With these strategies on the rise, Bloomberg polled over 150 quants, research analysts and data scientists in a survey conducted during a global series of client workshops to understand key trends and challenges in investment research.

Data coverage, timeliness, and quality issues with historical data was cited as the top challenge in the industry, with nearly two-fifths (37 per cent) of respondents selecting this option. This was followed by normalising and wrangling data from multiple data providers (26 per cent), and identifying which datasets to evaluate and research (15 per cent).

In line with these challenges, Bloomberg’s survey found that 72 per cent of respondents could evaluate only three or fewer datasets at a time, despite the need from quants and research teams to continually harness more alpha-generating data in today’s data deluge. The findings also show that the typical time it takes to evaluate a single dataset is one month or longer for more than half of respondents (65 per cent).

Firms are still trying to figure out their optimal strategy for managing research data in the face of the aforementioned hurdles. 50 per cent of respondents reported they currently manage the data centrally with proprietary solutions versus outsourcing to third party providers (8 per cent), with more than six in ten (62 per cent) of respondents preferring their research data to be made available in the cloud. Notably, 35 per cent of respondents also would like their data to be made available via more traditional access methods such as REST API, On premise and SFTP, indicating they prefer flexibility in the choice of data delivery channels.

“From in-depth conversations with our research clients, it’s clear there is a desire for new orthogonal datasets as well as a need to harness ‘AI-ready’ data. The journey from data sourcing to extracting alpha is difficult and the continuous ingestion, cleaning, modeling and testing of data is particularly challenging,” says Angana Jacob, Global Head of Research Data, Bloomberg Enterprise Data. “That’s why Bloomberg is committed to building out our multi-asset Investment Research Data product suite, targeted at quantitative and quantamental research, systematic strategies and AI workflows. Our datasets with modeled Python API access enable customers to reduce their time to alpha through deep granularity, point-in-time history, broad coverage and interoperability with traditional reference and pricing data.”

The statement calls on ESG data providers to enhance data offerings on ocean-related risks and opportunities at project and issuer-level.

“Introduction: Financial institutions are increasingly aware that alongside climate change, inequality and biodiversity loss are creating risks to businesses and increasing systemic risk for the financial system. The ocean is the world’s largest ecosystem, covering 71 per cent of the earths’ surface and playing host to an estimated one to two million species. Its annual economic value is estimated at USD2.5 trillion and millions of jobs depend on it. At the same time, biodiversity and ecosystem services are also the basis of countless new business opportunities. But today, the ocean’s health is being severely threatened by human activities.

The Kunming-Montreal Agreement (2022) has provided governments and non-state actors with frameworks for action that include overarching goals and targets, such as to protect 30 per cent of the planet’s lands and inland waters, as well as of marine and coastal areas, by 2030. The agreement recognises the vital role that we as investors can play to halt and reverse biodiversity loss. The focus now needs to move to translating the commitments made in Montreal into concrete meaningful actions. In 2023 a new international agreement on the conservation and sustainable use of marine biodiversity of areas beyond national jurisdiction (BBNJ) was adopted – highlighting the vital importance to ensure ocean biodiversity is considered within a just and equitable future.

This group takes stock of Target 15 in the Montreal agreement where governments for the first time have explicitly committed to require all large and transnational companies and financial institutions to assess and disclose their dependencies, impacts, risks and opportunities on nature, through their operations, supply and value chains, and portfolios. To help investors make informed decisions, and investment in, companies and activities that are causing or resolving this significant harm to ocean biodiversity and allocate capital in a way that provides solutions to protect biodiversity, credible data, consistent with international standards, are crucial. This will allow investors to:

–          Embed ocean-related data into our own analysis and assessments

–          Highlight areas of ocean-related risks and opportunities within our portfolios to improve our decision making

–          Engage on ocean-related topics with our investees

–          Identify and allocate capital for ocean-related opportunities

–          Improve ocean-related literacy within our workforces

This group of investors is strongly encouraging ESG data providers to further develop ocean-related data points and tools, and provide innovative ways to capture our investee’s dependencies, impacts, risks and opportunities related to ocean to support the implementation of the Kunming-Montreal Agreement.

Annex: Non-exhaustive list of suggested metrics and assessments to highlight ocean-related risks and opportunities at project and issuer-level.

Expectations to data providers:

As a guide in understanding the data needs of this group, ocean-related data principles (list not exhaustive) are suggested below:

·       Performance indicators: understand compliance against IMO or MARPOL regulation, data points reflecting real impact using physical units (ex. km2 of sea use change).

·       Supply chain: ability to check the potential sourcing of endangered species by food retailers. The data points must factor in the entire supply chain, as large investors are usually more exposed to downstream ocean-related supply chains.

·       Local context: FAO fishing areas, fishing effort in high seas, arctic route trips.

·       Asset level locations: fishing ships, shipping boats, aquaculture sites, ports, hotels.

