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PUBLISHER: Global Market Insights Inc. | PRODUCT CODE: 1532462

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PUBLISHER: Global Market Insights Inc. | PRODUCT CODE: 1532462

Algorithmic Trading Market Size - By Component (Software, Services), By Deployment Mode (On-premises, Cloud-based), By Trading Type (Foreign Exchange, Equity, Exchange-traded Funds, Bonds, Cryptocurrencies), By Industry Verticals & Forecast, 2024 - 2032

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Global Algorithmic Trading Market will witness over 13% CAGR between 2024 and 2032, fueled by the rising mergers and acquisitions M&A among leading financial companies. These strategic consolidations are driving innovation and expanding the reach of algorithmic trading solutions. M&A activities enable firms to integrate advanced technologies, enhance their trading platforms, and leverage data analytics capabilities. This results in more sophisticated algorithms that can analyze vast amounts of market data in real time, optimize trading strategies, and improve execution efficiency. For instance, in July 2024, cloud-based technology firm Clear Street unveiled plans to acquire an algorithmic execution solutions company specializing in Canadian and US equities. Clear Street has reached an agreement to purchase Instinet's Fox River algorithmic trading business.

Additionally, acquisitions allow firms to access new markets and client bases, broadening the adoption of algorithmic trading across different sectors and geographies. The increased investment in algorithmic trading technology by merged entities fosters competitive advantages and market liquidity. As financial institutions continue to pursue strategic mergers and acquisitions, the demand for cutting-edge algorithmic trading solutions is expected to rise, driving further growth in the market and enhancing the overall efficiency and profitability of trading operations.

The overall Algorithmic Trading Industry value is classified based on the component, deployment mode, trading type, industry vertical, and region.

The algorithmic trading market revenue from the services segment will register a commendable CAGR from 2024 to 2032. As companies increasingly rely on fleets for transportation, delivery, and logistics, robust insurance solutions become crucial to manage risks such as accidents, theft, and property damage. Comprehensive commercial auto insurance offers protection against financial losses and operational disruptions, safeguarding business assets and ensuring continuity. The rising number of commercial vehicles and growing awareness of risk management contribute to the increased demand for these policies. Consequently, the commercial auto insurance market is expanding to meet the evolving needs of businesses across various sectors.

The exchange traded segment will witness appreciable growth from 2024 to 2032. These vehicles are crucial for various commercial activities, including logistics, delivery, and field services. As businesses expand their fleets of vans and pickups, they require comprehensive insurance coverage to protect against risks such as accidents, theft, and damage. The increasing reliance on these vehicles for daily operations highlights the need for tailored insurance solutions that address their specific risks. Consequently, the commercial auto insurance market is expanding to cater to the diverse needs of businesses utilizing vans and pickups.

Asia Pacific algorithmic trading market will exhibit a notable CAGR from 2024 to 2032. Collision coverage protects businesses by covering repair or replacement costs for vehicles damaged in accidents, regardless of fault. As companies increasingly depend on commercial vehicles for their operations, the need for this type of coverage becomes essential to safeguard their assets. Rising road incidents and the desire for comprehensive risk management solutions further drive the demand for collision coverage. This trend highlights its importance in maintaining fleet safety and operational continuity, fueling market growth.

Product Code: 9512

Table of Contents

Chapter 1 Methodology & Scope

  • 1.1 Market scope & definition
  • 1.2 Research design
    • 1.2.1 Research approach
    • 1.2.2 Data collection methods
  • 1.3 Base estimates & calculations
    • 1.3.1 Base year calculation
    • 1.3.2 Key trends for market estimation
  • 1.4 Forecast model
  • 1.5 Primary research and validation
    • 1.5.1 Primary sources
    • 1.5.2 Data mining sources

