The Impact of Algorithmic Trading and Its Implications for the Nigerian Stock Market: A Case Study of Abuja Securities and Commodity Exchange, FCT
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1.1 Background of the Study
Algorithmic trading (AT), also known as algo-trading, refers to the use of computer algorithms to automate the process of trading financial instruments, including stocks, bonds, and commodities. These algorithms analyze vast amounts of market data and execute trades based on pre-set conditions without the need for human intervention. Algorithmic trading has revolutionized the global financial markets, allowing for faster, more efficient, and more precise trading decisions (Nguyen & Ojha, 2024; Dinesh et al., 2025). It has become a dominant force in advanced markets, contributing to liquidity, market efficiency, and price discovery. However, it has also raised concerns related to market volatility, systemic risks, and the potential for market manipulation.
The Nigerian stock market has begun to adopt algorithmic trading, with institutions such as the Abuja Securities and Commodity Exchange (ASCE) exploring its potential to enhance market efficiency and competitiveness. The introduction of AT in Nigeria comes at a time when the stock market is undergoing significant reforms, aimed at modernizing its infrastructure and attracting both domestic and foreign investors. The Nigerian Stock Exchange (NSE) and the ASCE have been working toward integrating technology into their trading platforms, but there remains limited understanding of the full implications of algorithmic trading on market dynamics, stability, and investor behavior.
This study examines the impact of algorithmic trading on the Nigerian stock market, specifically focusing on the Abuja Securities and Commodity Exchange. It aims to assess the benefits, challenges, and potential risks associated with AT and provide recommendations for maximizing its positive impact while mitigating its risks.
1.2 Statement of the Problem
While algorithmic trading has brought significant benefits to global financial markets, its implications for emerging markets such as Nigeria remain largely underexplored. The Abuja Securities and Commodity Exchange (ASCE) has recently adopted algorithmic trading as part of its modernization strategy. However, questions remain regarding its impact on market liquidity, volatility, and investor behavior. The adoption of AT in Nigeria’s stock market may also raise concerns about the regulatory framework, market manipulation, and the risks associated with high-frequency trading. Given the growing significance of the ASCE in the Nigerian stock market, it is crucial to evaluate how algorithmic trading affects market dynamics and whether its adoption aligns with the broader objectives of market development and stability.
1.3 Objectives of the Study
1. To evaluate the impact of algorithmic trading on market liquidity and volatility at the Abuja Securities and Commodity Exchange.
2. To assess the implications of algorithmic trading on investor behavior and trading strategies in the Nigerian stock market.
3. To identify the challenges and risks associated with the implementation of algorithmic trading in the Nigerian stock market and propose solutions.
1.4 Research Questions
1. How does algorithmic trading impact market liquidity and volatility at the Abuja Securities and Commodity Exchange?
2. What are the implications of algorithmic trading for investor behavior and trading strategies in the Nigerian stock market?
3. What challenges and risks are associated with the implementation of algorithmic trading in the Nigerian stock market, and how can they be mitigated?
1.5 Research Hypothesis
1. Algorithmic trading enhances market liquidity and reduces volatility at the Abuja Securities and Commodity Exchange.
2. Algorithmic trading influences investor behavior by encouraging more data-driven and high-frequency trading strategies.
3. The implementation of algorithmic trading in the Nigerian stock market presents risks such as market manipulation and systemic instability, which can be mitigated through regulatory measures.
1.6 Significance of the Study
This study provides valuable insights for policymakers, regulators, investors, and market participants. For regulators, it offers an understanding of the potential risks associated with algorithmic trading, allowing for the development of appropriate safeguards and regulatory frameworks. For investors and traders, the findings will provide insights into how algorithmic trading influences market behavior, offering a better understanding of the evolving market dynamics. Additionally, the study will assist the Abuja Securities and Commodity Exchange in optimizing the use of algorithmic trading to enhance market efficiency and attract investment, while managing potential risks effectively.
1.7 Scope and Limitations of the Study
The study focuses on the impact of algorithmic trading on the Nigerian stock market, specifically examining the case of the Abuja Securities and Commodity Exchange (ASCE). It investigates the effects of AT on market liquidity, volatility, investor behavior, and regulatory challenges. The study is limited by the availability of data, as detailed information on algorithmic trading in Nigeria's stock market is scarce. Additionally, the findings may be influenced by external factors such as global market conditions and the overall state of the Nigerian economy. The study’s focus on ASCE may also limit the generalizability of the findings to other exchanges within Nigeria.
1.8 Operational Definition of Terms
1. Algorithmic Trading (AT): The use of computer algorithms to automate the process of executing trades based on predefined criteria and market conditions.
2. Market Liquidity: The degree to which an asset or security can be bought or sold in the market without affecting its price.
3. Market Volatility: The extent to which the price of an asset or security fluctuates over time, often measured by price variability.
4. Investor Behavior: The patterns and strategies exhibited by investors in the stock market, including trading frequency, decision-making processes, and risk tolerance.
5. High-Frequency Trading (HFT): A subset of algorithmic trading that involves executing a large number of orders at extremely high speeds, often in milliseconds.
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