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Automated trading is a type of automation in which a computer program is used to carry out a defined set of instructions or rules, such as buying or selling an asset based on market data that is constantly changing. Timing, quantity, price, or any mathematical model are used to define sets of instructions or rules. It provides market participants with several advantages, including the ability to execute trades at the best possible prices, perform simultaneous automated checks on multiple market conditions, and reduce transaction costs due to the lack of human intervention.
The global automated trading market is segmented on the basis of component, deployment mode, type, type of traders, and region. Based on component, the market is bifurcated into solution and services. In terms of deployment mode, the market is categorized into cloud and on-premise. On the basis of type, the market is divided into stock, foreign exchange (FOREX), bonds, cryptocurrencies, and others. In terms of type of traders, the market is segregated into institutional traders, long-term traders, short-term traders, and retail traders. Geographically, the market is analyzed across several regions such as North America, Europe, Asia-Pacific, and Latin America, Middle East & Africa (LAMEA).
Top Impacting Factors
The growth of the global automated trading market is mainly driven by factors such as the rise in demand for reliable, fast, and effective order execution. In addition, the surge in demand for reducing transaction costs fuels the demand for automated trading. However, insufficient risk valuation capabilities are expected to hamper the growth of the market. Contrarily, the implementation of advanced technologies such as artificial intelligence, blockchain, and cloud computing is expected to provide lucrative opportunities for market growth during the forecasted period.
The Rise in Demand For Reliable, Fast and Effective Order Execution
Big brokerage houses and institutional investors are increasingly turning to automated trading to reduce operational costs. This is because automated trading allows for easier and faster order execution, making it appealing to exchanges. Furthermore, it allows investors and traders to profit quickly from small price changes. As a result, the growth of the market is driven by an increase in demand for effective trades, as it allows users to execute trades quickly.
The Emergence of AI and Algorithms in Financial Services
To capitalize on data from digitally driven channels, the majority of financial services companies are increasing their AI and machine learning adoption. It is used by several businesses in the banking, insurance, and asset management industries. During the last decade, this has resulted in the emergence of a data-driven investment trend.
The demand for high-frequency trading, also known as algorithmic trading, has grown tremendously in the market. AI-powered trading systems can analyze massive amounts of data much faster than humans can. As a result, AI and
algorithms in financial services are advantageous for market growth during the forecast period.
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