The acceleration of digital transformation in trading has pushed institutions into uncharted territory. In a landscape that’s increasingly dependent on artificial intelligence, algo execution has become imperative for anyone seeking meaningful results.
If your firm is still looking at artificial intelligence as a technology that could be implemented in the future, you may be missing out. AI is permeating the trading ecosystem today.
Already, as much as 70% of trades in US markets are executed by AI-powered algo trading systems. Institutions reliant on more traditional processes are at risk of being blown away by the growing levels of sophistication surrounding algorithmic trading models.
The pervasiveness of algo trading is only strengthening. Algorithmic trading markets are expected to expand at a CAGR of 12.2% throughout the decade.
This makes high-frequency algorithmic trading a necessity for institutional traders, and embracing the technology has never been more important.
Tapping into the Benefits of Algo Execution
The benefits of embracing artificial intelligence and algorithmic execution are clear for institutional investors in terms of leveraging improved efficiency and profitability. They include:
- Data-Driven Decisions: Because of the ability for algo execution models to interpret big data for stronger market insights, it’s possible for institutions to count on more powerful decision-making in real-time through trend analysis.
- Risk Mitigation: This use of big data can also help traders to identify riskier positions in a way that the human eye may not be as effective at interpreting-helping to avoid costly losses that could’ve been prevented.
- Higher Profit Potential: In a similar way to mitigating risk, traders can act faster on fleeting opportunities to capitalize on profits more effectively-helping them to make the most of their trading strategies.
Perhaps the biggest benefit of algo execution is that it helps institutions gain those valuable extra margins for efficiency over their rivals. In a landscape where 70% of trading is undertaken by artificial intelligence, algorithmic trading isn’t just about supercharging profit potential, it’s about leveling a playing field that’s leaning heavily towards automation technology.
The Necessity of Automation
The united front of AI and algorithmic trading is already a transformative presence for the Nanking, Financial Services, and Insurance (BFSI) industry. In utilizing the power of automation for trading, firms can actively tailor portfolios by risk tolerance and adapt instantly to market volatility.
In the case of hedge funds, it could be possible to utilize artificial intelligence to deliver more holistic arbitrage strategies with greater accuracy.
At the core of this strategy is machine learning (ML), which leans on algorithms and deep levels of historical data to shape its predictive capabilities on an ongoing basis.
This helps institutions to leverage pattern recognition which identifies trends or prospective anomalies across historical financial data to apply it to current markets in a way that can shape powerful future insights. For institutions with higher leverage, pattern recognition is imperative in terms of identifying opportunities while mitigating the prospect of risk.
At its core, this level of automation can help to deliver unprecedented high-frequency trading (HFT) at a pace that instantaneously analyzes market data and capitalizes on opportunities, particularly in arbitrage, that may only manifest for a matter of seconds.
Algo execution is also highly versatile and can be used for a number of key trading strategies that optimize institutional access to markets. With the help of prime broker services, algos can take the form of time-weighted VWAP or TWAP strategies, or automate iceberg orders to minimize the market volatility that could come with a large-scale trade.
Automation also helps to deliver greater control over areas of the market that have been notoriously difficult to manage for institutional investors in the past. Primarily, human error can enter the fray when it comes to managing emotional factors like fear, greed, mental fatigue, tilting, and developing flawed affinities to stocks.
Algo execution eliminates the confounding impact of emotions and human error and automatically executes trades based on pre-determined metrics-offering an unprecedented level of discipline.
Why Algo Compliments Human Counterparts
Inevitably when it comes to automation, there can be fears among human staff over their utility in the future. However, natural language processing (NLP) and large-language models (LLMs) can perfectly complement human staff without replacing them.
In the case of chatbots, it’s possible for AI algorithms to collaborate with traders to help support their awareness of changing market conditions, shifting market sentiment, and emerging trends within markets that may not have been previously discovered.
While automation and algo execution will change trading forever, chatbots can help to improve the power and pace of insights that can help shape the capabilities of human traders and brokers.
This flow of invaluable information like live account statements, real-time quotes, rapid troubleshooting, and alerts for price fluctuations means that chatbots can keep humans updated as they act on trends while they emerge.
NLP and machine learning algorithms can also help to comprehensively gauge market sentiment by interpreting human language across news sources, social media, and various trader insights. This can help to better understand market sentiment and for AI and humans alike to anticipate market movements in advance due to anticipated trader perception-helping to mitigate risk further.
Balancing Risk and Opportunity
At this stage, it’s important to note that embracing the AI trading revolution isn’t without its risks. The power of artificial intelligence can cause the technology to impact the market in unconventional ways.
For instance, as more institutions adopt similar algo execution technology, fresh stress could be placed on the market alongside the weakening of arbitrage opportunities for traders.
Likewise, the rapid performance of algorithmic trading could also lead to ‘spoofing’ which occurs when bids are placed to buy or sell securities which are then canceled before they can be executed. This leads to a false fluctuation in the sense of demand that could actively manipulate the market.
However, as the technology matures, we’ll see a more seamless integration with prime brokers that can help shape strategies based on their own intelligence bases, helping to nurture AI algorithms to help clients achieve their respective goals while staying true to the principles of the brokerage they represent.
At a time when the AI boom is only building momentum, algo execution times have never been more important. The technology is no longer a consideration for the future, and is very much an essential factor in building a successful strategy today.
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