OK, let's unpack this with a phrase that is,
I think, older than finance, older than banking,
probably older than written trade laws itself.
You're talking about the eggs in the basket.
Don't put all your eggs in one basket. Exactly.
It is the ultimate piece of folk wisdom, right?
It's applied to investing all the time. And yet
the the underlying mathematics, the economics
of why that simple principle actually works are,
well, they're often forgotten. even by really
sophisticated investors. And that's our mission
for this deep dive. We want to gain knowledge
quickly, but also thoroughly about this foundational
principle, financial diversification. We're going
to try and go beyond the cliche. Right. We want
to understand the real mechanisms of risk reduction,
what the limits of diversification are, and maybe
most importantly, debunk some of these persistent
myths about how risk behaves over time. At its
core, it's just a risk reduction technique. That's
all it is. It's the structured process of allocating
your capital across a whole variety of assets
and exposures. And the goal is to reduce your
exposure to any single point of failure. To any
one particular asset failure or, God forbid,
a catastrophic risk event. And this is where
the conversation and, frankly, the surprise really
begins. You might think, just intuitively, that
if you combine, say, 10 risky things, the total
risk is just the average risk of those 10 things.
That's the common sense assumption. But because
of the way different asset returns interact with
each other, a properly diversified portfolio
exhibits this remarkable property. it will have
less variance, so less volatility, less measured
risk, than the weighted average variance of its
parts. It's so counterintuitive, isn't it? It
is. You are actively combining volatile assets,
but the combination is somehow calmer than the
sum of its parts. Often, and this is the part
that really gets people, the volatility of your
entire diversified basket can actually be less
than the volatility of the least volatile asset
you hold inside it. So long as they don't move
in perfect lockstep. Exactly. Provided they don't
move in lockstep. This lower variance, that's
the prize we're all chasing. And we should probably
clarify for those of you following along that
diversification isn't the only way to manage
risk, even if it is the most sort of foundational.
The other main technique is hedging. Right. Hedging
is much more of a tactical maneuver. It's about
taking an offsetting position, maybe using a
financial instrument like a derivative. To mitigate
a specific. A known risk factor, exactly, like
currency risk or interest rate movements for
a very specific period. Diversification, on the
other hand, is strategic. It's a structural approach
to just diluting risk across multiple, hopefully
non -correlated assets, making the whole portfolio
inherently more robust. Rather than just shielding
it from one specific danger. Precisely. Okay,
so let's use the simplest and maybe the most
terrifying example to illustrate this need for
structural diversification. Imagine you the listener
hold an entirely undiversified portfolio. You
decide to put your entire retirement savings
into just one company stock. You're facing maximum
idiosyncratic risk. And we aren't talking about,
you know, hypothetical disasters here. It is
historically common for a single, even a large
cap stock, to drop 50 % or more in a single year.
Oh, easily. A specific scandal, a major product
recall, some new regulation, or just terrible
management. It happens all the time. And as you've
pointed out before, recovering from a 50 % drop
requires a 100 % gain just to get back to even.
The path back is... brutally difficult. So now
contrast that nightmare scenario with one where
you hold a portfolio of, say, 20 stocks randomly
selected from different industries, company sizes,
maybe different countries. The probability of
that entire aggregated basket dropping 50 percent
in the same year all at once due to unrelated
catastrophic failures. It just becomes astronomically
small. The diversification kind of smooths everything
out. It eliminates the trends that are specific
to one industry or one company class. That is
the immediate visceral benefit you get. You're
no longer betting on the fate of a single CEO.
You're betting on the long term health. of the
entire economy. And this structural dilution
of risk, it's not just limited to stocks and
bonds in one country. Since, what, the mid -70s,
the concept has really pushed aggressively toward
geographic diversification. That was a pivotal
shift, especially for institutional investment.
Managers began to heavily advocate for investing
in emerging markets. You know, think Asia, Latin
America, parts of Eastern Europe. Places where
the underlying economic growth rates were often
much higher. Higher than in the established G7
nations exactly. And the argument was simple.
