AppLovin (APP) is an “ad-targeting machine” — growing on the back of AI-driven optimization and a two-sided marketplace, while also warranting attention to the shadows of volatility and regulation.

* This report is prepared based on data as of 2026-01-07.

1. The simple version: What kind of company is AppLovin?

In one sentence, AppLovin (APP) provides the infrastructure that helps apps and online stores “sell / get installed” through advertising. The name can make it sound like “an app maker,” but the core business is very clearly an advertising platform.

AppLovin previously owned a mobile gaming business as well, but it sold that mobile gaming business at the end of June 2025 and made its focus on the advertising platform explicit. That shift also makes it easier for investors to understand “where the company wins.”

Two customer groups (who pays vs. who supplies inventory)

• Advertisers (the demand side): Mobile app operators (games, education, photo editing, etc.) and e-commerce businesses. They pay ad spend to drive “app installs” or “product purchases.”
• Publishers (inventory owners = app developers): Companies that can show ads inside their own apps. Because they want to monetize impressions, they look for tools that sell inventory at higher and more stable prices.

Breaking down the products: AXON AI (target) × MAX (sell)

At its core, AppLovin combines an “ad marketplace” (the venue where transactions happen) with an “optimization engine.” At a middle-school level, these two pieces matter most.

• For advertisers: AppDiscovery and AXON AI… The “brain” that predicts “this user is likely to buy / install if shown this ad,” then bids to win inventory auctions. Rather than just selling impressions, the system is built around outcomes (purchases, installs, etc.).
• For publishers: MAX (mediation)… An operating tool that connects multiple ad companies and helps serve whichever ad offers the best terms at that moment. Think of it as “a control panel that tunes an in-app ad vending machine to the most profitable settings.”

How it makes money: A marketplace model that spins faster as performance improves

Today, the advertising platform is the core (the gaming business has been sold). You can think about monetization through two main flows.

• Revenue from advertisers: The better the results advertisers see, the more likely they are to increase spend; AppLovin is designed to earn revenue tied to those outcomes.
• Supporting publisher monetization: If publisher ad revenue rises through MAX and related tools, more inventory comes onto the platform; that, in turn, improves liquidity and increases overall platform value.

Forward-looking initiatives: E-commerce and self-serve as the “next pillars”

App advertising is the main arena today, but the following are explicitly positioned as future upside.

• E-commerce advertising (beta): Within the AXON AI narrative, the company indicates an e-commerce solution is in beta. That creates a path to expand beyond apps into “ads that sell physical goods.”
• Platformization / self-serve under Axon: Around October 2025, it was reported that the ad platform was reorganized under Axon and that Axon Ads Manager (a self-serve tool) was introduced. Self-serve typically supports scaling “without adding headcount.”

An analogy to make it click

The easiest way to think about AppLovin is as a company running a “market” where advertisers (who want to run ads) and publishers (who can place ads) meet—and where AI rapidly decides “which pairing works best.”

2. What the long-term numbers say about the “type of company”: High growth, but with a choppy trajectory

Looking at long-term fundamentals, APP is not the classic “steady, straight-A student” that compounds smoothly every year. It’s better understood as a business with strong growth but meaningful swings in profitability.

Lynch classification: Fast Grower (primary) + Cyclical (secondary) hybrid

This stock fits best as a Fast Grower (high growth) as the primary classification, with Cyclical (high variability) characteristics as a secondary classification. Here, “Cyclical” is less about macro sensitivity and more about Lynch’s idea of “profit volatility / cyclicality.”

• Fast Grower rationale (FY): 5-year EPS CAGR +65.1%, 5-year revenue CAGR +36.5%, ROE (latest FY) 144.96%.
• High-variability rationale (FY/TTM): The FY history includes years with negative EPS and net income, and the TTM series also shows sharp reversals from negative to positive. Near-term growth also has the feel of an acceleration phase, with TTM EPS YoY +150.8%.

5-year and 10-year growth: Revenue and FCF remain strong even over longer horizons

• 5-year (FY): EPS +65.1% CAGR, revenue +36.5% CAGR, free cash flow +60.8% CAGR, net income +67.7% CAGR. Revenue, earnings, and cash are all rising together—very much the “shape” of a growth company.
• 10-year (FY): Revenue +46.1% CAGR, free cash flow +57.4% CAGR. Meanwhile, EPS and net income cannot be calculated over this period, which limits what we can say about ultra-long-term consistency from the data alone.

