Nairobi Securities Exchange (NSE) is considered as developing securities market and for many years has faced many challenges due to its low liquidity. NSE has continued to grow and implement reforms and innovations in order to raise their levels of efficiency. Between 2002 and 2009 a series of reforms were undertaken through the market regulator-Capital Markets Authority (CMA) and the exchange itself. Whether these reforms have improved the performance of bond market still remains unknown. The aim of this study was to investigate the effect of automation of bond trading on the performance of bond market at Nairobi Securities Exchange (NSE). Specifically the study sought; to determine the effect of automated bond trading on transactions cost, trading volumes, liquidity and growth of market size at the NSE. Data collection sheet was used to collect data from the NSE database spanning from January 2005 to December 2012. The data was divided into two sub periods corresponding to the pre-automation (January 2005-December 2008) and post automation period (January 2009-December 2012). The study adopted a comparative research design. The population comprised of all firms trading on bond market at the NSE from 2005 to 2012. This study used secondary data. Secondary data that targeted bond trading between the period 1 January 2005 and 31 December 2012 was identified from the NSE bulletins. The data collected was analyzed using descriptive statistics, correlations, and linear regression analysis. The influence of bond automation trading on the transaction cost is high and significantly affects its changes. The study findings indicated that; there is significant influence of bond market automation to the performance of the listed companies at NSE. Specifically, the effect of automation on bond trading determines the changes in the cost shift; the bond trading volume and market size changes are determined by the influence of the automation effect and the changes (shifts) in the bond trading liquidity are due to the influence of bond trading automation. In reaction to the current situation, policy should be imposed governing the bond trading which shall see efficiency in bond automation and consequently increasing the income of a nation through increased collection of revenue. Government securities market development must be viewed as a dynamic process in which continued macroeconomic and financial sector stability are essential to building an efficient market and establishing the credibility of the government as an issuer of debt securities.

Background of the Study 
The vital role played by capital markets all over the globe cannot be overemphasized. A capital market is a major barometer for measuring the aggregate performance in Kenya (Nzotta, 2002). Available evidence shows that there is a direct correlation between the level of development of a nation’s capital market and her overall social and economic development (Okereke-Onyiuke, 2000). There is therefore, the need for a fast growing capital market, through technological innovation so as to facilitate the speedy growth and development of an economy. Computer networks have altered the modus operandi of global stock markets. This is characterized by modern innovation and accompanying liberalization of global markets, which have influenced such markets. Older trading systems, which rely on personal contact between traders, are being replaced by computer networks in which traders throughout the world communicate and trade over microwaves and optical fibres (Wriston, 1999). Today it is strange to hear that a Stock Exchange is operating based on the call over system. 

Computerization of the order flow in financial markets began in the early 1970s, with some landmarks being the introduction of the New York Stock Exchange`s designated order turnaround system, which routed orders electronically to the proper trading post, which executed them manually (Alexander et al, 2000). Financial markets with fully electronic execution and similar electronic communication networks developed in the late 1980s and 1990s. In the U.S., for example decimalization, which changed the minimum tick size from 1/16 of a dollar (US$0.0625) to US$0.01 per share, may have encouraged algorithmic trading as it changed the market microstructure by permitting smaller differences between the bid and offer prices, decreasing the market-makers' trading advantage, thus increasing market liquidity (Kalimipalli and Warga, 2002). This increased market liquidity led to institutional traders splitting up orders according to computer algorithms so they could execute orders at a better average price. These average price benchmarks are measured and calculated by computers by applying the time-weighted average price or more usually by the volume weighted average price (Glosten, 2004). 

According to Chau and Gray (2002), the use of bond automated trading in some developed countries such as German took over according to market share and since their introduction they have continued to increase their importance for the bond market. One could have expected that once a supposedly superior electronic trading system had been introduced, floor trading would run out relatively near-term automatically due to market participants´ choice or by securities exchange decision. This however has not been the case (Frame and White, 2004). 

In 2006 a long desired implementation of automation of trading at the exchange was actualized. Live trading on the automated trading systems of the Nairobi Securities Exchange was implemented. As envisioned, the Automated Trading system delivered the promise of much more efficient trading and transaction processing, and shone the path for a modernized exchange ready for even greater growth. The system allowed for many changes and improvements that were not possible before, not least being longer trading hours per day and more options for transactions and trades. Besides trading equities, the ATS is also fully capable of trading immobilized corporate bonds and treasury bonds (NSE, 2013). 

