“The only free lunch in finance is diversification”
Suppose you have two assets that you can invest in, and $1000 dollars total to split between them. By the end of year 1, Asset A doubles in price and Asset B halves in price. And by the end of year 2, A halves and B doubles (so that they each return to their starting point).
If you had initially invested all $1000 in Asset A, then after year 1 you would have $2000 total. But after year 2, that $2000 is cut in half and you end up back at $1000. If you had invested the $1000 in Asset B, then you would go down to $500 after year 1 and then back up to $1000 after year 2. Either way, you don’t get any profit.
Now, the question is: can you find some way to distribute the $1000 dollars across Assets A and B such that by the end of year 2 you have made a profit? For fairness sake, you cannot change your distribution at the end of year 1 (as that would allow you to take advantage of the advance knowledge of how the prices will change). Whatever weights you choose initially for Assets A and B, at the end of year 1 you must move around your money so that you ensure it’s still distributed with the exact same weights as before.
So what do you think? Does it seem impossible to make a profit without changing your distribution of money between years 1 and 2? Amazingly, the answer is that it’s not impossible; you can make a profit!
Consider a 50/50 mix of Assets A and B. So $500 initially goes into Asset A and $500 to Asset B. At the end of year 1, A has doubled in price (netting you $500) and B has halved (losing you $250). So at the end of year 1 you have gained 25%.
To keep the weights the same at the start of year 2 as they were at the start of year 1, you redistribute your new total of $1250 across A and B according to the same 50/50 mix ($625 in each). What happens now? Now Asset A halves (losing you $312.50) and Asset B doubles (gaining you $625). And by the end of year 2, you end up with $312.50 in A and $1250 in B, for a total of $1562.50! You’ve gained $562.50 by investing in two assets whose prices ultimately are the same as where they started!
This is the magic of diversification. By investing in multiple independent assets instead of just one, you can up your rate of return and decrease your risk, sometimes dramatically.
Another example: In front of you are two fair coins, A and B. You have in your hand $100 that you can distribute between the two coins any way you like, so long as all $100 is on the table. Now, each of the coins will be tossed. If a coin lands heads, the amount of money beside it will be doubled and returned to you. But if the coin lands tails, then all the money beside it will be destroyed.
In front of you are two coins, A and B. You have $100 dollars to distribute between these two coins. You get to choose how to distribute the $100, but at the end every dollar bill must be beside one coin or the other.
Coin A has a 60% chance of landing H. If it lands H, the amount of money placed beside it will be multiplied by 2.1 and returned to you. But if it lands T, then the money placed beside it will be lost.
Coin B has only a 40% chance of landing H. But the H outcome also has a higher reward! If the coin lands H, then the amount of money beside it will be multiplied by 2.5 and returned to you. And just like Coin A, if it lands T then the money beside it will be destroyed.
Coin A: .6 chance of getting 2.1x return
Coin B: .4 chance of getting 2.5x return
The coins are totally independent. How should you distribute your money in order to maximize your return, given a specific level of risk?
(Think about it for a moment before reading on.)
If you looked at the numbers for a few moments, you might have noticed that Coin A has a higher expected return than Coin B (126% vs 100%) and is also the safer of the two. So perhaps your initial guess was that putting everything into Coin A would minimize risk and maximize return. Well, that’s incorrect! Let’s do the math.
We’ll start by giving the relevant quantities some names.
X = amount of money that is put by Coin A
Y = amount of money that is put by Coin B
(All your money is put down, so Y = 100 – X)
We can easily compute the expected amount of money you end up with, as a function of X:
Expected return (X)
= (0.6)(0.4)(2.1X + 2.5Y) + (0.6)(0.6)(2.1X) + (0.4)(0.4)(2.5Y) + (0.4)(0.6)(0)
= 100 + .26 X
Alright, so clearly your expected return is maximized by making X as large as possible (by putting all of your money by Coin A). This makes sense, since Coin A’s expected return is higher than Coin B’s. But we’re not just interested in return, we’re also interested in risk. Could we possibly find a better combination of risk and reward by mixing our investments? It might initially seem like the answer is no; after all Coin A is the safer of the two. How could we possibly decrease risk by mixing in a riskier asset to our investments?
The key insight is that even though Coin A is the safer of the two, the risk of Coin B is uncorrelated with the risk of Coin A. If you invest everything in Coin A, then you have a 40% chance of losing it all. But if you split your investments between Coin A and Coin B, then you only lose everything if both coins come up heads (which happens with probability .6*.4 = 24%, much lower than 40%!)
Let’s go through the numbers. We’ll measure risk by the standard deviation of the possible outcomes.
= (0.6)(0.4)(2.1X + 2.5Y – 100 – .26X)2 + (0.6)(0.6)(2.1X – 100 – .26X)2 + (0.4)(0.4)(2.5Y – 100 – .26X)2 + (0.4)(0.6)(0 – 100 – .26X)2
This function is just a parabola (to be precise, Risk2 is a parabola, which means that Risk(x) is a hyperbola). Here’s a plot of Risk(X) vs X (amount placed beside A):
Looking at this plot, you can see that risk is actually minimized at a roughly even mix of A and B, with slightly more in B. You can also see this minimum risk on a plot of return vs risk:
Notice that somebody that puts most of their money on coin B (these mixes are in the bottom half of the curve) is making a strategic choice is strictly dominated. That is, they could choose a different mix that has a higher rate of return for the same risk!
