0:13

Investors and analysts are constantly looking at financial markets to see

Â whether there are opportunities to buy and sell, and take advantage of

Â the mispricing and inefficiencies we discussed in the last video segment.

Â Suppose you want an overview of economic performance of the largest firms

Â in the world during the past 25 years, you'd likely look at the US S&P 500 Index.

Â The S&P is the Standard and Poor's equity market index which includes stocks of 500

Â prominent, large companies listed in the New York and NASDAQ stock exchanges.

Â Why would you look at the S&P?

Â One reason to do so is that the index includes a representative sample of

Â leading firms including companies like Apple, Exxon, GE and IBM.

Â Another reason is that due to the careful selection process, and

Â large number of stocks it tracks, the S&P is considered a bellwether indicator of

Â business cycles and market performance in the United States.

Â Here's the S&P index value for the past 25 years.

Â 1:23

What do you see?

Â Charts help to bring out patterns, and

Â humans are amazing pattern recognition machines.

Â But it's smart to be wary.

Â Looking at this chart, it's tempting to interpret several patterns, some of which

Â may appear to be conclusive which would imply some concrete actionable steps.

Â Take, for example, the twin peaks on the chart.

Â As mentioned in Course 2, Markets, Video, and Turbulence, you may have already

Â deduced that the first peak reflects the dot-com bubble collapse in 2000.

Â And that the second peak corresponds to the housing bubble and

Â the subprime mortgage crash eight years later.

Â Here we are today, in 2016, eight years hence,

Â there is an even more spectacular increase in the market.

Â If there is a pattern to be learned from the first two peaks,

Â you might conclude that we take steps to prepare for the next big correction.

Â But before we jump down that rabbit hole,

Â let's say you come across another related graph that includes data on margin debt,

Â which involves borrowed money to invest, which is that red line.

Â 2:32

As with the first graph, you might see some very familiar shapes.

Â You see the impressive increases in borrowing when the market was on the rise,

Â followed by sharp reductions in borrowing that coincide with the market crash.

Â This trend or

Â tendency can be explained in that borrowing to invest is associated with

Â speculation, which typically accelerates and artificially increases prices.

Â Or the reverse,

Â speculation precipitates price decreases during a market correction.

Â Speculators, which we discussed in the derivative sections of course 2 on

Â markets, bet on price movements and are often driven by the kind of herd mentality

Â explained in the Ascent of Money video which we included in our curation corner.

Â But back to the issue at hand.

Â Seeking patterns in our second graph, do you believe that this chart reinforces our

Â earlier intuition that a market correction is imminent?

Â 3:29

If you find these patterns disconcerting, you might find yourself pondering whether

Â you can identify any other signs that indicate a market correction is coming.

Â Actually, earlier in this specialization, we discussed the proliferation of

Â conditions and danger signals that raise alarm about market corrections.

Â Some of these signals include desperate central bank actions,

Â over-indebtedness in every economic sector, households, firms and

Â governments and issues like inequity that bear on political factors.

Â 4:01

Each of these types of situations have been happening with unprecedented

Â frequency and exacerbating this frightening state of affairs,

Â the event of one situation fueling the fire on the other.

Â Central banks are now stuck with zero or negative interest rates.

Â All sectors of the economy are indebted at dangerously unsustainable levels.

Â And extreme political candidates are poised to win elections,

Â all of this exaggerating uncertainty.

Â 4:55

Keep in mind that practitioners generally promote their own interests,

Â because most of them sell financial products and want to earn fees.

Â Because of this,

Â they have a very poor track records in advising us when to buy or when to sell.

Â This is why we must continue to explore several hypothesis and viewpoints so

Â that by the time you've completed the Capstone course in Finance for Everyone,

Â you will be in a better position with more confidence to make your own decisions.

Â Back to our issue at hand.

Â What would conventional wisdom suggest?

Â For one thing, you can expect to hear the refrain, don't do anything drastic and

Â the suggestion that two graphs are not enough to make a case for

Â a catastrophic event and a spectacular correction.

Â 5:40

Convention would repeat the points made in the previous video about

Â efficient financial markets,

Â where prices move in a random walk, where the markets self-correct.

Â And where it is very, very hard to profit by timing market exits and

Â entries since share prices already reflect all available information.

Â The academic conventional wisdom view would back this up.