Chapter 2 Executive Summary

  • 2.1 Industry 360° synopsis, 2021 - 2032

Chapter 3 Industry Insights

  • 3.1 Industry ecosystem analysis
  • 3.2 Supplier landscape
    • 3.2.1 Algorithm developers
    • 3.2.2 Technology providers
    • 3.2.3 Trading platform providers
    • 3.2.4 Consulting firms
    • 3.2.5 End user
  • 3.3 Profit margin analysis
  • 3.4 Technology & innovation landscape
  • 3.5 Patent analysis
  • 3.6 Key news & initiatives
  • 3.7 Regulatory landscape
  • 3.8 Impact forces
    • 3.8.1 Growth drivers
      • 3.8.1.1 Increasing adoption of automation in trading strategies
      • 3.8.1.2 Demand for faster execution and reduced transaction costs
      • 3.8.1.3 Expansion of electronic trading platforms and exchanges
      • 3.8.1.4 Globalization leading to cross-border trading opportunities
    • 3.8.2 Industry pitfalls & challenges
      • 3.8.2.1 Vulnerability to technological glitches and system failures
      • 3.8.2.2 Lack of transparency in algorithmic trading strategies
  • 3.9 Growth potential analysis
  • 3.10 Porter's analysis
  • 3.11 PESTEL analysis

Chapter 4 Competitive Landscape, 2023

  • 4.1 Introduction
  • 4.2 Company market share analysis
  • 4.3 Competitive positioning matrix
  • 4.4 Strategic outlook matrix

Chapter 5 Market Estimates & Forecast, By Component 2021 - 2032 ($ Bn)

  • 5.1 Key trends
  • 5.2 Software
    • 5.2.1 Algorithm
    • 5.2.2 Trading platform
    • 5.2.3 Risk management tools
  • 5.3 Services
    • 5.3.1 Consulting
    • 5.3.2 Implementation
    • 5.3.3 Support & maintenance

Chapter 6 Market Estimates & Forecast, By Deployment Mode, 2021 - 2032 ($ Bn)

  • 6.1 Key trends
  • 6.2 On-premises
  • 6.3 Cloud-based

Chapter 7 Market Estimates & Forecast, By Trading Type, 2021 - 2032 ($ Bn)

  • 7.1 Key trends
  • 7.2 Foreign exchange (Forex)
  • 7.3 Equity
  • 7.4 Exchange-traded funds (ETFs)
  • 7.5 Bonds
  • 7.6 Cryptocurrencies
  • 7.7 Others

Chapter 8 Market Estimates & Forecast, By Industry Verticals, 2021 - 2032 ($ Bn)

  • 8.1 Key trends
  • 8.2 Banking & finance
  • 8.3 Broker-dealers
  • 8.4 Others

Chapter 9 Market Estimates & Forecast, By Region, 2021 - 2032 ($ Bn)

  • 9.1 Key trends
  • 9.2 North America
    • 9.2.1 U.S.
    • 9.2.2 Canada
  • 9.3 Europe
    • 9.3.1 UK
    • 9.3.2 Germany
    • 9.3.3 France
    • 9.3.4 Italy
    • 9.3.5 Spain
    • 9.3.6 Russia
    • 9.3.7 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 China
    • 9.4.2 India
    • 9.4.3 Japan
    • 9.4.4 South Korea
    • 9.4.5 ANZ
    • 9.4.6 Southeast Asia
    • 9.4.7 Rest of Asia Pacific
  • 9.5 Latin America
    • 9.5.1 Brazil
    • 9.5.2 Mexico
    • 9.5.3 Argentina
    • 9.5.4 Rest of Latin America
  • 9.6 MEA
    • 9.6.1 UAE
    • 9.6.2 South Africa
    • 9.6.3 Saudi Arabia
    • 9.6.4 Rest of MEA

Chapter 10 Company Profiles

  • 10.1 AlgoTrader
  • 10.2 Automated Trading SoftTech
  • 10.3 Codebase Technologies
  • 10.4 CQG
  • 10.5 Deltix
  • 10.6 InfoReach
  • 10.7 Marquee by Goldman Sachs
  • 10.8 MetaTrader
  • 10.9 Nasdaq
  • 10.10 Optiver
  • 10.11 Pragmatic
  • 10.12 QuantHouse
  • 10.13 Raptor Trading Systems
  • 10.14 Refinitiv (formerly Reuters)
  • 10.15 Tethys
  • 10.16 Tick Data
  • 10.17 Trading Technologies
  • 10.18 Virtu Financial
  • 10.19 Wissolution
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Jeroen Van Heghe

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Christine Sirois

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