← Series 7 Whisperer
Loading summary
Transcript213 lines
- [00:00]
A
What if, what if the entire multi billion dollar hedge fund industry is actually just this like highly sophisticated data driven casino?
- [00:10]
B
Oh, wow. I mean, that is a provocative thought.
- [00:13]
A
Right. But what if the certification exam that you are studying for right now actually expects you to know exactly why that might be true?
- [00:19]
B
Well, when you actually look at the underlying theories governing modern finance, it's a question you absolutely have to grapple with.
- [00:27]
A
Welcome to this deep dive. If you're listening to this right now, you are likely, you know, staring down the barrel of the series 65 or series 66 exam.
- [00:36]
B
Yeah. Trying to make that transition into a professional advisory role.
- [00:39]
A
Exactly. And our mission today is to be your ultimate study shortcut. Because we have gathered like a massive stack of source material for you.
- [00:47]
B
We're talking the official NASA test specifications, heavy finance textbooks from libertexts.
- [00:53]
A
Yeah. Plus analytical guys from Wall Street Prep and some really practical insights from SmartAsset and Bajaj Broking.
- [00:59]
B
It's a lot of reading.
- [01:00]
A
It is, but we sifted through all of it to extract the most important mechanisms behind portfolio risk and market theories.
- [01:07]
B
Because look, passing these exams isn't about rote memorization. I mean, they won't just ask you for a dictionary definition of a term.
- [01:13]
A
Right. They want application.
- [01:15]
B
Exactly. They are going to give you complex real world scenarios and test whether you actually understand the mechanical levers moving underne.
- [01:26]
A
So today we are decoding the exact differences between standard deviation and beta. We're going to figure out how to navigate the trickiest practice scenarios, map out the capital market line versus the security
- [01:37]
B
market line, and finally unpack how the efficient market hypothesis challenges the entire premise of active investing.
- [01:44]
A
Yeah. And we are keeping this strictly tailored to how these concepts actually appear on your exams.
- [01:49]
B
Which is critical, you know, when you're facing a testing landscape that covers literally everything from the time value of money to geopolitical risk factors. The key is understanding why these formulas exist in the first place.
- [02:01]
A
Right. So let's start right there by breaking down the two heaviest hitters of risk measurement, standard deviation and beta.
- [02:09]
B
The big ones.
- [02:09]
A
Yeah. And to really grasp these, not just memorize them, but really understand them, I was reading through our libertext sources and I want to build on this classic real world comparison.
- [02:19]
B
Okay, let's hear it.
- [02:20]
A
I want you to imagine that you are standing on the deck of a small boat out in the ocean.
- [02:25]
B
Right. I'm on a boat.
- [02:26]
A
Standard deviation represents the total overall rocking of that boat. It includes the relentless rolling of the ocean waves, plus let's say, the impact of a frantic golden retriever running back and forth across the deck.
- [02:40]
B
I love that. That captures it perfectly. Because standard deviation measures total risk.
- [02:45]
A
Right? The whole picture.
- [02:46]
B
Exactly. In financial terms, it's looking at how much an asset's returns bounce around in total. That includes both the broader market risk, which is your ocean waves, and the firm's specific risk, the dog. The dog running on the deck. It measures pure volatility, capturing every single variable, causing the boat to move.
- [03:04]
A
Now contrast that with beta. Beta is just the rocking of the boat that is caused by the ocean waves.
- [03:10]
B
It completely ignores the dog.
- [03:12]
A
Exactly. Strictly measures systematic risk or market risk. This is the risk you simply cannot escape. I mean, the ocean is going to do what the ocean is going to do and your boat is going to rise and fall with the tide.
- [03:23]
B
And what's fascinating here is how this ties directly into the core engine of modern portfolio theory, or MPT, which is
- [03:31]
A
pretty much the entire framework the series 65 and 66 exams are built upon.
- [03:35]
B
It is. And MPT is heavily focused on the mathematical power of diversification. The central premise is that you shouldn't judge investments in isolation.
- [03:45]
A
Right. You have to look at the whole portfolio.