Â Relying on a few data points that show the twin peaks is probably coincidental.

Â Patterns and correlations offer great explanations but only after the fact and

Â have a poor track record to predict.

Â And the margin debt data?

Â That comes in from a two-month lag representing only 2% of the $19

Â trillion S&P index, and so the speculator theory is probably over-the-top.

Â Conventional wisdom also invokes a measurement view.

Â It looks at the lessons learned from modern efficient market theory and

Â a large body of subsequent work on how prices reflect risk.

Â 6:41

A lot of investment courses focus on these ideas and

Â at the risk of oversimplifying, I'm going to take the next few minutes to highlight

Â the measurement aspects of conventional wisdom.

Â And then, come back to the more intangible factors that will help us to bridge

Â theory with practice.

Â Recall from the last video that risk has many definitions to many people but

Â the financial definition focuses on volatility because this notion is

Â measurable.

Â 7:13

For as single asset, like a stock,

Â the most common measure of risk is the dispersion of possible outcomes from

Â its central tendency known in statistical jargon as the standard deviation.

Â For example, say you're measuring the risk of earning an expected return on a stock

Â that has an equal 15% chance to earn either a positive or

Â a negative return of 20%, and the remaining 70% chance to earn a 10% return.

Â Now you can calculate the expected return which will work out to 7% and

Â a standard deviation of 11.9%,

Â which I will illustrate to you in the following table.

Â We begin by making some assumptions about scenarios.

Â We have three scenarios here.

Â Two extreme scenarios, one for extraordinarily optimistic Boom scenario.

Â And then the opposite and extraordinarily pessimistic Bust scenario.

Â And then the remainder, which we'll call a Normal scenario.

Â 8:18

In these three different states,

Â what we do have of course are chances for each of these to occur.

Â And those chances we can denote under probability of occurrence.

Â So let's assign some probabilities as was given in this particular case.

Â We had an equal chance of a Boom and a bBust scenario given to be 15%.

Â And since all of these add up to 100%,

Â the remainder is the expected Normal scenario of 75%.

Â Now here is where we focus our expected returns for each of these.

Â So our expected return for the boom scenario that was given to me 20% and

Â then, for the pessimistic one -20%, and for the normal one 10%.

Â So that's the data that we need to be able to calculate the expected return

Â of these different scenarios.

Â And we can come up with a standardized formula for

Â that, which is to calculate the expected return.

Â What we're going to do is take the sum of each of these

Â individual probabilities and expected returns.

Â So, we'll take each probability and

Â then multiply it by the associated return to come up with the expected values.

Â So if we do that right now, for this example we're

Â going to have the product of these two, 0.03, 0.07.

Â And then, of course, this is going to be -0.03.

Â This will cancel out, and so we will be left with a 7% expected return.

Â 10:06

So one this gives us the return calculation what we still

Â need to do is calculate our first measure of risk.

Â Right, so we're going to use this expected return data

Â to help us compute our first measure of risk and

Â that measure of risk is going to be known as the standard deviation.

Â And this standard deviation by the Greek symbol sigma

Â is simply equal to the square root of the variance.

Â 10:33

And this variance is going to be the sum of each of these

Â expected outcomes that we have minus the mean squared,

Â multiplied by the associated probability.

Â So, let's write that down.

Â So, it's going to be the difference between each of these expected outcomes,

Â minus the mean, squared, times the associated probability.

Â Now why don't we plug the numbers in to see how this would work out

Â in our example.

Â We have the first expected outcome .20 minus the mean of .07 and

Â 11:57

So what you have here are two measures, the mean and standard deviation

Â that explains the dispersion of how far will each outcome is from the mean.

Â So if we draw this on a graph, you can see here,

Â this is going to be a mean, and this distance the standard deviation.

Â 12:18

In this example, this is the 7%, and this distance here is the 11.87%.

Â So, risk is always relative to return, so we first calculated the expected return.

Â And here, again, just to repeat and summarize.

Â The 7% is nothing more than the sum of each of these expected outcomes

Â with their associated probabilities.

Â And that starts to look like a normal distribution if you have many,

Â many data points.

Â So what do I mean by many, many data points?

Â Well think about it like this, suppose you are going to flip a coin 1,000 times.