By capturing those higher return rates while
at the same time reducing your overall portfolio
risk because those foreign economies don't move
in perfect lockstep with your home market. You
generate superior risk adjusted returns. That's
the holy grail. So if the U .S. market is struggling
with, I don't know, some internal political crisis,
your exposure to booming manufacturing in Southeast
Asia helps cushion that blow. You're using the
global economic cycle to flatten your personal
volatility curve. You're recognizing that the
events that influence one market are not necessarily
the same events that influence another. OK, so
we've firmly established that diversification
reduces risk. It reduces volatility. But now
we have to deal with the inevitable objection
from any aggressive investor. What does this
mean for my returns? The big question. If I'm
deliberately choosing to spread my capital around,
am I diluting my potential gains? Am I sacrificing
my chance to find that next hundred bagger stock?
Let's turn our attention from safety to the performance
side of spreading your bets. This is truly the
essential, maybe even existential question for
investors considering diversification. It speaks
to a core psychological hurdle we all have. It
feels like you're choosing to be average. It
can feel that way. Let's look at the math first.
When we talk about expected returns, and by that
I mean our prior rational data -driven expectations
of the average returns on all assets, if those
expectations are identical across all the assets
we choose, then the expected return on the diversified
portfolio will also be identical to the expected
return on the undiversified one. Okay, so if
I buy 20 stocks that I think will have an average
return, my portfolio's expected return is still
just that average return. Diversification doesn't
give me a free lunch where I get less risk and
more average return. That is the tradeoff. Diversification
is not an engine for increasing your average
returns. Its core purpose is purely statistical.
It narrows the range of possible outcomes. It
brings the goalposts closer together. Exactly.
The extremes, both good and bad, are mitigated.
Okay, let's unpack that mitigation. Why do I
lose the extremes on the high side? That's the
part that hurts. Because you are now holding
a weighted average of everything, in any given
period, a group of investments will naturally
have a spread of performance. There's going to
be a winner, a loser, and everything in between.
And you don't know which is which ahead of time?
You don't. So you have to hold the whole basket.
Therefore, the return on your diversified portfolio
can never exceed the return of the single top
performing investment in your basket. Unless
all the assets return the exact same amount,
which never happens, the diversified return will
always be mathematically lower than that single
highest return. That feels like the hard truth
of diversification. You are deliberately, consciously
sacrificing the chance to hit the jackpot. You
are choosing not to chase the highest possible
return. And in exchange, you get certainty or
more certainty. It's the price of safety. It's
the explicit price of safety. But look at the
flip side, which is far more critical for long
term survival. Conversely, the return of your
diversified portfolio will always be higher than
that of the single worst performing investment.
By diversifying, you sacrifice the chance of
achieving the maximum theoretical gain. But in
return, you completely avoid the risk of suffering
the maximum theoretical loss. That distinction
is paramount, isn't it? especially for capital
preservation. You're giving up the tail of maximum
upside, that lottery ticket win, to completely
chop off the catastrophic tail of maximum downside.
It stabilizes your returns. Diversification doesn't
inherently boost or penalize your expected returns,
but it makes them vastly more predictable. If
your goal is preserving capital and ensuring
a smooth journey toward a financial goal, that
narrow path provided by diversification is really
the only viable road. It manages the inherent
uncertainty of, well, of capitalism. It smooths
the path. It turns a jagged mountain range of
potential outcomes into a gentle hill. Which
naturally leads us to the practical side, the
how -to. If the goal is stability, how much diversification
is enough? Is there a magic number of assets
I need to hold before I can say, you know, I'm
safe? This is a point of, um... extensive debate
in financial literature, and it's because there
is no single magic number that just instantly
transforms a high -risk portfolio into a low
-risk one. But there are rules of thumb. There
are. The number 30, 30 stocks, is often cited
historically, particularly in the context of
achieving sufficient diversification within a
single large equity market like the U .S. Okay,
so why 30? Is that based on some observation,
or is it just a nice round number? It's an approximation,
really, informed by empirical data, which we'll
definitely get into later. But the consensus
among quantitative researchers suggests that
the vast majority of the risk reduction benefit
actually arrives much earlier than 30 stocks.
