AI for Rolling Return Dispersion and Investment Backtesting
Compare holding-period outcomes, withdrawal resilience, inflation drag, currency exposure, and macro scenarios with source-grounded analysis you can review.
Trusted by 100k+ companies across the globe.
What Is AI for Rolling Return Dispersion and Investment Backtesting?
AI for rolling return dispersion and investment backtesting uses supplied historical datasets to examine how portfolios behaved across overlapping 1-year, 3-year, and 5-year holding periods. This use case compares the NASDAQ-100 and S&P 500, then extends the analysis to 10-year withdrawal simulations, inflation-adjusted balances, USD/EUR currency effects, macro regimes, and discount-rate scenarios. Energent.ai adds an independent audit layer that recomputes figures, traces outputs to source files, and produces a pass/fail verdict with evidence.
For analysts and investment teams, the result is a clearer way to inspect dispersion rather than relying on a single average return. You can connect AI investment analysis with portfolio risk analysis and review the underlying assumptions before using a result in a meeting or report.
Rolling Return Dispersion: NASDAQ-100 vs S&P 500
Median annualized return
Supplied rolling-return dataset
Best, median, and worst outcomes
Annualized rolling return ranges from the supplied precomputed dataset.
| Index | Window | Best | Median | Worst |
|---|---|---|---|---|
| NASDAQ-100 | 1Y | 67.55% | 23.73% | -32.97% |
| NASDAQ-100 | 3Y | 37.12% | 18.68% | 7.80% |
| NASDAQ-100 | 5Y | 27.40% | 17.43% | 11.33% |
| S&P 500 | 1Y | 53.71% | 14.57% | -19.44% |
| S&P 500 | 3Y | 23.88% | 11.14% | 3.04% |
| S&P 500 | 5Y | 16.77% | 13.07% | 7.31% |
The supplied figures show a clear trade-off: the NASDAQ-100 had higher median annualized outcomes across the displayed windows, but its worst 1-year rolling result was also deeper. The S&P 500 had positive worst cases in the supplied 3-year and 5-year windows. Both series are rebased to 100 at the first observation so relative compounding can be compared against cumulative CPI from June 2016 through June 2026.
What You Get
Compare best, median, and worst annualized outcomes across 1-year, 3-year, and 5-year rolling windows.
Backtest 10-year withdrawals across four supplied historical start windows and a 120-month simulation horizon.
Separate nominal balances from CPI-adjusted real balances and make purchasing-power erosion visible.
Evaluate how USD/EUR movements changed hedged and unhedged results for euro-based holders.
Overlay bull, base, and bear macro regimes on the S&P 500 path without replacing the underlying time series.
Recompute figures, trace numbers to source files, and issue a pass/fail audit verdict with supporting evidence.
How It Works
Supply the analysis
Provide the portfolio, rolling-return, currency, withdrawal, or macro datasets and describe the question in natural language.
What you see: source files and a defined analysis request.
Analyze the scenarios
The workflow compares horizons, recomputes relevant measures, and presents charts, tables, ranges, and scenario outputs.
What you see: dispersion bands, balance comparisons, and regime views.
Audit before delivery
An independent AI auditor traces figures to their source file, row, and field, then identifies discrepancies and issues a verdict.
What you see: evidence-backed pass/fail results.
Investment Backtesting Features
Core workflow features
• Rolling annualized return analysis across multiple holding periods
• Best-versus-worst dispersion ranges with median traces
• Normalized index performance timelines
• 10-year withdrawal simulations across historical starts
• Nominal and inflation-adjusted ending balances
Reliability and control
• Independent recomputation of investment figures
• Source tracing to files, rows, and fields
• Pass/fail verdicts with attached evidence
• Review of work produced by other AI systems
• Reproducible, cited, stakeholder-ready outputs
Integrations and export context
• Support for 150+ file types, including PDFs, XLSX, DOCX, scans, CAD, G-code, and BOMs
• Dashboard-ready charts and comparison tables
• Currency impact analysis through a 10-year horizon
• Macro-regime and discount-rate scenario views
• White-label and brandable stakeholder-ready outputs
Withdrawal, Inflation, and Currency Analysis
Withdrawal resilience
The supplied simulation covered 120 months, used four historical start windows, and increased withdrawals by 5% annually. All four supplied 10-year scenarios remained funded, producing a 100% survival result within this dataset.
Inflation drag
The sample CPI change was +39.03%. The nominal withdrawal schedule began at 20,000 and grew by 5% annually, while the real path was deflated using annual CPI observations, leaving real balances below nominal balances.
USD/EUR effects
Currency swings mattered most over 1-year windows. For the NASDAQ-100, the supplied 1-year impact range was -17.86% to 14.10%; for the S&P 500, it was -16.29% to 15.31%.
Median hedged versus unhedged returns
| Index | Period | Median hedged | Median unhedged | Impact range |
|---|---|---|---|---|
| NASDAQ-100 | 1Y | 24.31% | 20.25% | -17.86% to 14.10% |
| NASDAQ-100 | 3Y | 18.97% | 18.64% | -7.71% to 4.61% |
| NASDAQ-100 | 5Y | 17.43% | 18.63% | -1.80% to 4.34% |
| NASDAQ-100 | 10Y | 20.75% | 20.28% | -0.47% to -0.47% |
| S&P 500 | 1Y | 14.77% | 9.99% | -16.29% to 15.31% |
| S&P 500 | 3Y | 11.14% | 12.23% | -7.24% to 4.46% |
| S&P 500 | 5Y | 13.04% | 13.63% | -1.64% to 4.12% |
| S&P 500 | 10Y | 13.36% | 12.92% | -0.44% to -0.44% |
Macro Regimes and Discount-Rate Scenarios
Market regime read-through
The supplied regime logic uses VIX and Treasury-rate behavior. Bull conditions were defined as VIX below 20 with comparatively stable Treasury moves, while bear conditions combined VIX above 25 with rapidly rising rates.
