Cameron Brandt: We are in the early stages to testing and adopting ChatGPT
Cameron co-authored an International Financing Review (IFR)-published study of fund flows and produces regular reports on trends that emerge from the data, directing the focus of the research based on clients’ interest. He previously worked as a journalist at a number of regional papers in the U.S., as well as the managing editor of now-closed World Paper in Boston, publishing a theme-driven international affairs supplement in emerging economies.
Cameron, who grew up in Scotland and Ireland, spends most of his time outside work with his two teenaged children. He also spends much of his time fishing for tuna, trout, carp and salmon, partaking in a pastime he sees as one of the last bastions of true social diversity.
B.A., Economics and Political Science; Yale University
“I spent years as a journalist canvassing communities and interviewing people to find the most compelling stories for our readers. At EPFR, my job is no different –I scour data tracking trillions of dollars looking for stories our clients can’t see. I build powerful narratives they can use to grow as a firm.”
What has been your /your firm’s top 3 priorities for the coming year?
Lay the foundations for operating as a standalone company after 10 years as part of UK-based Informa PLC, improve the user experience for clients and prospects working with EPFR data and launch a new dataset tracking Fund of Funds.
What do you think are the biggest challenges facing data scientists/AI experts/quantitative practitioners for 2023 and beyond?
Getting credible data that is clean enough to work with. We are hearing that data scientists in the fintech space frequently spend more than half their time cleaning up and reformatting datasets so they can be ingested and used. EPFR scores highly with clients when it comes to providing clean data, but there is still room to improve.
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Market and political uncertainty over the last year has seen unpredictable outcomes for some quant firms – how do you think quant firms can prepare for increased uncertainty to come and manage the 40-year inflation high that was seen in 2022?
Inflation, while it will likely settle at a slightly higher level than pre-Covid, may already be yesterday’s story. Higher interest rates, China exporting deflation, demographics and the productivity gains promised – if yet to be delivered – by AI all point to lower price pressures going into 2024 and beyond. Our Inflation Protected Bond Fund flows, which have been consistently negative for nearly a year, are certainly consistent with this view.
When it comes to political and geopolitical uncertainty, accepting that they are part of the “new normal” is an important shift that quant shops need to plan for. In much the same way 1,000-year weather events are now occurring once or twice a decade, money managers can no longer assume that geopolitical events will generate ripples rather than waves. Dealing with this will mean paying more attention to qualitative factors and a willingness to adjust quantitative models and strategies when those factors appear significant.
What is your advice to funds hoping to get new systematic strategies into production quickly and more often?
Only experience is from a data provider perspective. From that experience, we know that a thorough back test and simulation of strategy is crucial to running an effective strategy, but it can – and often needs to be -- a lengthy process.
A willingness to use the resources available, both in-house and external, is one way to expedite the process. EPFR offers data dictionaries, curated back test environments and strategy notebooks to help clients and prospects find value which can speed up the process.
ChatGPT is everywhere and being used everywhere. How do you see quant funds using this new technology and what advice can you give people using it?
a. What are your predictions for generative AI in the coming years?
We are in the early stages to testing and adopting ChatGPT. But one thing we are finding is the importance of the right ‘prompts’ in generating good results. That will become even more important when firms start using it on a larger scale to gain competitive edges. From our conversations with clients, we know that are testing potential applications for ChatGPT even though they know they can’t fully trust it. But we should talk again when it has passed the CPI exam.
Quant investing in other asset classes such as corporate bonds is increasing – what do you think has caused this shift to look at quant investing outside of equities and where do you see this going?
Central banks have released their grip on asset markets, which manifested itself in the ‘financial repression’ of bond yields over the past decade, as they pivoted to fight inflation. That means that equities and property are not the only games in town, and quant shops that can navigate multiple asset classes have more scope for outperformance.
Are you seeing quant investing being used in new geographies/where are you expecting some interesting quant stories to be emerging from?
The ESG/SRI space looks ripe for greater quant involvement. While the theme has been widely embraced, with it is very hard for investors to measure what they are getting in return for their money. There is wide disagreement over definitions of ESG, investors tend to emphasize the ‘E’ – and see that in terms of fighting climate change – and the timeframes that fund managers are judged on do not correspond well with the timeframes for meaningful environmental and social change. So, there’s demand for new, comprehensible metrics in this space.
NLP continues to be a big area of interest during our research – is the industry really using it to its full potential? Where else can we go with NLP and have you seen examples in other industries that we can learn from?
NLP has been evolving for a while now, and we believe it will continue to develop and find new applications. But I think it will be a long time before we use NLP to its full potential and, given the social considerations and desire for safeguards, we may never use NLP to its full potential.
Among the areas that EPFR sees potential value for our day-to-day operations are:
- Real-time Analytics
- Name Entity Recognition (NER)
- Fraud Detection
- Customer Service
- Education
- Language translation
At Quant Strats, we always discuss the challenges and opportunities of blending quant and fundamental strategies and this is always a popular topic – why do you think this is? What do you think is the most important questions for quants when considering this strategy?
Blending the two approaches has its obvious appeals, as it in theory leverages the strengths of algorithm based decision-making and human judgement. However, I think this remains a popular topic because there is no “one-size-fits-all” optimal balance for integrating both methods, so creating a blended strategy that works for a given user frequently requires reinventing of the wheel.
It is very important for quants to consider if they have enough variety in their data sources, and if there is some angle they are missing when assessing their data options. If there is not enough variety in their sources, the chances of bias or missing movements in the market rise significantly.
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