As readers may be aware, I prefer selling options on the Russell 2000. The main reason for this is that the spread between implied volatility (the cost of the option) and historical volatility is normally greater for the Russell 2000 than it is for the S&P500, so you end up getting paid more for the risk you are taking. The RUT (Russell 2000 index) has pretty good liquidity too, so I rarely have a problem getting into and out of position.
With that, I’ve pretty much written everything I have to say about my option strategies (see here and here), so I think it’s about time I wrote about how I realize these option strategies in my portfolio.
2015 Option Strategies +32.0%
2015 was a good year, and marked the first time that the cash generated on my portfolio exceeded the income from my job. A little over half the returns came from RUT option strategies, with the rest from puts and calls on individual companies. I threw caution to the wind a little bit during the year, and began writing naked puts and calls on the RUT instead of iron condors. I was fed up of losing a ton of money on the short component of an iron condor leg, and having the long component do virtually nothing to hedge me. So instead, I drastically reduced the number of options I traded, and went naked. I feel way more at ease with my portfolio now. The chances of a large loss are much smaller now, even though the risk of a very large loss is slightly greater.
Still, I feel happier with these risks. I modeled my worst case scenario on 2008, and even with a drawdown in the markets on that scale, this options strategy performs better over the long term.
2016 YTD Option Strategies +6.92%
This year so far has been a bit of a frustration even though I spent the first three and half months traveling in India and South-East Asia (one of the perks of trading is having travel money, another is having time to enjoy it).
I’ve been having a great deal of difficulty with selling calls on the RUT – every time I am about ready to pull the trigger, the market gyrates lower. In the January/February drawdown I chased the RUT lower, so even though my short puts miraculously survived, I was clobbered when the market came roaring back. I had to roll my calls up while in Goa, India, which wasn’t much fun. When the Brexit caused a sudden crash in markets, I sold some more puts. I knew I needed to wait for markets to come back up before selling any more calls. Right now the calls I sold prior to the Brexit vote may be needing some adjustment soon, so I am not ready to sell any more just yet.
In all, my RUT trades are down marginally on the year. This has been more than offset by my individual option strategies though. Naked puts (mostly cash-secured) and covered calls around Chevron and General Mills have been big positive drivers, and I’ve recently started selling puts on Chipotle Mexican Grill, which have been doing pretty well too. I eat there quite regularly, and I’ve been seeing customers coming back. The P/E ratio is not too bad (only 65% higher than the P/E ratio for McDonalds), and there is obviously much more potential for growth.
Right now the Russell 2000 is at 1177, and I’m looking for the market to take a breather and head back down. My current RUT calls are AUG 1200, and my puts are at AUG 1040 and AUG 1000. I’m a little reluctant to sell any more puts until we have a vol spike. Given that we’re about to enter Scary September, I am hoping I shouldn’t have to wait too long. In all, my option strategies are still performing, and I’m still very happy with the service provided by Interactive Brokers
I remember during the 2007/8 crisis a common refrain to hear from finance guys was “flat is the new up”. Well, it felt exactly like that with oil prices for the last two months, with oil finally ending flat/up for the first week in eight thanks to a great spurt today (16th January, 2015). And boy, did the oil majors respond, with four out of the top five gainers (by market cap) being well known oil stocks.
Much like during the crisis of 2007/2008, I’m being asked by people what I think of prices here. I always respond with the two golden rules of investment advice:
Rule number one: never take investment advice.
Rule number two: never give investment advice
Having worked in FX/equity sales, trading, and research for nearly 10 years, I’ve learnt that nobody has a clue where the market will go next. Only the astrology industry has as much bullshit in it as finance does.
That said, I still like reading about it. So here’s some oil price related weekend reading that you may like. Enjoy!
An old-ish article about how America and its allies will benefit from the falling oil price from The Economist
Another old one (when prices were still above $70) along similar lines as the article above from Thomas Friedman
Back to The Economist for a short one about the relationship between the oil price and Russian politics
A recent short one about shale and geopolitics from Bloomberg
And finish up with an update on today’s move from Reuters
Have a nice weekend, but remember – if anyone asks you where stocks are going from here, just do the right thing and tell the truth – “I DON’T KNOW!”