- [03:47]
B
You have to judge how they behave together. By combining assets that don't move in the same way at the same time, you achieve diversification.
- [03:54]
A
Okay, so let's unpack this mechanism, because the exam is going to test this heavily. If a test question specifically asks you which type of risk diversification can actually reduce, what are they looking for?
- [04:06]
B
They are looking for unsystematic risk.
- [04:08]
A
Unsystematic, got it.
- [04:10]
B
Also known as firm specific risk. Going back to your boat analogy, if you want to stabilize the rocking, you don't just tie the dog up. No, no. True diversification is getting a second dog that naturally likes to run in the exact opposite direction of the first dog.
- [04:25]
A
Ah, okay. So their movements basically cancel each other out. That's negative correlation.
- [04:31]
B
Precisely. Or, you know, even better. Diversification is upgrading your tiny boat to a massive thousand foot cruise ship.
- [04:39]
A
Which makes sense because on a ship that size, a single dog running across the deck has absolutely zero impact on the stability of the vessel.
- [04:47]
B
Exactly. The unsystematic risk has been completely absorbed and neutralized by the sheer scale of the diversified portfolio.
- [04:54]
A
Wow.
- [04:55]
B
But, and this is the crucial part for the exam, no matter how big the cruise ship gets, it still rises and falls with the massive ocean swells.
- [05:02]
A
Because you can't get rid of the ocean.
- [05:04]
B
Right. Diversification cannot reduce systematic risk. The market risk is always going to
- [05:09]
A
be there that makes the distinction so much clearer. So, knowing that standard deviation measures the whole boat and beta measures just the ocean, how do the exam writers actually test this?
- [05:20]
B
They love to use scenarios.
- [05:21]
A
Right. Let's move away from the definitions and look at some fluid narrative case studies based on our source material. Because you really have to know how to apply this.
- [05:30]
B
Lay one on me.
- [05:31]
A
Okay, let's imagine a client walks into your office and they've just inherited some money and they decided on a whim to dump their entire life savings into just three highly volatile tech stocks.
- [05:43]
B
Oh, boy.
- [05:44]
A
Yeah. How do you even begin to measure the risk of that portfolio?
- [05:48]
B
Well, in that scenario, the exam requires you to use standard deviation.
- [05:52]
A
Wait, why standard deviation and not beta? I mean, they are tech stocks, so they definitely move with the broader NASDAQ market.
- [05:57]
B
They do, absolutely. But because the portfolio only holds three stocks, the unsystematic firm specific risk has not been diversified away.
- [06:05]
A
Oh, I see.
- [06:06]
B
Yeah. If one of those three tech companies suddenly faces a massive lawsuit or like a product recall, that portfolio is going to plummet regardless of what the broader market is doing.
- [06:17]
A
The dogs are running wild all over the deck of a very small boat.
- [06:20]
B
Exactly. You absolutely must measure the total risk standard deviation, because both the market waves and the individual company's quirks are are going to dramatically affect the client's returns.
- [06:32]
A
That tracks logically. Yeah, but what if a different client comes in? Let's say this client already has their life savings parked in a massive, well diversified S&P 500 index fund.
- [06:42]
B
Okay, the cruise ship.
- [06:43]
A
Yes, the cruise ship. And they want to buy a single share of a new tech startup to add to their holdings. How do we evaluate the risk that this single new security brings to their overall situation?
- [06:54]
B
For this client, the rule for the
- [06:56]
A
exam is to use beta because they are already diversified.
- [06:59]
B
Exactly. When you add one single stock to a highly diversified portfolio of 500 different companies, that new stock becomes just a drop in the bucket. It's firm specific, unsystematic risk. You know, whether the CEO resigns or a product flops, gets entirely absorbed and diversified away by the 499 other stocks you own.
- [07:18]
A
So the dog's movement on the deck no longer matters.
- [07:21]
B
It doesn't. All that actually matters for this client's overall risk profile is how that new startup stock moves in relation to the overall market waves.
- [07:29]
A
And since beta measures that market risk, it's the only appropriate measurement.
