Equity risk drivers, factors, international and emerging markets

Level 1 Plain words

For every reader: the idea in plain words, no formulas.

What moves a stock price

Four forces move stock prices, and every market event can be traced to one or more of them.

Earnings. If a company's profits fall, its shares are worth less. In a recession most companies' profits fall together.

Interest rates. Stocks compete with bonds for savers' money. When safe bonds pay more, investors need a higher return from stocks too, and they get it by paying less for them.

The risk premium. On top of the safe rate, investors ask for extra return to hold something as uncertain as stocks. When they grow fearful, that extra rises and prices fall, even if profits and rates have not moved.

Flows. Sometimes buyers or sellers must trade whatever the price: a fund that has to sell because savers are withdrawing, an index that adds a company and forces every index fund to buy it, or traders on borrowed money who face a margin call. Flows move prices for days or months; the other three drivers matter more over years.

Market risk and company risk

Part of a stock's ups and downs comes from the whole market. When the economy turns down, nearly every share falls. The rest comes from the company itself: a failed product, a lawsuit, a new chief executive. Owning many companies cancels out most of the company-specific part, because one firm's bad news is another's good news. Market risk cannot be diversified away inside the stock market, and that is the risk investors get paid to carry. A stock's beta measures how much of it a share carries.

Buying abroad adds a currency

When a US investor buys a Japanese or Brazilian stock, she owns two things: a share priced in yen or reais, and the yen or reais themselves. If the share rises 10 % at home while the currency falls 8 % against the dollar, the dollar gain is much smaller than 10 %. Over a year, currency moves can be as large as the stock moves. Chapter 14 explains what drives currencies; here we measure what they add to a stock return.

Level 2 How it works

For readers who want the mechanism, with the diagrams.

Beginner path stops here

The four drivers with numbers

Each driver can be sized with the valuation tools of Chapter 10. We start from a stock priced by the Gordon model at a P/E of 20, with a risk-free rate of 4 %, an equity risk premium of 4 % and growth of 4 %.

The earnings driver gets most attention in the news, yet over a single year the discount rate and the premium often move prices more. In 2022 S&P 500 earnings fell far less than the index, so most of the loss came through the second and third rows of that table.

Beta and the security market line

Beta measures how far a stock tends to move when the whole market moves 1 %. A beta of 1.2 means the stock has moved about 1.2 % for each 1 % move in the market, on average. Utilities and food companies tend to have betas below 1; chip makers and airlines, above 1.

The CAPM, the capital asset pricing model of William Sharpe (1964), turns beta into a required return. Investors earn the risk-free rate plus the market's premium scaled by beta. Plotted with beta across and expected return up, the CAPM is a straight line called the security market line. A stock above the line offers more return than its beta warrants; below it, less.

The CAPM is simple and widely used for setting costs of capital, and it explains returns poorly in the data. High-beta stocks have not earned as much more than low-beta stocks as the line predicts. That gap opened the door to factor models.

Factors beyond the market

A factor is a common source of return shared by many stocks. Eugene Fama and Kenneth French (1992, 1993) showed that two characteristics besides beta explained US stock returns: size (small companies earned more than large ones) and value (stocks with low prices relative to book value earned more than those with high prices). Mark Carhart (1997) added momentum: stocks that had risen most over the past year kept outperforming for a few months. Later work added profitability (quality) and investment.

Factors are measured as long-short portfolios. The value factor, called HML for "high minus low", holds cheap stocks and sells expensive ones; SMB ("small minus big") holds small and sells large. French publishes these returns monthly back to 1926.

Why would a factor pay? Two explanations compete. In the risk story, value companies are fragile and suffer most in bad times, so their higher return pays for that risk. In the behavioural story, investors over-extrapolate, paying too much for exciting growth and too little for dull or troubled firms. Both may hold in part. Either way, a factor premium is an average over decades, and Level 4 shows how long a factor can lose.

International stocks and the currency layer

A foreign stock's return in dollars combines two returns: the local return in its home currency and the change in that currency's value against the dollar. The two can reinforce or cancel each other. Over long periods currency moves have partly averaged out for developed markets, yet over five or ten years they can add or subtract several points a year.

