Macro-economic determinants of the IPO waves: An assessment of G-7 countries

MUSTAFA ÖZYEŞİL; Esin Benhür Aktürk

doi:10.12688/f1000research.122849.1

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Research Article

Macro-economic determinants of the IPO waves: An assessment of G-7 countries

[version 1; peer review: 1 approved, 1 approved with reservations, 1 not approved]

MUSTAFA ÖZYEŞİL¹, Esin Benhür Aktürk²

PUBLISHED 08 Jul 2022

Author details Author details

¹ Business Administration, Istanbul Aydin Universty, Istanbul, Turkey
² Management organization, İstanbul Aydin University, İstanbul, Turkey

MUSTAFA ÖZYEŞİL
Roles: Funding Acquisition, Investigation, Project Administration, Supervision

Esin Benhür Aktürk
Roles: Data Curation, Methodology, Supervision, Writing – Original Draft Preparation

OPEN PEER REVIEW

REVIEWER STATUS

Abstract

Background: The purpose of this study was to reveal the macroeconomic determinants that companies take into account when scheduling initial public offerings (IPOs).
Methods: For this purpose, the relationship between the frequency of the IPOs and the selected macroeconomic indicators in the stock market of the G-7 countries for the period 1999-2020 was examined by panel data analysis method. A random effect model was used as econometric model in the analysis, and the Driscoll-Kraay resistant standard estimator developed against deviations was used in the application of the model.
Results: According to the results of the analysis, it was determined that stock market returns have a positive and significant effect on the volume of public offerings.
Conclusions: Based on our findings, it can be concluded that companies take into account the periods when stock market returns are relatively high for timing the IPO.

Keywords

IPO Wave, Stock Returns, Panel Data, Random Effect Model, Driscoll-Kraay Standard Error, New Product Development, Operations Management, Organization Development Change, Continuous Improvement

Corresponding author: MUSTAFA ÖZYEŞİL

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2022 ÖZYEŞİL M and Benhür Aktürk E. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: ÖZYEŞİL M and Benhür Aktürk E. Macro-economic determinants of the IPO waves: An assessment of G-7 countries [version 1; peer review: 1 approved, 1 approved with reservations, 1 not approved]. F1000Research 2022, 11:763 (https://doi.org/10.12688/f1000research.122849.1) First published: 08 Jul 2022, 11:763 (https://doi.org/10.12688/f1000research.122849.1) Latest published: 29 Jan 2024, 11:763 (https://doi.org/10.12688/f1000research.122849.2)

Introduction

Initial public offerings (IPOs) are one of the equity financing methods that companies apply in capital markets. As a strategic financing process, public offerings play a critical role in the financial success of companies. The advantages and disadvantages of IPOs for companies are a matter of debate in the literature. The company that makes the IPO gains the status of a publicly traded company, is listed on the stock exchange, and thus the stocks are traded among the investors in the secondary market, that is, a liquid market is formed for the investors. In addition to this, after issuance process, the firms, as a publicly traded firm, have the opportunity to raise capital under much more favorable terms in the coming years (Ibbotson and Ritter, 1995). On the other hand, public offerings have many direct and indirect costs, such as the intermediary costs paid to the financial institutions and the listing fee paid to the regulatory institutions; these are accepted as one-time costs, while the obligation of the firms to provide information to the regulatory institutions on a regular basis is accepted as a continuous cost (Ibbotson and Ritter, 1995). Considering other factors such as company owners are sharing the management with others, companies should carefully evaluate advantages and disadvantages during the public offering process.

Public offerings are one of the most important issues in corporate finance. There have been many studies on initial public offerings in the literature. While some of the studies have examined the macro- and micro-determinants that lead companies to go public, some have examined the short and long-term price performance of stocks after the public offering. For example, Chemmanur and He (2011), Rydqvist and Högholm (1995) and Draho (2004) have studied the reasons for companies to go public and the frequency of public offerings. Loughran and Ritter (2004), Booth and Chua (1996), Ljungqvist (2007), Ritter and Welch (2002), Ritter (1984) et al. focused on price movements and their determinants. In studies on the causes and frequency of public offerings, hypotheses such as hot and cold public offerings have been developed. Many hypotheses such as Winner’s Curse by Rock (1986) have been put forward regarding the post-IPO price performance.

In this study, macroeconomic determinants that affect the frequency of IPOs was examined. In other words, we tried to determine which indicator companies take into account for timing of the public offering. The study is carried out on the G-7 countries for the period 1999-2020 using the panel data analysis method.

The number, volume and returns of IPOs are influenced by both the firm-related and macroeconomic environment and intermediary institution behaviors. For example, investment banks have a very high public offering experience, as they constantly manage public offerings. For this reason, the information produced by investment banks for market participants in the public offering processes provides significant guidance both companies and investors. Particularly during periods of heavy issuance and high initial returns, the information produced by investment banks improves the quality of IPOs, enabling previously low-quality firms to go public and increasing secondary market prices, synchronizing high IPO volumes and high first-day returns (He, 2007).

In the following part of the study, research in the international literature on this subject were examined. The relationship between the frequency of public offerings and the macroeconomic indicators selected for this study were then analyzed. In the last part of the study, the findings were interpreted and evaluated.

Research limitations and further research suggestions

The sample for the study consisted only of G-7 countries, which are developed markets.

In this respect, a comparative analysis and interpretation between country groups cannot be made, since the results of emerging or underdeveloped markets were not included in the study. However, it is recommended to conduct similar analyzes on other country groups in order to obtain more comprehensive and comparative results.

In addition, in this study, macroeconomic factors that are thought to be effective on the number of IPOs were included, and micro factors related to the company were ignored. It will be possible to obtain more meaningful results by including micro factors in the model.

Literature review

Studies on the number of initial public offerings were examined. The majority of the studies in the literature have analyzed the trend of IPOs in terms of number and volume and have created the concepts known as IPO Wave and Hot and Cold Issue markets in the literature. In most of the studies in the literature, the markets in which the number and volume of IPOs increase gradually are defined as “hot issue markets”, while the markets where the number and volume of IPOs decrease are defined as “cold issue markets”. In hot issue markets, many companies can plan their IPOs for this period, as the investor’s demand for the stock market is increasing. This way, the company makes a successful public offering and the investors have the opportunity to obtain abnormal returns in the short term thanks to the high discounts they obtain.

