- The information in this
video might be more important than anything that I've ever told you. In every one of my videos, I tell you things that hinge
on one of the landmark ideas in financial economics, the efficiency of the capital markets. As fundamental as market efficiency is to good financial decision-making, it is poorly understood by most investors. I have been telling you information that is based on this
foundational principle of finance, but I've never taken the
time to explain to you why it is so important. Condensing the foundations
of modern financial theory into a short video is ambitious, but I'm going to do my best. I'm Ben Felix, portfolio
manager at PWL Capital. In this episode of Common Sense Investing, I'm going to tell you why the
efficient market hypothesis matters to your investment decisions. (upbeat music) Financial theory starts
with asset pricing. How do investors decide how
much to pay for a stock? Theoretically, rational
investors are willing to pay a price for a stock
based on the present value of expected future earnings. All else equal, investors will pay more for higher expected earnings and they will pay less for
riskier future earnings. Whether or not investors are rationally evaluating
investments this way, brings us to the concept
of market efficiency. As defined by Eugene Fama in 1970, a market in which prices
always "fully reflect" available information
is called "efficient". This is a simple sounding concept, but it is also extremely important and terribly misunderstood. Informational efficiency in a market stems from competition for
profits, low transaction costs, and readily available information. If there is information
suggesting that an asset's value will be higher in the future, competitive traders with low
cost access to the market will buy that asset today,
increasing its price. The competition to incorporate
new information into prices for profit, means that
prices should change quickly as new information develops. New information is random, and therefore price
changes should be random. This point is at the
core of index investing. If price changes are
based on new information and therefore not predictable, then trying to predict them as any traditional active
manager or stock picker does, should not be expected
to improve your outcome. And that's before the
higher costs and risks of active management are considered. In an efficient market, low-cost index investing makes sense. Explaining why market efficiency
matters is easy to do. I just did it in one sentence. Explaining how we know
that efficient markets reflect reality is a much bigger task, but it is a task worth undertaking. To continue this discussion, I need to make sure that we all have the same baseline understanding
of a few concepts, starting with the meaning of the words, "empirical" and "theoretical". Empirical research means
looking at real data. For example, noticing
that day-to-day changes in stock prices are random,
is an empirical observation. Empirical observations are concrete, but drawing insight from
empirical observations is more abstract. This is where we see the
importance of theory. Theory is an idea about why things work the way that they do. In financial economics,
empirical research and theory are tied together by scientific method, forming a hypothesis, collecting data, and testing the hypothesis. If economists make consistent
empirical observations about stock returns, they
might develop a hypothesis about what is driving
that empirical result. Once a hypothesis has been created, it can be tested in the data. If a theory ends up doing a
good job of explaining reality, it becomes a useful decision tool. In 1900, 70 years before Fama published his landmark work on market efficiency, Louie Bachelier, a French statistician, noticed that stock prices
seem to follow a random walk. In the 1950s and 1960s,
more empirical work emerged, suggesting that stock
prices moved randomly. With no theoretical explanation
for this randomness, economists at the time
concluded that stock prices did not have any economic meaning. In 1965, Paul Samuelson
brought forward the idea that in a well-functioning
and competitive market, we would expect prices to change as investors' expectations
adapt to new information. This theoretical explanation
called the "fair game model", started to bring meaning to
the randomness of stock prices. The work of Samuelson was followed by Fama's now famous 1970 paper, "Efficient Capital Markets: "A Review of Theory and Empirical Work." A simple summary of past work is not what made Fama's paper famous. Fama formalized an empirical approach to testing the theory
of market efficiency. Fama never claimed that the
market is perfectly efficient. Perfect efficiency is an ideal state that real markets can only approach. That is not a shortcoming
of the efficient hypothesis. Theory is not meant to be reality. The map is not the territory. Theory allows us to predict
what the world might look like in an ideal state and compare
that prediction to reality. In the case of efficient markets, reality does look very similar to what we would predict an
efficient market to look like. Market efficiency predicts that
prices should move randomly. Active managers should not be successful at beating the market, and prices should change quickly
based on new information. Each of these predictions
describe real markets. Stock price changes are random. Active managers do on average,
trail the market after costs. Even managers with the best
returns are no more likely to have strong future returns. Event studies that is observing X post how prices moved based on new information have indicated that markets
are generally very quick to incorporate new information. One of the challenges with the
efficient market hypothesis is that it cannot be
definitively proved or disproved. This was acknowledged by Fama as the Joint Hypothesis Theorem. Any attempt to test market efficiency is really a test of two
distinct hypothesis. It is jointly a test of the
efficient market hypothesis, and a test of the model
of market equilibrium. What I just said will make sense
in a second, stick with me. The model of market equilibrium is the model of how the
market prices assets. We can think about value
stocks as an example. Under the capital asset pricing model that is defining risk as
only the risk of the market, value stocks produce
higher average returns than would be expected
based on their riskiness. Under the capital asset pricing model for equilibrium pricing, value stocks violate the
efficient market hypothesis. They create a systematic excess return that cannot be explained by risk. This result could mean one of two things. Markets are not efficient, or the model being used for
market equilibrium is flawed. The implication of the
joint hypothesis theorem is that the concept of market efficiency is only as empirically useful
as the model that we have for market equilibrium. Specifying this equilibrium
model has been the grounds for much of the work in
empirical finance since 1970. In the case of value stocks,
later research revealed that the single factor
capital asset pricing model for market equilibrium was not accounting for the independent risk of value stocks. Adding the independent risk
and value stocks to the model takes away the efficient market violation. Value stocks do not
violate market efficiency, they're just riskier than
we originally understood. Improving the equilibrium
