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Algorithmic Trading Definition
Algorithmic trading is a system of trading whereby advanced mathematical tools and computer programs are used in facilitating trade and making decisions in the financial markets. Algorithmic trading is a method that helps in facilitating trade and solve trading problems using advanced mathematical tools. This system of trading uses automated trading instructions, predetermined mathematical models and human oversight to execute a trade in the financial market.
In algorithmic trading, traders leverage powerful computers that have the capability to process complex mathematical formulas. The use of high-frequency trading techniques also helps traders to make multiple trades within seconds.
A Little More on What is Algorithmic Trading
Algorithmic trading was introduced in the 1970s, this was then highly computerized trading systems emerged in the American financial markets. The New York Stock Exchange also introduced a system in 1976 which enhanced the acceptance of electronic trader by traders.
Algorithmic trading was popularized by Michael Lewis, an author who drew the attention of market traders and the public to high-frequency algorithmic trading.
The combination of an algebraic equation and rules of algebra is an example of an algorithm. When algorithms are used as a system of trading, complex formals and mathematical models are combined to be trading decisions in the financial market. Algorithmic trading can be applied in different market situations such as order execution and arbitrage.
Do it Yourself Algorithmic Trading
When algorithmic trading was introduced in America, companies defined the structure of electronic trading, in the sense that average investors do not have access to electronic trading or do not know how to go about the system. Michael Lewis, an author who polarized algorithmic trading to the public also argued that companies who use the algorithmic trading system gained an advantage over other traders and engage in trading that only benefitted them and are detrimental to average investors.
In recent times, ‘Do-it-yourself’ algorithmic trading has become popular, this enables average investors to facilitate the execution of trades in the financial markets using high-frequency computers. The innovation of high-speed computers and the high speed on the internet has made this type of algorithmic trading even more popular and widely accessible by average investors.
Below are the key points to know about algorithmic trading;
- Algorithmic trading is a system of trading that uses high-frequency computerized trading systems to execute a trade in the financial market.
- With the help of high-frequency computers, investors are able to solve complex mathematical problems that facilitate their decision making in the financial market.
- Algorithmic trading first emerged in the 1970s, it was popularized by an author, Michael Lewis. This system of trading became widely acceptable in the 1980s.
- When it first emerged, the algorithmic trading system was used by institutional investors and companies, but more recently, ‘Do-it-yourself’ algorithmic trading has become popular
Advantages and Disadvantages of Algorithmic Trading
The advantages of algorithmic trading are;
- Algorithmic trading reduces the costs associated with trading.
- This system of trading facilitates faster execution time in the trade.
- It offers varieties of benefits to institutional investors and large companies who have large trading volume because it aids an easier and faster execution of large orders.
- Market makers can create market liquidity using algorithmic.
Disadvantages of algorithmic trading;
- The speed at which orders are executed can create a problem in the financial market, especially if there is an absence of human oversight.
- Algorithmic trading has the tendencies of causing flash crashes in the market.
- It can cause a sudden disappearance or instant loss of liquidity which was created through rapid buying and selling orders.
Reference for “Algorithmic Trading”
Academics Research on “Algorithmic Trading”
Does algorithmic trading improve liquidity?, Hendershott, T., Jones, C. M., & Menkveld, A. J. (2011). Does algorithmic trading improve liquidity?. The Journal of Finance, 66(1), 1-33. Algorithmic trading (AT) has increased sharply over the past decade. Does it improve market quality, and should it be encouraged? We provide the first analysis of this question. The New York Stock Exchange automated quote dissemination in 2003, and we use this change in market structure that increases AT as an exogenous instrument to measure the causal effect of AT on liquidity. For large stocks in particular, AT narrows spreads, reduces adverse selection, and reduces trade‐related price discovery. The findings indicate that AT improves liquidity and enhances the informativeness of quotes.
Rise of the machines: Algorithmic trading in the foreign exchange market, Chaboud, A. P., Chiquoine, B., Hjalmarsson, E., & Vega, C. (2014). Rise of the machines: Algorithmic trading in the foreign exchange market. The Journal of Finance, 69(5), 2045-2084. We study the impact of algorithmic trading (AT) in the foreign exchange market using a long time series of high‐frequency data that identify computer‐generated trading activity. We find that AT causes an improvement in two measures of price efficiency: the frequency of triangular arbitrage opportunities and the autocorrelation of high‐frequency returns. We show that the reduction in arbitrage opportunities is associated primarily with computers taking liquidity. This result is consistent with the view that AT improves informational efficiency by speeding up price discovery, but that it may also impose higher adverse selection costs on slower traders. In contrast, the reduction in the autocorrelation of returns owes more to the algorithmic provision of liquidity. We also find evidence consistent with the strategies of algorithmic traders being highly correlated. This correlation, however, does not appear to cause a degradation in market quality, at least not on average.
Algorithmic trading and the market for liquidity, Hendershott, T., & Riordan, R. (2013). Algorithmic trading and the market for liquidity. Journal of Financial and Quantitative Analysis, 48(4), 1001-1024. We examine the role of algorithmic traders (ATs) in liquidity supply and demand in the 30 Deutscher Aktien Index stocks on the Deutsche Boerse in Jan. 2008. ATs represent 52% of market order volume and 64% of nonmarketable limit order volume. ATs more actively monitor market liquidity than human traders. ATs consume liquidity when it is cheap (i.e., when the bid-ask quotes are narrow) and supply liquidity when it is expensive. When spreads are narrow ATs are less likely to submit new orders, less likely to cancel their orders, and more likely to initiate trades. ATs react more quickly to events and even more so when spreads are wide.
Algorithmic decision-making framework, Kissell, R., & Malamut, R. (2005). Algorithmic decision-making framework. The Journal of Trading, 1(1), 12-21. The emergence of algorithmic trading as a viable and often preferred execution mechanism has created a need for new suites of trading analytics to assist investors compare, evaluate, and select appropriate algorithms. Unfortunately, many of the existing algorithms do not provide necessary transparency to make informed trading decisions. In this paper we provide a dynamic algorithmic decision making framework to assist investors determine the most appropriate algorithm given overall trading goals and investment objectives. The approach is based on a three step process where investors choose their price benchmark, select trading style (risk aversion), and specify adaptation tactic. The framework makes extensive use of the Almgren & Chriss (1999, 2000) efficient trading frontier.
Algorithmic trading, Nuti, G., Mirghaemi, M., Treleaven, P., & Yingsaeree, C. (2011). Algorithmic trading. Computer, 44(11), 61-69. In electronic financial markets, algorithmic trading refers to the use of computer programs to automate one or more stages of the trading process: pretrade analysis (data analysis), trading signal generation (buy and sell recommendations), and trade execution. Trade execution is further divided into agency/broker execution (when a system optimizes the execution of a trade on behalf of a client) and principal/proprietary trading (where an institution trades on its own account). Each stage of this trading process can be conducted by humans, by humans and algorithms, or fully by algorithms.