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Walmart
Consumer Staples · Consumer Staples Merchandise Retail

Global retailer operating hypermarkets, discount stores, wholesale clubs, and e-commerce platforms for everyday consumer goods.

HQ: United StatesListed: United States

Walmart (WMT) Stock Analysis: Compare AI Forecasts & Scenarios

Walmart has 14 published AI advisor reports. Compare stock forecasts, individual ratings, price targets and bull/bear scenarios, separately from the consensus AI rating.

Updated on 26 October 2026Deep analysis 21 September 2026

Approved byRusslan Ramdowar, Editor-in-Chief
25 min readAudit All Past Forecasts

Walmart AI Forecast Summary

14 advisor opinions · Base subscription

Unlock the rating distributions, return ranges and advisor comparison with a Base subscription.

1 Year Ratings & Returns

Ratings: 11 Sell All · 3 Partial Sell

Return range: -23.7% to -9.4%

14 downside · 0 lateral · 0 upside

14 comparable price-return forecasts · Target

5 Year Ratings & Returns

Ratings: 9 Partial Sell · 5 Neutral

Return range: -13.9% to +11.1%

6 downside · 1 lateral · 7 upside

14 comparable price-return forecasts · Target

From $121.25 · End Of Date

What do advisors disagree on

The primary disagreement across reports centers on whether high-margin advertising and automated logistics justify sustaining an earnings multiple near thirty-nine times in a five percent interest rate environment. More constructive perspectives argue that retail media structurally transforms earnings quality into software-like margins, supporting thirty-plus multiples. Conversely, skeptical analyses emphasize that grocery merchandise represents eighty percent of volume and requires twenty-six billion dollars in capital expenditures, leaving free cash flow yields too thin to resist multiple compression toward historical norms. Resolving this tension requires tracking whether consolidated operating margins break sustainably above five percent.

Compare all 14 opinions

1 Year & 5 Year ratings, targets & returns · Swipe table to compare

AdvisorModelResearch1 Year5 YearFull Report
Target dateRatingPrice targetPrice returnReturn incl. dividendsTarget dateRatingPrice targetPrice returnReturn incl. dividends
Universal Investor AI The Polymath FrameworkGPT-6 AstraResearcher Mode with Web Search UsedSell All$102.24-15.7%-15.3%Neutral$134.75+11.1%+14.2%Full report
Universal Investor AI The Polymath FrameworkClaude Opus 5Researcher Mode with Web Search UsedPartial Sell$109.85-9.4%-8.9%Neutral$133.87+10.4%+13.5%Full report
Universal Investor AI The Polymath FrameworkGemini 3.8 FlashResearcher Mode but Web Search Wasn't UsedSell All$100.42-17.2%-16.8%Neutral$127.41+5.1%+8.0%Full report
J.P. Morgan AI The Titan FrameworkGemini 3.8 FlashResearcher Mode with Web Search UsedPartial Sell$108.85-10.2%-9.8%Neutral$130.15+7.3%+10.3%Full report
Superintelligence AI The Anthropologist FrameworkGemini 3.8 FlashResearcher Mode but Web Search Wasn't UsedPartial Sell$107.77-11.1%-10.7%Neutral$126.34+4.2%+7.1%Full report
Universal Investor AI The Polymath FrameworkGemini 3.8 FlashThinker Mode with Web Search DisabledSell All$106.65-12.0%-11.6%Partial Sell$124.78+2.9%+5.8%Full report
Ray Dalio AI The Strategist FrameworkGemini 3.8 FlashResearcher Mode with Web Search UsedSell All$106.70-12.0%-11.6%Partial Sell$123.74+2.1%+4.9%Full report
Niccolo Machiavelli AI The Insider FrameworkGemini 3.8 FlashResearcher Mode with Web Search UsedSell All$103.54-14.6%-14.2%Partial Sell$121.36+0.1%+2.9%Full report
Superintelligence AI The Anthropologist FrameworkGemini 3.8 FlashThinker Mode with Web Search DisabledSell All$101.45-16.3%-15.9%Partial Sell$119.97-1.1%+1.7%Full report
Warren Buffett AI The Value Seeker FrameworkGemini 3.8 FlashThinker Mode with Web Search DisabledSell All$104.58-13.7%-13.3%Partial Sell$118.92-1.9%+0.8%Full report
Sherlock Holmes AI The Whistleblower FrameworkGemini 3.8 FlashResearcher Mode with Web Search UsedSell All$104.52-13.8%-13.4%Partial Sell$114.22-5.8%-3.2%Full report
Elon Musk AI The Visionary FrameworkGemini 3.8 FlashResearcher Mode but Web Search Wasn't UsedSell All$98.37-18.9%-18.5%Partial Sell$109.52-9.7%-7.1%Full report
Michael Burry AI The Vulture FrameworkGemini 3.8 FlashResearcher Mode but Web Search Wasn't UsedSell All$92.55-23.7%-23.3%Partial Sell$108.39-10.6%-8.1%Full report
Warren Buffett AI The Value Seeker FrameworkGemini 3.8 FlashResearcher Mode but Web Search Wasn't UsedSell All$100.42-17.2%-16.8%Partial Sell$104.35-13.9%-11.5%Full report
View all 14 opinions

All 14 opinions are displayed in the table above.

Prices, dates & return assumptions

1 Year and 5 Year identify the original published checkpoints. Current returns compare the dated close with each fixed target over its remaining horizon. Original quarterly tables use the publication-time anchor. Targets and observed prices share a split-adjusted basis.

Lateral means a price return from -1% to +1%, inclusive. Downside is below -1%; upside is above +1%. Ratings use the shared volatility-adjusted calculation and can differ from these price-direction categories.

Dividend returns model a 0.57% annual net yield, reinvested over the remaining time to target.

Ranges and rating distributions use matching currencies, price bases and target dates. Missing or expired checkpoints are excluded; targets are never extrapolated. Shared frameworks and models can produce correlated opinions.

How this research is produced

iPulse AI analyzes each asset through structured investment frameworks using curated data and frontier models. Individual reports feed consensus, rankings, and reviewed selections. Past forecasts are preserved for evaluation as outcomes emerge.

Walmart Stock Analysis & Forecast — Universal Investor The Polymath Framework on GPT-6 Astra Model

Universal Investor AI advisor icon

Universal Investor AI · The Polymath Framework

GPT-6 Astra AI Researcher Web Search Used

5 Year Rating

Neutral

5 Year Price Return

+11.1%

Incl. net dividends

+14.2%

Framework Description & Key Record Dates

Investment Framework

The Polymath Framework: Eight-Lens Investment Analysis Framework

Analysis Framework Description: Select the lenses with the greatest explanatory power, resolve their disagreements across horizons, and connect the evidence to a consistent thesis, scenario path, and verdict.

