세계의 알고리즘 트레이딩 시장 규모 : 유형별, 전개별, 최종사용자별, 지역별 범위 및 예측

Global Algorithmic Trading Market Size By Type, By Deployment, By End-User, By Geographic Scope And Forecast

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한글목차

알고리즘 트레이딩 시장 규모 및 예측

알고리즘 트레이딩 시장 규모는 2024년 163억 7,000만 달러로 평가되며, 2026년부터 2032년까지 10%의 CAGR로 성장하여 2032년에는 319억 달러에 달할 것으로 예상됩니다.

• 일반적으로 알고리즘 트레이딩 또는 자동 거래로 알려진 알고리즘 트레이딩은 다양한 시장에서 금융 거래를 실행하는 데 사용되는 컴퓨터 알고리즘으로, 데이터를 분석하고 의사결정을 내리고 주문을 실행하기 위해 미리 프로그래밍 된 명령을 활용합니다.
• 이 기술은 고속 컴퓨터, 저지연 데이터 연결, 코로케이션 서비스, 프록시미티 호스팅 등 첨단 기술 인프라를 활용하여 거래를 신속하게 실행하고 경쟁이 치열한 시장에서 경쟁합니다.
• 알고리즘 트레이딩은 수학적 모델과 컴퓨터 알고리즘을 사용하여 거래 의사결정을 자동화합니다. 이러한 알고리즘은 통계 분석, 기술 지표, 차익 거래 기회, 기계 학습, 인공지능 등 다양한 전략을 기반으로 합니다.
• 주식, 채권, 원자재, 통화, 파생상품 등 다양한 금융시장에 적용되고 있습니다. 알고리즘 트레이딩은 전자거래 플랫폼과 거래소에서 널리 사용되고 있으며, 알고리즘이 실시간으로 경쟁하고 상호 작용하여 시장 기회를 포착하고 수익을 창출하고 있습니다.

세계 알고리즘 트레이딩 시장 역학

알고리즘 트레이딩 시장을 형성하는 주요 시장 역학은 다음과 같습니다:

주요 시장 촉진요인

• 금융기관의 알고리즘 트레이딩 채택 : 알고리즘은 트레이딩 비용과 인력을 크게 줄이고 세일즈 데스크 업무를 개선할 수 있습니다. 또한, 거래소에 주문 전송을 자동화함으로써 유동성, 가격 설정, 브로커 수수료를 개선하기 위해 브로커의 필요성을 없애고 있습니다. 은행 기관의 자동 거래 소프트웨어 사용이 증가함에 따라 클라우드 기반 솔루션 및 시장 모니터링 소프트웨어에 대한 수요가 증가하고 있으며, 시장을 주도하고 있습니다.
• 인공지능(AI)과 머신러닝(ML)의 통합 : AI 알고리즘은 밀리초 단위의 시장 변화에 반응하여 인간의 능력을 훨씬 능가하는 속도로 거래를 실행할 수 있습니다. 이는 순간의 기회를 포착하고 불안정한 시장에서의 손실을 최소화하는 데 매우 중요합니다.
• 금융 부문의 복잡성 : 알고리즘은 방대한 양의 데이터를 분석하고 인간보다 훨씬 빠른 속도로 거래를 실행할 수 있기 때문에 순간의 기회를 포착하고 변화하는 시장 환경에 빠르게 대응할 수 있습니다. 따라서 알고리즘 트레이딩 전략을 과거 데이터로 엄격하게 백테스트하고, 그 유효성을 평가한 후 특정 시장 환경에 맞게 최적화함으로써 세계적으로 확립된 시장을 만들어 낼 수 있습니다.
• 리스크 관리 전략의 자동화 : 거래 실행 전에 거래의 잠재적 영향을 평가하는 거래 전 리스크 점검을 도입함으로써 주문 규모 상한, 포지션 상한, 필요 증거금, 규제 제약 준수 여부를 점검할 수 있을 것으로 예측됩니다. 따라서 알고리즘 트레이딩 솔루션과 같은 자동 리스크 관리 소프트웨어는 실시간으로 거래 파라미터를 분석하여 미리 정의된 리스크 임계값을 위반하는 주문을 거부할 것으로 예상됩니다.
• 다양한 기업의 자동 알고리즘 트레이딩 채택 : 알고리즘 자동매매는 일류 증권사, 개인투자자, 신용조합, 보험사들 사이에서 점점 더 널리 보급되고 있습니다. 그 이유는 거래 관련 비용을 절감할 수 있기 때문입니다. 자동 알고리즘 트레이딩을 채택함으로써 주문을 더 빠르고 쉽게 실행할 수 있어 거래소에 이상적인 거래가 될 수 있습니다. 특히 인간 트레이더가 대량의 거래에 대응할 수 없는 경우에 효과적입니다.