·       Ownership data: to reflect the ultimate owner, need to link vessels to operators, hotels to hotel chains, fishing vessels to fish processors, estimate seafood supplier links to food retailer/services.

·       Sector estimates: sector assessment grids should make it possible to make estimates tailored to the specificities of each sector.

We also recommend the following when considering the format of the data:

·       Ease of use, for a variety of purposes: integration into the investment process, communicating about impact on marine biodiversity, providing more extensive reporting, etc.

·       Flexibility and transparency: the methodology must be compatible with the public taxonomies, guidance and internal environmental assessment systems already in use (e.g. EU Taxonomy).

·       Application scope: the approach must be applicable to companies active in the main market indices (listed equities and fixed income funds). Ideally the method should be compatible with other asset classes (listed and unlisted equities, fixed income funds, infrastructure, real estate, etc.).

As large investors with high ocean-related exposure, we have identified the main ocean-related sectors (list not exhaustive) where we find major data gaps in understanding risks and opportunities as:

·       Aquaculture – algae / seaweed / seafood

·       Coastal and deep-sea mining

·       Coastal and marine tourism

·       Desalination

·       Dredging and coastal protection

·       Marine bioprospecting / biotechnology

·       Marine renewable energy

·       Offshore oil & gas

·       Ornamental marine products

·       Ports

·       Shipping

·       Wild capture fishing

For some of the sectors mentioned above, the below flags the data gaps (not exhaustive) in better understanding risks and opportunities present:

·       Based on its business/project activity, what risks does it pose on the ocean? – flagging any activities that have negative impact on the ocean and contribute towards ecosystem degradation. For example:

o   Pollution (wastewater, air, noise, plastic, ghost fishing gear) and invasive species

o   Fishing practices – Assessment of company’s fishing methods, does it undertake any sustainable practices, Overfishing and destructive fishing practices

o   Aquaculture – Evaluation of company’s operations; habitat modification, use of chemicals, feed sourcing, waste management, disease outbreaks

o   Shipping and transportation – Fuel used/emissions, measures to prevent oil spills and marine pollution

o   Coastal development – Exposure to coastal infrastructure projects, impact on habitats, mangroves and coral reefs

o   Energy sector – Offshore oil and gas extraction rates, offshore windfarm development and capacity, impact of energy extraction on marine biodiversity and habitats

·       Based on the business/project activity (including Capex, Opex), what are the opportunities? –  flagging any activities that either minimise risk posed on the oceans or help restore. For example:

o   Does the issuer invest in any conservation activities linked to the ocean or minimise its impact? Flag any policies and guardrails in place to minimise negative impact.

o   Any investment in innovation undertaken by the issuer to help mitigate above risks?

o   Current working conditions and human rights of employees and local communities and how are these considered?”

This group writes that it is “inspired by the great work of their ocean-related partners and stakeholders, such as Planet Tracker, FAIRR, UNEP FI, WBA, TNFD, SASB, Minderoo Foundation, IUCN, WWF, GRI, CDP or The Economist, just to name a few”.

Powered by billions of US consumer credit card and debit card transactions, Bloomberg writes that the BSM data analytics feeds deliver valuable insights into company performance and consumer trends in near-real-time at a three-day lag. The transaction data comes from a subset of a US consumer panel that includes 20+ million consumers and covers 3,000+ public and private companies and 4,000+ brands across industries.

Making Bloomberg Second Measure’s transaction data analytics available for use across the enterprise is Bloomberg’s latest step in developing solutions tailored for quant customers requiring new and advanced data solutions and technologies to find an edge in their investment process. The firm writes that this new offering follows the launch of Bloomberg’s Company Financials, Estimates and Pricing Point-in-Time solution that connects and integrates a broad, diverse range of datasets from multiple sources, provides historical point-in-time data and will enable linking traditional company data to more esoteric data like alternative data.

“By continuing to build out our interconnected suite of company research products, Bloomberg is a catalyst for change to the typically complex quant workflow that requires sourcing and organizing datasets from multiple providers,” says Tony McManus, Global Head of Enterprise Data at Bloomberg. “Delivering our proprietary alternative data directly alongside our traditional financial data through Data License allows quants and research analysts to make efficient, better-informed market projections with unique insights.”

The BSM transaction data analytics feeds are also the flagship data source for the ALTD <GO> function on the Bloomberg Terminal, which launched in September 2023 in an effort to democratise access to alternative data by seamlessly integrating it alongside traditional market data, broker research, estimates and news on the desktop. With this latest development, Bloomberg is building on its investment in expanding the applicability of alternative data to new use cases for its clients, the firm says.

“Making our Bloomberg Second Measure transaction data analytics feeds available for use across the enterprise with Data License is the next step in our effort to lower the barrier to entry for investment analysts to use alternative data for generating differentiated insights,” says Richard Lai, Global Head of Alternative Data in Bloomberg’s Office of the CTO. “We’re excited to continue building on this momentum to support additional research workflows and create new use cases for Bloomberg’s alternative data solutions.”