The REIT Screen

Five Checks, One Blind Spot

Five checks before buying a REIT — and the caveat each one needs before it's trusted.

A REIT (Real Estate Investment Trust) owns real estate and pays out most of its rental income as distributions. That structure is why it needs its own screening approach, not the ratios used for an ordinary company.

CHECK 01

Sector, Before Ratios

Before any ratio, decide what kind of real estate the REIT actually owns — because no ratio tells you whether that property type has a future. An office REIT and a nursing-home REIT can post identical debt ratios and coverage numbers while facing completely different demand trajectories: one shaped by remote work eroding tenant need, the other by demographic aging increasing it. The ratios below screen for financial health within a sector; they say nothing about whether the sector itself is growing or shrinking.

CHECK 02

Price-to-Book, Relative Not Absolute

Price-to-book compares the share price to the REIT's book value per unit — the real estate carried at its last appraised value.

There's no meaningful flat cutoff here. Across the REIT market, sector medians range from meaningful discounts to book (office, historically 50%+ below NAV in weak periods) to consistent premiums (self-storage, data centers have both traded above NAV for extended stretches) — so "below 1" often just reflects the structural norm for that property type, not mispricing. The more useful read: compare the REIT's current P/B against its own multi-year average, and against peers in the same property sector.

Book value also lags — it only updates at each appraisal cycle, so it can sit a year or more behind real conditions. The REIT-specific check that captures cash performance directly is FFO (Funds From Operations): net income with depreciation added back and property-sale gains stripped out, since REITs write off large non-cash depreciation on buildings that don't actually lose value the way accounting assumes. Divide price by FFO per unit to get P/FFO, and compare it the same way — against the REIT's own history and against peers in the same property sector, since typical multiples differ by sector. FFO isn't on general free screeners like Reuters or Yahoo Finance — it's in the REIT's own quarterly earnings release.

CHECK 03

Interest Coverage, Above 3x

Interest coverage divides EBIT by interest expense — how many times over the REIT can pay its interest bill from earnings. Above 3x is the standard threshold for absorbing an earnings dip or a rate shock without slipping toward distress.

Sector averages have run higher than that floor — post-2008 deleveraging lifted the REIT industry average coverage ratio to around 5.4x, so a REIT above 5x is comfortably ahead of the pack, not just clearing the safety bar. Regulatory floors sit lower still: Singapore's MAS requires a minimum of 1.5x for all S-REITs since 2024. Treat 3x as the safety line, 5x+ as strength above the sector norm.

CHECK 04

Leverage, Under 50%

Total-debt-to-equity below 50% is generally read as conservative gearing — roughly 66% equity funding the portfolio against 33% debt, the shape of a conventional mortgage.

This isn't the ratio regulators cap. MAS limits aggregate leverage — total debt against total assets, a different denominator — to 50% for all S-REITs since 2024; Hong Kong's cap is 45%. The two measures move together but aren't interchangeable, and the cap varies by market. Check debt-to-equity as your own filter, separately from whatever regulatory leverage cap applies to the REIT.

CHECK 05

Occupancy and Lease Expiry

Occupancy is the share of a REIT's floor space or units currently earning rent — the easiest of the five checks to find, in every REIT's quarterly factsheet.

Occupancy is a snapshot, not a forecast. WALE (weighted average lease expiry) shows how much of that occupied space needs re-letting soon — a REIT at 95% occupancy with a large share of leases expiring within 12 months carries rollover risk occupancy alone doesn't show. Check lease-expiry profile in the annual report alongside the headline occupancy number.

The Blind Spot Isn't the Numbers

The ratios test financial health; the sector call tests whether that health is worth having. Get the second one wrong and the first four won't save you.

Why the Math Rarely Works

Early retirement isn't a lifestyle. It's an equation — and most people never see all three variables.

Every early-retirement plan eventually runs into the same wall: the numbers don't add up, and it isn't for lack of ambition or discipline. It's because most plans are built on three assumptions that don't hold. There is no truly risk-free place to park capital. Life runs longer than the plan accounts for. And the safety net most people are quietly counting on is structurally weaker than it looks. None of these are pessimistic takes — they are the starting conditions anyone serious about retiring early has to work within, not around.

No Safe Harbor

We're taught to think of certain assets as "risk-free" — government bonds, cash in the bank, a pension promise. In practice, none of them are, once you account for what governments do to their own currencies over long stretches of time. Debt gets monetized. Money supply expands. The purchasing power of a "safe" asset erodes quietly, year after year, while the nominal balance stays exactly where it was. A retirement plan that treats any single asset as immune to this is starting from a false premise.

This isn't a call to abandon caution — it's the opposite. It's why the entire idea of a portfolio built to survive different economic regimes, rather than one built around a single "safe" holding, exists in the first place. The point stands on its own: safety has to be engineered, not assumed.

A Longer Runway

The second problem is quieter but just as corrosive. Life expectancy keeps extending — not dramatically year to year, but steadily, decade after decade, through better medicine, better prevention, better everything. A retirement plan calculated for a 25-year runway that actually needs to cover 35 doesn't fail loudly. It fails slowly, in the last years, exactly when there's the least room left to adjust.

The honest response isn't to guess a number and hope. It's to build in a margin wide enough that being wrong about your own timeline doesn't wreck the plan — and to revisit that estimate periodically rather than setting it once and forgetting it.