  • Ownership and value: examine economic rights, competitive advantage, capital allocation, and the price paid.
  • Regimes and constraints: connect credit, liquidity, policy, demand, and physical bottlenecks to the asset.
  • Innovation and adaptation: test adoption economics, AI and robotics exposure, substitution, and value capture.
  • Forensics and incentives: reconcile accounting, cash conversion, dilution, governance, and missing evidence.
  • Expectations and reflexivity: distinguish changes in fundamentals from changes in narratives and valuation.
  • Probabilities and resilience: use base rates, competing scenarios, catalysts, and the capacity to survive the wait.
  • Power and the rules of the game: assess political interests, regulatory influence, protection, and dependency.
  • Industry structure and consolidation: identify pricing power, chokepoints, alliances, and the economics of integration.
Full framework methodology
Model Producer:
OpenAI
Model Version:
GPT-6 Astra
Mode Used:
Researcher
Web Search Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor. Dividend-inclusive returns assume 0.57% annual net yield, reinvested until the target.

Read full advisor report

Walmart: Historical Prices & Published Forecast (The Polymath Framework on GPT-6 Astra model)

Walmart: Historical Prices & Published Forecast (The Polymath Framework on GPT-6 Astra model)Observed prices and the selected advisor's published projection share a split-adjusted price basis. Prices after the forecast start are later observations, not information known at publication. Values in USD.30.458.8687.32115.77144.23Sep 2021Mar 2024Sep 2026Mar 2029Sep 2031Forecast startsToday (UTC)
  • Observed price
  • Published advisor forecast
Observed prices and the selected advisor's published projection share a split-adjusted price basis. Prices after the forecast start are later observations, not information known at publication. Values in USD.

Walmart: Quarterly Price Forecasts & Scenarios (The Polymath Framework on GPT-6 Astra model)

Price targets, price returns and complete scenario reasoning

Forecast prices in USD. Price returns are cumulative from the original forecast anchor dated 2026-09-18; they exclude dividends. Swipe horizontally to read every column.

QuarterForecastPrice returnScenario
$100-6.0%

Holiday demand remains resilient, but price reinvestment and elevated delivery costs prevent investors from annualizing the refund-assisted profit surge. The projected decline reflects a lower forward multiple, not an assumption that Walmart loses its value proposition.

$97.3-8.8%

FY2028 guidance is projected to emphasize another year of elevated investment, limiting hopes for an immediate free-cash-flow inflection. Earnings still advance, but investors trim the premium paid for growth whose cash realization remains deferred.

$100-6.1%

Spring trading is projected to show steady transactions and better digital contribution margins. Advertising and membership offset softer discretionary mix, prompting modest earnings upgrades; the stock recovers part of its earlier valuation decline without regaining peak multiples.

$102-4.2%

As tariff-refund comparisons become harder, recurring advertising and fulfillment gains provide a cleaner earnings signal. Investors reward modest underlying progress rather than headline growth. Nontrading period-end labels refer to the latest available trading-session close.

$105-1.3%

Holiday order density improves utilization of automated capacity, while membership supports repeat demand. The projected gain comes from stronger incremental profit per delivery, with competitive pricing preventing a disproportionate rerating of the core merchandise business.

$107+0.6%

FY2029 planning is projected to show capital spending growing more slowly than revenue, although absolute investment remains substantial. Better prospective cash conversion supports the shares, while the market waits for commissioning benefits to survive another operating cycle.

$105-1.4%

Rivals are assumed to intensify delivery promotions as Walmart's economics improve. Management reinvests part of the savings to protect retention, causing a temporary earnings-estimate pause; the share-price setback reflects competitive sharing of productivity rather than failed automation.

$108+1.6%

Automated fulfillment gains begin to outweigh the prior quarter's promotional pressure. Lower handling cost and improved inventory placement strengthen cash conversion, allowing investors to distinguish durable operating benefits from savings temporarily recycled into customer acquisition.

$112+4.6%

Holiday marketplace activity expands monetizable advertising inventory without requiring Walmart to own every additional product. Higher fee income supports profit growth, while first-party merchandising remains price competitive; the stock advances primarily with normalized earnings estimates.

$114+6.7%

FY2030 guidance is projected to confirm disciplined reinvestment and improving free cash flow after several years of heavy deployment. Investors accept slower digital revenue growth because fulfillment contribution and fee-income quality matter more at this larger scale.

$116+8.9%

Supplier analytics and retail-media targeting are projected to improve advertiser retention, supporting earnings without proportional inventory requirements. The shares rise modestly as higher-margin revenue diversifies profit, although maturation and advertiser budget discipline keep valuation expansion limited.

$117+10.0%

Membership growth moderates against a larger base, and renewal economics become more important than subscriber additions. Recurring fees still support earnings, but reduced growth optionality limits the stock's advance as investors demand evidence of sustained cash conversion.

$121+13.2%

Peak-season utilization strengthens the return on earlier automation spending, improving delivery contribution without equivalent fixed-cost growth. The projected appreciation follows stronger cash earnings, while management preserves price gaps rather than maximizing short-term merchandise margins at competitors' expense.

$123+15.5%

FY2031 capital plans are projected to shift toward maintenance, selective capacity additions and technology refreshes. This releases more cash for shareholders, but replacement investment remains necessary; the stock gains as distributable cash grows faster than reported revenue.

$122+14.4%

A routine equipment and software refresh cycle temporarily raises spending, while wage and healthcare costs pressure operating leverage. Investors temper near-term free-cash-flow expectations, producing a modest valuation setback without overturning the longer-run improvement in fulfillment productivity.

$124+16.6%

Refreshed systems restore throughput and improve inventory availability, while recurring fee businesses cushion merchandise volatility. Cash conversion resumes its modeled improvement, supporting a modest share-price recovery as investors separate temporary replacement spending from structural capital intensity.

$128+20.1%

Holiday scale and more mature fulfillment operations lift profit dollars even as percentage sales growth normalizes. Selective repurchases add modest per-share support, provided they remain funded by residual cash rather than an increase in financial leverage.

$131+22.5%

FY2032 planning is projected to show an increasingly balanced retailer: slower expansion but stronger retained cash generation. The market values the earnings stream near the model's terminal multiple, so operating progress rather than narrative expansion drives appreciation.

$133+25.0%

Advertising and membership remain incremental profit contributors, while automation benefits approach a mature run rate. Earnings estimates edge toward the terminal assumption, supporting the shares; limited additional margin optionality prevents investors from restoring the starting valuation premium.

