Can Good Stocks Still Create Bad Portfolios? A Nobel-Winning Intellectual Journey

Chicago, 1952: A Conversation That Changed Everything

Before he changed the way the world thinks about investing, Harry Markowitz was simply a PhD student at the University of Chicago, wondering what on earth he should choose as his doctoral research topic.

He wasn’t trying to revolutionise finance. He was just looking for a subject that would keep him interested through the long years of academic work ahead.

One afternoon in 1950, while waiting for an appointment with his advisor, he found himself discussing portfolio selection. According to later accounts, a casual conversation about how difficult it was to build good portfolios for clients planted a seed in his mind.

Everyone already knew the old wisdom: “Don’t put all your eggs in one basket.”

Your grandmother said it. Every investment book repeated it. Diversification was common sense.

But here’s what bothered Markowitz: nobody had actually done the math. Nobody could explain why diversification worked, how much was enough, or which combinations of stocks were truly better than others.

He decided to find out.

The Insight That Started It All

Markowitz began with a simple observation. Imagine you own shares in two companies:

• Company A makes umbrellas. When it rains, people buy umbrellas. Profits soar. The stock jumps.

• Company B makes sunscreen. When it’s sunny, people buy sunscreen. Profits soar. The stock jumps.

Now here’s the interesting part. If you own both companies, something magical happens.

When it rains, your umbrella company thrives while your sunscreen company suffers. When it’s sunny, the reverse happens. But you—the investor who owns both—sleep remarkably well regardless of the weather. Your overall wealth stays steadier than either stock alone.

This wasn’t just luck. This was mathematics.

Markowitz realized that what mattered wasn’t just picking “good” stocks. What mattered was picking stocks that moved differently from each other. When one fell, another might rise. When one stagnated, another might soar.

Statisticians already had a word for this: covariance—a mathematical way to capture how two things move together. Markowitz applied this tool to investing in a way nobody had before.

• High covariance → stocks rise and fall together

• Low covariance → stocks move independently

• Negative covariance → stocks move in opposite directions

The revelation was powerful:

A portfolio of individually excellent stocks that all move together is risky. A portfolio that mixes good stocks that move differently is safer—and often more rewarding on a risk-adjusted basis.

The Efficient Frontier: Finding the Sweet Spot

Markowitz went further. He showed that for any level of risk you’re willing to accept, there’s a best possible portfolio—the one that gives you the highest return for that level of risk.

Imagine plotting all possible portfolios on a graph. Risk on one axis, return on the other. Most portfolios scatter randomly. But some sit on a special curve—they give you the maximum return possible for each level of risk. Markowitz called this the efficient frontier.

Think of it like shopping for a phone. For any budget, there’s a best phone you can buy. Below that curve? You’re overpaying or settling for less. On the curve? You’re getting the best value possible.

For the first time, investing had a mathematical framework that went beyond gut feeling and rules of thumb.

Beautiful Theory, Impossible Practice

Markowitz published his findings in 1952. Academics were impressed. Here, finally, was mathematical proof of why diversification worked.

But practitioners—the people managing real money—hit a wall.

To build a portfolio of 100 stocks using Markowitz’s method, you’d need to calculate how each stock relates to every other stock. That’s nearly 5,000 relationships. For 500 stocks? Almost 125,000 calculations.

Remember, this was the 1950s. No Excel. No computers on every desk. Just ledgers, pencils, slide rules, and patience wearing dangerously thin.

Markowitz had created something beautiful.

But beauty without usability is just decoration.

Enter William Sharpe: A Different Question

By the early 1960s, William Sharpe was wrestling with the same problem. He admired Markowitz’s brilliance—but also saw its burden.

Sharpe asked a different question:

Not “Is Markowitz right?” but “Is there a simpler way to capture the same truth?”

He noticed something that now feels obvious: when the overall stock market crashes, most stocks fall. When the market rises, most stocks rise.

What if, instead of measuring how every stock relates to every other stock, we just measured how each stock relates to the overall market?

Think of the market like ocean waves. Some boats rock wildly with every wave. Some are steadier. But all boats are affected by whether the sea is calm or stormy.

Statisticians already had a measure for this relationship: beta.

• Beta = 1.0 → moves with the market

• Beta = 1.5 → moves 50% more than the market

• Beta = 0.5 → moves half as much

Suddenly, those 5,000 calculations became just 100—one number per stock.

Same insight. Vastly simpler math.

That simplification didn’t just make portfolios easier to build—it opened the door to a bigger question:

If risk can be measured, how should it be rewarded?

This became known as the Single Index Model.

From Portfolio Tool to Pricing Model

Sharpe’s simplification led naturally to a deeper problem.

Why would you buy a risky stock instead of a safe government bond? Only if the risky stock promises higher returns. But how much higher?

In 1964, Sharpe answered this with the Capital Asset Pricing Model (CAPM).

The logic is elegant:

• Safe investments give you a baseline return

• The market is riskier, so it offers a higher return

• The difference is the reward for accepting market risk

• A stock twice as risky as the market should earn twice that premium

For the first time, investors had a benchmark:

Is a stock worth buying? Compare its expected return with what it should earn for its level of risk.

