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알고리즘 거래 시장 : 성장, 동향, COVID-19의 영향, 예측(2022-2027년)

Algorithmic Trading Market - Growth, Trends, COVID-19 Impact, and Forecasts (2022 - 2027)

발행일: | 리서치사: Mordor Intelligence Pvt Ltd | 페이지 정보: 영문 | 배송안내 : 2-3일 (영업일 기준)

※ 본 상품은 영문 자료로 한글과 영문 목차에 불일치하는 내용이 있을 경우 영문을 우선합니다. 정확한 검토를 위해 영문목차를 참고해주시기 바랍니다.

알고리즘 거래 시장은 예측 기간(2022-2027년)에 10.5%의 연평균 복합 성장률(CAGR)을 나타낼 것으로 예측됩니다.

목차

제1장 서론

  • 조사의 전제조건과 시장의 정의
  • 조사 대상 범위

제2장 조사 방법

제3장 개요

제4장 시장 인사이트

  • 시장 개요
  • 업계의 매력 - Porter's Five Forces 분석
    • 신규 진출업체의 위협
    • 구매자/소비자의 교섭력
    • 공급 기업의 교섭력
    • 대체품의 위협
    • 경쟁 기업간 경쟁 관계
  • COVID-19가 시장에 미치는 영향
  • 기술 현황
    • 알고리즘 거래 전략
      • Momentum Trading
      • 차익 거래
      • 추세 추종
      • 집행 기반 전략
      • 감정 분석
      • 인덱스 펀드 재조정
      • 수학적 모델에 근거한 전략
      • 기타 알고리즘 거래 전략

제5장 시장 역학

  • 시장 성장 가속요인
    • 고속, 고신뢰성, 효율적인 주문 집행 필요성
    • 거래 비용 절감에 의해 높아지는 시장 감시 수요 증가
  • 시장 성장 억제요인
    • 순간적인 유동성 상실

제6장 시장 세분화

  • 거래자 유형별
    • 기관 투자자
    • 개인 투자자
    • 장기 거래자
    • 단기 거래자
  • 컴포넌트별
    • 솔루션
      • 플랫폼
      • 소프트웨어 및 툴
    • 서비스
  • 전개 형태별
    • 온클라우드
    • 온프레미스
  • 조직 규모별
    • 중견/중소기업
    • 대기업
  • 지역별
    • 북미
    • 유럽
    • 아시아태평양
    • 라틴아메리카
    • 중동 및 아프리카

제7장 경쟁 구도

  • 기업 개요
    • Thomson Reuters
    • Jump Trading LLC
    • Refinitiv Ltd
    • 63 Moons Technologies Limited
    • Virtu Financial Inc.
    • MetaQuotes Software Corp.
    • Symphony Fintech Solutions Pvt. Ltd
    • Info Reach Inc.
    • ARGO SE
    • IG Group
    • Kuberre Systems Inc.
    • Algo Trader AG

제8장 투자 분석

제9장 시장 기회와 향후 동향

LSH 22.10.27

The Algorithmic Trading Market is anticipated to witness a CAGR of 10.5% throughout the forecast period (2022-2027). Traders have traditionally used market surveillance technology to keep track of their trading operations and investment portfolios. Applications with built-in intelligence, like algorithmic trading, can explore the market for various opportunities based on the yield and other parameters the user specifies.

