CASE STUDY — May 16, 2025
An Investment Management Firm Enhances Stock Selection Process with AI-Ready Data
THE CLIENT:
A mid-sized investment management firm
USERS:
The data analytics team
In today’s rapidly evolving fintech landscape, the integration of artificial intelligence (AI) is revolutionizing how financial organizations analyze data and make investment decisions. With traditional sources of differentiation becoming increasingly commoditized, more firms are utilizing alternative data and AI for stock selection to stand out from the pack and generate additional alpha.
A mid-sized investment firm specializing in equity investments across various sectors and countries faced increasing competition while, at the same time, slower growth was putting pressure on the firm's profitability.
Members of the data analytics team were charged with finding a way to leverage AI's ability to analyze patterns across vast datasets to help the firm's portfolio managers make data-driven decisions when selecting securities and devising asset allocation strategies. The team began to investigate third-party offerings that could support the initiative.
Pain Points
Members of the data analytics team recognized that good AI models require vast amounts of data for training purposes. They also recognized that fragmented datasets and legacy systems can hinder AI’s effectiveness. They wanted to find a solution that would enable them to create "AI-ready data" to use in machine-learning models to enhance their predictive analytics. They especially wanted:
- Access to rich data that could provide deep insights into stock performance drivers, enabling the portfolio managers to make more informed investment decisions and identify high-potential stocks early in the selection process.
- Automation of data preparation to reduce the time needed to prepare the data, enabling the portfolio managers to focus on strategy development and investment analysis.
- Industry-leading analytics and content in a single platform to reduce time to market without compromising the complexity and uniqueness of their investment strategies.
The team contacted S&P Global Market Intelligence ("Market Intelligence") to learn more about the firm's offerings.
The Solution
Market Intelligence specialists described ClariFI®, a robust alpha research and portfolio management platform. ClariFI provides powerful analytics and global market data solutions that let portfolio managers and quantitative researchers easily access Market Intelligence’s data libraries through a secure, hosted, or locally installed environment. This would provide the data analytics team with:
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Access to rich data |
The team could tap into an integrated set of data, including: Premium Fundamentals with global standardized financial statement data for 180,000+ companies, including 95,000+ active and inactive public companies, and As Reported data for 150,000+ companies. This enables users to extend the scope of historical analysis and back-testing models with consistent data from all filings of a company's historical financial periods, including press releases, original filings and all restatements. Consensus Estimates with the most comprehensive global estimates based on projections, models, analysis and research. This dataset can be used to evaluate earnings estimates to select stocks and manage investment performance, track the direction and magnitude of upgrades and downgrades and more. Key Developments with structured summaries of material news and events that may affect the market value of securities. With deep history back to 2003, this dataset helps derive signals and support trading models across asset classes, trading styles and frequencies. Market Data with end-of-day traded currency, both split-adjusted and as quoted pricing data across open, close, high, low, volume, adjustment factor, shares outstanding, volume-weighted average price and other data points. |
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Feature engineering |
ClariFI’s advanced analytical tools can be employed for feature engineering, which transforms raw data into meaningful inputs (features) for predictive models. This creates hundreds of new variables (factors) that capture critical financial metrics and market trends. |
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Data enrichment |
ClariFI's Factor Backtesting workflow evaluates factors based on their correlation to future returns across diverse investment universes. Features include:
Factor backtests conducted within ClariFI, and the resulting insights, can be integrated into an AI algorithm alongside raw factor scores. |
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AI-ready dataset creation |
ClariFI’s data preparation capabilities transform enriched data into an AI-ready format by normalizing data, handling missing values and accurately representing corporate actions. The result is a high-quality dataset easily ingested by various AI models. |
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Model development and testing |
ClariFI Quantitative Consultants empowered the client to harness the full potential of ClariFI by meticulously preparing data for advanced machine-learning models designed to predict stock performance through innovative engineered features. By training and validating these models on an extended period of in-sample and out-of-sample historical data, users can not only better evaluate predictive accuracy but also enhance their stock selection strategies, driving more informed investment decisions. |
Key Benefits
The data analytics team quickly saw the power of ClariFI and subscribed to the offering. The implementation of the solution and Market Intelligence data has led to significant improvements in the firm’s stock selection process. The portfolio managers now have:
- Enhanced Predictive Accuracy: Machine learning models powered by the AI-ready dataset achieved a significant improvement in predictive accuracy compared to previous models utilizing traditional data sources.
- Informed Decision-Making: The enriched dataset provided deeper insights into stock performance drivers, enabling the portfolio managers to identify high-potential stocks earlier in the selection process.
- Increased Efficiency: Automation of data preparation and feature engineering through ClariFI significantly reduced time spent on manual data handling, enabling analysts to focus on strategy development and investment analysis.
- Competitive Edge: Leveraging advanced analytics and AI capabilities positioned the firm as a leader in quantitative investing, enhancing its ability to identify and capitalize on market opportunities.