CAPM became one of the most influential ideas in finance—used in valuation, capital budgeting, and investment decisions worldwide.

The Measuring Stick: The Sharpe Ratio

Sharpe wasn’t done yet.

Imagine two mutual funds:

• Fund A earns 15% but swings wildly.

• Fund B earns 12% but moves steadily.

Which is better?

Before Sharpe, people just compared returns. Bigger number meant better fund. But that ignored the most important question: How much risk did you take to get that return?

In 1966, Sharpe introduced what we now call the Sharpe Ratio:

Sharpe Ratio = (Return – Risk-Free Return) / Risk

Think of it like fuel efficiency. You don’t just ask how far a car went—you ask how much fuel it used to get there.

As a rule of thumb:

• Above 1.0 is good

• Above 2.0 is excellent

• Below 0.5 means you’re taking a lot of risk for little reward

Today, most professional mutual fund fact sheets include this number. It has become the standard way to compare investments fairly.

Over the years, finance has developed several theories to answer one simple question: how should we invest better? Here’s a simple comparison of the most influential ideas that shape modern investing.

Comparison of Major Portfolio Theories

Theory / Model	Key Thinker & Year	Core Idea (in simple words)	Risk Focus	Key Measure Used	Best Used For	Main Limitation
Modern Portfolio Theory (MPT)	Harry Markowitz, 1952	Don’t just choose good stocks—choose the right mix of stocks.	Total portfolio risk	Variance, Covariance	Building diversified portfolios	Assumes investors are always rational and markets behave normally
Single Index Model	William Sharpe, 1963	A stock’s risk can be understood mainly by how it moves with the market.	Market risk	Beta	Simplifying portfolio construction	Ignores company-specific relationships between stocks
Capital Asset Pricing Model (CAPM)	Sharpe (1964), Lintner (1965), Mossin (1966)	Higher risk should earn higher return—no free lunch.	Systematic (market) risk	Beta, Risk-free rate	Estimating expected return, valuation	Assumes one market factor explains everything
Arbitrage Pricing Theory (APT)	Stephen Ross, 1976	Returns depend on multiple economic forces, not just the market.	Multi-factor risk	Economic factors	Advanced asset pricing	Hard to identify the right factors
Efficient Market Hypothesis (EMH)	Eugene Fama, 1970	Prices already reflect all available information—beating the market is hard.	Information risk	Market prices	Index investing, passive strategies	Underestimates investor psychology
Behavioural Portfolio Theory	Hersh Shefrin & Meir Statman, 2000	Investors are human—emotions and goals shape portfolios.	Psychological & emotional risk	Mental accounting	Understanding real investor behaviour	Hard to model mathematically
Post-Modern Portfolio Theory (PMPT)	Rom & Ferguson, 1993	Investors fear losses more than volatility.	Downside risk	Semi-variance	Goal-based investing	Less standardised than MPT

Stockholm, 1990: The Recognition

On December 10, 1990, Harry Markowitz and William Sharpe stood in Stockholm, Sweden, alongside economist Merton Miller, receiving the Nobel Prize in Economic Sciences.

Markowitz was 63. His dissertation from 1952 had taken nearly four decades to be fully appreciated.

Sharpe was 56. His simplifications had made those ideas usable by millions.

The Nobel Committee recognized them for work that had fundamentally changed the practice of portfolio management.

What Indian Investors Can Take From This

So what does all this mean for you as an investor? First, don’t judge your portfolio by how many stocks or funds you own—ask instead how differently they behave when markets rise and fall. Markowitz’s work gently suggests that real diversification comes from mixing unrelated risks, not just adding more names to your list. Sharpe then nudges you to go one step further: take risk only when you are being properly rewarded for it. Before chasing returns, pause and ask—am I earning enough for the risk I’m taking? And here is the most important part: don’t stop at listening to advice, not even mine. If you truly want to grow as an investor, commit to learning, reading, and understanding these ideas for yourself. Informed decisions are always stronger than borrowed opinions.

One important thing to remember: these ideas are not just about choosing the right stocks. They apply to your entire investment portfolio—from equity and mutual funds to debt, gold, and even how you balance risk across all your financial goals.

What This Means for You

You may never personally calculate these formulas, unless you are studying finance. But even then, the ideas behind them quietly shape how investments work for everyone.

When your mutual fund mentions its Sharpe Ratio, that’s Sharpe’s legacy.

When advisors talk about diversification, they’re thinking about how investments move relative to each other—Markowitz’s insight.

When you see a stock’s beta, that’s measuring its sensitivity to market swings—Sharpe made that practical.

More importantly, you’ve seen how progress really happens.

Not through perfect answers—but through better questions.

Markowitz asked: Can diversification be measured mathematically?

Sharpe asked: Can that measurement be made practical?

Both questions mattered. Both answers changed finance.

The models aren’t perfect. Markets still surprise us. Human emotions still dominate decisions. Rare crises still humble sophisticated investors.

But the core truths remain:

• Diversification works.

• Risk and return are connected.

• How you measure performance matters.

And all of this began with simple curiosity.

What questions are you asking?

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Note: This article simplifies complex financial models for clarity. Real-world investing involves additional factors, and readers should consider multiple perspectives before making financial decisions.