Pástor and Veronesh (2005) developed an optimal IPO timing model for firms. They emphasized that many studies in the literature explained the change in IPO volume over time with the concept of market inefficiency, where IPOs are made when stocks are overvalued. Contrary to previous literature, they created a model in which the fluctuation in the volume of initial public offerings occurs without any mispricing mechanism and the volume of the IPO is more closely related to recent changes in stock prices rather than the level of market stock prices. In the model, the variables that companies take into account during the public offering are, respectively: expected market return, expected aggregate profitability and prior uncertainty about the average future profitability of IPOs. According to the results of the analysis, they determined that there were high market returns before the IPO waves and low market returns after the IPO waves.

Buttimer, Hyland and Sanders (2005), in their study, comparatively examined the waves of IPOs in the real estate investment trust (REIT) sector and the waves of general initial public offerings. In terms of initial returns, it was observed that the REIT sector provided lower returns than the general market average after the IPO waves. In addition, the long-term underperformance anomaly, which is frequently observed in stock markets according to many studies such as Ritter (1991), Loughran and Ritter (1997), Levis (1993), Schultz (2003) among others, has not been observed in the REIT sector. Based on the findings of their study, authors stated that the Capital Demand Hypothesis best describes the REIT public offering market.

Yung, Colak and Wang (2008) created a model in which real investment opportunities that change over time cause adverse selection in the market for IPOs. As a result of the analysis, it has been determined that economic expansions are highly correlated with the number of companies that go public, which is positively correlated with underpricing. In line with the model they created, greater cross-sectional return variance and higher delisting rates were detected for hot market public offerings.

Christoffersen, Nain and Tang (2010) examined the waves and the quality of IPOs. They stated that the quality of the public offering will not be the same in the first and later periods of the IPO wave. They noted that in the early stages of the IPO wave, the average quality of IPO stock is low when initial returns and IPO demand are low. However, later in the IPO wave, when initial returns and IPO demand are low, IPOs have better operating performance, higher market share, and consequently higher long-term abnormal returns. According to the results of the study, they found that institutional investors benefit from short-term high returns in early public offerings, but they prefer IPOs that are done in later stages of the IPO wave thanks to their higher quality in the long run.

In their study, Tran and Jeon (2011) analyzed the macroeconomic factors that were effective on the initial public offerings in the US capital market during the 1970-2005 period. By applying time series econometric methods in the analysis, they determined the long-term balance between macroeconomic variables and public offerings. As in many studies, Tran and Jeon also found that stock market return performance and volatility have the most significant impact on the timing of the IPO. In their study, they also examined the effects of interest rates and bond market returns. They found that there are not only long-term but also short-term statistically significant relationships between macroeconomic variables and IPO activities.

Angelini and Foglia (2018) examined the short- and long-term relationships between initial public offerings and macroeconomic factors in the UK stock market for the period 1996-2016. In the analysis, they sought answers to the questions of how macroeconomic conditions affect the initial public offerings and how long the recent shock effect lasted. Business cycle, volatility, interest rates and stock returns were used as macroeconomic variables. According to the results of the correlation analysis made in the study, it was determined that the business cycle, volatility and interest rate variables could explain the change in the number of IPOs. However, unlike many studies in the literature, no significant effect of stock market performance on the number and timing of IPOs could be detected.

Thanh (2020) used macroeconometric models to examine the cycles of initial public offerings in US stock markets. According to the results of the time series analysis they applied in their studies, they determined the strong and negative effect of macroeconomic uncertainty on the public offering activity. It has been determined that a one-unit standard deviation increase in macroeconomic uncertainty reduces the monthly number of initial public offerings by approximately four units in the long run. In the analysis, they also found that both the decrease in the number of IPO applications and the increase in withdrawn IPOs contributed to the decrease in the number of IPOs in response to an uncertainty shock.

Carosi and Mengoli (2021) analyzed local IPO waves in their studies. They analyzed public offerings on the basis of region and sector. They found that the waves of public offerings overlapped on a sectoral and regional basis. In other words, public offerings on a sector basis are similar to public offerings on a regional basis. They observed that IPOs at the beginning of the IPO wave were equally priced lower than IPOs at the end of the wave. According to the results of the analysis, it was determined that the IPO decision is sensitive not only to the high valuations of the companies in the same sector, but also to the high valuations of the companies in the same region but in different sectors.

Methods

Dataset and sample structure

In the study, we examined whether macroeconomic variables have an effect on the frequency of public offerings. Since the aim is to determine the factors affecting the IPO wave through developed capital markets, the sample consists of G-7 countries. The analysis period covered the period 1999-2020, and annual data were used in the analysis.

Macroeconomic indicators such as annual consumer inflation, stock market returns and economic growth over the years (GDP growth) were used as independent variables. Explanations regarding on variables included in analysis are provided in Table 1 as follows.

Table 1. Descriptions of variables.

Variable	Description
Consumer inflation	Inflation is measured through consumer price index. This index indicates annual change in % in cost to average individual of buying goods and services.
Stock market returns	Average annual return over the daily closing prices of each country's national share index.
GDP growth	Annual change in % of GDP based on market prices and constant local currency.

Our preliminary expectations were a negative relationship between consumer inflation and the frequency of public offerings, and a positive relationship between stock returns and economic growth and frequency of public offerings.

Methodology

In the study, the relationship between the IPO waves (frequency) and macroeconomic variables were analyzed in terms of country sections. For this reason, panel data analysis method was used. In the analysis, it was deemed appropriate to apply the random effect model as the econometric model. While macroeconomic variables were included in the analysis as independent variables, the number of IPOs was included in the analysis as a dependent variable.

Since there was a cross-section dependency between the series in the analysis, the stationarity of the data was measured with the Peseran (2007) unit root test, which is one of the second-generation unit root tests. Afterwards, the Hausman test was applied to determine the econometric model to be applied. According to the results of the test, the application of the random effect model was deemed appropriate and the Driscoll-Kraay resistant standard estimator developed against deviations was used in the application of the model.

Results

Descriptive statistics

Descriptive statistics for the sample used in the study are shown in Table 2 below.

Table 2. Descriptive statistics.

