Two representatives from Deutsche Bank’s quantitative research team presented on the summit day of QuantMinds International on 13 November 2023 in London about the impact of artificial intelligence (AI) on the future of investing.
Part of the LLMs & Advanced ML Summit, the Deutsche Bank session looked at how best to harness this exciting new technology and how best to:
- Identify investable themes
- Recognise score relevance
- Construct portfolios
AI has applications from trend detection to risk management, but for the QuantMinds attendees at the InterContinental O2 Hotel in the UK capital, the focus was firmly on presenting an AI framework to identify and invest in emerging themes before mainstream adoption, thereby gaining an edge.
A process that uses natural language processing (NLP) to continuously scan for innovative topics and convert them into tradable thematic investment baskets was unveiled by the presenters. Their session offered quants a roadmap to mine sentiment analysis with AI tools in order to:
- ID and target future investable trends,
- manage risk,
- & generate alpha from media analytics.
The QuantMinds conference, running 13-16 November, attracted 500+ senior quant professionals and academics from around the world, plus 110 asset managers, thereby covering the buy- and sell-side. All of them were reminded “that AI isn’t new” but that it is powerful and weighted with the prospect of introducing radical change to previous investment and trading strategies.
Since November of last year, and the advent of the generative AI ChatGPT LLM chatbot, a new wave of innovation has been unleashed.
The presenters used Google’s Bard tool for their experiment to compile tradable thematic baskets, rather than the more famous OpenAI [and Microsoft] supported Chat Generative Pre-trained Transformer (GPT) tool. But this just illustrates how many other tools are now already easily available since the original ‘big bang’ moment of ChatGPT’s unveiling on 30 November 2022.
ChatGPT signposted the start of a new advance in AI LLM capabilities, aided by the vast data lakes of information that are now available from unstructured social media resources, online news outlets and other data providers covering financial markets. A web of data is just waiting to be mined by AI tools seeking patterns and trends with ever greater granularity. The presentation’s agenda addressed:
- Asset allocation and how to identify long-term emerging economic trends, such as the rise of ESG metrics, that might move markets and business models.
- Risk management procedures that can protect and optimise portfolios much more easily than in the past. AI can now be built into models fast. It is especially useful if the models are tasked with specific granular end goals looking at currency fluctuations, for example, or market volatility, credit defaults and so on in greater and greater detail. “We modelled all different scenarios with our AI LLM,” explained the quantitative researchers.
- Stock picking can be aided by AI analysing more efficiently financial statements, news and social media sentiment. The aim is to consistently identify companies with strong growth potential. Picking an investment theme is important, but once the trend has been identified specific companies within it will also need identifying.
- Trading execution – what time, day or week is best to go short or long on particular stocks is a common refrain on financial markets, “but AI can reduce the human bias”.
AI can have its own bias as well of course. For instance, if it is only designed by rich white academics its output will reflect that. AI might be dangerous too if a human isn’t kept in the loop as the ultimate arbiter of what can and cannot be done by the machine.
But the technology is undeniably powerful and transformative – harnessing it in a good way is what will be necessary in the year ahead. More end use cases are no doubt in the pipeline for the 2024 QuantMinds conference. Watch this space.
