Quantitative and strategy research

Strategy teams must extract investible signals from growing datasets while adapting models to changing macroeconomic relationships and market regimes. Resource-intensive models can slow model refreshes, constrain coverage and reduce capacity for differentiated research and client engagement.

Crisil Integral IQ supports equity, macro, rates, FX and cross-asset strategy research through quantitative modelling, back-testing, forecasting and analytics. We combine asset-class expertise with Python-based and technology-enabled frameworks to expand coverage, improve model efficiency and create capacity for higher-value research and client-facing insights.

20+ years

Experience in investment and quantitative research

15 of top 20

Global investment banks served, alongside 20+ regional sell-side firms

800+

Research analysts globally across Crisil Integral IQ

Our solution components

We develop quantitative frameworks that translate market, fundamental and macroeconomic data into systematic signals supporting differentiated investment and trading strategies

We build scalable forecasting models that connect macroeconomic drivers with equity indices and securities to strengthen strategy research

We test quantitative relationships across historical periods and market regimes to assess signal robustness and improve confidence in strategy outputs

We support strategy research across equities, fixed income, rates and FX, helping teams analyze market relationships and develop coherent cross-asset views

We apply quantitative screening and data-led frameworks to identify patterns, relative opportunities and potential investment ideas across large security universes

We use Python and analytics-led workflows to automate model updates, calculations and recurring analysis, reducing maintenance effort and expanding research capacity

The problems we solve

Research

Scaling strategy coverage without multiplying model complexity

Overview

Modular quantitative frameworks help strategy teams extend coverage across indices, securities and markets without replicating resource-intensive models for every research universe.

Keeping quantitative models relevant as market relationships change

Overview

Back testing and systematic evaluation of underlying drivers help teams reassess model relationships and incorporate signals that improve research..

Reducing analyst time spent maintaining models

Overview

Technology-enabled modelling and automated workflows reduce recurring model-maintenance effort, creating more capacity for interpretation, differentiated research and client engagement.

Turning scattered market
data into meaningful
business insights

Mutual fund case

Transforming scattered data into
actionable insights for smarter business
strategies

Quantitative and strategy research

Quantitative strategy development

Market and index forecasting

Back-testing and model validation

Cross-asset strategy analytics

Systematic screening and idea generation

Research automation and model engineering

Discover why the world's most prestigious global and regional financial institutions chose us as their strategic partner

Businesses

Businesses

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Our Organisation

Investor Relations

Newsroom

More from Crisil

Our Organisation

Investor Relations

Newsroom

Discover why the world's most prestigious
global and regional financial institutions
chose us as their strategic partner