- [07:34]
B
You got it.
- [07:34]
A
Okay. So if they aren't diversified, they eat the total risk standard deviation. If they are heavily diversified, the Firm specific risk is gone. So you just measure the market risk beta, perfectly summarized. But let me push back on this with a trickier scenario, though. What if you were looking at two completely different mutual funds? The prospectus for both funds claims they are perfectly 100% well diversified.
- [07:58]
B
Okay.
- [07:59]
A
The test asks you how to evaluate the risk between them. Which metric do you use?
- [08:04]
B
According to the principles we've discussed, you could actually use either standard deviation or beta.
- [08:08]
A
Wait, hold on. If I'm looking at two totally different portfolios, how can those metrics be interchangeable?
- [08:15]
B
It seems counterintuitive. I know.
- [08:17]
A
Yeah. Because what if mutual fund A has a higher standard deviation but a lower beta than mutual fund B? Wouldn't they give me conflicting advice on which fund is riskier?
- [08:28]
B
That is a brilliant trap. And it's exactly the kind of conceptual math question the test writers love to throw at you.
- [08:35]
A
So it's a trick.
- [08:35]
B
It is. If a portfolio has a higher standard deviation but a lower beta than another portfolio, it tells you one definitive mathematical fact. Which is the portfolio is not actually well diversified.
- [08:48]
A
Oh, I see. Because the premise of the question is secretly flawed.
- [08:52]
B
Exactly. Think about the underlying math here. We established that total risk equals systematic risk plus unsystematic risk, right? Well, if both mutual funds are truly perfectly diversified, their firm specific unsystematic risk has been eliminated entirely. It is zero.
- [09:08]
A
Okay.
- [09:08]
B
Yeah. And if unsystematic risk is zero, then total risk and market risk are essentially the exact same number.
- [09:14]
A
Oh, wow. So in a truly diversified portfolio, a higher standard deviation must mathematically equal a higher beta.
- [09:21]
B
Exactly. If they don't align, it means firm specific risk is still hiding in the portfolio. And it's a trick answer choice designed to test if you really understand the relationship between these two metrics.
- [09:32]
A
That is exactly the kind of underlying mechanic that separates a passing score from a failing one. You can't just memorize the terms. You have to really understand the equation.
- [09:41]
B
Which brings us perfectly to how the series 65 and 66 exams visualize this math.
- [09:47]
A
Oh, right, the graphs.
- [09:48]
B
Yeah. They won't just use word problems. They're going to map these risk metrics onto two very famous lines on a graph. The capital market line, or cml, and the security market line or sml.
- [09:59]
A
And I know these graphs intimidate a lot of candidates.
- [10:02]
B
They do. But honestly, they are just visual representations of the exact same boat metaphor we've been discussing.
- [10:08]
A
So let's break down the cheat code for telling them apart from. Let's look at the CML first. The capital market Line is strictly used for evaluating portfolios. And because it evaluates portfolios, specifically efficient portfolios that combine a risk free asset like a Treasury bill with a basket of risky assets, it uses standard deviation on its X axis.
- [10:29]
B
Right. It plots expected return against total risk.
- [10:32]
A
Exactly.
- [10:32]
B
And understanding why it uses standard deviation is key. Here the CML is drawing what's called the efficient frontier.
- [10:38]
A
The efficient frontier. Right.
- [10:40]
B
It is trying to find the absolute maximum possible expected return you can get for any given level of total risk. The theory assumes that rational investors will only hold perfectly diversified portfolios.
- [10:52]
A
And because these theoretical portfolios are perfectly diversified, we use standard deviation to measure their total risk footprint.
- [10:58]
B
Yes. If an actual real world portfolio falls below that CML line on a graph, it means you are taking on unnecessary risk for the mediocre returns you're getting.
- [11:07]
A
Now contrast that with the sml, the security market line. The SML is used to evaluate individual securities, not entire perfectly efficient portfolios.