Descriptive statistics	IPOs	GDP growth	Stock returns	Inflation
Mean	84.94156	0.012248	0.036149	0.014812
Median	56	0.01695	0.05445	0.016
Maximum	486	0.0687	0.3999	0.039
Minimum	0	-0.094	-0.3632	-0.013
Std. dev.	93.52817	0.02532	0.157667	0.01052
Skewness	1.996234	-1.804778	-0.318524	-0.237892
Kurtosis	7.271059	7.328356	2.391868	2.596243
Jarque-Bera	219.3329	203.8162	4.977113	2.498583
Probability	0	0	0.08303	0.286708
Sum	13081	1.8862	5.5669	2.281
Sum Sq. Dev.	1338370	0.098089	3.803388	0.016934
Observations	154	154	154	154

Correlation analysis is one of the most typical analyzes used to examine the relationship between variables. Pearson’s correlation analysis was used in this study, as the data showed normal distribution.

The correlation relationship between the variables is shown in Table 3 below.

Table 3. Correlation analysis.

	Stock-returns	Inflation	GDP-growth	IPOs
Stock-returns	1
Inflation	0.05	1
GDP-growth	0.49	0.32	1
IPOs	0.30	0.12	0.25	1

According to the results of the correlation test, since the degree of correlation between the variables varies between 0.05 and 0.49, there was a positive but weak relationship. The degree of correlation differed according to the variables used. For example, there was a very weak correlation of 0.05 between stock returns and inflation, while a weak correlation of 0.49 between the GDP growth and stock returns. A strong and very strong relationship between the variables in the correlation analysis should have a degree of correlation between 0.70-0.89 and 0.90-1.00, respectively. As a result, according to the results of the analysis, there is a weak relationship between the variables in this sample.

Unit root test

In order not to encounter the spurious regression problem in the analysis, it is necessary to measure the stationarity of the series before the regression analysis. However, in order to determine which stationarity test will be used, a cross-section dependency test should be applied. According to the result of the cross-sectional dependence, the appropriate unit root test was applied. Cross-section dependence was tested with Pesaran (2004) cross-section dependence method.

The hypotheses of this test were as follows:

H0: There is no cross-section dependence between the series.

H1: There is a cross-section dependence between the series.

The results of the test are shown in Table 4 below:

Table 4. Pesaran (2004) cross-section dependency (CD) test.

Variable	CD-test	Correlation coefficient	abs (corr)	Result
IPOs	7.84	0.365	0.366	H0 cannot be accepted
StockReturns	16.49	0.767	0.767	H0 cannot be accepted
Inflation	10.24	0.476	0.51	H0 cannot be accepted
GDPGrowth	17.84	0.83	0.83	H0 cannot be accepted

According to the results of the test, there was a cross-sectional dependence between the series. As a consequence, Pesaran (2007) second generation unit root test was applied, taking this stiuation into account.

The hypotheses of these tests were as follows:

H0: Series are not stationary.

H1: Series are stationary.

The results of the test are shown in Table 5 below.

Table 5. Unit root test results.

Variable	p-value
Variable	Origin level	First difference	Decision
IPOs	0.636	0.00	I(1)
Stock returns	0.09	-	I(0)
Inflation	0.072	0.00	I(1)
GDP growth	0.04	-	I(0)

According to the unit root test results, IPOs and Inflation were found to be stationary after taking the first difference, while the GDP growth and stock returns variables were found to be stationary at level values. Therefore, non-stationary IPOs and inflation variables were included in the regression analysis after taking their first difference. For this reason, a cointegration test was not be applied in the study.

Econometric model

A Hausman test was used to determine the econometric model to be established. The Hausman test results are summarized in Table 6 below.

Table 6. Hausman test results.

Chi² (3)	p-value	Decision
4.74	0.8956	Random effect model is used

According to this result, the random effect model was used as the econometric model.

Tests related to autocorrelation and heteroscedasticity problems in the model were performed in Table 7 and Table 8 below.

Table 7. Autocorrelation test.

F	p-value	Decision
1.874	0.2201	There is no autocorrelation problem.

Table 8. Heteroscedasticity test.

Chi²	p-value	Decision
4.74	0.0295	There is no autocorrelation problem.

In the model, macroeconomic variables were used as independent variables, and IPO numbers were used as dependent variables, and a random effect regression analysis with the Driscoll-Kraay resistant standard estimator was applied.

Regression analysis results are shown in Table 9 as follows.

Table 9. Regression with Driscroll - Kraay standard errors.

GLS: generalised least squares.

Regression with Driscroll - Kraay standard errors				Number of observations = 154
Method: Random effects GLS regression				Number of groups = 7
Group variable: code				Observations per group:
R-square				minimum = 21
within = 0.2037				average = 21.0
between = 0.1687				maximum = 21
overall = 0.1959				Wald Chi² (3) = 9.43
				Probability > Chi² = 0.0241
IPOs	Coefficient	Robust standard error	z	p > (z)	[95% Confidence interval]
GDP growth	-203.6567	225.7047	-0.90	0.367	-646.0297	238.7164
Stock returns	190.9641	78.77502	2.42	0.015	36.56787	345.3603
Inflation	-669.5706	412.4192	-1.62	0.104	-1477.897	138.7561
_constant	-6.434738	3.141505	-2.05	0.041	-12.59197	-.2775006

According to the results of the regression analysis, stock returns is the only variable that has a positive and significant effect on the number of IPOs. The effects of all other variables on the frequency of IPOs were not found significant. Based on the analysis findings, it can be stated that companies take into account the stock returns variable in timing the public offering. The correlation between stock returns variable and IPOs was found to be positive. In other words, as the stock market index returns increase, companies may choose to go public in order to enter the hot public offering market and maximize their issuance proceed (or reduce the underpricing degree of public offerings). The findings are compatible with many studies in the previous literature, especially Pástor and Veronesh (2005), and Tran and Jeon (2011).

Discussion and conclusions

IPOs are one of the most important methods used by companies as long-term equity financing in the capital market. In an IPO, the issuer receives issuance income in exchange for shares sold to investors. Investors, on the other hand, become a shareholder of a newly publicly traded company, often taking advantage of the discounted issue price, and as long as they hold the stock in the future, they will receive dividend income or can get capital gain by selling the discounted shares at a higher price. Considering the above-mentioned advantages and long-term results of public offerings, it was found that this strategic financing process has an important place in the financial success of companies. For this reason, the success of the IPOs is critical for all parties in the IPO process as well as for the general market functioning. Due to the importance of IPOs, there have been numerous studies on IPOs in the literature. The majority of the studies focused on the short- and long-term price performances and determining factors of IPOs, as well as the cycle of IPOs.

