Nonlinear determinants of listing day returns: Evidence from spline regression analysis
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Author(s)Amit Kumar SinghLink to ORCID Index: https://orcid.org/0000-0002-8095-7237
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Mamta DhandaLink to ORCID Index: https://orcid.org/0000-0003-4038-8452
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DOIhttp://dx.doi.org/10.21511/imfi.23(1).2026.29
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Article InfoVolume 23 2026, Issue #1, pp. 395-410
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2 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
Type of the article: Research Article
Abstract
This study aims to identify the nonlinear determinants of listing day returns globally using a comprehensive dataset. It further examines the regional distinctions and complexities in the international context using spline regression analysis. It also assesses variation in the linear and nonlinear relationships of listing day returns and their determinants across various geographical regions worldwide. Using a set of 8,914 initial public offerings issued across the globe from January 2011 to October 2024, this study employs a restricted cubic spline methodology. Spline knots for each determinant under study were identified to examine the nonlinear influence of the already studied determinant variables. The results of the analysis depict the offer price and listing delay as major non-linear determinants, whereas issue size and market timing significantly influence listing day returns based on linear analysis. In addition, it was found that the Asia-Pacific market substantially differs from other markets geographically, based on splines. The findings of this study provide valuable insights for associated stakeholders by focusing on issue performance, predictions, and market understanding. There is a substantial presence of nonlinear relations among listing day returns and their determinants worldwide.
- Keywords
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JEL Classification (Paper profile tab)G01, G10, G15
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References52
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Tables9
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Figures7
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- Figure 1. Evolution of listing day returns
- Figure 2. Listing day returns across the globe
- Figure 3. Regional listing day returns across the globe
- Figure 4. Spline graph: Adjusted (price)
- Figure 5. Spline graph: Marginal difference (price)
- Figure 6. Spline graph: Adjusted (listing delay)
- Figure 7. Spline graph: Marginal difference (listing delay)
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- Table 1. Sample derivation
- Table 2. Variable description
- Table 3. Correlation matrix
- Table 4. Variance inflation factor
- Table 5. Regression analysis
- Table 6. Regional regression analysis
- Table 7. Restricted cubic spline regression analysis
- Table 8. Restricted cubic spline regression: Regional analysis
- Table 9. Restricted cubic spline regression: Regional analysis
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The impact of listing on banks’ performance: case study from Saudi Arabia
Mohammad Alsharif
doi: http://dx.doi.org/10.21511/bbs.15(3).2020.01
Using quarterly data from 2010 to 2019, this paper investigates the impact of listing status on the performance of the National Commercial Bank (NCB), the largest commercial bank in Saudi Arabia, by applying a combination of financial ratios analysis and efficient frontier analysis with a mix of parametric and non-parametric tests. The overall results show that although the NCB performance is superior compared to their counterparts, this superiority has deteriorated after the bank was listed in 2014. This result was captured by the deterioration in the efficiency measures of NCB, indicating the significance of using the efficient frontier analysis as an additional monitoring tool by the Saudi regulators. The financial ratios analysis also shows that even though the NCB profitability has increased, there is an increase in the bank’s overall risk after being listed. Therefore, Saudi regulators should closely monitor their listed banks as these banks are directed toward high-risk assets.
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Influence of world stock markets on the development of the stock market in Ukraine
Inna Shkolnyk ,
Serhiy Frolov ,
Volodymyr Orlov ,
Viktoriia Dziuba ,
Yevgen Balatskyi
doi: http://dx.doi.org/10.21511/imfi.18(4).2021.20
Investment Management and Financial Innovations Volume 18, 2021 Issue #4 pp. 223-240 Views: 1212 Downloads: 501 TO CITE АНОТАЦІЯViewing the development of the stock market in Ukraine, the economy, which world financial organizations characterize as small and open, is largely determined by the trends formed by the global stock markets and leading stock exchanges. Therefore, the study aims to analyze Ukraine’s stock market, the world stock market, stock markets in the regions, and to assess their mutual influence. The study uses the data of the World Federation of Exchanges and National Securities and Stock Market Commission (Ukraine) from 2015 to 2020. Stock market performance forecasts are built using triple exponential smoothing. Based on pairwise correlation coefficients, the existence of a significant dependence in the development of the world stock market on the development of the American stock market was determined. Regarding the Ukrainian stock exchanges, only SE “PFTS” demonstrated its dependence on the US stock market. The results of the regression model based on an exponentially smoothed series of trading volumes in all markets showed that variations in the volume of trading on the world stock market are due to the situation on the US stock markets. Trading volume dynamics on Ukrainian stock exchanges such as SE “PFTS” and SE “Perspektiva” is almost 50% determined by the development of stock markets in the American region. Although Ukraine is geographically located in Europe, the results show a lack of significant links and the impacts of stock markets in this region on the major Ukrainian stock exchanges and the stock market as a whole.