How much earlier? A key finding established as
early as 1985 and validated many times since
showed that most of the measurable value you
get from diversification. So the elimination
of that. company specific risk is achieved by
incorporating just the first 15 or 20 different
stocks into a portfolio. So the heavy lifting
is done by the 20th stock and the 30th stock
is just kind of polished. That's a perfect way
to put it. The overall principle holds. Adding
more stocks generally leads to lower price volatility.
But the marginal benefit, the risk reduction
you gain by adding the 31st stock versus the
16th, it's statistically negligible. That efficiency
is so important for the average investor. If
I only have the resources or, frankly, the time
to research 20 companies, I've already done most
of the risk mitigation I need to do. You've neutralized
what we call security -specific risk. By holding
20 different companies across different sectors,
the risk that one specific CEO engages in accounting
fraud. Or that one specific factory catches fire.
And shuts down the company's production line.
That has almost no measurable effect on your
overall financial well -being. That kind of company
-specific disaster becomes an isolated, small
event in your diversified landscape, rather than
a catastrophe for your entire wealth. So that
baseline, maybe 15 to 20 assets, becomes the
critical cycle. psychological threshold where
you shift from a high risk concentrated approach
to a structurally sound low idiosyncratic risk
portfolio. So we've established that, okay, 15
to 20 assets gives you enough basic diversification
to handle that company specific risk. But what
if we want to go further? What if we want to
achieve maximum diversification? What's the theoretical
peak of spreading your capital? The theoretical
ideal, the thing that quants and academics are
always chasing, is often described as buying
the market portfolio. That sounds like a concept
ripped straight from an economics textbook. It
is, pretty much. The earliest and most influential
formal definition of this maximum diversification
came with the advent of the Capital Asset Pricing
Model, or CAPM. Which dominated financial thought
from the 60s onward. Right. And CAPM posited
that the perfectly diversified investor should
hold a pro -rata share of all available assets
in the entire investment universe weighted by
their market value. Okay, pro -rata share of
all available assets. What does that translate
to in practical terms for someone listening today?
It translates directly to the modern index fund.
When you invest in a fund that tracks the total
global stock market, you are attempting to mimic
this theoretical market portfolio. Trying to
own a slice of everything. You're achieving maximum
diversification within that particular asset
class, global equity, because you own a tiny
piece of everything weighted by how big that
thing is in the global economy. That feels like
the ultimate definition of dilution. If I buy
the world, I can't possibly be destroyed by the
failure of one specific company. That's the logic.
And theoretically, diversification has no maximum
so long as more unique assets are available.
Every equally weighted, uncorrelated asset you
add will mathematically contribute to reducing
your portfolio's measured variance. But the real
world is messy. As we saw in 2008 and during
the pandemic, when a serious crisis hits, correlations
tend to jump towards one. Everything starts moving
down together. A very real problem. So how do
the more advanced strategies account for assets
that aren't uniformly uncorrelated? This is where
practitioners move beyond just market cap weighting,
which simply uses market size to determine your
holding. We move into alternative risk based
weighting approaches. And the first major strategy
here is risk parity. OK, explain risk parity
simply for us. Risk parity aims to weight assets
in inverse proportion to their historical or
their forecasted risk, which is usually measured
by volatility. So if stocks are twice as volatile
as bonds. A risk parity strategy would dictate
holding twice as much value in bonds as in stocks.
Wait. So instead of measuring my portfolio's
contribution by dollar value, I'm measuring its
contribution by risk. Exactly. The result is
a portfolio where every asset class, be it commodities,
stocks, bonds, real estate, contributes an equal
amount of risk to the overall portfolio volatility.
And why do it that way? What's the justification?
It's highly pragmatic. Forecasting future risk
or volatility is statistically much easier and
more reliable than forecasting future market
prices or economic growth. So you're structuring
the portfolio around a known quantity risk contribution
rather than an unknown quantity future return.
