How Does the Stock Market Work?

How does the stock market actually work? Understanding this can help improve your personal investment strategy.

​The stock market is essentially a big auction where buyers and sellers negotiate their transactions and are able to efficiently exchange shares of existing companies.

A transaction always includes two parties – a buyer and a seller. As with any free market, stock prices are based on expectations and demand. A stock’s price is a reflection of the collective view of all buyers and sellers in the market. Their opinions are affected by new information related to the specific company.
 
According to financial theory, a stock’s price is determined by three forces: (1) expected company earnings (dividends), (2) the expected rate of return, and (3) the expected earnings growth rate.

​As new information arises, buyers and sellers make judgments about how the news will affect a company’s future earnings and risk. Market pressures move the stock price to reflect the aggregate opinions of market participants. For example, if new information indicates that a company's earnings will be higher than expected in the next period, investors bid up the stock price to match. The expected rate of return increases only when the stock appears to be getting riskier to own.

How the Efficient Market Hypothesis can help investors

The efficient market hypothesis (EMH) is an organizing principle for understanding how markets work. Professor Eugene F. Fama of the University of Chicago performed extensive research on stock market patterns. In the 1960s, he developed the efficient market hypothesis, which asserts that:

• Securities prices reflect all available information and expectations.
• Current prices are the best approximation of intrinsic value.
• Price changes are due to unforeseen events.
• Although stocks may be mispriced at times, this condition is hard to recognize.

If market prices are the best estimates of the current value, stock mispricing should be rare and is something that cannot be systematically determined through forecasting or analysis.
 
The EMH implies that no investor will consistently outperform the stock market except by chance. Market efficiency suggests that the market forces mispriced securities back to their fair value. The market mechanism gathers the information available and builds it into prices.
 
While the markets are volatile, it can be hard to agree that the prices are rational. Some investors may earn above-market returns by exploiting occasional price differences, but the number of investors who do so will be no greater than expected by chance.
 
Market efficiency can be tested by evaluating active managers. If the market was not efficient and mispriced stocks were easily identified, most managers should be able to add value by finding these opportunities.
 
In the mid-1960s researchers began studying professional money manager performance to compare the returns of active managers to the overall market (indexes). Noteworthy researchers on mutual fund returns include Sharpe (1966), Jensen (1967), Malkiel (1995), and Carhart (1997). These are only a few of many academics who asked whether professional money managers could apply their research and skills to consistently outperform the broad market. Survey papers that describe this work include Davis (2001) and Malkiel (2003).
 
The research shows that that the number of successful managers who were able to outperform on a long-term basis is no greater than what would be expected by chance.
 
For example, a DFA study examined 10, 15- and 20-year periods ending December 31, 2018. The study compares active equity mutual fund performance to their respective benchmarks. In the last 10 years, out of 3,097 active equity funds, 59% survived and only 21% outperformed their benchmark. In the last 15 years, out of 2,786 funds, 51% survived and only 18% outperformed their benchmark. In the last 20 years, out of 2,414 active equity funds, 42% survived and only 23% outperformed.
 
It can be tempting to try and pick the best performing managers, out of the 23% mentioned above. Unfortunately, there is no proven strategy to identify the “winners” in advance. Reviewing managers past performance is an unlikely indicator of future outperformance. Another study examined returns from 2004 to 2018 and concluded that out of the 25% top performing active equity funds based on previous 5 years, on average only 21% remained in the top category in the following 5 years. Again, suggesting that there is no consistency in outperformance and is likely nothing more than what would be expected by chance.
 
It is even more complicated for the managers attempting to time the market. To have a shot at successfully timing the market, they must make the call to buy or sell stocks correctly not just once, but twice.
 
