Traditional Asset Management

Traditional asset management focuses on professionally managing investment portfolios that mainly include publicly traded assets like equities and fixed income securities. The goal is to help investors meet their financial objectives by balancing expected returns with risk tolerance through effective asset allocation and performance monitoring.

S&P Global Market Intelligence enhances this process with its Risk & Valuation Services, which combine portfolio analytics and investment risk management. By leveraging market intelligence and data-driven insights, asset managers can better assess portfolio exposures, monitor performance, and make informed decisions.

This improvement in visibility allows traditional asset managers to transition from reactive oversight to proactive decision-making, ultimately leading to better risk management and increased confidence in volatile markets.

Drive high-conviction public market decisions with integrated credit, macro, and valuation insight across liquid securities.

• Screen large public universes faster and more consistently when markets shift quickly, using comparable credit, risk, and macro signals across listed equities and liquid fixed income.
• Increase conviction in issuer selection by combining integrated credit and macro insight to assess issuer strength and downside risk across liquid portfolios, supporting decisions aligned to mandates and benchmarks.
• Improve relative‑value positioning across instruments by enabling consistent comparison of valuation, liquidity, and credit risk across public securities and derivatives.
• Act earlier on real‑world inflection points by surfacing early operational and market signals when traditional financial data lags, supporting better entry/exit timing.
• Strengthen investment committee outcomes with independent benchmarks and forward‑looking risk signals that validate theses against public comparables. 

Power systematic public strategies with scalable data that strengthens factor design, testing, and disciplined alpha generation.

• Build differentiated factor strategies in crowded markets using scalable time‑series datasets that plug into models for factor construction, testing, and refinement, supporting repeatable alpha within benchmark constraints.
• Reduce model risk across regimes and cycles through robust back‑testing with normalized historical datasets, enabling consistent testing across time, regions, and asset classes.
• Add alternative alpha signals earlier by incorporating indicators linked to physical trade flows, supply chains, and economic activity, revealing shifts not visible in financial statements.
• Improve timing and risk management with transparent indicators of investor positioning and market behavior to support sentiment‑aware strategies.
• Move from research to production faster using standardized, production‑ready datasets that integrate cleanly into research, portfolio construction, and monitoring workflows. 

Monitor market, credit, and liquidity exposures to manage tracking error and align portfolios with mandate objectives.

• See market‑driven losses and tracking deviation earlier when rapid moves threaten benchmark alignment, using integrated market risk analytics to measure sensitivities across liquid portfolios.
• Identify downgrade and default risk faster when credit deteriorates quickly in bonds/loans, using forward‑looking credit risk metrics and independent ratings for continuous surveillance.
• Manage liquidity and exit constraints with confidence using liquidity indicators and pricing transparency to assess tradability and redemption risk.
• Quantify downside under stress with scenario‑based analytics using macro and credit assumptions, improving preparedness for market shocks.
• Explain performance drivers and optimize risk through risk decomposition that clarifies factor, credit, and market contributors, aligning risk/return objectives. 

Ensure accurate daily NAV, margin processes, and trusted data foundations across liquid portfolios.

• Increase confidence in daily NAV accuracy when large liquid portfolios make valuation complex, using independent, market‑consistent valuations for NAV and shareholder reporting.
• Reduce operational risk and margin disputes by improving the calculation and monitoring of margin and collateral obligations in time‑sensitive workflows.
• Cut breaks and downstream exceptions by maintaining a standardized, validated security master as a single source of truth.
• Support benchmark tracking and compliance with precise, timely index/ETF data updates and intraday valuation support.
• Improve funding efficiency and liquidity control using transparent pricing and financing inputs to support treasury decisions across cash, collateral, and securities financing. 

Automate screening, classification, and reporting to support compliance for registered investment vehicles.

• Speed compliant onboarding and reduce manual risk by automating screening of investors/counterparties against sanctions, PEPs, and adverse media using trusted regulatory data.
• Reduce regulatory breaches and reputational risk with continuous portfolio and look‑through screening to detect direct and indirect exposure to sanctioned entities.
• Clarify which rules apply (with less interpretation) using regulatory classification data to determine obligations by instrument, jurisdiction, and vehicle structure.
• Lower reporting risk and findings by supporting accurate, timely regulatory reporting using standardized regulatory and reference datasets for registered investment vehicles.
• Strengthen governance without slowing decisions by automating checks against mandates, restrictions, and exclusion lists across portfolios. 

Improve execution outcomes through pre-trade liquidity transparency and financing insight.

• Reduce market impact and execution risk by assessing pre‑trade liquidity conditions with transparent pricing and liquidity indicators before trading.
• Improve entry/exit timing by detecting crowding and sentiment through indicators of positioning and borrowing activity, beyond price data alone.
• Lower funding costs and improve liquidity access with transparency into securities lending, repo markets, and collateral availability to support financing decisions.
• Deliver more predictable execution outcomes by combining pre‑trade transparency and liquidity context to balance timing, benchmark alignment, and risk. 

Mitigate broker and vendor risk with forward-looking credit and operational intelligence.

• Mitigate exposure earlier when broker/custodian credit quality deteriorates quickly by applying forward‑looking credit risk insight to assess counterparty strength and emerging risk.
• Improve operational resilience and regulatory confidence by centralizing and standardizing third‑party vendor due diligence to identify and manage service‑provider risk.
• Respond faster to emerging events by enabling continuous monitoring of counterparties and third parties using dynamic risk signals and alerts.