Long-term profitability trend: It appears to have moved to a “different range” in 2023–2024

On an FY basis, both margins and cash generation have improved materially in recent years.

• Free cash flow margin (FY): 44.5% in 2024 (with a trajectory such as 28.5% in 2018, 14.6% in 2022, 32.2% in 2023).
• Operating margin (FY): -1.7% in 2022 → 19.7% in 2023 → 39.8% in 2024.

Based on FY figures, profitability appears to have shifted into a higher band in 2023–2024.

Where we are in the cycle: Not at the bottom, but on the “post-recovery high” side

While the FY history includes a swing from losses to profits, the most recent period shows substantial profitability (FY net income 15.80億USD). TTM levels are also high; at least numerically, the company sits on the “post-recovery high” side.

• TTM scale: Revenue 55.21億USD, EPS 8.30, free cash flow 33.54億USD, free cash flow margin 60.7%.
• TTM growth (YoY): EPS +150.8%, revenue +55.2%, free cash flow +95.3%.

Keep in mind that some metrics (including margins and safety indicators) can look different between FY and TTM. That’s a difference in how the time window is captured; rather than treating it as a contradiction, the real question is “which view is closer to the underlying reality,” to be tested with additional evidence.

3. Near-term (TTM / last 8 quarters): Growth momentum is “accelerating”

Near-term momentum looks Accelerating, because EPS, revenue, and free cash flow all show that the most recent 1-year growth rate is higher than the 5-year average.

Growth rates: The most recent year is strong (the Fast Grower profile holds in the short term as well)

• EPS (TTM YoY): +150.8% (above the FY 5-year CAGR of +65.1%)
• Revenue (TTM YoY): +55.2% (above the FY 5-year CAGR of +36.5%)
• Free cash flow (TTM YoY): +95.3% (above the FY 5-year CAGR of +60.8%)

The last 2 years (roughly 8 quarters) also show strong continuity to the upside

Even on a 2-year CAGR basis, the numbers are elevated: EPS +184.4% CAGR, revenue +29.7% CAGR, and free cash flow +79.7% CAGR. Directionality (correlation) is also strong—EPS +0.99, revenue +0.94, free cash flow +0.99—suggesting this is more than a one-year spike and has had real upside continuity.

What’s driving the momentum: Revenue growth + profitability (efficiency) improvement happening simultaneously

Recently, EPS (+150.8%) and FCF (+95.3%) have grown faster than revenue (TTM YoY +55.2%). Without making a structural claim, the shape of the numbers is consistent with the kind of momentum you often see when profitability/efficiency improves alongside growth.

Supplementary check on profitability momentum (FY): Operating margin improved sharply

FY operating margin rose from -1.7% in 2022 to 39.8% in 2024, which lines up well with the short-term EPS acceleration.

4. Cash flow quality: EPS and FCF are broadly consistent, but “divergence” is the detection point

In the latest TTM, free cash flow is 33.54億USD and the free cash flow margin is 60.7%, which is very strong and moving in the same direction as EPS growth. At least today, this does not look like a business where “profits are reported but cash never shows up”—if anything, it’s the opposite.

That said, the core idea behind Invisible Fragility is that the better things look, the harder it becomes to see how the model could break. As a practical detection mechanism, the key is whether “divergence” starts to appear—such as profit/cash growth slowing first even while revenue keeps rising, or margins expanding in an unnatural way even as revenue growth cools.

5. Financial soundness (the part that directly informs bankruptcy-risk assessment)

APP is a growth company, but financially it should be viewed not as “debt-free and bulletproof,” but as a levered capital structure. That means interest-paying capacity and the cash cushion need to be evaluated together.

Debt looks different depending on the metric: Debt/Equity is high, Net Debt/EBITDA is more contained

• Debt/Equity (latest FY): 3.26x (debt looks large relative to equity)
• Net Debt / EBITDA (latest FY): 1.20x

Because the structure can make equity look small, Debt/Equity screens high, while Net Debt / EBITDA sits around 1x. The fact that these metrics tell different stories is itself an important analytical point; this is a situation where it’s better not to declare “safe” or “unsafe” based on a single ratio.

