The Effect of Automation on Stock Market Price Volatility: A Case of Nairobi Securities Exchange

Omuchesi, Jonathan Ang ani; Bosire, Mary

doi:10.9790/5933-0537179

Cited by 5 publications

(9 citation statements)

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“…It was found out that macroeconomic policies of Ghana were characterized by volatile and generally high inflation, high interest rates and large exchange rate swings. Omuchesi, et al (2014) established that the introduction of the Automatic Trading System did not have statistically significant effect on market efficiency. Therefore, automation didn't provide the expected benefits of improving efficiency of the Nairobi Securities Exchange.…”

Section: 1mentioning

confidence: 99%

“…This study used Case 2 as outlined by Baron and Kenny (1986) since automation is dichotomous given the fact that the periods of study are divided into twopost and pre-automation whereby automation happened in 2006. Similar basis of measurement was applied by (Omuchesi, et al 2014) except that the study did not consider implementation period of 54 months in their sample of post automation periods. The study tested the following null hypothesis: Firm Size is market capitalization of firms at the end of the first day of the IPO (Switzer and Zhai, 2019).…”

Section: Moderating Effect Of Automation On the Relationship Between ...mentioning

confidence: 99%

“…It has been observed that electronic trading enhances efficient settlement and reduces transaction processing costs, any opportunity costs and associated risks (Simiyu, et al 2014). Technological breakthrough does not only benefit Capital markets automation as one of the players of the financial trading game, but also all the players in the sector (Omuchesi, et al 2014). Security exchange automation started in the 1970s and the transaction of securities became electronically traded through the support of information and communication technology (Jain, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…The automation of NSE took place in 2006.The performance of IPO stocks before and after this event was studied and analyzed. These strategies were used byOmuchesi et al, (2014),Kaya (2012),Odipo, et al (2020).…”

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confidence: 99%

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The Moderating Effect of Automation on Firm Specific Factors and Performance of Initial Public Offering Stocks at The Nairobi Securities Exchange In Kenya

Okumu¹,

Olweny²,

Muturi³

2022

Journal of Applied Finance &Amp; Banking

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Performance of IPO stocks depicts two anomalies: initial underpricing and long run underperformance. A plethora of studies have established the causes of these IPO puzzle to include information asymmetry, institutional ownership, IPO size, market volatility, the intrinsic value of the IPO and issue characteristics among others. None of these studies on IPO performance as far as research has shown considered moderating effect of automation of securities market on firm specific factors. Kenya’s equity market capitalization has been on a downward trend being surpassed by fixed income securities. IPO stocks performance offer long-term as well as short-term capital to companies. This study attempts to establish whether there is a nexus between NSE automation and firm specific factors regarding performance of IPO stocks at NSE. Longitudinal and descriptive study designs were used on the secondary panel data. Multiple linear regression was used to analyze the data with Stata statistical software. The Hausman test was used to determine between fixed and random effect model. The results showed that automation had negative correlation with firm age and positive correlation with firm size. The study will assist the Kenyan government in developing financial stability measures and investors in making informed decisions. Keywords: Automation, Firm specific factors, Initial Public Offerings, Nairobi Securities Exchange.

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Section: 1mentioning

confidence: 99%

Section: Moderating Effect Of Automation On the Relationship Between ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Moderating Effect of Automation on Firm Specific Factors and Performance of Initial Public Offering Stocks at The Nairobi Securities Exchange In Kenya

Okumu¹,

Olweny²,

Muturi³

2022

Journal of Applied Finance &Amp; Banking

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“…Performance of IPO stocks is a combination of both price and returns. Previous studies on performance of IPO stocks at the NSE include: Simiyu, Mutunga, Barasa and Matete (2016) established that 51.5% of the variation in long-run performance of shares was explained jointly by 1st day pricing differential between the offer price and closing day one price; Kiluku (2014) found positive relationship between IPO price and the first day price with a significance level of underpricing; Mwendwa (2014) established that the results of IPO performance Open Journal of Business and Management differed from one methodology to the other; Wamari (2014) found that share price, price earnings ratio and the overall stock performance decreased in the long run after IPO and the earnings per share increased after IPO; Kinyua, Nyanumba, Gathaiya and Kithitu (2014) revealed a positive relationship between IPO and firm performance and Omuchesi and Bosire (2014) established that the introduction of the ATS had no statistically significant effect on market efficiency.…”

Section: Statement Of the Problemmentioning

confidence: 99%

Theoretical Review of Effect of Firm Specific Factors on Performance of Initial Public Offering Stocks at the Nairobi Securities Exchange in Kenya

Okumu¹,

Olweny²,

Muturi³

2021

OJBM

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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 as How to cite this paper: Okumu, A. B.,

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Impediment in Adaptation of Algorithm Trading: A Case of Frontier Stock Exchange

Rahat,

Siddiqui,

Pervez

et al. 2023

KJCIS

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The global financial markets have been significantly affected by the rapid change in technology. The study is an attempt to get to know the barriers to not adopting algorithmic trading in conventional stock exchanges. This research aims to plan and analytically proposed a model for explaining the reasons why frontier stock exchange traders and investors are hesitant to adopt algorithmic trading as a tool. The research includes variables; Lack of awareness, Trust, Lack of Government interest, unemployment, and unnecessary investment, which were extracted from previously available literature based on the theory of reason and technology acceptance model (TAM). A sample of 50 traders/investors from Pakistan stock markets was taken by using convenience sampling. Data was collected through a questionnaire and analyzed using correlation and linear regression techniques. The results show trust factor is the biggest hurdle in implementing Algorithm Trading which means countries like Pakistan which are following conventional methods for trading in stock markets have great doubts about the efficiency of Algorithm base trading because of the less human interaction and dependency on machines. Fear of miscalculation and the inexperience of data engineers are also one of the reasons conventional stock exchanges are reluctant to adopt algorithm trading. Similarly, variables like Lack of Government interest, unnecessary investment, and employment have a significant effect on the implementation of algorithm trading. Moreover, lack of awareness is the least significant factor, which shows the traders and investors in the Pakistan Stock Exchange are well aware of algorithm trading but the results cannot be generalized to the population due to a limited sample size of the study.

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The Effect of Automation on Stock Market Price Volatility: A Case of Nairobi Securities Exchange

Abstract: This study investigated the effect of the automation on stock market price volatility of the NairobiSecurity Exchange (NSE). Two study periods were considered, pre-automation

Cited by 5 publications

References 20 publications

The Moderating Effect of Automation on Firm Specific Factors and Performance of Initial Public Offering Stocks at The Nairobi Securities Exchange In Kenya

The Moderating Effect of Automation on Firm Specific Factors and Performance of Initial Public Offering Stocks at The Nairobi Securities Exchange In Kenya

Theoretical Review of Effect of Firm Specific Factors on Performance of Initial Public Offering Stocks at the Nairobi Securities Exchange in Kenya

Impediment in Adaptation of Algorithm Trading: A Case of Frontier Stock Exchange

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