Capital market theory makes reference to multiple forms of analysis that aim to predict the value of securities and the flow of supply and demand in the market. In this section, we’ll discuss a model, theory, and hypothesis, all of which are considered integral components of capital market theory. They are:
The capital asset pricing model (CAPM) predicts a security’s expected return based solely on factors related to systematic risk. CAPM utilizes the following formula, which we loosely discussed in a previous chapter:
The three components of the formula - the risk-free return, beta, and market return - all relate to a security or portfolio’s systematic risk. The risk-free rate of return refers to the rate of return on the 3-month (or 91-day) Treasury bill. While technically not free of all risk, Treasury bills are considered to have the lowest level of risk in the securities markets, and therefore provide insight into the returns that can be expected with little-to-no systematic risk. Beta refers to a security or portfolio’s volatility as compared to the general market. The market return provides insights into expected volatility in the market.
Let’s take a look at an example of a 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?
Can you figure it out?
Answer = 17%
Let’s first determine the necessary components of the expected return formula:
Now, let’s establish the expected return formula:
The standard deviation is not necessary to perform this calculation.
All components of the expected return formula relate to the market and have little to do with the actual security itself. Non-systematic risks like business risk, financial risk, and liquidity risk are not factored into this model. Bottom line - the calculation above only determines expected return based on market dynamics and risks.
If the expected return formula above seems similar, it is. When you learned how to calculate alpha, we utilized all the components of this formula. The only difference between the two is the alpha calculation involves subtracting the expected return (what we’re calculating above) from the actual return of the security or portfolio. CAPM only determines the expected return, while alpha compares that expected return to the actual return to determine if the portfolio or security is over or underperforming expectations.
In 1952, economist Harry Markowitz published an essay on investing that still remains relevant in the financial industry today and is often referred to as the birth of modern portfolio theory (MPT). Aptly named ‘Portfolio Selection,’ this essay established a number of rules and protocols for attaining an efficient portfolio. The most efficient portfolio is one with the highest return potential for the lowest risk exposure.
In order to set forth protocols for creating an efficient portfolio, Markowitz made several assumptions about investors, including:
With those assumptions in place, investors face a catch-22. They want the highest possible returns but also don’t want to expose themselves to risk. They want to avoid risk, but would not be content with obtaining a risk-free rate of return. As we’ve established throughout this material, with more risk potential comes more return potential. So, how does MPT recommend investors deal with this conundrum? One key component is diversification.
An investor may seek high returns with a highly volatile (risky) security. While a fair amount of risk may be present with any given investment, it can be balanced out with the potential returns of other securities. For example, a loss experienced on luxury cruise line stock due to an economic downturn may be offset by the returns on a defensive investment like pharmaceutical company stock.
When a portfolio is properly diversified, the risk/return profile of any one given security is not important. Instead, the risk/return profile of the overall portfolio should be the primary focus. This means a conservative, risk-averse investor may allocate a small portion of their assets to a high-risk security and still maintain a suitable portfolio.
An investor can utilize correlation coefficient to determine the best security to add to a portfolio for further diversification. Correlation references the similarities in the market price fluctuations of two different securities or portfolios. It is measured on a scale of negative 1 to positive 1. Two securities or portfolios with a perfect correlation of 1 have market prices that have historically moved at the same speed and in the same direction. For example, the S&P 500 index and an S&P index fund should have maintain a correlation of 1. If the S&P 500 index is up 10% one day, then the index fund tracking its performance should also maintain a 10% return. The two have a perfect correlation.
On the other side, a security with a perfect negative correlation (-1) moves at the same speed, but in opposite directions. For example, the S&P 500 index should maintain a -1 correlation with an inverse S&P 500 exchange-traded fund (ETF). If the S&P 500 index is up 10% one day, the inverse S&P 500 ETF should be down approximately 10%. The two have a perfect negative correlation day-to-day. However, the correlation will decay in the long run due to the investors’ holdings effectively being adjusted daily.
All other correlations will fall between -1 and 1. A correlation of zero means there’s no relationship between the two securities or portfolios being compared. A correlation of 0.5 roughly translates to the two comparisons acting similarly 50% of the time. A correlation of -0.5 roughly translates to the two comparisons acting as inverses 50% of the time. And so forth.
A key test point integrates diversification and correlation. If an investor wants to further diversify their portfolio, they should invest in securities with negative correlations to the portfolio. By doing so, they’re introducing new components that act inversely to the rest of the investor’s securities (on average). When the overall portfolio is losing value, the new securities with negative correlations should increase in value, thereby reducing the investor’s losses.
