Algorithmic Trading Market - Growth, Trends, COVID-19 Impact, and Forecasts (2022 - 2027)

Market Overview

The algorithmic trading market is expected to witness a CAGR of 11.23% over the forecast period (2021-2026). Traditionally, traders keep track of their trading activities and investment portfolio by using market surveillance technology. Applications, such as algorithmic trading, have built-in intelligence to search for opportunities that exist in the market, as per the yield and other criteria defined by the user. Factors, such as favorable government regulations, increasing demand for fast, reliable, and effective order execution, growing demand for market surveillance, and reducing transaction costs, are expected to spearhead the need for the algorithmic trading market. Institutional investors and big brokerage houses use algorithmic trading to cut down on costs associated with bulk trading.

• Factors, such as favorable government regulations, increasing demand for fast, reliable, and effective order execution, growing demand for market surveillance, and reducing transaction costs, are expected to spearhead the need for the algorithmic trading market. Institutional investors and big brokerage houses use algorithmic trading to cut down on costs associated with bulk trading. Furthermore, the emergence of AI in the financial service sector is expected to be a major factor in the growth of the algorithmic trading market. Regulators are also starting to take note of the ways by which individuals interact with the market due to advances in artificial intelligence. For instance, in May 2010, the House of Representatives announced the creation of a Task Force on Financial Technology and a Task Force on Artificial Intelligence.
• Furthermore, Algos, with the help of pre-defined rules, backtested and placed at pre-defined levels, can also analyze every quote and trade in the stock market. Thus, these programs identify liquidity opportunities and turn such information into intelligent trading decisions. For instance, in April 2020, Bitcoin suddenly jumped by 20%. Some observers speculated that Algo trading may have been the factor behind the sudden move in the world's most popular cryptocurrency. Liquidity plays a vital role in the financial market. Thus, algorithmic trading creates a situation to maintain the liquidity, exceptionally high liquidity and demand, due to rapid buy and sell orders, without any human intervention. Algorithmic trading works when big trades are fed into computers running relevant programs.
• In the age of cloud deployment, the cloud-based algorithmic trading platforms are expected to play a significant role in the growth of the market, owing to various benefits, like a gain of maximum profits, as cloud-based trading solutions help traders to automate their trading process, easy trade data maintenance, cost-effectiveness, scalability, and effective management. Cloud-based trading works on the cloud computing model that uses networks of remote servers usually accessed over the internet to store, manage, and process data. Attributed to the convenience of the cloud, traditional traders can deploy algorithmic trading in the cloud to check new trading strategies, backtest, and run-time series analysis, while executing trades.
• Due to the spread of COVID-19, stock markets plunged in March 2020, triggering circuit breakers that halted market-wide trading several times. Algo trading has been contributing to the market rebound after the March lows. Thus, algorithmic execution tools in foreign exchange have increased significantly since March 2020. As per the latest Survey by JPMorgan, more than 60% of trades for ticket sizes are bigger than USD 10 million were executed in March via an algorithm. This was compared to less than 50% a year ago. Hedge funds and real money accounts are leading the end-user industry. Additionally, a report on algorithmic trading by the National Institute of Financial Management, submitted to the Department of Economic Affairs in May 2010, found that algorithms accounted for half the orders on the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE).

Scope of the Report

Algorithmic trading (also known as algo-trading, black-box trading, or automated trading) is defined as a method of executing trade orders with the help of automated pre-programmed trading instructions. Considering several variables, such as time, price, and volume, the programs send small slices of the order to the market over time. The study characterizes the algorithmic trading industry by types of traders (institutional investors, retail investors, long-term traders, and short-term traders), component (solutions and services), deployment (on-cloud, on-premise), organization size (small and medium enterprises and large enterprise), and geography (North America, Europe, Asia Pacific, Latin America, and Middle-East and Africa).

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Key Market Trends

Institutional Investors are Expected to Hold Major Share

• Institutional investors are banks, credit unions, insurance companies, hedge funds, investment advisors, and mutual funds companies, which pool their money to purchase securities, real estate, or any other kind of investment assets. Institutional investors daily use multiple computer-driven algorithmic strategies in the volatile trading markets, which succumb to the trade influence and the market makers. These techniques enable the traders to cut down the costs of trades and improve their profitability.
• These investors need to execute high-frequency numbers, which are not possible every time. It helps them break the whole amount into small parts and continue to perform in particular time intervals or according to any dedicated strategies. For instance, instead of placing 1,00,000 shares at a time, an algo-trading technique may push 1,000 shares out every 15 seconds and incrementally put small amounts into the market over the period or the entire day.
• With high-frequency traders making a large number of trades per day, automated trading using computer programs and artificial intelligence is required, primarily to speed up the execution of trades. Therefore, only institutional investors can afford this technology. Moreover, they get an unfair advantage to profit off from value, which is based around millisecond arbitrage. Furthermore, when the institutional-based investors want to take advantage of various occasional tiny market price discrepancies that arise in the security’s market price trading available on two different exchanges, they incorporate algorithmic trading to follow up on arbitrage strategy.
• Trend following is also one of the most popular techniques used by traders, which is based on algorithms. The approach identifies specified patterns used to carry out the buying and selling of assets. Many traders use the programming trend following algorithmic trading platforms, as they are simple to implement. Additionally, there is high adoption of algorithmic trading because traders do not need to make any predictions or price forecasts to become profitable, as the algorithmic trading strategy relies on the desired trends rather than predictive analysis.

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