How Artificial Intelligence Is Reshaping Market Liquidity Through Algorithmic Trading

By Lekhan

Introduction

 Human judgment, experience, and response to information have formed the historical basis of the stock market; however, a rapid transformation is occurring as artificial intelligence becomes entrenched in trading systems, moving from a trader's gut instinct and personal analysis of performance to a situation defined by information, speed, and adaptive algorithms. One of the most obvious changes taking place is market liquidity, or the ease with which an asset can be purchased or sold without a significant change in its price.  

Source: ciowomenmagazine 

 Exploring the concept of Market Liquidity & why it matters  

Liquidity is critical to the efficient operation of a market; it facilitates more efficient transactions, reduces volatility, and increases investor confidence in the market. Algorithmic trading powered by artificial intelligence is significantly improving liquidity in the markets, although there are pros and cons to being able to increase liquidity due to the influence of trading algorithms. 

AI-based trading systems enable the analysis of large amounts of data almost instantly, unlike traditional trading models that had limited data sets and used fixed rules to determine how a trader should behave. Instead, modern AI trading systems continually learn and adjust their trading strategies based on the information being fed into them, including company reports, economic indicators, news articles, and social media sentiments. The ability to perform this type of deep dive and analysis enables traders to make better-informed decisions and, thus, increases the amount of participation in a particular market, subsequently increasing market liquidity. 

For many years now, human opinion, past experience, and responses to developing information have determined stock purchases/sales on the stock market. Moving forward at an accelerating pace, artificial intelligence is now evolving into a large, integral component of the electronic trading system. Therefore, what was once based purely on an intuition and manual analysis of available information is being replaced by a data-driven, speed-of-trade, and learning/adapting algorithm-based decision process. 

One example of this evolutionary change is the change in liquidity of the financial markets (liquidity refers to how easily an asset can be bought/sold without major price changes). For a market to function properly, liquidity must be present in order to create better transaction experiences for all participants, reduce volatility, and enhance investor confidence. 

 How Artificial Intelligence contributes to improving Market Liquidity 

 AI-based algorithmic trading is making significant contributions to providing liquidity in the markets; however, the advantages of algorithmic trading also come with some disadvantages. 

The biggest driving force behind AI-based trading is the ability to process vast amounts of data in real time. Unlike traditional trading systems that had limited-sized data sets and utilized fixed rules for evaluation, current AI-based trading systems constantly learn from their experiences. By gathering information from various sources such as economic data, company financial reports, news articles, and social media sentiments, AI-based trading systems are able to observe trends in the economy and subsequently predict market behaviour. The large number of transactions made by traders as a result of AI-driven data analysis will lead to greater market participation and thus greater liquidity. 

Source: TimNao

A key contribution of artificial intelligence to trade execution is assisting big institutional traders in executing their large orders while minimally disrupting the marketplace. By providing an algorithmic solution to break down a large order into several smaller orders and executing them at strategic intervals, AI helps minimize the price impact of a large order. In turn, this produces a more efficient execution of the large order as well as a more stable market for buying and selling securities. Consequently, the number of buy and sell orders in the market helps increase market liquidity.  

Finally, AI may provide improved response times of markets to new information released into the marketplace. The financial services market can be heavily impacted by a variety of international events, changes in government policy, and changes in individual companies. AI applications that deploy an artificial intelligence language processor allow for rapid interpretation of major events reported in the media.  

When a corporate earnings announcement or a central bank makes a monetary policy announcement, AI systems are able to quickly scan the data and change their trading strategies based on that new data in an effort to ensure the price reacts quickly to new information and maintains a liquid and efficient marketplace.   

The transition from conventional trading methods to data driven approaches

 Though the use of this AI can be beneficial, there are also growing concerns about the reliance on artificial intelligence, especially with regard to reducing diversity in trading decisions. If numerous traders are using similar AI trading models or getting data from the same sources, the decisions they make could become highly correlated. Consequently, when multiple traders are making decisions at the same time, this could create large spikes and drops in liquidity.   

 Also, because many of the latest AI systems are based on a learning mechanism that does not require explicit communication between AI systems, there may be potential for algorithmic coordination. Thus, traders may make decisions that may look similar to those of their fellow traders when they have never communicated or compared notes, thereby creating distortions in competition and creating changes in market behavior that are unexpected.  While highly studied, these scenarios indicate an even higher need for constant review of the operations of AI systems in the future.   

Source: Justtotaltech  

 Existing challenges & Limitations 

While AI is designed to work optimally under the most normal trading conditions, it may not perform well when trading is occurring under extremely volatile market conditions or the occurrence of unexpected events (ex., a natural disaster). 

Automated systems may be unable to provide stability to markets and, in some cases, will amplify the volatility of market movements. Previous experiences with electronic markets demonstrate that the rapid response of automated systems can cause sudden intraday trading halts, which cast doubt on the viability of AI-driven trading systems.  

Another challenge presented by AI is the differential access to technology. The development and maintenance of AI requires considerable investment, and most advanced AI systems are beyond the accessibility of smaller financial institutions. Thus, there exists a significant disadvantage to smaller investors relative to larger investors in a trading environment with AI sophistication. A significant imbalance could result in decreased overall market fairness and ultimately lay the foundation for decreased liquidity and stability over the long term.  

As such, AI will affect the liquidity of the market in both positive and negative ways. The short-term benefits of AI in the marketplace have been shown to yield more active markets, narrowed spreads, and faster transaction processing. However, in the long term, risks due to lack of diversity among participants, potential for coordinated activities, and systemic instability may become more apparent as the number of AI-driven traders increases.  

Artificial Intelligence (AI) doesn't replace the way people interact with each other in financial marketplaces; rather, it shapes the way people interact in financial markets. There are many instances where AI will do an excellent job at traditional "analytical" or data-oriented jobs (e.g., analyses of data streams; trade execution; change the I/O and policy of other "non-traditional" employees); however, AI does not replace the need to provide judgment to interpret certain situations that affect how humans make decisions when trading in today's financial marketplaces.