- [11:16]
B
And because individual stocks still have wild, unpredictable firm specific risk, the SML uses beta on its X axis. It measures systematic risk, which is a
- [11:27]
A
fundamental concept of the capital Asset pricing model, or capm. Right. Which the SML visually represents.
- [11:34]
B
It is. CAPM theory states a very harsh truth. Honestly, the market does not care about your unsystematic risk.
- [11:40]
A
It doesn't?
- [11:41]
B
No. The market will not compensate you for taking on firm specific risk because you could have easily diversified it away. The market only rewards you for taking on systematic risks. Beta. Therefore, when evaluating a single stock's expected return, we only care about where it plots on the x axis relative to its beta.
- [12:00]
A
So the SML is basically drawing a line of fairness. It's saying like, based on this amount of inescapable market turbulence, you deserve exactly this much profit.
- [12:08]
B
That's a great way to frame it. The SML establishes the baseline for what a stock should return. And this is huge for valuation concussions on the exam. Well, if you look in a graph and a stock plots above the security market line, it means its expected return looks unusually high for the amount of systematic risk it carries.
- [12:24]
A
So you're getting more return than the market risk dictates you should. Meaning it's a bargain.
- [12:30]
B
Precisely. It implies the stock is undervalued. It's a massive buy signal for an active portfolio manager.
- [12:37]
A
And I assume the reverse is true.
- [12:39]
B
Yeah. Conversely, if a stock plots below the sml, the return isn't high enough to justify the risk. It's overpriced. Don't buy it.
- [12:46]
A
Okay, so let's say an active portfolio Manager uses the sml, does their research, spots a dot floating high above the line, and buys that undervalued stock for a client.
- [12:57]
B
A classic active management move.
- [12:58]
A
Right. How do we. Or more importantly, how does the exam grade that manager's performance? Because this brings us to a massive testing area from our smart asset sources. Measuring success through time weighted versus dollar weighted returns.
- [13:12]
B
This is one of those areas where the definitions sound deceptively similar, but the real world mathematical outcomes couldn't be more different.
- [13:18]
A
Let's define the two metrics before we look at the math.
- [13:20]
B
Good idea.
- [13:21]
A
First, we have the time weighted return or twr. This metric completely isolates the performance of the investment itself. It entirely ignores when the investor deposited new money or withdrew cash.
- [13:34]
B
It just looks at the asset.
- [13:35]
A
Exactly. Then we have the dollar weighted return, which is often called the internal rate of return or IRR on the exam.
- [13:42]
B
Right.
- [13:43]
A
This metric heavily factors in the exact timing and the specific size of the investor's cash flows into and out of the account.
- [13:50]
B
Because real human beings rarely just put a single lump sum into a stock and walk away for 30 years.
- [13:55]
A
No, they don't.
- [13:56]
B
They add to their account when they get a year end bonus. Or they panic and withdraw cash when they see the market dropping. On the evening news and our sources
- [14:04]
A
provided a phenomenal historical example to illustrate how this timing destroys returns. Let's look. Investor A and investor B.
- [14:11]
B
Okay, let's hear the story.
- [14:12]
A
On January 1st, both of them buy a stock at $20 a share. Over the next few months, the stock has a great run and goes up to $25. Investor A gets excited, suffers from a bit of performance chasing and decides to dump a massive amount of new money into the stock at $25.
- [14:28]
B
Oh no.
- [14:29]
A
Yeah. Almost immediately after he does, the market corrects and the stock price tanks down to $18.
- [14:36]
B
A classic emotionally driven mistake. He bought heavy at the absolute peak.
- [14:41]
A
Meanwhile, investor B is patient. She waits out the volatility. When the stock hits that bottom of $18, she recognizes a bargain.
- [14:49]
B
Smart.
- [14:50]
A
She adds her new money right then. And by December 31st, the stock rebounds and closes the year at $22.
- [14:57]
B
If we connect this to the bigger picture, the math here tells a fascinating story about human.
- [15:02]
A
How so?
- [15:03]
B
Because both investors were holding the exact same stock over the exact same 12 month period. They both experienced the exact same time weighted return.