Interest coverage and cash cushion: Recent observations suggest improvement

• Interest coverage (FY): 5.95
• On a quarterly basis, interest-paying capacity has improved, with the latest quarter observed in the 20s
• Cash ratio (FY): 0.70 (1.55 is observed on a quarterly basis)

There are stretches where interest coverage and liquidity look stronger on a quarterly basis than on FY, and recently the direction has not been one-way deterioration. From a bankruptcy-risk standpoint, based on currently available information, signals like “imminent liquidity stress” appear limited; however, given the leverage profile, it still requires ongoing monitoring whether interest-paying capacity is the first thing to weaken when profits roll over.

6. Capital allocation and shareholder returns: Dividends are difficult to position as a “core” element

On a TTM basis, dividend yield and dividend per share cannot be obtained, which makes this period hard to evaluate. Annual data show years where dividend payments can be confirmed, but the history is limited (years with dividends: 3), and there are also years when dividends were reduced (or suspended).

As a result, APP is better framed today not as an income stock built around dividends, but as a name where the main debate is the balance between growth investment, margin expansion, and financial leverage. Notably, while TTM free cash flow is large at 33.54億USD, the structure also includes Debt/Equity of 3.26x.

7. Sources of growth: In addition to revenue growth, margin improvement is a major contributor

EPS growth can be framed as coming not only from strong revenue growth, but also meaningfully from operating margin expansion (from negative territory in FY 2022 to ~40% in FY 2024). Shares outstanding show a slight decline on an FY basis, so the share-count effect appears secondary.

8. The success story: Why it has won (APP’s “path to winning”)

APP’s path to winning can be boiled down to the single most important point in advertising: raising the odds that ad spend turns into outcomes (hit rate).

• Outcome-centric (ROI-centric) design: Advertisers spend when ROI works and stop when it doesn’t. APP is built around that reality, aiming for a structure where more outcomes translate into more transactions.
• Two-sided marketplace flywheel: It brings advertisers (demand) and app inventory (supply) into the same venue, and as transactions grow, learning opportunities expand. That increases the odds of “it gets smarter the more it’s used.”
• Productizing operations (self-serve): By moving from human-driven operations to tool-driven, repeatable workflows, it can aim to avoid a linear relationship between customer acquisition and operating cost.

9. Is the story still intact?: Recent moves and consistency (narrative alignment)

Recent corporate actions appear broadly consistent with the success story, while a newer issue—data handling—is increasingly moving to the center of the narrative.

From a “game company” to an “advertising platform company”: Unifying the narrative

With the sale of the gaming business at the end of June 2025, it has become easier—even at the headline level—to describe the company as “advertising-focused.” This isn’t a judgment of good or bad; it’s simply that the narrative has become more coherent.

From “human-operated” to “self-serve”: Changing how the company scales

From the second half of 2025 onward, the self-serve narrative has strengthened, and messaging has shifted toward “customer growth through the product” rather than “sales execution.” That is consistent with a push to distribute APP’s existing winning formula—“outcome-centric × AI optimization”—more broadly.

At the same time, “data handling” is becoming the central issue: A phase where operating conditions are more likely to be questioned

The advertising business is highly sensitive to privacy, regulation, and platform policies, and APP has continued updating state-law compliance and privacy disclosures. In addition, in October 2025, it was reported that authorities were investigating data collection practices. Separate from performance, this shift makes operability and accountability more likely to become central to the story.

10. Invisible Fragility: Eight ways it can break—worth checking most when it looks strongest

Here, without claiming “it is bad now,” we lay out structurally plausible failure modes as practical detection points.

• ① Concentration in customer dependence (continued reliance on app advertising): If expansion into non-gaming areas such as e-commerce doesn’t progress, maturation in the core domain becomes more exposed.
• ② Rapid shifts in the competitive environment: The mediation-adjacent space is concentrated, and feature/term changes by major players like Google/Unity can reshape the landscape quickly.
• ③ Loss of differentiation (commoditization of hit rate): Optimization technology can diffuse, pushing differentiation toward data, operations, and connection density. If measurement constraints make differentiation harder, competition can tilt toward price/terms.
• ④ Supply constraints from “platform dependence”: Rule changes by OS owners or ad-ecosystem gatekeepers (identifiers, consent, measurement specs) can function as supply constraints.
• ⑤ Deterioration in organizational culture (internal wear): There is not sufficient high-quality primary information after August 2025, making evaluation difficult for this period; however, as a general point, in rapid-growth/high-load phases, frequent priority shifts and cross-team friction can later show up as weaker implementation and support quality.
• ⑥ Deterioration in profitability (the high-profit “new range” reverting): Even if revenue is still growing, profits/cash slowing first—i.e., “divergence”—often shows up as an early signal.
• ⑦ Worsening financial burden (interest-paying capacity): While interest-paying capacity has improved recently, it could deteriorate quickly if profits fall due to weaker performance.
• ⑧ Rising regulatory/authority-response costs: Regardless of whether investigations are reported, legal, audit, disclosure, and technical compliance burdens can rise, reducing operating flexibility.

