Reassessing the Long-Run Abnormal Performance of Jordanian IPOs: An Event Study Approach

The efficient market hypothesis is based on the assumption that stock prices reflect all available information relevant to a financial event. The main objective of the event study strategy is to test the reaction of stock prices in the market when announcing information related to financial disclosure data for new issues that are offered for trading or the reissue from existing companies. The study of the event is one of the important methodologies applied in empirical research in various fields of business administration and economics, and not only in the field of finance. It can be applied in testing the effectiveness of financial markets, in testing theoretical models and their suitability in predicting future returns in the areas of corporate finance, and in evaluating administrative decisions facing enterprises, such as evaluating acquisitions, mergers, and restructuring, in addition to knowing the impact of these events on the market value of the enterprise in the short and long term. However, there are many thorny issues regarding the methodology for studying the event in the long term. Many studies specializing in economic and financial affairs have focused on the most important statistical problems that researchers can face in how to deal with these issues when measuring or estimating long-term returns. Among the most important researchers in this field are Barber and Lyon (1997), Kothari and Warner (1997), and Barber and Tsai (1999).

These researchers presented extensive discussions regarding the statistical inference methods that can be followed to achieve accurate results when implementing return measurement tests. In the long run, using various methods and different methodologies and by reviewing studies that dealt with methodologies that measure financial performance in the long term after important financial events, we find that these studies provide evidence that companies are gaining an unexpected or unusual return during the long term (1–3 or 5 years). The “abnormal return” (AR) is the difference between the real return of a security or financial asset and the expected return of the financial market, represented by the financial performance of the market index. This AR results from financial events related to the financial market that affect the price of traded financial assets, and the events can include mergers, dividend announcements, company profit announcements, interest rate increases, lawsuits, and others, as all these things can contribute to the formation of this AR. However, the availability of evidence of this long-term performance (long-term ARs) contradicts the effective market hypothesis that the prices of shares or securities offered in the market will reflect the information available in the market within a short period of no more than a day or two. To solve this problem, Fama (1998) presented an explanation that the main reason for this phenomenon is attributed to the methodology used in estimating AR in the long term, and he indicated that the estimation of this return will differ when the method of calculating it changes, or it will disappear in the event of a change in the method of calculation.

This paper discusses the purposes and objectives of the event study, gives examples from previous studies, provides a detailed explanation of the application of the event study, and gives a guide to the most important procedures to be followed if this methodology is used in applied studies. A financial event study methodology has been developed to measure the impact of an unforeseen event on stock prices, determine the direction of this effect, and know the size of the perceived effects on stock prices under study.

This research addresses several key questions that can be presented as follows:

Is the measurement of the long-run AR of the study sample different when compared to the employed benchmark?

This research aims to review and summarize the most important models related to the subject of event studies and also aims to discuss the necessary steps to apply the methodology used in these studies. The research will discuss the methodology known as the BHARs methodology as well as the return CARs, and the research will also address the application of statistical inference hypotheses for both methods, indicating the strengths and weaknesses of each from a statistical perspective. The research also deals with the application of this methodology to the Jordanian market to evaluate the long-term performance of a sample of companies that were listed in the market and offered their shares for the initial public offering (IPO) in the Amman Stock Exchange during a certain period of time as an example of a financial event that has importance and impact on the performance of the stock exchange in general.

Event studies are frequently employed in finance research to examine the effects of announcements of corporate initiatives, regulatory changes, or macroeconomic shocks on stock prices. The methodological difficulties of conducting event studies in international finance research are examined in the article by Ghoul (2023). The author emphasized how researchers should pick an event, decide on the study period (short vs. long term), estimate ARs, determine statistically whether the event in question causes a reliable price reaction, and investigate the role of formal and informal institutions in explaining cross-country variations in price reactions. Also, the author offered an extension of event studies to the global fixed-income market, a significant but understudied asset class. The author concluded by outlining potential for future research as well as offering helpful advice for scholars doing cross-country finance event investigations. Their work is considered to be especially pertinent in light of the rising number of significant world events, such as the coronavirus disease 2019 (COVID-19) pandemic, Brexit, and the Paris and Trans-Pacific Partnership agreements.

