Nvidia Q2 2026 Earnings and AI Capex Boom

Nvidia will report its second-quarter fiscal 2027 results on August 26, 2026, giving investors a fresh measure of demand for accelerated computing, AI data centers and related infrastructure. The report will also arrive after Nvidia announced financing partnerships designed to mobilize more than $500 billion of third-party capital for AI infrastructure over time.

60-Second Investor Summary

Nvidia's second-quarter fiscal 2027 earnings call is scheduled for August 26, 2026. The quarter ended July 26. Investors will focus on data center revenue, gross margin, Blackwell and Vera Rubin demand, supply availability, customer spending plans and management's outlook.

Nvidia also announced partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to establish independent AI compute financing platforms. The platforms are designed to mobilize more than $500 billion of third-party capital over time. That figure is not Nvidia revenue, a single fund or a guaranteed purchase commitment.

Key Takeaways

• Nvidia will discuss second-quarter fiscal 2027 results on August 26, 2026.
• The quarter ended July 26, 2026.
• Nvidia's first-quarter fiscal 2027 revenue was $81.6 billion.
• The May 2026 quarter showed data center demand remained very strong.
• Investors will focus heavily on the next revenue outlook rather than only the reported quarter.
• AI infrastructure spending depends on data center power, networking, memory, cooling and financing as well as GPUs.
• Nvidia's new financing platforms are designed to mobilize more than $500 billion of third-party capital over time.
• The $500 billion figure should not be interpreted as Nvidia's sales guidance.
• NVDA stock remains sensitive to earnings expectations, valuation, AI capital expenditure and supply conditions.

Table of Contents

When will Nvidia release its Q2 2026 earnings?

Nvidia will discuss its second-quarter fiscal 2027 financial results on August 26, 2026, at 5:00 p.m. Eastern Time.

Nvidia announced the earnings schedule on July 29, 2026. The company said the second quarter of fiscal year 2027 ended on July 26, 2026. The earnings call will take place after the company publishes its financial results.

This distinction matters when investors search for Nvidia Q2 2026 earnings. Nvidia's fiscal calendar does not match the calendar year. The quarter reported in August 2026 is the second quarter of fiscal 2027.

Nvidia said written commentary from Chief Financial Officer Colette Kress would be provided after the financial results are released. The company also scheduled a question-and-answer session for financial analysts and institutional investors.

Investors should therefore separate the calendar date from Nvidia's fiscal reporting period. The August 26 release covers business activity through July 26, not through the entire month of August.

What does Nvidia Q2 2026 actually mean?

Nvidia's August 2026 report is the second quarter of fiscal 2027, even though the report is commonly described by investors as a Q2 2026 earnings event.

Nvidia uses a fiscal year that ends in January. That means its quarterly labels differ from calendar-year quarters used by many other companies.

The company reported first-quarter fiscal 2027 results in May 2026. That quarter ended April 26, 2026.

The second fiscal quarter ended July 26, 2026. The company will therefore report the quarter on August 26.

For search purposes, terms such as Nvidia earnings August 2026, Nvidia Q2 2026 earnings and NVDA stock may all refer to the same earnings event.

Investors should use the company's fiscal label when comparing revenue, margins and guidance with earlier Nvidia reports.

Why is the Nvidia earnings report important for AI infrastructure?

Nvidia's results provide direct financial evidence about demand for accelerated computing used in AI training, inference and large-scale data center workloads.

Nvidia sells GPUs, networking products, systems and software that form part of modern AI computing infrastructure. Large cloud providers and technology companies use Nvidia systems to train and run AI models.

That makes Nvidia's financial results useful when investors assess the broader AI infrastructure spend cycle.

The company does not capture every dollar spent on an AI data center. A complete facility also requires electricity, land, buildings, cooling equipment, power systems, networking, storage, memory and other components.

Nvidia sits near the computing layer of that investment chain.

If customers increase spending on AI clusters, Nvidia can benefit through higher demand for accelerators and networking systems. If customers slow purchases, Nvidia's revenue outlook can provide an early indication of weaker infrastructure orders.

This relationship does not mean Nvidia's stock is a perfect measure of total AI spending. The company can gain market share, lose market share, change product mix or experience supply constraints even when total industry spending remains high.

