Understanding the Efficient Market Hypothesis

(Originally posted in 2020)

Why does Camber believe in a passive investment philosophy? Why has Camber implement it in both our clients’ portfolios as well as our own? These are good questions to ask a financial advisor. At Camber, we take a data based approach to everything we do. From our financial planning dashboards to our investment principles this approach never waivers. One of the fundamental concepts that contributes to that approach is the efficient market hypothesis. This theory lets us test if a person, team, or computer can enter a market and consistently outperform it.

To understand the data behind the efficient market hypothesis we have to go back in time. In the 1970s, Nobel Prize winning economist, Eugene Fama, first introduced the term efficiency. He also developed a way to test it. According to Fama, “A market in which prices always “fully reflect” available information is called “efficient.”^[i] This theory sounds simple, but is often misunderstood. It is also one of the cornerstones of understanding investment strategies and our investment principles. To get a better understanding into why this theory is so important, we ask two key questions. Where do stock prices come from and what role does market efficiency have on return?

A stock’s price is determined by how much investors are willing to pay for that stock. A sensible investor is willing to pay a certain price for a stock based on how much they believe the present value of the stock’s future earnings are worth. Investors will pay more for higher future expected earnings and less for riskier future earnings.^[ii] Meaning that the price an investor actually pays defines what return they expect to receive. This relationship between risk and return, helps us understand what to pay for a stock.

The Fama-French Model

There are many different types of risks that investors consider when determining a stock’s price. These include changing interest rates, differences in currency prices, and the effect that low priced stocks create. Eugene Fama and Kenneth French came together to develop the Fama-French model. Their model outlined that by adjusting the expected return of one diversified portfolio by these different risks, you can explain the difference in the expected return of another. The famous Fama-French model has since become very accurate. Today, it can explain where over 90 per cent of the difference in return between two diversified portfolios comes from.

Since Eugene Fama’s initial work, we have determined two key findings about efficient markets. First, stock prices move randomly. Second, stock prices respond quickly to new information. These conditions make it improbable that investors can exceed market returns by trying to predict stock price changes. Especially when you factor in the higher risk and cost of an active investment strategy.

Understanding Warren Buffett’s Success

Remember, that the efficient market hypothesis is just a theory and theories do not always reflect reality. However, in this case, the theory does look very similar to what we actually see. Using the Fama-French model we are even able to explain why Warren Buffett has performed so well. Warren Buffett simply understood what risks to take and used an abundance of discipline and leverage to intensify the results. Ultimately, there is no way to exploit random stock price changes to generate higher average returns. Instead taking what the market has to offer is where the real “smart money” goes.

Despite all the evidence, investors are still quick to react to the words of a big bank CEO, a hedge fund manager, or a media analyst. Where in reality, each one of these sources has their own agenda. Empirical evidence is all that we can base sound investment decisions on. After reviewing all this information we are adamant about the merits of a passive investment strategy. For there is one simple fact. over the long-term, markets are efficient. With that understanding investors should look at low cost index funds, like those offered from Dimensional, as a solution.

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