What's Our "Edge" Trading Options?

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In this video, we're going to talk about defining our edge as an options trader. Now, point blank, to start you off, I'm going to tell you, this is the most important concept that you'll need to learn if you want to be successful trading options.

Without completely understanding what we're going to cover today, you can all but forget about making money trading. See, every successful business has some edge that gives them a long-term advantage over someone else or in the marketplace, and before we even talk about options, I first want to cover how casinos and insurance companies make money.

Now, you're probably thinking to yourself, "Wait for a second, casinos and insurance companies?" But listen, follow me on this. I promise your portfolio will thank me later on, once you're done.

Now, this is a little bit longer video, but it's worth going over because this is the core foundation of how you can be successful trading options. So, here's the deal. Here's how casinos make money.

It's dead simple, and you probably already knew what it is, or know what it is, but they make money on small theoretical probability imbalances in each of the hundreds of gambling games they create, either through payout or odds.

Put simply, if you know the probability of getting heads and tails from flipping a coin is 50%, then the casino might pay you $10 when it landed on heads, but take 11 when it landed on tails.

They either fix the games so that the probabilities are in their favor, and we'll talk about that here in a second with roulette, or they either pay out a sum based on the odds of an occurrence happening.

So, they could have a game about flipping a coin at a casino, they could pay 10 when you land on heads, and they could take 11 when you land on tails. Now, that's too obvious, most people would get that because it's just heads and tails, but what they do is they just kind of mask it in some of their probabilities.

Okay? So it's very strategic, and we'll talk about it here right now. So, roulette. Most of you know the roulette game, it's the one with the big wheel and little ball, marble that rolls around there.

Basically, a roulette wheel has 36 numbered spaces, black and red, and you'll often hear, I'm going to go to Vegas and put it all on red or put it all on black. It's got 36 individually numbered spaces, black and red, and this is kind of how they break down with blacks and reds in the numbers.

But what most of these roulette wheels have is they have either one or two, or sometimes three, they have three green dots, or green spaces, either zero, double zero, triple zero, right?

Basically, what happens is, is that when you go into a casino, those numbers, those double zeros, triple zeros, and all that stuff, they tilt the odds in the favor of the casino.

Now, you can go in, and sometimes you can bet just straight up on black or red, so that's ... This is one of the more popular plays in the casino, and the payout for black and red is 1:1, meaning that if you go in, and you put $10 down, and you're right, you can get back 10 more dollars.

You make 100% gain on your $10. You going to go in and you can put down $50, and if you're right, you get $50 back, and you get a $50 profit on top of that. Okay, so the payout is 1:1.

You would think that in a fair, and even, or balanced game, that the probability of landing on black or red, would be 50%. The reality is that in most markets, especially American type roulette wheels, the probability of winning every single time on black or red is just 47.37%.

Why is that? That is because on the extra green spaces that are on the roulette wheel. Again, every roulette wheel has 36 number spaces, black and red but they have extra green spaces, or green numbers, zero and double zero, that tilt the odds in favor of the casino.

In this case, in roulette, they tilt the odds in the favor and they also gyp you on the payout because you don't get paid out what the actual probability of winning is, or the probability of your risk is.

This is just one quick example of how casinos make money. In roulette, here are the entire odds of making money based on any one particular combination of things that you have on the wheel.

The probability of a red number or a black number is 1:1 and the payout are 46.37%, again payout's 1:1. An even number or an odd number that would be like one, three, five, all that stuff. Again, payout's 1:1, 46.37%.

They don't include in here, green spaces, okay, green spaces, that's where they gain their edge, and zero or double zero. Those aren't even or odd numbers; they're zero and double zero.

That's again, where the casino gets its edge on every single freaking roll of the roulette wheel, or every turn of the roulette wheel. Here's the deal. As you can see, the odds are not stacked in your favor.

The question becomes, and this is a key point that nobody in this entire industry talks about, is why casinos have table limits. They have table limits because it increases the number of plays that a person will make, which thus increases the house edge back to the casino.

You see, the longer you play, the more you stand to lose, period, end of story. Casinos deliberately put table limits so that they can control the number of times that somebody plays a game. They will put a table limit of say, $10 per play or $100 per play.

The reason they do that is that they don't want you coming in and placing a million dollars on black or red, for one single roll. That's too big of a risk for one roll.