주요 과제

• 데이터 오류 및 불일치 가능성 높음 : 부정확하거나 일관성 없는 데이터는 잘못된 정보에 기반한 거래 결정으로 이어질 수 있습니다. 잘못된 데이터가 거래 알고리즘에 입력되면 잘못된 신호가 생성되고, 그 결과 불충분한 거래 실행이나 손실이 발생할 수 있습니다. 시장 데이터의 오류는 운용 리스크와 시장 리스크를 증가시킬 수 있습니다. 예를 들어, 거래 알고리즘이 부정확한 가격 데이터에 의존하는 경우, 불리한 가격으로 거래가 체결되어 손실이 확대되거나 예상치 못한 노출이 발생할 수 있습니다.
• 시장 파편화 및 유동성 문제 : 자동 거래 시스템은 플랫폼과 자산 카테고리 간 유동성 분산으로 인해 높은 실행 비용과 유동성 제한이라는 문제에 직면해 있습니다. 이러한 문제를 극복하기 위해 시장 진입자들은 고급 주문 라우팅 알고리즘을 개발하고, 체결 방법을 최적화하고, 다양한 유동성 풀에 접근해야 합니다.
• 주문과 체결의 시간 지연 증가 : 주문 체결의 시간 지연은 특히 빠르게 움직이는 시장이나 유동성이 낮은 증권에서 시장 영향력을 증가시킬 수 있습니다. 주문 체결 지연으로 인해 의도한 가격과 다른 가격으로 거래가 체결되는 슬리피지(Slippage)가 발생하여 거래 비용 상승 및 수익성 저하로 이어질 수 있습니다.
• 갑작스러운 시스템 장애 및 네트워크 연결 문제 : 하드웨어 고장, 소프트웨어 결함, 서버 충돌 등의 시스템 장애는 자동 거래 운영을 방해하고 주문 실행을 지연 또는 중단시킬 수 있습니다. 그 결과, 거래 기회를 놓치고, 주문이 정체되어 시장 진입자가 손실을 입게 될 수 있습니다.

주요 동향

• 암호화폐 시장 확대 : 암호화폐의 인기가 상승세를 타고 있으며, 이에 따라 디지털 자산 시장에서의 알고리즘 트레이딩 활동이 확대되고 있습니다. 알고리즘 트레이딩은 암호화폐의 가격 비효율성, 차익거래 기회, 시장 동향을 이용하기 위해 자동화 전략을 활용하고 있습니다. 이를 통해 암호화폐 생태계의 유동성과 혁신성을 높이고 있습니다.
• 양자 컴퓨팅의 가능성 : 양자 컴퓨팅은 아직 개발 초기 단계에 있지만, 컴퓨팅 파워를 크게 향상시키고 전례 없는 속도로 복잡한 계산을 가능하게함으로써 알고리즘 트레이딩에 혁명을 가져올 수 있는 잠재력을 가지고 있습니다. 시장 진입 기업들은 양자 컴퓨팅 기술의 발전을 주시하며 알고리즘 트레이딩에 대한 적용 가능성을 모색하고 있습니다.
• 고빈도 거래(HFT)의 진화 : 고빈도 거래(HFT) 시장 개발 : 고빈도 거래(HFT) 업체들은 거래 전략을 개선하고, 주문 실행을 최적화하고, 순간적인 시장 기회를 활용하기 위해 끊임없이 새로운 알고리즘을 개선하고 개발하고 있습니다. 이러한 알고리즘은 고급 수학 모델, 통계 분석 기술, 기계 학습 알고리즘을 활용하여 최소한의 대기 시간으로 시장 데이터에서 알파 값을 추출합니다.

