Showing posts sorted by date for query new normal. Sort by relevance Show all posts
Showing posts sorted by date for query new normal. Sort by relevance Show all posts

Saturday, May 16, 2026

CAPE is an Issue, But How Much?

Nobody can know for certain--beyond the fact that U.S. financial markets are in historically above-average valuation levels--what could happen next. 


Some rationally expect a reversion to mean, which will mean lower valuations.


Others just as rationally argue that above-average valuations can persist for some time, and that a correction is not in store. AI might be among the reasons, if it changes growth expectations.

source:  Ark Invest


Consider the Cyclically Adjusted Price-to-Earnings Ratio (CAPE), widely considered a valuable long-term valuation metric. The current CAPE is high, suggesting caution and a likely correction to lower levels.  


But many analysts believe that changes in accounting rules since the early 2000s make the standard version look artificially high relative to its historical average. Adjusted, it might still be high, but not at internet bubble levels. 


The CAPE is calculated as the current S&P 500 Price divided by the average of 10 years of inflation-adjusted earnings.

The issue is that the denominator uses reported GAAP earnings, and those earnings have become more conservative over time, leading to a boost in CAPE that make comparisons with past levels misleading, the argument goes. 


Key accounting changes include: 

  • Goodwill impairment rules (FAS 142, adopted in 2001)

  • Large acquisition write-downs now hit earnings immediately.

  • Before 2001, many such costs were spread over decades.

  • This depresses modern earnings compared with earlier periods.

  • Mark-to-market accounting

  •  lk;juring crises, companies must recognize large non-cash losses.

  • These can sharply reduce earnings even if long-term economics are less affected.

  • One-time charges

  • Restructuring costs and impairments are recognized more aggressively.


The result is that the denominator in today’s CAPE is lower than it would have been under earlier accounting rules, making the ratio appear higher.


Economist Jeremy Siegel argues for using National Income and Product Accounts instead of GAAP earnings, to better normalize over time. 


The standard CAPE can overstate market valuation materially because recent earnings include unusually large accounting write-downs by roughly 10 percent to 25 percent.


Others argue for using operating earnings rather than reported earnings, which also can adjust earnings by 15 percent.


Estimated Distortion

Standard CAPE 38

Adjusted CAPE

10%

38.0

34.2

15%

38.0

32.3

20%

38.0

30.4

25%

38.0

28.5


Using such methods, the market still appears expensive, but less so than it might appear. 


Other issues:

  • Lower interest rates over long periods

  • Higher profit margins

  • Global diversification of large U.S. firms

  • Greater use of stock buybacks instead of dividends

  • Stronger institutional ownership and retirement savings flows.


These factors may justify a structurally higher "normal" CAPE than the 19th- and 20th-century average.


So some will argue a practical adjustment for accounting changes is to reduce the published Shiller P/E by 10 percent to 25 percent.


This suggests the market may still be richly valued, but not as dramatically overvalued as the unadjusted Shiller P/E implies.


It is a useful gauge of long-term valuation, but it is not a short-term market timing tool, as history shows that markets can continue to rise for years, even when the CAPE ratio is well above its historical average.


Several forces can keep markets rising despite expensive valuations:

  • Earnings continue to grow

  • Corporate profits may rise fast enough to justify higher prices

  • Investor optimism and momentum

  • Strong sentiment can sustain elevated valuations for extended periods

  • Low interest rates

  • When bond yields are low, investors are willing to pay more for equities

  • New technologies can create expectations of stronger future growth

  • Retirement contributions, buybacks, and institutional inflows can support prices.


Period

Approximate CAPE at Start

Years Until Major Peak

Additional Market Gain After CAPE Became Elevated

What Happened

1925–1929

25–32

4 years

+150% to +200%

Roaring Twenties speculation pushed valuations higher before the 1929 crash

1995–2000

25–44

5 years

+200% to +250%

Dot-com bubble drove extraordinary gains

2017–2021

30–38

4 years

+80% to +120%

Continued growth in Apple Inc., Microsoft Corporation, NVIDIA Corporation and other large-cap firms

2023–2026

Mid-30s (approx.)

Ongoing

Still developing

Strong enthusiasm around artificial intelligence and large technology firms


The point is that valuation is a poor short-term timing tool:

  • A CAPE above average tells you expected long-term returns may be lower, but it does not predict when prices will stop rising

  • Markets can stay expensive for years

  • If profits rise rapidly, high valuations can become more sustainable

  • Structural changes matter (lower inflation, global market reach, and dominant technology companies may justify higher valuation ranges than in earlier eras).


We still have to make our own choices about timing, though!


Saturday, April 18, 2026

It's Just Math

It’s normal for commentators to note that any new tax plan “gives” more to the wealthy than to working people. So it is with the latest tax plan


Such claims normally rely on absolute dollar amounts of benefit, rather than the impact of rates, which normally are progressive, with higher rates for wealthier payers, and lower rates for lower-income earners. 