model for asset pricing by identifying independent risks led to the Fama French
Three Factor Model in 1992, which includes the independent
risks of the market, small stocks, and value stocks. And the Five-Factor Model in 2014, adding the independent risks of stocks with robust profitability, and stocks that invest aggressively to the Three Factor Model. Without the efficient market hypothesis, empirical finance would just
be a collection of anecdotes. Efficient markets as a framework, has allowed financial
economists to evaluate theories by their rejectable predictions, as opposed to observing
the individual outcomes of successful investors. We now have the ability to gain insight into how markets work, as opposed to relying on anecdotes that we hope to replicate. An anecdote is like a story, one sample of a successful outcome with no theoretical explanation and no empirical corroboration. In a social science like economics, we want to build an
understanding of the world that allows us to make better decisions, while avoiding the type of bias that anecdotes often promote. Warren Buffet or anyone else being successful at picking
stocks is an anecdote, but asking why he was successful is not a productive question. It might be similar to asking a doctor why your grandpa lived to be 98, even though he smoked a
pack of cigarettes a day. Science does not have the
explanation for every outcome but this does not make it a good idea to start smoking or stock-picking. Interestingly, Buffet's
performance has now been explained within the framework
of an efficient market. He simply knew which types of
risks to maintain exposure to, and used extreme discipline
and leverage to do so. Again, we see the joint
hypothesis problem. Buffet is not proof of market inefficiency when the appropriate model
for equilibrium pricing is specified. As the model for market
equilibrium pricing has developed over time, it has gotten increasingly difficult to find violations of the
efficient market hypothesis. With the Fama French Five Factor Model, the vast majority of
differences in returns between two diversified
portfolios can be explained by differences in exposure to the independent risks
identified in the model. Adding more factors to a model
to make it explain returns sounds like over-fitting or data mining. But the way that this model was developed is another critical aspect
of modern financial theory. In their 2006 paper, "Profitability, Investment,
and Average Returns", Fama and French documented
the body of empirical work, showing that three specific
common characteristics of stocks predict higher average returns. Price relative to book value where cheaper stocks have
higher average returns, profitability where all else equal, a more profitable stock must
have a higher expected return, and investment, where all else equal, a stock that reinvests
profits conservatively must have a higher expected return. They took these individual
empirical observations and use the framework evaluation theory to construct a new set of empirical tests. Valuation theory predicts
that there should be a relationship between
these three characteristics. Past empirical work had
observed them individually. Based on the theory, properly observing the effects of each characteristic must be done by controlling
for the other two. Fama and French verified
this empirically in 2006, which eventually led to
the Five Factor Model, the most complete model of
market equilibrium to date. This takes us to 2014, which
is when the Fama French paper introducing the Five
Factor Model came out. We're not talking about
ancient history here. Remember, equilibrium pricing
is the theoretical model for how the market prices assets. The Five Factor Model
suggests that the market prices more risk into
certain types of assets as identified in the Five Factor Model. The Five Factor Model can be used to empirically test the
concept of market efficiency, subject, of course, to the
joint hypothesis problem that we talked about earlier. In its current form, the Five Factor Model is able to explain over 90%
of the difference in returns between any two diversified portfolios. The room for arguments
about market inefficiency has gotten very small. Any returned difference that
appears to be an anomaly, like the returns of dividend
paying stocks, for example, can most likely be explained by exposure to the risk factors
identified in the model. Understanding that this
was not an exercise in blind data mining is important. It comes from making
empirical observations, developing logical hypothesis,
and testing the hypothesis. Today, the theory of efficient markets and equilibrium pricing is robust enough that in empirical testing, it
explains almost any difference in returns over any historical time period in any country that we have data for. It is also able to explain
anomalies like low beta stocks and even the performance of Warren Buffet. What used to seem like proof that the market was not efficient, has today been explained
by how assets are priced in an efficient market. Asset pricing is extremely important to how investors allocate their capital. The price that you pay for a stock defines your expected return. If prices are constantly wrong, investors will not know
where to allocate capital. Fortunately, there is a strong theoretical and empirical case that
markets are efficient. They price stocks based on
their expected future earnings, and the riskiness of those earnings. Market efficiency has
sweeping implications on how you should invest your money. At a bare minimum, market
efficiencies should push investors toward low cost index funds. There is no way to exploit
random stock price changes to generate higher average returns, so taking what the market
has to offer is a smart bet. At a more advanced level, there is a strong,
theoretical, and empirical case that the Five Factor Asset Pricing Model is a good model of market equilibrium. Based on this, allocating more capital to the types of stocks
that the model predicts to have higher expected
returns could be sensible. Finally, I think it is worth
mentioning the role of evidence in decision-making. The medical field has a
hierarchy of evidence, ranking types of evidence
based on their quality and their risk of bias. The lowest form of evidence in this hierarchy is expert opinion, while the highest is systematic reviews of randomized controlled trials. In the world of financial economics, we do not have randomized
controlled trials, but we do have a nearly
unlimited pool of data for empirical testing. There is a scientific approach to accessing the capital
markets efficiently. Despite this, investors will
be quick to listen to the words of a successful hedge fund manager, or the CEO of a big bank, while being quick to dismiss
the theory and evidence that we have discussed today. As I mentioned earlier, the
map is not the territory. The models are not perfect
reflections of reality, but we have a choice
between making decisions based on the theory and empirical data, or making decisions based
on what we hope will do well even if that hope is
at odds with the data. Thanks for watching. My name is Ben Felix of PWL Capital, and this is Common Sense Investing. If you enjoyed this video, please share it with someone who you think could benefit
from the information. Don't forget, if you've run out of Common Sense Investing videos to watch, you can tune into weekly episodes of the Rational Reminder Podcast, wherever you get your podcasts. (upbeat music)