- [15:11]
A
Oh, right. Because the stock is the stock.
- [15:13]
B
Exactly. The stock itself generated a TWR of 10%. It went from 20 to 22 over the year. The underlying asset picked by the manager
- [15:22]
A
performed well, but the reality of their bank accounts look wildly different night and day.
- [15:28]
B
Because of his terrible cash flow timing, weighting and his portfolio heavily right before a drop, investor A actually ended up with a negative dollar weighted return.
- [15:37]
A
Wait, really?
- [15:38]
B
Yes. His personal IRR was negative 0.4%. He literally lost money on a stock that went up 10% for the year.
- [15:46]
A
Wow. And investor B?
- [15:48]
B
Well, investor B, because she timed her cash flow to buy heavily at the dip, achieved a dollar weighted return of over 7.6%.
- [15:56]
A
So TWR is grading the asset and DWR is grading the investor's behavior.
- [16:01]
B
It. Exactly.
- [16:02]
A
Here is your crucial exam tip based on this mechanism. You know, for anyone listening, if a test question asks you how to evaluate a portfolio manager's pure skill at picking investments, the unassailable answer is time weighted return.
- [16:15]
B
You use TWR because the manager generally cannot control when clients deposit or withdraw funds.
- [16:21]
A
Right. So you shouldn't be penalized because investor A bought at the top.
- [16:24]
B
Exactly. But if the question asks about evaluating the individual client's actual financial outcome, the answer is dollar weighted return because that captures the reality of their personal cash
- [16:35]
A
flow timing, that distinction is paramount. Dollar weighted shows the messy reality of the investor's journey, while time weighted shows the mathematical purity of the asset's performance.
- [16:46]
B
Perfectly said.
- [16:48]
A
So we've spent all this time discussing how to pick undervalued stocks using the SML and how to isolate and measure a manager stock picking skill using time weighted returns.
- [16:59]
B
We are deep in the mechanics of active management right now.
- [17:02]
A
We are, but now we have to zoom out because our Wall street prep sources bring up a theory that suggests all of this effort, like all of this analysis, is entirely pointless.
- [17:12]
B
Enter the efficient market hypothesis.
- [17:14]
A
Yes, the great philosophical clash at the exam. The emh.
- [17:18]
B
It's a big one.
- [17:19]
A
Introduced by economist Eugene Fama, the efficient market hypothesis theory states that asset prices instantly reflect all available information in the market.
- [17:27]
B
All of it.
- [17:28]
A
Meaning every stock is always priced at its exact, mathematically perfect fair value. There are no undervalued dots floating above the SML and no overvalued dots sinking below it.
- [17:39]
B
Everything is perfectly priced all the time.
- [17:41]
A
Exactly.
- [17:42]
B
And for the exam, you need to deeply understand Eugene Fama's three specific forms of this efficiency, because they will definitely test you on the nuances.
- [17:52]
A
So let's outline the mechanisms behind them.
- [17:55]
B
First is the weak form of emh. This suggests that current stock prices fully reflect all past historical trading data, volume and price movements.
- [18:05]
A
So if weak form is true, looking at past stock charts, which is called Technical analysis is completely useless.
- [18:12]
B
Completely.
- [18:13]
A
You can't predict tomorrow's price by looking at yesterday's squiggly lines.
- [18:16]
B
Exactly. Then you step up to the semi strong form. This argues that prices instantly reflect all publicly available information. Like what? Earnings reports, news articles, macroeconomic data, interest rate changes. The literal second that information hits the Internet, the massive army of Wall street algorithms has already priced it into the stock.
- [18:35]
A
So that implies fundamental analysis is completely dead too.
- [18:38]
B
Yeah.
- [18:39]
A
Pouring over balance sheets or P E ratios won't give you an edge because the market already knows exactly what you know.
- [18:44]
B
Right. And finally, you have the strong form emh. This is the most extreme academic view.
- [18:50]
A
Okay.
- [18:50]
B
It claims that stock prices reflect absolutely all information, both public and completely private or insider information.
