Capital market theory refers to several approaches used to estimate the value of securities and to explain how supply and demand affect market prices. Three ideas commonly grouped under capital market theory are:
The capital asset pricing model (CAPM) estimates a security’s expected return using only factors tied to systematic risk. CAPM uses the following formula (introduced earlier in a previous chapter):
Each input in the formula is market-based:
Let’s work through a typical CAPM question:
An investor is analyzing a large-cap stock fund prior to making a potential purchase. The expected return of the S&P 500 is 12%, while the security reflects a beta of 1.5 and a standard deviation of 22. Additionally, the 3-month T-bill rate is 2%. Assuming the investor is utilizing the capital asset pricing model, what is the expected return of the large-cap stock fund?
Answer = 17%
Start by identifying the inputs:
Now plug them into the formula:
The standard deviation is not needed for this calculation.
Notice what CAPM leaves out: it does not include security-specific risks. Non-systematic risks such as business risk, financial risk, and liquidity risk aren’t part of the model. In other words, CAPM estimates expected return based on market dynamics and systematic risk only.
CAPM also connects directly to alpha. The CAPM formula gives you the expected return. Alpha then compares performance by taking the security’s (or portfolio’s) actual return and subtracting this expected return. CAPM produces the benchmark; alpha measures over- or underperformance relative to that benchmark.
In 1952, economist Harry Markowitz published an essay on investing often viewed as the starting point of modern portfolio theory (MPT). The essay, titled Portfolio Selection, laid out principles for building an efficient portfolio. An efficient portfolio is designed to offer the highest return potential for the lowest risk exposure.
To develop these principles, Markowitz made several assumptions about investors, including:
Given these assumptions, investors face a tradeoff: higher expected returns generally require taking more risk. MPT’s key tool for managing this tradeoff is diversification.
An investor might pursue higher returns by holding a volatile (risky) security. Even so, the overall portfolio’s risk can be reduced by combining investments whose returns don’t move together. For example, losses in a luxury cruise line stock during an economic downturn might be offset by gains in a defensive investment like a pharmaceutical company stock.
With proper diversification, the risk/return profile of any single security becomes less important than the risk/return profile of the portfolio as a whole. That’s why a conservative, risk-averse investor might still allocate a small portion of assets to a high-risk security while keeping the overall portfolio suitable.
To evaluate diversification benefits, investors use the correlation coefficient, which measures how similarly two securities or portfolios have moved historically. Correlation ranges from -1 to +1:
All other correlations fall between -1 and +1:
A common test point combines diversification and correlation: to further diversify, an investor should add securities with negative correlations to the existing portfolio. On average, those positions tend to move differently from the rest of the portfolio, which can reduce losses when other holdings decline.
Diversification isn’t only about adding more securities. Asset allocation matters too. Strategic asset allocation builds on MPT by emphasizing a suitable long-term allocation, avoiding market timing, and periodically rebalancing. Used correctly, it helps keep the portfolio’s overall risk/return profile aligned with the investor’s goals.
In today’s markets, information spreads quickly, and prices can adjust almost immediately when new data becomes available. That raises a practical question: if the market reacts so fast, does research still help you find mispriced securities?
Investors often review metrics before buying or selling, such as assets and liabilities on balance sheets, profitability on income statements, and market trends suggested by technical analysis. But even if those metrics look favorable, the market may have already responded to the same information. If so, the information is already “baked into” the current price.
That idea is the core of the efficient market hypothesis (EMH). EMH states that available market data and information about a security are reflected in its current market price. Information travels efficiently and is incorporated into prices quickly. If EMH holds, then research won’t reliably produce an investing edge: by the time you identify “good news,” the market price may already reflect it.
Investors who accept EMH tend to avoid selecting individual securities and instead focus on passive investing strategies. This approach aims to capture the returns of broad market segments rather than trying to outperform by security selection. It can also reduce costs by avoiding higher expense ratios often associated with actively managed funds.
There are three versions of EMH to be aware of:
Strong form EMH states all public and private information is reflected in the market prices of securities. Under this view, even non-public inside information* wouldn’t consistently help an investor earn excess returns.
As you may already know, there is an undeniable history of investors making significant returns or avoiding large losses when illegally using inside information. Strong form EMH doesn’t deny that history. Instead, it argues that even private information may not be as reliably useful as it appears, because unexpected events can overwhelm any advantage.
*Trading on material, non-public information, also known as inside information, is explicitly illegal. The specifics related to this concept are discussed in a future chapter.
For example, suppose an executive at an oil company knows an upcoming earnings report will show profitability far above expectations. Expecting the stock price to rise when the report becomes public, the executive buys a large amount of company stock just before the release (illegally). A few days before the earnings report is released, an explosion occurs at one of the company’s rigs, causing a catastrophic environmental disaster. Cleanup costs and legal liabilities create billions of dollars in losses, and the stock price drops sharply despite the favorable earnings report.
In this scenario, even private insider information didn’t lead to a profit. Strong form EMH argues outcomes like this are more common than they may seem.
Semi-strong form EMH states all public information is reflected in the market prices of securities. This is a step down from strong form. It suggests that while public information is quickly priced in, non-public information can consistently predict future market movements. Under this view, scenarios like the oil company example can happen, but they’re considered infrequent.
Weak form EMH states most public information is reflected in the market prices of securities. Like semi-strong EMH, weak form also argues private information can consistently predict future market movements. It adds another idea: complex fundamental analysis may provide insights into future supply and demand.
In the fundamental analysis chapter, we covered the basics of balance sheets and income statements. Many analysts spend years learning to interpret the details in these documents. For example, here’s Nike’s 2021 annual report, which includes 109 pages of information about the company’s operations and finances.
If only a small portion of investors can accurately interpret complex financial statements, weak form EMH argues it’s reasonable that those investors may consistently predict the direction of market prices.
Proponents of EMH believe future market movements are random and unpredictable. How strongly they believe this depends on which form of EMH they accept:
Strong form EMH
Semi-strong form EMH
Weak form EMH
Across all three forms, technical analysis is not believed to consistently predict market movements. As discussed in a previous chapter, technical analysis assumes market patterns repeat. EMH conflicts with that assumption by treating price movements as random and unpredictable.
Some investors describe EMH’s conclusion using another label: the random walk hypothesis. The random walk hypothesis states market dynamics are consistently unpredictable, so attempts to evaluate an investment using available information are useless. The more an investor believes in EMH or the random walk hypothesis, the more likely they are to use passive strategies. The reverse is also true: the less an investor believes in EMH or the random walk hypothesis, the more likely they are to use active investment strategies.
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