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July’s trading performance is out!

Here’s the full, unfiltered breakdown of every stock and options trade I closed in July, including both wins and losses.

These are real trades, taken at modest position sizes.

One month is a sample size of one, and P&L in dollars says nothing about the size of the account behind it.

This is a transparency log rather than a performance pitch.

The headline numbers

• Net P&L: +$487.83

• Trades closed: 27

• Win rate: 19 wins / 8 losses (70%)

• Average winner: +$36.15

• Average loser: –$24.87

• Win/loss ratio: ~1.5x

Win rate of 70% is good, but it tells you nothing about whether the system is working.

The number that actually matters is the ratio between average winner and average loser. At 1.5x, wins didn’t just happen more often than losses but they were meaningfully bigger as well.

That combination is what turned a busy but choppy month into a green one.

Strategy breakdown

MRT (Mean Reversion Trend) - the workhorse, 25 trades

This systematic strategy did almost all of the heavy lifting this month: 18 wins against 7 losses (72% win rate) for a net of roughly +$496.89 on its own.

Names like ALAB (+$88.43), FTNT (+$55.98), and VRSN (+$53.51) were the standout winners; CAR (–$35.73) and CRUS (–$28.04) were the biggest drags.

Most of these positions were held anywhere from 2 to 9 days - short, mechanical, in-and-out trades.

MOMO (Momentum) - 1 trade, and it stung

The only MOMO trade closed for the month, MPWR, was also the single worst trade on the sheet at –$84.94.

With a sample size of one, there’s nothing statistically meaningful to conclude here, except that it’s a reminder of why position sizing matters more than any individual system’s edge.

Options - 1 trade, a slow burn

The single options trade of the month was a bull put spread on AMZN, opened May 19 and closed July 31.

This trade was initially in the red. By rolling the trade to a further expiration, it resulted in a 10-week hold that eventually turned green and closed for +$75.89.

What this month actually shows (and doesn’t)

• Process over outcome: the green total is nice, but the more useful data point is that the 72% win rate on MRT held up over 25 trades in a single month, enough reps to say something, though still far from enough to call it “proven.”

• One bad trade can move the whole month: MPWR alone accounted for over 17% of gross losses. It didn’t sink the month, but it’s a good example of why sizing losers small is non-negotiable even inside a systematic approach.

• Not everything needs to be fast: the AMZN bull put spread was open longer than every stock trade combined, and it was still one of the better individual results of the month.

The usual caveat

This is one month of data from one small account running specific systematic systems (bar the options trade).

The process stays the same whether the bottom line is green or red.

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A colleague of mine was laid off recently.

No warning, no performance issues, no story you could point to and say “well, that explains it.”

Just a restructuring, a headcount number on a slide somewhere, and a job that had been a stable part of their life for years - gone in a day.

I’ve been thinking about it since.

Not because it’s an unusual story cause it isn’t, but because of how quietly dependent most of us are on a single source of income without ever pricing in the risk.

The employer is not a diversified asset

If you had 100% of your portfolio in one stock, most of us would call that reckless. We’d talk about concentration risk, about how a single bad earnings call or a single regulatory shift could wipe out years of gains. We wouldn’t need convincing.

But swap “one stock” for “one employer” and somehow the same math stops applying in most people’s heads.

Your salary is the biggest financial asset most people have, bigger than their investment portfolio, often bigger than their home equity in the early years, and yet it’s almost always concentrated in a single position: one company, one manager’s decisions, one industry’s cycle.

That’s not a criticism of having a job.

It’s a reminder that a job is not a guarantee, even when it feels like one. Restructurings don’t check tenure or performance reviews before they happen.

Why “just build multiple income streams” isn’t the useful part of the advice

You’ve heard the advice before- build multiple income streams, don’t rely on one employer. It’s repeated so often in personal finance content that it’s basically wallpaper at this point.

The reason it doesn’t stick isn’t that it’s wrong.

It’s that it’s usually said without the part that actually matters: what counts as diversification, and what doesn’t.

A side hustle that depends entirely on your ability to trade hours for dollars - freelancing, consulting, a second job, reduces your reliance on one employer, but it doesn’t reduce your reliance on your own labor.

If you’re injured, burnt out, or simply unable to work for whatever reason, that income stops too.

It’s diversification in name, not in structure.

What actually changes your risk profile is income that doesn’t require your continuous, active input:

• Dividend income from a portfolio built over time - it keeps paying whether or not you’re working that week

• Systematic or rules-based trading income - not because it’s passive (it isn’t, it requires process and discipline), but because it’s decoupled from any single employer’s fortunes

• Content or a small audience-based business - slower to build, but it compounds in a way a second job doesn’t

• Royalties, licensing, or equity-like stakes in something you built once

None of these are quick. That’s the uncomfortable part of this advice that gets skipped over in favour of the punchier version.

Building a second income stream that actually reduces your risk usually takes years, not a weekend project and a Canva template.

What I’d actually suggest doing this week

Not “quit your job” or “start a side hustle immediately.”

Something smaller and more honest:

1. Write down what percentage of your income comes from one source. For most people it’s 100%. Just seeing that number is useful. My non-work income currently sits at between 15-20% of my total income and I’m looking to increase it over time.

2. Separate your emergency fund from your investment plan. If a layoff happened tomorrow, how many months could you cover before you’d need to touch investments or take on debt? If you don’t know, that’s the actual first project - not picking a side hustle.

3. Pick one income stream that doesn’t require your daily labour, and start it small. Doesn’t have to be trading or dividends specifically. The point is starting something that keeps compounding whether or not you show up to it every day.

The point isn’t to be afraid of employment

I’m not trying to make anyone anxious about their job, or to sell the idea that everyone needs to become a full-time investor or entrepreneur.

Most of the income streams I write about here still take years to matter, and a stable job is still, for most people, the highest-return asset they have in their 20s and 30s.

The point is smaller than that: don’t mistake “stable” for “guaranteed.”

My colleague didn’t do anything wrong. The company’s decision had very little to do with them personally.

That’s exactly the kind of risk that diversification exists to manage, not because you did something wrong, but because you can’t control the one variable you’re fully dependent on.

Build the second stream slowly.

Start this week, even if it’s just the spreadsheet.

P.S. Speaking of the spreadsheet: I put together the actual income tracker I use, free to download it here.

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Every few months, someone asks me the same question in a different costume: “What’s the one thing I should do with my money?”

They’re usually expecting a stock tip. A sector to rotate into. Some options play.

Instead, I give them something much less exciting, but much more useful: buy a broadly diversified index fund, and hold it for at least three years.

That’s it. That’s the strategy. Let me show you why it works.

Point 1: Buy a broadly diversified index fund

The single biggest risk in investing isn’t market crashes, it’s concentration.

Betting on one stock, one sector, or one theme means your outcome depends entirely on being right about that one thing.

Most people aren’t, consistently, over long periods.

Even professionals struggle with this (so what makes us think we can)?

A broadly diversified index fund like the Vanguard S&P 500 ETF (VOO) sidesteps this problem entirely.

You’re not betting on any single company, you own a slice of 500 of the largest, most established businesses in the US economy, weighted by size.

When one sector stumbles, another often picks up the slack.

You’re buying the average outcome of American capitalism, and it turns out the average outcome has been extraordinary.

Since VOO’s inception in September 2010, it has returned roughly 15% annualised, turning a hypothetical $10,000 investment into over $90,000 today.

No stock-picking. No market-timing. No conviction calls on earnings. Just consistent exposure to the broad market.

The lesson isn’t “VOO is magic.”

It’s that diversification removes the need to be right about anything specific. You just need the overall economy to keep growing, which, historically, it has.

Point 2: Hold for at least 3 years

Here’s where it gets interesting, and where most people go wrong.

Zoom out to the last 100 years of the US stock market, and a pattern emerges: the market has finished the year positive roughly 74 times out of 100 — close to three years out of every four. Down years happen, but they cluster around specific shocks (the Great Depression, the dot-com bust, 2008, 2022) rather than showing up randomly every other year. The rest of the time, the market simply goes up.

Here's the case for holding at least 3 years, a single calendar year has been a "win" about 74% of the time, but stretch that same bet to any rolling 3-year period, and the win rate climbs even higher, into the high-80s or better.

You're not changing the market.

You're changing your exposure to the bad years by giving the good years more room to outweigh them.

That alone should reframe how you think about “risk.” Betting on any single year is closer to a coin flip weighted heavily in your favour. But stretch your holding period out, and the odds tilt even further in your direction.

Diversification alone doesn’t protect you from losses if you panic-sell during a downturn.

The real protection comes from time in the market, specifically, from holding long enough that short-term volatility washes out.

I looked at every possible 3-year holding period for VOO since its 2010 inception.

Not just neat calendar blocks like 2020–2022, but every single rolling 3-year window, starting from any month in the fund’s history.

The result:

The worst 3-year holding period in VOO’s entire history still returned over 5% annualised.

Not the best period. The worst.

Even if you had the misfortune of buying right before the COVID crash or right before the 2022 bear market, and held for three years, you still came out ahead every time.

Compare that to shorter windows.

Buy VOO and check your account a month later, and you might be down double digits.

Check a year later, and you might catch a year like 2022, when the fund fell over 18%.

But stretch that same holding period to three years, and the picture changes completely.

The table below shows this using clean calendar-year blocks:

Notice something: even the weakest of these blocks, 2020 to 2022, which includes both the COVID crash and the 2022 bear market, still finished up nearly 25% cumulative. There is no 3-year window in VOO’s history that lost money.

That’s not a coincidence. It’s math.

Short-term price swings are driven by sentiment, headlines, and fear.

Over three years or more, those swings tend to average out, and what’s left is closer to the actual earnings growth of the underlying businesses, which, for the S&P 500 as a whole, has been remarkably resilient.

The takeaway

You don’t need to predict the next Nvidia. You don’t need to time the bottom of the next crash. You only need two things:

1. Own something broad enough that no single failure can sink you.

2. Give it enough time that the noise cancels itself out.

Everything else - the stock picks, the sector calls, the “this time is different” narratives, is optional.