Yarovaya, et al. (2021) examined the impact of human capital efficiency on equity fund performance during the COVID-19 outbreak. Data was gathered for 799 open stock funds spread across five European Union economies. The findings showed that during the COVID-19 outbreak, equity funds with higher human capital efficiency (HCE) scores outperformed their peers. The long-term performance of IPOs in various domestic and international markets has been the subject of numerous studies. For a sample of 220 American IPOs issued during the period 1960–1969, Ibbotson (1975) demonstrated a negative relationship between the initial return of the public offering and the long-term performance of the stock price. He also demonstrated that there was an overall positive performance in the first year, a negative performance in the following 3 years, and an overall positive performance in the fifth year.

Sharif, et al. (2020) investigated the link between the COVID-19 outbreak, geopolitical risk, oil prices, and economic uncertainty in the USA. They used a wavelet-based approach to demonstrate the relationship between different time periods and investment horizons. It has been discovered that COVID-19 has a greater impact on US geopolitical risk as well as economic uncertainty. In another study, French (2018) investigated the patterns of market returns predicted by the occurrence of an event. From 2007 to 2016, 64 events were observed in six countries with both developed and developing economies. Market returns are said to be influenced by investor sentiment. Furthermore, the study’s findings provide some critical information about the rate of price adjustment to obtain new information.

Another important study that examined the long-run stock performance is Dutta (2014). This paper investigates the robustness of existing long-run event study methodologies using the UK security market data. The study employs the BHAR approach and the calendar time approach method (CTA) to identify the long-term abnormal performance following corporate events. Although many recent studies consider the application of these two widely used approaches, each of the methods is a subject of criticism. This paper uses the standardized calendar time approach (SCTA) of Dutta (2014a), which presents a number of significant improvements over the traditional calendar time methodology. The simulated results reveal that all the traditional methodologies perform well in the UK stock market. Their findings further report that SCTA documents better specification and power than the conventional approaches.

The study of the financial event has evolved over time by improving statistical models that can capture the impact of the financial event on the company and on the financial market in general. Brown and Warner (1980 and 1985), Ritter (1991), Levis (1993), Aktas, et al. (2007), Binder (1998), Corrado (2010), and Kritzman (1994) were among the first to contribute to providing financial event studies and understanding its effects on the financial market (which is why these studies are considered a cornerstone in this field). Previous research has demonstrated that the event study methodology can be used effectively in a variety of areas, including studying the impact of important financial decisions (MacKinlay, 1997), accounting disclosure and its impact on the market (Johannesen and Larsen, 2016), and many other areas such as mergers and acquisitions (Adnan, et al., 2016; Sorescu, et al., 2017), and the global financial crisis (Miyajima and Yafeh, 2007).

Using various benchmarks, including the Fama–French three-factor (FF3F) model, the market value-weighted index (VWMI), and the reference index made up of the remaining companies, Espenlaup and Gregory (2000) studied 588 public offerings that were listed during the period 1985–1992 in the UK, the study sample is prepared and categorized by size (size decile model). Regardless of the benchmarks employed, the results obtained supported the existence of long-term IPOs with subpar performance and high statistical significance.

Gregory, et al. (2010) investigated 2499 UK IPOs issued between mid-1975 and the end of 2004 using precisely built benchmarks based on an equal and value weighting schemes to estimate returns and revealed a compelling proof of long-term underperformance that lasts 36-60 months after listing.

According to Ritter (1991), which examined the performance of 1526 American IPOs issued between 1975 and 1984, the indices (NASDAQ, AMEX, and NYSE) had a dismal 3-year performance of about 27.39%. The ages of listed companies and their long-term performance were significantly correlated. After 5 years, IPOs or stock offerings (SEOs) performed significantly worse than non-issuing companies, according to Loughran and Ritter’s (1995) study of a sample of 4753 US IPOs issued during the 1970–1990 period.

The average annual return for businesses conducting IPOs over the 5 years following issuance was only 5% and for SEOs was only 7% for stock offerings (SEOs). Barber and Lyon (1997) used the CAR, BHAR, and FF3F models to analyze 1798 US IPOs issued between July 1963 and December 1994. They displayed three intriguing findings: the reference return test statistics-based CAR is positively skewed, and the size of the bias grows with the magnitude of the cumulative return, also, when CAR is calculated using an equally weighted market index, this positive bias is more obvious, finally, the researchers showed that FF3F leads to negatively biased test statistics at 12- and 36-month horizons. The methods of using the matching firm (MF) as a benchmark all produce well-defined test statistics.