That's a fascinating inversion of the typical
approach, which is always chasing returns. This
chases stability. And this leads to an even more
nuanced approach, correlation parity. This is
a further refinement. Instead of just aiming
for equal volatility contribution, like in risk
parity, correlation parity tries to structure
the portfolio so that each individual asset have
an equal correlation with the overall portfolio
itself. So how does balancing correlation beat
balancing volatility? Well, think of volatility
as the speed of a car. And correlation is how
many other cars on the road follow your exact
speed changes. OK. Risk parity ensures every
part of your portfolio is equally responsible
for the overall speed bumps. Correlation parity
tries to make sure every part is equally independent
of the overall portfolio's movement. In mathematical
terms, it's considered the true most diversified
portfolio because it aims to reduce shared dependency
across the board. So risk parity is just a special
case of it. It's a special case of correlation
parity that only applies if you assume all the
correlations are uniform. These complex methods
exist because they recognize that correlation
fundamentally is the chief enemy of effective
diversification. OK, let's bring it back to the
CAPM model because it is so crucial for our listener
to internalize the distinction the model introduced.
We have to define the two faces of risk clearly.
This separation is the true genius of modern
portfolio theory. The first type of risk is the
one we can get rid of, diversifiable risk. It's
also called idiosyncratic risk, unsystematic
risk, or security -specific risk. Give us a concrete
example of diversifiable risk. Let's say you
own shares in Exxon Marble. If a specific oil
rig malfunctions or they lose a patent on a specific
drilling technology, that incident only impacts
ExxonMobil. It's specific to that company. Right.
And because it's only one company in the S &P
500, you can effectively dilute or eliminate
the financial impact of that one event by holding
the other 499 stocks. And the risk we absolutely
cannot eliminate no matter how clever we get.
That is the non -diversifiable risk. We also
call this systematic risk or market risk, and
it's often quantified by an asset's beta. So
if you own an index fund tracking the entire
S &P 500. You are still exposed to movements
of that index as a whole. No matter how many
individual stocks you buy within that market,
you cannot eliminate the risk that the entire
market dives due to a coordinated recession,
a sudden spike in inflation or a global war.
Because that risk applies to all companies, more
or less. Directionally, yes. It applies to all
of them. So the TAPM's argument is essentially,
stop worrying about the risk you can control.
Focus on the risk you can't. Absolutely. And
they made an extremely powerful, logical leap
about compensation. They argued that investors
should only be financially compensated, meaning
they should only expect a premium return for
bearing non -diversifiable risk. And the logic
there is? unassailable. Diversifiable risk is
entirely within your control as an investor.
If you choose to hold only one stock and you
suffer a 50 percent loss due to a management
scandal, the market views that as a self -inflicted
wound. The market doesn't pay you extra just
for failing to diversify. It does not. It creates
an efficient market structure where unnecessary
risk is not rewarded. Which puts the onus entirely
on the investor. It does. But, you know, in this
pursuit of maximum diversification, we have to
introduce a necessary practical caveat. of over
diversifying. Over diversifying. How can too
much of a good thing possibly be bad? Well, while
mathematically every uncorrelated asset helps,
practically diversification has to be cost efficient.
If you start adding assets where the per asset
investment fees, the transaction costs or just
the increased monitoring expenses begin to outweigh
the marginal tiny gains you get from more diversification.
Your net portfolio performance will suffer. Exactly.
That was a much bigger problem, say, 30 years
ago when mutual fund fees were high and trading
was expensive. For sure. But even today. if you're
actively trading small amounts of complex, illiquid
assets, those fees or just the difficulty of
managing a massive number of positions can quickly
consume that marginal risk benefit. It's a pragmatic
warning. It's a warning that the pure quantitative
models sometimes overlook. The theoretical pursuit
of maximum diversification must always be timbered
by the practical costs of execution. All right,
let's move beyond the theory and get into the
engine room. We've claimed that diversification
lowers variance even if assets aren't perfectly
opposed. How does the math actually make this
magic happen? We have to look at the mathematical
structure of variance, which is our formal measure
of risk, sigma squared. The beauty of diversification
lies in the fact that when you combine assets,
you're including both their individual risk contributions
and, critically, their shared risk relationship,
the covariance. Let's start with the base case
again. Two perfectly uncorrelated assets, X and
Y. Zero covariance. So we use our simple thought
experiment. A total portfolio of, say, W dollars.