목차

제1장 세계의 알고리즘 트레이딩 시장 소개

• 시장 개요
• 조사 범위
• 가정

제2장 주요 요약

제3장 VERIFIED MARKET RESEARCH 조사 방법

• 데이터 마이닝
• 검증
• 1차 자료
• 데이터 소스 리스트

제4장 세계의 알고리즘 트레이딩 시장 전망

• 개요
• 시장 역학
  • 성장 촉진요인
  • 성장 억제요인
  • 기회
• Porter's Five Forces 모델
• 밸류체인 분석

제5장 알고리즘 트레이딩 세계 시장 : 유형별

• 개요
• 주식시장
• 외환(FOREX)
• 상장 투자신탁(ETF)
• 채권
• 암호화폐
• 기타

제6장 알고리즘 트레이딩 세계 시장 : 전개별

• 개요
• 온프레미스
• 클라우드 기반

제7장 알고리즘 트레이딩 세계 시장 : 최종사용자별

• 개요
• 단기
• 단기 트레이더
• 장기 트레이더
• 개인투자가
• 기관투자가

제8장 알고리즘 트레이딩 세계 시장, 지역별

• 개요
• 북미
  • 미국
  • 캐나다
  • 멕시코
• 유럽
  • 독일
  • 영국
  • 프랑스
  • 기타 유럽
• 아시아태평양
  • 중국
  • 일본
  • 인도
  • 기타 아시아태평양
• 세계 기타 지역
  • 라틴아메리카
  • 중동 및 아프리카

제9장 세계의 알고리즘 트레이딩 시장 경쟁 구도

• 개요
• 각사의 시장 순위
• 주요 발전 전략

제10장 기업 개요

• 63 Moons Technologies Ltd
• Software AG
• Virtu Financial
• Thomson Reuters
• MetaQuotes Software
• Symphony Fintech
• InfoReach
• Argo SE
• Kuberre Systems
• Tata Consulting Services

제11장 부록

• 관련 조사

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영문 목차

영문목차

Algorithmic Trading Market Size And Forecast

Algorithmic Trading Market size was valued at USD 16.37 Billion in 2024 and is projected to reach USD 31.90 Billion by 2032, growing at a CAGR of 10% from 2026 to 2032.

• Algorithmic trading, commonly known as algo trading or automated trading, is a computer algorithms used to execute financial transactions in various markets, utilizing pre-programmed instructions to analyze data, make decisions, and execute orders.
• The technology leverages advanced technological infrastructure like high-speed computers, low-latency data connections, co-location services, and proximity hosting to execute trades quickly and compete in highly competitive markets.
• Algorithmic trading involves the use of mathematical models and computer algorithms to automate trading decisions. These algorithms can be based on various strategies, including statistical analysis, technical indicators, arbitrage opportunities, machine learning, and artificial intelligence.
• It is applied across various financial markets, including stocks, bonds, commodities, currencies, and derivatives. Algorithmic trading has become prevalent in electronic trading platforms and exchanges, where algorithms compete and interact in real-time to capture market opportunities and generate profits.

Global Algorithmic Trading Market Dynamics

The key market dynamics that are shaping the Algorithmic Trading Market include:

Key Market Drivers

• Adoption of Algorithmic Trading by Financial Institutions: Algorithms are significantly lowering trading costs, headcount, and improving sales desk operations. They also help automate order sending to exchanges, eliminating the need for brokers for enhancing liquidity, pricing, and broker commissions. The increasing use of automated trading software by banking organizations is demanding for cloud-based solutions and market monitoring software, driving the market.
• Integration of Artificial Intelligence (AI) and Machine Learning (ML): AI algorithms can react to market changes in milliseconds, executing trades at speeds far exceeding human capabilities. This is crucial for capitalizing on fleeting opportunities and minimizing losses in volatile markets.
• Increasing Complexity in Financial Sector: Algorithms can analyze vast amounts of data and execute trades much faster than humans, allowing them to capitalize on fleeting opportunities and react swiftly to changing market conditions. Thus, algorithmic trading strategies can be rigorously backtested on historical data to assess their effectiveness and then optimized for specific market conditions, creating an established market globally.
• Automating Risk Management Strategies: Implementing pre-trade risk checks to evaluate the potential impact of a trade before it is executed is projected to help upkeep checks for order size limits, position limits, margin requirements, and compliance with regulatory constraints. Hence, automated risk management software, such as algorithmic trading solutions, is projected to analyze trade parameters in real time and reject orders that violate predefined risk thresholds.
• Adoption of Automated Algorithmic Trading Across Diverse Companies: Automated algorithmic trading is becoming more and more popular among top brokerage firms, individual investors, credit unions, and insurance companies. The reason for this is that it helps to reduce the costs associated with trading. By adopting automated algorithmic trading, orders can be executed faster and more easily, making it ideal for exchanges. It is particularly useful in situations where a human trader is unable to handle large volumes of trading.