11. Competitive landscape: Who it competes with, and what determines outcomes

APP’s competitive battle is less about “who has the newest feature,” and more about repeatable outcomes and whether it can maintain a default position in mediation (the operating foundation).

Key competitors (competing in the same arena / potentially disintermediating)

• Google (AdMob/Google Ad Manager, etc.): Strong influence on the OS/measurement/ad-infrastructure side; spec changes can reshape surrounding conditions.
• Unity (Unity Ads / ironSource＝LevelPlay): Deep touchpoints in the game development stack and the supply side, and widely viewed as a major force in mediation.
• Meta (Meta Audience Network): A large source of demand, observed as a top-tier SDK presence.
• Liftoff (formerly Vungle), Mintegral: Established app-focused ad networks, observed as top-tier SDK presence.
• (Supplementary) Appodeal, Chartboost, etc.: Often cited as smaller, peripheral players.

As a supplement, note that SDK presence rankings are estimates of “the share of apps with the SDK installed,” and do not directly represent transaction volume or revenue share.

Competition map by domain (where the battles are fought)

• Advertiser side (acquisition / outcome optimization): The key issues are repeatability of outcomes, optimization under measurement constraints, and expansion into non-gaming (e-commerce, etc.).
• Publisher side (in-app ad monetization / mediation): The key issues are standardized implementation, operational quality, incident recovery, and support for migration to bidding-based formats.
• Measurement / privacy response (the domain that sets competitive conditions): OS/regulation/policy set the “terms,” and operating design under data constraints is what gets tested.

12. Moat (barriers to entry): Not a single wall, but “cumulative”

APP’s moat isn’t one impenetrable barrier; it’s built through the accumulation of multiple advantages.

Elements that build the moat

• Two-sided connectivity (demand × supply): A flywheel is more likely than in a one-sided model.
• Accumulation of operating data and learning: Can translate into more repeatable outcomes.
• Embedding into mediation as an operating standard: Once embedded, SDK/adapters, validation, and operating procedures often become switching costs.
• Expansion via self-serve: Productizing operations changes the shape of scaling.

Elements that erode the moat (factors that can shake durability)

• Tightening privacy/measurement constraints (erosion of data advantage)
• Rule changes by OS owners / large ad platforms (less room for third-party optimization)
• Operational instability tied to bidding migrations or SDK updates (friction often discussed in the community)
• Changes in ad experience or UX feeding back into publisher KPIs, potentially affecting inventory and operating decisions

Switching costs: Heavy on the publisher side but not “absolute lock-in”; advertisers move depending on performance

• Publisher side (app developers): SDK/adapter implementation, configuration, A/B validation, and incident-response workflows are real cost drivers. However, because mediation aggregates multiple networks, there is generally more room to switch than with a single network.
• Advertiser side: Creative, learning, measurement alignment, and operating know-how are cost drivers, but churn can be fast when performance is judged insufficient. The real stickiness comes from repeatable outcomes.

13. Structural position in the AI era: A tailwind, but optimization competition and data constraints “intensify simultaneously”

In the AI stack, APP is not an OS; it sits in the middle layer (marketplace and optimization infrastructure) that handles ad transactions and optimization. Expanding self-serve tools is a move to thicken the middle layer’s value by adding an “easy-to-use UI” (application layer).

Factors that tend to be tailwinds

• Network effects: The more demand and supply circulate, the more learning opportunities expand, making a flywheel where improvement drives usage more likely.
• High degree of AI integration: AI isn’t a bolt-on feature; it’s core logic designed to directly drive outcomes.
• Mission-critical nature: For advertisers, it ties directly to ROI, and budgets tend to flow as long as outcomes are delivered.