Professor Robert Merton, a Nobel laureate, said it well in a recent interview with Dimensional:
“Timing markets is the dream of everybody. Suppose I could verify that I’m a .700 hitter in calling market turns. That’s pretty good; you’d hire me right away. But to be a good market timer, you’ve got to do it twice. What if the chances of me getting it right were independent each time? They’re not. But if they were, that’s 0.7 times 0.7. That’s less than 50-50. So, market timing is horribly difficult to do.”
 
If current market prices offer the best estimate or real value, investors should avoid spending time and effort trying to identify and exploit mispricing.
 
If professionals with all their resources cannot apply research to pick winning stocks, it is unlikely that individuals can outperform the market. The futility of speculation is good news for the investor. It means that prices are fair and that the differences are explained by differences in expected risk.
 
Investors who believe that markets are fair choose a different path to building wealth. Rather than trying to outguess the capital markets, they let the markets work for them by continuously and efficiently targeting the dimensions of higher expected returns.

Risk and Return Are Related

Risk and return are related. There is no free lunch in the capital markets. To earn higher returns we must accept higher risk. But not all risks are the same.​

The Modern Portfolio Theory

It might seem obvious to invest in portfolios that offer the most potential for higher returns, but this hasn’t always been so obvious, and with all of the options out there, it can be difficult establishing what exactly those portfolios are. Harry Markowitz developed fundamental work on portfolio diversification in the 50’s (Journal of Finance, Portfolio Selection, 1952), which helps show the union of risk and return in our portfolios.
 
Markowitz’s theory worked around an “efficient frontier” where investment decisions are based on level of risk, investment performance, and the risk of not being diversified within your portfolio. Looking at these factors, the efficient frontier provides optimal portfolios that can provide either the lowest volatility for the highest expected return, or the highest possible return for a given level of volatility. To be efficient, investors should first consider their personal comfort with risk and portfolio volatility, and then choose a portfolio based on where they land on the frontier. 
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​The theory behind the Efficient Frontier was developed before we had all of the resources, such as the power of a computer and other data processing tools, that we use to today to implement Modern Portfolio Theory, making the work done by Markowitz truly fundamental. In 1990, Markowitz shared the Nobel Prize in Economics, along with Merton Miller and William Sharpe, for their contribution to modern finance and setting the stage for an investment approach that is based on a risk-aware and quantitative process.

The M&M Theorem and the Cost of Capital
 
A theory developed by Merton Miller and Franco Modigliani, known as the “M&M theorem” helps explain the dynamics of risk and return, and proposed that the value of a firm is unaffected by the way it is financed. The market value is independent of how the company finances investments, and value is actually based on the ability to earn revenue combined with the risk of its underlying assets. Companies can decide to fund operations by issuing debt, in the form of bonds, or equity, in the form of stocks, but there is not an ideal financing strategy that will maximize the market value.
 
An investor in the company expects to be compensated for the money they provide the company, and the higher the perceived risk, the higher the expected rate of return. This expected return is the price a company pays in order to attract investors. Miller, one of the original directors of Dimensional Fund Advisors and past professor at University of Chicago’s School of Business, points out that in an efficient market, the cost of a firm’s capital equals the investor’s expected return.
 
When the stocks of companies have different levels of perceived risk, they must offer different expected returns in order to attract customers. Companies that are higher-risk must offer higher returns, to make up for the increased risk and uncertainty that the investor bears.

The Capital Asset Pricing Model and the Three Factor Model

​The Capital Asset Pricing Model
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​Following the work done by Markowitz on portfolio theory, risk and diversification, the Capital Asset Pricing Model (CAPM) was developed in the mid 60’s by William Sharpe. This model helps calculate investment risk and the return that should be expected for such risk, and later earned Sharpe the Nobel Prize in 1990.
 
The CAPM starts by identifying two types of risk. Systematic risk is the risk that comes from the market, and cannot be “diversified away”, such as the current interest rate, recession and war. The second risk is Unsystematic, which includes the specific risks that come from the individual stock. Unsystematic risks are not correlated with general market movement, and can be managed with diversification.
 