When I worked at my old bank job, I would constantly cajole my co-workers into betting on things. Anything, it didn’t really matter. Football games, non-farm payrolls, S&P closing price, anything. Our coffee machine was so useless we’d even bet on how many times we’d have to operate it before we got a proper coffee out of it (my employer was a real tightwad with “extras” like coffee machines). The idea was always the same – we tried to work out the expected value of a bet.
I like to use dice games to illustrate points about trading because of the similarities between rolling dice and entering the market. In both instances there is a huge element of chance. You need to be aware of the expected value of your bet, but that’s not all.
For example – Let’s say I have a die. I’m going to roll it, and if it’s a 1, 2, 3, or 4, I will give you $10. However, if it’s a 5 or a 6, you have to give me $10. This is a game you’d want to play, right?
The chances are clearly in your favor – two thirds of the time you will win $10, but only one third of the time will you lose$10. If you played the game with me 100,000 times, you would expect to end up with a profit of around $333,333. Not bad, right? This is definitely a game you would want to play.
Now let’s change the rules of the game. Instead of playing 100,000 times, we’ll play just once. But we’ll up the stakes a bit. If I roll a 1,2,3, or 4, then I will give you $1 million. However, if I roll a 5 or a 6, you have to give me $1 million. Do you still want to play?
Why not? The expected value of the bet is the same – according to the probabilities, if I play this game with a lot of people, the average person should still win $333,333. For each roll of the die, the chance of winning is still 4/6.
The DELTAS of the two games are the same – there is the same probability that you will win or lose before you start playing, and the expected profit is the same.
However, the GAMMAS of the games are different. In the first game, you can quit if you start to lose money. You can demand that we change the game. You can inspect the die if it seems like it’s biased. Even if the first ten rolls of the die go against you, your probability of making $333,333 from me hasn’t really changed very much. Your delta in the first game remains unchanged. It is a “low gamma” game.
In the second game, you can be wiped out in an instant. After a single roll, your probability of earning $333,333 (or more) changes hugely and instantly. You delta has moved massively. This is a “high gamma” game.
Just because an option has a 10 delta seven weeks before expiration does not mean that it has the same risk as a 10 delta option one week to expiration. Even though the probabilities are roughly the same, with seven weeks to expiration we have plenty of time to “inspect the die” if it starts rolling against us, we can simply stop playing altogether, or we can just play with another die by picking a different expiration month. With one week to expiration, one wrong roll of the die and you could lose a lot of money.
Play often, accept that you will lose often, but make sure the odds are in your favor.
Understanding option prices is important because your job, as an options trader, is to evaluate the price of an option and decide whether you should buy or sell it.
Too many people don’t really ‘get’ options prices, even though they trade them regularly. We should really understand expected return and theoretical value before we start thinking too much about the greeks. Here’s a quick intro to how and why option prices vary.
You already know some of the factors that must be included in an analysis of options prices. Here’s a short list of some of the most important factors:
The current price of the stock
The strike price of the option
The time left until the option expires
How quickly the price of the stock can change (the volatility)
Ideally, we’d know all of these inputs exactly, stick them into an equation, then get an answer for the true value of an option. We know the first three inputs exactly – they are all features of the option that we are considering trading. I’ve highlighted the last factor because it is the only thing we don’t know for sure when we trade an option. Volatility is the only thing we have to estimate.
For a discussion of volatility, let’s consider expected returns.
Let’s say you have a six-sided die. We all know that there is an equal probability of rolling any number between 1 and 6. Now let’s say I offer you the opportunity to roll the die and I will give you a dollar amount equal to whatever number come ups. If you roll a 1, you get $1. If you roll a 2, you get $2. And so on. How much would you pay to play this game with me?
Well, the first thing we need to think about is the expected return of the die. The average roll of the die will be (1+2+3+4+5+6)/6 = 3.5. So, on an average roll, I will give you $3.5. For you to be profitable over the long term (assuming you love my game and want to keep playing it), the fee you should pay to play this game with me has to be under $3.5. The theoretical value of the bet is $3.5, so if you paid more than this, over the long run you would be a loser.
Now let’s do the same thing with a call option on a stock.