Previous studies summarize that event study methodology estimates price changes or wealth effects after company events during the event window of a few days around an announcement by companies. Studies of long-term events focus on the performance of stock prices over a period of 3–5 years after the event. Existing literature has found a variety of findings, including cases of long-term over- and under-performance after corporate events (see Fama, 1998 and Ritter, 2002 for comprehensive surveys).

The purpose of this study is to provide fundamental insights into the methodology’s implications for market efficiency as well as the effectiveness of event study in capturing the impact of events on stock behavior. The use of synthetic benchmarks to measure aftermarket performance and to compute cross-sectional CAR and BHAR to test the efficiency of portfolio-based (CAR. BHAR) was a major driving force behind the questions of this paper. The key findings of the aforementioned studies concur that most short-run tests are well specified, whereas the majority of the long-run tests are not well specified.

The stationarity test is a crucial step in analyzing time series data as it helps to determine if the statistical properties of the data remain constant over time. Stationary time series data is important because it allows for accurate forecasting and modeling, which is essential in various fields such as finance, economics, and engineering.

There are several tests available for checking the stationarity of time series data, such as the Augmented Dickey–Fuller (ADF) test, the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test, and the Phillips–Perron (PP) test, among others. Each of these tests has its strengths and weaknesses and should be chosen based on the nature of the data being analyzed.

It is important to note that stationarity is not a characteristic of all-time series data, and nonstationary data may require additional preprocessing before meaningful analysis can be performed. Therefore, it is essential to understand the underlying properties of the data and to perform proper testing to ensure accurate results.

Table 1 shows the results of a time series test indicating that the data is stationary, which means that the stationary time series data has a constant mean and variance and the statistical properties of the data remain stable over time. This characteristic allows for easier modeling, forecasting, and identification of patterns in the data. The table provides detailed information about the statistical properties of the data and displays the results of different statistical tests used to confirm the stationarity of the data, such as the ADF test and the PP test.

Having a table displaying these results can make it easier for analysts to identify the key characteristics of the data and make informed decisions based on that information. They can use this data to create models that accurately capture the patterns in the data and use those models to make predictions and forecast future values. In summary, a table showing the results of a time-series test indicating that the data is stationary provides valuable information that can aid in the analysis, modeling, and forecasting of the data. It can make it easier for researchers and decision-makers to understand the behavior of the data over time and use that knowledge to make informed decisions.

An analytical and descriptive method is presented by developing research hypotheses to test selected theories in this research. The descriptive study provides a view of the relevant aspects of the Jordanian IPO market. The event study methodology is chosen on the basis that it is a method of investigation to answer the research questions and to identify the important variables related to the research problem. When designing the study of a financial or an economic event, the length of the event period and the availability of the necessary data will be taken into account when determining the returns for the time period. The test period (TP) can be daily, weekly, monthly, or yearly. However, most previous studies focused on daily or monthly data. Therefore, we will explain below the most important steps that must be followed to implement the event study methodology.

Step 1: This step requires determining the type of event under study, as well as specifying the start date of the event, where the date for the announcement of the event is considered zero. Using the specific date of the financial event, the selection of a sample of companies must be appropriate to the nature of the event, so that companies can be classified into different groups based on their performance. The start date of the event may differ from one company to another according to the occurrence of the financial event in the market.

Step 2: The exact date of the announcement of the financial event must be specified. To monitor the reaction of this information on the prices of securities in the financial market, the period of study or testing must be determined. In event studies, determining the time horizon when studying the event in each time period is a crucial step as the researcher must determine TP (also known as the event window) and the estimation period (EP), which comes before the event window.

The effect of the event on stock prices (returns) is studied and analyzed in TP, which has a range between T1 and T2 around the date of the event (the event announcement), which is day 0 as shown in Figure 1.

Figure 1.

Step 3: Estimate the expected return E(Ri,t) for each share in the sample during the period of the EP window. The expected return is used as a reference return in the normal situation, where the actual return is compared to it during the period of the event window (TP), which represents the return. There are several models that can be used to estimate the expected return, including measuring the normal return, as there are many models that can be used to measure the expected or normal return for the market (benchmark) to compare it with the return of a financial asset or security. Among the studies are these models for comparing performance in a variety of contexts. The most important of these models are the following:

Market-adjusted returns model

This form can be used as follows: 1ARit=rit−rMt$$A{R_{it}} = {r_{it}} - {r_{Mt}}$$

This return (Rmt) is susceptible to statistical and economic biases that do not provide the required accuracy in measuring the unexpected or unusual return, resulting in misleading results when evaluating long-term stock returns. The average return is the rate of return during the appreciation period (EP), so that each stock in the sample can use the rate of return during EP as its expected return (Brown and Warner, 1985; Lambertides, 2009). The expected return is the market return (Rm, t) in the same time period, assuming that all stocks on average generate the same rate of return (Ritter, 1991; Bruner, 1999; Weber, et al., 2008). The use of market-adjusted returns does not require an EP.