Key Challenges:

• High Chances of Error and Inconsistency in Data: Inaccurate or inconsistent data can lead to misinformed trading decisions. If trading algorithms are fed with erroneous data, they may generate incorrect signals, resulting in poor trade execution or losses. Errors in market data can increase operational and market risk. For example, if a trading algorithm relies on incorrect pricing data, it may execute trades at unfavorable prices, leading to increased losses or unexpected exposures.
• Market Fragmentation and Liquidity Challenge: Automated trading systems face challenges due to liquidity dispersion across platforms and asset categories, resulting in higher execution costs and limited liquidity. To overcome these issues, market participants should develop advanced order routing algorithms, optimize execution methods, and access various liquidity pools.
• Increase in Time lags in Order and Executions: Time lags in order execution can lead to increased market impact, especially in fast-moving markets or illiquid securities. Delayed order execution may result in slippage, where trades are executed at prices different from the intended price, leading to higher transaction costs and reduced profitability.
• Sudden System Failures and Erroneous Network Connectivity Issues: System failures, such as hardware malfunctions, software glitches, or server crashes, can disrupt automated trading operations, leading to delays or interruptions in order execution. This is likely to result in missed trading opportunities, order queuing, and potential losses for market participants.

Key Trends:

• Expansion of Cryptocurrency Markets: The popularity of cryptocurrencies is on the rise, and as a result, algorithmic trading activities in digital asset markets are expanding. Automated strategies are being used by algorithmic traders to take advantage of price inefficiencies, arbitrage opportunities, and market trends in cryptocurrencies. This is leading to increased liquidity and innovation in the crypto ecosystem.
• Quantum Computing Potential: Although quantum computing is still in its early stages of development, it has the potential to revolutionize algorithmic trading by providing a significant boost in computing power and enabling complex calculations at unprecedented speeds. Market participants are closely monitoring advancements in quantum computing technology and exploring potential applications in algorithmic trading.
• The Evolution of High-Frequency Trading (HFT): HFT firms are continuously refining and developing new algorithms to improve trading strategies, optimize order execution, and capitalize on fleeting market opportunities. These algorithms leverage advanced mathematical models, statistical analysis techniques, and machine learning algorithms to extract alpha from market data with minimal latency.

Global Algorithmic Trading Market Regional Analysis

Here is a more detailed regional analysis of the Algorithmic Trading Market:

Asia Pacific:

• According to Verified Market Research, Asia Pacific is estimated to grow at a faster rate over the forecast period due to the rise in private and public sectors making substantial investments to improve their trading technologies, driving the demand for solutions to automate trading processes.
• In addition, trading companies are increasingly deploying algo trading technology, which is creating lucrative opportunities for market players. Furthermore, the adoption of cloud-based technologies in this region is increasing, contributing to the growth of the regional market.
• Tokyo serves as Asia's primary financial hub and a major center for algorithmic trading. The Tokyo Stock Exchange (TSE) and Osaka Exchange (OSE) are key venues for algorithmic trading in Japanese equities and derivatives markets. Japanese regulators oversee market regulation and infrastructure development.

North America:

• North America currently dominates the Algorithmic Trading Market, holding the largest share. This is due to the high number of market participants, making it a highly competitive industry. Consequently, there have been significant investments in trading technologies and government support for global trade, leading to the development and adoption of algorithmic trading solutions.
• The widespread use of algorithmic trading in financial institutions, along with extensive technology enhancements, is boosting industry expansion, particularly in banks.
• The New York Stock Exchange (NYSE) and NASDAQ are prominent venues for algorithmic trading. High-frequency trading (HFT) is prevalent, driven by advanced technology infrastructure and a regulatory environment conducive to electronic trading.