They know that if you come in, and you place a million dollars on the table and you slowly invest or play that million dollars, $10 at a time, they would absolutely take that bet any day of the week because they know that over time, their edge is greatest with a lot of plays or occurrences or rolls or however you want to call it. Okay?

They know what their probabilities are, but they're not going to take a chance on a one off, stupid bet, of a million dollars. They'll let you blow your million dollars $10 at a time or $50 at a time because they know, the longer you play, the more you stand to lose.

Here are how the actual numbers work out, really on roulette, as far as it goes with the black or red. The first time that the wheel spins, you are theoretically behind by 51%. Meaning, the first and only spin of the roulette wheel, is your best shot at making money in a casino.

You go in, you spin it one time, you got the best chance of making money at a casino, and that's about 49%. Again, depending on where you look and what numbers you bet on, black, red, or other numbers. Okay? Your edge is lost on the first spin.

After 100 spins of the wheel, the number of players who get behind, meaning they owe money or they don't have more money than they came in with, is 64%. Think about that.

After a hundred times that that wheel spins, that that game gets played out, their edge or their consistency as far as being profitable, casino-wise, goes up. Your profitability or your consistency goes down as a player.

Now, your chance of making money is about 36% or the likelihood that you make money is about 36%. After a thousand spins, and you got to be there for a long time, this is why table limits are there, the chance of the casino making money is about 81%, your probability of making money is about 19%.

Now, get this, after 10,000 spins, this is the real key here, this is a key, key point, after 10,000 spins or 10,000 plays. However, you want to look at it, the likelihood that the casino makes money is almost 100%.

Meaning, if you're there for a long time and you keep investing your money, play after play after play, gamble after gamble, hand after hand, there's almost 100% chance that you walk away with no money.

Now, think about this logically. This is why casinos offer people so much money to come back. That's why they give you a free room, and they give you free food, and they give you all this.

They know, as long as you are there long enough or as many people as they can get into the casino, there long enough, the likelihood of them winning, is almost guaranteed. They know they have the odds in their favor and it's just a matter of time before they get enough people to play.

That's why they want you to play. That's why they give you free drinks. That's why they have all these shows. They want you there in the casino. Okay? It all makes logical sense when you start thinking about it.

All right, let's talk about how insurance companies make money. Again, I told you, this wasn't going to be a traditional video but it's important that we understand the thought process on how other companies make money in the other industries because it relates directly to how options traders can make money.

Insurance companies make money through actuaries. Actuaries are people who do all the hard statistics and probabilities. Like what is the likelihood that you, as a male, in your 30s or whatever, dies before 60, whatever the case is, okay, living in Boston and with a family heritage of heart disease.

All that stuff, that's what an actuary does. They make money through their actuaries that give them expected probabilities, the probability that you die or not, based on all of these different things, probability that the house burns down, the probability that you get in an accident because of your age and if you're partially blind or not blind, whatever the case is.

Okay. They got all this stuff that they give them expected probabilities. This works in all cases but let's take life insurance for example or the payout that your family gets when you die. Okay?

Now, before we go into this, I just want to mention, real quick, Buffett has huge bets in insurance and options. In fact, we've done multiple, multiple videos showing you exactly inside of Warren Buffett's 10Ks, 10Qs, all of that stuff, on exactly how he's trading options.

He's doing it exactly like we're doing it. He's got a ton of money invested in insurance. He loves insurance. He's been quoted as saying, insurance is one of the most profitable businesses that he has. He's got Geico; he's got other insurance companies that he runs. This is why he does it because the odds are in his favor, he can control it.

All right, here's a life insurance example. You have two events with life insurance, you either die or you live, right? Sorry to say but that's how it is, okay. If the actuary, for whatever case you are, for your person, says okay, the probability that you die in the next year is .0025%, okay?

It's .25% that you die, and you have $100,000 policy. Which means that if you die, which is a .25% chance of happening over the next year, the company has to pay out $100,000 to your heirs, right?

The probable value of this, if we just take the expected probability, times the lost value or revenue to the company, is $250. Which means that there might be a $250 chance that they lose money over the next year. You could die tomorrow, and they pay out $100,000, or you could not die, and they don't pay out $100,000.