In this study, macroeconomic factors that were thought to be effective on the cycle of IPOs were examined, and especially whether economic growth, inflation and stock market returns are determinants on the number of IPOs. The analysis was applied to the G-7 countries representing the developed world economies, for the period 1999-2020.

In the analysis, first of all, the stationarity of the series was tested with the Pesaran (2007) unit root test, which takes into account the cross-section dependence. The series that were not stationary at the values level values were included in the regression analysis by considering their primary difference. Before the regression analysis, we tested whether the problems of autocorrelation and heteroscedasticity existed in the model, and it was determined that there were no such problems in the model. A Hausmann test was applied in the selection of the econometric model to be established, and according to the results obtained, it was deemed appropriate to use the random effect model. Regression analysis was performed using the Driscoll-Kraay resistant standard estimator, which was resistant to deviations and errors.

According to the results of the analysis, it was determined that only the stock returns variable, among the independent variables in the model, had a significant effect on the number of initial public offerings. The stock returns variable has a positive effect on the number of IPOs. In other words, as stock return increases, the number of IPOs also increases. Based on this finding, it can be stated that stock returns is one of the macroeconomic factors that affect companies’ public offering decision. In other words, companies prefer to go to public offerings during periods when stock market returns increase. The inflation and GDP growth variables, which were the other variables investigated in the analysis, could not have a significant effect on the frequency of the initial public offering.

In this analysis, only the macroeconomic determinants, which are thought to be effective on the IPO waves, were examined. In addition, more comprehensive and meaningful results can be obtained by including microeconomic factors that may affect the number of IPOs in the analysis. A study on developing economies in addition to developed world economies will be extremely useful in terms of showing comparatively how macro- and microeconomic factors change according to the level of development of countries.

Data availability

Underlying data

Inflation and GDP growth rate data were retrieved from WorldBank databank and filtered for the G-7 countries for the period of interest (https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG). In the menu on the right of the home page, the indicators for sample countries can be selected. No account creation is needed.

IPO numbers were retrieved from Statista. Scholars don’t have to create account and make subscription on Statista when they are downloading IPO numbers for each country seperately. For multiple countries and periods, the system requires to create an account.

Stock returns were retrieved from Investing.com. In thesearch box located at the top of the home page of investing, equity code is written and profile page of related stock wil be opened. The historical data section is located just above the price chart. After determining start and end dates all information regarding on closing price, opening price, highest and lowest will be visible.

All data used are open access.

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VERSION 2 PUBLISHED 08 Jul 2022

Author details Author details

¹ Business Administration, Istanbul Aydin Universty, Istanbul, Turkey
² Management organization, İstanbul Aydin University, İstanbul, Turkey

MUSTAFA ÖZYEŞİL
Roles: Funding Acquisition, Investigation, Project Administration, Supervision

Esin Benhür Aktürk
Roles: Data Curation, Methodology, Supervision, Writing – Original Draft Preparation

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

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© 2022 ÖZYEŞİL M and Benhür Aktürk E. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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ÖZYEŞİL M and Benhür Aktürk E. Macro-economic determinants of the IPO waves: An assessment of G-7 countries [version 1; peer review: 1 approved, 1 approved with reservations, 1 not approved]. F1000Research 2022, 11:763 (https://doi.org/10.12688/f1000research.122849.1)

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Reviewer Report 15 Aug 2023

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https://doi.org/10.5256/f1000research.134892.r185960

The paper addresses a relevant issue which is however not very new. I find the following concerns

The paper suffers from flow and connectivity issues
Why will a researcher be interested

The paper addresses a relevant issue which is however not very new. I find the following concerns

The paper suffers from flow and connectivity issues
Why will a researcher be interested to read this article? What is the value addition?
Why have the authors undertaken this work?
Literature review is outdated. It is required to study the papers published in 2020-2023.
The selection of methodology needs justification
The results need to be aligned with the key research questions and limitations need to be elaborated

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Multicriteria Decision Making, Multivariate analysis, Logistics & Supply Chain Management, Sustainability

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Author Response 29 Jan 2024

Esin Benhür aktürk , Management organization, İstanbul Aydin University, İstanbul, Turkey

29 Jan 2024

Author Response

First of all, we’d like to thank the respected professor reviewer for invaluable comments. We consider all these comments as crucial to improve our study. You can kindly find responses ... Continue reading First of all, we’d like to thank the respected professor reviewer for invaluable comments. We consider all these comments as crucial to improve our study. You can kindly find responses to your comment as follows:

1. The paper suffers from flow and connectivity issues
We improved this issue. Thank you very much.

2. Why will a researcher be interested to read this article? What is the value addition?
The study aims to assist investors in the stock market with market timing. By examining the relationship between macroeconomic indicators and the hot and cold issue market, it is intended to provide higher returns to investors. Investors can use macroeconomic indicators as a signal to gain insight into the trend of initial public offerings (IPOs) and, accordingly, time their investments to achieve higher short-term returns.

3. Why have the authors undertaken this work?
The reason for our study is the relatively limited presence of research in the literature examining the relationship between stock returns and IPO trends. Through this study, we aim to contribute to the literature. We believe that the findings of this analysis, particularly focusing on the developed G-7 countries' capital markets, could serve as guiding insights for researchers delving into this subject.

4. Literature review is outdated. It is required to study the papers published in 2020-2023.
As mentioned earlier, there exists a significant subject area in the literature on this matter. Consequently, the number of studies available is limited. Furthermore, due to its publication in 2021, this study naturally does not encompass research from the years 2022 and 2023. Nevertheless, three relevant studies published between 2021 and 2023 have been included in the literature.

5. The selection of methodology needs justification 6.The results need to be aligned with the key research questions and limitations need to be elaborated.
In panel data analysis, the dataset encapsulates information from different cross-sections (e.g., various firms or countries) and across different time dimensions (e.g., different time periods). The Discroll-Kraay regression analysis is a method utilized for analyzing data within a panel data structure. This method specifically provides robust predictions against common issues such as autocorrelation and heteroscedasticity present in panel datasets.
The Discroll-Kraay method is recommended to address concerns like autocorrelation and heteroscedasticity in panel data analysis. Autocorrelation denotes the situation where relationships between observations remain similar over time, whereas heteroscedasticity indicates varying variance among observations. By addressing such scenarios, this method ensures more reliable and robust regression outcomes.
Moreover, the Discroll-Kraay method is widely recognized in the literature and is acknowledged for its reliability and effectiveness in panel data analysis. Hence, this method has been chosen for panel data analysis and forms part of the reasons behind its selection in this study.
First of all, we’d like to thank the respected professor reviewer for invaluable comments. We consider all these comments as crucial to improve our study. You can kindly find responses to your comment as follows:

1. The paper suffers from flow and connectivity issues
We improved this issue. Thank you very much.

2. Why will a researcher be interested to read this article? What is the value addition?
The study aims to assist investors in the stock market with market timing. By examining the relationship between macroeconomic indicators and the hot and cold issue market, it is intended to provide higher returns to investors. Investors can use macroeconomic indicators as a signal to gain insight into the trend of initial public offerings (IPOs) and, accordingly, time their investments to achieve higher short-term returns.