What did Nvidia report in its previous quarter?

Nvidia reported first-quarter fiscal 2027 revenue of $81.6 billion, up 20% from the previous quarter and 85% from a year earlier.

The May 20, 2026 release showed the scale of Nvidia's current business cycle.

Data center remained the central source of demand. The company's accelerated computing products serve large AI deployments operated by cloud providers, enterprises and AI developers.

The first-quarter result also established a high comparison point for the August report.

Investors will therefore ask two separate questions. First, did Nvidia meet or exceed the market's expectations for the quarter that ended in July? Second, did management provide a forward outlook that supports continued high AI infrastructure spending?

The second question can matter more for the stock because equity prices reflect expected future earnings, not only the results of a completed quarter.

What will investors watch in the new earnings report?

Investors will focus on revenue growth, data center demand, gross margin, product transitions, supply availability and forward guidance.

The first number will be total revenue. Investors will compare the result with consensus expectations and Nvidia's own previous guidance.

The next area will be data center revenue. Nvidia's data center business has become the primary financial indicator of the AI computing cycle.

Gross margin will also receive close attention. New products can carry different costs during their early production stages. Supply constraints, product mix and manufacturing costs can affect reported margins.

Management commentary about Blackwell and Vera Rubin systems will provide information about the product transition.

Investors will also listen for comments about hyperscale customers, AI labs and enterprise demand.

The earnings call may provide information about whether customers are adding capacity for model training, inference or both.

Another issue is geographic demand. AI infrastructure investment now involves companies and governments outside the United States. Export restrictions and regulatory rules can affect which products Nvidia can sell into specific markets.

How important is data center revenue to Nvidia?

Data center revenue has become the main financial indicator for Nvidia's AI infrastructure exposure.

Nvidia originally built its reputation around graphics processors for gaming and professional visualization. Its accelerated computing architecture later became central to machine learning workloads.

Modern AI systems require large amounts of computing capacity for both model development and deployment.

Training a large model can require thousands of accelerators operating together. Inference also requires computing capacity when users interact with AI systems.

This creates two related sources of demand.

Training demand depends on companies building and updating large models. Inference demand depends on the number of users, applications and workloads that run those models in production.

For Nvidia, a transition toward greater inference demand could change product mix and system requirements over time.

Investors should therefore avoid focusing only on the number of GPUs shipped. The financial result also depends on system configuration, networking, software, customer contracts and product generation.

How large is the AI capital expenditure cycle?

AI capital expenditure involves a much larger investment chain than semiconductor purchases alone.

A hyperscale data center requires power infrastructure, buildings, cooling systems, networking, storage and computing equipment.

AI clusters can require unusually high power density. That creates investment needs outside the semiconductor sector.

Utilities may need additional generation and transmission capacity. Data center operators may need new substations and power-management systems.

Construction companies, electrical-equipment suppliers, networking vendors, memory manufacturers and cooling-system providers can therefore gain exposure to the same capital cycle.

This is why technology investors often examine AI capital expenditure across several companies rather than using Nvidia alone.

Nvidia's results can provide a direct view of accelerator demand. Cloud-company capital expenditure plans provide information about the customer side of the market.

The two measures should be considered together.

If Nvidia reports strong orders while customers reduce future capital expenditure plans, investors may question the durability of the growth rate.

If both Nvidia and major customers maintain strong spending plans, the evidence for continued infrastructure investment becomes stronger.

What is the $500 billion AI financing partnership?

Nvidia announced financing platforms with six major investment firms designed to mobilize more than $500 billion of third-party capital for AI infrastructure over time.

On August 10, 2026, Nvidia announced strategic partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR.

The companies plan to establish independent financing platforms for AI compute infrastructure.

Nvidia described the platforms as a way to help AI labs, enterprises and AI cloud operators obtain financing for large computing deployments.

The structure is different from Nvidia simply selling GPUs for cash.

Under the proposed model, financial institutions can help provide capital for infrastructure projects. Customers can then obtain computing capacity through financed infrastructure arrangements.

This structure could matter because AI data centers require very large upfront investments.

A financing platform can spread infrastructure costs over a longer period. That may allow companies with strong expected demand but limited immediate capital to expand computing capacity.