Borrowed Time

The third headwind is the one most people lean on without examining it: the public pension waiting at the end of the working years. Most public pension systems are pay-as-you-go — today's contributions fund today's retirees, not tomorrow's. That structure depends on a widening base of workers to stay solvent. When the base narrows — fewer births, longer retirements, more retirees per worker — the system doesn't quietly self-correct. It has to be reformed, and reform usually means later retirement ages, lower real payouts, or both.

None of this means early retirement is impossible. It means the plan has to be built assuming these three conditions are permanent features of the landscape, not temporary inconveniences. That's a very different starting point than most retirement content offers — and it's the one we start from.

Three Variables, Not One

The math doesn't fail because people aren't disciplined enough. It fails because the plan assumed a risk-free asset, a fixed lifespan, and a reliable safety net — and none of the three actually exist.

The Math Behind the Price Target

Four studies, one pattern: the more optimistic the call, the less you should trust it.

When Goldman Sachs, JPMorgan, or any other major bank publishes a 12-month price target, it reads like a forecast. The research says we should treat it as something closer to a position statement — informative about what the bank is willing to put its name behind, far less informative about where the stock will actually trade.

The research desk: where the target gets written, and where the incentives live.

The Track Record

Bradshaw and Brown (Harvard Business School / Georgia State, 2006) examined roughly 100,000 twelve-month price targets issued between 1997 and 2002. By the end of the twelve months, the stock had reached or exceeded the target in only about a quarter of cases. Even allowing for the target being touched at any point during the year — a much looser bar — it happened less than half the time. A separate study, Asquith, Mikhail and Au (2005), found a similar pattern: targets were achieved at some point within the year in 54.28% of cases.

The Direction Problem

Hitting the exact number is one thing. Getting the direction right is a lower bar, and even there the record is weak. Lee, Miao and co-authors (2024, International Review of Economics & Finance) found that only 54% of targets correctly predicted whether the stock would rise or fall — barely better than a coin flip. The same study documented a systematic upward bias of 9.4% and an average absolute pricing error of 24.8%.

This sample was an emerging market, not US large-caps — worth flagging, since it likely overstates the problem for the most liquid, heavily covered names, and understates it for smaller or less-followed ones.

Why the Bias Runs One Direction

Kerl and Walter, studying German stocks, found something specific: the further a target sits from the current price, the less accurate it tends to be, ex post. The most aggressive, most optimistic calls are precisely the ones the literature says to trust least.

A separate strand of research — Dugar and Nathan; Lin and McNichols; Michaely and Womack — ties the upward bias itself to a structural incentive: analysts at banks with underwriting or advisory relationships to the company they cover have something to lose by publishing an unfavorable number. Losing management access, or future deal flow, is a real cost; being wrong about a price target a year later rarely is.

A Rare Call

Sell ratings remain rare today. As of December 2025, FactSet counted 12,696 analyst ratings across S&P 500 stocks: 57.5% Buy, 37.7% Hold, and just 4.8% Sell. That figure has sat in roughly the 5–6% range for years — a small fraction of all coverage, regardless of where the market itself was heading.

We won't claim that scarcity makes a sell call more accurate — we don't have a study that tests that directly, and we'd rather say so than invent one. What we can say is that when a bank does go negative, it's choosing to issue the rating its own incentive structure pushes against. That alone makes it worth a second look, even without a verified accuracy edge attached to it.

Our Own Rule

None of this means ignore Wall Street. It means reading a target for what it is: one institution's public position, shaped by incentives that don't always point toward accuracy. We treat a price target the same way we'd treat a single data point in any model — useful in context, useless as a conclusion on its own. The temptation, especially when a target is far above the current price, is to let the number do the thinking. That's exactly the case the research says to be most careful with.

A Sentiment Reading, Not a Forecast

A price target tells us what a bank is willing to publish about a company it often has a commercial relationship with. It is a data point about sentiment — not a forecast we should weight as a probability.

One Currency

Unequal by Design

The Balassa-Samuelson effect explains why sharing a currency does not mean sharing a standard of living — and why it probably never will.

A German factory worker and a Greek café owner both spend euros. But their euros come from very different productive foundations. In 2023, the average gross monthly wage in Germany was approximately €4,250; in Greece, around €1,450 — both countries inside the same currency union, bound by the same monetary policy, yet separated by a threefold wage gap (approximate figures, European Commission). This is not a flaw in the euro. It is a structural feature of economies at different levels of productivity, first described independently in 1964 by economists Béla Balassa and Paul Samuelson.

Two Tracks

Every economy runs on two tracks. The first is the tradable sector — goods and services that compete internationally: cars, semiconductors, pharmaceuticals, financial services. Prices here are set by global competition and tend to converge across borders. The second is the non-tradable sector — haircuts, restaurant meals, plumbers, taxi rides. These cannot be exported. A Greek barber does not compete with a German barber.

Here is the key mechanism. In a highly productive economy like Germany's, wages in the tradable sector are high — driven by world-class output per worker in manufacturing and industry. Because workers can move freely between jobs within the country, wages in the non-tradable sector get pulled upward too. A Berlin barber charges more than an Athens barber not because he cuts hair faster, but because his alternatives — working in a factory, an office, a lab — pay far more than they do in Greece.

This is the Balassa-Samuelson effect in its purest form: productivity differences in tradables spill over into wages and prices across the entire economy, including sectors where productivity between the two countries is essentially identical.