$135+26.2%

The forecast ends near $135 as roughly $4.50 of modeled FY2032 earnings commands a 30-times multiple. That outcome reflects successful business adaptation but restrained shareholder returns; all quarterly changes are price-only, compounded sequentially, with dividends excluded.

Show remaining 18 quarters

All 20 quarters are displayed in the table above.

Walmart: Investment Thesis Base Case (The Polymath Framework on GPT-6 Astra model)

Walmart common stock is a residual claim on an omnichannel retailer. Its listing moved to Nasdaq on December 9, 2025; the supplied price anchor remains unchanged. (nasdaq.com) The base case assumes the business improves faster than the stock: advertising, membership and automation lift earnings, but reinvestment and valuation normalization absorb much of that progress. These are conditional forecasts, not promised quarterly catalysts. Factor impacts are overlapping counterfactual sensitivities, not additive returns. Reducing exposure is preferable to paying a premium for a projected return that barely compensates for equity risk.

  • Assumed FY2032 revenue of $925 billion and 5.4% operating margin support approximately $4.50 earnings per share.
  • Applying 30 times modeled earnings implies $135, matching the compounded quarterly path's approximately 26% price gain.
  • At 7.7 billion projected shares, equity value approaches $1.04 trillion, against approximately $26 billion annual free cash flow.

Walmart: Scenarios & Signals (The Polymath Framework on GPT-6 Astra model)

Bull Case

Scale finally becomes operating leverage rather than another round of price investment. By 2029–2031, automation and agent-led commerce accelerate profitable fulfillment, while advertising retains strong economics and customer acquisition costs fall. Earnings approach $5.30–$5.70 per share, and investors sustain a 35–36 times multiple because cash conversion improves alongside margins. That combination supports approximately $185–$205, or 73%–92% above the anchor, materially exceeding the base case without requiring a software-business valuation.

Bear Case

Value wins the shopper but loses the shareholder when cost inflation and a delivery price war arrive together. Walmart keeps traffic, yet promotions, slower discretionary demand and underutilized automation hold normalized earnings near $2.80–$3.10 through 2031. Investors then pay only 24–26 times earnings rather than a technology-enabled growth premium. The resulting $67–$81 range implies roughly 24%–37% downside from the anchor, even without financial distress or a collapse in revenue.

Behavioral Alpha Signals

Sentiment, repricing cycle, crowd narrative, catalyst, and macro alignment.

Expected Volatility Regime: Moderate

Greed and Fear Index: +18 / 100 (Greed)

+18

Cycle Position: Reversal

The thesis is unwinding and reflexive decline is underway.

EarlyAwareMomentumOvershootReversalCapit.StabilizeREVERSAL
Figure: Advisor position within the seven-stage market-recognition cycle. The highlighted point marks Reversal.
What does Media Tell? (Crowd Consensus)

The implied consensus remains defensive growth with a digital margin kicker, not conventional grocery economics. August's comparable-sales miss dented that confidence. (axios.com) Nevertheless, the supplied 38.6-times trailing earnings multiple still assumes durable profit outgrowth. The anchoring bias is to extrapolate advertising momentum while underestimating how much productivity must return to customers through lower prices and better service.

What Crowds Get Wrong? (Alpha/Value Gap)

The crowd overestimates these shares, although it correctly recognizes a strengthening business. Walmart's Q2 FY27 release shows digital sales contributing about 510 basis points to U.S. comps of only 260 basis points, suggesting material channel substitution rather than wholly incremental demand. (stock.walmart.com) That distinction matters: Walmart incurs fulfillment investment while only part of digital growth expands the total basket. With reinvestment limiting cash conversion, my modeled annualized return including dividends remains around 6%, insufficient compensation for valuation and execution risk.

When will Value Gap Repricing Happen? (Repricing Catalyst)

The clearest recognition catalyst is the post-refund earnings comparison during FY2028. Recurring margin gains and free cash flow must then justify the premium without temporary assistance. If capital expenditure stays elevated, the first repricing signal should be downward normalized-EPS revisions and a lower forward multiple.

How is Asset Influenced by Macro Regime?

Renewed Federal Reserve tightening is a valuation headwind, not an existential funding constraint. The September 16, 2026 increase to 3.75%–4.00% raises the opportunity cost of Walmart's low cash yield. (federalreserve.gov) Energy inflation also squeezes discretionary baskets, although trade-down can redirect grocery spending toward its value proposition.

Walmart: Positive & Negative Factors, Risks & Opportunities (The Polymath Framework on GPT-6 Astra model)

Base-Case Forces

Top Drivers / Tailwinds

Base-case positive forces

Structural or operating forces that support this advisor thesis. Base-case means above 70% probability of occurrence. Probabilities are model estimates; unquantified forces retain their published group.

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Top Drivers / Tailwinds with asset-specific estimated impacts and thesis rationale
Driver / TailwindCategoryEst. stock-price impactEst. earnings impactWhy it matters
Advertising Monetizes THE Shopping GraphInnovation And Product+28%+30%Walmart increasingly monetizes the shopping trip twice: through merchandise and through advertiser access to purchase intent. Global advertising grew 38% in Q2 FY27, corroborating demand for its transaction-linked audience. I assign 85% likelihood to continued expansion; incremental advertising and supplier-data income should lift earnings faster than merchandise sales, although the forecast assumes material deceleration from today's growth rate. (corporate.walmart.com)
Automation Earns ITS Capital BACKOperational Efficiency+17%+19%Automation converts Walmart's physical footprint from a fulfillment burden into a density advantage. The company reported roughly half of U.S. eCommerce fulfillment-center volume automated in Q1 FY27, establishing deployment rather than mere aspiration. I assign 80% likelihood that robotics, routing and inventory AI lower unit costs over five years, improving fulfillment contribution margins after allowing for depreciation and reinvestment. (corporate.walmart.com)
Value Leadership Captures Recurring BASKCompetitive Positioning+16%+18%Walmart's low-price proposition keeps recurring food and household demand inside its distribution network when consumers scrutinize budgets. Q2 FY27 materials report share gains across income cohorts, suggesting convenience reinforces value rather than replacing it. I assign 85% likelihood to continued share capture; higher throughput spreads store and logistics costs, supporting earnings even if aggregate discretionary consumption remains uneven. (corporate.walmart.com)
Membership Deepens THE Spending MOATCompetitive Positioning+10%+12%Membership makes convenience habitual and converts occasional transactions into recurring fee income. FY26 membership fees exceeded $4.3 billion, establishing a meaningful revenue stream alongside merchandise sales. I assign 80% likelihood that Walmart+ and Sam's Club deepen retention and wallet share, improving earnings visibility and delivery density; the model deducts the continuing cost of member benefits rather than treating fees as pure profit. (corporate.walmart.com)
Top Frictions / Headwinds

Base-case negative forces

Expected frictions that can slow, cap, or damage this advisor thesis. Base-case means above 70% probability of occurrence. Probabilities are model estimates; unquantified forces retain their published group.

