing

firms and non-issuing firms. The results of time series regressions also show long-run

underperformance of IPOs and SEOs. Therefore, the authors conclude that mangers possess the

market timing ability and take advantage of overvaluation while issuing equity. Loughran and

Ritter (1997) extend the argument of Ritter (1991) and Loughran and Ritter (1995) and show that

the firms conducting SEOs not only perform badly in terms of stock returns but the operating

performance of the issuing firms also deteriorates after the issuance of equity. The sample of the

study consists of 1338 SEOs which issued equity during 1979 – 1989. Their results show that the

operating performance of issuing firms improves prior to equity issuance but decline

significantly after the issuance. The decline in the post-issue market-to-book value shows that

issuers tried to take advantage by issuing overvalued equity. The average annual stock returns of

issuing firms for post-issue five year period are found to be significantly less than average annual

returns on value weighted market index and on non-issuing firms. This leads to their conclusion

that both investors and managers are too optimistic about the future prospects of the company

and managers take advantage of over optimistic investors by issuing overvalued equity.

Brav, Geczy and Gompers (2000) re-examine results of Ritter (1991) for the long-run

stock performance of IPOs and SEOs by using improved methodologies. Taking into

consideration the shortcomings of event-time approach (CARs and BHRs), they use calendar-

time approach (Fama-French three factor model (1993) and Carhart (1997) four factor model)) to

analyze long-run performance of firms issuing equity. They analyze a sample of U.S. 4622 IPOs

and 4526 SEOs during 1975-1992. The event-time results indicate that IPO firms perform similar

to those non-issuing firms having similar characteristics which are size and book-to-market ratio.

However, SEOs underperform relative to benchmarks. The results of calendar-time approach

show that only small IPO firms with high market-to-book ratio underperform in the long-run

whereas SEOs returns co-vary with the returns of non-issuing firms. This leads to the conclusion

that in calendar-time approach, the long-run underperformance is not because of equity issuance

per se but raised a question that why small size firms with high book-to-market ratio did

underperform in the long-run.

Pagano, Panetta and Zingales (1998) use direct test to address the question, “Why do

companies go public?” and examine the determinants of IPOs. They analyze a sample of 69

Italian companies which consists of 40 independent IPOs and 29 carve-outs3 during 1982-1992.

To examine ex-ante determinants of firms, they use Probit regression in which the dependent

variable is dummy variable which takes value 1 if the company goes public and 0 if it stays

private. The independent variables are size, CAPEX, growth, ROA, Leverage, industry market-

to-book ratio, relative cost of credit, Herfindahl index and a calendar year dummy. The highly

significant variable affecting the probability of a company to go public is found to be industry

market-to-book value which reflects two possibilities: high growth opportunities in the sector to

which the firm belongs or issuers possess the market timing ability to sell their equity at higher

prices. However, the second set of results (ex post effects of IPOs on each of the above

mentioned variables) show that investment and profitability decrease after the issuance of IPO.

This indicates the support for second explanation that the companies go public in order to time

the market. One serious shortcoming of the paper is that, both ROA and leverage are included in

the regression which may cause endogenity.

The previous studies, Ritter (1991), Loughran and Ritter (1995, 1997) and Pagano et al

(1998) mainly conclude that issuers possess the market timing ability and take advantage of

‘windows of opportunity’ because market returns/valuations predict the events like equity

issuance whereas Baker and Wurgler (2000) view market timing ability of managers from the

perspective that events predict future returns. They claim that equity share in aggregate new

equity and debt issues predict aggregate market returns. They examine equity and debt issues

3 Carve-out – When a parent company takes its own subsidiary company public.

data for the period 1927 – 1996 and compare equity share with other market returns predictors

which are book-to-market ratio and dividend yield. They find that equity share is a better

predictor than other variables and its significance is consistent across time. They also show that

firms issue equity before low returns periods (or at the time of high returns) and prefer to issue

debt before high returns periods (or at the time of low returns). In particular, all the results show

negative relationship between equity issue and subsequent market returns which means equity

share predicts negative stock market returns. They conclude that the markets are inefficient and

managers take advantage of those inefficiencies.

Continuing with the argument of market timing, Baker and Wurgler (2002) examine the

impact of this market timing on the capital structure. In particular, they analyze the impact of

historical market-to-book value on the capital structure. They also investigate whether market

timing has persistent effects on the capital structure. They examine 2839 U.S. IPOs from 1968 –

1999. The results show negative relationship between market-to-book value and the leverage

which indicate that that firms issue more of equity when their equity valuations are higher. In

order to examine the persistent effects of market timing on capital structure they analyze the

impact of weighted average of market-to-book ratio (on the basis of past 10 years) on different

leverage variables for up to ten years. Book leverage, market leverage, cumulative change in

leverage since the pre-IPO level and the future leverage are the different types of leverage

variables. All the regression results indicate that the historical market valuation has a negative

relation with the leverage. So, the authors conclude that the market timing has persistent effects

on capital structure. The regression analysis suffers from endogeneity since profitability and size

are included in the regression as control variables.

By using direct test, Lowry (2003) also gives the evidence of market timing. She shows

that U.S. quarterly IPO volume has a negative relation with post-issue quarterly equal-weighted

returns. She states that this is due to the fact that firms issue equity when equity is overvalued

and the prices come back to fundamentals in the post-issuance period leading to low equity

returns.

Another study which uses direct test to examine market timing is Aydogan (2006). His

work is similar to Baker and Wurgler (2002) as he also examines the impact of market timing on

capital structure but his measure to capture market timing is more direct because he directly

deals with IPO hot issue markets where the chances of market timing are high. By doing so, he

makes use of market timing measure which is a function of market conditions not of firm level

characteristics as seen in the case of Baker and Wurgler (2000). The sample consists of 2200

U.S. IPOs which issued equity during 1971 – 1999. He defines hot and cold IPO markets on the

basis of IPO volume per month. He uses regression with industry-fixed effects in which the two

dependent variables are proceeds of equity and number of equity issues. He uses a dummy

variable called ‘HOT’4 as a measure of market timing which indicates if the equity is issued

during hot issue market. The other control variables are M/B, profitability (Earnings before

interest, tax, depreciation and amortization divided by total assets), size (log of sales), R&D,

tangibility of assets and lagged book leverage. The results show that HOT market has a

significant and positive effect not only on the amount of equity proceeds but also on the quantity

of equity issues. The author also shows that the effects of market timing on capital structure are

not persistent because the issuers tend to reverse (increase) the leverage just after two years of

equity issuance.

4 An IPO market is considered as ‘HOT’ if it is characterized by high volume of IPOs or large number of issuers

whereas it is considered as ‘COLD’ if it is characterized by low volume of IPOs or small number of issuers.

Marisetty and Subrahmanyam (2010) analyze the underpricing and long-run performance

of 2713 Indian stand-alone IPOs and the IPOs which are affiliated with domestic business

groups, government owned firms and foreign firms IPOs which issued equity from 1990 to 2004.

They test two hypotheses: certification hypothesis5 and tunneling hypothesis

6 with regard to

group affiliation. Certification hypothesis creates confidence among investors for family

business firms and leads to less underpricing for business group firms as compared to stand-

alone firms. Under tunneling hypothesis, the investors have less confidence in family managed

firms and this leads to greater underpricing of business group firms as compared to stand-alone

firms. The authors show that group affiliated firms have more underpricing than stand-alone

firms. Firms affiliated to foreign groups also show greater underpricing. Government owned

firms show least underpricing. Investors’ overconfidence proxied by oversubscription explains

the maximum underpricing of business group IPO firms. In this way, authors find support for

tunneling hypothesis. This result also goes in line with overreaction hypothesis. The long-run

performance of IPO firms measured by using CAARs and BHARs7 is negative for all IPOs.

Though the paper has examined the long-run performance of IPOs but it uses event-time

approach to measure the performance which has been criticized by various researchers such as

Fama (1998), Mitchell et al (2000) and Schultz (2003). The use of calendar approach to analyze

the long-run performance is advocated because the negative performance in event-time approach

tends to disappear in calendar-time approach.

5According to certification hypothesis, family managed business firms provide financial help to their member firms

through internal capital markets in case of need. 6 According to tunneling hypothesis, the controlling family firms try to expropriate cash flows from the other

member which have less control. 7 When market CARs and BHRs are subtracted from firm’s CARs and BHRs respectively then they are called

CAARs (cumulative abnormal adjusted returns) and BHARs (Buy-and-hold adjusted returns).

5.2.2 Pseudo Market Timing/Market Conditions

So far, the previous literature has claimed that managers can time the market because

they have the insider information about the true value of equity and they sell equity when it is

overvalued. Schultz (2003) is the first paper which claims that markets are efficient and

managers do not possess the market timing ability. He argues that even in the absence of

managers who possess insider information, one can find evidence which is consistent with the

market timing. He argues that firms issue more equity when they can receive high price for their

equity and this is plausible when markets on an average are rising. This does not mean that

equity of issuers is mispriced and issuers take advantage by selling overvalued equity. Instead,

rising markets show that there are more growth opportunities and firms issue more equity at high

prices in anticipation of new investment projects. Through a simulated model, he shows that

managers react to market-wide conditions by issuing equity believing that the markets are

inefficient even though markets are efficient and when managers do not possess market timing

ability. In such a scenario, equity issuance will be concentrated at higher prices ex post even

though managers cannot determine those price peaks ex ante. He refers this situation to ‘Pseudo

Market Timing’. He claims that analyzing long-run performance of IPOs or SEOs through event-

time approach can lead to false conclusions because under event study, we analyze the impact of

an event on the stock prices but here the event equity issuance (number of IPOs or SEOs) is not

an exogenous variable but rather depends on the level of market returns. In such a scenario, it is

inevitable to observe underperformance which leads us to conclude that the managers time the

market. He advocates the use of calendar-time approach over event-time approach to analyze the

long-run performance.

Lowry (2003) finds fluctuations in IPO volume across time and tries to understand

whether these variations are explained by efficient market or inefficient market factors. She

analyzed 5349 U.S. IPOs which went public during 1960 to 1996. She finds that IPO volume is

positively related to various indicators of economic growth, new business formations and more

efficient market features like low information asymmetry. She also shows that most significant

factors which contribute to the variations in IPO volume are market demand and investor

sentiment factors.

Bulter, Grullon and Weston (2005) affirm Schultz’s (2003) pseudo market timing

hypothesis and suggest an efficient market explanation for what the previous literature has called

as market timing ability of managers. Bulter et al (2005) raise their concern against Baker and

Wurglers’ (2000) results which conclude that markets are inefficient and managers can time the

market. The conclusions of Baker and Wurgler (2000) are based on in-sample results which

show that equity share in aggregate issues is negatively related to future returns. Butler et al

(2005) not only conduct in-sample (ex-post analysis) tests similar to those of Baker and Wurgler

(2000) but they also conduct out-of-sample (ex-ante analysis) tests to test aggregate pseudo

hypothesis. According to aggregate pseudo market hypothesis, we cannot conclude that

managers can time market only on the basis of predictive power of equity (negative relation

between equity share and future returns) in in-sample (ex-post) tests but we need to investigate

whether future returns are predictable in out-of-sample (ex-ante) tests. If returns are not

predictable in out-of-sample tests then it can be said that the markets are efficient. The sample of

the study is same as of Baker and Wurgler’s (2000) sample with a little extension of five years.

They show the evidence of aggregate pseudo market timing hypothesis and also claim that

aggregate pseudo market timing occurs only at large market shocks. They carry out their analysis

on the complete sample period as well as sample period excluding two major unpredictable and

structural shocks in the U.S. economy: Great Depression (1927-1931) and Oil Crisis (1973-1974)

during the same time period identified by Baker and Wurgler (2000). Equity share has a strong

negative relation with future returns in regression of complete time period which includes both

the shocks whereas the significant relation between equity share and future returns disappears in

the regression which excludes both the shocks. The authors state that their results cast doubt on

managerial market timing ability shown by Baker and Wurgler (2000) because the in-sample

predictive power of equity share comes from the two big economic shocks which are

unpredictable. Overall results are consistent with the aggregate pseudo market timing hypothesis.