3. Why have the authors undertaken this work?
The reason for our study is the relatively limited presence of research in the literature examining the relationship between stock returns and IPO trends. Through this study, we aim to contribute to the literature. We believe that the findings of this analysis, particularly focusing on the developed G-7 countries' capital markets, could serve as guiding insights for researchers delving into this subject.

4. Literature review is outdated. It is required to study the papers published in 2020-2023.
As mentioned earlier, there exists a significant subject area in the literature on this matter. Consequently, the number of studies available is limited. Furthermore, due to its publication in 2021, this study naturally does not encompass research from the years 2022 and 2023. Nevertheless, three relevant studies published between 2021 and 2023 have been included in the literature.

5. The selection of methodology needs justification 6.The results need to be aligned with the key research questions and limitations need to be elaborated.
In panel data analysis, the dataset encapsulates information from different cross-sections (e.g., various firms or countries) and across different time dimensions (e.g., different time periods). The Discroll-Kraay regression analysis is a method utilized for analyzing data within a panel data structure. This method specifically provides robust predictions against common issues such as autocorrelation and heteroscedasticity present in panel datasets.
The Discroll-Kraay method is recommended to address concerns like autocorrelation and heteroscedasticity in panel data analysis. Autocorrelation denotes the situation where relationships between observations remain similar over time, whereas heteroscedasticity indicates varying variance among observations. By addressing such scenarios, this method ensures more reliable and robust regression outcomes.
Moreover, the Discroll-Kraay method is widely recognized in the literature and is acknowledged for its reliability and effectiveness in panel data analysis. Hence, this method has been chosen for panel data analysis and forms part of the reasons behind its selection in this study.
Competing Interests: No competing interests were disclosed. Close
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COMMENTS ON THIS REPORT

Author Response 29 Jan 2024

Esin Benhür aktürk , Management organization, İstanbul Aydin University, İstanbul, Turkey

29 Jan 2024

Author Response

First of all, we’d like to thank the respected professor reviewer for invaluable comments. We consider all these comments as crucial to improve our study. You can kindly find responses ... Continue reading First of all, we’d like to thank the respected professor reviewer for invaluable comments. We consider all these comments as crucial to improve our study. You can kindly find responses to your comment as follows:

1. The paper suffers from flow and connectivity issues
We improved this issue. Thank you very much.

2. Why will a researcher be interested to read this article? What is the value addition?
The study aims to assist investors in the stock market with market timing. By examining the relationship between macroeconomic indicators and the hot and cold issue market, it is intended to provide higher returns to investors. Investors can use macroeconomic indicators as a signal to gain insight into the trend of initial public offerings (IPOs) and, accordingly, time their investments to achieve higher short-term returns.

3. Why have the authors undertaken this work?
The reason for our study is the relatively limited presence of research in the literature examining the relationship between stock returns and IPO trends. Through this study, we aim to contribute to the literature. We believe that the findings of this analysis, particularly focusing on the developed G-7 countries' capital markets, could serve as guiding insights for researchers delving into this subject.

4. Literature review is outdated. It is required to study the papers published in 2020-2023.
As mentioned earlier, there exists a significant subject area in the literature on this matter. Consequently, the number of studies available is limited. Furthermore, due to its publication in 2021, this study naturally does not encompass research from the years 2022 and 2023. Nevertheless, three relevant studies published between 2021 and 2023 have been included in the literature.

5. The selection of methodology needs justification 6.The results need to be aligned with the key research questions and limitations need to be elaborated.
In panel data analysis, the dataset encapsulates information from different cross-sections (e.g., various firms or countries) and across different time dimensions (e.g., different time periods). The Discroll-Kraay regression analysis is a method utilized for analyzing data within a panel data structure. This method specifically provides robust predictions against common issues such as autocorrelation and heteroscedasticity present in panel datasets.
The Discroll-Kraay method is recommended to address concerns like autocorrelation and heteroscedasticity in panel data analysis. Autocorrelation denotes the situation where relationships between observations remain similar over time, whereas heteroscedasticity indicates varying variance among observations. By addressing such scenarios, this method ensures more reliable and robust regression outcomes.
Moreover, the Discroll-Kraay method is widely recognized in the literature and is acknowledged for its reliability and effectiveness in panel data analysis. Hence, this method has been chosen for panel data analysis and forms part of the reasons behind its selection in this study.
First of all, we’d like to thank the respected professor reviewer for invaluable comments. We consider all these comments as crucial to improve our study. You can kindly find responses to your comment as follows:

1. The paper suffers from flow and connectivity issues
We improved this issue. Thank you very much.

2. Why will a researcher be interested to read this article? What is the value addition?
The study aims to assist investors in the stock market with market timing. By examining the relationship between macroeconomic indicators and the hot and cold issue market, it is intended to provide higher returns to investors. Investors can use macroeconomic indicators as a signal to gain insight into the trend of initial public offerings (IPOs) and, accordingly, time their investments to achieve higher short-term returns.

3. Why have the authors undertaken this work?
The reason for our study is the relatively limited presence of research in the literature examining the relationship between stock returns and IPO trends. Through this study, we aim to contribute to the literature. We believe that the findings of this analysis, particularly focusing on the developed G-7 countries' capital markets, could serve as guiding insights for researchers delving into this subject.

4. Literature review is outdated. It is required to study the papers published in 2020-2023.
As mentioned earlier, there exists a significant subject area in the literature on this matter. Consequently, the number of studies available is limited. Furthermore, due to its publication in 2021, this study naturally does not encompass research from the years 2022 and 2023. Nevertheless, three relevant studies published between 2021 and 2023 have been included in the literature.

