5 reasons AI trading floor jobs are still less exciting than you think
At face value, AI seems like the most attractive place to be in banking. Huge salaries, adulation and innovative tech certainly draw attention. When you get to the trading floor, however, some of that luster tends to go away. A risk analysis report on AI in EU securities markets by the European Securities and Markets Authority (ESMA) has investigated the various ways in which AI is used in trading and more often than not it's kind of underwhelming.
Portfolio managers are doing just fine without it
When AI is used by portfolio managers at quant funds, the report claims it's "mostly as a tool to execute specific tasks that leverage large amounts of data." The ability to extract and analyze data with minimal human interaction is "driving AI adoption." Natural language processing in particular is one of the more popular techniques implemented.
If it sounds exciting, it's not really - yet. ESMA concludes that, "AI does not seem to be transforming portfolio managers' investment practices in a disruptive or revolutionary fashion." It adds that machine learning isn't yet an obvious way to improve fund performance.
This is in part due to the fact that "many of the AI models currently in use [are] better suited for short-term frameworks rather than long term decision-making" as "structural breaks in time series" limit its ability to effectively forecast.
You won't be put working for elite funds...
A peculiar finding is just how few investment funds are open about their AI use. The ESMA report screened documents from over 22,000 funds for published uses of the phrases "artificial intelligence" and "machine learning" only to find just 65 funds from 40 different fund management companies indicating the use of AI in investment strategies.
...Because results are mixed
When ESMA looked at the performance of AI funds compared to others in the market, results were neither terrible nor spectacular. Returns, interestingly, were up, though merely by 0.04%, while the total expense ratio (TER) was down 0.1%.
With the newness and uncertainty of AI in this space, most investors will be more inclined to trust the established investment techniques.
The 'next big thing' in AI won't change much
The biggest issue for AI in finance currently is the issue of explainability. The report claims that "industry executives were unanimous in stating that AI is not tantamount to autonomous decision-making without human oversight."
Enter, XAI. - 'Explainable artificial intelligence.' For AI to be viable, investors need to understand what's going on inside the black box. Unfortunately, this is easier said than done, and explainable AI models tend to be more limited than their opaque counterparts.
It works well in live trades... by doing the dirty work
AI may not be a game changer in terms of finding investments, but it has a lot of great uses when it comes to actually executing trades. It's a shame, however, that those "great uses" are predominantly the work done in the background.
When machine learning is implemented in pre-trade analysis, the report claims it's primarily used for "liquid instruments for which plentiful and timely data is available" (spot FX, equities, for example). When used outside those parameters, it's either employed as a means of reducing the market impact of trades or to feed brokers client data in order to optimize their price rates accordingly.
Trade execution is therefore where the report says "AI finds some of its most promising applications." The machine learning models used by brokers and buy-side investors here are used "to split and execute metaorders optimally across different trading venues and times," in an effort to minimize market impact again.
Post trade processing has also seen developments where ML methods are used "to predict the probability of a trade not being settled given the resources allocated to it, so as to optimally distribute said resources." Another use there is for "anomaly detection, data verification, data quality checks and automated data extraction from unstructured documents." Not exactly the glitz and glamour of cushy research roles at AI companies.
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