Key Highlights

  • The need for the algorithmic trading industry is anticipated to be driven by favorable governmental rules, rising demand for quick, reliable, and efficient order execution, increasing demand for market surveillance, and declining transaction costs. Large brokerage firms and institutional investors use algorithmic trading to reduce the expenses of bulk trading. Additionally, it is anticipated that the development of artificial intelligence (AI) and financial service algorithms would create attractive market expansion opportunities. A rise in the demand for cloud-based solutions is also anticipated to support the growth of the algorithmic trading market.
  • In recent years, especially in the last ten years, FinTech tools have been developed significantly to increase the capacity of the financial industry, and algorithmic trading has dominated the capital markets, particularly the trading business. The general public now has access to data science tools, high-speed internet, and computing power. The proliferation of online trading platforms and apps has increased the accessibility of trading financial items. Trade stocks and currencies only take a few mouse clicks.
  • The market growth for algorithmic trading is projected to be significantly influenced by the financial services industry's broad adoption of AI, ML, and big data. Technological improvements have caused regulators to pay attention to how consumers interact with the market. For advancing Algo trading, some of the global central banks began employing such technologies. Moreover, algorithmic trading can maintain exceptionally high market liquidity due to quick buy and sell orders placed without human interaction. The increased application of algorithms across asset classes, particularly cross-asset automation, has been a trend over the past two years.
  • As per TRADE's 2021 Algorithmic Trading Survey, hedge funds increasingly use algorithms to trade most of their portfolios. For a multi-asset portfolio, hedge funds highly depend on a more significant number of suppliers for this. To address the demand from hedge funds, algorithm providers are now emphasizing multi-asset solutions. The survey found that implementation insufficiency - single stock (53.14%), VWAP (54.71%), and dark liquidity seeking (72.94%) were the three most employed types of algos. Furthermore, some of the primary reasons behind the utilization of the algos in 2021 include increased trader productivity (10.32%), reduced market impact (10.45%), consistency of execution performance (10.19%), ease of use (12.04%), and low commission rates (8.69%). Also, there has been a noticeable rise in the overall amount of automation and electronification. Moreover, the market volatility increase has maximized the need for algorithmic trading services and solutions.
  • Algorithmic trading has thus increased because of the volatile market circumstances, large trading volume, and need for quick digital transformation to deal with distant working environments. The necessity for algo trading expanded during the COVID-19 pandemic since there was no way for geographically diversified trading to function effectively without the requirement for more advanced routing and electronic algos to assist and offer liquidity for traders. Moreover, due to a growing tendency toward algorithmic trading to make quick decisions while minimizing human mistakes, the pandemic had a positive effect on the growth rate of the algorithmic trading sector.

Key Market Trends

Institutional Investors Expected to Hold Major Share

  • A group or institution's accounts are managed by institutional investors, who also buy and sell stocks on their behalf. Pension funds, mutual fund families, insurance companies, and exchange-traded funds are institutional investors. Institutional investors and large brokerage firms primarily use algorithmic trading to save trading costs. Large order sizes benefit significantly from algorithmic trading.
  • Institutional investors employ several computer-driven algorithmic tactics daily in the volatile trading markets that power the stock market. These strategies allow investors to lower trade expenses and increase their profitability.
  • These investors must execute high-frequency numbers, which isn't always achievable. Institutional investors can divide a large sum of money into smaller portions and continue to trade according to predetermined time frames or strategies due to algorithmic trading. For instance, an algorithmic trading strategy may push 1,000 shares out every 15 seconds and progressively place modest quantities into the market studied throughout the period or the entire day rather than depositing 100,000 shares at once.
  • Due to the massive volume of trades made by high-frequency traders daily, automated trading leveraging software and artificial intelligence is necessary, primarily to accelerate trade execution. Therefore, this technology may only be purchased by institutional investors. Moreover, they gain the benefit of value based on millisecond arbitrage to profit from it. Additionally, institutional-based investors use algorithmic trading by adhering to the arbitrage strategy when they want to benefit from various occasional tiny market price discrepancies.
  • Institutional investors are very concerned about their capital; thus, they require a system capable of making wise choices. Automation of processes reduces overtrading dramatically because some traders buy and sell at the first indication of a trade window opening. These techniques lessen the possibility of errors brought on by people. It responds to marketing conditions in a split second, making it a desired investment option.

North America Expected to Dominate the Market

  • North America is anticipated to have the most significant market share in the market studied. The main drivers of market growth throughout the forecast period are the rising investments in trading technologies (such as blockchain), the growing presence of algorithmic trading suppliers, and the expanding government backing for international trading.
  • According to Wall Street data, Algorithmic trading accounts for around 60-73% of the overall US equity trading. As per Select USA, the US financial markets are the largest and most liquid globally. An AI company, Sentient Technologies, based in the United States, operates a hedge fund that built an algorithm processing millions of data points to find trading patterns and forecast trends.
  • Modern technology is rapidly transforming the formats of conventional investment models by automating all associated trading procedures, enabling the development of a secure and effective ecosystem that will be accessible to all potential investors. In February 2022, a group of developers established the Dex Finance ecosystem. Dex Finance developed a low-risk algorithmic trading model that almost anyone can utilize by automating sophisticated trading tactics and encouraging investors to leave their deposits within the protocol.
  • In order to compete with one another, businesses are offering low pricing in the highly competitive financial market. Stankevicius Group launched quant finance algorithmic trading services in December 2021 with no advance payments leading to success-based fees exclusively. The business has been researching and creating advanced financial services like algorithmic trading. The Stankevicius Quant Financial algorithm can trade numerous pairs simultaneously in bullish and bearish markets. Professional traders also monitor trading activities, and in case of unexpected mistakes or defaults, admin-side human contact is enabled to stop losses.
  • Most traders trade with based on tips, gut feeling, as they are limited by current platforms and are often blind-sided by market movements against them that create unexpected losses. Streak, a supplier of algorithmic trading and strategy building for retail investors, announced the launch of its Streak application in the US to address this issue. It is anticipated that the company, which already serves more than 300,000 retail investors and has handled over half a billion in trading turnover, will soon give American users access to a broad range of advanced trading capabilities for various asset classes. This will enable them to develop new trading ideas and strategies and quickly seize new trading opportunities. Streak removes the obstacles to algo trading, which typically necessitates traders and investors to learn how to program or purchase costly, sluggish, and clunky legacy software.