Wagner (2007) examines the role of market timing in equity issues by analyzing a sample

of 2400 IPOs and 5300 SEOs during 1970 to 2005. The study is similar to Baker and Wurgler

(2002) as its study mainly addresses two questions: Are equity issuances driven by market

timing; and does market timing has persistent effect on capital structure? However, the author

does not use market-to-book ratio, he uses a more direct measure given by Aydogan (2006) to

capture market timing. The study examines market timing in hot market issue markets vs. cold

issue markets. The author also uses four ex ante characteristics of firms which reflect the

opportunities of firms to time market and are: valuation uncertainty, financial constraints, price

momentum and information content in stock prices. IPO and SEO proceeds are regressed on

these four variables, hot issue dummy and other control variables. The regression results show

that the firms which have opportunities to issue equity to take advantage of favorable conditions

to issue equity. However, the performance of issuing firms measured in calendar-time is similar

to the performance of match firms. Also, the effects of market timing on capital structure are not

persistent in the sense the firms reverse their leverage immediately after the equity issuance. In

nutshell, the results support pseudo market timing explanation of equity issuances.

Chan, Ikerberry and Lee (2007) test Shultz’s (2003) pseudo market hypothesis in the

context of share repurchases in order to examine whether managers time the market while

repurchasing equity. The motivation of the study comes from the fact that previous researchers

have shown positive abnormal long-run stock performance of firms after share repurchase

activity. The authors study a sample of U.S. 5508 buyback announcements which took place

during 1980 -1996. They use Carhart’s (1997) four-factor regression to examine long-run

performance of firms repurchasing equity. The time series regression results show a positive and

significant intercept which is an indication of abnormal performance. The authors conclude that

the repurchase activity of firms is driven by managerial market timing, not by pseudo market

timing.

Gregory, Guermat and Shawawreh (2010) test behavioral market timing as against

Schultz’s (2003) pseudo market timing hypothesis in the context of UK IPOs. Their sample

consists of 2499 IPOs of London market which went public during 1975 to 2004. They find

long-run underperformance, both in event-time as well as in calendar-time approach. So, they

dismiss pseudo market hypothesis and suggest that managers time the market while launching

IPOs.

Another study to test the market timing against pseudo market hypothesis8 is done by

Ball, Chiu and Smith (2011). They claim that it is still not clear whether IPOs are driven by

market conditions or due to market timing by managers. They analyze this issue in the context

where venture capitalists exit via IPOs and acquisitions. Going public and selling the firm to

8Market conditions hypothesis and pseudo market hypothesis are used interchangeably.

another firm, are the two ways through which private investors or venture capitalists can exit

from the business. They examine a sample of 3477 IPOs and 4686 mergers and acquisitions

which took place in U.S. during 1978 to 2009. They state that market timing by managers could

be driven by market wide /sector wide opportunism (where firms take the advantage of broad

based phenomenon by predicting market or sector returns) or firm specific opportunism (where

firms need not necessarily predict market or sector returns but can predict issuers’ market or

sector adjusted returns). In either of these cases, the firm prefers IPO over M&A and experiences

decline in market or sector returns (if it is aggregate market timing) and lower market or sector

adjusted firm returns (if it is firm specific market timing). They also hypothesize that firms prefer

IPOs over M&As if market conditions lead to high capital demand, reduction in adverse

selection cost and reduction in cost of going public. This is called market conditions hypothesis.

The results based on univariate analysis of aggregate market timing and firm-specific market

timing variables are consistent with pseudo market timing with a weak evidence of market

timing for biotech sector on which the study pays the special attention. The authors also use

probit regression where dependent variable dummy equals 1 if the event is IPO and 0 if the event

is M&A. This dummy variable is regressed on aggregate market timing (market BHRs) and firm-

specific variables (underpricing and BHARs of the firm) then the results support market timing

hypothesis. However, when market conditions variables are introduced in the regression then the

market timing variables’ coefficients turn out to be insignificant. In other words, the results

become more consistent with market conditions or pseudo market hypothesis.

5.2.3 Hypotheses Development

The literature on long-run performance of IPOs and SEOs concludes that the firms

underperform relative to benchmarks (either market or match firm) in the long-run after the

equity issuance because managers time the market. Managers issue equity when their equity is

overvalued and repurchase equity when their equity is undervalued. Now, this market timing can

be aggregate or firm-specific.9 Aggregate market timing is market timing attempts by managers

due to market inefficiencies. A firm may take the advantage of industry over optimism or overall

market over optimism. Firms’ reaction to industry over optimism can be seen when that IPO

volume of a firm is positively related with its past industry market to book (Panetta et al 1998).

Firms’ reaction to overall market over optimism can be seen when equity issues are preceded by

high market returns and followed by low market returns. In other words, when IPO volume has a

positive relation with past market returns and a negative relation with post issue market returns

(Baker and Wurgler, 2002; Lowry, 2003; and Ball et al, 2011). Hence, equity issuance has a

positive relation with past industry and market returns and negative relation with post-issue

industry and market returns.

The hypotheses which are raised from the above discussion on aggregate market timing effects

on equity issuance are as follows:

H1: The relationship between past market returns (industry)10

and equity issuance (number of

IPOs/SEOs) is positive.

H2: The relationship between post-issue market returns (industry)11

and equity issuance (number

of IPOs/SEOs) is negative.

H3: The relationship between market-wide (industry)12

market-to-book ratio and equity issuance

(number of IPOs/SEOs) is positive.

9When market timing is driven by firm over optimism (Ball et al 2011) then it is called firm-specific market timing.

When issuers take the advantage of market wide or sector wide over optimism then it is called aggregate market

timing (Baker et al 2002). 10

We provide our industry results in the next chapter. 11

We provide our industry results in the next chapter. 12

We provide our industry results in the next chapter.

In addition to aggregate market timing, equity issuance can also be influenced by firm-

specific market timing. Firm specific overvaluation also leads to equity issuance. Ex-post impact

of equity issuance on issuer’s stock return reflects the firm-specific market timing by managers.

This effect can be examined in two ways: one, short-run initial returns13

and other, long-run

stock returns. Initial returns are considered as a proxy of underpricing. Higher the initial returns

are higher is the underpricing. The issuing firms which have higher first day initial returns

experience low ex post long-run stock returns relative to the match firms (Ritter, 1991; and Ritter

and Loughran, 1995). Since, long-run underperformance of issuing firms is considered as the

result of market timing by managers so higher initial returns are positively and post issue long-

run stock returns are negatively related to the market timing or equity issuance (Ball et al

2011)14

. However, recent study by Purnanandam and Swaminathan (2004) directly examines the

relationship between valuation of IPOs and its underpricing. They show that IPOs are

underpriced and at the same time overvalued as well. They also show that overvalued IPOs earn

high initial returns and low long-run stock returns. Their result makes our argument of market

timing even stronger because overvalued firms have more opportunities to time the market.

Hence, we expect initial returns to be positively related and long-run ex post stock returns to be

negatively related with equity issuance. We take post issue buy-and-hold adjusted returns

(BHAR) as the proxy of ex-post long-run stock returns.

From the above discussion of firm-specific market timing, we frame the following hypotheses:

H4: The relationship between initial returns (underpricing) of the issuer equity issuance (number

of IPOs/SEOs) is positive.

13

Initial return is difference of first day closing price and the offer price as a percentage to offer price. 14

Ball et al (2011) test firm-specific and aggregate market-timing as against pseudo market timing in the context of

venture capitalists’ exits via IPOs or M&As. The study mainly examines the question, “Does market timing affect

the exit choice of venture capitalists.

H5: The relationship between BHARs of issuers and equity issuance (number of IPOs/SEOs) is

negative.

Market timing is challenged on the ground that it is not the market timing rather it is

pseudo market timing which leads to IPO waves. According to pseudo market timing hypothesis,

IPO waves occur due to favorable market conditions. Managers simply respond to favorable

market conditions which can give appearance to market timing. Firms issue equity when they

can receive good price of their equity and that is common when economy is performing well.

Therefore, equity issues will be concentrated at peak prices ex post even if managers cannot

determine those peak prices ex ante (Schultz, 2003; and Pastor and Veronesi, 200515

). The

measurement of market conditions is also a point of concern. Past market returns can be a proxy

of market conditions to test pseudo market timing. Past market returns have a negative (positive)

relation with equity repurchases (equity issuance) (Chan et al 2007; and Gregory et al 2010).

However, dependence of equity issuance on past market returns cannot help us differentiate

between market timing and pseudo market timing. There is only a thin line of difference between

test of aggregate market timing and pseudo market timing using past market returns. The

negative relationship between number of IPOs and past market returns is an evidence of both,

market timing as well as pseudo market timing. In addition to this, if we see post-IPO market

returns are less than pre-IPO returns then we can say that IPOs or equity issuances are driven by

pseudo market timing (Ball et al 2011).

In addition to market returns, IPO waves can also be driven by high aggregate demand

for capital which in turn makes capital more expensive. High aggregate demand for capital is

15

The term ‘pseudo market timing’ is given by Schultz (2003). However, Pastor and Veronesi (2005) develop a

model which predicts that IPO waves are rational and depend on the market conditions rather than manager

opportunism or investor optimism.

another proxy of market conditions (Lowry, 2003; and Poulson and Stegemoller, 2008). Gross

Domestic Product (GDP) and S&P P/E earnings ratio are proxies of capital demand and are

expected to have a positive relation with equity issuance. One year T-Bill rate is a measure to

capture the variations in risk-free rate and to control inflation and expected to have a positive

relation with equity issuance (Ball et al, 2011).

The above discussion leads to formulation of following hypotheses to test pseudo market timing:

H6: Post-issue market returns are lower than pre-issue market returns.

H7:The relationship between each of pseudo market timing/market conditions variables – stock

market index price-earnings ratio (P/E), T-Bill rate, gross domestic investment and equity

issuance (number of IPOs/SEOs) is positive.

The significant relation of market timing variables with equity issuance is not sufficed to

conclude that managers can time the market. To conclude that managers can time the market, we

need to observe that issuing firms underperform in long-run after the equity issuance. The strong

argument which was given by researchers for the market timing by managers is the observed

long-run underperformance of IPOs and SEOs after equity issuance over a period which ranges

from one beyond five years. The decline in the stock prices after equity issuance which persists

in long-run is an indication that stock prices reach the fundamentals after the issue and were

overvalued at the time of issuance. This overvaluation is a window of opportunity for managers

to issue equity (Ritter, 1991; and Ritter and Lounghran, 1995). However, objections have been

raised to the above conclusion on the ground that the event study methodology which has been

used in the abovementioned studies to measure the long-run stock performance is considered as

flawed methodology, the validity of which has been questioned on the ground on its assumptions

(e.g. Mitchell and Stafford, 2000). Solution to this problem is to use calendar-time approach

instead of event –time approach to measure stock returns as calendar time returns are not

affected. Post-issue abnormal stock returns of US IPOs which are significantly negative in event-

time become close to zero or insignificant when calendar-time approach is used (Schultz, 2003).

The support for Schultz’s (2003) hypothesis is found in Butler et al (2005) and Wagner (2007)

but Chan et al (2007) and Gregory et al (2010) show the counter evidence. Since, the recent

literature has documented the advantages of calendar-time over event-time approach; we intend

to measure the long-run performance of IPOs and SEOs using calendar-time approach. If we

observe underperformance of IPOs and SEOs using calendar-time approach then we will

conclude that the managers can time market.

Another indication of successful market timing by managers is that long-run

underperformance is concentrated in hot issue (high volume) markets. However, cold issue

markets perform well relative to hot issue markets (Ritter, 1991; Loughran and Ritter, 1995; and

Lowry, 2003). This result is based on event-time study which has been criticized recently. There

is need to reexamine the performance of IPOs and SEOs in hot issue markets vs. cold issue

markets by using calendar approach as hot issue markets provide more windows of opportunities

to time the market. Moreover, Schultz’s (2003) simulation results show that firms which issue

equity in the periods of heavy issuance experience poor long-run stock returns ex post in

calendar-time even when abnormal returns on IPOs are zero ex ante which makes his pseudo

market timing argument stronger. Hence, we expect that long-run underperformance of hot issue

markets is more than cold issue markets.