5. The selection of methodology needs justification 6.The results need to be aligned with the key research questions and limitations need to be elaborated.
In panel data analysis, the dataset encapsulates information from different cross-sections (e.g., various firms or countries) and across different time dimensions (e.g., different time periods). The Discroll-Kraay regression analysis is a method utilized for analyzing data within a panel data structure. This method specifically provides robust predictions against common issues such as autocorrelation and heteroscedasticity present in panel datasets.
The Discroll-Kraay method is recommended to address concerns like autocorrelation and heteroscedasticity in panel data analysis. Autocorrelation denotes the situation where relationships between observations remain similar over time, whereas heteroscedasticity indicates varying variance among observations. By addressing such scenarios, this method ensures more reliable and robust regression outcomes.
Moreover, the Discroll-Kraay method is widely recognized in the literature and is acknowledged for its reliability and effectiveness in panel data analysis. Hence, this method has been chosen for panel data analysis and forms part of the reasons behind its selection in this study.
Competing Interests: No competing interests were disclosed. Close
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Reviewer Report 14 Jul 2023

Daniel Penido L. Amorim, Universidade Federal de Minas Gerais, Belo Horizonte, State of Minas Gerais, Brazil

Not Approved

https://doi.org/10.5256/f1000research.134892.r182871

The article "Macro-economic determinants of the IPO waves: An assessment of G-7 countries" aimed to analyze which macroeconomic factors affect the number of IPOs in the G7 countries. The authors estimated a static model using a conventional panel data econometric method (random effects model). The results suggest that stock market returns influence the number of IPOs.

The results are not new. Many articles on the topic have also found positive effects of stock market returns (or prices) on IPOs. However, the study contributes to the literature because it considers the countries that compose the G7. Adding this contribution and others in the introduction would be relevant to improving the article. Except for this, the introduction is good.

Regarding the literature review, some citations are missing in the first paragraph of this section. The way the authors wrote the paragraphs, dedicating each to a single study, is not convenient. The authors should discuss the articles together, according to some questions they have in common. Furthermore, the authors need to add some articles on IPOs that are more recent. I list these articles at the end of this referee report.

It is necessary to add at least the variables of interest rate and economic policy uncertainty (Backer et al., 2016) to the analysis. In several previous studies, the authors considered the interest rate in the models. Effects of economic policy uncertainty have appeared in recent studies such as Thanh (2020) and Amorim et al. (2021).

The authors stated that the data used had a normal distribution, but this is not what the Jarque-Bera test suggests for some variables in the table of summary statistics. Moreover, the authors can remove the less common summary statistics from this table. IPO number data from Statista does not appear to be openly available, even after a basic registration.

The more critical problems in the article arise from the econometric methods employed. The Hausman test is an asymptotic method. Therefore, it is not convenient to choose between fixed effects and random effects models based on it when the panel has only seven countries.

Still on the models, we know that the process of deciding to go public (IPO) is dynamic. Agents can make these decisions with information from past periods. Therefore, it is common that the empirical models include lags. Moreover, stock prices affect IPOs because of the choice to go public in periods that favor IPO proceeds. However, IPOs also affect stock prices because these events cause stock market fluctuations. Thus, endogeneity is probable. Finally, there is also cross-sectional dependence. This requires the use of more advanced econometric methods than conventional panel data estimators with robust standard errors.

I recommend that the authors estimate a dynamic model for long panel data, specifically the cross-sectionally augmented ARDL (CS-ARDL) approach (Chudik & Pesaran, 2015). The lags included in this model will overcome endogeneity and autocorrelation. The author can apply this method with variables I(0) (stationary) and I(1) (with a unit root). In addition, the method deals with cross-sectional dependence. In this approach, it has been common to use some tests jointly (two of them even the authors have used), which the authors can apply using the Stata software:

Pesaran's (2007) CIPS unit root test [command xtcips];
Pesaran’s (2021) cross-sectional dependence CD test [command xtcd];
Westerlund's (2007) cointegration test [command xtwest];
Chudik and Pesaran's (2015) CS-ARDL estimator [command xtdcce2]

These methods appear in studies such as Herzer (2019), Vo et al. (2022), and Uddin et al. (2023). Other dynamic models (for example, DOLS and FMOLS estimators) can be useful as robustness tests.

Finally, the article needs to have the text revised. The authors need to improve their writing. Firstly, they need to use accurate vocabulary. For example, it is better to write "evidence indicates" or "evidence suggests" when referring to the results (no single study has a result "determinant"). There are well-known concepts that the authors miswrite, such as "resistant standard estimator" (I believe they meant "robust standard error" or "robust estimator") and "macro-economics" (do not use a hyphen). Furthermore, authors should avoid adopting passive voice in English and writing very long sentences. Some tools, such as Grammarly and Quillbot, contribute to the improvement of texts written by non-native English speakers.

I hope the authors will continue to seek publication of the article.

References

Ameer, R. (2012). Macroeconomic factors and initial public offerings (IPOs) in Malaysia. Asian Academy of Management Journal of Accounting and Finance, 8(1), 41–67. https://ejournal.usm.my/aamjaf/article/view/aamjaf_vol8-no1-2012_3

Amorim, D. P. L., Camargos, M. A., & Ferreira, C. M. B. L. (2021). Macroeconomic Factors and Initial Public Offerings in Brazil. Global Business Review. https://doi.org/10.1177/09721509211003696

Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636. https://doi.org/10.1093/qje/qjw024

Chudik, A., & Pesaran, M. H. (2015). Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors. Journal of Econometrics, 188(2), 393–420. https://doi.org/10.1016/j.jeconom.2015.03.007

Deloof, M., de Jong, A., & Legierse, W. (2022). Going public: evidence from stock and bond IPOs in Belgium, 1839–1935. Cliometrica. https://doi.org/10.1007/s11698-022-00258-5

Herzer, D. (2018). The long-run effect of aid on Health: Evidence from panel cointegration analysis. Applied Economics, 51(12), 1319–1338. https://doi.org/10.1080/00036846.2018.1527449

Mehmood, W., Mohd-Rashid, R., & Ahmad, A. H. (2020). The variability of IPO issuance: evidence from Pakistan stock exchange. Global Business Review. https://doi.org/10.1177/0972150920929198

Mehmood, W., Mohd-Rashid, R., Abdullah, Y. et al. (2023). Inclusive mapping of initial public offerings: a bibliometric and literature review study. Quality & Quantity, 57, 655–700. https://doi.org/10.1007/s11135-022-01387-9