Europe:

• Europe is expected to exhibit a steady growth rate in the trading industry. The market in Europe is analyzed across various countries, including Germany, France, the U.K., Italy, and others. The use of advanced trading approaches and novel infrastructures has increased due to regulatory platforms, technological advancements, and increased competition among trading participants.
• Additionally, the government has implemented special rules and regulations to promote security and performance, which has further nurtured the market growth.
• For instance, MiFID II, a European Union framework that regulates financial markets, has implemented a comprehensive set of algorithmic and high-frequency trading regulations in 2021. These achievements offer immense opportunities of growth for to the Algorithmic Trading Market in Europe.

Global Algorithmic Trading Market: Segmentation Analysis

The Algorithmic Trading Market is Segmented based on Type, Deployment, End-User, And Geography.

Global Algorithmic Trading Market, By Type

• Stock Market
• Foreign Exchange (FOREX)
• Exchange-Traded Fund (ETF)
• Bonds
• Cryptocurrencies
• Others

Based on Type, the Algorithmic Trading Market is divided into Stock Market, Foreign Exchange, Bonds, Cryptocurrencies, Exchange-Traded Fund (ETF), and Others. The stock market segment is projected to dominate the market. Algorithms are becoming increasingly popular on online trading platforms, creating a large consumer base for stock market. These mathematical algorithms analyze all prices and trades on the stock market, identify liquidity opportunities, and convert the information into intelligent trading results. Algorithmic trading reduces trading costs and enables stock managers to manage their trading processes more efficiently. Algorithm modernization continues to offer returns for firms with the scale to absorb the costs and reap the benefits.

Global Algorithmic Trading Market, By Deployment

• On-Premise
• Cloud-Based

Based on Deployment, the market is divided into On-Premise, and Cloud-Based. The cloud-based segment currently holds the largest market share and is expected to grow at the highest rate during the forecast period. This is due to financial organizations' adoption of cloud-based applications to increase their productivity and efficiency. Moreover, traders are increasingly opting for cloud-based solutions as they ensure effective automation of processes, data maintenance, and cost-friendly management. These factors are likely to fuel the growth of cloud-based algo trading software during the forecast period.

• Global Algorithmic Trading Market, End-User
• Short-term
• Traders
• Long-term Traders
• Retail Investors
• Institutional Investors

Based on End-User, he market is divided into Short-term Traders, Long-term Traders, Retail Investors, and Institutional Investors. The short-term traders segment is expected to grow at the highest CAGR. They focus on price movements to profit from market volatility. The institutional investors segment holds the largest market share and includes mutual fund families, pension funds, exchange-traded funds, and insurance firms. Algorithmic trading benefits significantly from large order sizes.

Key Players

The "Global Algorithmic Trading Market" study report will provide valuable insight with an emphasis on the global market. The major players in the market are The major players in the market are 63 Moons Technologies Ltd, Software AG, Virtu Financial, Thomson Reuters, MetaQuotes Software, Symphony Fintech, InfoReach, Argo SE, Kuberre Systems, and Tata Consulting Services, among others.

Our market analysis also entails a section solely dedicated to such major players wherein our analysts provide an insight into the financial statements of all the major players, along with product benchmarking and SWOT analysis. The competitive landscape section also includes key development strategies, market share, and market ranking analysis of the above-mentioned players globally.

• Algorithmic Trading Market Recent Developments
• In August 2020, Non-deliverable forwards algorithms were introduced by Barclays on the BARX electronic trading platform. To give clients a variety of options, this algorithm incorporates large investments in electronic offerings.
• In March 2022, the trading software company Trading Technologies International, Inc. announced that it had acquired RCM, a provider of algorithmic execution methodologies and quantitative trading tools. With its exceptional staff, this acquisition of RCM-X provides best-in-class implementation tools.
• In June 2022, Agency-broker FIS's trading operation will be acquired by Instinet. The acquisition reduces execution costs, minimizes information leakage, and enhances customer execution quality.
• In June 2024, one of the top platforms for automated trading and bot building, Kryll, recently partnered with KuCoin Futures via an API. By incorporating TradingView signal features and Kryll's algorithmic trading bots into the KuCoin Futures platform, this ground-breaking partnership seeks to transform futures trading.
• In June 2024, one of the top software platforms for measuring, analyzing, and data in digital media, DoubleVerify, has partnered with Scibids, a major global provider of artificial intelligence (Al) for digital marketing, to produce DV Algorithmic Optimizer, an advanced measure and optimization tool. With Scibids' AI-powered ad decisioning and DV's proprietary attention signals, advertisers can find the best inventory that maximizes advertising ROI and business outcomes without compromising scalability.

