To evaluate Bloom Energy (NYSE: BE) risk, use a dated, consistent price series and separate two calculations: volatility, which measures how widely periodic returns vary, and drawdown, which measures the fall from a previous high-water mark to a later low. State the lookback dates, daily or weekly frequency, adjusted or unadjusted closes, volatility formula and annualization convention. Then compare BE with a broad-market benchmark on the same basis and examine recovery time. The available company disclosures provide risk context, but they do not establish a current volatility or maximum-drawdown number without a usable price series.
What volatility and drawdown tell you
Volatility and drawdown answer different investor questions. Volatility asks, “How much do returns typically fluctuate during this interval?” Drawdown asks, “How far did the investment fall from a prior peak before reaching a trough?” A stock can record very large daily swings yet remain near its high-water mark, or it can suffer a prolonged decline with relatively moderate day-to-day movements.
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| Measure | What it measures | Basic calculation | What it does not tell you |
|---|---|---|---|
| Periodic volatility | Dispersion of returns over a selected frequency and window | Standard deviation of consecutive-period returns | Whether the next move will be up or down, or the largest possible loss |
| Maximum drawdown | Largest peak-to-subsequent-trough loss in the window | Lowest value of current price divided by running peak minus one | How frequently prices fluctuate between the peak and trough |
| Recovery time | Time from a trough until the prior peak is regained | Count calendar or trading days to a new high | Whether another drawdown occurs before recovery |
Always identify the observation window. “BE volatility” is incomplete without dates, frequency and price convention. A one-year daily estimate and a three-year weekly estimate are different statistics, not competing answers.
Choose a reproducible price series
Set the start and end dates
Pick exact dates, such as the most recent three complete calendar years or the 12 months ending on a stated analysis date. Record whether the end date is the latest trading day available or a month-end. Re-running the same method with a different end date can materially change both volatility and drawdown.
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Decide between adjusted and unadjusted closes
An adjusted close normally reflects splits and, where the data vendor applies them, distributions. An unadjusted close is the quoted closing price for that session. Use one convention for BE and every benchmark, and state whether dividends and splits are included. Mixing adjusted BE prices with unadjusted benchmark prices makes relative-risk conclusions unreliable.
Bloom Energy’s investor-relations site provides a historical-price lookup, but the rendered page reviewed for this analysis did not expose a downloadable series. Before publishing a current numerical result, obtain a dated series from a reliable market-data source, verify corporate-action treatment and retain the extraction date. If only a rendered lookup is available, manually transcribing a few observations is not an adequate basis for a multi-year statistic.
Use closing prices consistently
The standard procedure below uses closing prices. An intraday low can be useful for a separate stress analysis, but it is not the same as a closing-price drawdown. Do not report an intraday percentage as if it came from daily closes.
Calculate return volatility step by step
1. Convert prices to periodic returns
For prices Pt and Pt-1, the simple return is:
rt = (Pt / Pt-1) − 1
Log returns are:
ln(Pt / Pt-1)
Choose one method and use it for BE and the benchmark. Daily observations generally produce more data points; weekly observations reduce the influence of individual trading sessions and can be useful when the question concerns medium-term variability. Do not mix daily BE returns with weekly benchmark returns.
2. Compute standard deviation
For n returns and sample mean r̄, sample standard deviation is:
s = √[Σ(rt − r̄)2 / (n − 1)]
Report the unannualized result if the reader needs the dispersion of one trading day or one week. A daily standard deviation is not directly comparable with an annual figure until you apply a stated annualization convention.
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3. Annualize only when the convention is clear
For daily returns, a common convention is:
annualized volatility = daily s × √252
For weekly returns, a common convention is:
annualized volatility = weekly s × √52
The factors 252 and 52 are conventions for trading days and weeks, not guarantees of the number of observations in your file. If your source contains holidays, missing sessions or a partial period, document how you handled them. Annualization scales historical dispersion; it does not forecast future volatility.
4. Inspect the return distribution
A single standard deviation can hide asymmetric losses and occasional extreme moves. Add the largest positive and negative periodic returns, the median return and selected percentiles when the dataset is large enough. These diagnostics do not replace standard deviation; they show whether the average measure is being driven by a few sessions.
5. Use rolling estimates for changing risk
A full-window statistic blends calm and turbulent periods. A 30-session or 90-session rolling standard deviation can show when realized risk expanded or contracted. Label the window length and frequency, and avoid presenting a rolling peak as a permanent characteristic of BE.
Calculate drawdowns and maximum drawdown
1. Build the running high-water mark
For each date, calculate the highest closing price observed up to that date:
Ht = max(P1, P2, …, Pt)
The drawdown on date t is:
Dt = (Pt / Ht) − 1
Values are zero at a new high and negative after a decline. The maximum drawdown is the most negative Dt in the selected window.
