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| Report Code: A08567 | Pages: 242 | ||
| Tables: 120 | Charts: 66 |
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The global algorithmic trading market size was valued at $12,143 million in 2020, and is projected to reach $31,494 million by 2028, registering a CAGR of 12.7% from 2021 to 2028. Algorithmic trading or automated trading is a form of automation, in which computer program is used to execute a defined set of instructions or rules that includes the buying or selling of an asset in regards to the varying market data. The defined sets of instructions or rules are based on timing, quantity, price, or any mathematical model. It offers several benefits to market participants such as it executes trades at the best possible prices; simultaneous automated checks on multiple market conditions; trades timed correctly and instantly; and reduced transaction costs due to lack of human intervention.
An algorithm is fed into a computer program to automatically perform the trade whenever the command is met. An algorithm is anticipated to be based on different number of input pointers such as price, quantity, timing, or other metrics. It offers several benefits to market participants such as it executes trades at the best possible prices; simultaneous automated checks on multiple market conditions; trades timed correctly and instantly; and reduced transaction costs due to lack of human intervention.
COVID-19 pandemic has impacted positively on the growth rate of the algorithmic trading market owing to the increased shift towards algorithmic trading for taking the decisions at a very rapid pace by reducing human errors. For instance, Reserve Bank of Australia, in its recent publication stated that the Covid-19 pandemic may have only furthered the industry's shift toward electronic trading. Also, there is upsurge in popularity of High-Frequency Traders (HFT) during the pandemic; thus, fueling the market growth. For instance, Virtu Financial, one of the leading high-speed traders reported $784.5 million in net trading income for the first quarter of 2020.
On the basis of component, the solution segment exhibited the highest growth in the algorithmic trading market share in 2020, and is expected to maintain its dominance in the upcoming years. The demand for algorithmic trading solutions is mainly driven by its benefits such as reduced transaction costs due to lack of human intervention and instant and accurate trade order placement. In addition, the market players are introducing advanced algorithmic trading solutions to serve various needs of their customers. For instance, in September 2019, BNP Paribas introduced an upgraded FX trading platform with real-time analytics and interactive algorithms. Moreover, strong technological advancements along with the considerable application of algorithm trading across several applications such as financial institutions and banks fuel the growth of the market However, the services segment is expected to witness the highest growth, due to an extensive adoption of professional services among end users, as it ensures effective functioning of algorithmic trading solution throughout the process.
By deployment mode, the global algorithmic trading market share was dominated by the cloud segment in 2020, and is expected to maintain its dominance in the upcoming years, due to increase in adoption of cloud-based applications by financial institutions to enhance their productivity and efficiency. In addition, the cloud-based algorithmic trading solutions are gaining popularity among the traders as it ensures the effective automation of processes and data maintenance along with cost-friendly management. This factor further propels the growth of the segment during the forecast period.
North America dominates the algorithmic trading market, owing to a number of factors including huge investments in trading technologies and increase in government support for global trading. In addition, extensive presence of algorithmic trading vendors in the region propels the growth of the market. However, Asia-Pacific is expected to witness highest growth rate during the forecast period, due to heavy investments by public and private sectors to enhance their trading technologies, driving the demand for algorithmic trading solutions to automate trading processes.
The report focuses on the growth prospects, restraints, and algorithmic trading market analysis. The study provides Porter’s five forces analysis of the algorithmic trading industry to understand the impact of various factors such as bargaining power of suppliers, competitive intensity of competitors, threat of new entrants, threat of substitutes, and bargaining power of buyers on the algorithmic trading market trends.
The algorithmic trading market is segmented on the basis of component, type, deployment mode, type of traders, and region. On the component, it is categorized into solution and services. On the basis of type, it is classified into stock markets, FOREX, ETF, bonds, cryptocurrencies, and others. As per the deployment mode, it is classified into cloud and on-premise. Depending on type of traders, it is divided into institutional investors, long-term traders, short-term traders, and retail investors. Region wise, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA.
The current estimation of 2028 is projected to be higher than pre-COVID-19 estimates. The COVID-19 outbreak has low impact on the growth of the algorithmic trading market, as the adoption of algorithmic trading solutions have increased in the face of unprecedented circumstances. COVID-19 pandemic has significantly fueled the growth rate of the algorithmic trading market, owing to the increased shift toward algo trading for taking the decisions at a very rapid pace by reducing human errors. For instance, Reserve Bank of Australia, in its recent publication stated that the COVID-19 pandemic may have only furthered the industry's shift toward electronic trading.