Competitive Landscape

Due to the existence of numerous market participants worldwide, such as Virtu Financial, Inc., Algo Trader AG, MetaQuotes Software Corp., and Refinitiv Ltd., the global algorithmic trading industry is moderately fragmented. To maintain and grow their market share, key firms are mostly focusing on producing innovative solutions and successful marketing plans.

  • February 2022 - Software AG successfully partnered with the largest rural lifestyle retailer in the United States, Tractor Supply, to manage customer demand and enhance the shopping experience. Tractor Supply utilizes Software AG's integration and APIs solution to allow its customers to connect experiences across the store, mobile, and click-and-collect channels. Software AG solutions improve its operational excellence by integrating the supply chain from supplier to customer.
  • November 2021 - Refinitiv and Pio-Tech declared their collaboration in order to deliver smart, modern solutions with numerous unique business benefits to both firms' banking clients in the Middle East and Africa. The goal of this alliance is to increase the effectiveness of various internal anti-money laundering (AML) operations across all banking functions.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET INSIGHTS

  • 4.1 Market Overview
  • 4.2 Industry Attractiveness - Porter's Five Forces Analysis
    • 4.2.1 Threat of New Entrants
    • 4.2.2 Bargaining Power of Buyers/Consumers
    • 4.2.3 Bargaining Power of Suppliers
    • 4.2.4 Threat of Substitute Products
    • 4.2.5 Intensity of Competitive Rivalry
  • 4.3 Impact of COVID-19 on the Market
  • 4.4 Technology Snapshot
    • 4.4.1 Algorithmic Trading Strategies
      • 4.4.1.1 Momentum Trading
      • 4.4.1.2 Arbitrage Trading
      • 4.4.1.3 Trend Following
      • 4.4.1.4 Execution-based Strategies
      • 4.4.1.5 Sentiment Analysis
      • 4.4.1.6 Index-fund Rebalancing
      • 4.4.1.7 Mathematical Model-based Strategies
      • 4.4.1.8 Other Algorithmic Trading Strategies

5 MARKET DYNAMICS

  • 5.1 Market Drivers
    • 5.1.1 Rising Demand for Fast, Reliable, and Effective Order Execution
    • 5.1.2 Growing Demand for Market Surveillance Augmented by Reduced Transaction Costs
  • 5.2 Market Restraints
    • 5.2.1 Instant Loss of Liquidity

6 MARKET SEGMENTATION

  • 6.1 By Types of Traders
    • 6.1.1 Institutional Investors
    • 6.1.2 Retail Investors
    • 6.1.3 Long-term Traders
    • 6.1.4 Short-term Traders
  • 6.2 By Component
    • 6.2.1 Solutions
      • 6.2.1.1 Platforms
      • 6.2.1.2 Software Tools
    • 6.2.2 Services
  • 6.3 By Deployment
    • 6.3.1 On-cloud
    • 6.3.2 On-premise
  • 6.4 By Organization Size
    • 6.4.1 Small and Medium Enterprises
    • 6.4.2 Large Enterprises
  • 6.5 By Geography
    • 6.5.1 North America
    • 6.5.2 Europe
    • 6.5.3 Asia Pacific
    • 6.5.4 Latin America
    • 6.5.5 Middle-East and Africa

7 COMPETITIVE LANDSCAPE

  • 7.1 Company Profiles
    • 7.1.1 Thomson Reuters
    • 7.1.2 Jump Trading LLC
    • 7.1.3 Refinitiv Ltd
    • 7.1.4 63 Moons Technologies Limited
    • 7.1.5 Virtu Financial Inc.
    • 7.1.6 MetaQuotes Software Corp.
    • 7.1.7 Symphony Fintech Solutions Pvt. Ltd
    • 7.1.8 Info Reach Inc.
    • 7.1.9 ARGO SE
    • 7.1.10 IG Group
    • 7.1.11 Kuberre Systems Inc.
    • 7.1.12 Algo Trader AG

8 INVESTMENT ANALYSIS

9 MARKET OPPORTUNITIES AND FUTURE TRENDS

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