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Top Frictions / Headwinds with asset-specific estimated impacts and thesis rationale
Friction / HeadwindCategoryEst. stock-price impactEst. earnings impactWhy it matters
Quality Premium Meets Discount RatesMacroeconomic And Macrofinancial-26%+0.0%Walmart's earnings are defensive, but the price paid for them is not. The supplied snapshot values the stock at 38.6 times trailing earnings despite a 1.59% free-cash-flow yield. I assign 80% likelihood to some multiple compression as higher discount rates compete with defensive equities; terminal valuation falls to 30 times earnings in the base case, offsetting substantial operating improvement. (federalreserve.gov)
LOW Prices Reclaim Productivity DividendCompetitive Positioning-8.0%-8.0%Low prices are Walmart's moat and a recurring claim on its productivity gains. FY26 management commentary described grocery rollbacks helping customers afford general merchandise, demonstrating that efficiencies also finance demand creation. I assign 90% likelihood that competition keeps reclaiming part of automation and advertising gains through lower prices, limiting incremental margins even while the retailer strengthens its competitive position. (corporate.walmart.com)
Physical Infrastructure Keeps DemandingCapital Allocation-6.0%-7.0%Physical retail cannot become software merely because transactions move online. Walmart's FY26 capital expenditure exceeded depreciation, while fulfillment retrofits and remodels keep cash conversion constrained; subsequent depreciation also suppresses earnings. I assign 90% likelihood that sustained investment intensity restrains distributions and valuation. Normalizing the supplied dividend-payment signs implies roughly 0.9% cash yield, not the snapshot's sign-distorted 0.02%. (stock.walmart.com)
Labor AND Liability Costs CompoundOperational Efficiency-5.0%-8.0%Labor productivity does not erase Walmart's exposure to medical inflation, claims severity and a vast service workforce. The Q2 FY27 presentation identifies healthcare and self-insured liability claims as sources of expense deleverage. I assign 85% likelihood that recurring wage and benefit pressure absorbs part of gross-profit gains; unlike a temporary tariff refund, these obligations reset upward and constrain sustainable operating-margin expansion. (stock.walmart.com)

Risks & Opportunities

Tail Risks

Plausible downside scenarios

Less likely downside scenarios that could materially hurt the outcome if they occur.

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Tail risks with plausibility, asset-specific potential impact and scenario rationale
Tail scenarioChance of OccurringStock Price ImpactWhy plausible / what changes
Stagflation Breaks THE Earnings Bridge30%-32%A prolonged energy shock combined with sharply weaker employment during 2027–2029 could turn defensive traffic gains into an earnings disappointment. In this scenario, food and fuel absorb household budgets while financing, freight and labor costs remain sticky, forcing Walmart to defend volume through pricing. The trigger is a simultaneous discretionary-sales contraction and broad margin compression, not merely slower growth. Its probability stays below half because Walmart can gain share and flex expenses before that combination becomes severe.
Delivery Subsidies Trigger Price WAR25%-28%A nationwide subsidized delivery offensive by Amazon or another scaled competitor during 2027–2030 could force Walmart to reset delivery fees and membership benefits abruptly. Matching the offer would lower contribution margins just as automation depreciation matures, weakening free cash flow and invalidating the assumed earnings-growth premium. This is distinct from ordinary competitive reinvestment in the base case. Probability remains below half because sustained subsidies are costly and Walmart's local fulfillment density makes displacement difficult.
Tail Opportunities

Plausible upside scenarios

Less likely upside scenarios that could materially improve the outcome if they occur.

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Tail opportunities with plausibility, asset-specific potential impact and scenario rationale
Tail scenarioChance of OccurringStock Price ImpactWhy plausible / what changes
Automation Clears THE Margin Hurdle35%+35%Completion of a nationwide automated fulfillment rollout during 2028–2030, accompanied by disclosed unit-cost reductions and sustained consolidated operating margin above 6%, would change the earnings ceiling. It would establish that robotics and AI generate savings Walmart can retain after reinvestment, lifting free cash flow and the terminal multiple. Probability remains below half because deployment alone does not prove retained economics; competitors and customers can capture the savings. (corporate.walmart.com)
Shopping Agents Choose Walmart First25%+25%A major distribution agreement during 2027–2029 could make Walmart a preferred fulfillment and checkout partner for widely used shopping agents, while preserving customer data and attractive transaction economics. Existing AI-shopping partnerships establish a starting point, not that outcome. If an expanded contract delivers demonstrably incremental baskets rather than channel migration, advertising demand and fulfillment utilization could accelerate together. Probability remains below half because platforms may insist on neutrality, fees or control of the customer relationship. (corporate.walmart.com)

Walmart: References & Context (The Polymath Framework on GPT-6 Astra model)

Research Provenance11 sources · 8 search terms
Researcher modeWeb Search Used

Web Search was used. The retained search terms and consulted sources are shown below.

Context supplied to the model

Public-safe inputs retained with this immutable forecast publication.

  1. Market datainmemory_base_placeholders__latest_eod_close_price_with_stats_and_fundamentals__var1
  2. Global context in this runUsed
    2026 Year-to-Date Global Market and World-Events Context Through September 20

    January 1-September 20, 2026: monetary tightening, energy security, trade restrictions, AI financing and divergent growth; five leading market themes.

  3. Fundamental data in this runUsed
    Fundamental Context Passed as Input to the Models

    Time range: Annual fiscal periods: 2015-01-31–2026-01-31, 12 periods; Quarterly fiscal periods: 2023-10-31–2026-07-31, 12 periods. Currencies cited: USD (quote USD; primary reporting USD; converted/valuation USD).

  4. Global contextStandard global market and cross-asset context
  5. Subject contextEquity-specific subject and market context
  6. Task frameworkStandard investment-forecast task guidelines
  7. Advisor frameworkUniversal Investor The Polymath
  8. Forecast output requestedEquity Extended Investment Thesis (4 Quadrants and Alpha Asymmetry) + Pct Change Timeseries for Close Price with Rationale, (5Y Quarterly)

Search terms retained

  1. 1.site.stock.walmart.com Q2 FY2027 earnings release August 2026 Walmart
  2. 2.site.stock.walmart.com 2026 annual report 2026 operating income ecommerce advertising
  3. 3.site.corporate.walmart.com 2026 automation ecommerce profitable advertising fiscal 2027
  4. 4.site.corporate.walmart.com September 2026 AI Sparky agents Walmart shopping partnerships
  5. 5.site.corporate.walmart.com Walmart Nasdaq December 9 2025 transfer listing
  6. 6.site.corporate.walmart.com "Q2 FY27" "transcript" "2026"

Sources retained for this advisor

Original Published Forecast

Inspect the original revision and its sealed receipt.