SEOs underperform more than IPOs as Ritter (1991) shows that IPOs underperform at

the rate of 7 percent per year whereas SEOs underperform at the rate of 8 percent per year. Scope

of market timing is more in SEOs than in IPOs the percentage of secondary shares sold by SEO

firms having high market-to-book ratio is more than those sold by IPO firms having high market-

to-book ratio (Ritter, 1991). Secondary shares are the shares which are sold by insiders not by the

firm and do not bring cash to the firm. This suggests that insiders take advantage of overvalued

equity by selling their own shares (Kim and Weisbach, 2008). Having evidence that SEOs firms

have more market timing opportunities than IPOs, we expect that long-run underperformance

calculated in calendar-time of SEOs is higher than that of IPOs.

On the basis of above discussion, we frame the following hypotheses:

H8: IPOs and SEOs underperform in the long-run.

H9: The post-issue long-run underperformance of firms issuing equity in hot issue markets

(periods) is higher than that of firms issuing equity in the cold issue markets (periods).

H10: The post-issue long-run underperformance of SEO firms is higher than that of IPOs firms.

5.3 Data, Variable Definitions and Methodology

5.3.1 Data

We examine 3958 IPOs and 724 SEOs for twenty years which issued equity during the

period 1991-2009.The data on individual equity issuance for IPOs and SEOs like company

name, filing date, issue date, offer price, deal size, etc. is collected from Prime Database and

Thomsonone Database of Securities Data Corporation. The stock price data for all the firms is

collected from PROWESS database maintained by Centre for Monitoring Indian Economy

(CMIE). Data on equally-weighted COSPI index prices and industry return is also collected from

the PROWESS database. The data on macro-economic variables is collected from BUSINESS

BEACON database maintained by CMIE and EPWRF (Economic and Political Weekly Research

Foundation) maintained by Economic and Political Weekly. The remaining data on accounting

and financial variables is collected from PROWESS database. Our aim is to make the study data

as comprehensive as possible. We drop post-2009 years in our analysis as we intend to examine

the long-run performance of issuing firms’ performance for three years after the equity issuance.

Our sample is more comprehensive than any other study in India. Indian primary market

provides a perfect setting to analyze IPOs and SEOs from three different dimensions: regulatory

time regimes, ownership structure (dominance of business group affiliated firms vis-à-vis

standalone firms) and industry-wise.

The complete time period of IPOs is classified into three sub-period regimes: Regime I

i.e. 1991-1996, Regime II i.e. 1997-2002 and Regime III i.e. 2003-2009. Regime I is a post-

liberalization era immediately after the economic reforms which were initiated in India in 1991.

This time period is characterized by high growth rate of the economy, maximum number of IPOs

and presence of very few regulations in Indian IPO market. SEBI introduced regulations on

pricing of IPOs and imposed restriction on promoters holding in 1996, the impact of which is

seen in Regime II in the form of very few IPOs as compared to Regime I. So, we call this era as

regulated era. In order to encourage equity participation after the slump of IPOs in Regime II,

SEBI again introduced few changes for example, new allotment norms, book building process

etc. in 2000, the impact of which is seen after 2002. The period after 2002 is considered as

Regime III i.e. 2003-2009 and we call this regime as reformed regulated era.16

Since, SEOs are very few in number as compared to IPOs and according to their

clustering in different years, we classify whole time period of SEOs into two sub-period regimes:

Regime A i.e. 1991-1996 and Regime B i.e. 1997-2009. Regime B is post liberalization era in

16

Our classification of whole time period into three sub-period regimes is similar to the time classification of

Marisetty and Subrahmanyam (2010). They stop their time period till 2004 but we extend it to 2009.

which very few SEOs took place in India and Regime B is a combination of initial regulated and

reformed regulated regime in which maximum SEOs took place in India.

5.3.2 Variable Definitions

In this section, we describe the construction of the variables which we use in the study.

Number of equity issues is total number of IPOs and SEOs in the time period from 1991 to 2009.

Number of IPOs/SEOs are used in two ways: One, market wide number/volume of equity issues

represented by MktIPOs and MktSEOs is total number/volume of IPOs and SEOs respectively in

the overall market and two, Industry wide number/volume of equity issues represented by

IndIPOs and IndSEOs is the total number/volume of IPOs and SEOs respectively in a given

Industry. We follow the CMIE17

’s (Centre for Monitoring Indian Economy) industry sector

classification for our study. Two types of market timing variables are used: aggregate market

timing and firm-specific variables. The variables which we use to test aggregate market timing

are quarterly market (industry) BHRs and market wide (industry-wide) market-to-book ratio.

Market BHRs are represented by BHR which is the holding gain or loss of the overall stock

market in a quarter whereas Industry BHRs are represented by IndBHR which is the holding gain

or loss of each industry in a quarter. The computation of BHRs is explained in research

methodology section. We use COSPI18

index return as a proxy of market return and equal-

weighted return of all the firms in the industry as proxy for industry returns. Market/Industry

BHRs are calculated for four quarters prior to equity issuance and four quarters after the equity

issuance. MktM/B is the equally weighted average of quarterly M/B of all listed firms. Industry-

17

CMIE is an Indian database having several products which provides financial data of Indian companies and also

Indian macro-economic variables. 18

COSPI index is maintained by CMIE (Centre for Monitoring Indian Economy) as a proxy for the market and it

gives value-weighted and equal-weighted index prices. COSPI is considered as the most comprehensive index for

India.

wide M/B ratio which is represented by IndM/B is also calculated in the similar manner by taking

all the firms of each Industry. Market-wide and industry-wise M/B ratios are calculated for four

quarters prior to equity issuance. The variables which we use to test firm-specific market timing

are: initial returns (underpricing) of the issuer and post-issue buy-and-hold adjusted returns of

issuer. Initial returns which is also known as underpricing is represented by UP and is the

average of initial returns of all the firms which issues equity in a given quarter. Underpricing of a

firm is calculated as the ratio of the difference between the first trading day closing price and the

offer price to its offer price. Post-Issue buy-and-hold adjusted returns of issuer which are

represented by BHAR are calculated for the market as well as industry. BHAR of a firm is

computed as BHR of the firm in a particular quarter minus BHR of the market/industry of the

same quarter. COSPI index is taken as proxy of market return and equally-weighted average

return of all the firms in the industry. The variables reflecting market conditions which we use in

the study are: BSE Sensex P/E Ratio, Gross Domestic Product (GDP) at constant prices and one

month T-Bill rate. These variables are measured quarterly. Sensex P/E ratio reflects changes in

the stock market before equity issuance. GDP is an indicator of growth of the economy. T-Bill

rate which is also considered as risk free rate is considered to control inflationary conditions. All

these variables are taken at quarterly frequency. The proxy of risk-free rate which is used in the

study is monthly one-year Treasury-Bill rate.

5.3.3 Research Methodology

(A) Assessing Market Timing and Pseudo Market Timing

The evidence of market timing can be seen in following ways: (1) Negative BHARs of

the issuers in post-equity issuance period; (2) High degree of underpricing; (3) Market returns in

pre-equity issuance period are more than market returns in post-equity issuance period; (4)

Positive relation of IPO/SEO activity with pre-issue market returns; (5) Negative relation of

IPO/SEO activity with post-issue market returns; (6) Positive relation of IPO/SEO activity with

underpricing; (7) Positive relation of IPO/SEO activity with BHARs; and (8) Long-run

underperformance of equity issuers in post-issue period.

The evidence of pseudo market timing can be seen in following ways: (1) Positive relation of

IPO/SEO activity with market conditions variables; (2) No long-run underperformance of equity

issuers in post-issue period.

We follow Ball et al’s (2011) methodology19

to analyze the impact of market timing and market

conditions on IPOs and SEOs.

1.1 Univariate Analysis of Aggregate Market Timing

First of all, we examine the relationship of equity issuance activity and market returns.

This is the first and basic test of aggregate market timing. Decline in the market returns from pre-

issue to post-issue period reflect the aggregate market timing attempt of firms. We compare pre-

issue and post-issue mean buy-and-hold return on equal-weighted and from COSPI20

. We

compute market return as cumulative daily returns over quarter one, quarter two, quarter three

and quarter four before and after the issuance. We test for the difference of means of market

returns for Qtr -3-4 vs. Qtr +3+4, Qtr-4 vs. Qtr+4, Qtr-3 vs. Qtr+3, Qtr -2 vs. Qtr +2 and Qtr -1

vs. Qtr +1. We use the difference of mean test (pair-wise t-test) for IPO and SEO firms

separately.

BHR for the market is calculated in the following way:

19

Ball et al (2011) test market timing against pseudo market timing hypothesis to analyze the IPO and M&A exit

choices of venture backed companies and to understand whether these choices are driven by market timing or

market conditions. 20

COSPI index is maintained by CMIE (Centre for Monitoring Indian Economy) as a proxy for the market and it

gives value-weighted and equal-weighted index prices. COSPI is considered as the most comprehensive index for

India.

EQ1

where is the proportional daily change in the price of market index for period t and t =1 to

T.

1.2 Univariate Analysis of Firm-Specific Market Timing

Under this, we carry out univariate analysis for two firm-specific market timing variables

which are BHARs and underpricing of the issuing firm. Quarterly BHARs are computed for four

quarters after equity issuance which are Qtr+1, Qtr+2, Qtr+3 and Qtr+4 and also cumulative

BHARs for 3rd

and 4th

quarter i.e. Qtr+3+4. We use t-test to examine if quarterly BHARs are

significantly different from 0. The negative BHARs are evidence of firm-specific market timing.

We also test if the average underpricing of IPOs/SEOs is different from 0. Underpricing of

IPOs/SEOs is also an evidence of firm-specific market timing. Both the tests are carried out on

overall market as well as for each industry.

BHAR for the firm i is computed in the following way:

2

where is the proportional daily change in the price of security of firm i for period t and t=1

to T.

1.3 Regression Analysis to examine the differences in market timing in different time

regimes

In order to examine how market timing differs from one regulatory regime to other

regulatory regimes, we use regression in which the dependent variable is market timing variable

and dummy variables representing different time regimes. We estimate following regression

equation to differentiate market timing in three regulatory regimes:

EQ3

The above regression is estimated three times for three market timing variables. In this way,

Y=Underpricing/BHARs/Difference between pre-issue market BHRs and post-issue market

BHRs. The regression makes use of two dummy variables:TD2 and TD3. TD2 take the value 1 if

IPO is issued in regime II i.e. 1997-2002 and 0 otherwise. Similarly, TD3 takes the value 1 if

IPO is issued in regime III i.e. 2003-2009 and 0 otherwise. Regime I (IPOs belonging to the

period 1991-1996) is used as reference category. Intercept in the regression captures the value of

Y for Regime I. The model is estimated in a similar way for SEOs with one regime dummy for

1997-2009.

1.4 Multivariate Analysis: Effect of market timing and pseudo market timing on equity

issuance

In this section, we directly examine the impact of market timing and pseudo market

timing on number of equity issues by using the time series regressions. We run time series

regressions at aggregate level in the following way:

Market Level Time Series Analysis

The impact of aggregate market timing, firm-specific market timing and pseudo market

timing (market conditions) variables on equity issuance at market level is analyzed by estimating

following time series regression model:

In the EQ4, t represents a quarter. We run EQ4 for IPOs and SEOs independently. In EQ4,

is the market-wide number of equity issues (IPOs/SEOs) in each quarter for the

complete sample period.BHR and MktM/B are aggregate market timing variables, UP and BHAR

are firm-specific market timing variables and GDP, P/E and TBill are pseudo market timing or

market conditions variables. is pre-issue buy-and-hold market returns for each quarter

from t-4 to t-1 and is post-issue buy-and-hold market returns for each quarter from t+1

to t+4. We use equally-weighted COSPI index returns as a proxy for market returns.

is the market wide market-to-book ratio for the prior quarter i.e. t-1. is the

underpricing/average initial returns of all the firms which issued equity in the quarter prior to

equity issuance quarter. is the post-issue market adjusted buy-and-hold returns of the

issuers for each quarter from t+1 to t+4. is the natural log of GDP at constant prices

for the quarter prior to the equity issuance quarter. is the price earnings ratio of BSE

Sensex for quarter t-1. is the is the one-month T-Bill rate for the quarter prior to the

equity issuance quarter.