You invest a fraction Q into asset X and the
remaining fraction 1 minus Q into asset Y. Because
we assumed zero correlation, the formula for
the portfolio variance, sigma P22, it simplifies
to 2Q dollars sigma X2 plus 1Q2 sigma E22. And
I see the crucial element right there. The portfolio
variance is the weighted sum of the individual
variances. But the weights... The Q and the 1
minus 4 squared. They're squared. And since Q
and 1 minus Q are both less than 1, their squares
are even smaller. Right. If Q is a half, 50 -50
weighting, then Q squared is a quarter. Precisely.
So instead of each asset contributing 50 % of
its risk, it contributes only 25 % of its risk
to the total variance pool. It's the squaring
of the weights that creates this geometric reduction
in risk. And furthermore, mathematically, you
can always find a specific variance minimizing
weighting, a Q, that is strictly between 0 and
1. Which proves that the most stable portfolio
requires holding both assets. It demonstrates
that diversification... reduces risk below the
level of even the least volatile single asset.
That's the core mathematical proof. Even if the
assets offer zero benefit from offsetting movements,
just subdividing your capital across them immediately
kills a portion of the total risk. And this favorable
effect is, of course, enhanced exponentially
if the assets are negatively correlated. One
goes up when the other goes down, creating a
natural hedge. And it's diminished but not eliminated
if they're positively correlated. Diminished
but still present, assuming the correlation is
not perfectly positive, not equal to plus one.
So let's scale this up dramatically. What happens
when we look at N assets, all equally weighted,
mutually uncorrelated? And they all have the
same individual variance, sigma x222. The resulting
portfolio variance simplifies to this elegantly
clear expression, sigma p22 equals sigma x22
divided by n. That is beautiful in its simplicity.
It is. The portfolio variance is simply the individual
asset variance divided by the number of assets
you hold. This result is mathematically monotonically
decreasing, as in increases. Meaning it only
ever goes in one direction. Downwards. As you
add more and more uncorrelated assets, the risk
only ever goes down. If you start with 10 assets
and you double your holdings to 20, you cut the
variance in half. This is the clearest possible
mathematical statement on the power of diversification.
But we need to spend a moment on a critical technical...
distinction that financial theory demands. The
difference between true diversification and simply
adding risk. You brought up the example of an
insurance company. Yes. When we talk about sigma
squared divided by n, we are defining diversification
as subdividing a fixed total amount of capital.
We have one pie and we slice it into n pieces.
Right. Now consider the opposite scenario as
explored by thinkers like Paul Samuelson. What
if an entity is simply aggregating independent
risks? Like an insurance company adding policy
X1, then policy X2, then X3, and so on? Exactly.
Each policy is an uncorrelated volatile risk.
If the insurance company simply adds N uncorrelated
policies without changing its capital base, the
total variance of their exposure is N times sigma
squared. The variance is increasing. It's increasing
linearly with N. They are adding risk, not diluting
it. That's a key conceptual barrier. The insurance
company... becomes riskier, the more policies
it writes, all else being equal. The company
aggregates the risk. The diversification in this
case doesn't happen at the corporate level by
writing more policies. It happens when that risk
is ultimately dispersed among the company's investors,
the shareholders who spread their investment
across many companies and other asset types.
The company aggregates risk. The investor dilutes
it. Okay, that's the theory. That's the mathematical
scaffolding. Let's make this palpable for the
listener by looking at the classic empirical
proof, the study that quantified exactly how
fast this risk falls off. We're talking about
the 1977 work by Elton and Gruber. This study
is the bedrock of the how many stocks are enough
debate. They looked at a huge population of available
securities and calculated the average risk, the
standard deviation of annual returns over all
possible randomly chosen portfolios of size N
with an equal amount held in each asset. The
power here is that it wasn't theoretical. It
was based on historical market data. So let's
look at the steepness of that risk reduction
curve. For a portfolio with just one stock, they
found the average standard deviation was 49 .24%.
That is maximum idiosyncratic risk. We'll use
that as our baseline, a ratio of one. Now watch
the immediate return on diversification. How
much risk is killed by just adding three more
stocks? With four stocks, the standard deviation
plummeted to 29 .69%. That's a ratio of 0 .60.