This isn’t.

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On 15 July, GovTech retrenched 93 officers as the first phase of a restructuring that will eventually affect 7-9% of its workforce over two years, around 300 people in total, with another 200-plus redeployed or moved onto apprenticeships.

A Singapore statutory board, the kind of employer people specifically choose because it’s supposed to be the “iron rice bowl”, just carried out its first retrenchment exercise since 2006-2010.

Analysts covering it called it a genuine surprise precisely because government jobs here are seen as “stable”, the ones nobody expects to lose.

The safe jobs were never actually safe

For a long time the story was: repetitive, low-skill work is at risk; knowledge work is insulated.

Learn to code, get a degree, move up the value chain.

That story is dead.

It’s not that it was disproven gently, it collapsed.

The work getting displaced now includes drafting, analysis, first-pass coding, customer support, document review, basic design, junior-level research.

These were the jobs people were told to aim for as the “safe” ones.

They were the plan B, but they’re now the ones under the most direct pressure.

I’m not saying every one of these roles disappears.

Most don’t disappear outright, they shrink.

A team of ten becomes a team of four plus a lot of software.

The four who remain are usually the ones who can direct the software, catch its mistakes, and do the judgment calls it can’t.

Everyone else’s job doesn’t vanish in a headline; it just quietly stops being refilled.

I’ve seen this in my own workplace.

Our team of 12 has become a team of 7; the 5 roles don’t get filled.

Why this matters more than the headlines suggest

The mistake is treating this as a future problem, something to worry about once the tech “matures.”

It’s already showing up in hiring data, in the shape of headcount at companies that used to hire aggressively at the junior level, in how long it takes new grads to land their first role.

This isn’t speculative.

It’s a rate-of-change problem happening now, and rate-of-change problems are the ones people consistently underprice, because nothing looks dramatic year to year, until you look back three years and the org chart is unrecognisable.

I don’t think this is a reason for panic. I think it’s a reason to stop treating your salary as the whole plan.

What actually follows from this, practically

I’m not going to pretend I have a formula that makes anyone’s job automation-proof, anyone who tells you that is selling something (nothing wrong with that but worth being discerning).

But there are a few things that seem directionally right, and they’re the same things I keep coming back to in how I think about my own setup:

1. Income concentration is the real risk, not the job itself.

If 100% of your cash flow depends on one employer’s continued need for your specific role, you have taken on a risk you didn’t choose to price.

That’s true whether or not automation ever touches your industry. GovTech’s staff weren’t at risk because of a skills gap, they were at risk because their entire income sat on one organisation’s org chart.

It’s just made more evident now.

2. Diversify income the same way you’d diversify a portfolio.

Nobody who reads this would put their entire net worth into one stock, no matter how stable that company looked.

We hold a spread of positions precisely because we can’t know in advance which one takes a hit, or when.

A single employer is a single position, concentrated, illiquid, and with a “management team” (your employer) whose decisions you have zero input into.

Multiple income streams, a side project, freelance work, dividend income, a content brand, consulting, function exactly like uncorrelated assets in a portfolio: none of them need to be large individually, but together they mean no single decision by a single institution can zero out your cash flow.

It’s not that you expect your job to disappear tomorrow — it’s the same logic as holding bonds alongside equities. You diversify before you need to, not after.

3. Skills that involve judgment over pattern-matching hold up better.

Reviewing model output, making a call under ambiguity, taking responsibility for a decision, dealing with a client who is upset for reasons that aren’t in the spreadsheet - this is the residual work.

It’s not glamorous and it doesn’t scale the way “10x engineer” narratives do. But it’s sticky.

4. Compounding doesn’t care about your job title.

This is the part I keep returning to.

Money that’s already invested doesn’t need you to keep your job to keep working.

It’s the one part of your financial life that isn’t sitting on the other side of a performance review.

The earlier that engine is running, the less any single employment decision, yours or theirs, determines your outcome.

The honest caveat

I don’t have a crystal ball on which roles get hit hardest or on what timeline, and anyone predicting specific job categories with confidence is guessing same as the rest of us.

Markets and technology both have a habit of moving slower than the panicked take and faster than the complacent one.

I could be wrong about the pace of any of this.

What I’m more confident about is the response, regardless of pace: don’t build a financial life that only works if your job stays exactly as secure as it feels today. That was never a great assumption. It’s an even worse one now.

No job is safe, not even the ones we used to call the safest.

That’s not a reason to freeze.

It’s the argument for building the parts of your financial life that don’t ask permission from an employer, the same way you wouldn’t ask permission from a single stock.

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Imagine two investors, both sitting on a 30% drawdown.

One just got $847 deposited into their brokerage account this month.

The other is staring at a management deck promising AI-driven margin expansion “over the next several years.”

Same loss on paper. Radically different headspace.

This asymmetry costs growth investors real money, not through bad stock picks, but through the quiet exit that happens at exactly the wrong time.

The psychology of getting paid to wait

Dividends do something stock price appreciation cannot: they make waiting feel productive.

Every quarter, the income investor gets tangible proof their capital is working, regardless of what Mr. Market is doing.

That distribution hits the account whether the stock is up 40% or down 40%.

Behavioural finance calls this the bird in hand effect.

Investors systematically overvalue certain, immediate payouts over uncertain future gains, even when the math favours the future.

It’s why high-yield portfolios see dramatically lower turnover during bear markets.

Growth investors, by contrast, are playing a game of pure faith.

The entire thesis lives in the future: future revenue, future margins, future free cash flow. When the stock falls 30%, there’s nothing in the present tense to hold onto.

Meta vs. ParkwayLife: a tale of two drawdowns

Both are high-conviction holdings of mine.

Both have sat in the red.

The experience of holding them couldn’t be more different.

The Meta investor who held peak to trough and back earned extraordinary returns, one of the best-performing large-caps of 2023.

But the ParkwayLife investor held without the psychological trauma.

Paid quarterly. No leap of faith required.

That’s the real trade-off. Meta’s upside is asymmetric and exciting. ParkwayLife’s return is steady and survivable.

The right choice depends entirely on one question: can you stay in your seat when a position falls 50% with nothing to show for it?

Why growth investors exit at the worst possible moment

It follows a predictable pattern: discomfort at -10%, rationalisation at -20%, capitulation at -35%.

Investors call it discipline.

What it usually is: converting a temporary paper loss into a permanent real one, thus missing the recovery entirely.

Dividend investors rarely follow this arc.

The cash flow is a psychological anchor.

The quarterly payment gives investors holding power, an underrated trait that growth stocks don’t offer.

Dividend Investing

Growth Investing

The covered call bridge: manufacturing your own “dividend”

There’s a middle ground, one Meta holders could have used during the brutal 2022 drawdown: selling covered calls.

Own 100 shares?

Sell someone the right to buy them at a higher price by a set date. You collect a premium upfront, a synthetic dividend, immediately in your account.

During Meta’s 2022 free-fall, spiking implied volatility made those premiums exceptionally rich. Income precisely when the pain was highest.

The catch: you cap your upside.

Meta’s recovery was violent and fast. Covered call writers who sold strikes too close to the money missed the run from $130 to $500+.

The income came at a real cost.

Rule of thumb: sell covered calls in high-volatility, low-near-term-conviction environments.

Never when you believe a sharp recovery is imminent, or if your thesis depends on capturing a near-term catalyst.

The only problem: This involves timing the market, a game that nobody wins.

“Covered calls don’t solve the problem of owning a falling stock. They make the waiting more tolerable, which, for most investors, is more than half the battle.”

So which approach is better?

The short answer: The one you can hold through a 40% drawdown without selling.

Growth investing wins on long-run math, a dollar compounding at 25% beats one returned to shareholders reinvesting at 8%.

Berkshire never paid a dividend.

Neither did Amazon, for decades.

But the compounding only works if you actually stay invested.

A growth investor who capitulates at every -35% will systematically underperform the ParkwayLife holder who quietly collects distributions through every rate cycle.

The alpha is real. It’s just only accessible to investors who can stomach the ride.

Most people only find out during a drawdown that their risk tolerance was lower than they thought.

The bottom line

Dividend stocks pay you to wait.

Growth stocks demand you wait for nothing but a promise. Covered calls let you manufacture income, but with trade-offs attached.

Know yourself before you build the portfolio.

While I maintain a diversified mix of growth and income assets, the weight of my holdings is significantly tilted toward growth.

The market will find whichever version of yourself you were hoping to hide.

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The romanticised leap into full-time entrepreneurship is often a trap.

Your salary isn’t the enemy, it’s the seed capital for everything you’re trying to build.

A counterintuitive case for staying put

Every week, someone in your feed announces they’ve quit their job to pursue their passion full-time.

The post gets hundreds of likes. It feels brave, decisive, cinematic.

But almost nobody talks about what happens six months later when the bills are due and the hustle hasn’t broken even yet.

This essay is for the person (and a reminder to myself) who has a side income they’re building , a newsletter, a portfolio, a small business, a creative practice, and who feels the growing pressure to make it their everything.

I want to make a case for patience. Not complacency. Patience.

The Invisible Asset on Your Payslip

When you receive your salary each month, you’re not just being paid for your time.

You’re being handed something far more valuable in the context of building a side income: pressure-free capital.

Most people treat their salary as money to spend. The sharper move is to treat a portion of it as your venture fund, the quiet backer that lets you make decisions based on what’s right for your business, not what’s right for your survival.

When your side income doesn’t have to feed you, it gets to grow. That’s not a small distinction. That’s the entire game.

Think about it from a first principles perspective.

A freelance writer who needs $4,000 a month to cover living costs is a different animal from a freelance writer who earns a salary and needs their writing to produce zero.

The first writer takes every client, at every rate, saying yes when they should say no. The second writer can afford to wait for the right brief, build a body of work that commands better fees, and walk away from clients who undervalue them. Same skill. Radically different leverage.

The Scarcity Trap Nobody Warns You About

When you quit your job before your side income is ready, you trigger something psychologists call scarcity mindset.

Suddenly, every creative or business decision is filtered through the lens of immediate survival.

