SimCorp has introduced the Axioma Factor Library Suite, giving quantitative investors access to a broad set of proprietary equity and macro factors derived from the research underpinning its Axioma risk models. The dataset is aimed at hedge funds, systematic investors and asset managers seeking to expand the signal universe available for portfolio construction and strategy research.
The launch reflects a broader shift in institutional analytics infrastructure, where research outputs once embedded within modelling systems are increasingly exposed as reusable data assets. By making these factors available as a standalone dataset, SimCorp is effectively opening the research layer behind its risk models to external users.Exposing the Factor Research Layer
The Axioma Factor Library combines fundamental, technical and macroeconomic signals covering more than 50,000 securities across over 90 countries, with history extending back to 1997. Alongside traditional equity style factors, the dataset includes macro sensitivities linked to variables such as interest rates, inflation, foreign exchange, credit and commodities.
Historically, these factors were generated internally as part of the research used to construct Axioma’s production risk models.
“We generate these factors daily as part of our modelling infrastructure, but many were never previously distributed,” explains Ian Lumb, Head of Analytics Product Management at SimCorp, in conversation with Market & Alt Data Insight. “Risk models include only the signals that best fit a specific investment universe, leaving other research factors unused. The new library gives clients access to that broader research set so they can apply and customise the signals within their own investment frameworks.”
The library therefore exposes a broader research universe than the models themselves, allowing clients to combine and adapt factors according to their own investment approaches.
Supporting Quantitative Research Workflows
For quantitative investment teams, the factors are intended primarily as research inputs rather than pre-packaged trading signals.
“Clients are integrating the factors directly into their research pipelines,” says Lumb. “Hedge funds use them to test hypotheses, analyse correlations between proprietary alpha signals and our research factors, and use the signals as controls or building blocks for new composite factors.”
Because the dataset spans multiple decades of market history, it can also be used to analyse how strategies perform across different market regimes.
This approach reflects how systematic investors typically conduct research. Rather than relying on a single factor framework, research teams experiment with multiple signals, combining them with proprietary data and statistical techniques to develop differentiated sources of alpha.Cloud Distribution and AI-Ready Data
A significant element of the launch is the distribution model. In addition to being available through SimCorp’s Axioma Risk Model Machine (RMM), the factor library is delivered via the Snowflake cloud data platform, allowing clients to access the dataset directly within their research environments.
“Making the data available via Snowflake significantly reduces the data engineering work required to access it,” notes Lumb. “Clients can simply activate the dataset and start analysing it immediately, rather than dealing with traditional data delivery methods such as SFTP.”
SimCorp also plans to add machine-readable metadata and semantic layers describing each factor and risk model. These will enable tasks such as risk calculations to be performed directly within the cloud environment, allowing researchers to analyse portfolios and strategies alongside the factor data without exporting or recreating modelling logic.
Data Governance and Institutional Standards
For systematic investors, the usefulness of any factor dataset ultimately depends on the robustness of the underlying data. The factor library therefore uses the same infrastructure that supports SimCorp’s production risk models, explains Lumb.
“Our risk models are used daily by hundreds of clients worldwide within live trading and portfolio management environments. The dataset therefore incorporates the same standards for point-in-time construction, daily model estimation, corporate action adjustments and security master management.”
Applying the same governance standards ensures the data can be used consistently across research, risk and portfolio construction workflows.
“The key distinction from many academic datasets is that this library uses production-grade infrastructure from the outset,” notes Lumb. “The data meets the same standards used in our published risk models and remains consistent across research, risk and portfolio construction workflows, whether delivered via Snowflake or traditional channels.”
Customisation and the Question of Crowding
One question raised by widely distributed research signals is whether they increase the risk of crowding. If multiple institutions rely on the same factors, strategies could begin to converge, potentially reducing signal effectiveness.
“There is always some risk of crowding if everyone uses the same risk model, but in practice the landscape is already highly diversified,” observes Lumb. “Even within our own suite there are multiple models covering different regions and styles, alongside models from other providers. The factor library actually increases flexibility by giving clients access to signals not included in the published models, allowing them to build customised factor combinations aligned with their investment universes and views.”
SimCorp argues that the library provides shared research inputs rather than a shared strategy framework. Institutional investors typically combine factors differently and integrate them with proprietary datasets, meaning the same signals can lead to very different portfolio outcomes.
Analytics IP as a Data Product
From an industry perspective, the launch reflects a broader evolution in how analytics vendors package intellectual property. Historically, research generated by analytics providers remained embedded inside modelling systems and was primarily accessed through packaged tools such as risk models.
Increasingly, however, vendors are exposing elements of that research as standalone datasets that can be integrated directly into clients’ analytics environments.
“This launch reflects the recognition that the intellectual property generated by our research has multiple uses,” says Lumb. “Risk modelling remains the backbone of the platform, but by giving clients access to the underlying research we are effectively industrialising that layer, allowing them to customise models and build additional analytics on the same foundations.”
Industrialising Factor Research
The Axioma Factor Library reflects a broader move toward industrialising factor research. Signals already generated within SimCorp’s modelling infrastructure are now available in a governed, production-grade format that can be consumed directly by investment teams.
Combined with cloud-based distribution and machine-readable metadata, the dataset is designed to integrate with modern quantitative research environments. For systematic investors, the result is not simply access to additional factor signals, but a structured dataset that fits directly into contemporary data science and portfolio research workflows.
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