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

·7 min read

The Algorithmic Trading Market is expected to witness a CAGR of 10. 5% over the forecast period (2022-2027). Traditionally, traders keep track of their trading activities and investment portfolio by using market surveillance technology.

New York, March 18, 2022 (GLOBE NEWSWIRE) -- announces the release of the report "Algorithmic Trading Market - Growth, Trends, COVID-19 Impact, and Forecasts (2022 - 2027)" -
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.

Key Highlights
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.
FinTech tools have been developed to expand the capacity of the financial industry in recent years, particularly in the last decade, and algorithmic trading has dominated the capital markets, particularly the trading business. The digital revolution reduced several access barriers to the market. The general public now has high-speed internet, computational capacity, and data science tools. Trading financial products have become more accessible due to the rise of internet trading platforms and apps. Trading stocks, futures, and currencies now take simply a few mouse clicks.
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.
Furthermore, the emergence of AI, ML, and big data in the financial service sector is expected to be a major factor aiding 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 technologies. Some of the major banks across the world started using such technologies for advancing Algo trading.
In April 2021, the management at JP Morgan, a prominent investment bank, announced that the growth in the fixed income futures algorithmic trading accelerated rapidly in 2020, as THE buy-side traders turned to the company’s machine-learning equipped algos to grapple with the intense market volatility.
Further, there has been a noticeable increase in the amount of electronification and automation. During the epidemic, buy-side and sell-side desks shrink as commissions and fees shrink as well. The increase in volatility has increased the need for algorithmic trading solutions and services for handling the surge.
In order to understand whether the calibration and deployment of circuit breakers have been effective in the European Union, the European Securities and Markets Authority (ESMA) analyzed the trading data during the start of the COVID-19 pandemic in 2020. The period between the end of February 2020 and March 2020 was characterized by a significant sell-off and high volumes traded.

Key Market Trends

Institutional Investors are Expected to Hold Major Share

Institutional investors handle accounts for a group or institution and buy and sell stocks on their behalf. Institutional investors include pension funds, mutual fund families, insurance firms, and exchange-traded funds (ETFs). Institutional investors and large brokerage firms largely utilize algorithmic trading to reduce trading expenses. Algorithmic trading is particularly helpful for high order sizes.
Institutional investors daily use numerous computer-driven algorithmic strategies in the volatile trading markets, which drives share markets. These techniques enable the investors to cut down the costs of trades and improve their profitability.
These investors are required to execute high-frequency numbers, which are not possible every time. Algorithmic trading helps institutional investors break the whole amount into small parts and continue to perform in specific time intervals or according to dedicated strategies. For instance, instead of placing 1,00,000 shares at a time, an algorithmic-trading technique may push 1,000 shares out for every 15 seconds and gradually put small amounts into the market studied over the period or the entire day.
With high-frequency traders making many trades per day, automated trading utilizing computer programs and artificial intelligence is required, principally to speed up the execution of trades. Therefore, only institutional investors are able to afford this technology. Furthermore, they get an advantage to profit off from value, which is based around millisecond arbitrage. Moreover, when the institutional-based investors targets to take advantage of various occasional tiny market price discrepancies, which arise in the stock available on two different exchanges, they incorporate algorithmic trading via following up on arbitrage strategy.
Institutional investors care a lot about their money; therefore, they need something that can make good decisions. Overtrading is significantly reduced by automating procedures since some traders purchase and sell at the first sign of a trade window opening. These methods reduce the likelihood of human-caused errors. It is a desired investment alternative because it reacts to marketing conditions in a fraction of a second.

North America Expected to Dominate the Market

North America is expected to hold the major market growth in the market studied. The rising investments in trading technologies (such as blockchain), with increasing presence of algorithmic trading vendors, and growing government support for global trading are the major factors contributing toward the market growth during the forecast period.
Algorithmic trading accounts for around 60-73% of the overall US equity trading (source: Wall Street). According to Select USA, US financial markets are the largest and most liquid globally. Sentient Technologies, an AI company based in the United States, operates the hedge fund developed an algorithm processing millions of data points to find trading patterns and forecast trends.
As algorithmic trading strategies, including high-frequency trading (HFT) strategies, have grown more widespread in the US securities markets, the potential for these strategies to impact the market adversely and firm stability has likewise grown.
Modern technologies are rapidly changing the formats of traditional investment models, automating all related trading processes, which makes it possible to create a safe and efficient ecosystem that will be available to every interested investor. In February 2022, a team of developers created a new ecosystem known as Dex Finance. By automating advanced trading strategies and incentivizing investors to leave their deposits within the protocol, Dex Finance created a low-risk algorithmic trading model that nearly anyone can use.

Competitive Landscape

The global algorithmic trading market is moderately fragmented due to the presence of various market players globally, including Virtu Financial, Inc., Algo Trader AG, MetaQuotes Software Corp., Refinitiv Ltd, etc. Key players focus on developing new solutions and creating effective marketing strategies for market surveillance to maintain and increase their market share.

June 2021 - IG Group completed its acquisition of brokerage and investor education platform Tastytrade. The purchase was worth USD 1 billion with a deal initiated in January 2021, seeing IG Group agree to pay an initial USD 300m in cash and also issue 61 million new IG Group shares at a price of USD 11.47 each. IG Group received all the necessary regulatory and anti-trust approvals and satisfied necessary pre-conditions to complete the deal, with the operator having made an application for the new shares.
November 2021 - Refinitiv and Pio-Tech announced the partnership to provide banking clients of both companies in the Middle East and African region with sophisticated contemporary solutions that offer many distinct business values. This partnership focuses on maximizing the level of efficiency of the various anti-money laundering (AML) internal operations across all banking functions.

Additional Benefits:

The market estimate (ME) sheet in Excel format
3 months of analyst support
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