You stop thinking in quarters and years. You start thinking in weeks.

This is the opposite environment from what a nascent business needs.

Early-stage ventures require experimentation, trying things that don’t work, pivoting, discarding bad ideas, investing time into things with long feedback loops.

Scarcity compresses your time horizon right when you need it to be longest.

A useful thought experiment

Imagine two people building the same newsletter.

One has quit their job and needs 500 paying subscribers to make rent. The other has a day job and is growing the audience for the love of the work.

Who writes better?

Who makes bolder creative choices?

Who is more likely to still be publishing in two years?

The salaried builder has one enormous structural advantage: they can afford to be bad at this while they get good at it.

That is an underrated luxury in a world that romanticises overnight success.

What the Day Job Actually Gives You

Beyond capital, a job provides a set of non-obvious gifts to the side hustler:

1. Credibility and network. Your professional identity - your colleagues, your industry relationships, your employer’s name, is social proof that carries over into your side work. A newsletter author who also works in finance writes with implicit authority. That stamp matters, especially early on.

2. Work expands to fill time. Constraints breed creativity. When you only have two hours in an evening, you use them. Many prolific side hustlers report that they become less productive, not more, after quitting, because infinite time removes urgency.

3. Insurance against failure. When an experiment doesn’t work, you don’t spiral. You learn, reset, and try again next month with fresh runway. This is how real iteration works. It is very hard to iterate when each failure means you can’t pay for groceries.

4. A live proving ground. Your job exposes you to problems, people, and dynamics that feed directly into your creative or business output. The best newsletter writers, consultants, and coaches draw on lived professional experience. Being embedded in the working world gives you content, perspective, and case studies that those who’ve “escaped” it can no longer access.

The Right Question to Ask

Most people frame the question wrong.

They ask: “When should I quit my job?” when the more useful question is: “How do I build something significant enough that the question answers itself?”

There is a meaningful difference between quitting because you’re sufficiently ready versus quitting because you’re emotionally exhausted, romanticising freedom, or feeling peer pressure from a curated feed of people who made the leap.

The former is strategy. The latter is escapism.

The goal is not to quit your job. The goal is to build something worth quitting for.

And the extraordinary irony is that you are most likely to build that thing while you still have the job. The salary keeps the lights on. It keeps you sane. It keeps your pricing honest, your standards high, and your time focused. It lets you treat your side income as a long-term project rather than a desperate lifeline.

When to Actually Make the Move

None of this is an argument for never leaving.

It’s an argument for leaving when the time is right, and not when it feels romantic.

A more defensible decision framework might look like this: your side income has been consistently covering at least half your living expenses for 6 months or more.

You have a clear thesis for how the remaining gap closes within 12 months. You have savings that extend your runway beyond a worst-case scenario.

And the bottleneck to growth is genuinely your time, not your skills or your market.

When those conditions are met, the leap isn’t brave. It’s inevitable. The timing becomes obvious rather than emotionally charged. That’s the version of the story worth telling.

Until then, show up to work. Cash the cheque. And use the space that gives you to build something real.

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Every crash produces the same reflex: open a screener, hunt for something that’s “finally cheap,” and convince yourself you’ve found a bargain nobody else has noticed.

I want to make the case for a less exciting, more disciplined move.

Before you go looking for a new stock, look at the ones you already own. Specifically, the ones that have made you money.

You already did the hard work on these names

Every stock in your portfolio that’s sitting on a gain got there because, at some point, you did real due diligence.

You understood the business model, you had a thesis, you watched it play out, and the market agreed with you enough to push the price up.

A new stock that just dropped 30% and “looks cheap” has none of that behind it. You don’t know if it’s cheap because the market overreacted, or cheap because something in the business actually broke.

Figuring that out under the pressure of a falling market, at a time when headlines are screaming and your emotions are loud, is the worst possible time to start due diligence from scratch.

Your winners come with a head start.

You’re not researching a thesis during a crash; you’re re-assessing one you already trust.

The behavioural case is just as strong as the financial one

There’s a psychological trap in crashes: the stocks that are down the most feel like the best opportunity, because the discount looks biggest.

But “biggest discount” and “best risk-adjusted opportunity” are not the same thing.

Buying more of a name you already understand and already trust is far easier to hold through volatility than a speculative new position bought in a moment of fear.

If the market keeps falling, you want conviction you’ve already tested throughout the years, not a fresh thesis you talked yourself into during a 24-hour news cycle.

This is also why it’s easier to size the trade properly. You already know your cost basis, your original thesis, and your risk tolerance for the name.

Adding to a known position is a sizing decision.

Buying a brand-new name mid-crash is a forecasting decision, and forecasting under stress rarely ends well.

How I actually apply this

When the market sells off hard, my checklist looks like this, in order:

1. Review winners first. Which of my current positions have made the most gains for me? Those go to the top of the list.

2. Valuation. Of these names, which are the most undervalued relative to their intrinsic value, and provides the best risk-adjusted opportunity.

3. Re-confirm the thesis, don’t assume it. A stock that has done well in the past doesn’t mean automatically add. Check that the fundamentals are still intact.

4. Position Size. This is portfolio management, not a new speculative position. Treat it with the same risk framework you’d use for any add.

5. Only then look outward. If there’s capital left after reinforcing what’s working (which is unlikely), that’s the time to evaluate something new, with full diligence, not panic-buying.

New names earn their way into the portfolio over time, through research done in calm markets, not during the worst week of the year.

The takeaway

Crashes don’t create good ideas. They create pressure to act, and that pressure makes “cheap” feel like “good.”

The stocks that have already proven their worth in your own portfolio are sending you a clearer signal than anything you’ll find by screening for new losers.

Before you go hunting for the next opportunity, take one more look at what’s already working.

Often, the best buy in a crash isn’t new at all.

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The S&P 500 and Nasdaq have been hitting record highs through the first half of 2026. So you’d think every stock is expensive right now, right?

Not quite.

Some of the biggest, most profitable companies in the world are actually trading cheaper than they usually do, based on how much cash they’re generating.

Here’s why that’s happening, and why it matters.

Wait, how can the market be at a high but stocks are still cheap?

Think of the stock market like a big basket of companies. The basket’s total value can hit a record high even if some companies inside it get cheaper, as long as other companies (or flashy new ones, like SpaceX’s huge IPO this month) go up enough to make up for it.

So “the market is at an all-time high” really just means: on average, things are up. It doesn’t mean every single stock is overpriced. Some companies are just getting overlooked.

A simple way to think about “fair value”

When investors try to figure out what a company is really worth, one common method is this: look at how much actual cash the business generates every year, and ask “if this kept growing at a reasonable pace, what should I be willing to pay for that today?”

This is the basic idea behind something called a DCF (discounted cash flow) analysis.

You don’t need to know the formula. Just know this: if a company’s cash flow is healthy and growing, but its stock price hasn’t kept up, that’s usually a sign the stock might be undervalued.

That’s exactly the pattern we see in Meta, Amazon, and Nvidia right now.

Meta Platforms (META)

What’s happening: Meta’s stock is priced cheaper right now compared to its own history. Investors are typically willing to pay around 27 times its yearly profit, right now they’re only paying about 21 times.

Why that’s surprising: Meta is making a lot of money. In the past year, it pulled in about $215 billion in revenue and $70 billion in profit, with very high margins (meaning it keeps a large chunk of every dollar it earns). After spending on equipment and infrastructure, it still has roughly $48 billion in free cash sitting there.

Why it’s cheap anyway: Meta has been spending heavily on AI data centers and other big bets (like the Reality Labs/metaverse division), which worries some investors because that spending hasn’t fully paid off yet. That’s a fair concern. But the core business, Facebook and Instagram ads, is still extremely profitable, and the stock price hasn’t fully given it credit for that.

The honest risk: If all that AI spending doesn’t eventually turn into more profit, the “cheap” label won’t hold up. This is the main reason the stock hasn’t already gone back up.

Intrinsic value: With a fair value of $818, Meta Platforms (META) stock is about 29% undervalued.

Amazon (AMZN)

What’s happening: At first glance, Amazon doesn’t look cheap, investors pay about 28 times its profit for the stock. But compare that to Amazon’s own history: over the last 5 years, that number has averaged around 55. Over the last decade, it’s averaged closer to 97. So by Amazon’s own standards, today’s price is unusually low.

Why that’s surprising: Amazon’s profit grew much faster than its sales recently - sales grew about 14%, but profit grew about 34%. That’s a great sign: it means Amazon is getting more efficient, not just bigger.

Why it’s cheap anyway: Amazon spent decades trading at sky-high prices because investors were betting on its cloud computing business (AWS) becoming hugely profitable. That bet has basically paid off - AWS is now a real profit engine, but the stock’s price hasn’t fully adjusted to reflect that the bet already worked.

The honest risk: Amazon’s actual free cash (after all its spending) dropped sharply recently, even though profit on paper went up. That’s a real warning sign, and it’s worth watching before assuming the stock is a screaming bargain. Heavy spending on AI infrastructure and warehouses is the likely cause, but it needs to turn around.

Intrinsic value: With a fair value of $354, Amazon (AMZN) stock is about 31% undervalued.

Nvidia (NVDA)

What’s happening: This is the most surprising one. Investors currently pay about 31 times Nvidia’s past profit for the stock, but only about 21–24 times its expected future profit. Compare that to history: Nvidia has usually traded at 50-70 times profit. By that measure, today’s price looks unusually low for a company growing this fast.

Why that’s surprising: Nvidia is wildly profitable. In the past year it made about $254 billion in revenue and kept $160 billion of that as profit, margins most companies could only dream of. It also doesn’t need to spend much to keep that machine running, so it’s sitting on about $119 billion in free cash.

Why it’s cheap anyway: Chip stocks have been getting sold off lately, partly because investors are nervous about how long the AI spending boom can last, and partly because money is flowing into newer, flashier stories (like recent hot IPOs). That’s caused a lot of profit-taking in semiconductor stocks broadly, Nvidia included, even though nothing about its actual business has gotten worse.

