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How These Data Science Scores Improve Confidence in Your Valuation Risk Analysis

Reading time: 6 min   |  By Sonia Chopra   |  Published in Articles,

In today’s financial markets, inherent uncertainties and complexities introduce significant risk into the asset valuation process. Valuation teams must be able to quantifiably measure pricing risks through critical metrics to compare, evaluate, and understand the accuracy and reliability of their asset pricing. These measurements offer a standardized and drill-down approach into specific trade and consensus data.

With the right solution, valuation teams are able to make more informed decisions, aligning their pricing closely with real-time market conditions. This process not only enhances the precision of asset valuation, but also supports strategic investment planning and risk management efforts. This ensures that portfolios are optimized for both current and future market scenarios.

Cuneiform for Valuation Risk: Data Science Scores

Cuneiform® for Valuation Risk provides transparent, statistically-weighted computations and scores to help you with your risk management and decision-making. Various data science metrics, such as the Consensus Density Score and Trade Alignment Score, help you identify potential areas of risk and opportunity within the market to support analysis and marking of your books. These metrics are consistently calculated across all asset classes for standardization. You can tailor your policies and procedures to perform scenario and risk concentration analysis to effectively guide your valuation teams.

Cuneiform for Valuation Risk offers the following scores:

Consensus Density Score

This Consensus Density Score allows you to evaluate the reliability of a consensus calculation based on the data available, including trades, indicative data, and submissions. It leverages the other scores (discussed below) in order to provide a holistic view of consensus results.
Valuation teams are able to gauge the credibility of the consensus value for a specific asset at a given snapshot in time, and identify if any investigations need to occur.

Bimodality Score

The Bimodality Score is calculated to determine whether or not submitted price points in a given consensus exhibit a bimodal distribution. This data science score demonstrates whether there are similar or differing opinions and valuations among market participants. Variations in this score may suggest underlying volatility or uncertainty regarding the asset’s valuation.

Valuation teams use this information to assess the risk level of an asset more accurately, considering the possible impact of divided market opinions on future price movements and asset stability.


Dispersion determines how scattered the pricing points appear to be. Valuation teams gain a clear picture of market agreement or disagreement. Lower dispersion indicates a tight cluster of submissions around the average value, suggesting a strong consensus among market participants. In contrast, higher dispersion reveals a wide range of opinions, indicating more uncertainty.

Alignment Scores

The Alignment scores allow you to measure how far the submissions are from traded prices and indicative prices. They indicate the reliability of a certain dataset in comparison to others, including prices, trades, indicative data, consensus results, etc.

Valuation teams are able to understand how closely related the trade / indicative data price is to all the participants’ submitted prices. If the submitted prices are not in line with the trade / indicative price, it may be an indication that the market has moved since that last trade was executed or the indicative price was captured.

Trade Alignment Score

The Trade Alignment Score assesses whether the last trade price is current with the market by comparing it against participants’ submitted prices to gauge the strength of their alignment.

Indicative Data Alignment Score

The Indicative Data Alignment Score measures how closely the prices align with indicative prices, providing insight into the accuracy and current market relevance of submitted valuations.

Expertise Rank and Score

The Expertise Rank compares firms’ distance from a price allowing you to quickly compare your placement amongst your peers for valuation analysis.

The Expertise Score reflects a participant’s ability to demonstrate deep market understanding and insight. A closer alignment suggests that valuation methods accurately reflect market conditions, whereas a lower score may indicate the need for adjustment or review.

Cuneiform® for Valuation Risk equips valuation teams with a comprehensive suite of data science based scores designed to enhance your risk management and decision-making processes. By offering transparent, statistically-weighted analysis across all asset classes, you will gain valuable insights into market dynamics and valuation accuracy.

From gauging the reliability of consensus calculations to assessing the impact of market opinions on asset stability, the solution ensures that your valuation teams can navigate the complexities of market valuations with confidence for more efficient analysis.

Contact Us

Please reach out today for more information or to schedule a demo. Take the next step towards redefining your valuation risk management strategy.


By Sonia Chopra

Sonia Chopra is PeerNova's Product Marketing Manager for the Valuation Risk product line. She has nearly a decade of marketing experience and has been with PeerNova for eight years. She specializes in crafting content and campaigns that address the complexities of product and valuation control, such as market volatility, asset pricing discrepancies, and regulatory compliance issues. Her ability to articulate the intricacies of these challenges, enables her to develop highly effective product marketing strategies that meet the evolving needs of the industry.

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