Little did you know, but you’ve actually just gotten an introduction to modern portfolio theory! Instead of putting money beside coins, portfolio managers consider investing in assets with various risks and rates of return. The curve of reward vs risk is famous in finance as the Markowitz Bullet. The upper half of the curve is the set of portfolios that are not strictly dominated. This section of the curve is known as the efficient frontier, the basic idea being that no rational investor would put themselves on the lower half.
Let’s reframe the problem in terms that would be familiar to somebody in finance.
We have two assets, A and B. We’ll model our knowledge of the rate of return of each asset as a normal distribution with some known mean and standard deviation. The mean of the distribution represents the expected rate of return on a purchase of the asset, and the standard deviation represents the risk of purchasing the asset. Asset A has an expected rate of return of 1.2, which means that for every dollar you put in you expect (on average) to get back $0.20 a year from now. Asset B’s expected rate of return is 1.3, so it has a higher average payout. But Asset B is riskier; the standard deviation for A is 0.5, while B’s standard deviation is 0.8. There’s also a risk-free asset that you can invest in, which we’ll call Asset F. This asset has an expected rate of return of 1.1.
Asset F: R = 1.1, σ = 0
Asset A: R = 1.2, σ = 0.5
Asset B: R = 1.3, σ = 0.8
Suppose that you have $1000 that you want to invest in some combination of these three assets in such a way as to maximize your expected rate of return and minimize your risk. Since rate of return and risk will in general be positively correlated, you have to decide the highest risk that you’re comfortable with. Let’s say that you decide that the highest risk you’ll accept is 0.6. Now, how much of each asset should you purchase?
First of all, let’s disregard the risk-free asset and just consider combinations of A and B. A portfolio of A and B is represented by the weighted sum of A and B. wA is the percentage of your investment in the portfolio that goes to just A, and wB is the percentage that goes towards B. Since we’re just considering a combination of A and B for now, wA + wB = 1. The mean and standard deviation of the new distribution for this portfolio will in general depend on the correlation between A and B, which we’ll call ρ. Perfectly correlated assets have ρ = 1, uncorrelated assets have ρ = 0, and perfectly anti-correlated assets have ρ = -1. Correlation between assets is bad for investors, because it destroys the benefit of diversification. If two assets are perfectly correlated, then you don’t get any lower risk by combining them. On the other hand, if they are perfectly anti-correlated, you can entirely cancel the risk from one with the risk from the other, and get fantastically low risks for great rates of return.
RP = wARA + wBRB
σP2 = wA2σA2 + wB2σB2 + 2ρwAwBσAσB
Let’s suppose that the correlation between A and B is ρ = .2. Since both RP and σP are functions of wA and wB, we can visualize the set of all possible portfolios as a curve on a plot of return-vs-risk:
The Markowitz Bullet again! Each point on the curve represents the rate of return and risk of a particular portfolio obtained by mixing A and B. Just like before, some portfolios dominate others and thus should never be used, regardless of your desired level of risk. In particular, for any portfolio that weights asset A (the less risky one) too highly, there are other portfolios that give a higher rate of return with the exact same risk.
In other words, you should pretty much never purchase only a low-risk low-return item. If your portfolio consists entirely of Asset A, then by mixing in a little bit of the higher-risk item, you can actually end up massively decreasing your risk and upping your rate of return. Of course, this drop in risk is only because the two assets are not perfectly correlated. And it would be even more extreme if we had negatively correlated assets; indeed with perfect negative correlation (as we saw in the puzzle I started this post with), your risk can drop to zero!
Now, we can get our desired portfolio with a risk of 0.6 by just looking at the point on this curve that has σP = 0.6 and calculating which values of wA and wB give us this value. But notice that we haven’t yet used our riskless asset! Can we do better by adding in a little of Asset F to the mix? It turns out that yes, we can. In fact, every portfolio is weakly dominated by some mix of that portfolio and a riskless asset!
We can easily calculate what we get by combining a riskless asset with some other asset X (which can in general be a portfolio consisting of multiple assets):
RP = wXRX + wFRF
σP2 = wX2σX2 + wF2σF2 + 2ρwXwFσXσF = wX2σX2
So σP = wXσX, from which we get that RP = (σP/σX)RX + (1 – σP/σX)RF = RF + σP (RX – RF)/σX
What we find is that RP(σP) is just a line whose slope depends on the rate of return and risk of asset X. So essentially, for any risky asset or portfolio you choose, you can easily visualize all the possible ways it can be combined with a risk-free asset by stretching a line from (0, RF) – the risk and return of the risk-free asset – to (sX, RX) – the risk and return of the risky asset. We can even stretch the line beyond this second point by borrowing some of the risk-free asset in order to buy more of the risky asset, which corresponds to a negative weighting wF.
So, we have a quadratic curve representing the possible portfolios obtained from two risky assets, and a line representing the possible portfolios obtained from a risky asset and a risk-free asset. What we can do now is consider the line that starts at (0, RF) and just barely brushes against the quadratic curve – the tangent line to the curve that passes through (0, RF). This point where the curves meet is known as the tangency portfolio.
Every point on this line is a possible combination of Assets A, B, and F. Why? Well, because the points on the line can be thought of as portfolios consisting of Asset F and the tangency portfolio. And here’s the crucial point: this line is above the curve everywhere except at that single point! What this means is that the combination of A, B, and F dominates combinations of just A and B. For virtually any level of desired risk, you do better by choosing a portfolio on the line than by choosing the portfolio on the quadratic curve! (The only exception to this is the point at which the two curves meet, and in that case you do equally well.)
And that is how you optimize your rate of return for a desired level of risk! First generate the hyperbola for portfolios made from your risky assets, then find the tangent to that curve that passes through the point representing the risk-free asset, and then use that line to calculate the optimal portfolio at your chosen level of risk!