Beyond diversifying a portfolio across a number of different securities, proper asset allocation also plays an important role. Strategic asset allocation builds upon the principles of MPT by encouraging the maintenance of a suitable long-term asset allocation, avoiding market timing, and periodically rebalancing. When implemented correctly, an investor can ensure their portfolio retains an appropriate risk/return profile.
In the modern digital age, news and information travel as fast as they ever have. When a piece of data becomes available, it may have a quick and significant influence on the market prices of securities. Investors must wrestle with this concept when planning to make investment decisions. The burning question is - does research even matter?
It’s common to look at a number of metrics related to an investment prior to buying or selling it. These metrics include the value of certain assets and liabilities on balance sheets, profitability on income statements, and identifiable trends in the market based on technical analysis theories. Even if the metrics look good, does it necessarily mean the investment should be purchased? What if the data provided by all those metrics has already resulted in demand for the security, and thus already “baked into” the market price?
Those that believe available information is already factored into an investment’s market price are proponents of the efficient market hypothesis (EMH). This theory states available market data and information related to a security are reflected in current market prices. Data and information “efficiently” and quickly travel, and thus are priced into a security near instantaneously. If true, then investment research is essentially meaningless. An investor may decide a security is worthy of an investment based on their own analysis, but the EMH argues their findings already resulted in past demand for the security and will have no effect on future price movement.
Those that believe in the EMH should avoid picking specific investments in the market and shift their focus to passive investing strategies. By doing so, the investor is betting on the momentum of large segments of the market rather than picking individual securities based on their merit (which is already “baked into” the market price). Additionally, they avoid paying higher expense ratios charged by actively managed funds in order to pay their analysts for “useless” research and data.
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. While it may seem counterintuitive, even non-public inside information* is considered useless when analyzing a security. 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 necessarily disregard the history, but instead states inside information may not be as useful as it seems.
*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, let’s assume an executive at an oil company is aware of an upcoming earnings report that will show the company’s profitability growing much more than expected. The executive assumes the stock price will rise dramatically when the information becomes public and buys a large amount of company stock just prior to the release (illegally, of course). A few days before the earnings report is released, there’s an explosion at one of the rigs owned by the oil company, resulting in a catastrophic environmental disaster. The clean-up and legal liabilities result in billions of dollars of losses. The stock price responds accordingly and plummets in the market regardless of the favorable earnings report.
In this scenario, even private insider information didn’t help the investor make a return. Strong form EMH argues circumstances like this are more prevalent and common than it 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, which argues that non-public information can consistently predict future market movements. While scenarios like the one involving the oil company insider (above) may occur, they’re infrequent and uncommon.
Weak form EMH states most public information is reflected in the market prices of securities. Similar to semi-strong form EMH, weak form also argues private information can consistently predict future market movements. However, it adds an extra layer of market predictability. In particular, complex fundamental analysis also offers insights into upcoming supply and demand in the market.
In the fundamental analysis chapter, we discussed the basics of balance sheets and income statements. The keyword is ‘basics.’ Many analysts go through years of education and training to accurately comprehend the data provided in these complex financial documents. For example, here’s Nike’s 2021 annual report, which includes 109 pages of information related to the company’s operations and finances. Can you analyze the data provided in this report to determine the value of the company’s stock? If so, congratulations! You’re part of a small portion of investors with the skillset and experience necessary to do so.
If not, no worries! You’re like the majority of investors out there that rely on the expertise and analysis of others. This is the crux of the argument for weak form EMH. If only a small portion of investors can understand the complexities of fundamental analysis, then it’s reasonable to believe those investors may consistently predict the trajectory of market prices.
Proponents of the EMH believe future market movements are random and unpredictable. The extent of their belief depends on the EMH form they believe in:
Strong form EMH
Semi-strong form EMH
Weak form EMH
No matter the EMH form, technical analysis is not believed to consistently predict market movements. As we discussed in a previous chapter, this type of analysis assumes patterns in the market repeat themselves. This is in direct contradiction with the EMH, which states market fluctuations are random and unpredictable.
Some investors refer to the conclusions provided in EMH as part of another market theory. The random walk hypothesis states market dynamics are consistently unpredictable, and therefore all efforts to determine the quality of an investment based on available information are useless. The more an investor believes in EMH or the random walk hypothesis, the more likely they’ll engage in passive investment strategies. This applies in reverse as well - the less an investor believes in EMH or random walk hypothesis, the more likely they’ll engage in active investment strategies.
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