Abstract:
During the period 1994 to 2020, a total of 18 firms in Kenya floated
16,530,781,060 shares at the Nairobi Securities Exchange (NSE) under Initial
Public Offerings (IPOs) raising over Kshs 91 billion. These stocks were significantly over-subscribed with the highest hitting 830%. The NSE became
fully automated in 2006. Similarly, in Africa between 2010 and 2019 there
were a total of 215 IPOs raising over Kshs 1.6 trillion. This could be explained
by divergence of opinion hypothesis. The initial returns were positive. However, in the long run, most of the firms underperformed. This under performance leads to losses incurred by investors and possible collapse of brokerage
and investment firms leaving investors with a bitter taste. This study will undertake to establish the effects of firm specific factors on IPO stock performance at the NSE in Kenya. The specific objectives will be: to establish the
effect of firm size on performance of IPO stocks at the NSE in Kenya, to determine the effect of age of firm on performance of IPO stocks at the NSE in
Kenya, to evaluate the effect of firm board composition on performance of
IPO stocks at the NSE in Kenya, to establish the effect of firm ownership
structure on performance of IPO stocks at the NSE in Kenya, and to analyze
the moderating effect of automation on the firm specific factors and performance of IPO stocks at the NSE in Kenya. The study will be built upon major
theoretical streams: Random Walk theory, Winners curse theory, Dow
Theory, Signaling theory and Agency theory and contextualize them to firm
specific factors and performance of IPO stocks. More studies have previously
been undertaken on the pricing of IPO at the NSE in Kenya and the few that
studied on performance of IPO stocks at the NSE in Kenya have provided
mixed findings depending on the methodology used. None of the studies asfar as research has shown have considered the automation of NSE in Kenya as
a moderating effect of performance of IPO stocks. The sample size will be the
same as population of 18 IPO firms between 1994 and 2020 with 8 IPOs during pre-automation and 10 IPOs post-automation period. This will be a longitudinal and event study that will adopt a descriptive study design. Data will
be analyzed using the Econometric Views (Eviews). Hausman test, Augmented Dickey Fuller (ADF) test and other diagnostic tests will be applied
to the panel data. The Capital Assets Pricing Model (CAPM) and the Nairobi 20 Share Index will be used as the benchmarks of performance of IPO
stocks.
