Probability of Financial Distress (%) - Definition, Formula & Calculator

What Is Probability of Financial Distress (%)?

Probability of Financial Distress (%) is a risk metric that estimates the likelihood that a company will fall into serious financial trouble or bankruptcy within the next 12 months based on its current financial condition and market behavior. On GuruFocus, the field name for this metric is PFD.

In practical terms, PFD is designed to answer a simple question: given a company’s profitability, leverage, liquidity, stock performance, volatility and size, how likely is it to become financially distressed in the near future? A lower value generally suggests stronger financial health, while a higher value suggests elevated bankruptcy or distress risk.

This metric is especially useful because it goes beyond traditional balance-sheet-only measures. Unlike simpler solvency ratios, Probability of Financial Distress (%) combines accounting data with market-based signals such as stock returns and volatility. That matters because financial distress often shows up in market prices before it becomes obvious in reported financial statements.

GuruFocus’s Probability of Financial Distress (%) is based on the distress-risk model developed by John Y. Campbell, Jens Hilscher and Jan Szilagyi. It is conceptually similar to the Altman Z-Score in that both are used to assess bankruptcy risk, but the Campbell-Hilscher-Szilagyi framework incorporates market information and is more broadly applicable across industries, including areas where working-capital-based models are less useful.

At a high level, the model first calculates a distress score using several explanatory variables, then converts that score into a probability using a logistic function:

A company with a very low PFD is not guaranteed to be safe, and a company with a high PFD is not guaranteed to fail. But as a screening and risk-monitoring tool, the metric can help investors quickly identify firms whose fundamentals and market signals suggest rising financial stress.

How Is Probability of Financial Distress (%) Calculated?

GuruFocus calculates Probability of Financial Distress (%) using a logit model based on eight explanatory variables. The model first computes a linear distress score, labeled LPFD, and then transforms that score into a probability.

The linear score is:

That score is then converted into a probability:

The Eight Inputs

1. NIMTAAVG = Net Income to Market Total Assets

This variable measures profitability relative to market-valued assets.

where:

GuruFocus notes that for companies reported quarterly, geometrically declining weighted quarterly net income data from the latest four quarters are used.

2. TLMTA = Total Liabilities to Market Total Assets

This variable captures leverage.

Higher leverage generally increases distress risk.

3. EXRETAVG = Weighted Excess Return Relative to the S&P 500

This variable measures how the stock has performed relative to the market over the past 12 months. GuruFocus uses geometrically declining weights on monthly excess returns, with the weight halved each quarter.

Weak relative stock performance tends to raise distress risk because the market may be pricing in deteriorating fundamentals before they appear clearly in financial statements.

4. SIGMA = Standard Deviation of Daily Returns

This variable measures stock-price volatility. GuruFocus uses the annualized standard deviation of a company’s returns over the past 92 days, or about 63 trading days.

Higher volatility is generally associated with greater uncertainty and higher distress risk.

5. RSIZE = Relative Size

This variable measures the company’s market capitalization relative to the total market capitalization of S&P 500 companies.

Larger firms tend to have lower distress risk, all else equal.

6. CASHMTA = Cash to Market Total Assets

For non-financial companies, GuruFocus measures this as:

More cash relative to obligations and market-valued assets generally lowers distress risk.

7. MB = Market-to-Adjusted-Book-Equity Ratio

This variable reflects valuation relative to adjusted book equity.

GuruFocus defines adjusted book equity as:

8. PRICE

PRICE is measured as the natural log of the stock price, capped at \log(15). Lower-priced stocks have historically been associated with higher distress risk.

Why the Formula Uses a Logistic Function

The logistic transformation converts the linear score into a value between 0% and 100%, making the result easier to interpret as a probability estimate rather than an abstract score.

In general:

• stronger profitability lowers LPFD,
• more cash lowers LPFD,
• larger company size lowers LPFD,
• while higher leverage, weaker returns and higher volatility raise LPFD.

Probability of Financial Distress (%) Trend Over Time

A company’s PFD is often most useful when viewed over time. A stable, low reading may indicate durable financial strength, while a rising PFD can be an early warning sign that leverage is increasing, profitability is weakening, liquidity is shrinking or the market is becoming more concerned about the business.

Because the model includes market-based inputs, PFD can sometimes move sharply before a company reports visibly weak earnings or balance-sheet deterioration. That makes trend analysis particularly valuable for investors monitoring credit risk, cyclical exposure or turnaround situations.

What Does Probability of Financial Distress (%) Tell You?

Probability of Financial Distress (%) tells you how vulnerable a company appears to near-term financial trouble based on a combination of fundamentals and market signals.

A low PFD generally suggests:

• healthy profitability,
• manageable liabilities,
• adequate liquidity,
• relatively stable stock performance,
• and lower market-implied stress.

A high PFD generally suggests the opposite:

• weak or deteriorating earnings,
• heavy leverage,
• poor stock performance relative to the market,
• elevated volatility,
• or a thin liquidity cushion.

For investors, the metric is useful in several ways.

First, it can serve as a screening tool. If you are reviewing a large list of stocks, PFD can help identify companies that may deserve extra caution before deeper analysis.

Second, it can serve as a risk-monitoring tool. A company whose PFD rises materially over several quarters may be experiencing worsening business conditions even if headline earnings still look acceptable.