Market model

The most common expected return model is the market model. It is based on a reference market’s actual returns and the correlation of the firm’s stock to the reference market. Formally, the model is described in equation (2). AR on a specific day within the event window is the difference between the actual stock return (rit) on that day and the normal return, which is predicted based on two inputs: the typical relationship between the firm’s stock and its reference index (expressed by the and parameters), and the actual return of the reference market (Rm, t): 2ARi,t=ri,t−(αi+βiRm,t)+εi,t$$A{R_{i,t}} = {r_{i,t}} - \left( {{\alpha _i} + {\beta _i}{R_{m,t}}} \right) + {\varepsilon _{i,t}}$$ where r_it and Rmt represent the return on the security and the return on the market portfolio, respectively. The two parameters of the model can be estimated from the data covering TP. The market model is probably the most commonly used option as a benchmark for comparison between the return of a security and the expected return in the literature for the study of financial economics. They are estimated using the least squares regression equation (OLS) over EP. This method is used to control the relationship between stock returns and market returns; it allows for a change in the risks associated with the selected stocks under study. The adjusted market return model was used in several studies before the event, such as Small, et al. (2007) and Homan (2006).

Capital Asset Pricing Model (CAPM)

When using the CAPM model, the expected return consists of the risk-free return (Rft) plus the market risk premium (Espenlaub, et al., 2000) and the model’s “beta” measures the risk of stock i assuming that the investor requires a higher return to compensate for greater risk. Several event studies have applied the Capital Asset Pricing Theory (CAPM) as a benchmark for comparing the real return of a financial asset and the expected return. 3ARCAPM=Ri,t−[ Rf,t+βi(Rm,t−Rf,t) ]$$A{R^{CAPM}} = {R_{i,t}} - \left[ {{R_{f,t}} + {\beta _i}\left( {{R_{m,t}} - {R_{f,t}}} \right)} \right]$$ where (R_i) represents the actual return, R_f the riskfree return, and (R_m)the expected return of the market portfolio.

Fama-French three-factor model (FF3F)

One of the models that have been used in previous studies to estimate the unusual return is this model, which is a modified version of the FF model. It includes more risk factors, such as the market return factor (Rm,t − Rf,t), the volume factor, which represents the difference between the rate of return of a portfolio of small and large stocks, and the book value factor to market value, which represents the difference in the rate of return between portfolios with high book value ratios. The market and its low ratios (Fama and French, 1993). This method uses the monthly returns over the length of the time period under study, and it can be applied similarly to the CAPM theory, as it has been widely used in the literature of previous studies (Barber and Lyon, 1997; Loughran and Ritter, 1995; Dutta and Jog. 2009). The model can be described as follows: 4FFARi,t=Rit− [ Rft+βff(Rmt−Rft)+ SffiSMBt+hiffHMLt$$F{F^{AR}}_{i,t} = {R_{it}} - \left[ {{R_{ft}} + {\beta ^{ff}}\left( {{R_{mt}} - {R_{ft}}} \right) + } \right.{S^{ff}}_iSM{B_t} + h{i^{ff}}HM{L_t}$$

Reference portfolio

The expected return according to this method is the return of a reference portfolio consisting of a large number of shares, classified on the basis of the following criteria: volume, the ratio of book value to market value, or both. To calculate this reference expected return, there is no need for an EP, as all companies are classified into different groups depending on certain characteristics. For example, companies are categorized into 10 groups of different sizes, and then the average return on all shares in each group is calculated. It is considered as an expected return for the reference portfolio (Ritter, 1991; Barber and Lyon, 1997). 5ARi,tp=Ri,t−Rp,t$$AR_{i,t}^p = {R_{i,t}} - {R_{p,t}}$$

Matched firm approach (MF)