Eng and Mak (2003) state that automated trading is the use of electronic platforms for entering trading orders with an algorithm which executes pre-programmed trading instructions whose variables may include timing, price, or quantity of the order, or in many cases initiating the order by a robot, without human intervention. Anthon (2002) adds that algorithmic trading is widely used by investment banks, pension funds, mutual funds, and other buy-side, investor-driven institutional traders, to divide large trades into several smaller trades to manage market impact and risk. Sell side traders, such as market makers and some hedge funds, provide liquidity to the market, generating and executing orders automatically. 

The introduction of the Central Depository System (CDS) in 2004, the Automated Trading System (ATS) in (2006) and the implementation of Wide Area Network (WAN) in 2007, was the onset for Automation to revolutionize security trading in Kenya. In Kenya as a result of bond automated trading systems in 2009, the NSE liquidity was up, while the number of days for settlement and cases of fraud were down in bond trading as fixed-income traders and investors flock to the Automated Trading System (ATS) of the Nairobi Stock Exchange. Citing data provided by NSE, the value of bonds traded by the end of May was KSh 177.5 billion ($2.2 billion), up 60% on the KSh 110.6 billion traded in the same period in 2009. The report says that 90% of bonds traded are transacted by banks and institutions, such as fund managers. High-net-worth individuals are also active (Kariuki, 2012). What is not clear is whether the automation has lowered the transaction cost, price discovery process, improved the transparency levels, trading volumes, increased the market size volatility of returns and growth of clientele base. 

Kariuki (2012) the increased liquidity is attributed to ATS. There is also a better match between inflation and bond yields. According to the Central Bank of Kenya (2013), annual inflation was 3.9% in May 2012, compared to 26.6% in 2008 and 12.4% per cent in 2009. Last October 2011 the Kenya National Bureau of Statistics started using the geometric mean method to calculate inflation, which gives lower figures than the arithmetic mean method used previously. In April 2011 KNBS adjusted the basket of goods and services used to calculate the consumer price index. The central bank reflects changed consumer tastes and makes the inflation rate comparable with that in other countries. Investors are now seeing a positive real return on their investments. Cases of fraud have also reduced, increasing investor confidence. 

The statement of the problem 
The Nairobi Securities Exchange's secondary bond market automation of trade in government paper started in the 2009, and there after the automation of other bonds. Studies done on market performance focus on stock automated trading but not the bond market. The market performance can be measured by several variables these being among them, transaction cost, levels of disclosure and transparency and efficiency of operations, liquidity, efficiency levels, volatility of returns, price discovery, market returns, stock market size, trading volumes, foreign currency risks, refinancing risks and credit risk. Studies have been carried on the impact of automated systems on efficiency and effectiveness of firms listed. Kariuki (2012) carried out a study on the impact of automated trading systems (ATS) on Share trading in the Nairobi Stock Exchange while Mailafia (2011), did a study to determine the effect of automation of the trading system in the Nigerian Stock Exchange. While these studies shade some light on the impact of automated system but they did not specifically deal with bond automated trading. Before the automation of bond trading, the Nairobi Securities exchange faced several challenges. There was low liquidity, lack of transparency, short trading hours, high transaction costs, low trading volumes, block trades board and high volatility. As envisioned, the bond automated trading delivered the promise of much more efficient trading and transaction processing, and shone the path for a modernized exchange ready for even greater growth. Whether the bond automated trading affected changes and improvements that were not possible before, by providing the necessary liquidity, trading hours per day and more options for transactions and trades. This study therefore established whether trading volumes, transactions cost, liquidity and market size had an effect on the bond trading. These variables are performance measures of automated bond trading, therefore it’s necessary to look at them since they are part of the bond market performance and they should not be neglected. This is the knowledge gap that this study fills.

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Item Type: Kenyan Postgraduate Material  |  Attribute: 64 pages  |  Chapters: 1-5
Format: MS Word  |  Price: KSh900  |  Delivery: Within 30Mins.


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