Based on the probabilities, their expected payout here's -250, if you die. Now, if you live, the probability of you living here is one, which is 100% minus the expected probability of you dying, where there's a 99.75% chance that you live, or whoever this case study was meant to be for.

The value to the company, if you live right now, is nothing because they haven't charged you anything for their policy. Okay? Right now, they're just saying, okay, if we're going to pay you $100,000 if you die, or your heirs $100,000 if you die, then we've got to determine how much money we need to charge you today so that we make money.

If we don't charge you any money, we just keep paying out, that's obviously not good, right? Premiums on insurance policies have to be determined this way. This is how they determine it.

They say, okay look, each policy, in this case, is costing us long term, based on the numbers and the probabilities and how everything works out, is costing us $250 per policy or per person.

Now, if the company wants to make money, they could charge some premium on top of that. Now, again, remember what I said, companies like casinos and insurance companies, they make money either on knowing the odds, fixing the odds, or based on payout and how they charge.

In this case, with insurance companies, they can figure out exactly what the odds are, they can't change those but they can figure out what the odds are and the probability that you die is, then they can change the payout to reflect a positive adjustment in their favor.

In this case, they can charge 250, they can add $40 to every single policy like this, and that means that every year, you pay $290, and the insurance company is expecting to make out of that $290 per year, about $40 per policy.

Okay? Now, again, this only works out, obviously, if they have lots and lots of people. Once they know the probabilities, their goal is to minimize risk by writing as many policies as humanly possible. That's why they go into all these different kinds of business because they diversify their risk.

The same thing goes for real estate and cars, and businesses, etc. Once the probabilities are known, it's just a game of numbers and math. If you think about it, you will not take out a policy, one policy, on one person.

The probability that that one person gets hit tomorrow by a car or dies or something happens, is too great of a risk for one person. That is called a black swan event.

The only way that you can avoid a black swan event, that your person that you just wrote a policy for gets hit by a car or dies or gets cancer or something horrible, the only way you can prevent and minimize it, is by writing lots and lots and lots of policies, so collecting lots and lots and lots of premiums.

So, that if one person dies, it doesn't hurt the profitability of the company. That's why insurance companies want to open up as many, many policies as possible. That's why the insurance business is very, very competitive because it's very, very profitable.

Again, as traders, we need to follow the same logic in how we run our options business. Namely, these three things. We have to know what the expected value is and we have to know what the probabilities are.

If you knew heading into every roulette game, or every blackjack game, how often you win or lose, do you think most people would play? Most people play because they get the excitement of playing because it's fun and whatever, and you might win a little bit of money.

Long term, if you thought about that as a business, you'd realize that's a bad business, okay. Same thing about insurance. We have to know exactly what the probability of the house burning down is or the probability that they get into a car accident or the probability that the person dies.

You've got to know those things if you ever want to be profitable. Number two is, you have to keep positions in investments small. You can see this is relevant to both casinos and insurance companies.

Casinos have table limits so that people are investing a small amount of money, keeping their position sizes small. Same thing with insurance companies.

They keep the number of people in their portfolios as high as possible so that each person, each person, is a small portion of their overall exposure to people dying or overall exposure to property values or some fire or whatever the case is.

That's why they want to be as diversified as possible. Number three is, you have to be consistent and frequent. It's just like anything else. The casino never makes money unless people continue to come in and make 10,000 rolls.

Once people play the game 10,000 times, they're guaranteed to make money. Same thing with insurance companies. The insurance company is not going to make money on their first investment in a person's life insurance.

They make money once they start getting, 1000, 10,000, 100,000 customers and are starting to collect those monthly premiums. Okay, hopefully, this is all making sense.

Now, here's the deal. We're going to now transition into implied volatility because now we're going to take everything that we've learned conceptually, with casinos and insurance companies, and apply it to figuring out what our edge is as traders, especially as options traders.

Now, here's the deal, implied volatility is derived from an options price and shows what the market implies or expects about the stock's volatility in the future.

Now, all option implied volatility is based on pricing from at the money calls and puts. That's usually the most active, the most volatile options that are out there, okay.

Now, remember, if the market is expecting a swing wildly in the future, then the value of options on both sides would be higher because traders expect a higher chance of the market swinging into a profit zone.

Now, before we continue, remember that almost everything about an options price can be determined, so the factors that go into an options price are known factors, meaning how much time is left until expiration?

