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    Finding the Weak Link with Financial Statement Forecasting

    October 2023

    Finding the Weak Link with Financial Statement Forecasting

    Financial statement forecasting through the business cycle is a pillar of credit risk management. Downturns lead to falling revenues and compressed margins in the aggregate; however, outcomes vary at both the industry level and across firms within the same industry. Fortunately, the introduction of EDF-X Smart Projections allows users to shock financial statements for a given portfolio and quantify resulting changes in credit risk.


    A comprehensive understanding of the financial capacity of a creditor, counterparty, or client financial capacity requires simulating the firm’s performance under hypothetical, yet realistic, scenarios. EDF-X Smart Projections offer a solution for forecasting financial statements under user-defined assumptions. The tool utilizes a detailed three-statement modeling approach with parameter values derived from Moody’s extensive financial statement database. Producing annual “pro forma” financials at a multi-year horizon, Smart Projections represent a scalable solution for forecasting financial statements for individual firms as well as across a portfolio.

    This article details a process for selecting a business cycle shock, as well as outlining a process for quantifying the pace of recovery in the wake of a shock. The subsequent analysis then looks at the shift in financial statement line items and change in probability of default (PD) across industries.


    Forecasting firm-level financial performance through the business is a two-step process. First, the appropriate shock and recovery dynamics are defined; second, these dynamics are applied to a given portfolio to produce PD measures. This paper outlines a data-driven example for selecting shock and recovery dynamics, forecasting financial statement line items and gathering PDs. While the present analysis is based on a given set of shock and recovery assumptions, the Smart Projections feature in EDF-X is flexible and allows users to define their own shock and recovery dynamics.

    Defining a shock

    When forecasting how a company’s financial statements will change, it is important to quantify the shock to the economy and subsequent recovery. Ultimately, the final results will depend on these assumptions. For this paper, we looked at business cycle data from 1990 onward and measured shock and recovery dynamics for different industries. We focus on key indicators—sales growth, profit margins, and interest rates—to get a sense of how rough things could get. For this study, we used the lowest 20th percentile of sales growth, profit margins, and interest rate metrics at the industry level.

    Table 1 presents shock values for selected sectors. Construction firms are most strongly impacted in terms of revenue. Agriculture firms experience the largest drop in EBITDA margin, and firms in HiTech face the steepest interest rate increase.

    Having defined a shock, the recovery path needs to be outlined. We assume the recovery takes place during the two years following the shock. The expected sales growth, EBITDA margin change and interest rate change post shock are calculated by drawing on sector-level dynamics from prior cycles characterized by similar levels of stress. For EBITDA margin, we assume a return to initial levels by the end of the recovery period. Further, we control for the fact that a share of firms are likely to exit during the stress period. Table 2 includes the full set of assumptions.

    For current analysis, we shock a portfolio of 47,813 U.S. companies with median firm-level total assets of $12 million. The most populous sectors are Trade and Services.

    Portfolio level: Strong PD increase when shock hits, followed by recovery

    Table 3 details portfolio level results. The average PD nearly doubles during the stress period before moderating during the recovery period. The median firm also experiences a negative shock - its PD nearly doubles and its PD implied rating falls outside of the investment grade range. During the subsequent recovery, we observe a strong reduction in credit risk for the median firm. By the end of the second year, the median PD falls to 0.43% and it further declines to 0.30% by the end of the third year, below its initial level of 0.54%

    Why does the average firm recover only partially, while the median firm is projected to have lower credit risk at the end of the recovery period relative to its starting value? First, the distribution of PDs is generally right-skewed, with many firms below 1% but also some with PDs of over 20%. Following the shock and recovery, the distribution of implied ratings becomes polarized, with the share of firms in the highest and the lowest rating categories increasing. These factors lead to a higher average PD. In contrast, the median PD improves relative to its initial level following the shock and recovery due to the assumption of a uniform recovery path within each sector. The absence of performance variation across firms improves the resulting overall risk of the portfolio.

    PD drivers: Profitability and interest coverage at the core of the PD impact

    The financial statement items influencing PD are impacted by the assumed sector-level shocks and recovery paths. Figure 1 illustrates the behavior of the primary PD drivers during the stress and recovery periods. Of note:

    1. Cash to Assets (liquidity) improves over time, thereby reducing default risk. Cash accumulation occurs as we assume firms retain profits
    2. Return on Assets falls during the stress year, lifting PDs, and then rebounds, weighing on PDs during recovery. This is driven by the assumption that EBITDA margin returns to prior levels following the initial shock
    3. Falling sales during the stress period lead firms to take on short-term debt. This increases the Current Liabilities-to-Sales ratio and leads to higher PDs
    4. Falling EBITDA to Interest Expense leads to higher PDs during the stress year. The ratio subsequently improves, reducing PDs during the recovery years
    5. Retained Earnings to Current Liabilities follows the path of Cash to Assets, increasing over time and reducing PDs

    Sector level: Agriculture, Construction and HiTech see largest PD increases

    Our sector-level shocks lead to higher PDs; however, the level of PD increase varies across industries. Agriculture, Construction and HiTech experience the largest jumps in median 1-year PD. In contrast, the Utilities sector experienced PD increase of only 11%.

