Algorithmic Trading Market to Cross USD 84.60 Billion by 2030 due to Increasing Emphasis on Data-driven Decision-Making | Research by SNS Insider
According to SNS insider research, regulators have implemented guidelines and controls to ensure fair and orderly trading practices, further boosting the credibility and attractiveness of algorithmic trading strategies to institutional investors.
Pune, Aug. 02, 2023 (GLOBE NEWSWIRE) — “The Algorithmic Trading Market, as per the SNS Insider report, had a valuation of USD 16.9 billion in 2022. It is projected to reach USD 84.60 billion by 2030, exhibiting a CAGR of 22.3% during the forecast period of 2023 to 2030.”
Market Overview
Algorithmic trading, also known as automated trading or algo-trading, is a sophisticated method of executing trades in financial markets using pre-defined computer algorithms. These algorithms are designed to analyze vast amounts of market data, identify patterns, execute trades, and manage positions without the need for direct human intervention.
Market Analysis
The algorithmic trading market’s exponential growth can be attributed to a confluence of technological advancements, data-driven decision-making, enhanced market liquidity, and efficient risk management. As the financial landscape continues to evolve, algorithmic trading is poised to play an increasingly crucial role in shaping the future of finance. By adapting to emerging technologies and regulatory frameworks, algorithmic trading firms can continue to thrive and drive innovation in the financial markets.
The Major Players Listed in this Report are:
- 63 moons technologies limited
- Tata Consultancy Services Limited
- Algo Trader
- Symphony
- Argo Software Engineering
- Refinitiv
- InfoReach, Inc.
- Kuberre Systems, Inc.
- MetaQuotes Ltd.
- VIRTU Finance Inc & Others
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Impact of Recession on Algorithmic Trading Market Growth
The algorithmic trading market has not been immune to the impact of recessions. Increased volatility, liquidity constraints, model recalibration, and regulatory scrutiny are some of the challenges that algorithmic traders face during economic downturns. Nevertheless, algorithmic trading has demonstrated resilience by evolving its strategies and incorporating advanced technologies to adapt to changing market conditions.
Impact of Russia-Ukraine War
The Russia-Ukraine War has introduced a new set of challenges for the algorithmic trading market, impacting market volatility, regulatory environment, and trading strategies. While algorithmic trading remains a critical component of modern financial markets, traders and institutions must remain vigilant and adaptive during geopolitical unrest.
Algorithmic Trading Market Report Scope:
| Report Attributes | Details |
| Market Size in 2022 | US$ 16.9 Bn |
| Market Size by 2030 | US$ 84.60 Bn |
| CAGR | CAGR of 22.3 % From 2023 to 2030 |
| Base Year | 2022 |
| Forecast Period | 2023-2030 |
| Historical Data | 2020-2021 |
| Algorithmic Trading Market: Key Segmentation | • By Solution (Platforms, Software Tools) • By Service (Professional Services, Managed Services) • By Deployment (Cloud, On-premise) • By Trading Types (Foreign Exchange (FOREX), Stock Markets, Exchange-Traded Fund (ETF), Bonds, Cryptocurrencies, Others) • By Type of Traders (Institutional Investors, Long-term Traders, Short-term Traders, Retail Investors) |
| Key Regional Coverage | North America (US, Canada, Mexico), Europe (Eastern Europe [Poland, Romania, Hungary, Turkey, Rest of Eastern Europe] Western Europe] Germany, France, UK, Italy, Spain, Netherlands, Switzerland, Austria, Rest of Western Europe]). Asia Pacific (China, India, Japan, South Korea, Vietnam, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (Middle East [UAE, Egypt, Saudi Arabia, Qatar, Rest of Middle East], Africa [Nigeria, South Africa, Rest of Africa], Latin America (Brazil, Argentina, Colombia Rest of Latin America) |
Impact of the COVID-19 Pandemic
The COVID-19 pandemic was a transformative event that left a lasting impact on the algorithmic trading market. Increased market volatility, a shift in trading strategies, and a greater reliance on machine learning and AI were some of the notable effects of the pandemic. Additionally, HFT firms faced unique challenges, and regulatory scrutiny intensified. The lessons learned from this experience prompted algorithmic trading firms to adapt, innovate, and prioritize risk management in an ever-changing financial landscape.
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Key Regional Developments
Algorithmic trading has emerged as a dominant force in global financial markets, and its growth has been shaped by various regional dynamics. The unique economic, technological, and regulatory factors in each region contribute to the diverse landscape of algorithmic trading. As the market continues to evolve, ongoing developments in technology and regulations will play a crucial role in shaping the future of algorithmic trading worldwide.
Key Takeaway from Algorithmic Trading Market Study
- The market is witnessing a transformative shift, with the Platforms Segment and the Foreign Exchange (FOREX) Segment emerging as dominant forces. These segments offer cutting-edge tools and vast opportunities for traders to capitalize on data-driven strategies.
- As technology continues to advance, further innovation, integration of AI and ML, and expansion into new markets are expected to shape the future of algorithmic trading. By staying at the forefront of these trends, traders can unlock new avenues for financial success in the ever-evolving landscape of algorithmic trading.
Recent Developments Related to Algorithmic Trading Market
- Exxe Group, a global leader in financial innovation and technology, has announced a landmark moment in the world of algorithmic trading. The company has recently signed a Letter of Intent (LOI) to launch its state-of-the-art Algorithmic Proprietary Trading Platform, promising to disrupt traditional trading practices and unlock new opportunities for investors worldwide.
- Virtu Financial, a leading global financial services firm, and Arqaam Capital, a prominent emerging markets investment bank, have announced a strategic cooperation aimed at expanding market access for equity algorithmic trading.
Table of Contents
1. Introduction
2. Research Methodology
3. Market Dynamics
4. Impact Analysis
5. Value Chain Analysis
6. Porter’s 5 forces model
7. PEST Analysis
8. Algorithmic Trading Market Segmentation, by Solution
9. Algorithmic Trading Market Segmentation, by Service
10. Algorithmic Trading Market Segmentation, by Deployment
11. Algorithmic Trading Market Segmentation, by Trading Types
12. Algorithmic Trading Market Segmentation, by Type of Traders
13. Regional Analysis
14. Company Profile
15. Competitive Landscape
16. USE Cases and Best Practices
17. Conclusion
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