by The Algorithmic Trading Market is estimated to be valued at US$ 2.03 Bn in 2022 and is expected to exhibit a CAGR of 7.2% over the forecast period 2023-2030, as highlighted in a new report published by Coherent Market Insights.
Market Overview:
Algorithmic trading is an automated trading process that uses advanced mathematical models and computer algorithms to analyze market data, identify trading opportunities, and execute trades. Algorithmic trading facilitates the implementation of well-defined trading rules and strategies through computer programs rather than human judgment. It helps reduce human emotions from impacting trading decisions and allows for trade execution at much faster speeds. Algorithmic trading capabilities offer advantages like market knowledge, minimized risk, increased productivity, reduced operational costs, scalability, and adaptability to changing market conditions.
Market key trends:
One of the major drivers for the growth of algorithmic trading market is the rising demand for automation in capital markets. Automation of trading processes helps market participants trade huge volumes efficiently and effectively. It reduces the dependency on manual inputs and human emotions to trade. Algorithmic trading assists in analyzing huge chunks of market data, identifies patterns, and executes trades at extremely high speeds that are impossible for humans. It ensures consistency in implementing chosen trading strategies. The automation further helps optimize order placement, minimize information leakage, and improve overall trading performance. Algorithmic trading provides tools for analyzing and achieving edge over other market actors.
Porter’s Analysis
Threat of new entrants: High capital requirements and economies of scale in data processing pose barriers for new entrants.
Bargaining power of buyers: Presence of many vendors giving buyers options to negotiate on pricing and technology.
Bargaining power of suppliers: Dominance of few tech solution providers like AlgoTrader, Trading Technologies gives them pricing power.
Threat of new substitutes: Low as algorithmic trading offers speed, scale and precision which are difficult to match.
Competitive rivalry: Intense as vendors continuously invest in technology and solutions to gain access to larger trading volumes.
SWOT Analysis
Strengths: Algorithmic trading provides speed, precision and scale advantages over manual trading. Most vendors have significant experience and client portfolio.
Weaknesses: Heavy reliance on technology exposes the industry to risks of technical glitches, cyber threats and regulatory risks. High initial investments and skills required.
Opportunities: Growing complexity of markets, asset classes provides scope for newer solutions. Increasing digitization across geographies and asset types to drive opportunities.
Threats: Disruptions from advanced technologies like AI, machine learning. Stringent regulations especially relating to market integrity and fairness.
Key Takeaways
Global Algorithmic Trading Market Size is expected to witness high growth, exhibiting CAGR of 7.2% over the forecast period, due to increasing digitization and complexities in trading. The market size is projected to reach US$ 3.5 billion by 2030.
Regional analysis is dominated by North America led by presence of financial technology hubs and stock exchanges in US. Europe and Asia Pacific also hold significant shares and are expected to witness strong growth with focus on financial technologies in major countries.
Key players operating in the Algorithmic Trading market are AlgoTrader GmbH, Trading Technologies International, Inc., Tethys Technology, Inc., Tower Research Capital LLC, Lime Brokerage LLC, InfoReach, Inc., FlexTrade Systems, Inc., Hudson River Trading LLC, Citadel LLC, and Virtu Financial. The industry is fairly concentrated with these 10 players accounting for over 70% of global volumes. Mergers and acquisitions are common as vendors look to broaden solutions and gain larger market share.
*Note:
1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
