Algorithmic Trading Market - Growth, Trends, COVID-19 Impact, and Forecasts (2023 - 2028)

Algorithmic Trading Market Analysis

The algorithmic trading market is anticipated to witness a CAGR of 10.5% during the forecast period. Traders have traditionally used market surveillance technology to keep track of their trading operations and investment portfolios. Applications with built-in intelligence, like algorithmic trading, can explore the market for various opportunities based on the yield and other parameters the user specifies.

• The need for the algorithmic trading industry is anticipated to be driven by favorable governmental rules, rising demand for quick, reliable, and efficient order execution, increasing demand for market surveillance, and declining transaction costs. Large brokerage firms and institutional investors use algorithmic trading to reduce the expenses of bulk trading. Additionally, it is anticipated that the development of artificial intelligence (AI) and financial service algorithms will create attractive market expansion opportunities. A rise in the demand for cloud-based solutions is also anticipated to support the growth of the algorithmic trading market.
• In recent years, especially in the last ten years, FinTech tools have been developed significantly to increase the capacity of the financial industry, and algorithmic trading has dominated the capital markets, particularly the trading business. The general public currently has access to data science tools, high-speed internet, and computing power. The proliferation of online trading platforms and apps has increased the accessibility of trading financial items. Trade stocks and currencies only take a few mouse clicks.
• The market growth for algorithmic trading is projected to be significantly influenced by the financial services industry's broad adoption of AI, ML, and big data. Technological improvements have caused regulators to pay attention to how consumers interact with the market. Some of the global central banks began employing such technologies for advancing Algo trading. Moreover, algorithmic trading can maintain exceptionally high market liquidity due to quick buy and sell orders placed without human interaction. The increased application of algorithms across asset classes, particularly cross-asset automation, has been a trend over the past two years.
• As per TRADE's January 2022 Algorithmic Trading Survey, hedge funds increasingly use algorithms to trade most of their portfolios. For a multi-asset portfolio, hedge funds highly depend on a more significant number of suppliers for this. Algorithm providers are emphasizing multi-asset solutions to address the demand from hedge funds. The survey found that implementation insufficiency - single stock (53.14%), VWAP (54.71%), and dark liquidity seeking (72.94%) were the three most employed types of algos. Furthermore, some of the primary reasons behind the utilization of the algos include increased trader productivity (10.32%), reduced market impact (10.45%), consistency of execution performance (10.19%), ease of use (12.04%), and low commission rates (8.69%). There has also been a noticeable rise in the overall amount of automation and electronification. Moreover, the market volatility increase has maximized the need for algorithmic trading services and solutions.
• Algorithmic trading has increased because of the volatile market circumstances, large trading volume, and need for quick digital transformation to deal with distant working environments. The necessity for algo trading expanded during the COVID-19 pandemic since there was no way for geographically diversified trading to function effectively without the requirement for more advanced routing and electronic algos to assist and offer liquidity for traders. Moreover, due to a growing tendency toward algorithmic trading to make quick decisions while minimizing human mistakes, the pandemic had a positive effect on the growth rate of the algorithmic trading sector.

Algorithmic Trading Industry Segments

Algorithmic trading, also known as automated trading, algo-trading, or black-box trading, is a method of implementing trade orders with the assistance of automated pre-programmed trading instructions. Considering variables like volume, price, and time, the programs send small slices of the order to the market over a period.

The algorithmic trading market is segmented by types of traders (institutional investors, retail investors, long-term traders, and short-term traders), by components (solutions (platforms, software tools) and services), by deployment (on-cloud and on-premise), by organization size (small and medium enterprises and large enterprises), and geography (North America, Europe, Asia-Pacific, Latin America, and the Middle East and Africa).

The market sizes and forecasts are provided in terms of value (USD billion) for all the above segments.

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Algorithmic Trading Market Trends

This section covers the major market trends shaping the Algorithmic Trading Market according to our research experts:

Institutional Investors Expected to Hold Major Share

• A group or institution's accounts are managed by institutional investors, who also buy and sell stocks on their behalf. Pension funds, mutual fund families, insurance companies, and exchange-traded funds are institutional investors. Institutional investors and large brokerage firms primarily use algorithmic trading to save trading costs. Large order sizes benefit significantly from algorithmic trading.
• Institutional investors employ several computer-driven algorithmic tactics daily in the volatile trading markets that power the stock market. These strategies allow investors to lower trade expenses and increase their profitability.
• These investors must execute high-frequency numbers, which is not always achievable. Institutional investors can divide a large sum of money into smaller portions and continue to trade according to predetermined time frames or strategies due to algorithmic trading. For instance, an algorithmic trading strategy may push 1,000 shares out every 15 seconds and progressively place modest quantities into the market studied throughout the period or the entire day rather than depositing 100,000 shares at once.
• Due to the massive volume of trades made by high-frequency traders daily, automated trading leveraging software and artificial intelligence is necessary, primarily to accelerate trade execution. Therefore, this technology may only be purchased by institutional investors. Moreover, they gain the benefit of value based on millisecond arbitrage to profit from it. Additionally, institutional-based investors use algorithmic trading by adhering to the arbitrage strategy when they want to benefit from various occasional tiny market price discrepancies.
• Institutional investors are very concerned about their capital. Thus, they require a system capable of making wise choices. Automation of processes reduces overtrading dramatically because some traders buy and sell at the first indication of a trade window opening. These techniques lessen the possibility of errors brought on by people. It responds to marketing conditions in a split second, making it a desired investment option.

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