(B) Long-run Performance of IPOs and SEOs using calendar-time approach

In this section, we examine the long-run performance of IPOs and SEOs. The observation

of long-run underperformance (negative abnormal stock returns) following equity issuance of

IPO firms by Ritter (1991) has been confirmed by various other researchers21

in the context of

IPOs and SEOs in different countries. This negative long-run performance after equity issuance

has been widely interpreted as market timing attempts by managers. However, recent

studies22

challenge this interpretation on the ground of the validity of the assumptions of event-

21

The studies which have reported negative long-run performance of U.S. firms issuing equity are Ritter (1991),

Ritter and Loughran (1995), Spiess and Affleck-Graves (1995) and Eberhart and Siddique (2002). Schultz (2003)

presents an overview of studies on long-run underperformance following equity issues in various other countries.

22Fama (1998) reviews literature on various corporate events like SEOs, share repurchase, stock splits, exchange

listings, dividend announcements, mergers, etc, which have used event time methodology to measure abnormal

long-run stock performance around these events. Mitchell and Stafford (2000) examine long-run abnormal stock

performance around various corporate like SEOs, share repurchase, and mergers by using event-time and calendar-

time approach and show that calendar-time approach has more power to detect abnormal performance as compared

to event-time approach. Both the authors advocate the use of calendar-time approach to measure abnormal

time approach and Schultz’s (2003) pseudo market timing hypothesis which reflects market

conditions. These studies advocate the use of calendar-time approach to measure the abnormal

performance and have shown that use of calendar-time approach tends to reduce the

underperformance and in some cases, the underperformance disappears. The difference between

event-time and calendar-time approach is event-time approach weights offerings (events) equally

whereas in calendar time approach, months are weighted equally even though offerings or events

cluster in time. In other words, event-time approach is a technique of investing equal amount in

each offering whereas calendar-time approach is a technique of investing equal amount in each

IPO month. Taking into consideration the shortcomings of event-time approach, we use

calendar-time approach to examine the long-run performance of IPOs/SEOs. We measure the

abnormal performance of IPO and SEO firms following equity issuance by using Carhart (1997)

four-factor model given (which is a combination of Capital Asset Pricing Model (CAPM) and

Three-Factor Model given by Fama and French (1993) given as under:

In EQ5, is the monthly portfolio returns calculated for the month t and is the one year

risk-free rate.23 is the market risk premium, where is the market return for the

month t, which is COSPI index return in this case.24 is the monthly return on the portfolio

of small stocks minus monthly return on the portfolio of large stocks. is the monthly

return on the portfolio of high book-to-market minus the monthly return on the portfolio of low

book-to-market returns. The forth factor added by Carhart (1997), is the momentum

performance. They show that the event time methodology is extensively flawed because it is based on the

assumption of independence of multiyear abnormal returns of event firms. This inflates the abnormal performance in

event time by about 4 times. Bravet al (2000) also show less underperformance of IPOs and SEOs in calendar-time

as compared to event-time approach. 23

We use one-year T-Bill rate as a proxy of risk-free rate. 24

Here, we use value-weighted COSPI index return as a proxy of market return.

factor which is returns on the portfolio of high momentum stocks (high past returns i.e. winners)

minus returns on the portfolio of low momentum stocks (low past returns i.e. losers). Momentum

is computed on the basis of previous one year returns. The intercept α is a measure of abnormal

performance. In case of no abnormal performance α should be zero. A positive α shows positive

abnormal performance whereas a negative α shows negative abnormal performance. We

follow the standard procedure of construction of all the factors given in Fama and French (1993)

and Jegadeesh and Titman (1993).

5.4 Empirical Results and Discussion

Empirical analysis is carried out in four stages: First, we use univariate analysis to test

firm-specific market timing and aggregate market timing. Negative BHARs and underpricing are

the evidence of firm-specific market timing in univariate analysis. Also, positive difference

between pre-issue market BHRs and post-issue market BHRs is an evidence of aggregate market

timing in univariate analysis. We use t-test to analyze if average BHARs of IPOs/SEOs in four

quarters subsequent to equity issuance and average underpricing are significantly different from

zero. We also use difference of mean test to test if pre-issue market BHRs are greater than post-

issue market BHRs. We carry out this analysis at aggregate level as well as for different

regulatory regimes: Regime I, Regime II and Regime III for IPOs and Regime A and Regime B

for SEOs. Second, we use regression to find out the differences of firm-specific market timing

and aggregate market timing in different regulatory regimes. Third, we use regression to analyze

the impact of firm-specific market timing, aggregate market timing and pseudo market timing on

the IPO/SEO activity of firms. Fourth, we examine the long-run performance of IPOs/SEOs for a

period of three years after the equity issuance by using calendar-time approach.

Table 5.1: Descriptive Statistics on aggregate and quarterly IPOs and SEOs

Aggregate Quarterly

Variable Name Mean Median Std dev

No. of IPOs/SEOs Mean Median Std dev

No. of Quarters

Panel A: IPOs UP 0.78 0.21 4.43 3958 1.01 0.52 1.73 85

MBHR(Q-1) 0.09 0.02 0.26 3181

0.07 0.03 0.27 85

MBHR(Q-2) 0.12 0.06 0.26 3181

0.08 0.07 0.23 85

MBHR(Q-3) 0.14 0.11 0.25 3181

0.09 0.07 0.23 75

MBHR(Q-4) 0.16 0.12 0.30 3181

0.10 0.05 0.24 85

MBHR(Q+1) 0.05 -0.02 0.23 3181

0.05 0.02 0.26 85

MBHR(Q+2) 0.04 -0.04 0.22 3181

0.03 -0.02 0.20 85

MBHR(Q+3) 0.01 -0.04 0.20 3181

0.04 -0.01 0.22 85

MBHR(Q+4) -0.01 -0.06 0.20 3181

0.03 0.01 0.21 85

BHAR(Q+1) -0.22 -0.22 1.17 3958

-0.19 -0.15 0.96 85

BHAR(Q+2) -0.19 -0.19 1.07 3958

-0.24 -0.1 0.44 85

BHAR(Q+3) -0.12 -0.2 3.34 3958

-0.24 -0.13 0.45 85

BHAR(Q+4) -0.19 -0.2 1.84 3958

-0.31 -0.15 0.46 85

MktM/B

2.63 2.56 1.08 84

GDP (in billion)

5450.872 4778.70 2451.851 85

P/E

21.48 19.22 8.97 81

Tbill

0.08 0.08 0.02 80

Panel B: SEOs

UP 4.82 0.07 18.8 724

4.68 1.86 7.09 69

MBHR(Q-1) 0.11 0.1 0.28 723

0.11 0.05 0.25 69

MBHR(Q-2) 0.14 0.12 0.31 722

0.12 0.07 0.29 68

MBHR(Q-3) 0.11 0.08 0.32 722

0.12 0.06 0.31 68

MBHR(Q-4) 0.07 0.02 0.27 722

0.12 0.07 0.23 68

MBHR(Q+1) 0.09 0.09 0.27 723

0.08 0.05 0.22 69

MBHR(Q+2) 0.09 0.08 0.23 723

0.1 0.07 0.22 69

MBHR(Q+3) 0.1 0.09 0.25 723

0.07 0.01 0.21 69

MBHR(Q+4) 0.1 0.1 0.21 723

0.11 0.07 0.23 69

BHAR(Q+1) -0.09 -0.12 0.33 718

-0.14 -0.09 0.19 65

BHAR(Q+2) -0.03 -0.06 0.29 718

-0.07 -0.06 0.24 65

BHAR(Q+3) -0.05 -0.09 0.3 718

-0.07 -0.06 0.15 65

BHAR(Q+4) -0.04 -0.07 0.32 718

-0.04 -0.03 0.22 65

MktM/B

2.72 2.63 1.12 69

GDP (in billion)

5899.582 5326.43 2494.145 69

P/E

22.09 19.12 9.4 68

Tbill 0.08 0.07 0.02 68

This table reports the descriptive statistics of IPOs and SEOs at aggregate level and quarterly level. Panel A and Panel B show descriptive statistics of IPOs and SEOs respectively during 1991-2009. UP is the underpricing or average initial return which is the difference of first day closing price and the offer price as a percentage to offer price. MBHRs are market buy-and-hold returns and are calculated using equally-

weighted COSPI index over sixty trading days for four quarters both before and after the issuance of IPO. BHAR is the average buy-and-hold market adjusted returns of issuers calculated over sixty trading days after the IPO is issued beginning with Q+1 through Qtr+4. MktM/B is the quarterly market-wide market-to-book ratio. GDP is quarterly gross domestic product at constant prices, P/E is the quarterly price-to-earnings ratio of BSE Sensex and Tbill is the monthly T-bill rate at the end of each quarter.

5.4.1 Descriptive Statistics

Table 5.1 presents the descriptive statistics of 3958 IPOs and 724 SEOs which we

examine in our study and were issued in India during 1991-2009. We provide descriptive

statistics on both aggregate and quarterly IPOs and SEOs. Panel A shows mean, median,

standard deviation and number of observations for IPOs and Panel B shows mean, median,

standard deviation and number of observations for SEOs. We find that average underpricing at

aggregate level in case of IPOs is 78% and in case of SEOs it is 482%. However, average

quarterly underpricing in case of IPOs is 101 % and in case of SEOs it is 468%. Thus, we can

conclude that underpricing of SEOs is more than that of IPOs. We also find that average pre-

issue market returns are more than the average post-issue returns and buy-and-hold adjusted

returns of the issuers are negative. Average market-wide market-to-book (MktM/B) ratio is also

greater than 1. This is an evidence of aggregate and firm-specific market and aggregate market

timing (we also provide the statistical evidence of firm-specific market timing and aggregate

market timing in subsequent sections). Gross Domestic Product (GDP), price-earnings ratio

(P/E) of BSE Sensex and T-Bill rate (Tbill) are market conditions variables and are only

computed quarterly. Since, we estimate quarterly regressions for our multivariate analysis so we

use only quarterly market conditions variables.

5.4.2 Univariate analysis of firm-specific market timing and aggregate market timing

We measure underpricing as the average of initial returns of all IPO/SEO firms. BHARs

are computed as the average buy-and-hold market adjusted returns of issuers calculated over

sixty trading days after the IPO/SEO is issued beginning with Qtr+1 through Qtr+4 and also for

Qtr+3+4 which the combined return of quarter 3 and 4. Market BHRs are calculated using equal-

weighted COSPI index over sixty trading days for four quarters both before and after the

issuance of IPO/SEO and also cumulative return of third and fourth quarter (i.e. Qtr-3-4, Qtr-4,

Qtr-3, Qtr-2, Qtr-1, Qtr+1, Qtr+2, Qtr+3, Qtr+4 and Qtr+3+4).

Table 5.2 reports the univariate results of market timing of IPOs for whole time period

and three regulatory regimes. Panel A of the table shows that both firm-specific variables are

significant in whole time period. Average underpricing of all IPOs is 16.14 % and BHARs for all

four quarters are significant and negative during 1991-2009. Panel B shows that the difference of

pre-issue market BHRs and post-issue market BHRs are positive which indicates that pre-issue

market BHRs are higher than post-issue market BHRs during 1991-2009. This proves the

presence of aggregate market timing. The results on complete time period give the evidence of

firm-specific and aggregate market timing. However, if we examine the three regulatory regimes,

underpricing is significant only in Regime I but not in Regime II and Regime III. BHARs are

significantly negative in all four quarters in Regime I and Regime III but not in Regime II.

Difference of pre-issue market and post-issue market BHRs is significant and positive in all four

quarters in Regime I and Regime III but not in Regime II. With this, we can conclude that

Regime I shows the strong evidence of market timing followed by Regime III and then Regime

II.