When unsystematic risk is removed through diversification, investors are still left with systematic risk, which is what CAPM has evolved to measure. The return on an individual stock, or portfolio of stocks, should equal the cost of capital, and the premium should reflect the excess return of the market over the “risk-free” rate one would find in US Treasury securities. Therefore, investors who take on more risk are rewarded relative to the amount of risk they take.
 
The idea that investments with more risk should earn a higher return is further explained with “beta”. Beta is considered the only relevant measure of risk in a portfolio or stock, and measures the stocks relative volatility, i.e. how much the price fluctuates of the specific stock or portfolio when compared to the movements of the entire stock market. For example, if a particular stock had a beta of 1.2, its historical price increases 20% more than the entire market in a positive return, and 20% lower when in a negative period.
 
When CAPM was first developed, it was a huge advance in the theory and application of financial economics. It emphasized the importance of diversification to mitigate risks we can control, and gave a tool to measure the risk we couldn’t. However, when applied to portfolio management, it didn’t always work as intended. When portfolios are very different from the market, the beta will not be as accurate, and will not always provide the higher return expected that correlates with the beta of the portfolio. Although still valuable, a new model was needed to successfully evaluate pricing.

The Three Factor Model
 
A more robust model was needed to explain and identify the differences in returns between stocks and portfolios, and in 1992 Eugene Fama and Kenneth French developed the Three Factor Model, which expands on the Capital Asset Pricing Model by adding additional risk factors to measure. After tests on several variables like earnings/price, leverage, cash flow, book-to-market and size, measured by running cross-sectional regressions on returns from the 1963-1990 period, Fama and French found that book-to-market (BtM) and size were the strongest determents of performance. Their research showed that on average, value stocks will outperform growth stocks, and small-cap stocks will out-perform large-cap.
 
When these two factors were combined with Sharpe’s market beta from the CAPM, the three gave a much more accurate tool for measuring performance.
 
Review:

1. Market. Stocks have higher expected returns than Treasury Bills, to compensate for added risk.
2. Size. Small company stocks have higher expected returns than large company stocks.
3. Relative Price. Defined by the BtM ratio, a lower-priced “value” stock will have a higher BtM, and offer higher expected returns than a higher-priced stock with a lower BtM, known as a “growth” stock.

 
Using these three factors, when indexed portfolios were organized by Fama and French, they found that the excess return (alpha) was close to zero. This suggests that a substantial portion of the return differences were explained by these factors. Fama and French also found that when size and BtM were added to the single-factor model, the market betas converged to 1.00, meaning most of the difference in returns are actually related to the exposure of size and value.
 
The work by Fama and French brought a more academic and science-based approach to the process of investing. Research continues and has been documented in other segments like emerging and developed markets, showing that higher returns can be targeted in globally diversified portfolios.
 
When compared to the single-factor model, the three-factor model gives investors an improved way of defining and capturing the potential returns in the market through portfolio structure.
 
Structure and Diversification
 
Guided by the principles that (1) markets are efficient and (2) stock prices reflect fair value, it is unreasonable to believe that one can obtain information that will give them an advantage to be able to consistently identified mispriced opportunities in the market. This is good news for the average investor, because they can expect the same returns as that of a professional. If risk and return are related, higher returns will only be earned with higher risk, and performance differences are explained by exposure.
 
Using these principles as a guide, investing turns into a process of identifying one’s own comfort level with risk, and then the risks that influence a portfolios returns. Tools like diversification* will help reduce unsystematic risks, and the portfolio can be structured in a way to achieve exposure that is ideal for the given risk dimensions, both in bond and equity markets. 

At OCTO Capital, we believe in a long-term investment strategy that is based in evidence and understanding of the market. If you are interested in learning more about our investment philosophy or how it can benefit you, please get in touch, or take our Investment and Financial Planning Portal for a test-drive, completely free of charge.


*Diversification does not guarantee protection from loss of principal. Investing involves risk.