Algorithmic Trading Market is Estimated To Witness High Growth Owing To Trend of Cloud Computing Adoption
The Algorithmic Trading Market is estimated to be valued at US$ 2.18 Bn in 2023 and is expected to exhibit a CAGR of 7.2% over the forecast period 2023 to 2030, as highlighted in a new report published by Coherent Market Insights.
Market Overview:
Algorithmic trading involves using advanced mathematical formulas and computer programs to automate trading activities such as security analysis, portfolio management, and order execution. Algorithmic trading helps optimize the trading process, reducing risks from human errors and enhancing execution quality and speed. This trading method allows trading firms to conduct transactions at higher speeds and lower costs than traditional methods of trading.
Market key trends:
The adoption of cloud computing across various industries is expected to drive the growth of algorithmic trading market. Cloud computing facilitates on-demand access to shared computing resources and services over the internet, allowing algorithmic trading firms to optimize infrastructure, harness big data, and operate in a scalable environment. Cloud services offer pay-as-you-go pricing models which help trading firms avoid high upfront capital investments and efficiently scale computing resources based on real-time demands. This has boosted the popularity of cloud-based algorithmic trading solutions.
Porter's Analysis
Threat of new entrants: Low barriers to entry allow new competitors to emerge easily. However, established players hold majority market share due to strong customer loyalty.
Bargaining power of buyers: Buyers have moderate bargaining power. They can negotiate on price and use substitution. However, switching costs act as a barrier.
Bargaining power of suppliers: Suppliers of trading platforms and solutions have low to moderate power. Well-established brands face less pressure on pricing and supply.
Threat of new substitutes: Technological advancement in artificial intelligence and machine learning led to new trading strategies like high frequency trading.
Competitive rivalry: Intense competition exists among existing players. Frequent product innovations and upgrades determine competitive edges.
SWOT Analysis
Strengths: Algorithmic trading enables automation of strategies and real-time execution. It helps eliminate human errors and offers consistent results.
Weaknesses: Over-reliance on algorithms can amplify losses during volatile periods. High setup and management costs are required.
Opportunities: Growing retail participation offers scope. Untapped markets in Asia and Latin America present opportunities for regional expansion.
Threats: Stringent regulations may affect flexible operations. Technical glitches can cause wrong trades and losses. Cyber security threats also loom large.
Key Takeaways
The global algorithmic trading market is expected to witness high growth, exhibiting CAGR of 7.2% over the forecast period, due to increasing automation in trading activities. Algorithmic trading facilitates automation of complex trades throughout global markets and enables real-time adjustments to thousands of changes in market conditions per second.
Regional analysis North America dominates the global algorithmic trading market currently. This is attributed to presence of liquid markets and adoption of new technologies by major market participants in the region. However, Asia Pacific is poised to lucrative growth owing to rapid developments in financial technologies and growing online brokerage industry in emerging nations like China and India.
Key players operating in the algorithmic trading market are 63 Moons Technologies Limited, MetaQuotes Software Corp., Algo Trader AG, Refinitiv Ltd, and Virtu Financial Inc. The key players adopt organic and inorganic growth strategies like new product launches, partnerships, and acquisitions to strengthen their global presence.
Get more insights on this topic: https://www.newswirestats.com/algorithmic-trading-market-size-and-opportunity-analysis-2023-2030/
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