What's the intrinsic value, which is the difference between the current stock price and the strike price? We know all of those factors. What we don't know are what is the unknown or the implied number, the guess, if you will, is implied volatility.

How much do we expect the stock to move in the future? Based on actual market participants, determining and bidding up the price of options, or not, we can imply how much markets expect the stock to move.

Here's a quick review of volatility. Again, if the stock price is right here in the middle at $100, a low implied volatility market would be this black line here. Which means that the stock is basically moving in a very tight range.

There's not a lot of volatility. It's not wildly swinging back and forth. If you had, for example, the 110 strike call options and the market was low in volatility, then these options probably don't have a good chance of being in the money.

Meaning, there's not a good chance that the market just randomly shoots higher and now you have a $110 call strike that is in the money and profitable. There's a low likelihood that that's going to happen. As a result, the price of those $110 options is much lower.

Think about this, if you are buying a lottery ticket, the likelihood that you win on a lottery ticket is very low, right? Therefore, the price of the lottery ticket is very low because it's only a dollar investment and you have almost no chance of winning, basically, on that lottery ticket.

Hence, the investment is low. Low likelihood of success, low cost to get it. Now, if the market is high volatility, which is this blue line here, where the stock is moving all kinds of crazy around $100.

It's still trading around $100, but you can see the volatility, in blue, is much higher. Now, if you have 110 strike call option, now there's a pretty good chance that at some point, it's going to maybe swing into a profitable zone.

Now, the value of that option goes up dramatically. Okay? Now you can see, high implied volatility or high expected move, increases the value of options, on both sides of the market.

If you had the 90 strike puts, you can see the value of those options will go up as well because implied volatility means that there's a higher chance that it could swing into a profit zone for you. Okay?

Now, here's the deal. We can determine exactly what implied volatility is to each and every security that we're looking at every single day. Here's a screenshot that we grabbed before. Historically, this is back for Apple.

It was basically when Apple was trading at about $113 a share, and we knew at the time that the market was pricing in a .3569 implied volatility move. That means a 35.69% move is expected at that exact moment in time for Apple. That's what the market was anticipating.

Market participants on both sides were expecting Apple to move about 35% up or down over the next year. That number is known, it's backed into, we can calculate it, we can figure it out. Most broker platforms have this. If yours doesn't, I don't even know why they're a broker, if they can't.

Now, here's the deal. We take this implied volatility range, and we have to assume a normal distribution. Now, this one is probably going to create a little controversy, with most people out there, especially if you're new to options trading or maybe new to stock trading, or even if you've been experienced.

You might assume, "Hey, Kirk, the market does not move in a normal distribution manner." I'm going to show you in a second that it does. What we're going to do is we're going to take that probability, and we're going to apply it to a normal distribution range.

What does normal distribution range mean? It means that 68% of the time, the stock will move within the expected range, where this is the expected range when you hear us say this. This is a 68% probability range.

That's a one standard deviation on either side of the market. Okay. So, one standard deviation move up and one standard deviation move down, that gives us our implied volatility range 68% of the time. Okay.

We'll look a little bit deeper into this here in a second. Now, the question becomes, that people have, is how do I know that the market is a normal distribution market? Well, the reality is, is that, we went back and tracked it.

We went back to the Dow over the last 26 years from the time that we're doing this video and tracked all of the daily percentage moves in the Dow Jones since 1990, and then we overlaid it here on this chart.

You can see that there's a nice, very even, normal distribution. Are there outliers, where the Dow moved huge when it had a big drop in a couple of days? Are there are outliers had a huge gain in a couple of days? Yes.

But are most of the distribution of moves inside of basically a normal distribution or a normal curve, bell curve shape and the answer is 100% yes.

You can see that even right here around zero, there is almost an exact amount, within I think two days or three days or three occurrences that the Dow moved up or by .1 or down by .1. I mean, almost the same exact likelihood of moving up by .1 or down by .1 every single day.

Okay? Now, this doesn't mean that markets can't then move a little bit higher because there's got to be positive skew. There's got to be some benefit to being invested in stocks versus like a risk-free bond.

We get that but the reality is that you can't assume that you know where the market's going in the future. It is a normal, random distribution. That applies to any liquid market out there.