How to understand these AI forecasts

How does an AI-generated forecast for Walmart differ from Wall Street analyst consensus?

Wall Street consensus aggregates estimates from human analysts. For listed companies, these typically include brokerage price targets, earnings forecasts and ratings. iPulse AI's forecast for Walmart is a separate AI consensus generated through its own research workflow. Different inputs, forecast dates, assumptions and weighting can therefore produce different targets.

We believe the era of investment research conducted only by human analysts is drawing to a close. Frontier models increasingly match or exceed expert work on selected professional benchmarks, including evaluations covering financial analysis. Research on multi-model synthesis also shows that combining model responses can improve on a single model's output in the tasks studied.

iPulse AI generates transparent investment research and market forecasts using a swarm of AI configurations, analytical frameworks and frontier models. The workflow applies multiple verification, challenge and synthesis layers, with selective editorial review under Russlan Ramdowar, Editor-in-Chief. Published assumptions, disagreements and past forecasts remain available so readers can inspect the reasoning and audit outcomes.

For a research cycle covering 426 assets with 14 perspectives per asset, our internal workload model estimates approximately 114,000 hours of traditional analyst work. It assumes 16 hours per individual research report, 28 hours per consensus for synthesis and validation, plus shared data preparation. Parallel AI execution allows a deep-analysis cycle across hundreds of assets to complete in up to one day. The human-hours figure is an illustrative workload estimate, rather than measured time savings. Our research methodology explains the automated workflow.

If iPulse AI produces thousands of investment reports - work estimated to require over 100,000 traditional analyst hours - isn't that just "AI slop" at scale?

No. iPulse AI produces AI-generated research at scale, but the purpose is depth of analysis, comparison, and accountability. The number of reports is a consequence of the research process; it is not the goal.

iPulse AI does not produce thin copies of existing websites, articles, or investment reports. We use frontier models as an Analytical Intelligence layer. Our Financial Experts select, validate, curate, and supply the input data, including proprietary inputs, so the models work from a prepared evidence base.

In Researcher mode, models can also search the web to check current information, test assumptions, and investigate evidence that may be missing from the supplied data. This can include lobbying expenditure, management developments, regulatory decisions, or other facts relevant to a particular investment thesis. Thinker mode focuses on deep reasoning over the supplied data and context.

Our process goes much further than asking ChatGPT to evaluate a stock. Our team designs structured investment frameworks inspired by the approaches of influential investors, economists, political thinkers, and technological visionaries. These are AI analytical frameworks, not reports written or endorsed by the people who inspired them.

Each asset is analyzed separately through multiple frameworks and configurations, using specialized inputs and task instructions. Different perspectives examine different questions: valuation, business quality, technological disruption, macroeconomic conditions, political influence, financial risks, and more.

Cloud infrastructure allows these workflows to run at scale. That scale also comes with quality assurance at scale: up to 30% of report-generation attempts fail our QA checks. We rerun failed attempts until a clean report passes those checks. Users are presented only with reports that have passed QA.

This explains the volume. The value comes from what we do with the analysis.

A second layer synthesizes the individual reports into an asset-level consensus. This is where we recommend starting when researching a particular asset: it brings together the central thesis, supporting evidence, risks, and disagreements while preserving access to the underlying opinions.

We then compare assets through our proprietary iPulse AI Consensus Score, whose methodology is publicly explained. This helps users move from understanding one asset to comparing opportunities across the analyzed universe.

A further research and editorial layer examines leading candidates, checks current evidence, and develops focused Editorial Top Picks across broad-market and dedicated asset categories. Human editors review the evidence and approve each published edition. For example, explore our Best Long-Term Investments.

But the process does not end when a report is published. We preserve past forecasts so users with the relevant subscription can inspect what the system actually said and compare it with subsequent market outcomes. As more forecasts reach their target dates, the historical record becomes more useful for evaluating which approaches worked, which failed, and under what conditions.

We publish research on our website, arXiv, and Zenodo, alongside explanatory writing on Medium. We use the evidence to refine our data, frameworks, validation, and workflows, and document important findings in Lessons Learned.

The standard we ask users to judge us by is the reasoning, evidence, and historical record behind the research.

What do the individual AI forecasts for Walmart represent?

Each report presents one AI advisor configuration's analysis of Walmart, including its investment framework, assumptions, forecast path, drivers, and risks. The reports provide different analytical perspectives on the same asset so you can examine the reasoning behind their conclusions.

How are these forecasts different from the AI Consensus?

The individual forecasts show each advisor's reasoning and outlook. The AI Consensus synthesizes those views into an overall assessment of Walmart. Start with the consensus for an overview, then explore individual reports to understand the strongest supporting arguments, objections, and disagreements.

Are the named AI advisors the actual investors or public figures?

No. They are AI analytical frameworks inspired by publicly documented investment approaches or broader schools of thought. Their reports are generated by AI within the iPulse AI research workflow. The names do not imply participation, endorsement, or a personal forecast from those individuals.

What is the difference between Thinker and Researcher mode?

Thinker mode reasons over the supplied data and context without external web search. Researcher mode can investigate additional external evidence when needed. Being configured as Researcher does not necessarily mean a particular report used web search; consult the report's research details where available.

Why do the AI advisors disagree about Walmart?

Different frameworks emphasize different evidence and assumptions. One may prioritize valuation, another technological growth, and another macroeconomic or political risk. Disagreement helps identify which assumptions deserve closer investigation. Agreement can also reflect shared inputs or model behavior, so it should not be treated as proof of accuracy.

How often are the reports updated?

The analytical content is updated during deep-analysis batch runs. We run each batch across all covered assets within the same research cycle, providing a consistent basis for comparison, scoring, and ranking. Currently, these batches run every few weeks; our aim is to increase the frequency to once a week.

Between batches, we update price-based expected returns and ratings daily using the latest available market prices. For example, a sharp price increase can reduce an asset's expected upside and change a previously published Buy rating. This keeps the price-based assessment current without presenting the underlying analytical report as newly generated.

Editorial Top Picks receive an additional weekly review, providing another check on whether current evidence continues to support the selected investment theses.

What can I read without signing in, and what requires a subscription?