Table 5.3 presents the univariate results of market timing of SEOs for whole time period

Table 5.2: Univariate Analysis of market timing of IPOs in different regulatory regimes

Time Period

No. of IPOs

Underpricing Qtr + 1 Qtr + 2 Qtr + 3 Qtr + 4 Qtr + 3+4

Panel A BHARs

1991-2009 3958 0.1614

-0.216 -0.186 -0.122 -0.187 -0.192

(11.47)***

(-11.57)*** (-10.98)*** (-2.31)** (-6.37)*** (-3.1)***

1991-1996 3300 0.1935

-0.244 -0.200 -0.121 -0.190 -0.181

(13.47)***

(-11.84)*** (-10.00)*** (-1.90)* (-5.45)*** (-2.44)**

1997-2002 183 -0.0605

0.179 -0.146 -0.185 -0.248 -0.360

(-0.62)

(1.4) (-3.07)*** (-4.2)*** (-5.33)*** (-5.65)***

2003-2009 475 0.024

-0.174 -0.110 -0.112 -0.139 -0.202

(0.50) (-5.06)*** (-5.45)*** (-5.59)*** (-6.87)*** (-6.63)***

Panel B Pre minus Post-Issue Market Buy-and-Hold Returns

1991-2009 3181

0.038 0.083 0.132 0.167 0.299

(7.00)*** (14.45)*** (20.74)*** (25.12)*** (30.13)***

1991-1996 2587

0.035 0.086 0.144 0.180 0.324

(5.77)*** (13.16)*** (20.5)*** (24.42)*** (29.39)***

1997-2002 160

0.053 0.060 0.080 0.053 0.132

(2.37)** (2.47)** (2.84)*** (1.46) (2.56)***

2003-2009 434

0.051 0.076 0.076 0.137 0.212

(3.44)*** (5.55)*** (4.45)*** (8.28)*** (8.9)***

The IPO sample includes 3958 IPOs issued in India over the time period from 1991 to 2009. The whole time period is classified in three sub-period regimes: 1991-1996 (post-liberalization era in which maximum number of IPOs was issued); 1997-2002 (regulated era in which restrictions were introduced in order to tighten the pricing of IPOs); and 2003-2009 (post-regulated era and the period of world-wide crisis). This table reports the results of univariate tests of underpricing, buy-and-hold market adjusted returns (BHARs) of the issuer for four quarters after the IPO and buy-and-hold returns of the market (BHRs) for four quarter before and after the IPO in three time regimes. Underpricing is the average initial return which is the difference of first day closing price and the offer price as a percentage to offer price. BHARs are the average buy-and-hold market adjusted returns of issuers calculated over sixty trading days after the IPO is issued beginning with Qtr+1 through Qtr+4. Qtr+3+4 show the combined return of quarter 3 and 4. Market BHRs are calculated using equal-weighted COSPI index over sixty trading days for four quarters both before and after the issuance of IPO. Panel A shows the test of significant means of average underpricing and average BHARs for whole time period and three sub-period regimes. Panel B shows the difference of mean test of four quarters prior to issuance of an IPO and four quarters after the issuance of and IPO. t-values are given in the parentheses. ***,** and * indicate significance at 1%, 5% and 10% respectively.

and two regulatory regimes. Panel A and Panel C show results of firm-specific market timing

variables and Panel B shows the results of aggregate market timing variables. The results show

clear evidence of firm-specific market timing for whole time period i.e. from 1991-2009but the

evidence of aggregate market timing is weak. However, if we examine two regulatory regimes

independently, the table shows that firm-specific market timing is strong in Regime B as

compared to Regime A and aggregate market timing is strong in Regime A and there is weak

Table 5.3: Univariate Analysis of market timing of SEOs in different regulatory regimes

Time Period

No. of SEOs

Qtr + 1 Qtr + 2 Qtr + 3 Qtr + 4 Qtr + 3+4

Panel A BHARs

1991-2009 718

-0.088 -0.027 -0.053 -0.036 -0.089

(-7.22)*** (-2.43)** (-4.69)*** (-2.98)*** (-5.23)***

1991-1996 96

-0.272 -0.110 -0.072 -0.008 -0.080

(-10.1)*** (-4.17)*** (-3.5)*** (-0.18) (-1.84)

1997-2009 622

-0.059 -0.014 -0.050 -0.040 -0.091 (-4.55)*** (-1.15) (-3.96)*** (-3.3)*** (-4.9)***

Panel B Pre minus Post-Issue Market Buy-and-Hold Returns

1991-2009 723

0.0155 0.0506 0.0141 -0.0306 -0.0166

(1.21) (3.44)*** (0.82) (-2.45)** (-0.76)

1991-1996 98

0.0535 0.1379 0.2408 0.1460 0.3868

(1.78)* (4.07)*** (6.62)*** (4.48)*** (7.41)***

1997-2009 625

0.0096 0.0371 -0.0211 -0.0580 -0.0792

(0.68) (2.3)** (-1.14) (-4.4)*** (-3.45)*** Panel C Underpricing

1991-2009 724

0.3154

(5.8)***

1991-1996 99

0.7741

(6.06)***

1997-2009 625

0.2427 (4.1)***

The SEO sample includes 724 SEOs issued in India over the time period from 1991 to 2009. The whole time period is classified in two sub-period regimes: 1991-1996 (post-liberalization era in which SEOs were not prominent); 1997-2009 (era in which most of the SEOs took place). This table reports the results of univariate tests of buy-and-hold market adjusted returns (BHARs) of the issuer for four quarters after the SEO, buy-and-hold returns of the market (BHRs) for four quarter before and after the SEO and underpricing in two time regimes. BHARs are the average buy-and-hold market adjusted returns of issuers calculated over sixty trading days after the SEO is issued beginning with Qtr+1 through Qtr+4. Qtr+3+4 shows the combined return of quarter 3 and 4. Market BHRs are calculated using equal-weighted COSPI index over sixty trading days for four quarters both before and after the issuance of SEO. Underpricing is the average initial return which is the difference of first day closing price and the offer price as a percentage to offer price. Panel A shows the test of significant means of average underpricing and average BHARs for whole time period and two sub-period regimes. Panel B shows the difference of mean test of four quarters prior to issuance of an SEO and four quarters after the issuance of and SEO. t-values are given in the parentheses. ***,** and * indicate significance at 1%, 5% and 10% respectively.

evidence of aggregate market timing in Regime B as we find positive and significant difference

of pre-issue and post-issue market returns in quarter 4. This suggests regulations introduced by

govt. in regime B created barriers of entry for SEO firms to take the advantage of overall market

valuations. However, the firms could take advantage of the firm-specific overvaluation.

5.4.3 Analyzing the differences in market timing of IPOs and SEOs in different regulatory

regimes

In this section, we examine how market timing differs from one regime to another

regime. We examine these differences by using regression in which the dependent variable is

market timing variable and independent variables are dummy variables which represent time

regimes, ownership types and different industries. We use three regression models: one for

regulatory time regime classification, second for ownership type classification and third for

industry classification. Each regression is estimated for each market timing variable. Since, we

have two firm-specific market timing variables and one aggregate market timing variable, we run

3×3=9 regressions for IPOs and 3×3=9 regressions for SEOs (The tables of this section are given

in the Appendix).

Table 5.4 reports results of regression EQ3 for IPOs. The results show that there is no

difference in BHARs of all three regulatory regimes which indicates that IPOs in all regimes

experience negative BHARs. However, underpricing of regime II and regime III is significantly

different from the underpricing of regime I. Underpricing in Regime I is the highest, followed by

underpricing in Regime III and underpricing in Regime II is the lowest. Similarly, evidence of

aggregate market timing is the strongest in Regime I. The difference of pre-issue minus post-

issue market BHRs is maximum in Regime I, minimum in Regime II and moderate in Regime

III. These results support our univariate analysis of market timing of IPOs that we find strong

evidence of market timing in Regime I, weak evidence of market timing in Regime II and

moderate evidence of market timing in Regime III.

Table 5.5 reports regression results of EQ4 for SEOs. For SEOs, we have one regulatory

regime dummy: TD2. TD2 represents time period 1997-2009. Intercept in EQ4 represents value

Table 5.4: Results of dummy regression of IPOs on the basis of regulatory regimes

Dependent Variable α TD2 TD3 R2 F-Value N

Panel A

BHAR1 -0.2439 0.4228 0.0701 0.0059 11.64 3958

(-11.96)*** (4.75)*** (1.22)

BHAR2 -0.1997 0.0540 0.0902 0.0008 1.62 3958

(-10.74)*** (0.67) (1.72)

BHAR3 -0.1205 -0.0642 0.0085 0.0000 0.03 3958

(-2.07)*** (-0.25) (0.05)

BHAR4 -0.1903 -0.0577 0.0509 0.0001 0.26 3958

(-5.93)*** (-0.41) (0.56)

BHAR3+4 -0.1807 -0.1791 -0.0209 0.0001 0.19 3958

(-2.67)*** (-0.61) (-0.11)

Panel B

Pre-Post_Qtr+1 0.0385 0.0146 0.0129 0.0003 0.66 3958

(8.12)*** (0.7) (0.97)

Pre-Post_Qtr+2 0.0711 -0.0167 0.0003 0.0001 0.29 3958

(14)*** (-0.75) (0.02)

Pre-Post_Qtr+3 0.1117 -0.0421 -0.0426 0.0024 4.79 3958

(19.96)*** (-1.72)* (-2.7)***

Pre-Post_Qtr+4 0.1457 -0.0967 -0.0189 0.0037 7.31 3958

(24.55)*** (-3.73)*** (-1.13)

Pre-Post_Qtr+3+4 0.3239 -0.1914 -0.1116 0.0093 14.98 3181

(29.56)*** (-4.22)*** (-3.86)***

Panel C

Underpricing 0.19349 -0.25396 -0.16948 0.0069 13.73 3958

(12.6)*** (-3.79)*** (-3.91)***

This table reports the regression results of the following regression equation:

Panel A reports regression results of the above equation when the dependent variable is quarterly BHAR. Panel B reports regression results of the above equation when the dependent variable is difference between quarterly pre-issue and post-issue market BHR. Panel C reports regression results of the above equation when the dependent variable is underpricing. Underpricing is the average initial return which is the difference of first day closing price and the offer price as a percentage to offer price. BHARs are the average buy-and-hold market adjusted returns of issuers calculated over sixty trading days after the IPO is issued beginning with Qtr+1 through Qtr+4. Qtr+3+4 show the combined return of quarter 3 and 4. Market BHRs are calculated using equal-weighted COSPI index over sixty trading days for four quarters both before and after the issuance of IPO. The above regression makes use of two dummy variables: TD2 and TD3. TD2 take the value 1 if IPO is issued in regime 2 i.e. 1997-2002 and 0 otherwise. Similarly, TD3 takes the value 1 if IPO is issued in regime 3 i.e. 2003-2009 and 0 otherwise. Intercept takes the value of dependent variable when IPO belongs to regime 1 i.e. 1991-1996. t-values are given in the parentheses. ***,** and * indicate significance at 1%, 5% and 10% respectively.

of Y for SEOs belonging to regime A i.e. from 1991-1996. Panel A and Panel C show the strong

evidence of firm-specific market timing as BHARs are less in Regime A as compared to Regime

B and underpricing is more in Regime A as compared to Regime B. Panel B shows strong

Table 5.5: Results of dummy regression of SEOs on the basis of regulatory regimes

Dependent Variable α TD2 R2 F-Value N Panel A

BHAR1 -0.2718 0.2125 0.0494 37.18 718

(-8.38)*** (6.1)***

BHAR2 -0.1098 0.0961 0.0124 9.03 718

(-3.69)*** (3.00)***

BHAR3 -0.0723 0.0219 0.0006 0.43 718

(-2.33)** (0.66)

BHAR4 -0.0077 -0.0325 0.0012 0.85 718

(-0.24) (-0.92)

BHAR34 -0.0800 -0.0105 0.0001 0.04 718

(-1.72)* (-0.21)

Panel B

Pre-Post_Qtr+1 0.0535 -0.0440 0.0019 1.38 723

(1.54) (-1.17)

Pre-Post_Qtr+2 0.1379 -0.1008 0.0076 5.50 722

(3.45)*** (-2.35)**

Pre-Post_Qtr+3 0.2408 -0.2620 0.0380 28.41 722

(5.27)*** (-5.33)***

Pre-Post_Qtr+4 0.1460 -0.2040 0.0430 32.32 722

(4.37)*** (-5.69)***

Pre-Post_Qtr+3+4 0.3868 -0.4660 0.0734 57.00 722

(6.74)*** (-7.55)***

Panel C

Underpricing 0.7741 -0.5314 0.0156 11.44 724

(5.3)*** (-3.38)***

This table reports the regression results of the following regression equation:

Panel A reports regression results of the above equation when the dependent variable is quarterly BHAR. Panel B reports regression results of the above equation when the dependent variable is difference between quarterly pre-issue and post-issue market BHR. Panel C reports regression results of the above equation when the dependent variable is underpricing. Underpricing is the average initial return which is the difference of first day closing price and the offer price as a percentage to offer price. BHARs are the average buy-and-hold market adjusted returns of issuers calculated over sixty trading days after the SEO is issued beginning with Qtr+1 through Qtr+4. Qtr+3+4 show the combined return of quarter 3 and 4. Market BHRs are calculated using equal-weighted COSPI index over sixty trading days for four quarters both before and after the issuance of SEO. The above regression makes use of one dummy variable: TD2. TD2 take the value 1 if SEO is issued in regime 2 i.e. 1997-2009 and 0 otherwise. Intercept takes the value of dependent variable when SEO belongs to Regime 1 i.e. 1991-1996. t-values are given in the parentheses. ***,** and * indicate significance at 1%, 5% and 10% respectively.

evidence of aggregate market timing in Regime A but no evidence of aggregate market timing in

Regime B as difference of pre-issue and post-issue market BHRs becomes negative in Regime B

Table 5.6: Impact of market timing and market conditions on IPO activity for whole period and Regime I

Panel A All IPOs

Panel B 1991-1996 IPOs

Model I Model II Model III Model I Model II Model III

Intercept 3.162 10.458 14.009

5.61 -32.44 33.16

(2162.82)*** (103.69)*** (57.38)***

(440.55)*** (203.71)*** (23.15)***

LnGDP(Q-1)

0.722 1.013

2.99 2.81

(98.34)*** (60.76)***

(308.76)*** (24.64)***

P/E(Q-1)

0.037 0.100

0.01 0.21

(576.3)*** (639.21)***

(20.03)*** (199.1)***

Tbill(Q-1)

21.498 18.558

8.54 56.43

(460.56)*** (91.37)***

(29.16)*** (69.55)***

UP(Q-1) 1.215

0.304

1.34

0.07

(404.52)***

(21.62)***

(43.09)***

(0.11)

BHAR(Q+1) -0.113

-0.579

0.18

-0.53

(6.4)***

(79.92)***

(0.76)

(6.26)***

BHAR(Q+2) -1.523

-0.853

-0.03

-2.85

(174.25)***

(44.25)***

(0.01)

(40.56)***

BHAR(Q+3) -0.560

-0.363

-0.43

-1.30

(80.92)***

(31.54)***

(33.55)***

(122.06)***

BHAR(Q+4) -0.959

-1.112

-1.07

-1.10

(91.11)***

(141.97)***

(147.92)***

(68.86)***

MBHR(Q-1) 1.868

1.515

-0.18

-0.85

(337.47)***

(157.29)***

(0.37)

(7.82)***

MBHR(Q-2) 1.580

0.986

0.15

-0.47

(243.1)***

(81.5)***

(0.98)

(6.43)***

MBHR(Q-3) 0.211

0.432

1.26

-1.72

(4.32)**

(15.4)***

(68.7)***

(38.51)***

MBHR(Q-4) 1.157

1.005

0.61

-1.18

(178.02)***

(133.05)***

(24.21)***

(54.7)***

MBHR(Q+1) -0.435

-1.168

-1.06

-2.23

(23.98)***

(128.96)***

(46.13)***

(117.65)***

MBHR(Q+2) -0.897

-0.846

-0.70

-1.66

(66.97)***

(55.25)***

(26.09)***

(65.95)***

MBHR(Q+3) -0.300

-0.591

-1.11

-1.17

(7.61)***

(24.5)***

(28.19)***

(24.01)***

MBHR(Q+4) -2.401

-1.993

-0.85

-2.02

(488.48)***

(320.05)***

(39.22)***

(76.99)***

MktM/B(Q-1) 0.396

0.157

-0.02

1.19

(270.17)***

(12.49)***

(0.03)

(79.39)***

LL 12547.570 13047.656 14282.102

13401.11 13053.24 13687.77

Full LL -2448.311 -1993.942 -713.779

-464.68 -812.56 -178.03

N 75 79 75 23 23 23

This table reports the regression results of the following count regression model (Poisson Distribution) for IPOs:

is the market-wide number of equity issues IPOs in each quarter in the given time period. is pre-issue buy-and-hold market returns for each quarter starting from t-4 to t-1 and is post-issue buy-and-hold market returns for each quarter from t+1 to t+4. We use equal-weighted COSPI index returns as a proxy for market returns. is the market-wide market-to-book ratio for the prior quarter i.e. t-1. is the underpricing/average initial returns of all the firms which issued equity in the quarter prior to equity issuance quarter. is the post-issue market adjusted buy-and-hold returns of the issuers for the each quarter from t+1 to t+4. is the natural log of GDP at constant prices for the quarter prior to the equity issuance quarter. is the price earnings ratio of BSE Sensex for the and it calculated as P/E ratio for the quarter prior to the equity issuance quarter. is the is the one-month T-Bill rate for the quarter prior to the equity issuance quarter. Panel A reports regression results of above equation for the whole time period from 1991-2009 and Panel B reports regression results of Regime I i.e. from 1991-1996. Chi-square values are given in parentheses. ***,** and * indicate significance at 1%, 5% and 10% respectively.

in most of the quarters. These results also support our univariate results of market timing of

SEOs.

5.4.4 Impact of firm-specific market timing, aggregate market timing and pseudo market

timing on equity issuance

In this section, we directly examine the impact of firm-specific market timing, aggregate

market timing and market conditions on the IPO/SEO activity by using regression models given

in EQ4. IPO/SEO activity is measured by number of IPOs/SEOs in a given quarter. In addition to

firm-specific market timing and aggregate market timing discussed already, we use three proxies

to capture market conditions which are GDP, P/E of BSE Sensex and T-Bill rate. In a growing

economy, we expect these variables to rise reasonably. If the economy is growing then the

economy will have more growth opportunities and demand for capital will rise. In such a

scenario, in order to meet the demand of capital in the economy the firms will supply capital by

issuing equity. In this way, we expect positive relationship between IPO/SEO activity and

market conditions variables.

Table 5.7: Impact of market timing and market conditions on IPO activity for Regime II and Regime III

Panel A

1997-2002 IPOs Panel B

2003-2009 IPOs

Model I Model II Model III Model I Model II Model III

Intercept -3.16 27.57 84.07

-0.27 10.42 16.96

(1.51) (6.61)*** (3.96)**

(0.21) (3.54)* (1.32)

LnGDP(Q-1)

2.36 -6.01

-0.80 -1.08

(7.63)*** (4.25)**

(3.47)* (1.03)

P/E(Q-1)

0.07 0.06

0.12 0.27

(26.41)*** (0.38)

(48.71)*** (13.95)***

Tbill(Q-1)

48.17 113.40

11.00 15.95

(27.78)*** (5.65)**

(5.99)*** (1.97)

UP(Q-1) 0.19

-0.84

0.03

0.02

(0.43)

(0.81)

(0.02)

(0.01)

BHAR(Q+1) -0.38

-1.06

0.19

0.03

(6.22)***

(2.17)

(0.39)

(0.00)

BHAR(Q+2) -0.36

-0.94

-0.10

0.14

(0.33)

(0.99)

(0.02)

(0.03)

BHAR(Q+3) -2.62

-5.07

-0.22

-0.67

(3.27)*

(1.36)

(0.21)

(1.69)

BHAR(Q+4) -1.34

-4.88

-0.59

-1.91

(1.57)

(2.34)

(0.35)

(2.78)*

MBHR(Q-1) -0.06

-1.91

0.13

-0.40

(0.01)

(1.00)

(0.13)

(0.56)

MBHR(Q-2) 2.35

1.03

-0.22

-0.42

(8.18)***

(0.22)

(0.37)

(0.72)

MBHR(Q-3) -2.57

8.73

0.81

0.61

(1.96)

(2.18)

(7.68)***

(2.76)*

MBHR(Q-4) -1.06

0.19

0.31

1.21

(1.41)

(0.03)

(0.93)

(3.3)*

MBHR(Q+1) 0.36

-5.03

0.39

0.03

(0.06)

(1.71)

(0.78)

(0.00)

MBHR(Q+2) -1.10

-2.08

-0.27

-0.91

(0.74)

(2.38)

(0.37)

(3.21)*

MBHR(Q+3) 1.81

-7.33

-1.00

-0.94

(0.97)

(2.22)

(5.49)**

(3.9)**

MBHR(Q+4) 0.37

-4.23

0.08

-0.18

(0.21)

(2.07)

(0.03)

(0.12)

MktM/B(Q-1) 3.34

-7.73

0.83

2.08

(4.73)**

(1.63)

(29.52)***

(33.51)***

LL 279.65 253.40 286.61

612.39 593.52 621.39

Full LL -45.49 -71.74 -38.52

-74.93 -93.79 -65.92

N 21 21 21 27 27 27

This table reports the regression results of the following count regression model (Poisson Distribution) for two regimes of IPOs:

is the market-wide number of equity issues IPOs in each quarter in the given time period. is pre-issue buy-and-hold market returns for each quarter starting from t-4 to t-1 and is post-issue buy-and-hold market returns for each quarter from t+1 to t+4. We use equal-weighted COSPI index returns as a proxy for market returns. is the market-wide market-to-book ratio for the prior quarter i.e. t-1. is the underpricing/average initial returns of all the firms which issued equity in the quarter prior to equity issuance quarter. is the post-issue market adjusted buy-and-hold returns of the issuers for the each quarter from t+1 to t+4. is the natural log of GDP at constant prices for the quarter prior to the equity issuance quarter. is the price earnings ratio of BSE Sensex for the and it calculated as P/E ratio for the quarter prior to the equity issuance quarter. is the is the one-month T-Bill rate for the quarter prior to the equity issuance quarter.Panel A reports regression results of above equation for Regime II i.e. from 1997-2002 and Panel B reports regression results of Regime III i.e. from 2003-2009. Chi-square values are given in parentheses. ***,** and * indicate significance at 1%, 5% and 10% respectively.

Table 5.6 presents the regression results of EQ4 for IPOs for whole time period and

Regime I. In Table 5.6, Panel A reports results for the whole time period and Panel B reports

results of Regime I i.e. 1991-1996. Model I and Model II in whole time period results show that

firm-specific and aggregate market timing variables have significant impact on IPO activity and

these variables maintain their significance even if we include market conditions variables in

Model III. In other words, all the variables, firm-specific market timing, aggregate market timing

and pseudo market timing have significant impact on IPO activity. Thus, we can infer that IPOs

activity in India as a whole is not only the results of market timing but is also influenced by

market conditions. Now, we investigate how market timing and market conditions play role in

different regulatory regimes. The results of Regime II and Regime III are given in Table 5.7.

Model III in all the regimes show the impact of all the variables on IPO activity. In regime I, we

find strong evidence of both, market timing and market conditions. In Regime II, we find weak

evidence of pseudo market timing but no evidence of market timing. Lastly, in Regime III, we

find some evidence of market timing as well as pseudo market timing. The possible reason for

Table 5.8: Impact of market timing and market conditions on SEO activity for whole

time period

All SEOs

Model I Model II Model III

Intercept 3.02 -36.62 -30.96

(797.21)*** (689.89)*** (69.67)***

LnGDP(Q-1)

2.92 2.56

(884.38)*** (100.67)***

P/E(Q-1)

0.05 0.07

(83.11)*** (69.1)***

Tbill(Q-1)

9.94 18.09

(43.21)*** (16.11)***

UP(Q-1) 0.39

-0.06

(94.8)***

(0.97)

BHAR(Q+1) -3.95

-0.11

(260.82)***

(0.11)

BHAR(Q+2) -0.58

-0.10

(5.81)**

(0.10)

BHAR(Q+3) -0.49

-0.69

(2.56)*

(3.6)*

BHAR(Q+4) -0.22

-0.22

(0.8)

(0.63)

MBHR(Q-1) 0.54

0.29

(11.00)***

(1.34)

MBHR(Q-2) 0.16

0.43

(1.16)

(3.63)*

MBHR(Q-3) 0.74

0.34

(23.23)***

(3.04)*

MBHR(Q-4) -0.21

-0.10

(1.8)

(0.37)

MBHR(Q+1) -0.44

-0.47

(5.46)***

(3.06)*

MBHR(Q+2) -1.58

-0.51

(57.34)***

(5.35)***

MBHR(Q+3) -0.37

0.22

(3.32)**

(1.07)

MBHR(Q+4) -1.80

-0.22

(101.57)***

(0.96)

MktM/B(Q-1) 0.19

0.18

(26.71)***

(7.83)***

LL 2638.87 3417.28 2978.51

Full LL -587.07 -296.42 -247.43

N 65 68 65

This table reports the regression results of the following count regression model (Poisson Distribution) for whole time period of SEOs:

is the market-wide number of equity issues SEOs in each quarter in the given time period. is pre-issue buy-and-hold market returns for each quarter starting from t-4 to t-1 and is post-issue buy-and-hold market returns for each quarter from t+1 to t+4. We use equal-weighted COSPI index returns as a proxy for market returns. is the market-wide market-to-book ratio for the prior quarter i.e. t-1. is the underpricing/average initial returns of all the firms which issued equity in the quarter prior to equity issuance quarter. is the post-issue market adjusted buy-and-hold returns of the issuers for the each quarter from t+1 to t+4. is the natural log of GDP at constant prices for the quarter prior to the equity issuance quarter. is the rice earnings ratio of BSE Sensex for the and it calculated as P/E ratio for the quarter prior to the equity issuance quarter. is the is the one-month T-Bill rate for the quarter prior to the equity issuance quarter. Chi-square values are given in parentheses. ***,** and * indicate significance at 1%, 5% and 10% respectively.

not so strong evidence of market timing could be that regime III consists of the time of global

financial crisis i.e. 2007-2008.