Now, how do we apply this? Again, back to the normal distribution graph that we talked about and with implied volatility. What we do is, take implied volatility ...

In this example, if we have a stock trading at $50 and implied volatility is 20% then we can assume 68% of the time, in the future, the stock will trade between $40 and $60, okay? Remember, 20% of 50 is a $10 move in either direction, okay.

That's how we get to that, 20% implied volatility of a $50 stock is a 10% up or down move. If the stock is trading at $50, again, we know 68% of the time, it should trade between 40 and 60 in the future.

This is how we start to build the foundation for high probability trading. It's figuring out what these data points are, where they are, and then building a high probability system around them.

Again, that's how we now take implied volatility, now basically overlay it on top of this normal distribution graph. If you want to think about it a different way because I always think about it kind of on this type of visual nature, you have a stock trading at whatever price is here, so let's say $50.

Okay, pricing on this stock doesn't matter because it's just an example. Let's say the stock is here, $50, and we use that same example of a 20% implied volatility, that means that there's a pretty good chance that it goes up to $60 here, okay.

Or a 68% chance that it trades between $40 down here and $60 up there. Now, you can visually see that this is your expected range for this expiration date. Okay? That range as we go off further in time might go a little bit higher.

Now, the expected range might be up here and might have a wider distribution based on time. We can calculate exactly how far the stock is expected to move into the future, and that's the a key difference between options trading and stock trading is now we know these ranges versus just having a set line in the sand ...

I drove my picture over there ... versus just having a set line in the sand saying, "Okay, I hope it goes higher or lower," we know that there's a 50/50 shot of every one of those events happening.

Here's the deal, as implied volatility increases, or Vega, as we talked about in previous videos here in track one, an options price will increase to compensate for the higher probability of the strike being in the money at expiration.

This is why we sell options when implied volatility is high and why we buy options when implied volatility is low. Again, we'll continue to cover this. If we have this normal distribution graph, when implied volatility is low, the distribution graph looks like this one, this red one.

Meaning that the likelihood of the stock making a huge move is really small. If this is the $50 stock price, the stock might move, let's say up to a total of $55 on the top side and then on the bottom side; it might move down to let's say $45. Okay.

A low volatility market might have a low distribution of prices, meaning most prices are going land somewhere between 45 and 50 and 55. If the market has high distribution, then what you see is a short volatility curve or a short normal distribution curve.

Something like this blue one, okay? This thing gets scrunched down. It gets compressed down and notices that the tails or the wings get distributed out further. Now, the one standard deviation expected move now might be up here.

So now, there might be the same 68% chance of the stock trading in range, but now that range might be from 30 down here up to 80, okay? Now, there's more volatility so there's a greater expectancy that the stock may move into a wider range.

Again, just continuing further, just to drive home this point. If this is let's say, normal distribution, a high distribution or a high-low implied volatility distribution might have a range that looks like this.

Okay? It might have a smaller range, where the stock is going to trade in between this range. A high probability distribution might look like this. There's a high likelihood that the stock reaches its extremes and makes huge price movements.

Again, just want to drive home that point. Here's the deal, the difference between expected implied volatility and actual becomes our edge.

This is one of the ways, as options sellers, that we make money is because of implied volatility, as we just talked about, what the market is expecting the stock to do, always overstates the actual move of the stock in nearly all cases long term.

Now, we are believers that markets are efficient, we've already proved that with the Dow Jones. In the same sense, there's no price edge that you can gain picking stocks directionally.

When it comes to options and option pricing, we can prove that selling rich implied volatility has been one of the rare historical and profitable edges to trade. Again, remember what we already proved here with casinos and insurance companies.

If you know the expected values and the probabilities, you can build a system where a business that profits from these known numbers. What everyone tries to do in this market, especially if you're stock trading, is you try to come in and assume they know where a company is going.

The reality is you don't because things change so fast and you're such a small player in the market; you don't know where the market is going. You don't know where a company is going.

With options and probabilities, we now know exactly what these numbers are. We have numbers that we can rely on, probabilities and expected moves and implied volatilities. All of this stuff we can rely on to build a system that makes money.

On the next page, we're going to track DIA's monthly implied volatility against the chart of actual realized volatility 30 days from the time of measurement for each period, since 2003.