The honest risk: Nvidia’s business depends heavily on big tech companies continuing to spend huge amounts on AI infrastructure. If that spending slows down even a little, Nvidia’s growth, and its stock price, could fall hard. A “cheap” stock in a cyclical industry can always get cheaper if the cycle turns.

Intrinsic value: With a fair value of $267, Nvidia (NVDA) stock is about 21% undervalued.

The bottom line

All three companies share the same basic story: they’re generating a ton of real cash, growing that cash flow, and yet are priced lower than the market has historically been willing to pay for them.

That gap between “what the business is actually worth based on its cash” and “what the stock costs today” is the heart of the undervaluation case.

That said, cheap isn’t the same as risk-free.

Each company has a real, specific reason the market is hesitant - Meta’s AI spending, Amazon’s recent cash flow dip, Nvidia’s dependence on the AI spending cycle continuing.

A smart investor weighs the discount against those risks, rather than assuming “cheap relative to history” automatically means “going up soon.”

This article is for informational and educational purposes only and does not constitute financial advice. I hold positions in META and AMZN. Do your own research before making any investment decisions.

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The market just dropped 5%. Your portfolio is red. Your feed is full of panic. And you’re staring at a blinking cursor wondering - now what?

This is the moment most investors get it wrong. Not because they lack conviction, but because they lack preparation.

Today I want to walk you through the exact framework I use to stay calm, decisive, and opportunistic when markets go into freefall, built around three things: a curated watchlist, a personal intrinsic value estimate for each stock, and pre-defined price levels that tell me exactly when to pull the trigger.

Why Downturns Feel Paralysing (Even When You Know They’re Opportunities)

Every long-term investor knows the drill in theory: buy low, sell high.

Downturns are when “low” finally arrives. Yet most people freeze.

Here’s why: when everything falls at once - quality and garbage, defensive and cyclical, dividend payers and speculative bets - the noise drowns out the signal.

You can’t tell if a stock is down 20% because the business is impaired, or simply because Mr. Market is having a bad week.

Without a framework anchored to fundamentals, you’re left reacting emotionally. You either panic-sell, or you buy something indiscriminately because it “feels cheap.”

The fix isn’t more market timing. It’s more preparation.

Step 1: Build a Watchlist Worth Having

Not all watchlists are equal. A good watchlist isn’t a screenshot of your brokerage’s “most popular” tab.

It’s a shortlist of businesses you’ve actually studied, companies you understand well enough to hold through volatility without losing sleep.

When building mine, I ask three questions for every candidate:

Do I understand how this business makes money? Where revenue comes from, who the customers are, what keeps them coming back.

Does it have a durable competitive advantage? A moat doesn’t have to be impenetrable. But there should be something that gives this company pricing power, switching costs, or network effects that make it hard to displace.

Would I be comfortable owning this for 5+ years? This question filters out the noise. If I’d panic sell it at -15%, it doesn’t belong on my list.

My watchlist tends to stay between 10–20 names. Any more than that and I’m spreading attention too thin to truly know each business.

Step 2: Anchor Every Stock to an Intrinsic Value Estimate

This is the step that most retail investors skip, and where most of the edge lives.

Intrinsic value is your best estimate of what a business is actually worth, independent of what the market is pricing it at today.

It’s not a precise number. It’s a range, informed by the business’s earnings power, growth trajectory, balance sheet quality, and the rate at which you discount future cash flows.

For the stocks on my watchlist, I maintain a simple one-page summary for each:

• Business description - what they do, who they serve

• Revenue and earnings trajectory - directional trend matters more than precision

• Key assumptions - growth rate, operating margins, terminal value inputs

• Intrinsic value estimate - expressed as a range (e.g., $180–$220 fair value)

• Current price vs. estimate - the gap is the opportunity

The DCF (discounted cash flow) model is one tool I use, though not the only one. For businesses with more predictable cash flows (think software or consumer staples), a DCF works well.

For others, I’ll use earnings multiples benchmarked to historical averages or compare to peers.

The method matters less than the discipline of doing the work.

The output?

You now know the difference between “this stock is down 20%” and “this stock is trading at a 30% discount to what I think it’s worth.”

Only the latter statement should move you to act.

Step 3: Pre-Define Multiple Buy Levels Below Intrinsic Value

Here’s the part that turns preparation into action: you set your buy levels before the crash happens.

This is critical.

When markets are falling fast, your cognitive bandwidth is at its lowest.

Decision fatigue kicks in. You either freeze or you act impulsively.

Pre-defined levels remove both failure modes - you’ve already made the decision when you were calm and clear-headed.

I define three tiers below intrinsic value for each stock on my watchlist:

Tier 1: The “Starter” Level (20% below intrinsic value)

This is where I begin building a position.

The stock isn’t deeply cheap yet, but the margin of safety is meaningful. I buy a partial position here, maybe 20–30% of my intended full allocation.

If the stock recovers from here, I’ve participated. If it falls further, I have dry powder.

Example: If my intrinsic value estimate is $200, my Tier 1 entry might be around $160–180.

Tier 2: The “Add” Level (25–35% below intrinsic value)

This is where the market is being more irrational than usual, and the business fundamentals haven’t changed. I add significantly here, another 30–40% of my allocation. At this price, I’m buying with a strong margin of safety, and the potential return on a reversion to fair value is compelling.

Continuing the example: Tier 2 might be $130–150.

Tier 3: The “Load Up the Truck” Level (40%+ below intrinsic value)

This rarely happens, but when it does, it’s the kind of price that defines a portfolio’s long-term performance.

At this level, the market is pricing in worst-case scenarios. I deploy the remainder of my allocation and may even exceed my original sizing.

In the same example: Tier 3 might be below $120.

The key insight is this: you’re not trying to catch the exact bottom. You’re averaging into conviction at prices that make the math work in your favour regardless of where the bottom ultimately is.

This exact system has allowed me to buy AMZN at $240.

Short-term price weakness doesn't concern me.

If anything, a continued slide toward Tier 3 would excite me in deploying my final bullet and maxing out the allocation.

What This Framework Is Not

A few honest caveats before you build yours:

It’s not a guarantee. Your intrinsic value estimate is only as good as your assumptions. Businesses change. Competition evolves. A stock trading at a 40% discount can still be overvalued if the business is in structural decline. This is why watchlist quality matters.

It doesn’t tell you when to sell. The buy levels are one side of the equation. You also need a framework for when you’re wrong, what would change your thesis, and at what point do you cut a losing position. That’s a separate conversation worth having.

It requires ongoing maintenance. Intrinsic value estimates go stale. When a company reports earnings, your assumptions need revisiting. This isn’t a set-and-forget system.

Final Thought: Clarity Is the Edge

Most investors lose money in downturns not because they picked bad stocks, but because they had no framework for what to do when prices fell. They bought in a panic, or they didn’t buy at all.

The edge in a downturn isn’t information, it’s preparation. It’s having done the work ahead of time, knowing what you own and why, and having a clear trigger for when to act.

A watchlist with pre-defined buy levels is that preparation made tangible.

The next time everything falls in tandem and your feed is screaming, you’ll have something most investors don’t: a plan.

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I closed four bull put spreads in May.

All four were profitable, for a combined realised P&L of +$249.38.

Before that number sets any expectations: this is an experiment I've only just started. Four trades, one contract each, deliberately small - a first month of live data, not a track record.

The win rate isn't the story; the process is.

An entry based on one indicator paired with a mechanical exit, and whether it still holds up with far more than four trades behind it.

Realistically, I'd want something like 100 trades before I'd trust any read on whether this actually has an edge in the markets.

The trades

All four were $5-wide spreads, one contract, opened with 43–66 days to expiry.

Total capital at risk across the four was roughly $1,515 (width minus credit), so the realised return on risk was about 16.5% , over an average holding period of one week.

What a bull put spread is

You sell a put and buy a further out-of-the-money put underneath it. You collect a net credit upfront. You keep the full credit if the stock stays above your short strike; your loss is capped by the long put you bought.

Defined risk, defined reward, and you’re being paid for time passing and for volatility being priced higher than it tends to be realised.

That last part, the volatility risk premium, is the actual edge.

How I’m timing the entries

The exit is the tastytrade-style rule I’ll get to below. The entry is where the experiment really sits.

I’m not picking these setups by feel. The entry timing is driven by a single indicator, one used in Rayner Teo's UST strategies, backtested to show a historical edge.

The idea I’m testing is whether that same signal, originally framed for directional swing entries, can also time the entries on a defined-risk premium-selling structure like a bull put spread.

I’m deliberately keeping it to that one input for now.

One indicator, one rule, one spread structure, so that when I have a real sample of trades, I can actually attribute what’s working to something specific rather than a tangle of discretionary calls.

Worth being clear-eyed about it, though: a backtested edge is a statement about the past, not a promise about the future, and four live trades tell me almost nothing yet.

That’s the whole point of running it small and logging it in the open.

Why I closed all of them around 50%

Every spread came off between 51% and 52% of the credit I took in. That’s not a coincidence, it’s the rule.

Here's the logic.

When you sell a spread, the premium doesn't decay in a straight line. The early portion comes off relatively quickly, while you're holding the same defined risk the whole way.

By the time you've captured half the credit, you're in a lopsided position: the remaining reward is small, but the risk of a sharp move against you is still fully on the table, and gamma only gets twitchier as expiry approaches.

So once a spread shows ~50% of max profit, I close it. I give up the back half of the credit on purpose. In exchange I get the capital back to redeploy, and I sidestep the part of the trade where the risk-to-reward quietly turns against me.

None of these four got anywhere near my 21-DTE backstop. They all hit the profit target first, the earliest after a single day.

That single-day one was MSFT: opened 14 May, closed 15 May for 52% of the credit. To be clear, that's not skill. The underlying moved my way and volatility came in, and the position handed me my target a day later. When the market gives you the exit early, you take it. That's the whole decision.

The research behind the 50% rule

The “manage winners early” idea isn’t mine - it comes from Tastytrade’s research team, who studied where the risk/reward on short-premium positions starts working against you.