Most such differences are the result of mathematics. A small percent of a big number is a big number; a small percent of a small number is still a small number. 


In the United States, for example, the top 10 percent of filers pay roughly three-quarters of all federal income taxes. 


The bottom half of filers produce 10 percent to 15 percent of all federal income tax receipts, and some effectively pay zero rates when one adds in the effect of income transfers and credits. 


Income Group (AGI Percentile)

Approx. Share of Total Income

Approx. Share of Federal Income Taxes Paid

Avg. Effective Tax Rate

Top 1%

~20–22%

~40–45%

~25–27%

Top 5%

~35–38%

~60–65%

~22–25%

Top 10%

~45–48%

~70–75%

~20–23%

Top 25%

~65–70%

~85–90%

~17–20%

Top 50%

~85–90%

~95–97%

~15–18%

Bottom 50%

~10–15%

~2–5%

~3–5% (often near zero or negative)


If you broaden from income taxes to total federal taxes, including payroll taxes (Social Security, Medicare), the overall system remains progressive, if less so than looking strictly at income taxes. 


But the point is that any tax plan that reduces rates will “give much more to the rich” than to lower-income taxpayers, simply because “the rich” pay most of the taxes. Even a progressive rate reduction is not going to change that. 


So it might come as no surprise that most people are relatively indifferent to any tax savings they received. It’s just math.

Friday, April 17, 2026

Enterprise Software Volatility Might be an Investor's Friend

In a few years we might look back and discover some investors were pretty significant winners; others losers as investors in enterprise software stocks, given the current volatility.


If software valuation multiples permanently contract lower, bears will have been proven correct. But if AI disruption proves less damaging, then enterprise software leaders are on sale.


According to Goldman Sachs analyst Matthew Martino, investors need to consider six different aspects of how artificial intelligence might affect an enterprise software supplier’s business:

  • Orchestration risk: The possibility that horizontal AI agent layers can bypass the platform and become the primary generator of value

  • Monetization model: Whether a business model is tied to users, which makes it more vulnerable, or assets and data, making it more durable

  • System-of-record ownership: If the platform governs approvals, compliance, and execution, it is harder to displace

  • Data and integration moat: Whether workflows depend on proprietary signals, structured data, and operational records that live inside the platform and must be accessed through it

  • AI execution: Whether the company is delivering real, embedded capabilities rather than conceptual roadmaps

  • Budget alignment: Determines whether AI adoption increases or decreases the strategic priority of the category. 


For investors, the key takeaways might be:

  • Pure workflow or UI-heavy tools are more exposed; systems handling intricate enterprise logic less so

  • Subscription models linked to business scale rather than headcount tend to hold up better as AI automates tasks

  • Core ERP, financial systems, CRM with compliance hooks, or regulatory platforms are harder to displace

  • AI agents amplify the value of clean, governed, real-time data rather than replacing it

  • Execution separates fast followers who reinforce their moat from those getting disrupted. Incumbents with domain data have an advantage here

  • Look for categories where AI creates tailwinds, like data platforms, cybersecurity, vertical SaaS with complex compliance, or physical-to-digital workflows.


Company

Orchestration Risk

Monetization Model

System-of-Record Ownership

Data & Integration Moat

AI Execution

Budget Alignment

Salesforce (CRM)

Moderate (−) Agentforce agents could be orchestrated externally, but platform workflows limit full bypass

Strong (+) Shifted to agentic ELAs + consumption (per conversation/action); Agentforce ARR ~$800M+ and growing fast

Strong (+) Core CRM is authoritative for customer data, approvals, compliance

Strong (+) Deep customer interaction history, proprietary signals, Data Cloud integrations

Strong (+) Shipping real agents (Agentforce) with measurable ROI; Einstein embedded

Strong (+) AI boosts CRM priority & spend (more data, governance, agent infra)

ServiceNow (NOW)

Moderate-Strong (+) Complex, multi-system IT/service workflows hard for horizontal agents to fully orchestrate

Strong (+) Now Assist “Pro Plus” premium tiers + consumption upside; ACV >$600M–$1B run-rate

Strong (+) System-of-record for IT/service management, approvals, audit trails

Strong (+) Proprietary operational workflows + deep integrations across enterprise tools

Strong (+) Now Assist agents delivering real value; infrastructure fully cloud-native for AI scale

Strong (+) AI adoption increases strategic priority of service/ops platforms

Oracle (ORCL)

Strong (+) Deep ERP/supply-chain/finance processes resist simple agent bypass

Moderate-Strong (+) Mix of license + cloud consumption; moving toward outcome-based

Strong (+) Authoritative ERP/finance/supply chain system-of-record with compliance hooks

Strong (+) Massive proprietary enterprise data across finance, supply chain, HR

Strong (+) Using AI to build full automated processes (not just copilots); ahead on domain execution per some peers

Strong (+) AI tailwinds for database/cloud infra spend

Workday (WDAY)

Moderate (−) HR/finance copilots can partially bypass UI; more replicable workflows

