Trading is in a sense the end result of a process, as a buy or sell transaction is the product of data and analysis.
But the trading function also generates its own data and insights that can improve subsequent trades.
JP Morgan created the Global Data Assets & Alpha Group in 2019 to optimize the information used within trading. Composed of three segments – market intelligence, data intelligence, and positioning intelligence – the group provides investors with a single touchpoint for trading data and associated signals and insights.
“We are creating and curating data sets from across our markets trading organization,” said Eloise Goulder, head of the data assets and alpha group. “We use them for predictive purposes within markets. We use them to help have a lens on which markets, which assets and which stocks will outperform or underperform.”
“We wouldn’t want to care about the data assets without the alpha focus,” Goulder added. “Alpha is at the core of everything we do.”
Goulder spoke with Edwina Lowe, product specialist within JP Morgan’s data assets and alpha group, about the group’s purpose, capabilities and uniqueness. A transcript of the conversation was provided to Markets Media.
By Trading, For Trading
Goulder emphasized that the data assets and alpha group sits within trading and is separate from the bank’s research function. Data assets include hedge fund positioning information sourced from the prime brokerage business, and social media sentiment that gives a view into retail trading; the group is catalyst-driven and aims to provide tactical, intraday insights.
The team analyzes key macro, micro and political themes in the context of high frequency trading data and proprietary signals. Whereas reports on markets, sectors and companies are ubiquitous in financial services, the data assets and alpha group has planted a flag on what it believes is under-explored territory.
“It’s this intersection from micro to macro via thematics, and from fundamental discretionary to quant, that’s the space in the middle, which I believe is relatively less tapped,” Goulder said.
Lowe, who works closely with central data sourcing teams at hedge fund and other investment firms, noted a trend of more clients asking for textual data, such as a transcript from an earnings call or a webinar, in as close to real-time as possible. The implication is that even fundamental investment firms are building knowledge bases, for which they apply large language models to produce actionable insights. The rise of LLMs is “arguably a game changer for the industry,” Lowe said.
“We now have discretionary investors utilizing LLMs to significantly systematize, speed up and scale up their investment decision-making processes,” Goulder said, noting a blurring of the lines between quantitative and fundamental investment processes.
The Data Assets and Alpha Group uses proprietary datasets, as well as publicly available or vendor-provided datasets that are then overlaid with proprietary analytical techniques. The group makes a point to make the underlying datasets available along with the insights. “It’s about opening up and being transparent, being as unbiased as possible,” Goulder said. “We’re showing our workings, which I think is critical.”
The team has been augmenting its analysis into positioning and sentiment, leveraging data from both institutional and retail investors including hedge fund, mutual fund, ETF and retail flows (and associated textual sources for sentiment). “One area we’re expanding upon is looking at reports or documentation from the industry to try and understand institutional sentiment,” Lowe said. “We think of our data sets as continuing to iterate.”
Podcast Insights
Goulder and Lowe highlighted their group’s podcast, which runs biweekly on JP Morgan’s Making Sense podcast channel, as a prime example of the collaboration within the team, across the J.P. Morgan Markets business and with key institutional investing clients. Recent episodes have covered what to watch in equity markets heading into year-end, innovations in quantitative investment strategies (QIS), retail vs institutional investor divergence, developments in buyside trend following strategies, and the impact of behavioral biases on investing decisions.
“It’s a way of creating new ideas and new insights, which you couldn’t create if you didn’t put those subject matter experts in a room together to have that discussion,” Goulder said.
“The beauty of the podcast lies in the nuance,” Goulder said. “And the breadth of our discussion topics is really a reflection of the Data Assets and Alpha Group’s wider work.”