Mehmood, W., Mohd-Rashid, R., Ong, C.Z., & Abbas, Y.A. (2021). Factors driving IPO variability: evidence from Pakistan stock exchange. Journal of Economics, Finance and Administrative Science, 26(52), 300-316. https://doi.org/10.1108/JEFAS-04-2021-0036

Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265–312. https://doi.org/10.1002/jae.951

Pesaran, M. H. (2020). General diagnostic tests for cross-sectional dependence in panels. Empirical Economics, 60(1), 13–50. https://doi.org/10.1007/s00181-020-01875-7

Uddin, I., Ahmad, M., Ismailov, D., Balbaa, M. E., Akhmedov, A., Khasanov, S., & Haq, M. U. (2023). Enhancing institutional quality to boost economic development in developing nations: New insights from CS-ARDL approach. Research in Globalization, 7, 100137. https://doi.org/10.1016/j.resglo.2023.100137

Vo, D. H., Ho, C. M., Le, Q. T., & Vo, A. T. (2022). Revisiting the energy-growth-environment nexus in the OECD countries: An application of the CS-ARDL approach. Energy, Sustainability and Society, 12(1). https://doi.org/10.1186/s13705-022-00375-z

Westerlund, J. (2007). Testing for error correction in panel data. Oxford Bulletin of Economics and Statistics, 69(6), 709–748. https://doi.org/10.1111/j.1468-0084.2007.00477.x

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

References

1. Ameer R: Macroeconomic factors and initial public offerings (IPOs) in Malaysia. Asian Academy of Management Journal of Accounting and Finance. 2012; 1 (8): 41-67 Reference Source
2. Amorim D, Camargos M, Ferreira C: Macroeconomic Factors and Initial Public Offerings in Brazil. Global Business Review. 2021. Publisher Full Text
3. Baker S, Bloom N, Davis S: Measuring Economic Policy Uncertainty*. The Quarterly Journal of Economics. 2016; 131 (4): 1593-1636 Publisher Full Text
4. Chudik A, Pesaran M: Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors. Journal of Econometrics. 2015; 188 (2): 393-420 Publisher Full Text
5. Deloof M, de Jong A, Legierse W: Going public: evidence from stock and bond IPOs in Belgium, 1839–1935. Cliometrica. 2022. Publisher Full Text
6. Herzer D: The long-run effect of aid on health: evidence from panel cointegration analysis. Applied Economics. 2019; 51 (12): 1319-1338 Publisher Full Text
7. Mehmood W, Mohd-Rashid R, Ahmad A: The Variability of IPO Issuance: Evidence from Pakistan Stock Exchange. Global Business Review. 2020. Publisher Full Text
8. Mehmood W, Mohd-Rashid R, Abdullah Y, Patwary A, et al.: Inclusive mapping of initial public offerings: a bibliometric and literature review study. Quality & Quantity. 2023; 57 (1): 655-700 Publisher Full Text
9. Mehmood W, Mohd-Rashid R, Ong C, Abbas Y: Factors driving IPO variability: evidence from Pakistan stock exchange. Journal of Economics, Finance and Administrative Science. 2021; 26 (52): 300-316 Publisher Full Text
10. Pesaran M: A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics. 2007; 22 (2): 265-312 Publisher Full Text
11. Pesaran M: General diagnostic tests for cross-sectional dependence in panels. Empirical Economics. 2021; 60 (1): 13-50 Publisher Full Text
12. Uddin I, Ahmad M, Ismailov D, Balbaa M, et al.: Enhancing institutional quality to boost economic development in developing nations: New insights from CS-ARDL approach. Research in Globalization. 2023; 7. Publisher Full Text
13. Vo D, Ho C, Le Q, Vo A: Revisiting the energy-growth-environment nexus in the OECD countries: An application of the CS-ARDL approach. Energy, Sustainability and Society. 2022; 12 (1). Publisher Full Text
14. Westerlund J: Testing for Error Correction in Panel Data. Oxford Bulletin of Economics and Statistics. 2007; 69 (6): 709-748 Publisher Full Text

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Financial Economics; Econometrics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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Reviewer Report 28 Jun 2023

Cemal Zehir, Yıldız Technical University, Istanbul, Turkey

Approved

https://doi.org/10.5256/f1000research.134892.r177271

In this peer-reviewed article, it has been examined whether the macroeconomic factors that are thought to be effective on the public offering cycle, and especially economic growth, inflation and stock market returns, are determinants of the number of public offerings. From this point of view, it will contribute to the literature.

The analysis employs a random effect model and applies the Driscoll-Kraay resistant standard estimator to account for deviations.

The findings reveal that stock market returns have a positive and significant effect on the volume of public offerings. This suggests that companies tend to time their IPOs during periods when stock market returns are relatively high.

This research provides insights into the factors influencing companies' decision-making processes regarding IPO timing, specifically emphasizing the role of macroeconomic indicators. By understanding the relationship between stock market performance and IPO frequency, policymakers and investors can gain valuable insights for strategic decision-making.

The analysis has been applied to the G-7 countries representing the developed world economies for the period 1999-2020. The countries analyzed and the fact that it covers a period of 20 years will be useful information for the readers.

Research results are also a guide for practitioners. This article is suitable for publication.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Strategic management, business administration, and economist,

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Version 2

VERSION 2 PUBLISHED 08 Jul 2022

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Cemal Zehir, Yıldız Technical University, Istanbul, Turkey
Daniel Penido L. Amorim, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
Sanjib Biswas, Calcutta Business School, Kolkata, India
Kamil Gemra, SGH Warsaw School of Economics, Warsaw, Poland

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03 Feb 2025 | for Version 2

Kamil Gemra, SGH Warsaw School of Economics, Warsaw, Poland

9 Views Cite this report Responses(0)

Approved With Reservations

The authors have undertaken an important research topic. The issue of factors influencing the IPO process is widely commented on in the literature. Here, it would be worth citing more new literature, especially that published after 2020. I think that the authors could supplement this.

The work is interesting because it draws attention to macroeconomic factors. However, their selection is controversial. First of all, the level of interest rates is missing in this study. After all, this mainly affects investors' decisions regarding the asset class in which they invest their capital. Another factor is the exchange rate and there is nothing about it here either. Among the listed macroeconomic factors, attention is often drawn to the level of unemployment. These factors are not in the article. If the authors believe that they are not worth including, they should write why they do not want to include them.