Â 13:07

Which we know is the probability of how many values occurred close to the mean,

Â or the average.

Â The volatility is the measure by the variance

Â of the average dispergence of each outcome from the middle known as the mean or

Â expected value as I have explained.

Â The variance is calculated as I showed to you again as the sum

Â of the squared differences of each outcome and the expected return.

Â And by standardizing the variance, we take the square root,

Â we get this 11.9%, our first measure of risk.

Â Now, let's add another stock so we have a portfolio consisting of two stocks.

Â We could now calculate the portfolio expected return and

Â the portfolio standard deviation.

Â This would be very useful, because investors own portfolios of assets and

Â not single stocks or single assets.

Â 14:00

Let's also assume that the second stock has an expected return of 13%,

Â so our first one here we have an expected return of 7,

Â let's assume the second one has an expected return of 13%.

Â Now if I put half of my money here, and I put half of my money in here?

Â Well we can see that my weighted average return is going to be the sum

Â of these two which will work out to 10%, that's easy to do.

Â Whether I have two assets or one hundred assets,

Â I just take a weighted average, and I can calculate my portfolio return.

Â But it's not such a simple matter when we try and

Â calculate the portfolio standard deviation.

Â This is because,

Â we not only have the different variances which we have to weight.

Â But we also have a third term known as core variance and

Â that has to do with the relationship of the individuals stock returns.

Â These relationships have to do with how well they are correlated.

Â Okay, and the idea of correlation is really key to understanding

Â the very powerful idea of diversification.

Â So imagine this.

Â If I had these two assets that are correlating like this,

Â you can see that the correlation, if this is time, right, and these are the returns.

Â You can see there is almost a perfect positive correlation between the first and

Â the second asset.

Â But supposed I had a correlation that looks something like this,

Â 15:38

now you can see the correlation between one and

Â three is actually negative because when one is moving up.

Â The other is moving down, now this implies that if I put some of my money in one and

Â some of my money in the second one.

Â What I could do is that I could smoothen out the variations that you see here.

Â So when one is going down, it's compensated by the other one going up.

Â So this idea of correlation is extremely important in

Â calculating the portfolio diversification effect.

Â 16:20

As long as these additional stocks in the portfolio spread and

Â reduce our overall risk without sacrificing returns,

Â we should of course keep adding stocks to our portfolio.

Â And generally we can show that by the time we have about 20 to 30 stocks

Â in our portfolio, then it is considered fully diversified.

Â 16:41

So note that the diversification is not going to eliminate risk.

Â But instead what it does is it spreads the risk across the stocks so

Â that losses of some are compensated by gains from others.

Â Harry Markowitz is the father of modern portfolio theory and

Â was recognized in 1990 with a Nobel prize in economics for

Â in fact looking at exactly this kind of stuff.

Â At the affects of risk, return, correlation and

Â diversification on investment portfolio returns.

Â Building on Harry's fantastic work is another

Â gentleman by the name of William Sharp, a joint Nobel recipient.

Â And he's credited for his contributions to the famous capital

Â asset pricing model, commonly refered to as the CAPM or the CAPM.

Â Again, without getting into the math,

Â the CAPM model uses a number of efficient market assumptions that we

Â discussed earlier to arrive at a very elegant result that can be measured in

Â a simple equation that uses a risk index to compute the expected return.

Â The following graph which I'm going to sketch for

Â you shows the main result of the CAPM model and applies it to the 13%

Â that we would expect, anticipating from buying that second stock.

Â 18:19

The relationship actually is linear and straightforward.

Â The CAPM suggests that the relationship looks like this.

Â And here I'm going to throw some numbers in.

Â Let's assume this is a point where there is no risk but

Â some return, and let's give this a value of 3%.

Â Let's also assume that the entire market has a risk index of 1,

Â and that corresponds to a return that is commensurate with a rate of 8%.

Â And then of course if we have a stock like ours,

Â which has a risk index of 2, well, that would suggest a much higher

Â return which would correspond according to this model to be 13%.

Â So how do we get that 13% value by looking at this risk index of 2?

Â Well we'll look at the famous CAPM equation.

Â And the equation is simply suggesting that the expected return,

Â these values that we're forecasting,

Â that you saw over here and then of course this particular one is

Â equal to this first component here which is the risk free component,

Â so this is the risk free rate plus we're adding to this part here a premium.