TABLE OF CONTENTS

1 INTRODUCTION OF GLOBAL ALGORITHMIC TRADING MARKET

• 1.1 Overview of the Market
• 1.2 Scope of Report
• 1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

• 3.1 Data Mining
• 3.2 Validation
• 3.3 Primary Interviews
• 3.4 List of Data Sources

4 GLOBAL ALGORITHMIC TRADING MARKET OUTLOOK

• 4.1 Overview
• 4.2 Market Dynamics
  • 4.2.1 Drivers
  • 4.2.2 Restraints
  • 4.2.3 Opportunities
• 4.3 Porters Five Force Model
• 4.4 Value Chain Analysis

5 GLOBAL ALGORITHMIC TRADING MARKET, BY TYPE

• 5.1 Overview
• 5.2 Stock Market
• 5.3 Foreign Exchange (FOREX)
• 5.4 Exchange-Traded Fund (ETF)
• 5.5 Bonds
• 5.6 Cryptocurrencies
• 5.7 Others

6 GLOBAL ALGORITHMIC TRADING MARKET, BY DEPLOYMENT

• 6.1 Overview
• 6.2 On-Premise
• 6.3 Cloud-Based

7 GLOBAL ALGORITHMIC TRADING MARKET, BY END-USER

• 7.1 Overview
• 7.2 Short-term
• 7.3 Traders
• 7.4 Long-term Traders
• 7.5 Retail Investors
• 7.6 Institutional Investors

8 GLOBAL ALGORITHMIC TRADING MARKET, BY GEOGRAPHY

• 8.1 Overview
• 8.2 North America
  • 8.2.1 U.S.
  • 8.2.2 Canada
  • 8.2.3 Mexico
• 8.3 Europe
  • 8.3.1 Germany
  • 8.3.2 U.K.
  • 8.3.3 France
  • 8.3.4 Rest of Europe
• 8.4 Asia Pacific
  • 8.4.1 China
  • 8.4.2 Japan
  • 8.4.3 India
  • 8.4.4 Rest of Asia Pacific
• 8.5 Rest of the World
  • 8.5.1 Latin America
  • 8.5.2 Middle East & Africa

9 GLOBAL ALGORITHMIC TRADING MARKET COMPETITIVE LANDSCAPE

• 9.1 Overview
• 9.2 Company Market Ranking
• 9.3 Key Development Strategies

10 COMPANY PROFILES

• 10.1 63 Moons Technologies Ltd
  • 10.1.1 Overview
  • 10.1.2 Financial Performance
  • 10.1.3 Product Outlook
  • 10.1.4 Key Developments
• 10.2 Software AG
  • 10.2.1 Overview
  • 10.2.2 Financial Performance
  • 10.2.3 Product Outlook
  • 10.2.4 Key Developments
• 10.3 Virtu Financial
  • 10.3.1 Overview
  • 10.3.2 Financial Performance
  • 10.3.3 Product Outlook
  • 10.3.4 Key Developments
• 10.4 Thomson Reuters
  • 10.4.1 Overview
  • 10.4.2 Financial Performance
  • 10.4.3 Product Outlook
  • 10.4.4 Key Developments
• 10.5 MetaQuotes Software
  • 10.5.1 Overview
  • 10.5.2 Financial Performance
  • 10.5.3 Product Outlook
  • 10.5.4 Key Developments
• 10.6 Symphony Fintech
  • 10.6.1 Overview
  • 10.6.2 Financial Performance
  • 10.6.3 Product Outlook
  • 10.6.4 Key Developments
• 10.7 InfoReach
  • 10.7.1 Overview
  • 10.7.2 Financial Performance
  • 10.7.3 Product Outlook
  • 10.7.4 Key Developments
• 10.8 Argo SE
  • 10.8.1 Overview
  • 10.8.2 Financial Performance
  • 10.8.3 Product Outlook
  • 10.8.4 Key Developments
• 10.9 Kuberre Systems
  • 10.9.1 Overview
  • 10.9.2 Financial Performance
  • 10.9.3 Product Outlook
  • 10.9.4 Key Developments
• 10.10 Tata Consulting Services
  • 10.10.1 Overview
  • 10.10.2 Financial Performance
  • 10.10.3 Product Outlook
  • 10.10.4 Key Developments

11 Appendix

• 11.1 Related Research

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