In addition, the market players have introduced innovative algorithmic trading products during the pandemic to ensure better serving the increased volumes of trading. This factor drives the market growth. For instance, in March 2021, Cowen, an American multinational independent investment bank and financial services company launched an algorithmic trading solution to help institutional clients navigate market dynamics caused by increased volumes of retail trading.
The growth of the global algorithmic trading industry is mainly driven by factors such as rise in demand for reliable, fast, and effective order execution; emergence of favorable government regulations; and the need for market surveillance primarily. In addition, rise in demand for reducing the transaction costs fuels the demand for algorithmic trading. However, insufficient risk valuation capabilities may hamper the market growth to some extent. On the other hand, emergence of AI and algorithms in the financial services is expected to provide lucrative opportunities for the market growth during the forecast period. In addition, rise in demand for cloud-based solutions is anticipated to be opportunistic for the algorithmic trading market growth during the forecast period.
Algorithmic trading is rapidly being used by big brokerage houses as well as institutional investors to cut down on costs associated with trading. This is attributed to the fact that the algorithmic trading enables easier and faster execution of orders, making it attractive for exchanges. In addition, it enables the investors and traders to quickly book profits off small changes in price. Therefore, rise in demand for effective trade drives the growth of the algorithmic trading market, as it enables the user to quickly execute trades.
Maximum number of financial services companies are increasing their AI and machine learning adoption to capitalize on the data from digitally driven channels. It is being used by several companies that operate in areas as banking, insurance, and asset management. This has led to the emerging trend of data-driven investments during the last decade. This in turn fueled the demand for high-frequency trading or algorithmic trading. Such AI-driven trading systems analyze massive amounts of data much quicker than people would do it. Thus, AI and algorithms in the financial services are opportunistic for the market growth during the algorithmic trading market forecast period, as it enables innovation in the market.
The need for market surveillance
Market abuse surveillance or trade surveillance includes capturing trade data, and then analysing and monitoring it to detect potential market abuse and other forms of financial crime, such as rogue trading. The legal definition of trade surveillance is varied by country. For instance, in the UK, it includes insider dealing, market manipulation, unlawful disclosure, and attempted manipulation. In addition, an increase in high-frequency related incidences has raised global concerns about market stability and integrity. Hence, there is rise in need for market surveillance, which fuels the demand for algorithmic trading solutions with market surveillance capabilities, driving the growth of the market. For instance, Software AG offers a trade surveillance solution based on the Complex Event Processing (CEP) engine that processes vast volumes of information, including both historical and live streaming data to detect positive and negative trading patterns.
Key Benefits For Stakeholders
Algorithmic Trading Market Report Highlights
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| By DEPLOYMENT MODE |
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| Key Market Players | TETHYS, ALGO TRADER AG, REFINITIV LTD., METAQUOTES SOFTWARE CORP., SOFTWARE AG, TATA CONSULTANCY SERVICES, VIRTU FINANCIAL, ARGO SE, SYMPHONY FINTECH SOLUTIONS PVT LTD., 63MOONS |
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The algorithmic trading market is going through enormous transformation and growth. Algorithmic trading has become the emerging technology for financial institutions to gain an edge over other market participants. There is very less possibility that the traders are profitable; however, algorithmic trading has improved such odds through better strategy testing, design, and execution; thus, increasing the profitability. More number of investors are adopting algorithmic trading technology to bring superior efficiency to financial markets.
Moreover, most of the investors and regulators are inclining toward algorithmic as well as high-frequency trading (HFT). Over the past few years, HFT has become the most pervasive use of the algorithmic trading technology, especially among large financial institutions. HFT enables the large volumes of shares to be bought and sold automatically at very high speeds. HFT is anticipated to continuously grow and become the dominant form of algorithmic trading in the upcoming years. This is attributed to its huge popularity among big insurers, banks, and hedge funds, owing to its ability to place large volumes of orders at high speed across different markets based on a number of algorithmic trading strategies.
COVID-19 pandemic has significantly fueled the growth rate of the algorithmic trading market, owing to increase in shift toward algorithmic trading. For instance, the Reserve Bank of Australia, in its recent publication stated that the COVID-19 pandemic may have only furthered the industry's shift toward electronic trading.
Furthermore, the algorithmic trading market is competitive and comprises number of regional and global vendors competing based on factors such as cost of solutions, reliability, and support services. The growth of the market is impacted by rapid advances in the electronic trading offerings, whereas the vendor performance is impacted by COVID-19 conditions and industry development. Moreover, vendors operating in the market are offering advanced algorithmic trading products and services to improve the experience of traders using an algorithmic trading platform.
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