The global algorithmic trading market size was valued at USD 35,701 million in 2021. It is expected to reach USD 1,04,971.2 million by 2030, growing at a CAGR of 12.73% during the forecast period (2022–2030). Algorithmic trading is also known as automated trading or Algo-trading and black-box trading. It is a method that uses a computer program to follow a defined set of instructions or an algorithm to administer the trading activity. An algorithm is fed into a computer program to perform the trade whenever the command is met automatically. An algorithm can be based on a different number of input pointers such as price, quantity, timing, or other metrics. It provides market players with several advantages, including the ability to execute trades at the best prices, run automated checks on numerous market situations, trades timed precisely and immediately, and reduce transaction costs due to lack of human intervention.
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Rising Demand for Reliable, Fast, and Effective Order Execution
Big brokerage companies and institutional investors utilize algorithmic trading to save expenses. Exchanges favor algorithmic trading because it enables easy and quick order execution. Investors and traders may swiftly benefit from modest price movements. The need for efficient trade encourages the rise of algorithmic trading, which enables users to complete transactions quickly.
Emergence of Favorable Government Regulations
Global government bodies are studying methods to control algorithms and implementing regulations to safeguard algorithmic trading. The U.S. Securities and Exchange Commission (SEC) has accepted a regulation proposed by the Financial Industry Regulatory Authority (FINRA) that requires algorithmic trading developers to register as securities traders to reduce market manipulation. A new order-to-trade ratio (OTR) framework was introduced by the Securities and Exchange Board of India (Sebi) in June 2020. The algorithmic trading market is expected to benefit from such governmental regulations.
Growing Need for Market Surveillance
Market abuse or trade surveillance includes capturing, analyzing, and monitoring trade data to uncover market abuse and other financial crimes, such as rogue trading. National regulations govern trade surveillance to prevent insider trading, market manipulation, unlawful disclosure, and attempted manipulation. High-frequency occurrences have sparked concerns about market stability and integrity. Increasing market surveillance demands algorithmic trading systems with surveillance capabilities, propelling the market growth. Software AG's Complex Event Processing-based trade monitoring system identifies positive and negative trading patterns.
Rising Demand for Reducing the Transaction Costs
The transaction costs include explicit costs and implicit costs. Implicit costs include time, market effect, opportunity, and investment delay. Explicit costs include commissions, taxes, fees, and bid-ask spreads. Using algorithmic trading, traders can conduct transactions without constantly watching, significantly reducing the trading time and lowering transaction costs.
Insufficient Risk Valuation Capabilities
Intraday algorithmic trading is risky, and losses may escalate quickly without proper controls. Investment companies must immediately reject or cancel orders that compromise risk management thresholds. Algorithmic high-frequency trading (HFT) carries concerns, including amplifying systemic risk. Insufficient risk valuation capabilities in algorithmic trading systems may hinder market development during the forecast period.
Artificial Intelligence and Machine Learning in Financial Services
Financial services companies employ Artificial Intelligence and machine learning to capitalize on digital data. It is used by banks, insurers, and asset managers. In the recent decade, data-driven investing has become popular. AI-powered trading algorithms analyze enormous volumes of data faster than humans. AI and algorithms in financial services are helpful for market growth during the forecast period because they enable algorithmic trading innovation.
Growing Demand for Cloud-Based Solutions
The market is anticipated to benefit from the shift from proprietary to cloud-based software. Cloud-based solutions are likely to rise since they lower deployment and ownership costs for end users. Cloud-based trading tools help traders maximize earnings and automate trading. Maintaining trade data is straightforward, scalable, cost-effective, and manageable. The rising demand for cloud-based algorithmic trading solutions is favorable for market growth during the forecast period.
The global algorithmic trading market is segmented by component, type, deployment mode, and the type of traders.
Based on the component, the global market is divided into solutions and services.
The solution segment dominates the global algorithmic trading market and is expected to grow at a CAGR of 12.03% during the forecast period. The solution section comprises software, platforms, and suites to improve trading. On-premise and cloud-based trading solutions automate trading with cost-effectiveness, easy trade data maintenance, scalability, and efficient management. Demand for quick, dependable, and effective order execution, market monitoring, and government regulations drive the growth of this industry. Reduced transaction costs as a result of eliminating human participation, as well as quick and accurate trade order placement, drive demand for algorithmic trading solutions.
The services section ensures that the algorithmic trading solutions run smoothly and transparently throughout the process. Controlled and professional algorithmic trading services include implementation, training, support, and consulting. Implementation services ensure the platform and software's scheduling, installation, and configuration. Quick issue detection and management, personalized help, and performance development optimization are all part of support and maintenance services.
Based on the type, the global market is divided into stock markets, FOREX, Exchange Traded Fund (ETF), bonds, cryptocurrencies, and others.
The Exchange Traded Fund is predicted to hold the highest market share and is expected to grow at a CAGR of 15.55% during the forecast period. The Exchange Traded Funds (ETFs) track an index, commodity, sector, or another asset. It may be bought and traded on a stock exchange like a conventional stock. ETFs have low average expenses, allowing traders to maximize returns, and boosting ETF demand. High-frequency trading, also known as algorithmic trading, aids in the stabilization of the ETF market by keeping fund prices near Net Asset Value. It has benefited all investors, accelerating the segment's growth.
Institutional and private investors buy and sell shares in stock markets, making trading in shares easier. Nowadays, shares are purchased and traded electronically. Artificial intelligence and digital information have powered algorithmic trading in the stock market. The desire for trading equities at high speeds and frequencies in sync with the market also promotes algorithmic trading. High-frequency trading (HFT) permits investors to make more money by supporting the market's expansion. Stock traders must preserve their earnings and manage their portfolios. Due to unstable markets and complex portfolio management, algorithmic trading helps traders control risk.
