Portfolio Risk Management: A Sample Scenario Analysis Using Climate Data

The purpose of this article is to show one approach for incorporating climate transition risk data into a scenario analysis process. Using hypothetical scenarios is useful for providing snapshots of how portfolios could perform under shocks.

We will use Entelligent’s climate transition and non-localized physical risk data, known as T-Risk and available in FactSet Workstation, for Net Zero 2050.

• A low T-Risk score indicates improved climate adjustments compared to peer companies

• A high T-Risk score indicates high exposure (and higher risk) in a transition to a low-carbon economy

A negative T-Risk score indicates superior adjustments in a low-carbon in which the global economy reduces carbon relative to Business-as-Usual, also known as Current Policies, in which the global economy keeps operating as it does now. For Carbon-Adjusted T- Risk Score, after estimating T-Risk at the security level, Entelligent adjusts the estimates by adding entity-level carbon footprint information, which improves the transition gamma of resultant T-Risk-based portfolios (also Gauss Rank-transformed)¹.

Companies classified with T-Risk scores are segmented into four categories—leaders, innovators, followers, and laggards—based on their performance.²

Single-Factor Shocks and Extreme Event Scenarios

We ran the stress test report on a broad world equity index as of June 30, 2025. The scenarios below include a few single-factor energy-related shocks:

• S&P Energy down by 20%

• World Oil & Gas 20% down

• Gasoline 30% up

• Natural Gas 30% up

These are factor event-weighted scenarios from the FactSet Workstation.³ We added one extreme event scenario from the FactSet Workstation⁴ to replicate a specific historical period relevant to oil/energy shocks from the July 2008 historic high price of crude oil. 

Under all scenarios with losses, the Percent Standalone Return column (return under scenario with no account of the portfolio weight) shows that leaders will lose consistently less than the rest of the groups across all scenarios. This complies with the expectation that companies with better transition profiles will be less impacted by energy shocks.

Therefore, under these scenarios, selecting companies that are more resilient to transition risks could help reduce the possible losses. However, so do companies in the positive return scenarios, where leader stocks provide the lowest return when gas and oil prices increase. In a sense, leader stocks as a group appear to be less sensitive to changes in both directions.

Drilling down the analysis on asset level, we will show how to quickly identify stocks with the same T-Risk score—but with substantially different returns under a given stress test. Selecting stocks with lower loss or higher return and the same Carbon-Adjusted T-Risk score (dotted yellow line between the two) could help improve portfolio performance and preserve the longer-term transition risk profile. This could be done on the index/portfolio level or within a particular industry.

In the first chart below, we focus on the scenario of the S&P Energy sector declining 20%. In that example, we could consider increasing the weight of a stock returning positive 1% (green outline) and reduce the weight of the ones with -22.5% loss (red outline), while preserving the T-Risk adjusted score at the index level, as both stocks have Carbon-Adjusted T-Risk of 0.1.

Alternatively, under the scenario we may select companies with the same return but with significantly different carbon adjusted T-Risk scores. This enables an improvement in the transition risk profile at the index/portfolio/industry levels while preserving the return under a given scenario. As an example, in the plot below we overweigh the stock with T-Risk score of -1 (green outline), while underweight the one with Carbon-Adjusted T-Risk score of 0.9 (red outline), as both provide a return of -13%.

The two examples below illustrate how to perform the same analysis within a given industry or sector.

In the plot below for the Financials sector, we easily spot companies with the same T-Risk adjusted score. For example, the companies with losses of -22% (red outline) and -2% (green outline).

By reducing the weight of the former and increasing the weight of the latter, we could stem the loss on industry and portfolio levels while preserving the same transition risk industry and portfolio profile.

When seeking to preserve the return and reduce the T-Risk score, we may consider a higher weighting for the company with a Carbon-Adjusted T-Risk score of -1 (green outline) and a lower weighting for the one with the score of 0.65 (red outline). That’s because both of them return -24% under this scenario (yellow vertical line).

What is even better, we can reallocate from stocks with large negative returns under the scenario and positive carbon-adjusted T-risk score (red outline on the right plot below) to those with smaller negative returns and a large, close to 1 T-Risk adjusted score. This way, within the same industry the portfolio will be reallocated to companies that would suffer lower loss in case the energy sector drops by 20% and at the same time more resilient to climate change.

In Conclusion

The examples above show one approach for incorporating climate transition risk data into a scenario analysis process. The perspective presented aims to show there is a way to analyze under different scenarios—historical or factor shocks, how would the portfolio and each of the Entelligent T-Risk score groups perform, and whether there are scenarios under which companies with higher or lower T-Risk scores are more vulnerable to loss or lower return.

Based on this analysis and the expectation for each scenario’s probability of realization, an investor might decide to reallocate the portfolio for the potential to perform better under a given scenario, improve its transition risk profile, or achieves both at the same time.

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This blog post is for informational purposes only. The information contained in this blog post is not legal, tax, or investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.

1 Entelligent Top-down climate transition risk calculations, while valuable in assessing macroeconomic scenarios, have limitations. They work well in macroeconomic stress test environments that rely on aggregated data and broad economic indicators specific to energy prices and the energy-technology transition to a low-carbon economy. However, their effectiveness diminishes when applied to macro indicators that are unrelated to energy or some micro-level company assessments that are beyond energy dependency. Entelligent models primarily utilize climate and energy data, applying it downstream to publicly available financial data on companies. It may not, by design, fully account for the self-reporting by companies. To address this limitation and provide greater accuracy, a double-verification system should be employed, involving financial practitioners who can evaluate a company's preparedness for climate and energy transitions. It is crucial to acknowledge that for many companies, there is substantial work required to align with climate and energy transition goals, and top-down calculations should be complemented by bottom-up analyses to create a more comprehensive and accurate risk assessment.

2 T-Risk (0/1/2/3: leader/innovator/follower/laggard) represents a quartile bin within each cohort. This intra-cohort ranking is consistent between T-Risk v2 and v3. However, the cohorts themselves are defined differently between the two versions. T-Risk v3: Cohorts are defined by 6 macro-regions (Asia/Pacific Ex Japan, Japan, North America, Latin America, Europe, Africa/Mideast ) and 32 RBICS L2 categories. T-Risk v2: Cohorts are defined by 6 macro-regions (Asia/Pacific Ex Japan, Japan, North America, Latin America, Europe, Africa/Mideast ) and 25 GICS Industry Groups. More information on T-Risk methodology is available in the T-Risk methodology white paper, which can be accessed for a detailed understanding of our classification process.

3 Factor event-weighted scenarios allow you to compare historical period changes to the supplied shock value saved with the stress test. Periods that have changes similar to the shock amount are weighted higher than non-similar periods. First you sort by the absolute value of the difference between historical change to the shock amount and then you exponentially weight the periods based on the saved decay rate.

4 Extreme Event Stress Tests take today's portfolio and hypothesizes what its return would be if an extreme event period were to happen again. Current factor exposures are used with factor returns from the event to derive the impact of this event on your portfolio returns.