- [18:58]
A
Wait, even insider secrets?
- [19:00]
B
Yes. If Strongform is true, the exam might give you a scenario where a CEO tries to trade on a secret memo before a merger is announced. Under StrongForm, even that CEO couldn't make an excess profit because the market has somehow intuitively already priced in the secrets.
- [19:17]
A
Wow. So how does this impact you, the listener sitting for the series 65 or 66?
- [19:23]
B
Yeah.
- [19:23]
A
You are preparing for a career as an investment advisor. You have to contrast EMH with the modern portfolio theory we discussed earlier.
- [19:31]
B
Right.
- [19:32]
A
Because if EMH is completely true, then active management, you know, hedge funds charging massive fees to try and beat the market is entirely futile.
- [19:40]
B
You cannot beat a market that is already perfectly efficient.
- [19:43]
A
Therefore, EMH is the theoretical backbone that completely supports passive investing. It's why advocates say you should just buy low cost S&P 500 index funds and hold them forever.
- [19:52]
B
And our sources tie this directly into the random walk theory as well. This theory claims that future stock price movements are totally random and unpredictable. Statistically akin to a drunk man stumbling down a street.
- [20:04]
A
You cannot predict his next step exactly.
- [20:07]
B
If the market is a random walk, it means that any active portfolio manager who does manage to beat the market didn't do it because they were highly skilled at reading the security market line.
- [20:16]
A
They just got lucky.
- [20:17]
B
They did. Under the random walk theory, long term past success is just a statistical illusion. Think of a bell curve. If you have 10,000 monkeys flipping coins, eventually one monkey is going to flip heads 10 times in a row.
- [20:32]
A
Whoa.
- [20:33]
B
You don't call that monkey a financial genius. You call it the inevitable result of probability.
- [20:37]
A
Right.
- [20:38]
B
EMH and the random walk theory suggest that star portfolio managers with a five year winning streak are just the monkeys flipping heads. Eventually they will regress to the mean.
- [20:48]
A
That is wild. Okay, we have covered some serious conceptual ground today. From the rolling deck of a boat to the deep philosophy of market efficiency we really have. Let's do a rapid fire recap so you can lock this into your study notes before exam day.
- [21:02]
B
Sounds good.
- [21:03]
A
Standard deviation measures your total risk. It's the boat, the ocean waves and the dog running on the deck. Beta measures your systematic market risk. It's just the inescapable ocean waves.
- [21:14]
B
And remember the exam applications. If you are evaluating a single stock or a poorly diversified portfolio, use standard deviation. If you are adding a stock to a massive, well diversified index fund, use beta.
- [21:28]
A
Right? And when you look at the graphs. The capital market line, the CML uses standard deviation to evaluate perfectly efficient portfolios. The security market line, the SML uses beta to evaluate individual stocks.
- [21:41]
B
Time weighted return judges the portfolio manager's pure skill because it ignores cash flows. Dollar weighted return judges the reality of the investor's outcome based on their personal timing.
- [21:50]
A
And finally, the efficient market hypothesis argues that all of this active analysis is a waste of time because asset prices instantly reflect all information, making it impossible to consistently beat the market.
- [22:01]
B
Which brings us back to that fascinating final thought to ponder as you prepare for this exam.
- [22:05]
A
The Casino.
- [22:06]
B
The Casino. If the random walk theory is correct, if accurately predicting stock movements is mathematically impossible, and historical success is just random chance, then the entire multi billion dollar active management and hedge fund industry is essentially a highly sophisticated data driven casino built entirely on the illusion of skill.
- [22:26]
A
Now that is something to think about while you're bubbling in your answers.
- [22:29]
B
Yeah.
- [22:29]
A
Good luck on your series 65 and 66 exams.
- [22:32]
B
Absolutely good luck.
- [22:33]
A
Remember, when the jargon feels overwhelming and you feel like you are drowning in formulas, just visualize that boat. Separate the dog from the ocean waves. Understand the mechanics, the math, and you will navigate these tricky scenarios just fine. Keep studying and we'll see you on the next deep dive.