Â This is the premium of risk that we're going to add for

Â the additional risk that we are taking.

Â And that premium is the function of the difference between

Â the return on a market portfolio.

Â This is the return on a market portfolio with the risk index

Â of 1 minus the risk free rate.

Â That gives us this distance over here, Rm- Rf.

Â And then, we adjust that with beta and

Â that gives us the additional risk premium and takes us all the way up to 13%.

Â So if we apply the numbers here, it's easy to see that the risk

Â free rate of 3 plus the return on the market of 8- 3, that's 5 times 2.

Â 10 + 3 gives us the 13%.

Â [COUGH] So just to summarize again.

Â 20:30

Instead of using the standard deviation,

Â this particular model uses a risk index known as beta.

Â The model assumes the entire market which for example is represented by the Standard

Â & Poor's market index to have a beta equal to 1.

Â Beta is actually computed using regression analysis.

Â It's a regression coefficient which is based on the changes of the entire

Â market relative to the changes of that particular stock in question.

Â Thus a beta of 2 means that a particular stock is twice as risky

Â as the average stock.

Â Or twice as responsive to changes to the market price

Â as opposed to changes of the stock.

Â So if the market goes up by 2%, the stock, because of the beta of 2,

Â would go up by 4%.

Â And vice versa.

Â So this model also assumes that all risky assets on the minimum

Â risk-free rate which in this example we assume to be at 3%.

Â The risk-free rate is typically offered in a government security like

Â a treasury bond.

Â So we can see that the expected return is the sum of the risk-free rate

Â plus the market risk premium adjusted by beta, that works out to 13% and

Â that's how the CAPM uses a neat equation to quantify risk.

Â Despite attracting a lot of criticism as an appropriate measure of risk that is

Â beta, due to its multiple assumptions and low predictive value, the CAPM,

Â nevertheless, continues to be used and taught as a good proxy of measuring risk.

Â 22:07

A good takeaway from this model is the validation of our intuition.

Â That by taking on more risk in the long run,

Â markets should be rewarding you with more return.

Â [COUGH] But does history prove this to be the case?

Â We've already considered this question during the earlier discussion

Â in the second course of the specialization market.

Â When we looked at the performance of another market index,

Â the famous Dow Jones index.

Â Looking at the performance of the Dow, the graph clearly shows in the long run

Â equities provide much higher returns even though

Â they're marked by bumps due to business cycles, recessions and market corrections.

Â They can be long periods when returns however are quite flacked.

Â But the overall trajectory is upwards as you can see.

Â 23:09

The challenge is not in sophisticated models and quantitive analysis.

Â But rather in the assumptions behind the models

Â that are used to explain the complex multidimensional nature of risk.

Â Conventional wisdom, we tend to hold tightly to these assumptions,

Â particularly today, as they appear to have contributed to the market upswing that so

Â many people have enjoyed and find themselves in.

Â 23:37

However, holding on to conventional wisdom may be an outdated option for many of us.

Â There are just too many danger signals which would give pause to anyone who wants

Â to protect themselves against perhaps a catastrophic financial correction.

Â Indeed the chance of a grand correction seems increasingly likely as each day

Â passes, but of course the exact timing is going to be anybody's guess.

Â Your opinion is likely to be better informed after you've completed the forth

Â course on debt,

Â as well as activities that will mark your capstone in finance for everyone.

Â Meanwhile if you are wondering

Â what do ordinary folk do who do not have access to expertise

Â who can not just shock proof their own portfolios from an imminent correction.

Â What are some of the first steps that you can take?

Â Consider the following.

Â First of all think cash.

Â 24:30

That is, convert as many of your assets in cash as possible.

Â Cash can insulate you from downward swings in both financial securities such as

Â stocks and bonds and anything related to financial products.

Â If you believe that the financial industry, including domestic banks

Â are vulnerable, perhaps you want to secure your cash in a debt free international

Â bank, or if that's not possible, then simply in a non bank vault.

Â 25:12

But because commodities in metals do appreciate in value, they have proven to

Â be a good hedge and a safe haven against problems in the financial industry.

Â And more broadly with bankrupt governments.

Â Take gold for example, which has a 5,000 year history of growing value, a longer

Â standing than that of the oldest paper currency, which is the British Pound.

Â