Their finding: roughly 21 days from expiration is, on average, the point where you’ve already captured about half of a position’s max profit.

Hold past that and you’re collecting less premium per day (declining theta) while carrying more risk from sharp moves (rising gamma). So the practical rule became exit at whichever comes first - 50% of max profit, or 21 DTE.

One honest caveat: that original work was run mostly on undefined-risk strangles and straddles, not on defined-risk vertical spreads like mine. The theta-vs-gamma logic transfers cleanly, but I’m applying their framework to spreads rather than quoting a spread-specific backtest.

The honest part

A few things I want on the record, because the point of this log is the real picture, not the highlight reel:

• Size is small. One contract each. The dollar figures are real but modest by design while I keep building the process.

• Four winners is a sample of four. Premium selling has a specific shape: a lot of small wins and the occasional loss that’s larger than any single win. May was clean. Plenty of months won’t be, and one bad gap through a short strike can erase a run like this. The strategy is the mechanical exits, not the streak.

• I’m not logging IV rank at entry yet. That’s on me to fix. The edge in selling premium is strongest when implied volatility is elevated, so entry IV is exactly the data I should be capturing to know whether I’m selling when it’s worth selling.

This is the boring side of the book - defined risk, mechanical entries and exits, no heroics. It’s also the side I trust most to compound. I’ll keep logging it as it is, win or lose.

Nothing here is financial advice. These are my own live trades, shared for transparency. Options carry real risk of loss, do your due diligence.

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I got a 20% raise this year without getting promoted.

No negotiation, no new title, no awkward conversations.

The raise came from somewhere else entirely, from income streams I’d been building quietly on the side, which now add up to roughly 20% of what my day job pays me.

If I’m being honest, I’m still in shock.

I certainly wasn’t expecting it to amount to this much.

It not only changed my bank balance, but how I show up to work...

I see a particular kind of anxiety that most working people carry but rarely name.

It surfaces in small ways - the slight tension before a performance review, the quiet dread of a restructuring announcement, the pause before you say no to a bad project because you feel you can’t afford to.

It’s the anxiety of depending on one source of income. Of being one financial decision away from crisis.

The solution most people reach for is more salary. A promotion. A better offer. Another rung on the ladder.

Salary growth matters, but it doesn’t solve the underlying problem.

A higher salary from a single employer is still a single point of failure. You’ve just raised the stakes.

What actually changes the equation is income diversification, building streams of income that exist outside your day job, outside your employer’s goodwill, and outside the fragility of any single relationship or role.

Freedom is a cash flow problem

We talk about freedom in vague, aspirational terms.

But when you pull it apart, most of what people mean by freedom is actually about optionality: the ability to say no.

To leave. To choose. To take time off without financial panic.

To pursue work that matters without needing it to immediately pay.

That kind of optionality has a price tag.

And the mechanism for buying it is not savings alone, savings deplete.

It’s recurring income that doesn’t require you to show up to a specific office for a specific employer every day.

Consider what happened this month.

Meta - profitable, growing, with over $200 billion in annual revenue, laid off 8,000 employees.

Standard Chartered, fresh off record wealth management inflows, announced plans to cut more than 7,000 back-office roles.

Neither company was in distress. Both were reallocating toward AI.

Your job security isn’t determined solely by your performance, it’s also shaped by decisions made in boardrooms you’re not in.

This is precisely what side income buys you.

Not just the dollars (though those matter) but the psychological shift that comes when your livelihood is no longer hostage to a single institution.

The permission you’ve been waiting for

A lot of people delay starting because they’re waiting to have a “real” idea, or enough time, or the right moment.

The reality is - side income is almost never built from a standing start with perfect conditions (they don’t exist).

It’s built during after-work hours, in the evenings, weekends, early mornings.

Start from something you already know, already do, or already care about.

You don’t need to launch a startup. You need to answer one question: What can I offer that someone else would pay for?

It might be writing - a newsletter, freelance work, content creation.

It might be investing your capital in dividend-generating assets, or writing options to generate premium income.

It might be teaching, coaching, or consulting in a domain where you already have genuine expertise.

The category matters less than the habit of starting.

The first dollar from a source outside your job is disproportionately meaningful. Not because it pays the bills but because it proves the model.

It tells you that someone other than your employer values what you produce.

That’s a more powerful realisation than it sounds.

The compounding you’re not thinking about

Everyone understands financial compounding - money growing on money, over time.

Few think about what might be called optionality compounding: the way each additional income stream makes the next one easier to build.

Your first side income stream teaches you how to deliver value outside a salary structure.

Your second teaches you to systematise.

Your third gives you enough slack to take risks your employed self couldn’t.

Eventually, you cross a threshold - not necessarily where you quit your job, but where you could if you wanted to.

That crossing point changes how you work.

You negotiate differently. You make different bets. Because you’re no longer afraid.

This is the freedom that matters: not the Instagram version of early retirement on a beach, but the version where you sit across from a difficult client or a mediocre boss and know, quietly, that you have options.

Start smaller than you think

The biggest risk with side income is overthinking the beginning.

You don’t need a polished product, a business entity, or a strategy deck.

You need to do something revenue-generating that isn’t your job, and then do it again.

Set a target that’s almost embarrassingly small. $200 a month. One freelance client. A single financial asset that throws off a quarterly dividend.

Not because the amount is transformative, but because the constraint forces you to actually start.

The goal isn’t to replace your income immediately, but to prove to yourself that your income can come from more than one place.

From there, the compounding, financial and psychological, does most of the work.

The job is a foundation, not a ceiling

A good job provides capital, structure, and skills worth having.

The argument here isn’t against employment - it’s against treating a salary as the total answer to financial security.

As recent events remind us, even well-paying roles at healthy, growing companies are not guaranteed.

Side income, at its best, is what lets you treat your job as a choice.

And a life where your income is diversified is a life where more of your decisions are made from clarity rather than fear.

That, ultimately, is what freedom looks like in practice.

Not the absence of obligation, but the presence of alternatives.

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Since the start of 2026, I’ve been closely tracking the P&L of my trading, specifically the systems taught in Rayner Teo’s proprietary UST program.

Are they profitable? Should I be scaling up or down?

These were the thoughts rattling around my monkey mind.

From my brokerage dashboard, I could tell some systems were outperforming others. But I wanted the actual P&L, broken down by system, no guesswork.

So instead of enjoying my Friday night like how a normal human being would, I sat down and combed through every trade I’d made in 2026.

Being the nerd I am, I fired up Google Sheets and tallied everything up. The number staring back at me: $5.9k in realised profits over the first five months, on a total capital base of around $20k.

More than I’d expected. And more than $1k a month in side income.

That’s over $1k a month in side income.

All these are realised profits (paper gains don’t count).

The month hasn’t ended yet but I’m just too excited that I had to post this.

Now, full transparency: my itchy fingers did some damage to these profits.

Losses from my own untested options strategies dragged the overall number down. Entirely self-inflicted.

Which brings me to the lesson I keep relearning - trade proven strategies. Strategies that have been stress-tested across decades, not just the last bull run.

So here’s the question worth sitting with: how much would you pay for a skill that reliably generates $1k a month? More, if you’re working with more than $20k in capital?

Only you can answer that.

So if you’ve been sitting on the fence, stop waiting. Start now.

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I want to walk you through a trade I closed this week on Microsoft ($MSFT), one that returned 15.8% in under 24 hours using a defined-risk options strategy called a bull put spread.

This isn’t a brag post. It’s a breakdown.

I’ll show you exactly what I did, why I did it, and what the numbers looked like at every step.

The Setup

When I entered the trade, MSFT was trading around $405.

The Stoch RSI was at 3.56, a sign that the stock was heavily oversold.

I sold a bull put spread at the $380/$375 strikes, collecting a credit of $1.20.

Here’s what that means in simple terms: I was getting paid $1.20 (or $120) to take on the obligation of buying MSFT at $380, but only if it fell there by expiration.

My long $375 put acts as a ceiling on my downside, capping my maximum loss at $3.80 per share (the spread width minus the credit received).

The key numbers going in:

• Credit received: $1.20

• Max loss: $3.80

• Breakeven price: $378.80

• Buffer to short strike: 6.17%

• Return on risk (if held to expiration): 31.6%

That last figure, the 6.17% buffer, is what I care most about at entry.

MSFT had to drop more than 6% from where it was trading for me to lose a single dollar.

That’s my margin of safety.

Why This Trade Made Sense

Bull put spreads work best when you have a view that a stock is unlikely to fall sharply in the near term.

You don’t need it to go up, you just need it to hold.

At $405, MSFT was sitting well above both strikes. The $380 short strike was a level I was comfortable defending.

The structure gave me a defined worst case scenario, a clear breakeven, and a measurable edge.

I wasn’t swinging for the fences. I was collecting premium for time and stability.

The Exit

This is where the trade gets interesting.

Less than 24 hours after entering, I bought back the spread for $0.60, locking in a profit of $0.60 per share, or 15.8% return on risk.

That’s roughly 50% of the maximum possible profit, which is my rule for early exits on premium-selling trades.

Why not hold for the full $1.20?

A few reasons.

First, once you’ve captured half your max profit, the remaining gain isn’t worth the continued exposure. The spread could still move against you in the time left.

Second, freeing up the capital lets you redeploy into the next opportunity.

Third, and most importantly, theta had already done most of the heavy lifting. The passage of time and a stable underlying had eroded the spread’s value quickly. There was no reason to stay.

A bird in hand beats two in the bush.

What This Trade Teaches

I share trades like this not to showcase winners, but to make the mechanics visible.

A few things worth pulling out:

Defined risk matters.

I knew my exact worst case before I entered. No surprises, no margin calls, no black-box exposure.

You don’t need the stock to move.

While MSFT stock price rose, it didn’t need to for this trade to work.

It just needed to stay above $378.80.

That’s a fundamentally different kind of bet than buying shares or buying calls.

Early exits are a strategy, not a concession.

Closing at 50% of max profit is a deliberate rule, not a reaction to fear.

Rules like this remove emotion from the exit decision, which is usually where traders give back gains.

The buffer is your edge.