The Currency Trap

Before the euro, the exchange rate performed a quiet but essential function. If Greece's economy was less productive than Germany's, the drachma would trade at a weaker level than the deutschmark. Greek wages in drachmas might look adequate domestically, but internationally they translated into lower purchasing power — which kept Greek exports competitive and the economy in rough balance.

The euro removed this valve. With a single currency, there is no exchange rate to adjust. A German wage of €4,250 and a Greek wage of €1,450 share the same denomination, with no automatic mechanism to rebalance them. The only adjustment paths that remain are internal devaluation (cutting wages and prices — politically brutal, as the 2010s demonstrated), labor migration (Greek workers relocating to Germany, which happened at significant scale after 2010), or fiscal transfers from richer to poorer members (which the Eurozone's architecture deliberately resists).

This is not a critique of the euro as a project. It is a structural observation: a common currency functions most smoothly when member economies have similar productivity profiles. When they do not, the Balassa-Samuelson effect transforms a monetary union into a permanent source of imbalance.

Hard Data

The productivity differential is not marginal. According to Eurostat, GDP per hour worked in Germany is more than double that of Greece. Wages roughly track that ratio, and the gap is not primarily a story about effort. Greeks consistently work more hours per year than Germans — the OECD data has shown this for decades. The difference is structural: capital stock, industrial complexity, export sophistication, institutional depth.

The Balassa-Samuelson framework predicts precisely this outcome. Where tradable-sector productivity is more than twice as high, economy-wide wages will tend to be higher — not because non-tradable workers are twice as productive, but because the entire productive base supporting their wages is twice as strong.

Structural, Not Circumstantial

Wages in a currency union reflect not just what workers produce, but the productive power of the entire economy around them — and no common currency can equalize that.

Buying Options: Four Rules

Four conditions before every trade. If any one fails, wait.

Options are more accessible than they used to be. More brokers now allow retail clients to buy calls and puts from the start — even when selling is restricted until you build a track record. That opening is welcome. It has also brought a steady stream of the same questions: which strike do I pick? How far out should the expiry be? Should I buy now or wait for a better entry? No framework answers these perfectly, and what follows does not pretend to. These are rules of thumb — a working estimate designed to avoid the most common mechanical traps. They are not a substitute for professional advice, and anyone trading real money should consult a qualified financial adviser before acting.

The four rules below each address a specific way options buyers lose money before a directional thesis even has a chance to play out. Together they form a filter, not a strategy. All four conditions must be satisfied before entering a position. One failure is enough reason to step back.

RULE 01

Time — Minimum 60 Days

Buy options with at least 60 days to expiry. The mechanism here is theta — the daily cost of holding an option as time passes. Theta decay is not linear. It accelerates sharply below 30 days, eroding premium with every session regardless of what the underlying does. Above 60 days, you remain on the flat part of that curve: decay is slow, the option holds its value while the trade develops, and there is time to be right without being pressured into an early exit.

Many experienced buyers prefer 90 days or more, particularly when the thesis requires time for a catalyst to materialise. Short-dated options are not inherently wrong, but they are a different instrument with a different risk profile — one that punishes hesitation and rewards only speed. For a buyer applying these rules, the practical minimum is 60 days.

RULE 02

Strike — At or In the Money

Buy at the money or slightly in the money. A delta in the range of 0.40 to 0.50 gives enough sensitivity to the underlying move without loading the premium with pure time value and directional hope. Delta measures how much the option price moves for each one-point move in the stock — at 0.40, a ten-point move in the underlying translates to roughly four points of option gain. Real leverage, with a real connection to what the stock does.

Deep out of the money options carry deltas of 0.10 or lower. They are cheap for a reason: the probability of profit is low, and most of their price is composed of hope rather than intrinsic value. Experienced traders use them for specific asymmetric bets with high conviction. As a general rule for buyers, they behave like lottery tickets — and lotteries are designed so the house wins.

RULE 03

Volatility — Check IV Rank First

Implied volatility is the premium the market charges above and beyond what current price movement would justify. When IV is elevated, options are expensive — you pay more for the same exposure, and the odds work against you even when the directional view turns out to be correct. When IV is low relative to its historical range, you are buying at a discount, and any move in your direction works in full.

IV Rank measures where today's implied volatility sits within its one-year range, from 0 (historically cheap) to 100 (historically expensive). Before entering any options position, check IV Rank for the specific ticker. The practical tool we use for this is the IV Rank chart from Pineify.

Volatility check: Visit pineify.app/options-iv-rank-chart, enter the ticker, and read the signal displayed on the right side of the chart. Proceed only if it reads Buy Options. If IV Rank is elevated, the premium is already working against you from the moment the position opens.

RULE 04

Earnings — Wait for the Crush

Earnings announcements are one of the most reliable ways to lose money as an options buyer without being wrong on direction. As the reporting date approaches, implied volatility inflates in anticipation of the move — pushing premiums higher regardless of your view. The moment results hit, IV collapses. This is called a volatility crush, and it is mechanical: it happens after every announcement, bull or bear, beat or miss. A buyer can call the stock's move correctly and still lose money if the actual move is smaller than what the inflated premium had priced in.

If earnings are fewer than 30 days away, do not enter. The ideal timing is the day after results have been reported. IV has just crushed — options are at their cheapest. The stock has made its post-announcement move, the uncertainty is resolved, and entering with 90 days to expiry gives approximately two months of clean runway before the next report becomes a factor.