Let’s say there is a stock, that is currently trading at $40. At expiration, the stock could be at $20, $30, $40, $50, or $60. Each of these prices is equally likely (each one has a 20% chance). Now let’s say you buy a call with an exercise price of $40. How much should this cost?
Well, you will only make money if the stock ends up at $50, or $60. If it ends up at $20, $30, or $40, you get nothing. If it ends up at $50, you make $10, and if it ends up at $60, you make $20.
So, what is the AVERAGE return of the call option? We need to weigh the returns by their probabilities in order to find out, like this:
(20% x 0) + (20% x 0) + (20% x 0) + (20% x $10) + (20% x $20) = $6
The average return of the call is $6. This is its theoretical value, so you should always pay under $6 for this call, in order to keep playing this game and make a profit over the long run.
Now obviously we simplified things by saying there was an equal chance that the stock would end up at each of these prices. That is never really the case. A more realistic situation would be if the stock had a higher probability of doing nothing, and a lower probability of moving significantly up or down, like this:
Now at the end of the period, the average stock price is the same (it’s still $40, as it was in the first example). But because we’ve changed the probabilities, we’ve changed how much we should pay (on average) for this option. If we do the same calculation as before, but with the new probabilities, we find that the average return on the call option is now:
(10% x 0) + (20% x 0) + (40% x 0) + (20% x $10) + (10% x $20) = $3
Wow. So even though the stock’s average ending price is still $40, the price we should pay for the call has HALVED to $3 from$6.
The probability distribution (of where we expect the stock to finish at expiration) has a massive effect on how much we should pay for the option
So, the key input into option prices is the probability distribution. If we know the ‘real’ probability distribution, we will know the real price we should pay for the option. To consider the ‘real’ probability distribution of a stock, we can do a little thought experiment. Let’s say that each day, a stock has a 50/50 chance of going up or down by $1. The stock’s movement is essentially a ‘random walk’. We cannot predict where a stock will end up in 30 days time, but we can predict a rough probability distribution of where the stock might end up. This video shows what happens if you go through a path many times where there are multiple 50/50 decisions:
Now that you’ve seen the video, imagine what would happen if the nails were much, much fatter. The distribution of balls at the bottom would be much, much wider. This is equivalent to a much more volatile stock.
The purple line is how it would look with normal sized nails (equivalent to a stock with low volatility), while the blue line is how the distribution would look with wider nails (equivalent to a stock with high volatility). Clearly, a call option with a strike price around 5 would be more valuable for the high volatility stock (the blue line). So, a clear conclusion is that higher volatility stocks will have higher priced options.
It seems that the probability distribution of a stock is roughly normally distributed. Is this a reasonable conclusion?
The problem is that the normal distribution is symmetrical, and as you can see in the distribution above, this means that there is the possibility of a negative stock price. No matter how high we set the middle of the distribution, the chart will always say there is the possibility of a negative stock price. Clearly, we can’t get a negative stock price, so this cannot be right. We can make the situation better by saying that the normal distribution is the distribution of the percent return of a stock, instead of the stock price itself. At every moment in time, the price of a stock can go up or down by a given percent, and it is these percent changes that are actually normally distributed, not the price itself.
If the percent return of a stock is normally distributed, then mathematically the stock price of that stock must belognormally distributed. The key features of a lognormal distribution is that it does not fall below zero (which is important, as stocks also do not fall below zero), and that it has a longer tail on the positive side, like this:
So if we want to think about the probability of a stock ending up within a particular range, we should allow that range to be slightly higher on the upside. If we include interest rates into our calculation (which I deliberately ignored when I listed the four factors that affect option prices at the start of this article), then we should assume an even greater upside bias to the stock price.
Knowing the theory behind the probability distribution of stock prices, and how this affects option prices, simply helps you understand options theory better.
The two conclusions you should make coming away from this article are as follows:
Probability theory shows us that the distribution of a stock’s price at expiration explains why options have different prices. I.e. volatility is the key ingredient in option pricing.
The probability distribution of a stock’s price is NOT normally distributed. A better approximation is a lognormal distribution, because stocks can (in theory) go up an unlimited amount but can only go down to zero.
It’s nearly a year old now, but this story of self-immolation by internet Apple guru Andy Zaky still makes for absolutely fascinating reading.