Foreign exchange (Forex) is a currency exchange and conversion facility for tourism, commerce, or travel. FX or Foreign exchange (FOREX) trading entails converting one currency into another, becoming more popular as more individuals become acquainted with the Forex market. Forex trading is now available online for part-time and aspiring traders. Also, forex trading is available 24/7, fuelling its growth.
A cryptocurrency is a digital asset protected by cryptography, making it difficult to double-spend or counterfeit. Bitcoin is the most popular blockchain-based asset, with competitors including Peercoin, Litecoin, Namecoin, Cardano, Ethereum, and EOS. Cryptocurrency markets are more volatile than traditional markets, resulting in more fluctuations in opportunities and pricing. The cryptocurrency market is available 24/7, enhancing automated trading possibilities.
The other segment comprises assets, commodities, collateral mortgage, Credit Default Swap (CDS), and Interest Rate Swap (IRS). As commodities trading automation grows, many traders are adopting algorithm trading. Algorithmic trading is gaining prominence in the mortgage, CDS, and interest rate swaps (IRS), which fosters market growth. A Credit Default Swap (CDS) protects the buyer against default and other risks.
Based on deployment mode, the global market is divided into on-premise and cloud.
The cloud segment is anticipated to maintain the highest market share and grow at a CAGR of 14.04% during the forecast period. Cloud-based algorithmic trading solutions are licensed and delivered remotely by a vendor or service provider. In this design, the software runs on CAM's cloud computing platform, and the provider also maintains trading software. Public or private clouds are popular owing to low upfront costs. Cloud-based algorithmic trading systems simplify operations and retain data while being cost-effective, which boosts segment growth during the forecast period.
On-premise deployment for algorithmic trading solutions permits software installation and application execution on in-house systems instead of server space or cloud. In this arrangement, algorithmic trading solutions providers deploy the solutions in a client's data center. On-premises systems provide more significant server maintenance, while continuous systems stimulate algorithmic trading. Large companies benefit from on-premise implementation, but it is expensive as the system requires networked computers and software. On-premise deployment permits software installation and application execution on the organization's premises instead of a server or cloud.
Based on the type of traders, the global market is divided into institutional investors, long-term traders, short-term traders, and retail investors.
Retail investors are predicted to hold the highest market share and grow at a CAGR of 13.84% during the forecast period. A retail investor, sometimes called an individual investor, is a non-professional investor who buys and sells ETFs and mutual funds. Algorithmic trading benefits retail traders in the financial markets. It includes faster execution, disciplined trading choices, expanded market reach, systematic trading, and removing continual market monitoring while doing real-time quantitative analysis. More retail traders are anticipated to adopt algorithmic trading due to the aforementioned benefits, boosting the market.
Institutional investors are firms or organizations that invest money on behalf of others or their members. Banks, credit unions, hedge funds, investment advisers, insurers, and mutual fund firms are institutional investors. Such investors purchase and sell large blocks of bonds, stocks, and other assets. In addition, institutional investors utilize computer-driven algorithmic tactics daily in volatile trading markets to boost profitability.
Mid- to long-term traders or buy-side businesses are active in pension funds, insurance, and mutual funds. Such traders employ algorithmic trading tools to buy significant volumes of stocks surreptitiously. Long-term trading advantages such as more time for money to develop, reduced trading costs, and more accessible operations fuel the expansion of this category. Position traders disregard short-term price changes and depend on long-term trends and fundamental research. Thus, employing algorithmic trading solutions by position traders encourages the market.
North America Dominates the Global Algorithmic Trading Market
The global algorithmic trading market is divided into four regions, namely North America, Europe, Asia Pacific, and LAMEA
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North America's largest market share is expected to grow at a CAGR of 10.22% during the forecast period. The United States and Canada make up the North American market. North America is projected to lead in algorithmic trading solution adoption and development due to its large number of market participants, making it a competitive industry. This results from massive investments in trading technologies and increased government support for global trade. Substantial technology improvements and widespread use of algorithm trading in financial institutions and banks propel industry expansion.
Europe is anticipated to proliferate at a CAGR of 13.85%, generating USD 12,871.63 million during the forecast period. The European market is analyzed across Germany, the UK, France, Italy, and others. The usage of novel infrastructures and trading approaches in the field has increased due to technological advancement, regulatory reforms, and increased participant competition in the trading market, which drives the algorithmic trading industry. European financial markets rely heavily on algorithmic trading. Also, the government is adopting special laws and regulations to promote algorithmic trading security and performance, which fosters market growth. MiFID II, a European Union (EU) framework to govern financial markets, adopted a comprehensive set of algorithmic and high-frequency trading regulations in 2021.
The algorithmic trading market in Asia-Pacific is analyzed in China, Japan, India, Australia, and the rest of Asia-Pacific. The 5G and network infrastructure and connectivity growth will boost the Asia-Pacific market for algorithmic trading. The growing adoption of mobile phones in China and India fosters industry expansion. Spending on 5G infrastructure is increasing quickly, encouraging economic growth by enabling new consumer services.
The algorithmic trading market in LAMEA is researched throughout Latin America and the Middle East and Africa. Latin America invests more in network infrastructure because companies want flexible, scalable, and controllable networks. The Middle East has a modest part of worldwide algorithmic trading expenditure, but its developing telecommunications sector is expected to boost the market. LAMEA's development in mobile data traffic encourages businesses to employ algorithmic trading for better data network management.