These four conditions function as a gate, not a guarantee. Markets can move against a position even when every box is checked. What the framework removes is the most common set of mechanical disadvantages: overpaying on premium, running out of time, entering into elevated volatility, and absorbing a post-earnings crush. Remove those disadvantages first. Everything else is the trade.

You can follow every rule and still lose.

If the stock does not move in your direction, none of this saves you. The four conditions remove the mechanical traps. They do not replace the hardest part: being right.

Gold Isn’t Broken

Governments Are Just Paying Their Bills

Gold just had its worst week since 1983. The sell-off wasn't a verdict on gold. It was a demonstration of exactly why gold works.

Gold fell more than 10% in seven days — its worst weekly performance in 43 years. From an all-time high of $5,595 in late January to an intraday low near $4,100 on Monday. Commentators reached for the word "collapse." I would use a different word: clarity.

What we witnessed is not a breakdown of gold's role. It is, on closer inspection, the most precise confirmation of that role in a generation. To understand why, you need to look at what governments have been doing quietly — and why they are doing it now.

01

Turkey Lit the Fuse

On March 26, the Central Bank of the Republic of Türkiye (CBRT) published its weekly reserve statistics. The data left little room for interpretation.

Official Data · CBRT Weekly Reserve Statistics · 26 March 2026

Week of March 13 — gold change − 6 t

Week of March 20 — gold change − 52.4 t

Total deployed, March 2026 ~ 56 t

Estimated market value ~ $8 billion

Total reserves (end March 20) 772 t

Approx. 22 t sold outright; ~31 t deployed via gold-backed swap agreements to generate FX liquidity.

The trigger was the US-Israeli military strike on Iran on February 28. The geopolitical shock sent the Turkish lira to successive all-time lows — eleven record lows in sixteen trading days. Since the conflict began, the CBRT has conducted $33.7 billion in foreign currency sales. When those reserves proved insufficient, it turned to gold.

This is the largest weekly gold drawdown in Turkey since August 2018, the last time the lira collapsed under external pressure. The mechanism is identical: sell or pledge the one asset that every counterparty accepts without question.

The historical context makes the move all the more striking. Turkey had spent five years building one of the most aggressive gold accumulation programmes of any central bank globally.

641 t Turkish gold reserves
January 2026

+55% Increase since 2021
(+219 tonnes)

#10 Global ranking
among official holders

Turkey had built the war chest for exactly this kind of moment. Now it is spending it — which is, of course, precisely what war chests are for.

02

Turkey Is Not Alone

The same logic is playing out across multiple balance sheets simultaneously. The Bank of Russia has been a net seller of gold since 2025, drawing reserves to a four-year low to fund its ongoing war in Ukraine — raising an estimated $2.4 billion in the first two months of 2026 alone. Poland — the most aggressive gold buyer of the past three years — is now openly discussing monetising unrealised gains from its holdings to fund defence spending rather than continuing to accumulate.

These are not isolated idiosyncratic events. They share a common structure: an energy or security shock, a currency under pressure, and a government that needs hard, unconditional liquidity in a hurry. Gold is the answer each time.

"It is likely that some central banks are selling gold to defend their currency and/or to fund energy purchases."

— Bernard Dahdah, Analyst, Natixis

03

How Much Did This Move the Market?

Turkey's $8 billion in two weeks is real and visible supply entering the market. But the global gold market trades $150–200 billion per day. That volume alone cannot arithmetically explain the recent drawdown.

Goldman Sachs provides a useful calibration: every 100 tonnes of net central bank purchases moves the gold price by approximately 1.7%. Applied inversely, Turkey's 56 tonnes of outright sales and swaps implies roughly a 0.95% mechanical price impact — meaningful, but not sufficient to account for the full move on its own, unless accompanied by others.

Also, the amplifier was a simultaneous macro regime shift. Rising real interest rates, a strengthening US dollar, and oil-shock-driven inflation fears all converged in the same fortnight — forcing leveraged paper traders to sell gold futures to meet margin calls on other positions. The futures market, not the physical market, drove the price. Physical gold premiums remained elevated throughout. The metal continued to change hands well above the futures screen.

BNP Paribas offered the clearest historical frame: the pattern is structurally identical to 2008, 2020, and 2022 — a sharp initial decline as financial stress forces liquidation, followed by recovery once the macro shock is absorbed and the fundamental bid reasserts itself.

Gold Does Not Fail Under Stress.
It Gets Spent

Consider what has actually happened in the past month. When Turkey needed to defend its currency, it did not sell US Treasuries first. It did not pledge equities. It did not liquidate real estate. It went to its gold.

When Russia needed hard currency to fund a war, it went to its gold. When Poland needed collateral for defence spending, it looked at its gold. Every single government under acute financial stress — regardless of political system, geography, or ideology — reached for the same asset.

Not out of habit. Because gold is the only asset whose value the counterparty accepts unconditionally: no credit risk, no issuer, no central bank that can print more of it overnight, no sanctions regime that can freeze it if held physically.

The sell-off is not evidence that gold has failed. It is the most powerful possible confirmation that gold works — liquid, universally valued, and convertible into real resources at precisely the moment when everything else is under strain.

Under stress, governments need to liquidate assets the others trust.

$5,000 remains the key technical level. Current institutional targets for 2026:

$6,300 J.P. Morgan
2026 target

$6,000 Deutsche Bank
2026 target

$5,000 Key support
level to watch

Both targets were set before the Iran escalation added a new structural demand driver. The correction is loud. The thesis is unchanged.

