Understanding Market Cycles and the Crash Probability Matrix

Looking back at history, every market boom has always felt rational  until it wasn’t.  The dot-com bubble in 2000 was a story of innovation that went too far. The 2008 crisis was fuelled by an obsession with home ownership and excessive leverage and 2020 was triggered by fear itself, a global shutdown that froze the world economy almost overnight.

Each of these moments began with confidence and ended with correction. I’ve never personally experienced a market crash as an investor. But based on history, crashes don’t arrive overnight. They form quietly, over months or even years, as small imbalances build up beneath the surface. That’s what makes them so hard to predict and so necessary to prepare for.

What a Crash Really Means

A market crash isn’t just a fall in numbers, it’s a reset of expectations. In 2000, the Nasdaq lost about 78% of its value after the dot-com bubble burst. In 2008, the S&P 500 fell roughly 57% from peak to trough. Even the pandemic shock of 2020, though shorter, erased 34% within five weeks.

To put it simply, if you had Rp100 million invested in 2008, your portfolio could have dropped to around Rp43 million at the bottom. (This is a simplified illustration, just to show how severe a major drawdown can be.)

Behind those numbers were millions of investors who believed the good times would last a bit longer. Every cycle follows a familiar rhythm: valuations rise, leverage builds, confidence peaks and then liquidity disappears faster than it came.

But after every crash comes recovery, often stronger than before. The goal isn’t to predict the exact timing of a crash. It’s to understand where we are in the cycle and act with awareness, not fear.

Why This Matters Now

It’s easy to say that “cycles repeat,” but 2025 doesn’t look like any cycle we’ve seen before. We’re four years past the COVID crash, yet the market has never truly reset. After the sharp dip in 2020, global equities rebounded rapidly and kept climbing.

This time, the story isn’t about cheap money, but about Artificial Intelligence. From chipmakers to cloud infrastructure, AI has become the new growth engine of the global market. The narrative feels different, yet the pattern is familiar, innovation driving valuations to record levels. Take NVIDIA, for example. Its valuation has soared to a point where investors are paying around 40 times its annual earnings, a price that assumes years of perfect growth.

Meanwhile, gold prices have reached record highs, signalling something else: quiet caution. Investors are hedging, not celebrating. Geopolitical tensions linger from Eastern Europe to the South China Sea and global debt remains at historic levels.

Yet markets stay calm, volatility remains low, and liquidity continues to flow.

So why hasn’t there been a crash?

From what I’ve read, markets don’t collapse because of events; they collapse under pressure. As long as earnings hold up and credit remains accessible, optimism can stretch further than logic suggests.

But that’s also when risk hides in plain sight in valuations, leverage, and complacency.

That’s why I use what I call the Crash Probability Matrix. I’m not an economist, and I don’t claim to have invented these measures. I simply gathered what I’ve read and learned, the indicators that many economists and fund managers consider reliable warning signs.

Out of the many possibilities, I focus on five that are both defensible and simple enough for individual investors to follow.

Learning to Read the Cycle

The Crash Probability Matrix is a framework based on macro indicators that professionals have relied on for decades to assess where we are in the market cycle. There are many ways to measure risk from credit spreads to volatility indices but five indicators consistently stand out across academic research and institutional practice.

They don’t predict crashes with precision, but they’ve historically given some of the clearest signals when financial conditions begin to turn.

The Yield Curve — When short-term interest rates rise above long-term ones (an inversion), it has preceded nearly every U.S. recession since the 1950s. Used widely by the Federal Reserve, IMF, and major investment banks such as JPMorgan and Goldman Sachs as a leading economic gauge.

Inflation — Persistent inflation above central bank targets tightens liquidity and erodes real returns. Monitored closely by the Federal Reserve, the European Central Bank (ECB), and institutional funds because it shapes monetary policy.

Unemployment (Sahm Rule) — A rise in unemployment of more than 0.5 percentage points above its 12-month low often signals the start of a downturn. Developed by economist Claudia Sahm and adopted by the U.S. Federal Reserve as an early-warning recession rule.

Central Bank Policy — Sharp tightening cycles or sustained high real rates can drain liquidity faster than markets can adjust. Fund managers watch this closely through cumulative rate hikes and forward guidance.

Valuation (Shiller CAPE Ratio) — When prices far exceed long-term earnings, expectations become fragile. Popularised by Nobel laureate Robert Shiller and used by firms such as GMO, Research Affiliates, and Vanguard to assess market expensiveness.

One more factor often discussed is the Fear and Greed Index, which measures investor sentiment, whether the market is dominated by optimism or fear. While it isn’t part of this matrix, it provides a useful psychological layer. Extreme optimism, when combined with high valuations, often signals that risks are being ignored.

Each indicator alone is imperfect, but together, they offer a composite view, not of where the market will go tomorrow, but of how fragile or resilient the system feels today.

Reading Between the Lines

We’re no longer in the “cheap money” era, yet the system isn’t in crisis either. Inflation has cooled, the yield curve has normalised, and the Fed has begun cutting rates again, a combination rarely seen before a crash.

HOwever, one thing stands out: valuations. At a Shiller CAPE of around 40×, the market today is priced very expenive, meaning investors are paying about 40 times companies’ average earnings.

That’s historically high and suggests that prices already assume everything will go right. When that happens, even small disappointments, like slower earnings or policy surprises can cause sharp pullbacks. In short, the higher the expectations, the smaller the room for mistakes.

How I Use the Matrix

Understanding the cycle doesn’t mean trying to time it. For me, it’s more about reading the conditions, like checking the weather before sailing. When the matrix is mostly green, opportunities are broad. When amber appears, it’s time to tighten risk and prepare liquidity and when red dominates, survival becomes more important than growth.

As of October 2025, the mix is balanced: enough stability to stay invested, but enough caution to stay alert. It’s the kind of environment where being steady and rational matters more than making bold moves.

I’m holding my core assets, strengthening my cash buffers, and watching valuations more closely than headlines.

Final Thoughts

Every generation of investors faces its own test. For some, it was 1999. For others, 2008. For me, it might be learning to navigate a world that’s neither cheap nor collapsing, a high plateau that tests discipline, not courage.

As I move closer to my retirement years, being aware of this cycle helps me protect what I’ve built. Crashes, to me, aren’t enemies to avoid; they’re part of the journey. I haven’t experienced one yet, at least not as an investor, but what matters isn’t whether it comes. It’s whether I’m ready when it does.

That’s the purpose of the Crash Probability Matrix: not to forecast fear, but to remind me that readiness itself is a form of resilience.

It’ll also be a good test to see if this framework can really help me recognise when the next storm is forming.