So you eliminated 40 % of the total measurable
risk just by going from one to four. 40%. That
is the massive initial gain we were talking about.
And if we move up to 20 stocks, which is that
comfortable threshold for eliminating idiosyncratic
risk. At 20 stocks, the standard deviation was
21 .68%, a risk ratio of 0 .44. So 20 random
stocks reduced the risk to less than half of
what a single stock entailed. And moving from
20 to the historical benchmark of 30. At 30 stocks,
the standard deviation was 20 .87%, a ratio of
0 .42. The incremental benefit of those extra
10 stocks was minimal. Just 2 % more risk reduction.
Tiny. And here's the truly astonishing part.
Let's compare that 30 -stock figure to a maximally
diversified holding of 1 ,000 stocks. At 1 ,000
stocks, the risk reduction essentially flatlines.
It drops only slightly further to a standard
deviation of 19 .21%, a ratio of 0 .39. The ultimate
takeaway for the listener is just undeniable
here. Four stocks capture nearly half the benefit.
And the risk level you get with 30 stocks is
extremely close to the risk level of holding
1 ,000. The curve flattens out so rapidly. Beyond
that point, that residual risk, that 39%, that
is the non -diversifiable systematic risk. It
confirms that your effort should be focused on
building that core diversified portfolio of 15
to 30 well -chosen non -correlated assets rather
than inefficiently trying to replicate the market
through thousands of individual high -cost trades.
the quantitative rules for the individual industry,
but these same concepts of risk and correlation,
they play at the corporate level too. Large companies
also have to manage risk, not just of single
assets, but of entire product lines or geographical
regions. Let's explore corporate diversification
strategies. When corporations diversify, they're
generally doing it for stability, either internally
within their supply chain or externally against
market volatility. The three common forms really
just define their relationship to the core business.
Okay, start with horizontal diversification.
This is the most common and often the lowest
risk approach. It involves expanding a product
line that uses existing resources or acquiring
companies that operate adjacent to the core business.
So a soft drink company starts selling energy
bars. Perfect example. They already have the
distribution networks. They have the marketing
muscle. They're just expanding the product offering
horizontally. And vertical diversification. That
sounds like capturing the supply chain. Precisely.
Vertical integration is all about consolidating
steps in the value chain amalgamating distribution
channels, or integrating the supply chain. A
large automaker might buy a lithium mining operation.
Or a software firm that writes their in -car
code. Exactly. The goal is efficiency, cost control,
and securing critical inputs, which mitigates
the risk of supply disruptions. And then the
most aggressive form. one that has fallen in
and out of favor dramatically over the decades,
non -incremental diversification. This is the
strategy of the pure conglomerate. It involves
acquiring or launching business lines that have
little to no connection to the company's core
activities. A classic example would be a company
that simultaneously owns aerospace manufacturing,
a commercial real estate portfolio, and a media
publishing house. The famous example being Berkshire
Hathaway, although they'd argue their strategy
is more about capital allocation than traditional
operational synergy. Why adopt such a disconnected
approach? The theory is purely diversification
driven. It's to stabilize the overall company's
earnings by mitigating external exogenous risk
factors. The hope is that when the aerospace
cycle is depressed, the commercial real estate
market might be booming or vice versa. The different
lines smooth out the total income flow. They
provide stability to shareholders and often lower
the overall cost of capital for the entity. Although
the complexity of managing such disparate businesses
is often what ultimately leads to conglomerates
being broken up. It's corporate diversification
applied on a grand scale. You're trying to kill
the systematic risk of specific sectors by owning
a basket of uncorrelated sectors. Exactly. And
its success often relies entirely on management's
ability to allocate capital effectively across
those disparate businesses, not on operational
synergy. Now, let's pivot to a point that you
need to be very clear on because it impacts retirement
planning for virtually every single person listening.
The fallacy of time diversification. This is
the belief that time itself is a risk reducer.