Headwinds (or areas where difficulty increases)

• The essence of AI substitution risk: The main risk is less that demand disappears, and more that optimization competition intensifies and differentiation becomes harder.
• Data-handling constraints: Regulation and platform policies can reduce not “performance,” but “operability and degrees of freedom” (reports of authority investigations are an important inflection point on this axis).

14. Leadership and corporate culture: Outcomes (ROI) × technology (AI) × discipline (lean operations)

Based on public information, APP consistently frames its value proposition to advertisers around ROI rather than “revenue”; SEC filings also clearly lay out the idea that advertisers spend when ROI targets are met, and that this alignment is a key driver of growth.

Consistency of CEO/management principles: Ad focus and self-serve can be read as an “extension of the culture”

• The 2025 sale of the gaming business clarified the priority of focusing on the advertising platform.
• Elements such as “a culture of innovation,” “a lean operating model,” “the product sells itself (closer to self-serve),” and “engineering-led refinement of AI” are described as an integrated set.

Persona and decision-making style (generalized)

• Tends to emphasize outcomes, speed, and execution (consistent with an ROI-centric worldview).
• Tends to favor a small, elite team and lean operations.
• Relatively, there may be phases where speed is prioritized over careful dialogue and consensus-building, and where self-serve is prioritized over high-touch human support.

Generalized patterns that tend to appear in employee reviews (no hard conclusions)

• Positive: Aggregations often show relatively high ratings for compensation and benefits; work is described as difficult but interesting/challenging.
• Negative (friction): Work-life balance ratings tend to be polarized; management ratings tend to be split; cohesion and sense of belonging may be cited as areas for improvement.

In addition, SEC filings explicitly note changes in senior roles (e.g., removed from executive officer in November 2024, and CMO stepping down/resigning in March 2025), which also serves as an observation point for culture and governance, indicating that the top-layer design is not fixed.

15. Where valuation stands today (historical comparison vs. the company’s own history only)

Here, rather than comparing to market averages or peers, we focus only on where today’s valuation sits versus APP’s own historical range (primarily the past 5 years, with the past 10 years as a supplement). Price-based metrics assume a share price of 632.91USD (as of the report date).

PEG: Above the normal range over the past 5/10 years (high vs. history)

• PEG (based on the most recent 1-year growth): 0.51

It is above the past 5-year range (20–80%) of 0.17–0.31, and even on a 10-year view it sits above the upper bound of the normal range. Over the past 2 years, PEG has been trending higher, and growth-adjusted valuation is skewed to the expensive side versus history (positioning only within the company’s own historical context).

P/E: High in absolute terms, but the historical range is wide—“mid to slightly high within the range”

• P/E (TTM): 76.24x

The past 5-year median is 71.01x, and the current level is modestly above that median. Because the normal range over the past 5/10 years is extremely wide at 47.94x–392.51x, today’s level still sits within the range. Over the past 2 years, P/E has been trending upward.

Free cash flow yield: Within the range but on the low side (yield trending down)

• Free cash flow yield (TTM): 1.72%

It is below the past 5-year median of 2.16% and sits toward the lower end of the past 5-year distribution. Over the past 2 years, the yield has been trending downward—often what you see when the market is pricing in a lot (no linkage to an investment decision is made here either).

ROE: Far above the normal range over the past 5/10 years (exceptionally high)

• ROE (latest FY): 144.96%

It exceeds the upper bound of the past 5-year normal range of 92.16% and also far exceeds the past 10-year upper bound of 76.91%. Over the past 2 years, it has been trending upward. However, because APP has a history of large swings in equity (including years with negative equity), we limit this to the factual statement that “its position in the distribution is extremely high.”

Free cash flow margin: Above the historical range (a different range of elevation)

• Free cash flow margin (TTM): 60.75%

It far exceeds the upper bound of the past 5-year normal range of 34.65% and the past 10-year upper bound of 31.45%, making it exceptionally high versus history. Over the past 2 years, it has been trending upward.

Net Debt / EBITDA: Below the historical range (smaller = more financial flexibility)

• Net Debt / EBITDA (latest FY): 1.20x

It is below the lower bound of the past 5-year normal range of 2.11x, and also below the 10-year lower bound of 2.15x. Note that this metric is an inverse indicator where smaller (more negative) implies more cash and greater financial flexibility, and the current level is smaller versus history. Over the past 2 years, it has been trending downward (toward smaller).