Here's the deal, here's what we're going to do here, because I think it's really important, again going back to that example that we had before, if the market is expecting a 20% move in a stock over a given period, we want to go back and track and see if the market actually moved 20%.

If market participants were expecting, let's say DIA's stock to move 20%, well did it move 20% in that period, right? Or did it move, let's say 21%? Or did it move, let's say 18%? What did the market do?

What is the actual move based on what people expected? Okay? That's the thing that we're going to track here next. All right, here on this chart is a couple of different things. One, you can see the timeline that we're looking at here is back to 2003.

Okay? We're taking a lot of data points here and proving this concept over time. The green line or I guess the teal line, the more blue line, is the implied volatility of DIA every single day over that period.

It's saying, okay, what does the market DIA to move over a given period, every single month from that given period? Now, the green line or the lighter green line here, is what happened, okay?

This is your historical volatility or what we call actual volatility or AV. All right? Now, you can see here that in any given an example, over the course of this period, from 2003, more often than not, vastly more often than not, the market expected DIA to move more than it did.

Okay? Using this example here, the market may have expected an 18% move in DIA when it played out, when the timeline played out from that date forward, and we retroactively went back and looked, and said, "Okay, how far did the market move?"

When the market was expecting an 18% move in the actual stock, and we look, and we see that the market only moved 10%, now we know that market participants were expecting the stock to be 8% more volatile than it was.

This about it like real estate. I always talk about real estate as a great proxy for this too. If people expect the real estate in San Francisco or New York to rise 20% and they buy assuming that it's going to rise 20%, when it only rises 2%, then they overestimated that the market would rise by 20% when it only rose 2%. That's the same thing that we have here.

Now, you will see, obviously that here are times where the market is not ... or the market expects the stock to move a less than expected than it does. Let me say it a little bit better. There are times where the market has implied volatility that is less than actual volatility.

That's going to happen. That is how it always happens because there have to be times where the rules don't work. This is not a 100% probability game. This is an 80, 90% probability game, not 100. You can see back in 2008, 2009; the market was expecting a lot of volatility.

It got a little bit more volatility than was expected. Okay? The vast majority of the time, you can see that the market implied volatility or IV in DIA's case is much higher than what happened, which is a key concept to prove.

Here's the deal. On average, DIA expected the market to have a slightly more volatile environment than had been realized over the last 13 years. The average difference between the DIA implied volatility and actual volatility in this period was 6.25%. I'm here to tell you, right here, right now, this is our edge in the market, okay?

Now, it's different for DIA than for SPY and GLD and all these other ones, and we'll show you some more examples but that right there, the difference between what the market price is as the expected move or implied volatility, and what actually happens, that difference creates our edge as an options trader, more specifically, as an options seller.

Now, here's the thing, I want to go over just two more examples with GLD and TLT because it proves the concept again in many different facets. Here's GLD, which is a gold ETF. You can see in nearly all cases; implied volatility was higher than the actually expected move, okay?

This overstatement sometimes in GLD, was really, really great. You can see like right here, back in late 2009, the market was expecting GLD move 25% when in fact it only moved, during that period, about 15%. Okay, still moved.

A 15% move is still a big move, but it's not as much as the options market was pricing in. Option prices were dramatically higher than what they turned out to be when the timeline passed because the market was expecting a big move in the underlying stock.

Again, you still get those little differences, so there's not ignoring that. You still get the differences where sometimes the market moves more than expected but the vast majority of the time, the market is moving less than the implied volatility that it's predicting.

Same thing with TLT. TLT is a bond ETF. You can see back in 2008, 2009, the market dramatically over-expected the market or over-predicted implied volatility at the height of the market crash here around 2009.

Traders were expecting TLT to move about 40% per year when it turned out to when the numbers came in, TLT was moving about 20% over that period.

The market was pricing in options like they were going to move, or like the stock was going to move 40% over the year when in fact the market only moved, or the stock only actually moved about 20%. So, huge, huge, discrepancy in pricing.

Again, this is where we gain our edge. You can consider the difference between these two lines as our theoretical edge as a trader. Now, most of you might be saying, "Kirk, what does all this stuff mean for me? I get it. I understand that the market over-anticipates that stocks will be more volatile or less volatile than they are. I get it but what does that mean for us?"

