By Alexey Afanassievskiy, Executive Director and Head of Portfolio Management at Mind Money, an investment technology and financial engineering hub
Finance 4.0., the latest phase of evolution in the industry, is set to embrace the utilisation of advanced technologies, and trading is no exception to the rule. The global algorithmic trading market is about to reach close to $31.5 billion by 2028, reflecting a substantial growth trajectory from its $12 billion valuation in 2020.
The stock market, in particular, has undergone a profound transformation with the widespread adoption of this cutting-edge technology. Notably, algorithmic trading now constitutes up to 73% of all equity trading, according to insights from Wall Street. As algorithmic trading gains increasing prominence, it becomes imperative for investors to examine the risks that this technology brings to the table.
Let’s delve into the primary types of risks associated with algorithmic trading and explore strategies for investors to adeptly navigate them.
Classifying algorithmic trading risks
In essence, the risks associated with algorithmic trading can be broadly categorised into two groups: technical risks and market/portfolio risks, the latter being considered as a unified entity. Distinguishing between market and portfolio risks proves challenging, given their often intertwined nature.
Furthermore, risks can be analysed through alternative lenses:
Firstly, there are the risks faced by algorithmic traders who are new to the game. They are usually characterised by a lack of experience, oversight, and susceptibility to technical errors. It is worth noting that even seasoned teams may grapple with these challenges, representing a sort of “childhood diseases” phase tied to understanding the intricacies of the subject.
Secondly, we encounter risks experienced by teams with several years of algorithmic trading work behind them when confronting “tail events” or low-probability industry situations. These events, inherent to the market but infrequent, require years of personal trading experience to navigate effectively.
Lastly, the third category encompasses fundamental or systemic changes induced by shifts in the global model, triggered by factors like political or technological transformations. Termed “black swans” in Aristotle and Taleb’s lexicon, or even “black ostriches,” as I call them, these highly unusual and impactful financial market events can catch even seasoned teams and investors off guard.
How to minimise risks for different types of investors
In my opinion, effectively mitigating risks in the realm of trading requires a perpetual state of thoughtful consideration, strategic calculation, and a healthy level of vigilance—a form of well-placed paranoia.
Crucial to this endeavour is the fundamental choice of identifying oneself within the market: a) a hands-on investor, steering one’s trades independently; b) an investor seeking an established team, software, or service to execute trading ideas; or c) an investor entrusting the process to asset, wealth, or trust managers.
For the first scenario, embarking on a self-directed trading journey entails a prolonged path of learning, inevitable mistakes, and a gradual accumulation of experience, with success far from guaranteed. Yet, if navigated adeptly, it opens doors to the realm of algorithmic trading, demanding proficiency in mathematics, programming, engineering, and research to effectively minimise the risks.
In the second case, for adept traders seeking automation of their ideas, the major task lies in identifying the right team or platform with the relevant experience to implement their trading concepts. This avenue is rife with risks, with success stories, however, slightly more common than with the first category of independent investors.
The third scenario emerges as a practical choice for 98% of investors. For those not treading the path of an independent algorithmic trader or those seeking automation, careful selection of algorithmic teams and products, coupled with a focus on diversification and precision in investments, becomes crucial in minimising risks within this framework.
Algorithmic trading trends: what to expect in the next five years
In the coming years, we can expect a diminishing role of high-frequency trading (HFT) as a primary source of initial capital accumulation for algorithmic traders. Simultaneously, as the market for ultra-fast exchanges grows, more platforms will introduce a potentially random delay for orders. This move aims to limit the physically low latency inherent in certain strategies.
Looking into advanced technologies, I anticipate a surge in the utilisation of AI systems for short-term market behaviour forecasting. These AI systems will operate at intervals ranging from fractions of seconds to several minutes, replacing traditional HFT strategies. Additionally, the emergence of fully AI-based strategies, leveraging approaches like large language model (LLM), is expected. While potentially highly effective, these strategies pose an augmented risk of “sudden death,” given their black-box nature and the challenge of comprehending their internal workings.
Beyond technological shifts, the prevalence of symbiotic human-machine models is expected to rise, encompassing both execution and control, as well as trading decisions. This fusion of human expertise and machine capabilities is seen to become more integral.
Lastly, an uptick in the prevalence of public “smart” algorithmic strategies is on the horizon. These strategies will be constructed through a combination of fundamental models, statistical systems, and pattern recognition models, marking a noteworthy evolution in the financial landscape.
The future is “humanely” algorithmic
Algorithmic trading has substantial potential for financial market participants, offering opportunities tempered by inherent risks. The magnitude of these risks depends on factors such as investor types, their level of experience and expertise, and the low-probability market events that even the most seasoned professionals are not entirely immune to.
To adeptly navigate this landscape and mitigate risks effectively, it is imperative to approach technology with a well-thought-out perspective. The future trajectory of technology in finance points towards a harmonious co-existence, where advanced algorithms complement and augment the capabilities of experienced professionals, fostering a symbiotic relationship for optimal results.