Some funds hedge the currency, selling it forward so that the investor earns roughly the local return plus the interest-rate gap between the two countries (Chapter 14 shows why). Most broad international funds do not hedge, which leaves the investor holding the currency as a second asset.

Emerging markets and country risk

Emerging markets add risks that developed markets mostly lack: governments that change rules for foreign owners, capital controls that trap money, banking systems that fail, and currencies that collapse when foreign money leaves. These risks cluster, because a country in trouble often suffers several at once, and because foreign investors tend to pull money out of many emerging markets together.

Analysts price this with a country risk premium added to the discount rate. A common method, set out by Aswath Damodaran, takes the extra yield that country's government bonds pay over US Treasuries and scales it up by how much more volatile the country's stocks are than its bonds. Index providers decide which countries count as emerging, using income, market size and how freely foreigners can trade; a country can be promoted to developed or demoted when its rules change. Growth in an economy and returns to its stock market have not lined up well across countries, partly because fast growth often comes with new share issues that dilute existing owners. So the usual reason to hold emerging markets is diversification, with a modest weight, more than a bet on GDP.

Level 3 The math

For Series 65 and CFA readers: formulas and worked examples.

Series 65 path stops here

The capital asset pricing model

With a risk-free rate $r_f$, an expected market return $\mathbb{E}[R_m]$ and a stock's beta $\beta_i$, the CAPM gives the stock's expected return.

In words, a stock's expected return equals the risk-free rate plus its beta times the market's premium over the risk-free rate.

Beta from covariance

Beta is the covariance of the stock with the market divided by the market's variance. Writing the covariance as correlation times the two volatilities gives a form that is easier to reason about.

In words, beta equals the stock's correlation with the market times its volatility divided by the market's volatility.

Multi-factor models

The Fama and French three-factor model adds a size and a value exposure to beta.

In words, expected return equals the risk-free rate plus the stock's exposure to each factor times that factor's premium, summed over the market, size and value factors.

With $r_f = 4\%$, a market loading of 1.0 on a 5 % premium, a size loading of 0.3 on a 2 % premium and a value loading of 0.4 on a 3 % premium (assumptions for the example), the expected return is 10.8 %. Fund analysts run the same regression backwards: they explain a fund's past returns by its factor loadings, and whatever return is left over, the intercept, is the manager's alpha. Many funds that beat the market turn out to have held small or cheap stocks, earning factor premia that an index fund could have delivered.

The factor premia in the data

French's monthly series let us measure each premium over almost a century. We annualise the monthly averages and give the compound rate beside the simple one.

Data run from 1927 (momentum from January 1927, the others from the same month) to September 2026, from the Kenneth French Data Library. The size premium is small next to its volatility, and researchers have debated for decades whether it survives once the smallest, least-traded stocks are left out. Value and momentum look larger. Their volatility tells us how hard they are to trust over a short span: the standard error of the value premium's average over 14 years is about 3.3 %, nearly as large as the premium itself. Over 99 years it falls to 1.2 %, and the premium sits 3.4 standard errors above zero.

In words, the standard error of an average return equals the yearly volatility divided by the square root of the number of years.

How many stocks it takes to diversify

Take stocks that each have a volatility of 30 % and an average pairwise correlation of 0.3. An equal-weight portfolio of $n$ of them has the volatility below.

In words, the portfolio's volatility is the square root of the shared part of the variance, which stays, plus the company-specific part divided by the number of stocks.

Most of the gain arrives with the first ten or twenty stocks; after that, each extra name helps little. What remains, the floor of 16.4 %, is market risk, which more stocks from the same market cannot remove. Adding other markets and other asset classes with lower correlations lowers the floor, and that is the reason to diversify across countries, put in numbers. Real portfolios need more names than the table suggests, because single stocks have fat-tailed returns and a few large winners supply most of the market's long-run gain.

Dollar return of a foreign stock

If the local return is $R_L$ and the currency's change against the dollar is $R_{FX}$, the dollar return compounds the two.

In words, one plus the dollar return equals one plus the local return times one plus the currency return.

Country risk premium

Damodaran's relative-volatility method scales the sovereign default spread by the ratio of equity to bond volatility in that market.