You can read the public preview and the methodology explanations available on this page. Additional advisor reports, comparisons, configuration details, and historical records may require signing in and a subscription that includes the relevant asset or feature. Access prompts explain what is needed.

How do these forecasts contribute to Live Rankings and Editorial Top Picks?

Individual forecasts contribute to the asset's consensus and quantitative scoring. Live Rankings compare assets using the published scoring methodology. Editorial Top Picks add a further review of selected candidates, current evidence, investment theses, and risks, followed by human editorial approval. For example, explore our Best Long-Term Investments.

Can I inspect previous forecasts for Walmart and see what happened?

Forecast History lets you inspect earlier published forecasts, subject to your subscription access. Read each forecast against its original publication date, assumptions, and target dates. As outcomes become observable, those records support evaluation of what worked and what did not. Broader findings and methodological changes appear in Research and Lessons Learned.

Walmart Stock Analysis & Forecast — Universal Investor The Polymath Framework on Claude Opus 5 Model

Universal Investor AI advisor icon

Universal Investor AI · The Polymath Framework

Claude Opus 5 AI Researcher Web Search Used

5 Year Rating

Neutral

5 Year Price Return

+10.4%

Incl. net dividends

+13.5%

Framework Description & Key Record Dates

Investment Framework

The Polymath Framework: Eight-Lens Investment Analysis Framework

Analysis Framework Description: Select the lenses with the greatest explanatory power, resolve their disagreements across horizons, and connect the evidence to a consistent thesis, scenario path, and verdict.

  • Ownership and value: examine economic rights, competitive advantage, capital allocation, and the price paid.
  • Regimes and constraints: connect credit, liquidity, policy, demand, and physical bottlenecks to the asset.
  • Innovation and adaptation: test adoption economics, AI and robotics exposure, substitution, and value capture.
  • Forensics and incentives: reconcile accounting, cash conversion, dilution, governance, and missing evidence.
  • Expectations and reflexivity: distinguish changes in fundamentals from changes in narratives and valuation.
  • Probabilities and resilience: use base rates, competing scenarios, catalysts, and the capacity to survive the wait.
  • Power and the rules of the game: assess political interests, regulatory influence, protection, and dependency.
  • Industry structure and consolidation: identify pricing power, chokepoints, alliances, and the economics of integration.
Full framework methodology
Model Producer:
Anthropic
Model Version:
Claude Opus 5
Mode Used:
Researcher
Web Search Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor. Dividend-inclusive returns assume 0.57% annual net yield, reinvested until the target.

Read full advisor report

Walmart Stock Analysis & Forecast — Universal Investor The Polymath Framework on Gemini 3.8 Flash Model

Universal Investor AI advisor icon

Universal Investor AI · The Polymath Framework

Gemini 3.8 Flash AI Researcher Web Search Not Used

5 Year Rating

Neutral

5 Year Price Return

+5.1%

Incl. net dividends

+8.0%

Framework Description & Key Record Dates

Investment Framework

The Polymath Framework: Eight-Lens Investment Analysis Framework

Analysis Framework Description: Select the lenses with the greatest explanatory power, resolve their disagreements across horizons, and connect the evidence to a consistent thesis, scenario path, and verdict.

  • Ownership and value: examine economic rights, competitive advantage, capital allocation, and the price paid.
  • Regimes and constraints: connect credit, liquidity, policy, demand, and physical bottlenecks to the asset.
  • Innovation and adaptation: test adoption economics, AI and robotics exposure, substitution, and value capture.
  • Forensics and incentives: reconcile accounting, cash conversion, dilution, governance, and missing evidence.
  • Expectations and reflexivity: distinguish changes in fundamentals from changes in narratives and valuation.
  • Probabilities and resilience: use base rates, competing scenarios, catalysts, and the capacity to survive the wait.
  • Power and the rules of the game: assess political interests, regulatory influence, protection, and dependency.
  • Industry structure and consolidation: identify pricing power, chokepoints, alliances, and the economics of integration.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Researcher
Web Search Not Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor. Dividend-inclusive returns assume 0.57% annual net yield, reinvested until the target.

Read full advisor report

Walmart Stock Analysis & Forecast — J.P. Morgan The Titan Framework on Gemini 3.8 Flash Model

J.P. Morgan AI advisor icon

J.P. Morgan AI · The Titan Framework

Gemini 3.8 Flash AI Researcher Web Search Used

5 Year Rating

Neutral

5 Year Price Return

+7.3%

Incl. net dividends

+10.3%

Framework Description & Key Record Dates

Investment Framework

The Titan Framework: Empire Dominance Assessment

Analysis Framework Description: The Titan looks for who sets the terms. It grades whether an asset controls price, infrastructure, distribution, capital flows, or ecosystem access, then asks whether that dominance is expanding or inviting a counterattack.

  • Measure pricing power and whether customers have credible alternatives.
  • Map infrastructure, distribution, data, supply, or capital choke points.
  • Evaluate whether acquisitions strengthen the moat or merely add complexity.
  • Identify antitrust, succession, and regulatory antibodies against dominance.
  • Ask whether the asset collects rent from the system or pays rent to someone else.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Researcher
Web Search Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor. Dividend-inclusive returns assume 0.57% annual net yield, reinvested until the target.

Read full advisor report

Walmart Stock Analysis & Forecast — Superintelligence The Anthropologist Framework on Gemini 3.8 Flash Model

Superintelligence AI advisor icon

Superintelligence AI · The Anthropologist Framework

Gemini 3.8 Flash AI Researcher Web Search Not Used

5 Year Rating

Neutral

5 Year Price Return

+4.2%

Incl. net dividends

+7.1%

Framework Description & Key Record Dates

Investment Framework

The Anthropologist Framework: Civilizational Compute Framework

Analysis Framework Description: The Anthropologist observes the financial system from above human emotion and asks whether an asset is biologically anchored, thermodynamically improving, informationally central, civilizationally aligned, and resilient to AI/quantum/space displacement.

  • Score biological demand anchoring: what human or civilizational need does the asset serve?
  • Measure thermodynamic efficiency and whether the asset reduces waste or increases throughput.
  • Map information network position, density, and dependency within the broader system.
  • Assess long-arc alignment with technological, demographic, and infrastructure evolution.
  • Estimate displacement risk from superior substitutes and frontier-tech transitions across AI, quantum, and space.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Researcher
Web Search Not Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor. Dividend-inclusive returns assume 0.57% annual net yield, reinvested until the target.