I recall avidly reading Andy Zaky’s free Bullish Cross articles in 2011 and early 2012, right before he nailed Apple’s earnings and the stock left Earth’s atmosphere.
It seems to me that being a good investor requires humility, which comes from experience. But being a good finance writer requires bold opinions. When you mix the two and become an investor with bold opinions, things eventually turn out badly.
John Paulson got crushed on his gold positions. Bill Ackman got his bottom spanked on JCP. Phil Falcone continues to insist that LightSquared is the future. Even now when you speak to some of the LTCM guys (immortalised in the book When Genius Failed), they still say that they were right and the market was wrong. Absolutely remarkable.
Possibly one of my favorite scenes from Seinfeld is when George Costanza decides that every decision he’s ever made has been wrong, resulting in the fact that his life is the complete opposite of everything he wanted it to be. Starting with chicken salad, he resolves to do the opposite of all his instincts. Immediately, his life turns around – he finds a girl, gets a job, and moves out of his parents’ house.
This pretty much describes how a rational investor should look at the stock market. There is the famous story of how JFK’s father, Joe Kennedy Sr., knew it was a good time to exit the market in 1929 when his shoeshine boy started giving him stock tips. Bernard Baruch, another well-known stock investor of his day, said:
Taxi drivers told you what to buy. The shoeshine boy could give you a summary of the day’s financial news as he worked with rag and polish. An old beggar who regularly patrolled the street in front of my office now gave me tips and, I suppose, spent the money I and others gave him in the market. My cook had a brokerage account and followed the ticker closely. Her paper profits were quickly blown away in the gale of 1929.
Unfortunately for us, the market has a remarkable capacity to inflict the most pain on the most people when you least expect it. It is easy to buy into the market when everyone around you is also buying, but you need to take George Costanza’s advice and do the opposite of what your instincts tell you.
Let’s take a look at a few sentiment indicators and see if they live up to their promise:
1. The American Association of Individual Investors (AAII) Sentiment Indicator
First up is perhaps the most widely cited sentiment indicator – a weekly survey done by the AAII that asks its members (retail investors) how they feel the stock market will do over the next six months. Are they bullish, bearish, or neutral? You can find out at the AAII website here. If you want to chart the figures, just go here and click on the links under “Major AAII Sentiment Survey Indicators”.
The survey gets interesting when we see extreme levels of bullishness or bearishness. We are basically looking for results that are a couple of standard deviations away from the average. Typically this means around 60% bullish or 60% bearish. We all know that people are idiots, so when the majority of retail investors have conviction which way the market will go, we should think about doing the opposite.
I like this sentiment indicator as it is one of the few that actually has some academic credence. CXO Advisory (a great website with some free and premium research) found that
[…]investors may be able to exploit extreme values of AAII net investor sentiment as contrarian signals[…]
In fact, they split the AAII sentiment readings into deciles, and compared the deciles with S&P500 returns for the following 6 months. The found that when sentiment was at its most bearish decile, forward S&P500 returns were highest, at an average of around 7%. They also found that when sentiment was in the most bullish decile, forward returns were at their lowest under 1%. The second most bullish decile also performed pretty poorly, generating a little over 2% over the next two months.
All the other deciles of sentiment (i.e. when the bullish/bearish readings were not at an extreme) didn’t really correlate well with future returns.
So what’s the takeaway?
Investors can exploit AAII data more easily at extreme bullish readings than extreme bearish readings. For extreme bearish readings to be useful to the investor, they must be further outside the normal range than extreme bullish readings
What this means that with the AAII data you will end up getting more sell signals than buy signals.
2. The Citigroup Panic/Euphoria Model
A lot of banks use a proprietary index of investor sentiment to find suitable buying and selling levels. This is good because it synthesizes a lot of information for us, but bad because it can be difficult to get hold of the information on a timely basis. We are also prevented from seeing how the model may have been changed to fit the data in the past. The Citigroup Panic/Euphoria model is said to provide signals that correlate with the forward 12 month returns on the S&P500. You can find a chart of the model at the bottom of this page at Barron’s, and you’ll see that it’s fairly easy to interpret.