Sources

CBRT Weekly Reserve Statistics, 26 March 2026

World Gold Council — Central Bank Gold Reserves by Country (IMF IFS, December 2025)

Bloomberg — "Turkey Sells and Swaps $8 Billion in Gold," 26 March 2026

Reuters / Kitco News — "Turkish gold reserves in largest drop in 7 years," 26 March 2026

Goldman Sachs · BNP Paribas · Natixis · J.P. Morgan · Deutsche Bank

The Case for Real Assets

In 2026

High public debt, persistent inflation risks, and the energy transition are combining to favor commodity producers and tangible-asset businesses over long-duration growth stories.

2026 looks like a reasonable moment to tilt the stock portion of a diversified portfolio toward companies linked to real assets: energy producers, commodity-related businesses — oil and gas, copper and other industrial metals — and certain infrastructure and real-asset stocks.

Several major market outlooks point to a convergence of three conditions that can support this tilt over time: high public debt levels, persistent inflation risks that keep nominal rates elevated, and the massive capital requirements of the global energy transition. Together, these factors create a backdrop that has historically rewarded businesses whose revenues are tied to physical production rather than to future earnings growth.

After years dominated by cheap money and a narrow group of tech winners, markets may be starting to price businesses whose worth is anchored in real production capacity — not in stories alone.

In past periods of higher inflation or supply shocks, stock allocations biased toward commodity producers and energy companies have often held up better than long-duration growth names. The reason is structural: companies that pump barrels, mine tonnes, or generate megawatt-hours can pass higher prices directly into revenues and profits. Long-duration growth stocks derive most of their value from earnings years in the future — and those future earnings are worth less when discount rates stay elevated.

How We Are Positioning

SimplyNoRisk is reflecting this view by gradually steering new stock investments toward companies backed by tangible assets and solid cash flows. The adjustment is deliberate and incremental — not a wholesale rotation. Overall diversification is maintained, and a comfortable cash buffer is kept in place to manage uncertainty and capture opportunities if conditions shift.

When money is no longer cheap, the value of things that are genuinely scarce tends to reassert itself.

Screeners

One set of rules, two independent tools, and a discrepancy worth reading instead of ignoring.

A lot of people ask us where we find good companies to buy. The honest answer is that we don't — not directly. What we do is set the rules first, then let a screener tell us which companies happen to satisfy them. This matters because "good company" is not an objective category. It is a set of parameters — a dividend floor, a debt ceiling, a growth minimum — that reflects a particular view of risk. Someone else's parameters will surface a completely different list, and neither list is more correct than the other. What follows is the method, not a stock pick.

There are plenty of strong screeners built specifically for the U.S. market. International coverage is thinner. Two free tools that do cover non-U.S. exchanges are Uncle Stock and TradingView's built-in screener — both let you filter by country, dividend yield, leverage ratios, and a range of other fundamentals without paying for a data terminal.

Rules Before Results

A screener is a filter, not an opinion. You give it a universe of stocks and a set of numerical thresholds, and it returns whichever companies clear every threshold — nothing more. It does not know a company's story, its management quality, or its competitive position. It only knows whether the numbers in its database happen to satisfy the conditions you wrote down.

That is precisely the appeal. Deciding on the rules before looking at any names forces the discipline that most retail research skips: you commit to "dividend yield above 4%, debt-to-equity below 50%" before you know which companies that implies, rather than picking companies you already like and rationalizing the numbers afterward. The screener is a discipline device as much as a discovery tool.

Two Tools, Not One

We run every screen through two independent tools rather than one, for a simple reason: each pulls its underlying fundamentals from a different data vendor, updates on a different schedule, and sometimes defines the same ratio differently — gross debt versus net debt, trailing yield versus forward yield, latest reported quarter versus latest available quarter. Run the identical criteria through two sources and you will rarely get an identical list back. That gap is not a flaw in either tool. It is information about how reliable a given data point actually is.

Say we want an Australian company yielding more than 4% with debt-to-equity under 50%.

Market Australia (ASX)

Dividend Yield > 4%

Debt / Equity < 50%

Set those three conditions in Uncle Stock and again in TradingView's screener, and it is genuinely useful — not just a curiosity — that the two result lists usually only partially overlap.

Reading The Gap

Names that clear the bar on both tools deserve the first look. Two independently sourced datasets agreeing on a dividend yield and a leverage ratio is a mild form of confirmation — it does not guarantee the number is right, but it rules out a single vendor's data error or a stale price feed being the whole explanation.

Names that appear on only one list are the more interesting case, and the instinct to discard them is usually wrong. The disagreement is worth tracing back to its source — a different fiscal-year-end being used, a recent capital raise one vendor has priced in and the other has not, a dividend that was just cut but only one database has updated. Sometimes the "missing" name is the more accurate one; the screener that excluded it was working from stale data. A five-minute check of the company's own latest filing settles it either way, and settling it is the actual work — the screener only narrows the pool of names worth checking.

One caveat worth stating plainly: free tools get discontinued, repriced, or restricted without warning. Confirm a screener is still active and still free before building a routine around it — the method above (set the rules, cross-check two independent sources, investigate the disagreement) is what's durable, not any specific tool name.

The Discipline Is The Product

A screener doesn't find good companies. It finds companies that match rules you set in advance — and the disagreement between two independent screeners is where the real due diligence starts, not where it ends.