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2. Name the peak and trough
A useful report identifies the peak date, peak closing price, trough date, trough closing price and percentage decline. If several dates share the same closing high or low, state your tie rule, such as the first occurrence. Keep the price convention in the same table or sentence so readers do not mistake an adjusted-price result for a raw close.
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3. Measure recovery separately
After the trough, find the first date on which the price equals or exceeds the prior peak. The elapsed trading days or calendar days are the recovery time. If the analysis end date arrives before that event, report “not recovered by the analysis date” rather than treating the remaining interval as a recovery duration.
A 50% drawdown requires a 100% gain from the trough to regain the old high; a 20% drawdown requires a 25% gain. This arithmetic is why recovery deserves its own measure rather than being inferred from volatility.
What can be stated about BE now
The 291% 2025 figure is a gain, not a risk statistic
Bloom Energy’s 2026 proxy-statement search result reports a 291% price increase for Bloom Energy Corporation during 2025. That is a reported year-over-year price increase. It is not annualized volatility, not a maximum drawdown and not evidence that losses were small during the year. A stock can rise 291% over a year while experiencing several severe interim declines.
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No current realized-volatility or maximum-drawdown number is established here
A usable dated adjusted-price series was not exposed in the reviewed pages, so no defensible current realized-volatility or maximum-drawdown figure is supplied. Do not fill that gap with a chart impression or with the 291% annual gain. Calculate the measures from a verified series immediately before publication and attach the exact date range.
Freshness matters
The latest periodic filing identified for this analysis is Bloom Energy’s Form 10-Q/A for the quarter ended June 30, 2026, filed July 29, 2026. Price statistics change with every trading session, and filings can add new information. Check the investor-relations section and public company communications—including SEC filings, releases, presentations and calls or webcasts—again before relying on a result.
Interpret BE’s business disclosures without claiming causation
Bloom Energy’s 2025 Form 10-K states, “Investing in our securities involves a high degree of risk.” That is the company’s own risk disclosure, not an independent risk rating. The filing warns that company risks can adversely affect business prospects, financial condition, cash flows and liquidity and may cause the common-stock price to decline.
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Use the filing as an investigation checklist
- Review the risk factors for dates when a filing, earnings release or other announcement coincided with an unusually large return.
- Check the filing’s discussion of liquidity, cash flows and operating results when interpreting a prolonged drawdown.
- Track bookings seasonality: Bloom reports that most completed bookings occur in the second half of its fiscal year. A quiet first half is not automatically evidence of deteriorating demand.
- Monitor changes in government, regulatory and tax incentives. The filing identifies these incentives as subject to change, so policy dates can be relevant event-study candidates.
These disclosures identify factors worth testing against the price timeline. They do not prove that a particular factor caused a particular daily return. To make a causal statement, you need event-specific evidence, such as a dated announcement and a defined comparison window.
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Select a defensible benchmark
A broad-market index can show whether BE’s realized risk exceeded the market over the same dates. An energy-sector index or peer basket can add context, but choose constituents for genuine business comparability rather than because their charts look alike. Document the benchmark symbol, index methodology and whether the series is a total-return or price-only index.
Align every input
| Item | BE | Benchmark |
|---|---|---|
| Observation window | Same start and end dates | Same start and end dates |
| Price convention | Adjusted or unadjusted close | Same convention, or an explicitly comparable total-return series |
| Frequency | Daily or weekly | Identical frequency |
| Volatility method | Simple or log returns; sample standard deviation | Identical method |
| Annualization | For example, √252 for daily data | Same factor |
| Drawdown basis | Closing-price running peak | Closing-price running peak |
| Recovery rule | First close at or above prior peak | Same rule |
Report absolute and relative results. For example, say that BE’s annualized volatility was higher or lower than the benchmark by a stated percentage-point difference, then provide each underlying number. A comparison without the underlying convention is not reproducible.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical calculation workflow
- Define the question. Decide whether you need short-term trading risk, a one-year investor experience or a multi-year cycle analysis.
- Set dates. Record the first and last trading dates and the analysis timestamp.
- Obtain prices. Download or export BE and benchmark closes from a source that documents adjustments. Preserve the raw file.
- Validate the series. Check for duplicate dates, missing sessions, obvious split discontinuities and mismatched currencies or time zones.
- Choose frequency. Keep daily data daily, or resample both securities to the same weekly rule, such as Friday close.
- Calculate returns. Apply one return definition to every series and remove the first row, which has no prior observation.