A quick review of the models signals show that over the recent past you would have done very well by selling when the model indicated euphoria, and buying when it indicated panic.
However, it is not this simple. According to Georg Vrba over at Advisor Perspectives, the model was changed after 2008 because it had incorrectly predicted positive market returns for 2008/2009. In his graphs we note that in the first quarter of 2008 the model had indicated panic (a buy signal) before briefly surging in May and almost indicating euphoria (a sell signal). However, according to the model at the time, you would not have initiated a sell order as the model did not quite touch the level required for it to be considered a sell signal. As a result, a strict interpreter of the rules would have been long stocks going into the massive drawdown of 2008 and 2009.
As result of this, the originator of the model changed the interpretation of the output, such that with these new rules it would have given you a profitable sell signal in 2008. Now it’s important to be aware that the actual model has not changed. All that happened is that the interpretation of the signals has been loosened. Technically, yes, this is curve-fitting, but if you believe that there is an a priori reason to believe that sentiment indicators are capable of hinting at future returns, you should be able to look at this model as a single part of a complex puzzle that you can interpret yourself in order to manage your equity exposure.
The takeaway regarding this sentiment indicator:
A strict interpretation of the output of the Citigroup Panic/Euphoria Model could have led to losses in 2008. A loosening of the interpretation of the outputs has been made in an effort to show enhanced backtesting results.
3. The CBOE Put/Call ratio
This ratio works as a measure of investor sentiment because when investors buy a lot of puts we can assume that they are fairly bearish; and when they buy a lot of calls we can assume they are fairly bullish.
What this means is that if the put call ratio is above 1, it means that more puts are being traded than calls, so the majority of people think the market will go down (or at least the people with the majority of the money think the market will go down). On the other hand, If the put call ratio is below 1, it means that more calls are being traded than puts, so the majority of people think the market will go up (or at least the people with the majority of the money think the market will go up).
Your option broker will provide you with put/call ratios that are provided by CBOE (Chicago Board Options Exchange). If you don’t yet have a broker you can always use www.stockcharts.com, which provides charts for the three most commonly used put/call ratios. These are the equity put/call ratio ($CPCE on stockcharts.com), the index put/call ratio ($CPCI on stockcharts.com), the the total put/call ratio ($CPC on stockcharts.com).
I prefer to use the CBOE total put/call ratio, smoothed out by a moving average. The chart below shows a 50-day smoothed put/call ratio (in blue) and the S&P 500 price (in black), courtesy of www.stockcharts.com. Take a look and you’ll find that when the ratio is high it is a good buying point, and when the ratio is low it is a good selling point. Of course, it is difficult to know in advance if the ratio has already hit an extreme, or if it is going to continue in the same direction, so it is always good to use it in conjunction with other indicators.
Unfortunately, the researchers at CXO Advisory crunched the numbers and found that the put/call ratio has very little predictive ability. Now I’d like to think that this is because of unfortunate sampling – the data they use for the put/call ratio is exceptionally noisy, and on a daily basis it swings about wildly. With a moving average of the ratio I believe they would have found a much higher correlation.
So there you have it, three sentiment indicators that’ll help you find a girl, get a job, and move out of your parents’ house. In a future post we’ll take a look at how VIX can be a great indicator, and why it’s doubly good for those of us interested in options trading.
In my former life as a hedge fund analyst, it was my job to find good hedge funds and recommend them to the firm’s clients. We looked for trustworthy hedge funds across a variety of strategies, which would have steady, positive performance. Despite the team all having studied economics and gaining the requisite industry qualifications like CFA, CAIA, and FRM, over the course of around 15 years the team managed to generate returns of a little over 4% per year, with volatility of around 8%.
Now let’s be honest, that’s pretty crap. And what’s more, that actually overstates the money made by our clients.
So why are hedge funds such a bad investment?
One of the main reasons hedge funds look bad is that people who tend to invest in hedge funds are just bad investors. Most investors like to see a good track record before giving their money to a manager, so what often happens is they will pile into a hedge fund that has just had a good year. Unfortunately, hedge funds tend to perform badly when they get too large. John Paulson is a prime example of this. After becoming famous in 2008 by a well-timed bet on the subprime crisis, his funds swelled right before suffering crushing losses (with one fund losing over half of it’s value). John Paulson has now lost far more money than he has ever made, or ever will make. Great job!