Nvidia stated that the more than $500 billion figure represents aggregate third-party capital that the platforms are designed to mobilize over time.

It is not a single $500 billion fund.

It is not Nvidia revenue.

It is not a guaranteed order book.

It is not a commitment that one customer will spend $500 billion.

This distinction is essential when analyzing the announcement from an investment perspective.

Which six financial institutions are involved?

The six announced partners are Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR.

Institution	Potential Function	Investor Relevance
Apollo	Long-duration capital and financing expertise	Can provide capital-market capacity for infrastructure financing.
BlackRock	Large-scale asset management and infrastructure investment	Provides access to institutional capital pools.
Blackstone	Alternative asset management and infrastructure investment	Adds private-market financing capacity.
Brookfield	Infrastructure investment and asset management	Provides experience with long-lived physical infrastructure.
Goldman Sachs	Capital markets and investment banking	Can support financing structures and distribution.
KKR	Long-term capital and infrastructure investment	Adds infrastructure and private-credit capabilities.

The institutions do not all perform identical functions. Their participation brings different forms of capital, infrastructure experience and financing capability to the proposed platforms.

Nvidia's announcement said the partnerships remained subject to final agreements. Investors should therefore distinguish between the announced framework and completed financing transactions.

Does $500 billion mean Nvidia will receive $500 billion?

No. The more than $500 billion figure refers to third-party capital that the financing platforms are designed to mobilize over time, not money that Nvidia will receive as revenue.

Nvidia announced on August 10, 2026 that it had signed memorandums of understanding with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to create independent financing platforms for AI computing infrastructure. Nvidia said those platforms are designed to mobilize more than $500 billion of third-party capital over time. 0

The distinction matters for investors. The figure does not represent a $500 billion Nvidia sales order. It does not mean Nvidia will add $500 billion to its balance sheet. It also does not represent one investment fund containing $500 billion.

The proposed structure connects institutional capital with customers that need large amounts of AI computing capacity. The financial institutions will independently assess each project, including the customer, expected demand, utilization, cash flow and residual value. 1

Nvidia supplies the computing platform and technology. The financial partners provide capital and financing expertise. If these arrangements lead to more funded data center deployments, Nvidia could benefit indirectly through additional demand for its hardware and software.

Nvidia has also disclosed that it may provide a residual-value support mechanism of up to 25% for certain opportunities, subject to project-specific assessment. That creates a potential financial exposure that investors should examine rather than treating the financing announcement as risk-free demand. 2

For an investor reading the Nvidia earnings report, the correct interpretation is simple: the $500 billion figure describes the intended financing capacity of the platforms, not Nvidia's expected quarterly revenue.

What could drive GPU demand after the earnings report?

GPU demand could remain strong if AI model training, inference workloads, cloud expansion, enterprise adoption and sovereign computing projects continue to require new capacity.

AI infrastructure demand is changing as companies move beyond model training toward continuous inference. Nvidia said in July 2026 that AI clouds and other infrastructure operators were building large, multi-tenant AI factories to provide computing capacity to many customers. 3

Training remains an important source of demand. Large AI developers need substantial computing capacity when they build new models, increase model size or repeat training runs with new datasets.

Inference creates a different demand pattern. Once an AI model enters production, computing resources must process user requests. A popular AI application can therefore create recurring demand for computing capacity rather than a single training event.

Cloud providers are another source of GPU demand. Companies that do not want to build their own data centers can rent accelerated computing through cloud platforms. This creates an indirect route for Nvidia hardware to reach thousands of businesses and developers.

Enterprise adoption could add another source of demand. Banks, software companies, manufacturers and other businesses are deploying AI for customer applications, internal automation, research and data analysis.

Government-backed and sovereign AI projects can also require large computing installations. Countries that want domestic AI capacity may invest in data centers, power infrastructure and high-performance computing systems.

Product transitions can create another demand cycle. Nvidia's Vera Rubin platform is designed for large AI factories and includes GPU, CPU, networking and other components in a broader computing platform. Nvidia announced in March 2026 that seven Vera Rubin chips had entered full production. 4

The important question after the earnings release will be whether customers continue to commit capital at a rate that supports Nvidia's expected revenue growth.