Moderate (−) Still largely seat/user-tied; slower shift to consumption

Strong (+) Core HR/finance system-of-record for employee data & compliance

Moderate (−) Data is relatively standardized vs. highly proprietary; easier for AI to replicate externally

Moderate (+) Has copilots but viewed as lagging some peers on full agentic execution

Moderate (+) AI helpful but could shift some budget away from traditional HCM suites

SAP (SAP)

Strong (+) ERP complexity & regulatory workflows make full orchestration bypass difficult

Moderate-Strong (+) Cloud shift + some consumption elements; historical pricing power

Strong (+) Dominant global ERP system-of-record for finance, supply chain, compliance

Strong (+) Deep structured operational data + decades of integrations

Moderate-Strong (+) Embedding AI into core ERP; fast-follower re-architecture

Strong (+) AI increases need for governed ERP data & execution layers

Adobe (ADBE)

Moderate (−) Generative tools could let agents bypass some creative workflows/UI

Moderate-Strong (+) “Generative Credits” + consumption model for Firefly; shifts value capture to usage

Moderate (+) Creative asset system-of-record but less regulatory/compliance weight

Moderate-Strong (+) Proprietary creative workflows + Firefly-trained data moat

Strong (+) Firefly & generative AI delivering real embedded capabilities; strong execution in content

Moderate-Strong (+) AI boosts content creation budget but compresses some per-seat value

Snowflake (SNOW)

Moderate (−) Data platform can be bypassed as pure backend; agents query elsewhere

Strong (+) Consumption-based (compute/data usage); Cortex AI adds monetization vectors

Moderate (+) Strong data platform but not always the business process system-of-record

Strong (+) Data cloud moat + governance; AI amplifies need for clean, governed data

Strong (+) Cortex AI services shipping real capabilities on customer data

Strong (+) AI dramatically increases data volume/query spend (force multiplier)

MongoDB (MDB)

Moderate (−) Flexible document DB can be commoditized by agents using open formats

Strong (+) Consumption-based usage; vector search/AI integrations drive upside

Moderate (+) Application data store but rarely the final system-of-record

Moderate-Strong (+) Developer-friendly data + vector capabilities; less proprietary than ERP data

Moderate-Strong (+) Vector search & AI app tools executing well; benefits from AI dev boom

Strong (+) AI apps drive more database consumption and developer spend


And even if all that is correct, the palpable investor fears about how to value enterprise software firms do not seem to be abating. But some analysts think a “new normal” valuation level is close to stabilizing.


Software Segment

New "Floor" P/E

Historical Context

Mega-Cap SaaS (Microsoft, SAP)

28x – 32x

Historic: 35x+

High-Growth / "Rule of 40" (ServiceNow, CrowdStrike)

45x – 55x

Historic: 80x – 100x+

Mature / Cyclical Enterprise (Salesforce, Oracle)

18x – 24x

Historic: 25x – 30x

Infrastructure / Dev Ops (Datadog, Snowflake)

50x – 60x

Historic: 100x+

Mid-Market / "Broken" SaaS

12x – 16x

Historic: 25x


In 2021, software was valued on Enterprise Value/Revenue (often 15x–20x). 


Analysts now believe the stable floor is EV/Free Cash Flow or Forward P/E. 


For a standard healthy software company, a 5x–6x revenue multiple is now considered "stable," whereas 10x or more is reserved only for elite AI-winners.


Analysts at firms including Goldman Sachs and Morgan Stanley believe "legacy" enterprise software could fall further (P/E of 12x–15x) if they cannot prove AI utility.


Based on those estimates, there is considerable danger of further downside.  


Company

Est. Forward P/E (FY 2026)

Trend vs. 5-Year Average

Valuation Driver

Microsoft

22.4x – 23.2x

~30% Compression

Balanced by Azure AI growth and office productivity dominance.

Adobe

23.5x – 25.0x

~20% Compression

Premium maintained due to Firefly AI integration in Creative Cloud.

Salesforce

20.8x – 22.0x

Significant Reset

Shift toward margin expansion and buybacks over aggressive M&A.

Oracle

18.8x – 19.5x

Relatively Stable

Lifted by OCI (Oracle Cloud Infrastructure) demand for AI training.

SAP

21.0x – 22.5x

~15% Compression

Resilient due to "sticky" ERP migration to S/4HANA Cloud.

Workday

43.1x – 43.5x

>50% Compression

Transitioning from high-growth SaaS to a mature HCM platform model.


Analysts at J.P. Morgan and Bessemer note that legacy firms are now strictly valued on their ability to maintain a combined growth and profit margin of 40 percent (“the rule of 40”). Firms falling below this are seeing P/E multiples dip into the mid-teens.


Also, private consensus is that software multiples will not return to 2021 levels unless the 10-Year Treasury falls below three percent. As long as rates remain "higher for longer," analysts are modeling a permanent 20-percent haircut on terminal valuation multiples compared to the last decade.


source: MacroMicro 


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