It is important to know the difference between time-weighted and dollar-weighted returns in this respect. If a hedge fund returns 10% per year for 9 years, then loses 10% in the tenth year, the overall returns are still pretty good for someone invested for the full 10 years. On a time-weighted basis the returns are fine. However, there is often very little money in the fund for the early years, and a lot of money in the fund in later years. This means that the -10% loss in the final year actually loses more money than all the combined 10% yearly gains earlier. This means that the returns on a dollar-weighted basis are negative. The result is that most investors in hedge funds actually end up not making the returns quoted by the hedge fund in their marketing literature.
Another reason that hedge funds are not a good investment is that the hedge fund fee structure is biased against investors. In his book The Hedge Fund Mirage: The Illusion of Big Money and Why It’s Too Good to Be True Simon Lack illustrates the outrageous nature of the hedge fund industry’s fee structure. He calculates that the real profits made by investors in hedge funds between 1998 and 2010 amount to $9 billion, whereas the fees made by the managers amount to $440 billion. Part of the problem is that while the fund takes 20% of all your profits, it doesn’t refund you these fees if it then loses your money. It’s a great set-up for the hedge fund manager – he takes your money when returns are positive, and then keeps it when returns are negative. Another problem is that if you invest in a few different hedge funds, you are subject to netting risk. If you put $1 million into two different hedge funds and one goes up by 10% and other goes down by 10%, you end up with an overall return of 0%. However, you are paying 2% fees on both funds, and you are also paying away 20% of your profits on the fund that increased in value.
Now let’s make the situation worse. Imagine that you are investing in these hedge funds via a fund-of-funds. These guys will take an additional 1% fee and 10% of any profits made.
A final reason for hedge fund underperformance is simply that hedge fund managers have gotten worse as the industry has grown too large. This has hurt performance in two ways. Firstly, there are now a lot of managers who simply aren’t that good. Secondly, if alpha is a scarce resource, there are now too many dollars chasing the same finite market inefficiencies.
The key takeaway is that any hedge fund investor is starting with a huge handicap due to the hedge fund industry’s standard fee structure of 2 and 20 (2% annual fee followed by 20% of any profits). Investing is tough enough already when you have to interview fund managers to determine if they are a fraud. Even if they are trustworthy and capable of generating consistent positive returns, you start with a significant disadvantage.
Options settlement is unfortunately an often overlooked aspect of index option trading, but one that can give you a nasty shock if you hold your positions to expiration (which, by the way, you shouldn’t).
With single equity options, it’s pretty easy to know if your options end up in-the-money on options settlement day. The options settlement value used is simply the closing value of the stock at the end of the day. But do you know what the options settlement value of the RUT (Russell 2000) is? Somewhat surprisingly, it’s not the RUT.
Options Settlement Price is Determined on Friday Morning
Monthly options on the RUT, SPX, and DJX stop trading on the Thursday before expiration. However, the actual settlement value is not determined until the next day. According to the CBOE:
The settlement value is calculated using the opening sales price in the primary market of each component security on the last business day (usually a Friday) before the expiration date
This means that the options settlement value is not the opening value of the index itself, but rather the value of the index as calculated from the opening prices of all the constituent companies of the index. Now this often isn’t a big deal for the SPX or the DJX. These indices are composed of big companies that mostly start being traded exactly at market open. However, it is a big deal for the RUT – because it is composed of fairly small companies, not all the constituents in the Russell 2000 start trading as soon as the market opens.
Imagine the RUT opens sharply down, and 900 companies print their opening values significantly lower than where they closed the day before. However, within a few minutes the market rallies strongly and the other 1100 components of the index finally start being traded. These companies are printing their opening price much higher than their previous closing price.
If we looked at a chart of the RUT, it would look like it opened down strongly. However, because of the odd way the it is calculated, the options settlement value used for options is actually higherthan the opening value of the RUT. In fact, depending on how the index moves, you can get an options settlement value that is higher than anything the actual index reached during the day’s trading. Naturally, this can be a pretty big shock if you are short a call option that you thought was safely out of the money. Unfortunately, the options settlement value for the RUT often isn’t published until late on Friday afternoon (on the options settlement page of the CBOE website), so you could be in for a nervous wait.