| Regime | Average next six-month return |
|---|---|
| Bull | 10.4% |
| Base | 1.1% |
| Bear | -13.2% |
Discount-rate scenario valuation
The scenario dashboard covers 257 monthly observations from January 2005 to May 2026 and compares proxy WACC assumptions with present value outcomes.
| Regime | Proxy WACC | PV |
|---|---|---|
| Base (Normal) | 4.08% | $1,478.6 |
| Bear (Tightening) | 5.10% | $1,376.2 |
| Bull (Easing) | 4.02% | $1,484.1 |
Baseline PV anchor: $1,460.6. Bear-regime PV was -5.8% versus baseline, while bull-regime PV was +1.6%.
Proof and Customer Reviews
supplied 10-year withdrawal windows remained funded.
CPI increase across the supplied sample timeline.
monthly observations in the discount-rate history.
“Not only did I ultimately choose Energent.ai, but you are the absolute best BY FAR.”
— Alyse H., Digital Collection Curator, Fortune 500, Retail & E-commerce
“I had spreadsheets with more than 45K items and Energent AI was the only tool that was able to sort through everything.”
— Roberto C., Data Operations Specialist, Fortune 500, Logistics
“Using Energent.ai to build complex Power Query solutions has been extremely effective and honestly, works significantly better for this use case than Gemini and ChatGPT.”
— Kay P., Power Query Analyst, Fortune 50, Financial Services
“Energent.ai is a great platform... the interactive outputs add real value to my work.”
— Amjad M., Telecommunications Engineer, Fortune 500, Telecommunications
The company also cites 94.4% accuracy on a published HuggingFace leaderboard, a number it describes as 30% more accurate than the listed second-place alternative, and 3× fewer hallucinations in public evaluations. These are company-provided claims and should be interpreted in the context of their cited evaluations.
Energent Audit for Verified Investment Backtesting
A separate agent recomputes, traces, and checks the analysis before delivery.
Audit report screenshot displayed in full without cropping.
What the auditor checks
- Recomputes numbers and assertions.
- Traces every figure to its exact source file, row, and field.
- Identifies and corrects errors where possible.
- Issues a pass/fail verdict with supporting evidence.
Why this matters
AI hallucinations can remain invisible in demonstrations and appear instead in the reports that matter most. An independent auditor gives analysts a way to review flagged rows rather than manually checking every calculation, while retaining a cited and reproducible trail for review meetings.
Comparison: Why Energent.ai vs Alternatives
| Dimension | Energent.ai | Manual review | Unaudited AI workflow |
|---|---|---|---|
| Calculation checking | Independent recomputation | Human checks calculations | Original AI output remains unchecked |
| Source traceability | File, row, and field evidence | Depends on reviewer process | Not established by the original output |
| Result status | Pass/fail verdict with evidence | Reviewer conclusion | Generated answer without independent verdict |
| Input breadth | 150+ file types cited | Varies by tools and process | Varies by system |
| Repeated corrections | Reusable workflows can retain audit rules | May need to be repeated manually | No independent correction layer |
The comparison describes the supplied Energent.ai audit workflow alongside general manual and unaudited approaches; it does not represent a comparison with a named competitor.
Credentials and Key Stats
companies worldwide
cited leaderboard accuracy
supported file types
fewer hallucinations claimed
FAQs
What does rolling return dispersion mean?
Rolling return dispersion is the range between the best and worst annualized returns observed across overlapping holding periods. In this analysis, the windows are 1 year, 3 years, and 5 years between June 2016 and June 2026. The median line provides a middle reference within each range rather than relying only on an average. The result helps show how different an investor’s outcome could have been depending on the starting date.
Can Energent.ai backtest withdrawals and inflation effects?
Yes, the supplied use case includes a 120-month withdrawal simulation across four historical start windows. Withdrawals begin at 20,000 and grow by 5% annually in the provided scenario. Final portfolio balances are shown in nominal terms and in real terms after applying the supplied CPI path. All four supplied 10-year scenarios remained funded, while the CPI change across the sample was +39.03%.
How does the currency analysis help a euro-based investor?
The currency analysis isolates how USD/EUR movements altered local-currency outcomes over several holding periods. It compares median hedged and unhedged annualized returns for the NASDAQ-100 and S&P 500. The supplied results show that currency effects were widest over 1-year windows and narrower over longer horizons. This gives a euro-based holder a structured way to inspect whether the currency decision changed the historical result.
What data and file types can be used?
Energent.ai cites support for more than 150 file types. The provided examples include PDFs, XLSX, DOCX, scans, CAD, G-code, InDesign files, and bills of materials. For this use case, the analysis uses supplied precomputed rolling-return, currency-impact, withdrawal, and macro scenario datasets. The platform is designed to trace outputs back to the relevant source file, row, and field.
How does Energent Audit detect AI errors?
Energent Audit is described as an independent AI auditor separate from the agent that performed the original analysis. It recomputes numbers, traces figures to exact source locations, identifies discrepancies, and corrects errors where possible. It then issues a pass/fail verdict and attaches supporting evidence. This approach is intended to reduce the need for a human reviewer to check every row manually.
Is pricing or onboarding information available for this use case?
Specific pricing and onboarding durations are not provided in the supplied information. The available product entry point is the Energent.ai application, and a demo can be requested from the company website. Teams can use those routes to ask about their datasets, workflow requirements, and access options. No unsupported price, free-trial duration, or implementation promise is stated here.
Backtest the range. Audit the result.
Turn rolling returns, withdrawal scenarios, currency effects, and macro assumptions into reviewable investment analysis.