Read full advisor report

Walmart Stock Analysis & Forecast — Universal Investor The Polymath Framework on Gemini 3.8 Flash Model

Universal Investor AI advisor icon

Universal Investor AI · The Polymath Framework

Gemini 3.8 Flash AI Thinker Web Search Not Used

5 Year Rating

Partial Sell

5 Year Price Return

+2.9%

Incl. net dividends

+5.8%

Framework Description & Key Record Dates

Investment Framework

The Polymath Framework: Eight-Lens Investment Analysis Framework

Analysis Framework Description: Select the lenses with the greatest explanatory power, resolve their disagreements across horizons, and connect the evidence to a consistent thesis, scenario path, and verdict.

  • Ownership and value: examine economic rights, competitive advantage, capital allocation, and the price paid.
  • Regimes and constraints: connect credit, liquidity, policy, demand, and physical bottlenecks to the asset.
  • Innovation and adaptation: test adoption economics, AI and robotics exposure, substitution, and value capture.
  • Forensics and incentives: reconcile accounting, cash conversion, dilution, governance, and missing evidence.
  • Expectations and reflexivity: distinguish changes in fundamentals from changes in narratives and valuation.
  • Probabilities and resilience: use base rates, competing scenarios, catalysts, and the capacity to survive the wait.
  • Power and the rules of the game: assess political interests, regulatory influence, protection, and dependency.
  • Industry structure and consolidation: identify pricing power, chokepoints, alliances, and the economics of integration.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Thinker
Web Search Not Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor. Dividend-inclusive returns assume 0.57% annual net yield, reinvested until the target.

Read full advisor report

Walmart Stock Analysis & Forecast — Ray Dalio The Strategist Framework on Gemini 3.8 Flash Model

Ray Dalio AI advisor icon

Ray Dalio AI · The Strategist Framework

Gemini 3.8 Flash AI Researcher Web Search Used

5 Year Rating

Partial Sell

5 Year Price Return

+2.1%

Incl. net dividends

+4.9%

Framework Description & Key Record Dates

Investment Framework

The Strategist Framework: Economic Machine Framework

Analysis Framework Description: The Strategist treats markets as cause-effect systems. The page explains how this persona separates cyclical expansion from deleveraging, temporary policy support from durable productivity, and short-term noise from long-wave regime change.

  • Identify the current short-term debt cycle and whether liquidity is expanding or contracting.
  • Map long-term deleveraging pressure and the asset's sensitivity to credit stress.
  • Assess reserve-currency, empire-cycle, and geopolitical pressure around the asset class.
  • Separate temporary policy support from durable productivity-driven value creation.
  • Translate macro regime into asset positioning, expected catalysts, and horizon risk.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Researcher
Web Search Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor. Dividend-inclusive returns assume 0.57% annual net yield, reinvested until the target.

Read full advisor report

Walmart Stock Analysis & Forecast — Niccolo Machiavelli The Insider Framework on Gemini 3.8 Flash Model

Machiavelli AI advisor icon

Machiavelli AI · The Insider Framework

Gemini 3.8 Flash AI Researcher Web Search Used

5 Year Rating

Partial Sell

5 Year Price Return

+0.1%

Incl. net dividends

+2.9%

Framework Description & Key Record Dates

Investment Framework

The Insider Framework: Power Audit Framework

Analysis Framework Description: The Insider asks who has power over the asset, how durable that power is, and whether the market is pricing the protection, threat, or political optionality correctly.

  • Map who can protect, subsidize, regulate, restrict, or destroy the asset's economics.
  • Track lobbying, procurement, sanctions, antitrust, and strategic policy exposure.
  • Identify insiders, capital networks, and institutions that are quietly positioned.
  • Estimate whether political power creates a valuation premium or discount.
  • Separate visible business performance from invisible power structure risk.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Researcher
Web Search Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor. Dividend-inclusive returns assume 0.57% annual net yield, reinvested until the target.

Read full advisor report

Walmart Stock Analysis & Forecast — Superintelligence The Anthropologist Framework on Gemini 3.8 Flash Model

Superintelligence AI advisor icon

Superintelligence AI · The Anthropologist Framework

Gemini 3.8 Flash AI Thinker Web Search Not Used

5 Year Rating

Partial Sell

5 Year Price Return

-1.1%

Incl. net dividends

+1.7%

Framework Description & Key Record Dates

Investment Framework

The Anthropologist Framework: Civilizational Compute Framework

Analysis Framework Description: The Anthropologist observes the financial system from above human emotion and asks whether an asset is biologically anchored, thermodynamically improving, informationally central, civilizationally aligned, and resilient to AI/quantum/space displacement.

  • Score biological demand anchoring: what human or civilizational need does the asset serve?
  • Measure thermodynamic efficiency and whether the asset reduces waste or increases throughput.
  • Map information network position, density, and dependency within the broader system.
  • Assess long-arc alignment with technological, demographic, and infrastructure evolution.
  • Estimate displacement risk from superior substitutes and frontier-tech transitions across AI, quantum, and space.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Thinker
Web Search Not Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor. Dividend-inclusive returns assume 0.57% annual net yield, reinvested until the target.

Read full advisor report

Walmart Stock Analysis & Forecast — Warren Buffett The Value Seeker Framework on Gemini 3.8 Flash Model

Warren Buffett AI advisor icon

Warren Buffett AI · The Value Seeker Framework

Gemini 3.8 Flash AI Thinker Web Search Not Used

5 Year Rating

Partial Sell

5 Year Price Return

-1.9%

Incl. net dividends

+0.8%

Framework Description & Key Record Dates

Investment Framework

The Value Seeker Framework: Value Ownership Framework

Analysis Framework Description: The Value Seeker turns market noise into an ownership decision: understand the asset, judge the moat, estimate owner economics, evaluate management or governance, compare price with value, and demand a financial fortress.

  • Treat every asset as a business to be owned, not a ticker to be traded.
  • Stay inside the circle of competence before assigning conviction.
  • Map the moat type, moat durability, and whether the moat is widening or eroding.
  • Estimate owner economics through cash generation, ROIC, and reinvestment runway.
  • Audit management integrity, governance, and capital allocation rationality.
  • Compare intrinsic value to the market price with a real margin of safety.
  • Stress-test the financial fortress before accepting long-term ownership risk.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Thinker
Web Search Not Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor. Dividend-inclusive returns assume 0.57% annual net yield, reinvested until the target.

Read full advisor report

Walmart Stock Analysis & Forecast — Sherlock Holmes The Whistleblower Framework on Gemini 3.8 Flash Model

Sherlock Holmes AI advisor icon

Sherlock Holmes AI · The Whistleblower Framework

Gemini 3.8 Flash AI Researcher Web Search Used

5 Year Rating

Partial Sell

5 Year Price Return

-5.8%

Incl. net dividends

-3.2%

Framework Description & Key Record Dates

Investment Framework

The Whistleblower Framework: Forensic Deduction Framework

Analysis Framework Description: The Whistleblower turns analysis into a case file: what is claimed, what evidence must exist if it is true, what evidence is missing, and what verdict follows from the exhibits.