Here’s a representative year (2007) for the opening prices of the RUT on expiration Friday alongside the actual options settlement values for each month:
What you see is that most of the time, the opening price of the RUT is the same (or near) the settlement value. However, a few times of year (in this case, April, June, and August, which I’ve bolded), you could end up getting screwed. The most bizarre example of this was in August. If you were short an 800 call, you would think you were fine on Thursday afternoon and Friday morning. However, somehow the settlement value ended up at over 802 despite the index actually opening at 784.
Now, while I’ve mentioned above that this isn’t as big a deal for the SPX and DJX, it still can happen, and it can happen regularly.
So what can you do to protect yourself? Well, let’s be clear about one thing – you probably shouldn’t be holding these positions in the first place. By trading close-to-the-money options you are exposing yourself to huge gamma risk (take a look at our options delta primer for a quick review). For most people, taking this risk is simply not worth the few pennies you’ll make. I like a stress free existence, and this means avoiding near-expiration options.
Trading options without knowing your greeks is like flying a plane without looking at your flight instruments. Knowledge of your greeks is ABSOLUTELY NECESSARY to trade options. They are like the “vital signs” of your portfolio, that can tell you if your portfolio is healthy, or about to get sick. The more complex your option strategy, the more important it is to be able to read these signs – they’ll help you to extract more money from the market, or avoid losing money in the future.
There are a few greeks you need to know (mainly delta, gamma, vega, and theta), but we’ll start with delta.
Delta is the simplest greek to understand.
Delta is the expected change in price of an option when the underlying asset moves by $1
This means that if you have an option with a delta of 0.5, then for every $1 increase in the stock, the option should increase by $0.5. On the other hand, if your option had a delta of -0.5, then for every $1 increase in the stock, your option would LOSE $0.5.
Call options almost always have POSITIVE DELTA between zero and plus one. They INCREASE in value as the underlying asset goes up.
Put options almost always have NEGATIVE DELTA, between zero and minus one. They DECREASE in value when the underlying asset goes up.
As expiration nears, the delta of an in-the-moneycall will move towards 1, whereas the delta of an out-of-the-money call will move towards zero. This is because, as expiration nears, the in-the-money call is likely to be exercised and turned into stock, whereas the out-of-the-money call is unlikely to be exercised so is virtually worthless and won’t react to the stock’s price movement at all.
For puts options it is very similar. The delta of an in-the-money put will move towards -1, whereas the delta of an out-of-the-money put will move towards zero as it becomes more obvious that it will be worthless at expiration. This means we can think about delta in another way:
We can think about option delta as the probability that the option will end up in the money at expiration. For example, a delta of 0.5 means that there is about a 50% chance that call option will end up in-the-money at expiration. A delta of -0.2 would mean there is about a 20% chance of that put ending up in-the-money at expiration
Now while this is a useful way to think about delta, you should be aware that it is not a proper textbook definition – it is really a side-effect of the way delta is calculated.
The following table is real data taken from a broker that shows the deltas of individual calls and puts with various strike prices, when the stock is at 1160 (the blue highlighted row).
In this example the underlying asset is the Russell 2000 index currently trading at 1160. The bright yellow line highlights an in-the-money calloption and an out-of-the-money putoption. The bright blue line highlights at-the-money call and put options. The bright green line highlights an out-of-the money calloption and an in-the-money put option. Notice how the deltas of the calls decreases as the strike price increases, and how the deltas of the puts get more negative as strike price increases. The table shows very well how delta is affected by the how close the strike price is to the price of the underlying asset. Here is a graph of that data above that shows the same thing:
How stock volatility affects option delta
If we think about delta as the chance that the option will end up in-the-money at expiration, then clearly the volatility of the stock will affect an option’s delta. If the stock moves up or down by 50% or more every day, then there is plenty of chance for almost any option to end up in-the-money. Take a look at Tesla (TSLA). This is a stock that is very volatile, so even when the stock is at $200, there is still a moderate chance that within the next month it could fall to $100. This means the delta of a $100 strike put option might be around 0.1 or 0.2. Compare this to McDonalds (MCD), which is a very stable company whose stock hardly fell at all during the 2008 crisis. If the stock is trading at $100, there is only a very small chance that it will fall to, say, $50 within the next month. This means the delta of a $50 strike put option would be very close to zero.