A 6.17% buffer to the short strike means the market had to be meaningfully wrong about MSFT’s near-term direction for this trade to lose. That’s not a guarantee, but a probability.

The Bottom Line

Entry to exit in under 24 hours. 15.8% return on risk. Full position accounted for.

This is one trade in a broader options approach I run alongside my long equity portfolio.

I track every position, entry, exit, reasoning, in the monthly performance recaps published here.

No hindsight, no cherry-picking.

Positions disclosed: Was short the MSFT $380/$375 bull put spread. Position now closed.

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28 trades, a volatile tape, and somehow a positive month.

Here’s how it happened.

While the MRT book took heat from a volatile macro tape, the MOMO positions, many of which I’d been holding since late 2025, delivered a strong month and more than made up the difference.

Net P&L: +$1,144.46 across 28 closed trades (57% win rate)

MOMO: The Carry That Saved April

The MOMO book closed 7 positions for +$1,120.43, driven almost entirely by three semiconductor names I'd been holding patiently: LRCX (+$517.60), KLAC (+$311.96), and MU (+$169.42).

LRCX and KLAC were both opened in September 2025, long-duration holds that finally paid out.

WBD (+$80.97) and WDC (+$75.34) were cleaner, quicker wins. The only drag was AMAT (−$39.27), exited after the signal triggered.

Bull Put: One Trade, Clean Win

My QQQ Bull Put spread, opened in mid-March, expired in late April for +$113.60. Given how choppy the tape was, I'll take it.

MRT: A Grind

MRT was the weak spot, 20 trades, net −$281.62, 45% win rate (9W/11L).

The damage was concentrated in two names: TPL (−$107.31) and NXST (−$93.80), both of which broke structure before I could get out cleanly.

Strip those two out, and the remaining 18 MRT trades were essentially breakeven (−$80.51 combined), which tells me the system itself held up fine.

Bright spots: ATI (+$54.24), ASTS (+$50.48), and MRNA (+$37.72) all executed well.

Key Takeaways

MOMO remains the primary P&L driver.

The patience on the semiconductor names paid off, and the longer-hold strategies naturally fared better in a macro environment that was hostile to short-duration setups.

The MRT and MOMO frameworks I trade come from the UST Program by Trading with Rayner. If April’s results made you curious, you can check it out here (Affiliate link - I earn a commission at no cost to you.)

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Meta, Amazon, Alphabet, and Microsoft all dropped their Q1 2026 results this week, and on paper, it was a clean sweep.

All 4 beat on revenue and earnings.

All 4 showed accelerating AI-driven demand.

Here’s a breakdown of what was reported in their latest earnings, including their latest intrinsic values.

Meta: 33% Revenue Growth, But the Capex Raise Stole the Headlines

Meta put up its best quarterly revenue growth since 2021, $56.3 billion, up 33% year-over-year.

The ad business continues to fire on all cylinders, with impressions up 19% and price per ad up 12%.

Zuckerberg also unveiled the first model from Meta Superintelligence Labs, framing it as a step toward delivering “personal superintelligence to billions of people.”

But investors zeroed in on the capex raise.

Full-year 2026 spending guidance was bumped from $115–$135 billion to $125–$145 billion, citing higher component costs and data centre build-out.

The EPS headline of $10.44 looked spectacular, but strip out the $8.03 billion one-time tax benefit and you get to $7.31.

Still a beat, but far less dramatic.

Meta is also cutting headcount, about 10% of its workforce, or 8,000 roles, even as it ramps infrastructure spending.

The message is clear: lean on humans, go heavy on compute.

Valuation: At a trailing P/E of around 21x and a forward P/E of roughly 23x, Meta is not expensive for a business compounding revenue at 30%+.

The capex raise is the legitimate concern, not because the spending is irrational, but because it compresses near-term free cash flow visibility.

On a DCF basis, Meta’s FCF for the last 12 months came in at approximately $48.2 billion, a decline from $52 billion in 2024, as aggressive AI infrastructure build-out ate into cash generation.

That FCF compression is temporary and well understood.

Operating cash flow of $124 billion tells a much cleaner story of underlying earnings power.

For the DCF, I use $48.2 billion as the base and apply a three-phase framework: 30% FCF growth in years 1–5 as revenue compounds and capex normalises, tapering to 15% in years 6-10 as the business matures, and a 4% growth rate in years 11-20.

Discounted at 10%, this yields an intrinsic value of about $818 per share.

On that basis, Meta is significantly undervalued today.

What’s more, there’s even an added upside optionality if the AI spend starts generating new revenue streams beyond advertising.

I hold a position and it’s one I’m most interested in adding on further weakness.

Amazon: AWS Firing on All Cylinders

Amazon was arguably the cleanest beat of the three.

Revenue of $181.5 billion came in well above estimates, net income more than doubled year-over-year to $30.3 billion, and AWS grew 28%, its fastest pace in over three years, generating $14.2 billion in operating income.

Advertising, now a $17+ billion quarterly business, grew 24% and continues to be one of the most profitable and under-appreciated segments in the company.

The AI pipeline is enormous. AWS backlog stood at $364 billion at quarter end, and that excludes the newly announced Anthropic deal worth over $100 billion.

Q2 revenue guidance of $194–$199 billion implies continued strong momentum.

The same capex anxiety applies here. Andy Jassy has committed roughly $200 billion in full-year spending, and the market wants to see a credible return on that.

Valuation: Amazon is the most nuanced of the three to value, and also where I’d make the strongest argument that headline FCF is the wrong number to anchor on.

FCF fell sharply to approximately $7.7 billion in 2025, as $131.8 billion in capex consumed nearly all of the $139.5 billion in operating cash flow.

On a pure FCF basis, the stock looks almost uninvestable. But that is precisely the wrong frame.

Last 12 months (TTM) operating cash flow of $148.5 billion is what I use as the DCF base.

Amazon is in a capex supercycle that is explicitly front-loaded. As infrastructure spend plateaus, FCF will recover sharply.

I apply a modified three-phase framework: 20% OCF growth in years 1–5 as AWS continues to accelerate and retail margins expand, tapering to 10% in years 6–10, with a 4% growth rate in years 11-20, discounted at 10%.

This yields an intrinsic value range of roughly $354 per share. With the stock currently trading around $265, Amazon is significantly undervalued.

This is the largest position in my portfolio.

While the stock is undervalued, the share price has made a parabolic move upwards and I’d like to see more price weakness before adding.

Alphabet: The Quiet Compounder Keeps Compounding

Alphabet reported $109.9 billion in revenue, up roughly 22% year-over-year, beating the $107.1 billion estimate.

EPS of $5.11 crushed the $2.62 consensus, though part of that outperformance reflects base effects from the prior year’s equity gains rather than pure operational upside.

The story here is the breadth of the beat. Search held up. YouTube ads are growing. Subscriptions crossed 270 million paid users.

And Google Cloud, increasingly the centrepiece of the AI thesis, continues to take share with Gemini embedded across the stack.

Sundar Pichai called out AI Overviews reaching 1.5 billion monthly users, a sign that AI is already reshaping Google’s core product at scale.

Alphabet doesn’t get the same breathless coverage as Meta or Amazon, but for a business of this size, the consistency is remarkable.

Valuation: Alphabet trades at a trailing P/E of around 32x and a forward P/E of roughly 23x, reflecting market confidence that earnings will continue to grow meaningfully over the next twelve months.

For the DCF, I use operating cash flow as the base input, $174.5 billion for the trailing 12 months, up 31% year-on-year and a record for the company.

The reason OCF is the right anchor here is the same logic that applies to Amazon: capex of $91.4 billion in 2025 is elevated and front-loaded into the AI infrastructure build, which suppresses FCF ($64.4 billion LTM) in a way that understates the business’s true earnings power.

OCF strips that distortion out and gives a cleaner read on what the machine is actually generating.

Applying a three-phase framework: 20% OCF growth in years 1–5 (Cloud is accelerating, Search is holding, and Gemini monetisation is early innings), tapering to 10% in years 6–10 as growth moderates, and a 4% rate in years 11-20, discounted at 10%.

This yields an intrinsic value range of approximately $371 per share. At the current price of around $375, it is fairly priced.

The stock has nearly doubled in a year and the gap reflects well-founded AI optimism being priced in ahead of delivery.

The key risk to the model is Search disruption; the key upside is Cloud re-rating as margins improve further from the current ~30% operating margin.

I hold a position and remain comfortable, and wouldn’t be adding at current levels.

Microsoft: Cloud and AI Compounding, Now at a Rare Discount to History

Microsoft reported Q3 FY2026 revenue of $82.9 billion, up 18% year-over-year, with operating income of $38.4 billion growing 20% and net income of $31.8 billion growing 23%.

EPS of $4.27 beat the $4.06 consensus, with revenue also clearing the $81.39 billion estimate.

The headline number from Nadella was the AI business. Microsoft’s AI revenue surpassed an annualised run rate of $37 billion, up 123% year-over-year.

Microsoft Cloud revenue hit $54.5 billion, up 29%, and commercial remaining performance obligations, essentially the forward revenue backlog, grew 99% to $627 billion. That backlog number is extraordinary and rarely gets the attention it deserves.

Microsoft’s Productivity and Business Processes segment (Office, LinkedIn, Dynamics) generated $35 billion in revenue, up 17%.

The company now surpasses 20 million seats for its Copilot AI assistant.

The capex story echoes the others.

Q3 capital expenditures came in at $31.9 billion, up 49%, below the $34.9 billion analyst consensus - a rare positive surprise on spend.

Full-year capex guidance was raised to $190 billion, up 61% from 2025, partly reflecting a $25 billion impact from higher component prices. Gross margin at 67.6% was the tightest since 2022, as depreciation costs from the data centre build-out mount. Despite the strong print, the stock fell roughly 1% in after-hours trading.

Q4 guidance: Amy Hood called for $86.7–$87.8 billion in revenue, implying continued double-digit growth.

Valuation: Microsoft trades at a trailing P/E of around 26.8x and a forward P/E of 22x, meaningfully below its 12-month average of 32x and well below its 10-year historical average of around 33x. For a business of this quality, that de-rating is notable and worth taking seriously.