- Calculate volatility. Compute sample standard deviation, then annualize only if the report states the factor.
- Calculate drawdown. Build the running maximum, divide each close by that maximum and identify the minimum result.
- Calculate recovery. Search after the trough for the first close at or above the named peak.
- Run quality checks. Recalculate with a second tool or spreadsheet, inspect extreme observations and verify peak and trough dates against the source.
- Write the result. Put the window, convention, formulas, numbers, peak/trough dates and recovery status next to the statistic.
Common mistakes and how to fix them
- Calling a one-year gain “low volatility.” Fix: report standard deviation of periodic returns; keep the 291% 2025 gain in a separate performance section.
- Using the all-time high when the study starts later. Fix: calculate the running high only inside the declared lookback window, or explicitly label an all-history analysis.
- Mixing intraday and closing data. Fix: choose closing prices for the main series and label any intraday stress result separately.
- Mixing adjusted and unadjusted prices. Fix: use the same convention across BE and the benchmark and explain dividend and split treatment.
- Annualizing weekly data with √252. Fix: use the factor that matches the observation frequency and state it.
- Ignoring missing dates. Fix: inspect the calendar, align securities by common sessions and document holidays or gaps.
- Treating a filing risk factor as proof of a price catalyst. Fix: require a dated disclosure and event-specific analysis before assigning causation.
- Reporting a drawdown without recovery status. Fix: state whether the prior high was regained by the analysis date and how recovery time was measured.
- Using a chart’s visual scale as a measurement. Fix: calculate from numerical observations and retain the underlying file.
How to present the finished analysis
A transparent BE risk table should contain at least the following fields:
| Field | Required detail |
|---|---|
| Security | Bloom Energy (BE) and the benchmark name or symbol |
| Window | Exact start and end dates |
| Price basis | Adjusted or unadjusted close; dividend and split treatment |
| Frequency | Daily or weekly |
| Return definition | Simple or log |
| Volatility | Standard deviation and annualized value, if used |
| Maximum drawdown | Percentage, peak date and trough date |
| Recovery | Recovery date and elapsed time, or not recovered by the end date |
| Data freshness | Last observation date and extraction date |
If a comparable value is unavailable, write “not stated” and identify the missing input. Do not substitute a different statistic merely to complete a table.
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High volatility with shallow drawdown
This pattern can occur when large gains and losses alternate around a rising trend. Position sizing and stop-loss assumptions may matter more than a single maximum-drawdown number, but avoid treating historical behavior as a guarantee.
Moderate volatility with a deep drawdown
A long, orderly decline can produce an ordinary standard deviation while still imposing a large capital loss. Recovery time and the gain required to regain the peak become central.
BE risk above the benchmark
Higher realized volatility or a deeper benchmark-relative drawdown indicates greater historical risk over that specific window. It does not establish that the difference will persist, nor does it identify the business cause.
Short window versus long window
A short window reacts quickly to current events but can be dominated by a few observations. A long window captures more cycles but may dilute a recent regime change. Present both only when they answer distinct questions and label each clearly.
Minimum evidence standard before publishing a current number
- A dated BE series with documented adjusted or unadjusted treatment.
- The identical date range and convention for the benchmark.
- Visible formulas or code that another reader can reproduce.
- Peak and trough dates checked against the source closes.
- A recovery test through the last available observation.
- A note that volatility is backward-looking and does not predict direction or future loss size.
- Fresh review of the latest filings and investor communications, including the Form 10-Q/A filed July 29, 2026, if it remains the latest report.
Until those checks are complete, the accurate conclusion is methodological: BE’s volatility and drawdown must be calculated from a specified, comparable series. The 291% increase reported for 2025 demonstrates that a strong annual result can coexist with substantial interim risk; it cannot answer the volatility or maximum-drawdown question by itself.
Frequently Asked Questions
What is the difference between BE volatility and BE drawdown?
Volatility is the standard deviation of periodic returns over a stated window. Drawdown is the percentage decline from a running prior peak to a later trough. They measure different dimensions of risk and should be reported separately.
Does Bloom Energy’s 291% increase in 2025 mean the stock was low risk?
No. The 291% figure reported in Bloom Energy’s 2026 proxy statement is a one-year price increase. It is neither a volatility statistic nor a peak-to-trough drawdown measure.
Can I quote a current maximum drawdown without downloading prices?
No. A defensible maximum drawdown requires a dated price series, a stated adjusted or unadjusted convention, a defined lookback window and explicit peak and trough dates.
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Use the same dates, price convention, return frequency, volatility formula, annualization factor and closing-price drawdown rule for BE and the benchmark. State whether each benchmark series is price-only or total return.
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