“Where we are now” across six metrics

• Valuation: PEG is above the historical range; P/E is within the range (slightly above the median); FCF yield is within the range but low (toward the bottom).
• Profitability / quality: Both ROE and FCF margin are above the historical range.
• Financials: Net Debt / EBITDA is below the historical range (on the smaller side).

16. What customers value / what they are dissatisfied with: Visualize “on-the-ground friction” in the product

For an ad platform, strength ultimately comes down to “whether it keeps getting used in the field.” Below are generalized patterns cited in source articles for investors.

What customers tend to value (Top 3)

• ① Perceived ability to deliver practical results: For advertisers, outcomes like acquisition and purchases; for publishers, higher ad revenue tends to be the key yardstick.
• ② Ease of operation (automation / self-serve): Automated setup and optimization—and the ability to run the program with a small team—tends to be valued in its own right.
• ③ Ease of flywheel dynamics from the two-sided structure: The more demand and supply circulate in the same venue, the more learning compounds—this flywheel tends to be valued.

What customers tend to be dissatisfied with (Top 3)

• ① Anxiety about rising dependence (black-boxing): The better the results, the more dependence can grow—while frustration can come from not being able to explain why results happen (or why they suddenly stop).
• ② “Fragility” of SDK/settings/mediation: Updates or integration mismatches can create sudden instability, showing up as real on-the-ground friction.
• ③ Burden of privacy/regulatory compliance: As opt-out and other requirements increase, implementation burden and accountability rise, affecting UX and how results show up.

17. A “two-minute” map for long-term investors (Two-minute Drill)

The long-term way to view APP can be reduced to one idea: it’s a “machine that improves advertising outcomes.” The foundation investors should build is a set of hypotheses like the following (this is not a buy recommendation—just an organized set of conditions for the thesis to hold).

• Repeatability of outcomes: Whether AXON AI’s “hit rate” remains repeatable even under measurement constraints and shifting inventory.
• The shape of scaling: Whether self-serve (Ads Manager, etc.) advances and creates a structure where customer acquisition and operating costs don’t rise linearly.
• Success of expansion: In non-gaming (e-commerce, etc.), how broadly the winning approach from app advertising translates (across a wide customer base, not just a handful of cases).
• Resilience to operating conditions: Whether changes in data handling, regulation, and platform policies stay in the realm of “added cost” rather than becoming “fatal damage.”

As a long-term “type,” this stock leans Fast Grower but also shows large profit swings (a Cyclical-like trait). That means the stronger the numbers look, the more important it is to look for “friction” across three areas: repeatability of outcomes, operational quality, and data constraints.

18. Understanding via a KPI tree: The causality that increases enterprise value, and the constraints that bind

APP can look complex, but once you unpack the cause-and-effect, the monitoring points become clearer.

Ultimate outcomes

• Expansion of profits and cash generation (free cash flow)
• Improvement and maintenance of profitability (margins and cash conversion)
• Improvement in capital efficiency and maintenance of financial durability (interest coverage and liquidity)

Intermediate KPIs (value drivers)

• Transaction scale: How much ad transaction volume flows through the platform
• Repeatability of advertiser-side ROI: Budgets tend to flow as long as outcomes are delivered, and can stop quickly when they break
• Publisher-side profitability: Inventory aggregates and the demand “sink” becomes deeper
• Connection density of demand × supply: Flywheel as a two-sided marketplace
• Optimization performance (hit rate)
• Penetration of self-serve operations: Reduces reliance on headcount and makes scaling non-linear
• Expansion of customer mix: From app-centric to non-gaming
• Operational quality: Stability of SDK/settings/measurement integrations
• Adaptation to regulatory and platform constraints: Operability under data constraints

Constraints and bottleneck hypotheses (Monitoring Points)

• Demand instability inherent in performance-based models: When outcomes deteriorate, advertiser budgets can move quickly.
• Operational friction: SDK updates, configuration differences, and integration mismatches can impair outcomes.
• Black-box anxiety: As self-serve expands, limited explanation can become friction.
• Data/regulatory/policy constraints: More than performance, operating freedom and compliance cost can become bottlenecks.
• Fixed burdens in the financial structure: Whether interest-paying capacity and liquidity deteriorate ahead of profit changes.