Similar to the practice of using a portfolio’s return as an expected return, this return is assumed to be the same as the return for a similar company during the testing period. The matching process is carried out on the basis of the relevant risk characteristics, matching them with shares not included in the event, and among the characteristics that can be adopted are the size (market value of shares), the ratio of book value to market value, or both. AR in this model is calculated as the difference between the return of the security in the sample and the return of the security of a similar company in the financial market that has not been exposed to the same financial event. To reach an accurate level in calculating the difference, there are several limitations that must be taken into account in a similar company in terms of matching in the level of volume as well as in the level of industry. The return of a large sample cannot be compared with the return of a small sample, even in the same sector or industry. 6ARMF=Rit−RMF$$A{R_{MF}} = {R_{it}} - {R_{MF}}$$

Step 4: Measure the abnormal performance of financial assets.

At this stage, AR is calculated for each share in the sample, which is the difference between the real (actual) return in time (t in the event window) and the expected return on the share, E(Ri,t):

Abnormal Return = Actual Return − Normal Return 7ARiτ=Riτ−E(Riτ)$$A{R_{i\tau }} = {R_{i\tau }} - E\left( {{R_{i\tau }}} \right)$$ where AR_iτ represents AR of a financial asset or security during the period of time τ, R_iτ represents the real return of a financial asset or security during the time period τ, and E(R_iτ) represents the normal return on a financial asset or security during the period of time τ.

After the unexpected or abnormal return is calculated about the event date for the sample of companies under study using the appropriate form to compare the return with the return of the general index of the financial market or the reference, the unexpected return is collected (aggregate ARs) either using the BHAR method or applying the CAR methodology, and the research will address both methods in detail later with a practical application. After that, the level of statistical significance is tested and the results are interpreted accordingly. At this stage, AR is calculated for each share in the sample, which is the difference between the real (actual) return in time (t) in the event window and the expected return on the share, E(Ri, t).

Step 5: Statistical test: The researcher will test the strength of the statistical test according to the indicator used and compare it with the unusual return that was calculated according to what was clarified in the methodology of the study of the event. Therefore, certain formulas will be employed, which the researcher will use to calculate the results of the statistical test.

In this study, the researcher used three reference criteria and compared each of these criteria with the IPO yield to calculate AR, and then calculated the aggregation model for each comparison to determine the degree of sensitivity of the standard and aggregate models to ARs in the long term. The researcher chose these criteria because they are appropriate and have not been employed before in the Jordanian market, in addition to the fact that the data needed by these criteria is available. Below is a discussion of these criteria and the collected models for further clarification.

When analyzing long-run returns, there is undoubtedly a need for a model that quantifies AR. The market-adjusted return model has been widely used in recent research on calculating AR for IPOs. This model directly measures AR, as shown below: 8ARit=rit−rBt$$A{R_{it}} = {r_{it}} - {r_{Bt}}$$

That is, AR is the result of subtracting the monthly return per share (IPO) (r_it) from the return on the benchmark (r_Bt) during a certain period, based on a specified criteria used to estimate abnormal return.

The purpose of this study is to use the financial event study methodology by measuring ARs of the IPO companies in the Amman Stock Exchange, bearing in mind that the ongoing public offering process itself is an influential financial event in the market. This is because it is crucial to test the various formulas for event study methodology in practice. After these businesses have been listed for 3 years, their returns are monitored; in cases where a sample of these businesses is involved in the event, the date of the event is established, and the return is then calculated from the date of listing and compared to the financial indicators, some of which represent the Amman Stock Exchange’s general index and the other was created using techniques for analyzing the event and theories that had already been established in the body of previous literature on the topic. Companies frequently decide to raise money through public offerings, also known as IPOs, when they need to do so. A company can raise the capital it requires through IPOs by selling stock to the general public for the first time. The money raised through an IPO can be used by the company for working capital, debt repayment, or future expansion. There are numerous perceptions that could be used to explain how IPOs perform over time, first, from the perspective of an investor, the existence of price patterns may present opportunities for active trading strategies to produce higher returns. Second, the ability of the IPO market to provide accurate information is called into question by the possibility of achieving abnormal (non-zero) performance profits after the sale. It supports the idea that rumors have an impact on market prices, and that this is true for both the general stock market and the IPO market in particular. Third, there are noticeable changes in the volume of IPOs over time (Ritter, 1991).

Whether the return is estimated using the FF3F model or the return of the ASEI model, the monthly returns of the shares of the IPO companies are calculated along with the monthly returns for each reference index (benchmark). The long-term performance of the stock is then examined for the companies included in the study sample after the calculation of AR, cumulative extraordinary return (CAR), and holding period return (BHAR).