For the DCF, I use operating cash flow as the base. FY2025 (ending June 2025) OCF came in at $136.2 billion, up 15% year-on-year, with TTM OCF now at $170.1 billion.

Given Microsoft’s fiscal year runs to June, I use the TTM figure of $170.1 billion as the most current base.

Applying a three-phase framework: 15% OCF growth in years 1–5 (Azure is accelerating, Copilot monetisation is early innings, and the $627 billion backlog provides strong revenue visibility), tapering to 10% in years 6–10 as the business matures at scale, and a 4% growth rate for years 11-20, discounted at 10%.

To approximate normalised cash yield, I apply a long-run maintenance capex ratio of around 15% of revenue, elevated versus pre-AI history but realistic given the infrastructure-heavy future.

This produces an intrinsic value range of roughly $491 per share.

With the stock trading at around $405, Microsoft is the one name among the hyperscalers where there is some margin of safety, and trading 14% below its 10-year historical P/E average.

The $627 billion RPO backlog is the key under-appreciated asset, providing multi-year revenue certainty that the market isn’t fully pricing in.

The risk is margin compression from the capex cycle persisting longer than expected.

I don’t currently hold a position in Microsoft, but at these levels it is the one I’m watching most closely.

The Pattern Across All Three

A week ago, conventional wisdom might have worried that macro uncertainty and tariff noise would weigh on ad spend and enterprise cloud budgets.

Instead, demand held up across the board. AI is pulling forward enterprise spending, not deferring it.

But the market’s muted reaction to 4 companies reporting near-perfect quarters tells you something.

Investors aren’t doubting the revenue, they’re discounting the returns.

Hundreds of billions in capex are being deployed into AI infrastructure with limited visibility on when that converts into new, durable revenue streams.

Advertising and cloud are absorbing the AI costs today - the question is whether AI creates genuinely new monetisation layers tomorrow.

The after-hours sell-offs on strong beats are a reminder that sentiment and fundamentals can diverge, sometimes for a while. The businesses are performing. The spending is large but purposeful.

And for long-term holders like myself, the compounding continues.

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In the world of options trading, the difference between a gambler and a professional often comes down to one thin g: positive expectancy.

While many beginners are lured by the unlimited upside of naked options, seasoned traders know that the path to consistent account growth lies in defined-risk strategies.

In one of his videos, Davis from Options with Davis breaks down two fundamental credit spread strategies that allow you to profit from market direction while limiting your downside exposure.

These are the bull put spread and the bear call spread.

The Foundation: Why Credit Spreads?

Unlike buying a call or put (where you need a massive move in your favour to win), a credit spread allows you to be “wrong” on the direction slightly and still walk away with a profit.

You receive money (a credit) upfront to take on the trade. Your goal is for the options to expire worthless so you can keep that entire credit.

The Power of Positive Expectancy

In these trades, your potential loss is often larger than your potential gain. To be profitable long-term, you must win more than 70% of the time.

The Formula for Success:

Expectancy = (Win Rate X Average Profit) - (Loss Rate X Average Loss)

By using the specific criteria below, you can tilt the “Probability of Profit” (POP) into your favour, ensuring that even if one loss wipes out two wins, the frequency of your wins keeps your account moving upward.

Strategy 1: The Bull Put Spread (Bullish)

Objective: Profit when you believe a stock will stay above a certain price.

The 3 Key Entry Criteria:

1. Oversold Condition: Use the Stochastic Oscillator or RSI. Wait for the market to be “oversold” (Stochastic below 20). This increases the probability of a bounce.

2. Support Levels: Place your short strike below a known support level or a previous low. This acts as a barrier that the price must break before you lose money.

3. The 20-30 Delta Rule: Your short put should have a Delta between 20 and 30. This mathematically implies a roughly 70-80% probability of profit.

The Ideal Setup:

• Timeframe: 30 to 60 days to expiration (DTE).

• Target Credit: Aim for $1.00 – $1.50 in premium for a 5-point wide spread.

Strategy 2: The Bear Call Spread (Bearish)

Objective: Profit when you believe a stock will stay below a certain price (or go down).

The 3 Key Entry Criteria:

1. Overbought Condition: Wait for the Stochastic Oscillator to cross above 80. An overextended market is much more likely to revert downward or stay flat.

2. Resistance Levels: Place your short call above a resistance level (a price the stock has struggled to move past) or a “dynamic” resistance like a moving average.

3. Delta Management: Again, look for a short leg around 20-30 Delta. This gives you a high “base” probability of success before you even factor in your technical analysis.

The Execution:

• Limit Orders: Don’t just take the market price. If the credit is too low (e.g. $0.80), set a limit order for $1.25 or $1.50 and let the market come to you.

• Width: For a 10-point wide spread, look for a minimum of $2.00 – $3.00 in premium.

Risk Management: The 1-5% Rule

Never bet the farm on a single spread.

Regardless of how “sure” a setup looks, limit your risk per trade to 1% to 5% of your total account size.

• Example: On a $10,000 account, if your max risk on a spread is $350, you are risking 3.5%. This allows you to survive a string of losses and stay in the game long enough for your “positive expectancy” to take over.

Conclusion

Trading isn’t about predicting the future; it’s about managing probabilities.

By combining technical indicators (Stochastic), price action (Support/Resistance), and options Greeks (Delta), you create a “Triple Threat” setup that puts the house odds in your corner.

As a trader, we want to be the house (casino) and not the player.

If you’re curious, you can watch the full video here: Grow Your Account Consistently With Just 2 Simple Strategies

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As a member of Rayner Teo’s UST course, I had the opportunity to attend MarketWise Asia’s event last weekend.

The session I was most looking forward to was Rayner’s, saved fittingly for last.

It didn’t disappoint.

Rayner opened by diagnosing why 95% of traders fail.

The culprits are familiar: no proven strategy, constant strategy-hopping, emotional decision-making, and a lack of discipline.

Nothing groundbreaking there.

But his follow-up reframe was worth the price of admission alone.

So what separates the top 1%?

3 things: Edge. Risk Management. Discipline.

Easier said than done.

And most traders stumble at the first hurdle, because they don’t actually understand what edge means.

Rayner illustrated this with three trader profiles (bear with me as this requires a little math):

Trader A - High Win Rate, Low R:R

Win rate: 80% | Avg win: +1R | Avg loss: −2R

Over 10 trades: 8 wins (+8R), 2 losses (−4R)

Net: +4R

Edge per trade: (0.8 × 1) − (0.2 × 2) = +0.4R

Trader B - Low Win Rate, High R:R

Win rate: 30% | Avg win: +5R | Avg loss: −1R

Over 10 trades: 3 wins (+15R), 7 losses (−7R)

Net: +8R

Edge per trade: (0.3 × 5) − (0.7 × 1) = +0.8R

Trader C - High Win Rate, Negative Edge

Win rate: 70% | Avg win: +1R | Avg loss: −4R

Over 10 trades: 7 wins (+7R), 3 losses (−12R) → Net: −5R Edge per trade: (0.7 × 1) − (0.3 × 4) = −0.5R

The numbers tell a counterintuitive story.

Trader C wins 70% of the time, and still loses money.

Trader B loses on 7 out of 10 trades, and walks away with the strongest edge of the three.

Win rate is seductive. It feels like signal.

But it’s noise without the reward-to-risk ratio attached.

The formula is deceptively simple:

Edge = (Win Rate × Avg Win) − (Loss Rate × Avg Loss)

But here’s the nuance: a high win rate isn’t inherently bad.

From a psychological standpoint, winning frequently has real value.

Frequent wins build confidence, reinforce discipline, and make it easier to stick to your system through drawdowns.

Traders who win often tend to stay in the game longer, and longevity matters.

The problem isn’t the high win rate. The problem is chasing it at the expense of positive expectancy.

Look back at Trader A.

An 80% win rate with a +0.4R edge per trade is a perfectly legitimate system.

It’s profitable. It’s sustainable.

For traders who struggle emotionally with consecutive losses, a high win rate setup can actually be the right fit, as long as the math checks out.

The danger is Trader C’s trap: letting losses run while taking profits too early, racking up wins that feel good in the moment but slowly drain the account.

High win rate, negative expectancy. The account doesn’t care how often you’re right, only how much you make when you are versus how much you lose when you aren’t.

So the real question to ask of any system isn’t “What’s my win rate?” It’s “What’s my expected value per trade?”

A high win rate with positive expectancy? That’s a great system. Pursue it.

A high win rate with negative expectancy? That’s a slow bleed dressed up as success.

What this means practically: you can build a profitable system around a 30% win rate if your winners are large enough relative to your losers.

And if a high win rate helps you stay disciplined and emotionally grounded, build around that too, just make sure the edge is real.

Most retail traders optimise for feeling right. The market rewards those who optimise for expected value.

One of those reminders that the fundamentals aren’t boring, they’re just underappreciated.

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March was a rough month.

No sugarcoating it, this was a losing month across the board, and the numbers deserve an honest post-mortem before we look ahead.

The Breakdown

Out of 31 closed trades, 10 were winners and 21 were losers, a 32% win rate that tells most of the story right there.

The average winner netted $24.68 while the average loser cost $52.36, meaning losses were more than twice the size of gains. That’s a painful ratio to overcome.

Stocks (MRT): -$369.85

The mean reversion trades (MRT) were the bread and butter of March, 27 of the 31 trades but the strategy ran into a buzzsaw.

The biggest culprits were BLD (-$125.00), SHC (-$90.83), and AS (-$48.58). These weren’t fringe positions; they were the kind of setups the strategy is designed for. The market simply didn’t cooperate, with many “oversold” names continuing lower rather than snapping back.

There were some genuine bright spots worth acknowledging.

APP (+$102.83) and AVGO (+$28.15) held their own under the MOMO strategy , were standouts, and BMY (+$18.53) and RNG (+$10.85) showed the MRT thesis can still work when conditions align.

Options: -$482.86

This is where the month really hurt.