Well, here's the thing. If you remember that an options price is mainly determined by implied volatility or future expected move, then that future move and that future move turns out to be less volatile than expected, than the option is theoretically overpriced long term.

If an option determines most of its value by future expected move and therefore a future expected move is high but turns out to be low, and it plays out when the days go forward, then that option is always going to be theoretically overpriced.

So, because we know and we have proved that implied volatility is always higher than historical volatility, or actual volatility, this means that long term, all options are theoretically overpriced by some margin.

Again, it might be different in DIA versus TLT and GLD, but the reality is, is that all options are overpriced theoretically by some margin. Hence, those traders who are net options sellers, more often than net option buyers, have a huge advantage or edge based on the math.

Again, you cannot dispute this. This is proven, I just proved it to you in all shapes and facets. The people who are net option buyers have a distinct long-term advantage, an edge, over option ... The people who are option sellers have a distinct long-term advantage over option buyers.

That doesn't mean that you can't be an option buyer sometimes and make money. Again, going back to TLT. There are some instances where the market does not assume that TLT is going to be volatile and it ends up being volatile.

Okay? In those cases, option buyers may win. The reality is, is that long term, option sellers have a much bigger advantage than options buyers. Again, this means that if the market is expecting a stock to move 25% over the year, it may only actually move 20%.

If your options are priced, assuming a 25% move in the stock, and it only moves 20%, you paid too much money for that options contract. Point blank, you're just going to pay too much money for that options contract.

Again, it gets back down to this; this is just a game of numbers. It is no different than running a casino or an insurance company. It's just a game of numbers. Most people assume that it's something different and it's not.

As traders, we have to follow the same logic on how we run our options business. Again, it's worth going over this slide again because you have to first know your expected value and probabilities.

We'll dig deeper and deeper and deeper into this, throughout track one, and two, and three, here at Option Alpha but the reality is, is that we know implied volatility's always overstated.

We know we can find out the expected move of stock or how often it's going to move. We know that option pricing is always overstated. Now, the key becomes, how do we minimize our risk? We minimize it by keeping our positions and investments small.

So that if we get into a situation where the stock moves more than we assumed it would, okay, where we have that black swan event, where if you're the life insurance company, someone dies the next day after you just took out a policy on them, the only way to prevent or minimize that risk, is to keep your investment small and to have a lot of them.

That's it. That's the only thing you can do. You cannot protect yourself from a black swan event. That's why they're black swans; they're unpredictable. You can keep your investments small, so if they happen, they don't kill you. They don't turn out the lights.

It's like insurance companies, and this is such a key point here, it's like insurance companies. If someone dies tomorrow or if three people die tomorrow for an insurance company, they don't care that three people die tomorrow because they've got 200,000 other people that didn't die and are still paying their premiums.

That's why they don't care. That, for them, could be the black swan event. The only way you prevent that keeps your positions and investments small.

The last and final thing here is you have to be consistent, and frequency leads to profits. The only way that this thing plays out is if you have enough diligence and enough consistency and persistence to keep doing it on a reoccurring basis, then it will work out.

It's like casinos; it's like everything else we just talked about. That's why they're such a great analogy for options traders because casinos just want you to play 100,000 times so that they know that they're going to make money.

If we go back to this chart here of implied volatility for DIA, if you started trading back in 2008, 2009, you might not have made money, but if you kept trading the entire time, you would've made money.

Although, you could've lost here early, by being consistent, the numbers eventually play out in your favor. It's just how the math works. You can't fight the math; it's just how it works. Implied volatility will always overstate the expected move.

The traders who are most successful, in my opinion, are the ones who are consistent and persistent. When you realize that frequency leads to profits and it's all just a big game of numbers and probabilities.

To end this, thank you so much for watching this video. Hopefully, this video changed your entire outlook on how to make money in the market, how to trade options, how to do it successfully.

Again, we can't go over everything in this video but we can lay the foundation here for where our edge is and now start to dig even deeper, throughout the rest of track one and as we get into track two and three, on Option Alpha, on how you can find these trades, how you can place these trades, how you can manage your risk, all of the other questions that you probably have.

If you have any questions or comments about what we talked about here, please add them in the comment box right below. If you loved this video and you want to share it with your friends and family, please do so online.

It's one of the ways that we spread the word here at Option Alpha, and I would be extremely grateful. Until next time, happy trading.