We should review some descriptive statistics using the three aggregation models employed in this study (BHAR, CAR, and AR) before beginning to analyze the financial event represented by the long-term performance of the Jordanian IPO. See Table 3 for a description of this descriptive statistic for the study sample, which spans a full 48 months from the start of listing and consists of 60 companies.

In Table 3, panel A, we observe that the mean values of AR, CAR, and BHAR are -0.00466, -0.0743, and - 0.2879 and the median values are -0.004, -0.065, and -0.087, respectively. They belong to values ranging between the minimum value of -0.765 for BHAR with a standard deviation of 0.476 and the maximum value of return achieved by AR (0.004), followed by CAR (0.002), and then lastly, we find that BHAR showed the worst performance of -0.007. In panel B, when the FF3F model is employed as a benchmark, we find that the average AR is -0.00987, while CAR for the whole period is -0.12845 and the average BHAR is -0.15709, which is the worst compared to the previous two aggregated models. When using the MF approach, the descriptive statistics are shown in panel C. CAR over the sample period is -0.0785, which is better than the performance of FF3F as a benchmark, while the performance of BHAR is the worst among the other models.

The main goal of this study is to analyze the financial performance of a sample of companies that are listed in ASE as IPOs based on CAR and by BHAR.To do this, we will use a technique developed by Barber and Lyon (1997) to compare the expected return in two different ways (CAR, BHAR), as well as the skewness-adjusted t-test. By examining the outcomes of both methods and the long-term performance of the IPO firms, the researcher will discuss both approaches.

The analysis must rely on the outcome of each measure to be used as a source for hypothesis testing to test the level of significance of AR for both methods (CAR, BHAR) and ascertain whether there are differences between them when using any of the three benchmarks (ASEI, MF, FF3F).

The results of this study are in agreement with the results of many previous studies regarding the poor long-term performance of IPOs (e.g., Ritter, 1991; Levis, 1993; Loughran and Ritter, 1995; and Drobetz, et al., 2005) and numerous studies that discussed the performance of IPOs in the long term. The study provides more evidence for the poor performance of the phenomenon of public offerings in the long term, given that the level of performance of the initial subscription varies according to the benchmark index (Espenlaub and Gregory, 2000).

Based on this study’s findings, which were for a sample of 60 companies that had made IPOs on the Amman Stock Exchange during the time period covered above, ASEI performed poorly compared to other indices over the period of the following 48 months. The results showed that the performance weakness reached 42.22% (CAR) and 63.88% (BHAR) when using ASEI as a benchmark. Also, poor performance was shown when FF3F model was used as a benchmark. The performance decline was 1.45% when using the CAR aggregation model and 6.63% when using the aggregative method (BHAR) after 48 months following listing. However, when the third benchmark was employed, the poor long-run performance reached 18.52% and 82.23% when CAR and BHAR were employed as aggregated models, respectively.

This study discussed the idea of using an event study methodology to examine the financial and economic events and their significance in assessing companies’ performance in the long term, demonstrating the theoretical aspect by reviewing the models and the common financial and economic tools that can be used. to assess the market performance of a sample of companies fairly. The strength of these models was also statistically tested to expose the extent to which they could be used to explain the performance of the listed firms. In general, research on financial and economic events demonstrates that stock prices in developed markets change in response to newly disclosed information, both in the long and short term. We anticipate that the method of analyzing economic and financial events will continue to be a highly useful resource in the fields of finance and economics.

Finally, multivariate regressions show that some performance indicators, such as asset turnover and return on equity, are negatively correlated with long-term returns. These two metrics serve as equity and asset efficiency indicators. The more a firm underperforms its pricing over the next 3 years, the higher its asset and equity efficiency was in the year of its IPO. A significant negative relationship between long-term returns and the hot period dummy variable is also shown by multiple regressions, demonstrating that hot issue IPOs underperform more over the long term. The same models highlight the association between proceeds and long-term anomalous returns.

The long-term implications of IPOs on issuing corporations are the focus of this study, which intends to assist investors in making more informed decisions about the equity holdings in their portfolios. Also, it can assist regulators in formulating equity market-related policy. This data makes it possible to more accurately estimate long-term IPO results and better plan the timing of IPOs. In-depth investigation into the causes of the simultaneous underperformance of price and operations is possible.