It follows from the entire work that the only factor is the rate of return on the stock market. I think it is obvious that the number of IPOs will increase during a bull market and decrease during a bear market. Returning to the previous comment, I wonder why the number of macroeconomic factors is small, and the factor related to a given stock market is controversial. Maybe it is worth expanding the number of factors and then approaching the study.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

My research areas are financing, especially with regard to the capital market. Therefore, the subject of IPO is close to me.

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2 Views

16 Dec 2024 | for Version 2

Cemal Zehir, Yıldız Technical University, Istanbul, Turkey

2 Views Cite this report Responses(0)

Approved

The article “Macro Economic Determinants of the IPO Waves: An Assessment of G7 Countries” is a highly insightful contribution to the field of financial economics. By examining the macroeconomic factors that drive IPO waves across G7 countries, the authors offer a unique perspective that bridges global economic trends with financial market activity.

The comprehensive analysis of variables such as GDP growth, interest rates, and market sentiment provides a well-rounded understanding of how broader economic conditions influence the timing and frequency of IPOs. The comparative approach across G7 countries adds significant value, highlighting both commonalities and country-specific dynamics.

Furthermore, the paper is exceptionally relevant for policymakers, investors, and scholars alike, offering actionable insights into how macroeconomic policies and global economic shifts can impact equity markets. The authors’ rigorous methodology and clear presentation make this work not only academically robust but also practically accessible.

Overall, this paper is a commendable effort that advances our understanding of IPO waves and their macroeconomic underpinnings, setting the stage for further research in this critical area.

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Strategic management, business administration, and economist,

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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8 Views

27 Feb 2024 | for Version 2

Daniel Penido L. Amorim, Universidade Federal de Minas Gerais, Belo Horizonte, State of Minas Gerais, Brazil

8 Views Cite this report Responses(0)

Not Approved

The authors did not make the suggested changes.

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Financial Economics; Econometrics

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40 Views

15 Aug 2023 | for Version 1

Sanjib Biswas, Calcutta Business School, Kolkata, India

40 Views Cite this report Responses(1)

Approved With Reservations

The paper addresses a relevant issue which is however not very new. I find the following concerns

The paper suffers from flow and connectivity issues
Why will a researcher be interested to read this article? What is the value addition?
Why have the authors undertaken this work?
Literature review is outdated. It is required to study the papers published in 2020-2023.
The selection of methodology needs justification
The results need to be aligned with the key research questions and limitations need to be elaborated

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Multicriteria Decision Making, Multivariate analysis, Logistics & Supply Chain Management, Sustainability

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Author Response

29 Jan 2024

Esin Benhür aktürk , Management organization, İstanbul Aydin University, İstanbul, Turkey

First of all, we’d like to thank the respected professor reviewer for invaluable comments. We consider all these comments as crucial to improve our study. You can kindly find responses to your comment as follows:

1. The paper suffers from flow and connectivity issues
We improved this issue. Thank you very much.

2. Why will a researcher be interested to read this article? What is the value addition?
The study aims to assist investors in the stock market with market timing. By examining the relationship between macroeconomic indicators and the hot and cold issue market, it is intended to provide higher returns to investors. Investors can use macroeconomic indicators as a signal to gain insight into the trend of initial public offerings (IPOs) and, accordingly, time their investments to achieve higher short-term returns.

3. Why have the authors undertaken this work?
The reason for our study is the relatively limited presence of research in the literature examining the relationship between stock returns and IPO trends. Through this study, we aim to contribute to the literature. We believe that the findings of this analysis, particularly focusing on the developed G-7 countries' capital markets, could serve as guiding insights for researchers delving into this subject.

4. Literature review is outdated. It is required to study the papers published in 2020-2023.
As mentioned earlier, there exists a significant subject area in the literature on this matter. Consequently, the number of studies available is limited. Furthermore, due to its publication in 2021, this study naturally does not encompass research from the years 2022 and 2023. Nevertheless, three relevant studies published between 2021 and 2023 have been included in the literature.

5. The selection of methodology needs justification 6.The results need to be aligned with the key research questions and limitations need to be elaborated.
In panel data analysis, the dataset encapsulates information from different cross-sections (e.g., various firms or countries) and across different time dimensions (e.g., different time periods). The Discroll-Kraay regression analysis is a method utilized for analyzing data within a panel data structure. This method specifically provides robust predictions against common issues such as autocorrelation and heteroscedasticity present in panel datasets.
The Discroll-Kraay method is recommended to address concerns like autocorrelation and heteroscedasticity in panel data analysis. Autocorrelation denotes the situation where relationships between observations remain similar over time, whereas heteroscedasticity indicates varying variance among observations. By addressing such scenarios, this method ensures more reliable and robust regression outcomes.
Moreover, the Discroll-Kraay method is widely recognized in the literature and is acknowledged for its reliability and effectiveness in panel data analysis. Hence, this method has been chosen for panel data analysis and forms part of the reasons behind its selection in this study.

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No competing interests were disclosed.

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14 Jul 2023 | for Version 1

Daniel Penido L. Amorim, Universidade Federal de Minas Gerais, Belo Horizonte, State of Minas Gerais, Brazil

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Not Approved

Pesaran's (2007) CIPS unit root test [command xtcips];
Pesaran’s (2021) cross-sectional dependence CD test [command xtcd];
Westerlund's (2007) cointegration test [command xtwest];
Chudik and Pesaran's (2015) CS-ARDL estimator [command xtdcce2]

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

References

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Financial Economics; Econometrics

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28 Jun 2023 | for Version 1

Cemal Zehir, Yıldız Technical University, Istanbul, Turkey

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Approved

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Strategic management, business administration, and economist,

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Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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Macro-economic determinants of the IPO waves: An assessment of G-7 countries

Abstract

Keywords

Introduction

Research limitations and further research suggestions

Literature review

Methods

Dataset and sample structure

Table 1. Descriptions of variables.

Methodology

Results

Descriptive statistics

Table 2. Descriptive statistics.

Table 3. Correlation analysis.

Unit root test

Table 4. Pesaran (2004) cross-section dependency (CD) test.

Table 5. Unit root test results.

Econometric model

Table 6. Hausman test results.

Table 7. Autocorrelation test.

Table 8. Heteroscedasticity test.

Table 9. Regression with Driscroll - Kraay standard errors.

Discussion and conclusions

Data availability

Underlying data

References

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