    The initial PD increase is related to the assumed shocks to sales growth, EBITDA margin, and interest rates. Figure 2 plots the relationship between sector level shocks and PD, along with a best fit line.

    Sectors above this line experience a severe PD impact relative to the magnitude of the shock, suggesting the shocks alone cannot fully explain sector dynamics - other factors, like the initial state of firms, also play a role.

    The most impacted sectors are the ones with the greatest shocks (Figure 2):

    1. Construction sales fall by 16.1% during the first year. This highly cyclical sector contracts during the downturn and experiences a large interest rate increase
    2. Agriculture suffers a 6.4% drop in EBITDA margin and a sales decline of 10.1%. The commodity-based nature of the sector leaves it vulnerable during downturns
    3. HiTech experiences the largest interest rate increase. Firms in the sector require constant R&D investments to stay competitive
    4. Utilities sees a modest decline in sales and EBITDA margin. The sector tends to be unaffected by business cycles

    Finally, Table 4 provides the forecasted sector-level PDs. Agriculture is the only sector where the median PD is higher in the final year relative to its initial level, reflecting the sector’s low profitability levels.

    Ratings results: Initial deterioration in credit quality, with polarization during the recovery

    At the ratings level, the first-year shock leads to higher PDs and a rightward shift of the rating distribution (Figure 3). The number of firms rated Aaa-A, Baa, or Ba declines, while the number of B and Caa-C firms increases. In contrast, the distribution during recovery led to a bimodal distribution, with more firms than initially in both the highest (Aaa-A) and lowest (Caa-C) rating categories.

    Of note, while the median credit risk improves after two years of recovery, 90% of credits initially rated Caa-C and over 50% of firms initially rated B or better transition to the Caa-C category, a reflection of the higher average PDs in Year 3.

    Firm level: Low-profitability firms are more vulnerable to shocks

    We categorize firms into two groups: those that experience a PD improvement from initial by Year 3 (“winners”) and those experiencing a PD deterioration (“losers”). Figure 4 shows two key PD drivers for these groups over time, along with the median firm. Of note, a firm’s initial profitability is crucial for its capacity to withstand a business shock and its subsequent recovery.

    Firms that end the third year with a higher PD than their initial state are characterized by a low return on assets. When the shock hits, profit margins turn negative and remain negative over the projection horizon. Crucially, this has implications for cash. While cash levels are initially consistent across all groups, they diverge over time - winners accumulate cash, while losers’ cash levels fall. Many firms flagged as “losers” take on additional short-term debt to fund their operations during the shock period.


    EDF-X Smart Projections allow users to shock financial statements and then use the resulting PDs to quantify credit risk changes for impacted firms. By quantifying firm-level performance under different business cycle assumptions, users are able to better understand their portfolio risk profile as the economy improves or worsens. For the current exercise, a one-year shock and two-year recovery consistent with prior business cycles were applied to firm-level sales growth, EBITDA margin change, and interest rate. However, EDF-X Smart Projections allow users to specify their own shocks, both to the upside and downside.

    For the portfolio used in this analysis, we observe significant variation in outcomes, with results differing by industry, rating and firm. Of note, a firm’s initial stage is crucial in determining its capacity to withstand stress, with the result that firms with thinner profit margins are more vulnerable to shocks.

    Key Takeaways

    • The magnitude of estimated typical financial shocks varies across sectors in the current analysis. Construction faces the most severe sales drop (-16%), Agriculture experiences the largest EBITDA margin decrease (-6.4 %-pts), HiTech see the steepest interest rate increase (+2.9 %-pts)
    • Overall portfolio default risk increases when the shock hits, with the average PD for the portfolio of interest almost doubling from 1.86% to 3.56%, before falling to 2.53% in the second year of recovery
    • During the recovery the distribution of firms across implied rating categories becomes more spread out. Firms’ ability to recover varies widely, with profitability playing a crucial role in determining outcomes
    • EDF-X Smart Projections are flexible and allow for user supplied assumptions; while a negative business cycle shock was applied in the current exercise, assumptions can be modified to meet users business and analytical needs
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