IPO subscription dynamics: A comprehensive inquiry into the Indian stock market

  • 456 Views
  • 297 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

The Indian IPO market showcased resilience during the global stock market downturn in 2022, emerging as a notable bright spot in regions such as Europe, the Middle East, India, and Africa. As the bullish rally of 2022 persists, Indian stock markets remain enticing for foreign institutional investors in 2023. A resurgence in IPO activity is anticipated, driven by increasing momentum and larger deals that are poised to overcome the constraints of subdued global sentiments and liquidity pressures, addressing the challenges posed by these factors. The study offers insights into factors influencing IPO subscriptions, capitalizing on the context of heightened stock market volatility and optimistic trends in the Indian stock market. A total of 132 IPOs listed on the Indian stock market between April 2019 and March 2023 were analyzed in this study. Multiple Linear Regression was used to assess the strength of the association between several factors outlined in the literature, and the overall subscription. Among the ten variables investigated in the study, it was observed that three variables under the external factors, specifically Grey Market Premium, IPO Rating, and Broker Recommendations, exerted a significant influence on the overall subscription. While other factors such as allocation proportion and issue attributes, were found to have no discernible influence on the overall subscription. The results indicate that the Indian IPO market demonstrates a prevalence of speculative behavior and a stronger reliance on expert recommendations, rather than being primarily driven by IPO characteristics.

Acknowledgment
Authors acknowledge that the publication fee is funded by Kingdom University, Bahrain.

view full abstract hide full abstract
    • Figure 1. Conceptual framework
    • Figure 2. Actual vs. predicted plot
    • Figure 3. Predictor profiler
    • Figure 4. Normal plot
    • Table 1. Summary of model fit
    • Table 2. Analysis of variance
    • Table 3. Parameter estimates
    • Table 4. Parameter estimate population
    • Conceptualization
      Chetan Shetty, Vinish P., Prakash Pinto
    • Data curation
      Chetan Shetty, Sumera Aluru
    • Investigation
      Chetan Shetty, Sumera Aluru, Prakash Pinto, Iqbal Thonse Hawaldar
    • Writing – original draft
      Chetan Shetty, Vinish P., Sumera Aluru, Prakash Pinto
    • Formal Analysis
      Vinish P., Sumera Aluru, Prakash Pinto
    • Methodology
      Vinish P., Prakash Pinto, Iqbal Thonse Hawaldar
    • Visualization
      Sumera Aluru, Prakash Pinto, Iqbal Thonse Hawaldar
    • Writing – review & editing
      Sumera Aluru, Prakash Pinto, Iqbal Thonse Hawaldar
    • Project administration
      Prakash Pinto, Iqbal Thonse Hawaldar
    • Supervision
      Prakash Pinto, Iqbal Thonse Hawaldar
    • Validation
      Prakash Pinto, Iqbal Thonse Hawaldar
Related Articles

  • Modeling Indian Bank Nifty volatility using univariate GARCH models

    Nikhil M. N.ORCID Researcher ID, Suman ChakrabortyORCID Researcher ID, Lithin B. M.ORCID Researcher ID, Sanket LedwaniORCID Researcher ID, SatyakamORCID Researcher ID doi: http://dx.doi.org/10.21511/bbs.18(1).2023.11
    Banks and Bank Systems Volume 18, 2023         Issue #1         pp. 127-138 Views: 1078 Downloads: 296 TO CITE АНОТАЦІЯ

    The crumble of financial markets due to the recent crises has wobbled precariousness in the stock market and intensified the returns vulnerability of banking indices. Against this backdrop, this study intends to model the volatility of the Indian Bank Nifty returns using a battery of GARCH specifications. The finding of the present research contributes to the literature in three ways. First, volatility during the sample period, which corresponds to a time of stress (a bear market), is more persistent, with an estimated coefficient of 0.995695. Moreover, when volatility rises, it persists for a long time before returning to the mean in an average of 16 days. Second, for a positive γ, the results insinuate the possibility of an “anti-leverage effect” with a coefficient of 0.139638. Thus, the volatility of the Bank Nifty returns tends to rise in response to positive shocks relative to negative shocks of equal magnitude in India. Finally, the findings demonstrate that EGARCH with Student’s t-distribution offers lower forecast errors in modeling conditional volatility.

  • Correlation of stock market returns in the West African region from 2008 to 2016

    Eseosa ObadiaruORCID , Alex Omankhanlen , Barnabas ObasajuORCID , Henry InegbedionORCID doi: http://dx.doi.org/10.21511/imfi.16(3).2019.12
    Investment Management and Financial Innovations Volume 16, 2019         Issue #3         pp. 120-130 Views: 858 Downloads: 141 TO CITE АНОТАЦІЯ

    Stock markets over the world have become more interconnected due to activities of foreign investors in search for alternative financial assets and markets to invest in order to diversify their portfolio. Stock market indices and index returns have been known to reflect linkages between different markets. This study assesses the extent of correlation of stock market index returns in West Africa and those of the United States of America (US) and United Kingdom (UK) from 2008 to 2016. The correlation between the index returns for the entire sample period and yearly samples were considered for Nigeria, Ghana, the BRVM, the USA and the UK. The indices selected for the five countries considered are the Nigerian All-Share Index, Ghanaian Composite Index, the BRVM Composite Index, the Financial Times 100 Index and the Standards and Poor’s 500 Index. Daily index returns data were used for the study and analyzed using correlation and multiple regression analysis. Findings revealed that the returns of the pairs of the United States of America (US) and the United Kingdom (UK) exhibited stronger positive correlation with each other than the other market pairs in the study both in the entire sample period and the yearly sub-period analysis. The correlations between the other market pairs were either positively or negatively weak or very weak indicating more diversification opportunities.

Download Preview
Close
Investment Management and Financial Innovations
Investment Management and Financial Innovations