  • Define the theory of the case behind the market narrative.
  • List the evidence that should exist if management or market claims are true.
  • Inspect filings, footnotes, revenue quality, related parties, and omitted metrics.
  • Stress-test contradictions between narrative, numbers, incentives, and behavior.
  • Deliver a clear case-file verdict rather than vague suspicion.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Researcher
Web Search Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor. Dividend-inclusive returns assume 0.57% annual net yield, reinvested until the target.

Read full advisor report

Walmart Stock Analysis & Forecast — Elon Musk The Visionary Framework on Gemini 3.8 Flash Model

Elon Musk AI advisor icon

Elon Musk AI · The Visionary Framework

Gemini 3.8 Flash AI Researcher Web Search Not Used

5 Year Rating

Partial Sell

5 Year Price Return

-9.7%

Incl. net dividends

-7.1%

Framework Description & Key Record Dates

Investment Framework

The Visionary Framework: Paradigm Shift Framework

Analysis Framework Description: The Visionary asks whether the asset is building the future or clinging to the past, then validates physics, S-curve timing, execution velocity, and a forced AI/quantum/space disruption verdict.

  • Start from first-principles feasibility instead of consensus market extrapolation.
  • Run a mandatory frontier-tech disruption test for AI, quantum, and space exposure.
  • Estimate where the asset sits on an S-curve: early, inflecting, maturing, or saturating.
  • Model future TAM from deployment constraints, infrastructure, and adoption friction.
  • Grade execution velocity through iteration speed, talent density, and learning loops.
  • Separate real paradigm shifts from narratives that only sound futuristic.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Researcher
Web Search Not Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor. Dividend-inclusive returns assume 0.57% annual net yield, reinvested until the target.

Read full advisor report

Walmart Stock Analysis & Forecast — Michael Burry The Vulture Framework on Gemini 3.8 Flash Model

Michael Burry AI advisor icon

Michael Burry AI · The Vulture Framework

Gemini 3.8 Flash AI Researcher Web Search Not Used

5 Year Rating

Partial Sell

5 Year Price Return

-10.6%

Incl. net dividends

-8.1%

Framework Description & Key Record Dates

Investment Framework

The Vulture Framework: Contrarian Forensic Framework

Analysis Framework Description: The Vulture starts with the crowd story and looks for the single fact, balance sheet pressure point, or dated catalyst that can force a repricing in either direction.

  • State the consensus story clearly before attacking it.
  • Search for balance sheet fragility, liquidity pressure, and hidden leverage.
  • Compare market narrative against hard evidence, filings, costs, and positioning data.
  • Identify the catalyst that could force the market to care.
  • Estimate upside and downside asymmetry if the crowd is wrong.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Researcher
Web Search Not Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor. Dividend-inclusive returns assume 0.57% annual net yield, reinvested until the target.

Read full advisor report

Walmart Stock Analysis & Forecast — Warren Buffett The Value Seeker Framework on Gemini 3.8 Flash Model

Warren Buffett AI advisor icon

Warren Buffett AI · The Value Seeker Framework

Gemini 3.8 Flash AI Researcher Web Search Not Used

5 Year Rating

Partial Sell

5 Year Price Return

-13.9%

Incl. net dividends

-11.5%

Framework Description & Key Record Dates

Investment Framework

The Value Seeker Framework: Value Ownership Framework

Analysis Framework Description: The Value Seeker turns market noise into an ownership decision: understand the asset, judge the moat, estimate owner economics, evaluate management or governance, compare price with value, and demand a financial fortress.

  • Treat every asset as a business to be owned, not a ticker to be traded.
  • Stay inside the circle of competence before assigning conviction.
  • Map the moat type, moat durability, and whether the moat is widening or eroding.
  • Estimate owner economics through cash generation, ROIC, and reinvestment runway.
  • Audit management integrity, governance, and capital allocation rationality.
  • Compare intrinsic value to the market price with a real margin of safety.
  • Stress-test the financial fortress before accepting long-term ownership risk.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Researcher
Web Search Not Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor. Dividend-inclusive returns assume 0.57% annual net yield, reinvested until the target.

Read full advisor report

Global Events Context Provided to the Model

GPT-6 Astra has a published knowledge cutoff of Apr 30, 2026. Curated global-events context covering events through Sep 20, 2026 was supplied to supplement its built-in knowledge with important developments.

2026 Year-to-Date Global Market and World-Events Context Through September 20

Download Archived Snapshot

Coverage 2026-01-01 to 2026-09-20 · Knowledge cutoff 2026-09-20

January 1-September 20, 2026: monetary tightening, energy security, trade restrictions, AI financing and divergent growth; five leading market themes.

Archived context covering 2026-01-01 to 2026-09-20. Its recorded scope includes monetary policy and rates; geopolitics energy and shipping; trade and industrial policy. Selected highlights reflect the analysis cutoff, not current world conditions.

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File size
84.8K bytes
Words
12.4K words
Characters
84.8K characters
Est. tokens (characters ÷ 4)
21.2K tokens

Fed raised rates to 3.75%-4.00%; ECB hike is in force and BOJ increase starts September 24. Markets through September 18, bitcoin through September 19.

Selected highlights from the archived 2026 Year-to-Date Global Market and World-Events Context Through September 20 snapshot
Selected highlights from this archived snapshotDateStatus
Renewed monetary tightening amid persistent inflation2026-01-30ACTIVE POLICY REGIME
Iran/Hormuz conflict and wider energy-security disruption2026-02-28ONGOING
Tariff legal reset and strategic supply restrictions2026-02-20ACTIVE POLICY REGIME

Representative Sources of the Context File

And more sources from the retained context package.

Fundamental Context Provided to the Model

Time range: Annual fiscal periods: 2015-01-31–2026-01-31, 12 periods; Quarterly fiscal periods: 2023-10-31–2026-07-31, 12 periods.

Currencies cited: USD (quote USD; primary reporting USD; converted/valuation USD).

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Income statement

9 fields

depreciationAndAmortization · ebit · ebitda · grossProfit · +5 more fields

Balance sheet

12 fields

cash · commonStockSharesOutstanding · longTermDebt · netDebt · +8 more fields

Cash flow

5 fields

capitalExpenditures · dividendsPaid · freeCashFlow · salePurchaseOfStock · +1 more field

Research datasets created by iPulse AI and published by Future Edge Group FZE. Use is subject to the iPulse AI Terms of Service and applicable source rights.