Eighteen Years…

and Counting

It's hard to believe, but eighteen years have passed since the first post here. September 2007 feels both distant and familiar — a different world in many ways, yet one whose questions still resonate.

From the beginning, we never aimed to build an audience, sell a product, or chase clicks. We wrote because we wanted to think aloud — about money, risk, freedom, and how to live with a bit more intention. Over the years, that habit quietly shaped its own rhythm: one post after another, sometimes frequent, sometimes rare, but always genuine.

Reading back through old entries is a strange experience. Some ideas could have been written yesterday; others belong clearly to their time, tied to specific market moments or contexts that have long passed. Yet together they form a map — not of forecasts, but of curiosity and persistence.

Consistency matters more than perfection. Restraint often ages better than opinion.

Eighteen years give perspective. They show that there's real value in keeping something independent and free of noise — personal, unhurried, and without shortcuts. No ads, no algorithms. Just words, written when there's something worth saying.

Here's to what's already been written, and to whatever still deserves to be said.

Rotation Theory: Gold vs. Stocks

Markets rarely move in straight lines. Over long cycles, leadership rotates from one asset class to another. For the past decade, stocks—especially U.S. large-cap technology—have strongly outperformed gold. That dominance has left equities, and particularly the S&P 500, trading at expensive valuations by historical standards.

Gold, on the other hand, has been largely ignored, yet it is starting to regain attention. Rotation theory suggests that when one asset has been stretched for too long, capital may begin to flow toward the alternative. If that pattern repeats, the next few years could see gold outperforming stocks.

Why investors are watching this shift

• Stock valuations, especially in the U.S., look elevated.
• Inflation and currency debasement risks increase demand for hard assets.
• Gold has a history of performing well when confidence in financial markets weakens.

Because of this, a number of investors are overweighting gold (through ETFs like GLD) and gold miners (through funds like GDX) relative to stocks. Some add silver into the equation.

A balanced perspective

Nothing in markets is certain. Gold may fail to outperform, or equities may continue their run. For investors who do not want to sell their stock portfolios outright, one approach is to reposition within equities—keeping exposure, but tilting away from the most overvalued parts of the market.

An alternative stock allocation for the non-gold part

As a thought experiment, here is one possible equity mix that avoids the most expensive U.S. growth stocks and instead emphasizes regions and sectors with more reasonable valuations:

Region / Sector	%	ETF	Rationale
Emerging Markets ex-China	25	IEMG	Growth from India, Mexico, Indonesia, lower valuations than U.S.
Japan	20	EWJ	Corporate reforms, attractive valuations, shareholder-friendly changes.
Europe (broad)	20	VGK	Exposure to developed Europe at cheaper multiples than U.S. peers.
Energy (global/US)	15	XLE / IXC	Hard assets, strong cash flows, hedge against inflation.
Industrials / Infrastruc.	10	EXI	Benefiting from reshoring, defense spending, infrastructure projects.
Healthcare (defensive global)	10	IXJ	Stable demand, demographics, defensive anchor.

Final word

This is not advice, just theory. Rotation may or may not happen. But if gold does gain leadership over stocks, holding a mix of gold, gold miners, and a diversified set of reasonably valued equity sectors could be one way to stay balanced.

The AI Job Crisis

Solutions Before Disruptions

Artificial intelligence (AI) is transforming our world, unlocking vast potential but threatening millions of livelihoods. The IMF’s 2024 analysis estimates that 40% of global jobs are at risk from AI, with up to 60% in advanced economies facing disruption. This isn’t just about factory workers—doctors, artists, and analysts face automation too. Past technological shifts created new roles, but AI’s speed and scope may overwhelm markets’ ability to keep up. If millions lose their jobs, societies could fracture, with economic instability inviting authoritarianism, as history warns.

Libertarian and Austrian economics, with their trust in individual ingenuity over state control, offer a way forward, but we need specific, practical solutions—not just faith in markets—to ensure freedom and opportunity endure. The crisis demands clarity. The IMF’s figures paint a stark picture: in advanced economies, nearly two-thirds of jobs could be affected, from routine tasks to high-skill professions. Austrian economics, rooted in entrepreneurial adaptation, suggests markets can respond, but not without deliberate steps to empower individuals. Left unchecked, mass unemployment risks desperation, eroding the liberty we cherish. We must act decisively, blending pragmatism with principle, to avoid a future where centralized power exploits economic chaos. Libertarianism prioritizes minimal government, rejecting heavy-handed policies like AI taxes that stifle innovation. Yet, the scale of this disruption calls for bold, market-compatible measures.

Friedrich Hayek, the Austrian economist, hinted at this balance in The Constitution of Liberty (1960), advocating a minimal income to prevent destitution while upholding market dynamics. Inspired by this, a universal basic income (UBI) intended to offer temporary stability amid the AI-driven job crisis is impractical, as funding through voluntary contributions—such as profits shared by firms across all industries benefiting from AI opting into a decentralized pool—cannot be sustained due to the absence of sufficient funds. This idea, while theoretically appealing, cannot be implemented in practice because no viable non-coercive revenue source exists, rendering it unfeasible regardless of libertarian compatibility.

Another game-changer lies in property rights, the cornerstone of Austrian and libertarian thought. Ludwig von Mises saw property as the foundation of markets, enabling prices to guide resources. Today, your data—your online habits, social media posts—is property, but tech giants like Google control it. Digital property rights, secured through blockchain or smart contracts, would let you own and monetize your data, creating income outside traditional jobs. Unlike traditional property, like land, digital property is intangible and often platform-locked, but it’s no less yours. By selling your data to advertisers or AI developers, you could earn a steady income, rooted in market-driven value, not state handouts. This empowers individuals, aligns with Austrian price mechanisms, and sidesteps dependency.

