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Algorithmic trading has emerged as the modern way of trading securities and other financial instruments by using sophisticated computer programs and models to analyze market data to determine when to buy and sell stocks, bonds, futures, or other assets. Algorithmic trading is typically employed by large institutional investors or investment banks to facilitate faster, more accurate, and timely execution of orders in large volumes. It allows institutions and funds to benefit from very small differences in market quotes. While algorithmic trading seeks to take advantage of market opportunities in fractions of a second, it also serves to increase liquidity in the market, tighten bid-ask spreads, and dampen volatility caused by large shareholders buying or selling large blocks of shares.
The global Algorithmic Trading Market is estimated to be valued at US$ 8.96 billion in 2024 and is expected to exhibit a CAGR of 10% over the forecast period 2024 to 2031, as highlighted in a new report published by Coherent Market Insights.
Algorithmic trading programs use pre-programmed trading instructions accounting for a variety of factors to take buy and sell decisions, automate the execution of orders, and manage various portfolios in seconds or even fractions of seconds. It incorporates various parameters such as pricing data, order flow data, financial news, and economic releases to size up the market situation and execute trades. This allows investment firms to deploy advanced quantitative techniques and leverage the dynamics of lower latency and computing power to their advantage. It filters out human emotions such as fear and greed from the decision-making process and aims to achieve efficiency in implementing strategies.
Market key trends:
One of the major trends in the algorithmic trading market is the incorporation of machine learning and artificial intelligence capabilities. Leading investment firms are focusing on developing algorithmic strategies that can learn from massive historical trading datasets and continuously enhance over time based on evolving market conditions. AI-powered algorithms can identify complex patterns and interpret unstructured data sources much faster than humans. Another key trend is the integration of algorithmic trading with blockchain technology. This enables developers to build decentralized applications and algorithmic strategies on distributed ledgers with enhanced security, transparency, and auditability features. It is also expected to facilitate peer-to-peer algorithmic trading between market participants in the coming years.
Porter’s Analysis
Threat of new entrants: The threat of new entrants in the algorithmic trading market is low as it requires significant investments in technology and infrastructure developments. Large brokers and high-frequency traders dominate the market.
Bargaining power of buyers: The bargaining power of buyers is low due to the presence of several algorithmic trading software providers and platforms. Buyers have many options available at competitive prices.
Bargaining power of suppliers: The bargaining power of suppliers is moderate as switching costs are relatively low for buyers. However, key suppliers have established brands and domain expertise.
Threat of new substitutes: The threat of substitutes is low as algorithmic trading offers unique benefits over traditional methods. However, social trading is emerging as a potential threat.
Competitive rivalry: The competitive rivalry in the market is high due to the presence of many global and local players. Players compete based on technology, pricing, and customer support.
Key Takeaways
The global algorithmic trading market is expected to witness high growth over the forecast period of 2024 to 2031. The global Algorithmic Trading Market is estimated to be valued at US$ 8.96 billion in 2024 and is expected to exhibit a CAGR of 10% over the forecast period 2024 to 2031.
Regional analysis: North America accounts for the largest share of the global algorithmic trading market revenue currently owing to technological advancements. The US and Canada are major revenue generators in this region. The Asia Pacific region is projected to grow at the highest CAGR over the forecast period due to growing investments in financial technology (FinTech) startups in countries such as India and China.
Key players: Key players operating in the algorithmic trading market are Thomson Reuters, 63 moons, Virtu Financial, Software AG, MetaQuotes Software, Symphony Fintech, InfoReach, Argo SE, Kuberre Systems, and TraderSoft. 63 moons provides algorithmic trading solutions through its business unit called Optiver. Virtu Financial is one of the largest high-frequency traders globally.
*Note:
- Source: Coherent Market Insights, Public sources, Desk research
- We have leveraged AI tools to mine information and compile it