Third, it can add context to valuation. A stock may appear cheap on earnings or book value, but if its PFD is elevated, the market may be discounting a real solvency risk rather than simply mispricing the shares.

That said, there is no universal cutoff that defines “safe” or “unsafe.” A 1% PFD may be high for a stable consumer staple company but low for a highly cyclical small-cap business. The most meaningful interpretation usually comes from comparing a company with its own history and with peers in the same industry.

Limitations of Probability of Financial Distress (%)

Like any model-based metric, Probability of Financial Distress (%) has important limitations.

It is a probability estimate, not a prediction

PFD does not say a company will or will not go bankrupt. It estimates risk based on historical relationships between certain variables and distress outcomes. Real-world outcomes can differ.

Market inputs can make it volatile

Because the model includes stock returns and volatility, PFD can change quickly when market sentiment shifts. That can be useful as an early warning signal, but it can also produce noise during broad market sell-offs or temporary price dislocations.

Industry context still matters

Although the model is broader than some traditional bankruptcy measures, industry structure still affects interpretation. Financial institutions, insurers, utilities, early-stage biotech firms and highly cyclical commodity businesses can all behave differently from the average public company used in distress modeling.

Accounting quality still matters

Several inputs rely on reported financial statement data. If earnings, liabilities or equity are distorted by unusual accounting, acquisitions, write-downs or one-time events, the resulting PFD may also be distorted.

It should not replace deeper credit analysis

PFD is useful for screening, but it is not a substitute for reviewing debt maturities, Interest Coverage, covenant risk, refinancing needs, Free Cash Flow and management’s capital allocation decisions.

For these reasons, investors should usually use PFD alongside other measures such as the Altman Z-Score, debt ratios, liquidity ratios and trend analysis.

Real-World Example

A useful way to understand Probability of Financial Distress (%) is to compare a financially resilient mega-cap with a more fragile, highly leveraged or volatile business.

Consider Apple (AAPL). Apple has historically combined strong profitability, enormous cash generation, deep market access and relatively stable investor confidence. Those characteristics tend to support a low PFD. Even if Apple carries substantial liabilities, its earnings power, liquidity and scale help offset distress risk in the model.

Now compare that with a smaller or more cyclical company whose earnings are inconsistent, whose stock has underperformed the market and whose share price is highly volatile. That company may post a much higher PFD even before a formal solvency problem appears. In other words, the model is designed to capture both current financial weakness and the market’s forward-looking concern.

This is why PFD can be especially helpful in spotting deterioration early. A company does not need to be in bankruptcy court for its distress probability to rise. Falling relative returns, rising volatility and weakening profitability can all push the metric higher well before the worst-case outcome occurs.

For investors, the key lesson is not that one number alone determines safety. It is that a company with a persistently low PFD usually has a stronger margin of financial safety than one with a rising or elevated PFD, especially when that higher reading is confirmed by weak balance-sheet and cash-flow metrics.

FAQs

What is a good Probability of Financial Distress (%)?

• Lower is generally better, but there is no universal threshold that applies to every company or industry. In most cases, a very low single-digit reading suggests relatively low near-term distress risk, while a much higher reading warrants closer review. The best comparison is against the company’s own history and industry peers.

What is the difference between Probability of Financial Distress (%) and related metrics?

• Probability of Financial Distress (%) is a model-based estimate of near-term distress risk that combines accounting and market variables. The Altman Z-Score is also a bankruptcy-risk measure, but it relies more heavily on accounting ratios and working-capital concepts. The Piotroski F-Score is different again: it measures financial strength and operating improvement rather than estimating bankruptcy probability directly.

Can Probability of Financial Distress (%) be negative?

• No. The final PFD value is expressed as a percentage probability between 0% and 100%. However, the underlying linear score, LPFD, can be negative before it is transformed by the logistic function.

How should investors use Probability of Financial Distress (%)?

• Investors should use it as a screening and monitoring tool, not as a standalone decision rule. It is most useful when combined with debt analysis, liquidity review, profitability trends, peer comparisons and other risk metrics.

Summary

Probability of Financial Distress (%) is a practical way to estimate a company’s near-term bankruptcy or distress risk using both financial statement data and market-based signals. On GuruFocus, it is displayed as PFD and calculated using a logistic model built from profitability, leverage, liquidity, relative returns, volatility, size, valuation and price.

Its biggest strength is that it can capture warning signs that do not show up in a single accounting ratio. A rising PFD may indicate that the market and the fundamentals are both pointing toward greater financial stress. Its biggest weakness is that it remains a model, not a certainty.

For that reason, PFD is best used as part of a broader risk framework. When combined with trend analysis, peer comparisons and deeper balance-sheet work, it can be a valuable tool for identifying companies whose financial condition may be stronger or weaker than it first appears.

Sources

1. John Y. Campbell, Jens Hilscher, and Jan Szilagyi, “In Search of Distress Risk,” The Journal of Finance. Available at: https://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.2008.01416.x
2. National Bureau of Economic Research, “In Search of Distress Risk” working paper. Available at: https://www.nber.org/papers/w12362
3. Investopedia, “Altman Z-Score Formula and How It Works.” Available at: https://www.investopedia.com/terms/a/altman.asp
4. Apple Inc. investor relations, annual reports and filings. Available at: https://investor.apple.com/financials/default.aspx
5. U.S. Securities and Exchange Commission, EDGAR company filings database. Available at: https://www.sec.gov/edgar/search/