To turn this vision into reality, targeted actions can bolster freedom and resilience:

• Establish digital property rights via legislation and blockchain, enabling individuals to profit from their data in a free market.

• Deregulate startups by removing licensing barriers, fostering new industries as satellite data once spurred weather forecasting growth.

• Strengthen decentralized platforms to keep economic power with individuals, reducing the risk of authoritarian overreach.

• Encourage voluntary innovation hubs where communities and businesses collaborate to create new economic opportunities.

Government’s role remains limited: enact these frameworks—protecting rights, easing regulations—then step back. The market has always surprised us with adjustments once deemed impossible, or perhaps this marks the quiet end of libertarian ideals in the face of relentless automation. 

The Shiller P/E Ratio

A Simple Guide for Everyone

If you’ve ever wondered how to tell if the stock market is overpriced or a bargain, the Shiller P/E ratio is a tool you’ll want to know about. It’s a popular way to measure the value of stocks, and it’s easier to understand than it sounds. In this article, we’ll break it down step-by-step: how it’s calculated, where you can find it, if it’s used beyond the S&P 500, and how to use it to guess what returns might look like. Let’s dive in!

1 How Is the Shiller P/E Ratio Calculated?

The Shiller P/E ratio, also called the Cyclically Adjusted Price-to-Earnings (CAPE) ratio, was created by economist Robert Shiller. Unlike the regular P/E ratio, which just looks at a stock’s current price divided by its earnings from the past year, the Shiller version takes a longer view to smooth out the ups and downs.

Here’s how it works in simple steps:

- Step 1: Take the price of the S&P 500 (or another index or stock) right now.

- Step 2: Gather the earnings per share (EPS) for the past 10 years.

- Step 3: Adjust those earnings for inflation so they’re all in today’s dollars (this keeps things fair over time).

- Step 4: Average those 10 years of adjusted earnings.

- Step 5: Divide the current price by that 10-year average.

For example, if the S&P 500 is at 5,000 and its 10-year average inflation-adjusted earnings is 150, the Shiller P/E would be 5,000 divided by 150 = 33.3. That’s it! The idea is to avoid getting thrown off by short-term booms or busts in earnings.

2 Where Can You Find It?

You don’t have to crunch the numbers yourself—plenty of free resources track the Shiller P/E ratio for you. Here are some great places to look:

- Multpl : A simple site with the current Shiller P/E and a chart going back over 100 years.

- GuruFocus: Offers the latest Shiller P/E for the S&P 500, plus other market insights.

3 Does It Exist for Other Markets?

Yes! While it’s most famous for the S&P 500, the Shiller P/E has been calculated for other markets too. Researchers and financial sites have applied it to indexes like the Dow Jones, NASDAQ, and even international markets such as the UK’s FTSE 100, Japan’s Nikkei 225, and emerging markets. However, the data might not be as widely available or go back as far as it does for the S&P 500. Sites like GuruFocus or research papers from economists often include CAPE ratios for these other markets if you dig a little.

4 How to Use It: Ranges and Expected Returns

So, what does the Shiller P/E tell us? It’s like a thermometer for the stock market—higher numbers suggest stocks are expensive (overvalued), and lower numbers hint they’re cheap (undervalued). Over time, it’s been linked to future returns: when it’s high, expect lower returns over the next decade; when it’s low, expect higher ones. Here’s a simple guide:

Historical Average: Since the late 1800s, the Shiller P/E for the S&P 500 has averaged around 16-17. Think of this as “normal.”

Ranges:

  - Below 15: Stocks are cheap! This has happened during big crashes, like 2008-2009 when it dipped to 13.

  - 15-25: Fair value territory—neither a steal nor overpriced. This is where it sits most of the time.

  - 25-35: Getting expensive. Investors are paying a premium, like in the mid-2010s or today (it’s around 33-35 in 2025).

  - Above 35: Very high! It hit 44 in 1999 before the dot-com crash and has only topped 35 a few times (1929, 2000, and recently).

Expected Returns Per Year:

  - Below 15: Historically, returns over the next 10-20 years averaged 8-10% per year or more.

  - 15-25: Returns drop to about 5-7% annually—still decent but not amazing.

  - 25-35: Expect 2-4% per year. That’s where we are now—modest growth ahead.

  - Above 35: Returns could be 0-2% or even negative, like after the 2000 peak.

For example, with a Shiller P/E of 33, history suggests S&P 500 returns might average 3-4% per year for the next decade—not terrible, but not the 10% many hope for. It’s a clue, not a crystal ball, so use it alongside other info.

P&F Charts: Good, Bad, and Ugly

Let’s talk about Point and Figure (PF or P&F) charts—a classic tool that’s been around forever but doesn’t always get the love it deserves. Unlike those flashy candlestick charts or time-bound bar charts, PF charts are all about price action. No time, no volume, just pure, unfiltered price movements. Sounds simple, right? Well, like any trading tool, it’s got its ups and downs. Let’s break it down: the good, the bad, and the ugly of using PF charts in the stock market.

The Good: Why Traders Love PF Charts 

1. Bye-Bye, Noise!

PF charts are like noise-canceling headphones for traders. They ignore all the tiny, meaningless price wiggles and focus only on the big moves. This makes it way easier to spot real trends without getting distracted by market drama.