In words, the country risk premium equals the extra yield on the country's government bonds times how many times more volatile its stocks are than those bonds.

A country whose dollar bonds pay 300 bp over Treasuries, with stocks 1.5 times as volatile as its bonds, gets a country premium of 4.5 %. Added to a US-based cost of equity of 10 %, the hurdle for a project there rises to about 14.5 %.

Level 4 History, analysed

For readers who want past episodes run through the Level 3 math.

Japan, 1989: the biggest market in the world falls 82 %

All three valuation drivers worked against Japan at once. The Bank of Japan raised rates from 1989 into 1990 to cool land and stock prices, lifting the discount rate. Banks loaded with property loans went bad through the 1990s, and profits stagnated. Investors who had once accepted very low earnings yields demanded more, raising the premium. Japanese P/E ratios at the peak were far above US levels (Gaps).

The gain-to-recover arithmetic shows how deep the hole was. From the 2009 low the index needed +452 % to get back to its peak. A global index fund in 1989 held Japan in proportion to its market value, about 45 %, so the largest share of a "diversified" world portfolio sat in the most expensive market. Market-cap weighting puts the most money where prices have risen most. That is usually a sensible default, and in 1989 it concentrated risk.

Thailand, 1997: when the currency goes too

For a US investor the currency move alone was ruinous. A move from 25 to 50 baht per dollar halves the dollar value of anything priced in baht: −50 %. Thai share prices fell sharply in baht at the same time (Gaps). With a local loss of 40 % and the currency down 50 %, the dollar loss compounds to −70.0 %. Compounding softens the total a little, and the result still leaves 30 cents of each dollar.

All four drivers fed one another. Thai companies had borrowed in dollars, so the falling baht raised their debts measured in baht, cutting earnings. Thailand's central bank lifted interest rates to defend the currency, raising the discount rate. Fear lifted the premium investors demanded for anything Asian. And foreign investors pulled money out of the whole region, a flow driver that hit countries with sounder finances too. Episodes like this are why analysts add a country risk premium and why emerging-market stocks and currencies tend to fall together.

2008: emerging markets fall further

The 2008 crisis began in US mortgages, yet emerging markets fell further. Beta explains part of it: emerging-market stocks have tended to move more than one for one with world markets. Currencies explain more: as investors fled to the dollar, most emerging currencies fell, adding a currency loss to the local one. And correlations rose. In calm years emerging markets looked like a diversifier; in the crash nearly everything risky fell together, the pattern Chapter 3 describes as correlations going to one.

Value's long drought, 2007 to 2020

For fourteen years, buying cheap stocks and selling expensive ones lost money. Over January 2007 to December 2020 the factor's cumulative return was −55.8 %. Many investors concluded the value premium was gone.

The Level 3 tools suggest caution in both directions. First, statistics: with a volatility of 12.3 % a year, the standard error of the average over 14 years is 3.3 %, so a long losing run is consistent with a positive long-run premium of about 4 %. Fourteen years is a short sample for a factor this noisy. Second, the discount-rate driver: growth stocks have more of their value far in the future, so they have longer equity duration, and the long fall in interest rates from 2007 to 2020 favoured them. When rates rose sharply in 2022, the same arithmetic ran the other way, and value had one of its best years. Third, the premium may have shrunk for good as more money chased it. The data cannot yet tell these explanations apart.

What the four cases share

Each episode combined several drivers, and each punished investors who treated a long average as a promise. Japan and value show how long an asset can trail: decades for one market, fourteen years for a factor with a positive long-run premium. Thailand and 2008 show that diversification abroad works over long periods and can let investors down in the crises when they want it most, because currencies and correlations turn against them together.

None of this argues against owning stocks abroad or tilting toward a factor. US investors who held only US stocks did well after 1989, while a Japanese investor who held only Japan did badly; nobody in 1989 knew which home market would win. Spreading across markets reduced the chance of being the investor stuck in the wrong one. For a portfolio, the practical tools follow from the math. Hold many markets in proportions that do not depend on recent winners. Decide in advance whether to hedge currencies, and keep the decision through a crisis. Judge a factor strategy over decades rather than years, using the standard error to tell a bad run from a broken premium.