Table 5.8 reports the results of EQ4 for SEOs in the whole time period. Model I and

Model II show evidence of market timing and pseudo market timing respectively. However, if

we include market timing variables along with pseudo market timing variables in Model III, we

find strong evidence of pseudo market timing and weak evidence of market timing. Table 5.9

shows the regression results of EQ4 for two regulatory regimes: Regime A and Regime B. Panel

A of Table 5.9 which presents results of Regime B shows that the regime experienced some

evidence of pseudo market timing but no evidence of market timing. On the other hand, we find

weak evidence of both, pseudo market timing and of market timing in Regime B. These results

confirm our univariate results and dummy regression results discussed before.

Table 5.9: Impact of market timing and market conditions on SEO activity for Regime A and Regime B

Panel A

1991-1996 SEOs Panel B

1997-2009 SEOs

Model I Model II Model III Model I Model II Model III

Intercept 1.54 -62.33 -119.01

0.48 -54.23 -48.46

(3.47)* (12.71)*** (1.66)

(2.96)*** (320.76)*** (37.79)***

LnGDP(Q-1)

4.95 8.92

4.32 3.95

(14.32)*** (1.64)

(371.24)*** (50.34)***

P/E(Q-1)

0.04 0.18

-0.02 -0.13

(7.54)*** (7.46)***

(3.06)* (11.17)***

Tbill(Q-1)

-3.21 33.91

-12.49 -15.00

(0.13) (1.24)

(26.13)*** (3.73)**

UP(Q-1) 0.21

0.08

0.02

0.09

(0.48)

(0.03)

(0.04)

(0.74)

BHAR(Q+1) -2.75

-3.29

2.81

1.73

(2.05)

(0.95)

(21.37)***

(7.5)***

BHAR(Q+2) -4.96

-3.90

4.90

1.94

(16.7)***

(6.71)***

(85.27)***

(10.22)***

BHAR(Q+3) 13.78

5.53

1.18

1.00

(17.95)***

(1.86)

(5.26)**

(2.98)*

BHAR(Q+4) 3.56

2.13

-0.35

-0.75

(15.9)***

(3.75)**

(0.74)

(2.39)

MBHR(Q-1) -0.23

-0.60

1.71

0.50

(0.08)

(0.37)

(38.13)***

(1.45)

MBHR(Q-2) 1.19

1.53

0.02

0.54

(1.33)

(1.65)

(0.01)

(3.18)*

MBHR(Q-3) 1.55

1.42

0.71

-0.05

(4.91)***

(2.44)

(9.54)***

(0.03)

MBHR(Q-4) -1.34

-0.87

-2.66

-0.54

(1.61)

(0.64)

(110.18)***

(1.56)

MBHR(Q+1) 0.53

-0.18

-0.37

-0.87

(0.46)

(0.02)

(1.51)

(4.58)**

MBHR(Q+2) -2.41

-0.14

1.08

0.37

(4.72)**

(0.01)

(12.62)***

(1.34)

MBHR(Q+3) 3.66

-1.25

2.53

0.87

(8.28)***

(0.14)

(57.45)***

(4.45)**

MBHR(Q+4) -7.90

-0.25

0.15

-0.01

(16)***

(0.00)

(0.24)

`(0)

MktM/B(Q-1) -0.02

-0.98

0.84

0.52

(0.01)

(4.71)**

(112.65)***

(6.83)***

LL 89.65 71.74 96.41

1314.21 1389.50 1420.18

Full LL -36.98 -54.89 -30.21

-202.91 -127.62 -96.94

N 20 20 20 39 39 39

This table reports the regression results of the following count regression model for two regimes of SEOs:

is the market-wide number of equity issues SEOs in each quarter in the given time period. is pre-issue buy-and-hold market returns for each quarter starting from t-4 to t-1 and is

post-issue buy-and-hold market returns for each quarter from t+1 to t+4. We use equal-weighted COSPI index returns as a proxy for market returns. is the market-wide market-to-book ratio for the prior quarter i.e. t-1. is the underpricing/average initial returns of all the firms which issued equity in the quarter prior to equity issuance quarter. is the post-issue market adjusted buy-and-hold returns of the issuers for the each quarter from t+1 to t+4. is the natural log of GDP at constant prices for the quarter prior to the equity issuance quarter. is the price earnings ratio of BSE Sensex for the and it calculated as P/E ratio for the quarter prior to the equity issuance quarter. is the is the one-month T-Bill rate for the quarter prior to the equity issuance quarter.Panel A reports regression results of above equation for Regime A i.e. from 1991-1996 and Panel B reports regression results of Regime B of SEOs i.e. from 1997-2009. Chi-square values are given in parentheses. ***,** and * indicate significance at 1%, 5% and 10% respectively.

5.4.5 Long-run Performance of IPOs and SEOs using calendar-time approach

Long-run performance of IPOs and SEOs are our last set of results. Examining long-run

performance of IPOs and SEOs is considered as indirect test of market timing and pseudo market

timing. In order to conclude that equity issues are driven by market timing, we should observe

long-run underperformance of firms issuing equity in the post-equity issuance period. So, if we

observe negative long-run performance of firms issuing equity then we can conclude that firms

not only raise capital due to market conditions but also issue equity in order to time the market.

Long-run performance of IPOs and SEOs is examined in a very comprehensive way. We

examine long-run performance of IPOs and SEOs for whole time period, regulatory regime-wise,

ownership category-wise and industry-wise. We also analyze long-run performance of IPOs and

SEOs in hot and cold periods. We use Carhart (1997) four-factor model given in EQ5 to examine

the long-run performance of IPOs and SEOs for three years after equity issuance. The intercept

in the regression model is proxy of abnormal performance of the firm. So, negative intercept

indicates long-run underperformance.

Table 5.10 reports factor regression of IPOs for whole time period and three regulatory

regimes. In Table 5.10, Panel A reports factor regression results for whole time period and

regime I and Panel B reports factor regression results for regime II and regime III. In whole time

period and all three regulatory regimes, the intercept is negative and significant in all the models.

Table 5.10: Calendar-time factor regressions of IPOs for whole time period and three regulatory regimes

Panel A

Full Sample 1991-1996

CAPM Fama-French Carhart

CAPM Fama-French Carhart

Intercept -0.05 -0.04 -0.04

-0.05 -0.04 -0.04

(-5.93)*** (-4.62)*** (-4.48)***

(-2.39)** (-2.03)** (-2.02)**

Rm-Rf 0.96 0.97 0.97

0.94 0.95 0.95

(82.29)*** (80.55)*** (79.32)***

(35.23)*** (34.92)*** (34.68)***

SMB

-0.05 -0.01

-0.01 0.00

(-2.66)*** (-0.80)

(-0.29) (-0.06)

HML

-0.02 -0.04

-0.06 -0.06

(-1.13) (-2.2)**

(-1.77)* (-1.45)

MOM

0.04

0.04

(1.07)

(0.38)

R2 0.9665 0.9674 0.9676

0.9324 0.9348 0.9349

F-Value 6771.07 2308.10 1732.40

1241.29 420.45 312.29

N 237 237 237

92 92 92

Panel B 1997-2002 2003-2009

CAPM Fama-French Carhart

CAPM Fama-French Carhart

Intercept -0.08 -0.06 -0.06

-0.05 -0.04 -0.03

(-3.85)*** (-2.92)*** (-2.85)***

(-4.80)*** (-3.23)*** (-2.29)**

Rm-Rf 0.90 0.93 0.93

0.94 0.96 0.99

(25.67)*** (24.93)*** (24.57)***

(45.68)*** (46.16)*** (47.48)***

SMB

-0.05 -0.05

-0.09 -0.07

(-1.65)* (-1.36)

(-2.53)** (-1.86)**

HML

-0.04 -0.04

-0.04 -0.02

(-1.40) (-1.19)

(-1.41) (-0.74)

MOM

0.04

0.22

(0.47)

(4.13)***

R2 0.8648 0.8689 0.8692

0.9521 0.955 0.9614

F-Value 658.97 223.17 166.15

2086.64 727.87 635.23

N 105 105 105 107 107 107

This table reports the regression results of the following regression for IPOs:

is the monthly portfolio returns calculated for the month t and is the one year risk-free

rate. is the market risk premium, where is the market return for the month t, which is

COSPI index return in this case. is the monthly return on the portfolio of small stocks minus monthly return on the portfolio of large stocks. is the monthly return on the portfolio of high book-to-market minus the monthly return on the portfolio of low book-to-market returns. The forth factor added by Carhart (1997), is the momentum factor which is returns on the portfolio of high momentum stocks (high past returns i.e. winners) minus returns on the portfolio of low momentum stocks (low past returns i.e. losers). Momentum is computed on the basis of previous one year returns. Panel A reports regression results of IPOs for the whole period and regime I i.e. 1991-1996 and Panel B reports regression results of IPOs for regime II 1997-2003 and regime III i.e. 2003-2009. We examine the long-run performance for three years after equity issuance. N denotes the number of months for which the performance is examined. The sum of N’s of all the regimes is not equal to N of the full sample because N in each regime includes the 24 more months after the last year of each regime. ***,** and * indicate significance at 1%, 5% and 10% respectively.

Table 5.11: Calendar-time factor regressions of SEOs for whole time period and two regulatory regimes

Panel A

Full Sample 1991-1996

CAPM Fama-French Carhart

CAPM Fama-French Carhart

Intercept -0.04 -0.04 -0.04

-0.05 -0.06 -0.06

(-2.8)*** (-2.42)** (-2.33)**

(-1.62)* (-1.95)** (-1.94)***

Rm-Rf 0.99 0.99 0.99

0.96 0.94 0.95

(45.86)*** (43.98)*** (43.25)***

(25.12)*** (24.79)*** (24.66)***

SMB

-0.02 -0.01

0.02 0.03

(-0.5) (-0.27)

(0.42) (0.60)

HML

0.00 0.00

0.12 0.12

(-0.06) (0.12)

(2.28)** (2.29)**

MOM

0.04

0.07

(0.64)

(0.47)

R2 0.8995 0.8996 0.8998

0.8776 0.8847 0.885

F-Value 2103.09 695.99 520.76

630.78 219.99 163.55

N 237 237 237 90 90 90

Panel B 1997-2009

CAPM Fama-French Carhart Intercept -0.06 -0.04 -0.03

(-3.56)*** (-2.30)** (-1.59)

Rm-Rf 0.95 0.97 0.99

(33.49)*** (35.27)*** (35.21)***

SMB

-0.10 -0.09

(-4.03)*** (-3.67)***

HML

-0.07 -0.06

(-2.62)*** (-2.21)**

MOM

0.18

(2.88)***

R2 0.8684 0.8813 0.887 F-Value 1121.74 415.92 327.58 N 172 172 172

This table reports the regression results of the following regression for SEOs:

is the monthly portfolio returns calculated for the month t and is the one year risk-free rate.

is the market risk premium, where is the market return for the month t, which is COSPI index

return in this case. is the monthly return on the portfolio of small stocks minus monthly return on the portfolio of large stocks. is the monthly return on the portfolio of high book-to-market minus the monthly return on the portfolio of low book-to-market returns. The forth factor added by Carhart (1997), is the momentum factor which is returns on the portfolio of high momentum stocks (high past returns i.e. winners) minus returns on the portfolio of low momentum stocks (low past returns i.e. losers). Momentum is computed on the basis of previous one year returns. Panel A reports regression results of SEOs for the whole period and regime A i.e. 1991-1996 and Panel B reports regression results of SEOs for regime B of SEOs i.e.1997-2009. We examine the long-run performance for three years after equity issuance. N denotes the number of months for which the performance is examined. The sum of N’s of all the regimes is not equal to N of the full sample because N in each regime includes the 24 more months after the last year of each regime. ***,** and * indicate significance at 1%, 5% and 10% respectively.