Investors should compare three pieces of evidence: Nvidia's own guidance, capital expenditure plans from large cloud customers and actual deployment schedules.

What supply issues should investors monitor?

Investors should monitor advanced packaging, high-bandwidth memory, manufacturing capacity, networking components, power availability and data center construction schedules.

Nvidia does not manufacture every component in its AI systems itself. The company relies on external suppliers and manufacturing partners for fabrication, packaging, assembly, testing, memory and other parts of the supply chain.

Advanced packaging can become a constraint when demand for sophisticated processors rises rapidly. AI accelerators also require high-bandwidth memory, which has its own manufacturing capacity and technology cycle.

Networking has become another major requirement. Large AI clusters connect thousands of processors, so the computing system needs high-speed interconnects and networking equipment.

Physical infrastructure can create an even larger constraint. Nvidia's August 2026 announcement about the PORTS-Pike campus in Ohio illustrates the issue. Nvidia said the project involves an initial 4.25 IT gigawatts of capacity, with an option for another 3.75 IT gigawatts. OpenAI is expected to be the customer for the planned 8 IT gigawatts of capacity. 5

This type of project demonstrates that GPU availability is only one part of the deployment process. Customers also need land, electricity, buildings, cooling and network connections.

Nvidia itself described land, power and shell capacity as important resources for AI factories in an August 17, 2026 company discussion. 6

Investors should therefore watch Nvidia's comments on product shipments as well as customer deployment schedules. A customer may have purchased GPUs but still face delays before the computing facility becomes operational.

Supply problems can affect Nvidia in two ways. A shortage can limit near-term revenue even when demand remains strong. A rapid increase in supply can later create inventory risk if customer demand slows.

What should investors watch in Nvidia's margins?

Investors should compare Nvidia's gross margin with product mix, manufacturing costs, new-product ramps and management guidance.

Nvidia's gross margin has remained unusually high for a semiconductor company because of its strong position in accelerated computing and its software ecosystem.

Its fiscal 2026 fourth-quarter results provide a useful reference point. Nvidia reported GAAP gross margin of 75.0% and non-GAAP gross margin of 75.2% for the quarter ended January 25, 2026. 7

Investors should not assume that a high margin will remain unchanged. Product transitions can affect manufacturing costs and product mix.

Complete AI systems contain more components than individual GPU boards. Nvidia sells computing platforms that can include processors, networking equipment and other components. The mix between these products can influence reported margins.

New architecture ramps can also create temporary cost pressure. Early production may involve lower manufacturing yields, higher logistics costs or other expenses before production becomes more efficient.

Investors should read management's margin guidance alongside the revenue forecast. A large revenue increase can have a different effect on earnings depending on the associated gross margin.

The same principle applies to operating expenses. Nvidia continues to invest in research, software and engineering as it develops new architectures and platforms.

The strongest earnings setup would combine sustained data center growth with healthy margins and controlled operating expenses. A weaker setup would involve strong revenue growth but falling margins caused by product mix or rising costs.

What does the report mean for NVDA stock?

NVDA stock will respond to the gap between reported results, investor expectations and Nvidia's forward outlook.

A strong earnings report does not automatically mean the stock will rise. Investors price shares based on expected future earnings, so the market response depends on what was already reflected in the share price.

Suppose Nvidia reports revenue above the previous quarter but below the level investors expected. The stock could decline even though the company continues to grow.

The opposite can also happen. If expectations are conservative and Nvidia provides stronger forward guidance, the stock could rise even when the reported quarter does not produce an unusually large surprise.

For NVDA stock, investors should examine several numbers together.

Valuation adds another variable. If investors already assign a high valuation to Nvidia based on expectations for future AI growth, the company may need to deliver strong results for the valuation to remain supported.

Interest rates also affect equity valuations. Higher discount rates can reduce the present value of future cash flows, while lower rates can support higher valuations for companies whose earnings are expected to grow rapidly.

For that reason, NVDA stock should be assessed using both company-specific earnings data and the broader macroeconomic environment.

What risks could weaken the AI capex cycle?

The main risks include weaker customer returns, financing costs, electricity constraints, supply problems, export restrictions, competition and slower AI adoption.

Customer return on investment

AI infrastructure requires substantial upfront spending. Companies need enough revenue or operating savings to justify the investment.