2. Trends Made Simple

Upward trend? You’ll see a column of Xs. Downward trend? A column of Os. It’s that straightforward. No overthinking, no complicated patterns—just clear, visual signals.

3. Support and Resistance on Steroids

PF charts are great at showing where prices might bounce or break through. Because they’re not cluttered with time or volume, key levels pop out like neon signs.

4. No Time, No Problem

If you’re a long-term investor who doesn’t care about what happened at 10:32 a.m. last Tuesday, PF charts are your best friend. They don’t care about time—just price. Perfect for keeping your cool during short-term market chaos.

5. Customizable AF

You can tweak the box size (how much the price needs to move to plot an X or O) and reversal criteria (how much it needs to reverse to switch columns). This makes PF charts super flexible for different trading styles.

6. Price Targets You Can Actually Use

PF charts often give you clear price targets based on patterns like double tops or bottoms. No guessing games—just actionable info.

The Bad: Where P&F Charts Fall Short

 1. Where’s the Time?

The lack of time context can be a double-edged sword. Sure, it’s great for filtering noise, but it also means you can’t see when a price move happened. Momentum traders, this one’s not for you.

2. Not for Day Traders

If you’re into scalping or short-term trading, PF charts might feel like trying to use a sledgehammer to crack a nut. They’re better for the big picture, not micro-movements.

3. Settings Can Be Tricky

Choosing the right box size and reversal criteria is key—but it’s also subjective. Pick the wrong settings, and your chart could give you garbage signals. It takes practice to get it right.

4. Missing Pieces

PF charts ignore volume and time, which can be a dealbreaker for traders who rely on those factors. If you’re a volume junkie, you’ll feel like something’s missing.

The Ugly: The Learning Curve

Let’s be real—PF charts aren’t the easiest to master. If you’re used to candlesticks or bars, the whole Xs and Os thing can feel like learning a new language. Plus, they’re not as popular as other chart types, so finding resources or communities to help you out can be tough.

So, Should You Use PF Charts?

Here’s the deal: PF charts are awesome if you’re a trend-focused trader or a long-term investor who wants to cut through the noise. They’re simple, objective, and great for spotting key levels and targets. But if you’re a short-term trader or rely heavily on volume and timing, they might not be your cup of tea. If you want you can play around with the settings, see if they vibe with your trading style, and decide for yourself. After all, the best tool is the one that works for you.

Hedging Portfolios with Reverse ETFs

When your outlook on the stock market turns bearish, selling your entire portfolio (or part of it) isn't the only option to reduce risk. A practical alternative is investing in a reverse or inverse ETF, which profits when the market declines. These ETFs are traded like regular ETFs through any broker and offer a straightforward way to hedge against market downturns.

Using Reverse ETFs Conservatively 

The primary use of a reverse ETF is to hedge your portfolio, not to speculate on market declines. Here's an example:

- Portfolio: $100,000 in stocks and $50,000 in cash.

- Hedge: Allocate $20,000 to a 3x leveraged reverse ETF.

This hedge provides approximate coverage for market drops, as the ETF is designed to move inversely to the market on a daily basis. While the calculation isn't perfect—because reverse ETFs are optimized for daily performance—the hedge can offset some losses if the market falls.

Which Reverse ETF Should You Buy? 

Choose the ETF that matches the market or sector you expect to decline the most. For example, if you anticipate the Nasdaq dropping more than the general market, you might consider a reverse ETF tied to that index. Leveraged options (e.g., 2x or 3x) amplify gains and losses, making them more volatile but potentially more effective for hedging small cash allocations.

Key Considerations

- Reverse ETFs are meant for short-term strategies, as their performance may deviate from expectations over longer periods.

- Not for Speculation: These are tools for protection, not gambling on a bear market.

- Alternative Protection: Selling part of your portfolio remains a valid option for managing risk.

For those interested, here’s a link to tickers of commonly used inverse ETFs.

Are They Really Fighting?

Take a look at this chart. It's a visualization of the S&P 500 divided by the price of gold—basically, what happens when you price the stock market in gold instead of dollars. The result? A story of financial cycles that many miss if they only focus on stocks or only on gold. This chart doesn’t just show market moves; it shows when one asset reigns supreme over the other.

In times when the line trends upwards, it's better to own stocks. Confidence is high, economies are expanding, and the return on equities outpaces the stability gold offers. But when the chart takes a sharp dive? That’s gold's time to shine. These moments represent financial turbulence, recession fears, or market corrections, where investors seek safety in gold’s enduring value.

Now, here's the kicker. Many analysts believe we’re on the verge of another significant downward leg in this chart. If that proves true, it would mean a shift in favor of gold over stocks—a warning shot for those clinging too tightly to equities. But let’s be clear, nothing is certain. What this chart does tell us is that these shifts happen, and when they do, it’s dramatic. Watching for these changes can make all the difference.

That said, it’s not about being all in on gold or stocks. The real strategy is balance. Holding both assets in a portfolio, but adjusting the weight depending on which part of the cycle we're in, is the key. Early in a downward segment? You might tilt toward gold. In the upswing? It’s time for equities to shine. Finding the exact mix is very complicated. What matters is to have the foresight to adjust the desired percentage with the cycles.

This isn’t advice—it’s a reminder to watch the clues, understand the patterns, and adjust your strategy before the next shift catches you off guard.