This shows underperformance of IPOs in complete time period and all regulatory regimes. This

is further evidence to our previous results which prove that IPOs in India issue equity in order to

time the market.

Results of factor regressions for whole time period and regulatory regimes of SEOs are

given in Table 5.11. In the case of SEOs also, we observe intercept to be negative and significant

in all the cases except in one model for regime B. This suggests that the evidence of strong

market timing in regime A and evidence of moderate market timing in regime B. Overall

conclude that SEOs also issue equity in order to time the market.

Table 5.12 and Table 5.13 report calendar-time factor regression results for hot and cold

IPOs and hot and cold SEOs respectively. Panel A in both the tables show the results for hot/cold

IPOs and hot/cold SEOs whereas Panel B in the both the tables show the results for the

difference between the long-run performance of hot IPOs/SEOs and cold IPOs/SEOs. The IPO

results show that IPOs under-perform in both hot and cold periods but under-performance for hot

period IPOs is higher than that of cold period IPOs. The SEO results show that only hot periods

SEOs underperform in the long-run not the cold period SEOs.

We also examine the difference in the long-run performance of IPOs and SEOs, the

results of which are given in Table 5.14. The results indicate that under-performance of IPOs is

higher than that of SEOs.

5.5 Conclusion

Despite research of many years, it is not yet found whether IPO/SEO waves are driven by market

timing or pseudo market timing/market conditions. Issuance of equity when equity is overvalued

and investors are overly-optimistic is an attempt of market timing by firms. Equity issuance

decision can also be driven by market conditions when there are more growth opportunities

Table 5.12: Calendar-time factor regressions of IPOs in hot and cold periods

Panel A

Hot IPOs Cold IPOs

CAPM Fama-French Carhart CAPM Fama-French Carhart

Intercept -0.05 -0.05 -0.05

-0.04 -0.04 -0.03

(-5.25)*** (-4.49)*** (-4.54)***

(-4.7)*** (-3.56)*** (-3.39)***

Rm-Rf 0.96 0.96 0.96

0.97 0.98 0.98

(66.03)*** (63.2)*** (61.79)***

(65.84)*** (64.67)*** (64.55)***

SMB

-0.02 -0.03

-0.05 -0.04

(-0.98) (-1.18)

(-2.38)** (-1.65)*

HML

0.00 -0.01

-0.04 -0.03

(-0.16) (-0.36)

(-2.07)** (-1.46)

MOM

-0.04

0.09

(-0.8)

(2)**

R2 0.9499 0.9501 0.9503

0.9511 0.9526 0.9534

F-Statistic 4359.58 1447.68 1084.24

4334.74 1478.97 1125.21

N 232 232 232 225 225 225

Panel B Difference between Hot IPOs and Cold IPOs

CAPM Fama-French Carhart Intercept -0.01 -0.02 -0.02

(-0.47) (-1.39)*** (-1.64)*

Rm-Rf -0.01 -0.03 -0.03

(-0.79) (-1.49)*** (-1.83)*

SMB

0.05 0.04

(2.58)*** (2.2)**

HML

0.06 0.06

(3.08)*** (2.7)***

MOM

-0.09

(-1.8)*

R2 0.0028 0.0573 0.071 F-Statistic 0.62 4.44 4.17 N 223 223 223

This table reports the regression results of the following regression for IPOs in hot and cold period:

is the monthly portfolio returns calculated for the month t and is the one year risk-free

rate. is the market risk premium, where is the market return for the month t, which is

COSPI index return in this case. is the monthly return on the portfolio of small stocks minus monthly return on the portfolio of large stocks. is the monthly return on the portfolio of high book-to-market minus the monthly return on the portfolio of low book-to-market returns. The forth factor added by Carhart (1997), is the momentum factor which is returns on the portfolio of high momentum stocks (high past returns i.e. winners) minus returns on the portfolio of low momentum stocks (low past returns i.e. losers). Momentum is computed on the basis of previous one year returns. Panel A reports regression results of IPOs belonging hot period and cold period. Panel B reports regression results of the difference between the performance of hot and cold IPOs. IPOs are classified as hot and cold on the basis of underpricing. Number of months in hot and cold issue markets are not same in case we have a month in which there is no IPO issued. ***,** and * indicate significance at 1%, 5% and 10% respectively.

Table 5.13: Calendar-time factor regressions of SEOs in hot and cold periods

Panel A

Hot SEOs Cold SEOs

CAPM Fama-French Carhart CAPM Fama-French Carhart

Intercept -0.04 -0.03 -0.03

-0.02 -0.01 -0.01

(-2.93)*** (-1.89)* (-1.79)*

(-0.59) (-0.45) (-0.42)

Rm-Rf 0.99 1.00 1.00

1.02 1.02 1.02

(48.27)*** (47.37)*** (46.65)***

(24.8)*** (23.89)*** -23.30

SMB

-0.07 -0.07

-0.02 -0.01

(-2.36)** (-1.98)**

(-0.28) (-0.2)

HML

-0.01 0.00

0.02 0.02

(-0.34) (-0.1)

(0.36) (0.4)

MOM

0.06

0.03

(0.85)

(0.2)`

R2 0.9084 0.9108 0.911

0.7313 0.7317 0.7318

F-Statistic 2329.98 792.72 594.02

615.01 203.65 152.1

N 237 237 237 228 228 228

Panel B Difference between Hot SEOs and Cold SEOs

CAPM Fama-French Carhart Intercept -0.02 -0.01 -0.01

(-0.71) (-0.51) (-0.4)

Rm-Rf -0.02 -0.01 -0.01

(-0.49) (-0.36) (-0.2)

SMB

-0.02 -0.02

(-0.49) (-0.35)

HML

-0.02 -0.01

(-0.35) (-0.22)

MOM

0.07

(0.59)

R2 0.001 0.0023 0.0038 F-Statistic 0.24 0.17 0.22 N 231 231 231

This table reports the regression results of the following regression for SEOs in hot and cold period:

is the monthly portfolio returns calculated for the month t and is the one year risk-free rate.

is the market risk premium, where is the market return for the month t, which is COSPI index

return in this case. is the monthly return on the portfolio of small stocks minus monthly return on the portfolio of large stocks. is the monthly return on the portfolio of high book-to-market minus the monthly return on the portfolio of low book-to-market returns. The forth factor added by Carhart (1997), is the momentum factor which is returns on the portfolio of high momentum stocks (high past returns i.e. winners) minus returns on the portfolio of low momentum stocks (low past returns i.e. losers). Momentum is computed on the basis of previous one year returns. Panel A reports regression results of SEOs belonging hot period and cold period. Panel B reports regression results of the difference between the performance of hot and cold SEOs. SEOs are classified as hot and cold on the basis of underpricing. Number of months in hot and cold issue markets are not same in case we have a month in which there is no SEO issued. ***,** and * indicate significance at 1%, 5% and 10% respectively.

Table 5.14: Calendar-time factor regressions for difference in the performance of IPOs and

SEOs

Difference between IPOs and SEOs

CAPM Fama-French Carhart

Intercept -0.02 -0.03 -0.03

(-1.65)* (-2.12)** (-2.02)**

Rm-Rf -0.04 -0.05 -0.05

(-2.18)** (-2.53)** (-2.36)**

SMB

0.04 0.04

(1.66)* (1.73)*

HML

0.02 0.02

(0.7)` (0.79)

MOM

0.03

(0.5)

R2 0.0197 0.0313 0.0323

F-Statistic 4.77 2.54 1.96

N 240 240 240

This table reports the regression results of the following regression for the difference between the performance of IPOs and SEOs:

is the monthly portfolio returns calculated for the

month t and is the one year risk-free rate. is

the market risk premium, where is the market return for the month t, which is COSPI index return in this case. is the monthly return on the portfolio of small stocks minus monthly return on the portfolio of large stocks. is the monthly return on the portfolio of high book-to-market minus the monthly return on the portfolio of low book-to-market returns. The forth factor added by Carhart (1997), is the momentum factor which is returns on the portfolio of high momentum stocks (high past returns i.e. winners) minus returns on the portfolio of low momentum stocks (low past returns i.e. losers). Momentum is computed on the basis of previous one year returns.***,** and * indicate significance at 1%, 5% and 10% respectively.

in a growing economy and there is more demand for capital. In this chapter, we examine market

timing hypothesis versus market conditions/pseudo market timing hypothesis in emerging

economy India. Market timing can be of two types: firm-specific market timing (when managers

take advantage of firm-specific overvaluation) and aggregate market timing (when managers

take advantage of overall high market valuations). We test market timing hypothesis against

pseudo market timing hypothesis for all IPOs and SEOs which issued in India during 1991-2009

in two ways. One, we directly analyze the impact of firm-specific market timing, aggregate

market timing and pseudo market timing on IPO/SEO activity. Two, we examine the long-run

performance of IPOs. and SEOs for a period of three years after equity issuance. The long-run

underperformance is also an evidence of market timing. In this way, by using both, direct and

indirect test, we provide comprehensive evidence on market timing and pseudo market timing.

We examine IPOs and SEOs at aggregate level and in different regulatory regimes. Since,

our time period is sufficiently large to understand the effect of structural breaks, we divide the

complete time period into three regulatory regimes for IPOs: Regime I (post-liberalized era from

1991 to 1996), Regime II (regulated era from 1997 to 2002) and Regime III (reformed regulated

era). Time period of SEOs is classified into two regimes: Regime A (post-liberalized era from

1991 to 1996) and Regime B (initial and reformed regulated era from 1997-2009).

When we examine the complete time period of IPOs, we find strong evidence of firm-

specific market timing, aggregate market timing and pseudo market timing. However, regime-

wise analysis show that there is strong evidence of market timing (firm-specific and aggregate)

and pseudo market timing in regime I; there is no evidence of market timing and weak evidence

of pseudo market timing in regime II; and there is moderate evidence of market timing and

market conditions in regime III. The reason of strong evidence of pseudo market timing in

regime I is that this is the initial phase of liberalization and economic reforms when economy

was opening up and there was higher demand for capital which led to heavy equity issuance

through IPOs. The possible reason for strong evidence of market timing in this era is that there

were very few regulations during this period and many entrepreneurs took this as an advantage

and eroded wealth of investors. Due to regulations imposed by Securities and Exchange Board of

India (SEBI) on IPO pricing and other constraints on promoters’ holding in regime II, we do not

find evidence of market timing and evidence of pseudo market timing is weak in this regime.

This also led to very few IPOs during regime II. To encourage equity participation after

observing the slump in IPO market in regime II, SEBI introduced norms for public equity

offerings in 1999 and 2000 such as norms related to allotment, norms related to financial

reporting, transparent book-building process, etc. We expect that the effect of these norms is

observable only after 1-2 years. That is why, we find moderate evidence of market timing and

pseudo market timing in regime III. As far SEOs are concerned, we find strong evidence of

pseudo market timing and moderate and weak evidence of firm-specific and aggregate market

timing respectively for the complete time period. In regime A (1991-1996), we do not find any

evidence of market timing but we find weak evidence of pseudo market timing whereas in

regime B (1997-2009), we find weak evidence of market timing and pseudo market timing. Our

results of long-run performance of IPOs and SEOs for complete time period and in different

regimes support above results. In other words, we observe long-run underperformance where we

find the evidence of market timing.

Thus at the end, we conclude that equity issuance in India is driven not only by market

timing but market conditions also play an important role in equity issuance decision of firms.

In the next chapter, we present our ownership-wise and industry-wise analysis of market

timing and pseudo market timing.