If AI applications produce lower returns than expected, customers may reduce future capital expenditure.

This risk is particularly relevant for newer AI companies that depend heavily on outside financing.

Electricity and data center capacity

Large AI clusters require substantial electricity.

A customer can have funding and access to Nvidia hardware but still face a delay because the required power connection or data center shell is unavailable.

Nvidia's recent focus on land, power and shell capacity reflects this physical constraint. 8

Financing costs

The new AI financing platforms are designed partly to address the capital requirements of large computing projects.

Higher interest rates can increase the cost of financing these facilities. That can affect the economics of projects whose returns depend on long-term AI service revenue.

Export restrictions

Government restrictions can affect Nvidia's ability to sell advanced computing products into particular markets.

Investors should read Nvidia's risk disclosures and management commentary for changes in export rules and their effect on product demand.

Competition

Nvidia faces competition from other accelerator suppliers and from custom chips developed by large cloud companies.

Customers may use a mixture of Nvidia GPUs, internally developed accelerators and competing products.

Nvidia's software ecosystem, CUDA compatibility and system-level products can influence customer decisions, but investors should still monitor changes in the competitive environment.

Capital spending discipline

AI spending has expanded rapidly. The next test is whether customers can convert that spending into enough revenue or productivity gains to justify continued investment.

A reduction in customer capital expenditure plans would eventually affect suppliers throughout the AI hardware chain.

Product transition risk

Nvidia's rapid product cycle creates opportunities but also execution risk.

Customers can delay orders while waiting for newer systems. Nvidia must also coordinate manufacturing, packaging, memory and system integration as new architectures enter production.

What should investors monitor after the report?

Investors should monitor forward revenue guidance, data center demand, margins, customer capital expenditure, product availability, power capacity and financing activity.

Frequently Asked Questions

Does the $500 billion financing announcement increase Nvidia's revenue immediately?

No. Nvidia described the more than $500 billion figure as aggregate third-party capital that independent financing platforms are designed to mobilize over time. It is not reported Nvidia revenue. 9

Who is providing the AI infrastructure financing?

Nvidia announced partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR. The institutions are expected to independently evaluate financing opportunities.

Why could financing affect Nvidia GPU demand?

Large AI facilities require substantial upfront capital. Financing can allow qualified customers to build computing capacity without funding the entire project from existing cash. More funded deployments could create additional demand for Nvidia systems.

What is the biggest physical constraint for AI data centers?

Power availability can become a major constraint. AI facilities also require land, buildings, cooling, networking and grid connections. Nvidia recently described land, power and shell capacity as resources required for AI factories. 10

What does gross margin tell Nvidia investors?

Gross margin shows how much revenue remains after the direct cost of products. Changes can reflect product mix, manufacturing costs, new product ramps and supply conditions.

Can Nvidia's revenue rise while NVDA stock falls?

Yes. The stock reflects market expectations. If investors expected stronger revenue or guidance than Nvidia delivers, the share price can decline even while the company's revenue continues to grow.

What could cause AI capital expenditure to slow?

Lower-than-expected returns on AI projects, higher financing costs, power constraints, slower AI adoption, competition, export restrictions and supply problems could reduce the pace of new infrastructure investment.

Why does inference matter for Nvidia?

Inference occurs when trained AI models process new requests. As AI applications gain users, inference can create recurring computing demand after the original model-training process.

What should investors compare with Nvidia's earnings?

Investors can compare Nvidia's results with capital expenditure plans from major cloud companies, AI infrastructure deployments, semiconductor supply conditions and changes in the broader interest-rate environment.

Technical and Economic Glossary

This glossary explains the main financial and technology terms used in the Nvidia Q2 2026 earnings analysis. The definitions are written for investors who want to understand the connection between AI infrastructure, semiconductor demand, financing and NVDA stock.

Sources and Verification

The following sources were used to verify the dates, financing arrangements and company-specific facts discussed in this Nvidia earnings analysis. The primary sources are Nvidia's own investor-relations and corporate news pages.

