Algorithmic trading, also known as “algo trading,” refers to the use of computer programs to execute trading orders at speeds beyond the capability of human traders. These programs operate based on predefined criteria such as timing, price, and quantity. By leveraging mathematical models and historical data, algorithmic trading systems aim to identify and capitalize on market opportunities more efficiently than manual trading methods.
The historical evolution of algorithmic trading took off in the late 20th century with advancements in technology, especially in computing power and data processing. Faster, high-performance computers enabled the rapid execution of complex trading algorithms, while big data and advanced analytics allowed these systems to analyze vast amounts of historical and real-time market data for precise, data-driven decisions. The advent of machine learning has further refined algorithmic trading, enabling systems to adapt and optimize based on evolving market patterns, making trades faster and more accurate than ever before.
In the early 2000s, the Regulation National Market System (Reg NMS) was put in place in the United States to improve how stocks were traded. Before Reg NMS, trading was done on different exchanges, which sometimes meant that buyers and sellers didn’t get the best prices. This was unfair, especially for smaller investors. Reg NMS made it a rule that all trades had to happen at the best available price, no matter which exchange they were on. This helped make the market fairer and allowed computer programs to quickly find and make trades at those better prices, boosting algorithmic trading.This evolution has transformed the financial markets, with algorithmic trading now accounting for a substantial portion of trading volumes—over 70% of total trading in major markets like the US and nearly 40% in the EU (Corporate Finance Institute, 2023; Deloitte, 2018).
The benefits of algorithmic trading are numerous. It allows for the rapid execution of trades, minimizing the impact of market fluctuations on order prices. Algorithmic trading also eliminates human errors and emotional biases from trading decisions, potentially leading to more consistent and profitable outcomes. Additionally, it can enable traders to handle large volumes of data and transactions, providing liquidity and efficiency to the markets by ensuring a continuous flow of buy and sell orders, narrowing bid-ask spreads, and allowing trades to be executed at optimal prices. This increased activity supports a smoother and more stable trading environment, making it easier for assets to be bought or sold with minimal price impact (Corporate Finance Institute, 2023; Investopedia, 2023).
However, there are several challenges associated with algorithmic trading. The reliance on technology means that system failures or network issues can lead to significant financial losses. The strategies often depend on historical data, which may not always predict future market conditions accurately, especially during black swan events. Furthermore, algorithmic trading can contribute to market volatility and flash crashes, raising concerns among regulators and market participants (Investopedia, 2023; Deloitte, 2018).
In summary, while algorithmic trading offers significant advantages in terms of speed, efficiency, and consistency, it also poses unique challenges and risks that need to be managed carefully to ensure market stability and fairness (Corporate Finance Institute, 2023; Investopedia, 2023; Deloitte, 2018).
Edited by: Dicky Poh Ting Le, Zaki
Reviewed by: Maryam Nazir Chaudhary
References
Corporate Finance Institute. (2023). Algorithmic trading – definition, example, pros, cons.
https://corporatefinanceinstitute.com/resources/equities/algorithmic-trading/
Investopedia. (2023). Basics of algorithmic trading: Concepts and examples.
Investopedia. (2022). Regulation NMS:
https://www.investopedia.com/terms/r/regulation-nms.asp
The Trade News. (2021). What is Reg NMS and could it be beneficial for Europe?