The META Iron Condor alone accounted for -$290.80, a position that ran against us as volatility expanded and the stock moved well outside the expected range.

The IWM and GOOGL Bull Puts added another -$252 combined. The lone winner was the AMZN Iron Condor (+$59.92), which wasn’t nearly enough to offset the damage.

In hindsight, March was a high-volatility environment that was hostile to premium-selling strategies.

Iron condors and bull put spreads are not built to survive outsized directional moves, and we got them on multiple names in quick succession.

What I’m Adjusting

A few things I’m taking into April:

1. Tighter leash on options positions in elevated VIX environments. The META and GOOGL losses were a timing issue. When the macro backdrop is uncertain, premium-selling works against you fast.

I’ll be more selective when selling iron condors and bull put spreads.

2. MRT stock selection filters. A 32% win rate with a 2:1 loss-to-win ratio is unsustainable. The mean reversion setups in March were valid, it was just one of those months.

3. Letting the MOMO strategy breathe. APP and AVGO, both MOMO trades held for months, were the only real contributors this month. The longer holding period allowed those positions to work.

Looking Ahead

March stings, but one month of losses doesn’t invalidate a strategy.

What it does demand is a clear-eyed review of what went wrong and the discipline to adjust without overcorrecting.

The plan is to trim options exposure until volatility settles, and keep a closer eye on position sizing relative to the overall portfolio.

The goal in April isn’t to make March’s losses back in one swing. It’s to trade well, stay within the framework, and let the edge reveal itself over time.

More updates to come.

As always, none of this is financial advice, just a transparent look at what’s working, what isn’t, and how I’m thinking about it.

31 trades closed | 10 winners / 21 losers | 32.3% win rate | Net P&L: -$852.71

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There are few companies in the world that command the daily attention of three billion people.

Meta Platforms is one of them.

Facebook, Instagram, WhatsApp, and Threads form a social operating system that is essentially impossible to replicate at scale.

Yet as the company pours $69.7 billion annually into AI infrastructure, investors face a real question: is the market pricing in the upside correctly, or is it getting swept up in the frenzy? We ran the numbers to find out.

The Bull Case

The advertising engine is one of the most profitable businesses ever built. Meta generated a 57.6% OCF margin in FY2025, nearly 58 cents of operating cash flow for every dollar of revenue, even as it absorbed record capex.

The four-app portfolio creates self-reinforcing network effects that no competitor has come close to matching. WhatsApp, with over two billion users and minimal monetisation to date, represents a substantial embedded option that management is only beginning to exercise.

The AI story adds another dimension. Meta’s Llama open-source strategy commoditises AI infrastructure costs, while AI-powered ad targeting is already lifting revenue per user.

The balance sheet is a further source of comfort: $81.6 billion in cash against $58.7 billion in debt leaves the company net cash positive, funding both the buildout and aggressive buybacks simultaneously.

The Bear Case

The bear case is not about the core business, it is about capital allocation risk.

At $69.7 billion in annual capex, Meta needs enormous incremental returns on its AI infrastructure to avoid destroying shareholder value.

The FTC antitrust case targeting the Facebook-Instagram-WhatsApp structure has not fully resolved; a forced divestiture of Instagram is low probability but catastrophic in impact.

European data privacy regulation continues to create friction in Meta’s most profitable ad targeting capabilities.

Structurally, over 97% of revenue comes from advertising, a cyclical concentration that hurt badly in 2022, when revenue fell year-on-year for the first time in the company’s public history.

And Reality Labs continues to burn roughly $5 billion per year with no clear monetisation timeline.

Our Valuation

Using FY2025 base FCF of $46.1 billion (the trough figure), we apply 30% growth for years 1–5 (capex normalisation + AI monetisation inflection).

This steps down to 15% for years 6–10 and 4% for years 11–20, all discounted at 10%.

No terminal value is applied beyond year 20, making this a deliberately conservative model.

The model produces an intrinsic value of ~$706 per share, implying roughly 20% upside from the $577 close.

This is a meaningful margin for a business of this quality, even before any terminal value is considered.

It is worth emphasising what the model does and does not capture.

The model does not include a terminal value beyond year 20, adding even a modest perpetuity growth rate at that point would push intrinsic value well above $800.

It also does not explicitly model the WhatsApp monetisation optionality, which could prove to be a multi-hundred-billion dollar business in its own right.

On the flip side, the model assumes the 30% growth rate is achievable, which requires capex to normalise on schedule and AI revenue to compound meaningfully.

If either of those assumptions prove too optimistic, then the true intrinsic value will be lower.

The Bottom Line

Meta screens as meaningfully undervalued on a conservative DCF basis, with a durable business, a net cash balance sheet, and a genuine AI monetisation runway ahead.

The key variable to watch is whether the capex cycle normalises on schedule. If it does, FCF recovery should be swift.

For long-term investors with a 5–10 year horizon, this remains a compelling entry point.

We rate Meta a cautious buy.

Not a recommendation to buy, but I’ve bought some myself.

Disclaimer: This article is for informational and educational purposes only and does not constitute financial advice. The author may hold positions in securities mentioned. All DCF figures are derived from publicly available financial data. Past performance is not indicative of future results. Always conduct your own due diligence before making any investment decisions.

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For years, the conventional wisdom among Singapore investors was to look westward to the U.S (towards the S&P 500), for growth.

That so-called wisdom is being put to the test.

Over the past two years, the Straits Times Index (STI) ETF has quietly staged one of its most compelling performances in decades, leaving the flagship US equity benchmark in the dust.

The Numbers Tell the Story

The scorecard is striking.

The SPDR STI ETF (ES3) delivered a total return of 28.14% in 2025, following a 22.11% gain in 2024.

The S&P 500, by contrast, returned 17.82% in 2025 and 24.98% in 2024 via the Vanguard S&P 500 ETF (VOO).

In 2025, the gap was particularly dramatic: the Singapore index surged 13.44% year-to-date by mid-year, nearly doubling the S&P 500’s 7.38% gain — a standout performance that caught the attention of global investors.

The momentum has carried into 2026.

The STI has been steadily climbing so far in 2026 while the S&P 500 has remained flat.

Year-to-date as of mid-March, the STI ETF is up 7.54%, with a one-year total return of 33.16%.

Meanwhile, VOO’s year-to-date return sits at -4.65%, a dramatic reversal from recent years when US equities seemed untouchable.

The STI also crossed a symbolic milestone. On 12 February 2026, the STI surpassed the 5,000 mark for the first time in its history.

SG Banks: The Engine Behind the Rally

Understanding the STI’s outperformance requires understanding its engine.

DBS, OCBC and UOB combined account for slightly over 50% of the index, making the three local banks the single biggest driver of returns.

And boy have those banks been firing on all cylinders.

Between 2020 and 2025, DBS returned +216%, OCBC +105%, and UOB +105%, all while maintaining prudent capital buffers and sustainable dividend policies.

The profits backing these moves have been record-breaking.

DBS achieved a record net profit of S$11.4 billion in FY2024, with total income growing 10% to S$22.3 billion. OCBC’s net profit grew 8% to a new high of S$7.59 billion, driven by robust growth across banking, wealth management, and insurance.

Crucially, these aren’t just price gains, they come with generous income.

All three banks are trading above a 4.5% dividend yield, even with share prices at all-time highs.

For Singapore-based investors, those juicy dividends are tax-free, with no withholding tax to erode returns.

This is in itself a meaningful structural advantage over US equities.

A Safe Haven Premium

Beyond the banks, broader macro forces have been working in Singapore’s favour.

Maybank has noted that Singapore offers a rare combination of macro resilience, structural reforms, and valuation support, and that with peers exhibiting far more volatility from a policy and geopolitical standpoint, Singapore’s “certainty premium” should widen going forward.

That premium has been tested and proven. When US tariff fears sent global markets into a tailspin earlier in 2025, Singapore’s relatively low reciprocal tariff rate of 10%, modest compared to other Asian economies, helped the index recover faster.

Singapore equities stand out as an attractive safe haven, with a dividend yield of approximately 5%.

The index itself has also broadened its base.

Pure-play data centre Keppel DC REIT joined the STI, increasing S-REITs’ combined weight in the index to over 10%.

After years of dragging on returns, the real estate sector turned around sharply in 2025, contributing +21.3% year-to-date as of August 2025, ranking as the third-best performer in the index.

The Currency Tailwind

There is another factor that Singaporean investors holding US equities have felt acutely.

A US$10,000 investment at the start of 2025 would have cost a Singapore-based investor more than S$13,700 to fund, growing to US$11,600 by year-end but converting back to only S$14,848.

This gives a total return on capital of just 8.4% in SGD terms, compared to the nominal 16% USD gain.

STI ETF investors, on the other hand, faced no such currency drag.

A Word of Caution

No market outperformance story is complete without its caveats.

The STI’s heavy concentration in three banks is a double-edged sword. Singapore banks experienced significant volatility in early 2025, with DBS falling more than 20% following the escalation of US-China trade tensions before subsequently recovering.

Longer-term, the S&P 500 retains a formidable track record: over 10 years, VOO’s total return stands at 275.34%, and over 15 years, 550.43%.

A two-year sprint does not erase decades of compounding.

Conclusion

The STI ETF’s recent outperformance is not a fluke. It reflects genuine earnings strength from Singapore’s banks, a rerating of Singapore as a geopolitical safe haven, a recovering REIT sector, and meaningful currency advantages for local investors.

Whether it sustains this edge over the next decade remains to be seen but for the first time in a long while, investors have a strong case for keeping more of their money closer to home.

I’ll admit it, I have long been a sceptic of the STI ETF.

For years, the index’s heavy bank concentration, modest growth profile, and reputation as a “boring” market made it easy to overlook in favour of the dynamism of US equities.

But recent performance has made me pause.

When an index delivers north of 28% in a single year, crosses the 5,000 mark for the first time in its history, and does so while the S&P 500 stumbles, even the most hard-core sceptic has to sit up and take notice.

For the first time in awhile, I’m genuinely reconsidering my allocation.

Sometimes the boring bet turns out to be the smart one.

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