1. Nvidia Investor Relations: Q2 Fiscal 2027 Earnings Date

Nvidia's official investor-relations calendar confirms that the company's second-quarter fiscal 2027 financial results are scheduled for August 26, 2026. Nvidia lists the financial-results event for 2:00 p.m. Pacific Time. 1

The company's July 29 announcement also confirms that Nvidia will discuss results for the second quarter of fiscal 2027, which ended July 26, 2026. The scheduled conference call is set for August 26 at 2:00 p.m. PT, or 5:00 p.m. ET. 2

Nvidia Investor Relations: Q2 Fiscal 2027 Financial Results

2. Nvidia: More Than $500 Billion of Third-Party Capital

Nvidia announced on August 10, 2026 that it had entered strategic partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to establish independent compute financing platforms.

The company said these platforms are intended to mobilize more than $500 billion of third-party capital for AI infrastructure over time. The announcement describes the capital as financing for the buildout of AI computing infrastructure and says the participating financial institutions will create dedicated pools of capital for Nvidia customers. 3

This distinction is important for financial reporting. The $500 billion figure should not be treated as Nvidia's quarterly revenue, booked sales or cash balance. It describes the targeted scale of third-party capital that the financing platforms are designed to mobilize.

Nvidia Investor Relations: AI Compute Infrastructure Financing Platforms

3. Nvidia Newsroom: AI Compute Financing Structure

Nvidia's corporate explanation of the financing initiative describes the purpose of the new structures and explains that the participating financial institutions will independently assess financing opportunities.

The company has described these arrangements as a way to connect capital with AI infrastructure projects. Nvidia has also discussed the potential use of residual-value support in certain transactions. The exact financial terms can vary by individual project and financing arrangement.

Nvidia: AI Factory Compute Financing

4. Nvidia: AI Infrastructure and Data Center Capacity

Nvidia's August 2026 disclosures provide evidence that AI infrastructure demand involves more than semiconductor production. Large facilities require electrical power, physical space, cooling systems, networking equipment and data center construction.

Nvidia's recent infrastructure announcements also demonstrate the scale of planned AI computing projects. Investors should therefore evaluate GPU demand together with the physical capacity required to operate those systems.

Nvidia Investor Relations

5. Nvidia: Vera Rubin Platform

Nvidia's Vera Rubin announcements provide information about the company's next-generation AI computing platform. Vera Rubin is designed as a broader computing system rather than a standalone processor, with GPUs, CPUs, networking and other components working together.

The product cycle matters for investors because new architectures can influence customer purchasing schedules, manufacturing requirements, product mix and margins.

Nvidia Newsroom: Vera Rubin Platform

6. Nvidia Historical Financial Results

Nvidia's previous financial reports provide the baseline required to evaluate the upcoming fiscal 2027 second-quarter results. Investors should compare the new report with the company's prior revenue, data center sales, gross margin, operating expenses, cash flow and guidance.

Historical results are useful for identifying the direction of the business, but they should not be treated as a forecast of future performance.

Nvidia Investor Relations Financial Reports

7. Current Market Context

Recent market coverage has focused on Nvidia's August 26 earnings release, the scale of AI infrastructure spending and the financing arrangements announced with major financial institutions. Reuters reported on August 21 that Nvidia's upcoming earnings were being watched as an important read on AI infrastructure demand. 4

Market coverage should be treated as context rather than as a substitute for Nvidia's own financial statements. The company's earnings release, regulatory filings and conference call remain the primary sources for revenue, earnings, margins and management guidance.

Verification Note

The financing figure in this article is presented as more than $500 billion of third-party capital targeted for mobilization over time, consistent with Nvidia's August 10, 2026 announcement. It is not presented as Nvidia revenue or as a guaranteed investment amount.

The August 26 earnings date is based on Nvidia's official investor-relations calendar and July 29 corporate announcement. 5

Financial figures and product information should be checked against Nvidia's latest filing after the earnings release because management can update guidance, accounting information and risk disclosures.

Financial Disclaimer: This article is provided for informational and educational purposes. It does not constitute personalized investment, tax or financial advice. Nvidia shares can rise or fall sharply after earnings releases. Investors should review Nvidia's official filings, earnings materials and risk disclosures before making investment decisions.

Source priority: Nvidia investor-relations materials and company disclosures were used as the primary sources for company-specific facts. Reuters and other financial publications provide market context where noted. 6