With use cases rapidly emerging throughout the investing ecosystem, it’s clear that artificial intelligence tools in institutional trading are here to stay. But how can AI improve access to liquidity for broker-dealers at scale?
The impact of the AI boom is already being felt. By 2030, AI in the finance market is expected to reach a value of $190.33 billion, representing a CAGR of more than 30% over the coming years.
The transformative potential of artificial intelligence can’t be understated, and the technology already has the ability to aid investors in rebalancing portfolios, paving the way for stronger trading volumes.
High-frequency, AI-driven trading is expected to become more prevalent in the years ahead, helping to boost liquid asset classes like equities, government bonds, and various derivatives. But what about assets that suffer from liquidity shortfalls?
Evidence is growing that AI could be a game-changer in empowering more institutions in accessing liquidity to take advantage of opportunities in more challenging market conditions.
With this in mind, let’s take a deeper look at how artificial intelligence can be utilized to streamline access to liquidity throughout financial markets and how it can improve the quality of service provided by broker-dealers:
Transforming Market Efficiency
Artificial intelligence as a disruptive technology in the financial landscape is a broad church. With use cases covering big data market analysis, reporting, pattern recognition, workflow automation, and liquidity sourcing, it’s clear that AI is a technology that draws on large-language model (LLM) communication, machine learning, and unstructured data analytics.
The investment ecosystem has also seen a proliferation of new players seeking to make the most of AI technology. Fintech firms like ALFO DeepTech, for instance, seek to utilize artificial intelligence as part of their risk management and hedging tools network. Elsewhere, Starfetch, a Swiss financial technology firm, specializes in the research and development of AI trading algorithms.
These AI-powered tools are all geared toward enhancing market efficiency at scale by increasing the size, frequency, and complexity of trades.
With the help of artificial intelligence, broker-dealers have more control over the service they provide institutional traders and can actively reduce transaction costs and improve execution speeds for users.
Even an LLM interface can leverage a frictionless process in which traders can quickly access relevant pricing information in real time to understand the best possible time, size, and venue for prospective trades. But what about liquidity risk? After all, the inability of broker-dealers to meet their respective obligations can be a major burden for the institutions they serve.
Overcoming Liquidity Shortfalls
So, how can broker-dealers improve access to liquidity? Artificial intelligence solutions for liquidity management can utilize advanced analytical tools to assess the most capable liquidity providers (LPs) within its machine learning algorithms to measure performance, as well as the quality of their own trades routed to LPs for mutually beneficial conversations and liquidity chains in various market conditions.
Platforms like OneZero are examples of liquidity management services that incorporate artificial intelligence into their platforms for the best results. Clients can also integrate their own proprietary execution algorithms within the platform.
Artificial intelligence can also help institutions to identify emerging trends in low-liquidity markets to recommend a buying opportunity before the wider market has a chance to get there first.
Thanks to AI-driven sentiment analysis and ultra-low latency trading, more broker-dealers are capable of fulfilling client orders at a pace that beats the competition and enhances efficiency.
Order execution processes can be further enhanced with the help of machine learning tools, which can take on board the most frictionless LP conversations to help open up access to broader markets.
Too Much of a Good Thing?
The role of AI in improving access to liquidity for broker-dealers comes with some risks that broker-dealers would need to be mindful of.
Crucially, the integration of artificial intelligence tools into trading systems could see more market participants using the same AI models to uncover the same trading opportunities at precisely the same time. This could lead to masses of investors rushing to make the same action, which could harm liquidity further or lead to flash crashes for low-liquidity assets.
With this in mind, ultra-low latency trading could be a victim of its success. If all players are using the best software with the same latency, they could theoretically receive the same recommendations at the same time.
There’s also the threat of algorithmic bias at a large scale should more market players use the same AI technology to inform their trading strategies. This could not only adversely affect market liquidity for certain assets but also damage the quality of trade actions made by institutional traders using broker-dealer solutions.
One way to counteract this danger is to utilize liquidity solutions that are fully customized to suit your client base with multi-asset liquidity. With the help of a multi-stream solution, you can accommodate both flow profiles that are tailored to your specific requirements.
Market sentiment towards AI is high, with some 80% of traders claiming that they already use the technology in some form, while 90% have identified artificial intelligence tools as a competitive advantage for the future.
This makes integrations with the emerging technology essential for broker-dealers and may push more institutions towards seeking faster trading tools to beat the competition in low-liquidity markets as AI capabilities continue to become more sophisticated.
The Future of Liquidity
Artificial intelligence will change the way broker-dealers improve access to liquidity for institutional investors. However, the proliferation of the technology is likely to push greater emphasis on streamlined execution times and the speed of trading.
In adopting a more unified approach to execution efficiency, broker-dealers can integrate AI tools to help improve access to low-liquidity markets without traders falling fowl of large-scale races to access fleeting opportunities before their competitors.
As an emerging technology, the true impact of artificial intelligence is yet to be fully realized throughout the trading landscape. However, its potential for putting more broker-dealers in touch with efficient liquidity solutions is a promising start that could open the door to far more asset classes for institutional investors to make low-liquidity markets more accessible than ever before.
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