This belief is so deeply rooted in investor psychology,
it's taught widely in personal finance. The argument
goes that because younger investors have decades
until retirement, they should aggressively favor
risky assets like stocks. Because they have time
to recover from any downturns. Right. The underlying
and very misleading premise is that over long
periods, the good returns will mathematically
cancel out any possible bad returns, making the
long run safer. It's that comforting voice that
says, Don't worry about the market crash today.
You have 30 years left. And while that voice
is attempting to calm panic, it fundamentally
misrepresents how risk operates over time. The
flaw was definitively identified by Nobel laureate
Paul Samuelson, among others like John Norstad
and Zvi Body. So what's the flaw? While it is
true that the standard deviation of annualized
returns decreases as the investment horizon increases,
meaning your average yearly return becomes more
stable, that is not the measure the retiree actually
cares about. The retiree cares about the final
dollar amount. the total return precisely the
uncertainty that matters is the final outcome
and due to the inexorable power of compounding
the standard deviation of the total return the
uncertainty surrounding your final accumulated
wealth actually increases dramatically with the
length of the time horizon let's visualize that
if i invest for one year the range of possible
outcomes from my final capital is relatively
small Maybe I earn 10 % or I lose 10%. Correct.
But if you invest for 40 years, the range of
possible total returns due to the cumulative
effect of those annual gains and losses compounded
over four decades is exponentially larger. The
absolute risk to your final nest egg is greater,
not less, the longer the timeline. So uncertainty,
measured as the standard deviation of my total
dollar return, increases with time. It increases
with time. So if I start with $10 ,000 and I
have a 40 year horizon, my best case scenario
might be $5 million, but my worst case scenario
might still be $50 ,000 depending on the sequence
of returns. And the spread between those two
numbers is the standard deviation of your total
return. And that spread is massive. It's huge.
The fallacy grants investors a false sense of
security. It encourages them to concentrate their
portfolio and high risk assets based on the mistaken
belief that time acts as a natural systematic
hedge. It does not. It magnifies potential outcomes.
So long -term investors need diversification
across asset classes, stocks, bonds, real estate,
just as much as short -term investors. Absolutely.
Simply to manage the volatility of that massive
final wealth number. That reframes the entire
purpose of diversification for a young person.
It's not about surviving the next quarter. It's
about controlling the enormous uncertainty inherent
in a multi -decade compounding experiment. It's
really remarkable that an economic concept so
mathematically complex in its modern form has
such simple ancient roots. Diversification in
its core form is just applied common sense about
survival. So let's trace this back through time.
Where does the concept first appear in written
history as a piece of financial advice? We can
trace the concept back to the Bible, specifically
the book of Ecclesiastes, which is often dated
around 935 BC. The text offers this remarkably
relevant financial advice. But divide your investments
among many places, for you do not know what risks
might lie ahead. That is nearly 3 ,000 years
of consensus. The underlying concern is identical
to modern risk management, preparing for unknown
future risks. Another ancient source provides
a highly specific prescriptive asset allocation
that is still studied today, the Talmud. It details
a specific financial strategy for wealth management.
What was the Talmudic formula? The Talmud advised
splitting one's assets into thirds. One third
was to be held in business, so active trading
and commerce. That was your high -risk, high
-growth engine. The second third was to be kept
liquid. perhaps in the form of gold coins or
readily available cash. This was the stable zero
risk component. And the final third was to be
invested in land or real estate, representing
the long term inflation protected tangible asset.
That is sophisticated risk management. You have
a growth engine, a safety net for liquidity and
a stable store of value protected against economic
turmoil. And this approach is now recognized
in modern research as naive diversification or
one end diversification because it instructs
the investor to split wealth equally among the
available major asset classes. And it actually
works. Research since the year 2000 has shown
that, surprisingly, this simple 1N allocation
strategy often performs remarkably well compared
to much more complex, optimization -driven models,
especially during periods of high market volatility.
The ancient wisdom holds significant practical
value. And we even see this concept woven into
the fabric of Renaissance literature, reflecting
how crucial it was to global trade back then.
You just have to think of Shakespeare's Merchant
of Venice, written around 1599. The character
Antonio, whose entire fortune is tied up in global
shipping, states that his ventures are not in
one bottom trusted, nor to one place. He's talking
about ships and ports. Bottoms and ports, exactly.
