MSCI has launched SignalLab, an investment analytics platform that brings together more than 600 research-based investment signals, giving asset managers and investment teams a single environment to evaluate, test and deploy investment strategies.

The platform combines artificial intelligence (AI)-enabled research tools, company classifications and existing factor and market-crowding signals. MSCI said the system is designed to address difficulties investment teams face when comparing data and research from multiple providers.

Investment signals are indicators that investors use to assess potential returns, identify market patterns or evaluate risks. However, differences in data quality, research methods and company identifiers can make signals from separate providers difficult to compare and integrate into portfolio strategies.

MSCI said SignalLab provides a standardised library of documented and validated signals, supported by its data infrastructure and research capabilities. Users can also incorporate their own proprietary data into the platform.

The launch reflects efforts by investment technology providers to streamline quantitative research, in which investors use mathematical models and data analysis to inform investment decisions.

More than 600 signals across four areas

MSCI said SignalLab initially covers four areas: risk premia, micro-industries, FactorLab and Crowding. The platform provides global coverage, with its signals updated daily.

Risk premia refers to the additional return investors seek for taking particular types of investment risk. MSCI’s risk premia offering draws on economic and financial research, with an AI agent analysing published studies and translating findings into tested and scored signals.

An MSCI researcher then validates the resulting signals, according to the company. The process is intended to combine AI-assisted research with human oversight before the signals are made available to investment teams.

The platform also includes micro-industries, a system for classifying companies into more detailed business categories. MSCI said it uses a proprietary language model to perform initial groupings, while large language models refine definitions and assign stocks to the relevant categories.

MSCI’s model validation team reviews the resulting classifications.

The other two areas build on the company’s existing FactorLab and Crowding signals. Factor investing involves selecting securities according to characteristics associated with investment performance, such as value, quality or momentum.

Crowding signals help investors assess when many market participants may be pursuing similar positions or strategies.

Together, the four areas are intended to give investment teams access to a broader set of research inputs within one environment rather than requiring them to assemble separate tools from multiple vendors.

Addressing fragmented investment data

Investment managers can develop proprietary signals internally or source them from external research and data providers. Both approaches can require resources to evaluate methodologies, reconcile datasets and integrate information into existing investment systems.

MSCI said these differences complicate the comparison of signals and can slow their adoption by investment teams.

SignalLab is designed to provide a common framework for assessing signals, with standardised documentation and validation processes. The company said users can test investment ideas against historical data, examine portfolio risks and complement existing investment models.

Mark Carver, MSCI’s global head of equity solutions and equity analytics, said clients wanted greater confidence that investment signals were reliable and added value to their existing processes.

“Our clients tell us the hard part isn’t necessarily finding a signal but proving that the signal is trustworthy and additive to their investment process,” Carver said in the company’s announcement.

He said the platform was developed in response to client demand for greater speed, control and transparency when testing investment ideas.

MSCI did not disclose financial details of the launch in its announcement.

Planned expansion into momentum and AI exposure

MSCI said it expects to expand SignalLab with additional signals and data modules, including tools focused on fundamental momentum and artificial intelligence exposure.

The planned Fundamental Momentum module will track changes in corporate earnings, profit margins, profitability and analyst estimates. These indicators can help investment teams assess whether a company’s financial performance is improving or deteriorating.

The proposed AI Exposure Intelligence module will classify companies according to how artificial intelligence affects their business value. MSCI said the framework will consider current AI-related revenue, competitive positioning and the risk that AI could disrupt existing business models.

The company said future development would also support signal customisation and integration with its index and risk-model production environments.

Competition in investment analytics

SignalLab adds to MSCI’s broader range of investment decision-support products, which includes equity indexes, portfolio analytics, risk models and quantitative research tools.

The company serves asset managers, asset owners, hedge funds, wealth managers, banks, insurers and other participants in financial markets.

Its existing data and analytics infrastructure provides the foundation for integrating the new platform into investment workflows.

MSCI said SignalLab would also support access through application programming interfaces (APIs) and Snowflake, a cloud data platform, enabling clients to connect the signals with other systems.

Historical testing can help investors evaluate a signal, but past performance does not guarantee future results. Investment teams must also account for transaction costs, changing market conditions and the possibility that a strategy may perform differently outside the period used to develop it.

MSCI’s launch comes as financial institutions increasingly incorporate AI into research and data-processing workflows. SignalLab’s initial offering combines AI-assisted analysis with established research processes and validation, while its planned modules point to further applications in corporate financial analysis and the assessment of AI-related business risks.

The company said the platform would continue to evolve as it adds signals and develops connections to its wider analytics ecosystem.