He fully understands that he has to mitigate
the risk of piracy, storms, or a single port
closure by spreading his ventures across multiple
vessels and geographic routes at the same time.
So the principle was intuitive for millennia.
driven by the realities of physical risk. But
the move towards scientific quantitative measurement,
the transition to modern portfolio theory, that's
where Harry Markovits steps in. Markovits' seminal
work in the 1950s provided the necessary mathematical
language. He moved the conversation from don't
put all your eggs in one basket to we must calculate
the covariance between the returns of the eggs
to achieve optimal risk reduction. MPT. Modern
portfolio theory. It formally defined how to
get the highest possible return for a given level
of risk or conversely the lowest possible risk
for a target return. But even before Markovits
formalized MPT, our sources highlight a major
financial figure who was practicing sophisticated,
intuitively driven diversification decades earlier.
John Maynard Keynes. Keynes' tenure managing
the King's College Cambridge endowment from the
1920s to 1946 is legendary. He was, by modern
standards, sometimes highly concentrated in his
stock picks. But his underlying philosophy was
profoundly diversified in the right ways. So
what defined his risk management philosophy in
a time without modern derivatives or index funds?
He was focused on the structural relationship
between assets. He recognized the crucial importance
of holding assets with opposed risks. Meaning
things that were likely to move and opposite
directions exactly assets that would move in
opposite directions during general market fluctuations
he was intuitively grasping the power of low
or negative correlation the key mathematical
insight markovits later formalized he understood
that while an individual asset might be volatile
if you paired it correctly the portfolio could
be stable and he was a pioneer in a geographic
sense too he was avoiding that infamous home
bias oh absolutely Keynes was a true maverick
in this regard. He aggressively invested internationally,
holding significant portions of the portfolio,
sometimes as high as 75 percent in non -U .K.
stocks, primarily U .S. equities. Which was revolutionary
at the time. It was. In the 20s and 30s, nearly
all university endowments in both the U .S. and
the U .K. were heavily, often entirely, invested
in domestic bonds and local assets. Keynes recognized
that the risks facing the British economy were
idiosyncratic on a global scale. He used international
diversification as a core risk mitigation tool
decades before it became institutional standard
practice. That global perspective, based on observing
risk factors rather than just relying on local
comfort, really cements his position as a crucial
historical figure in risk management. Hashtag
tag outro. We've taken a very deep dive into
this foundational principle. Let's try and synthesize
the core knowledge points that you can take away
right now. First and foremost, diversification
is a risk management tool. That's it. It's designed
to stabilize your returns, dramatically narrow
the range of possible outcomes. It is not a mechanism
for boosting your expected average returns. Second,
the empirical evidence that Elton and Gruber
study, it gives you a clear, actionable threshold.
The most impactful risk reduction happens very
quickly. Holding 15 to 20 well -chosen non -correlated
assets captures the vast majority of the benefit
available. It eliminates company -specific risk
with high efficiency. And finally, ditch the
comforting but misleading advice about time.
Time does not reduce the uncertainty of your
total final wealth. Due to compounding effects,
a longer time horizon actually increases the
standard deviation of your total return. This
demands that long -term investors still rely
heavily on diversification across asset classes
to manage that enormous potential spread between
their best and worst case retirement scenarios.
And to leave you with one final provocative thought
that circles back to where we started with systematic
risk, let's just recall the asymptotic limit
of diversification. We established that as the
number of assets n approaches infinity in a maximized,
equally weighted portfolio, the portfolio variance
does not go to zero. It bottoms out. It hits
a floor. Exactly. The variance tends asymptotically
toward the average of the covariances between
all the securities, what we call dom or sigma
d. This shared risk, this average relatedness
of all the assets, that is the ultimate non -diversifiable
risk. The systematic risk that is woven into
the fabric of the entire market. It's the risk
that's left over. So if mathematically, even
infinite diversification cannot eliminate the
risk shared by every asset in the economy, what
does that tell you about truly safe investing?
How can an investor truly escape systematic risk
if the entire global financial system is wired
together by shared economic and geopolitical
factors? That is the ultimate challenge for any
serious investor to contemplate.