If the volatility of a stock changes, it can change the deltas of the options. and cause you to make or lose money pretty quickly. When prices of options change due to changes in stock volatility, we call this VEGA RISK.
How time to expiration affects Delta
If we think about delta as a measure of the probability that the option will expire in-the-money, then it is common sense that if an option is way out-of-the-money and has very little time left until expiration, then it will have a delta close to zero, as there is very little time left for the stock to move a lot. If it is already way in-the-money and has very little time left until expiration, then it will have a delta close to 1 (if it is a call option) or -1 (if it is a put option). Essentially, at expiration, a call’s delta MUST be either zero (out of the money) or +/- 1 (in the money). Therefore, as time moves onward, deltas of options tend to gravitate towards these values. Take a look at this chart. With a lot of time left until expiration (171 days) the line is fairly flat. As time to expiration decreases, the line gets more curvy – i.e. the delta gravitates towards 1 or -1.
This results in something interesting around option expiration time – the deltas of at-the-money options tend to swing around wildly. Here’s an example:
Say you have a call option with a strike price of $25 and there is only one day left until expiration. The stock is also currently trading at $25, so the delta of the option is around 0.5. If the stock increases to $26, the probability of the call ending up in-the-money just increased a LOT simply because there is not much time left for the stock to go back down before the option expires. If you owned this call you would have suddenly made money on it. If you had sold it, you would suddenly have lost most of your money.
This is why trading options around expiration time can be pretty dangerous – small changes in the stock can cause very large changes in delta, and therefore very large changes in your profits.
This neatly introduces us to a second greek you need to know – GAMMA. Gamma is the rate of change of delta, i.e. how quickly delta changes. We don’t generally want delta to change too rapidly because it means we can lose (or make) money very quickly. As we approach expiration we say that our GAMMA RISK increases – our rate of change of delta increases which means that even though we’ve made money up until that point, we can lose it all in a matter of hours.
A friend of mine once called the internet “an ocean of crap with a thin layer of cream on top”.
The internet has democratized stock research and put the tools of professional investors into the hands of retail guys like you and me. In the past, people would pay exorbitant amounts to be able to get access to information that can now be reached at the click of a button. However, there is also a lot of garbage out there.
Here are the best stock screeners you should be using, measured by ease of use and breadth of screening criteria.
This web-based screener has pretty much everything you could want, including decile rankings, industry comparisons, and more financial ratios than FinViz. The software also has a bunch of pre-loaded screens, portfolios, and stock “grading systems” that can provide a starting point for your own research. The site has a more sophisticated feel to it than many of its competitors, and it surpasses them in ease of use, making it my go-to screener. The software also includes a pretty useful charting feature with graphical representations of events such as dividends or earnings, or max drawdowns over a specified period. Altogether, there are enough features in this screener to warrant keeping it open on one of your screens for most of the day.
This screener has about the same selection of fundamental and technical screening tools as StockRover.com, all packaged in an easy to use web-based form. This screener comes in a close second for the wide variety of things it can do, which should satisfy the majority of investors. One of my favorite features is the charts, where trend lines and support/resistance lines are automatically drawn on. However, a few minor gripes prevent this from being the standout winner.
Firstly, if you’d like to pre-load your own portfolio or watch-list, you are limited to 50 stocks. This is no big deal for most investors, but if you have your own larger list of potential stocks that you’d like to screen, you have to set up a number of separate portfolios.
Secondly, you can only filter by absolute values, rather than values relative to peers. If you want to work out which stocks are in the cheapest decile or quartile for their industry on a P/B basis, you have to work it out yourself.
Lastly, and less of an issue for many people, is that they don’t have EV/EBITDA. I understand they can’t accommodate every ratio for every investor, but given the growing consensus of this ratio as one of the best valuation metrics I would have expected to see it in this screener.
Google finance offers one of the simplest stock screeners out there, but when combined with Google Drive the power of the site is properly realized. Prior to using StockRover I made my own stock screener by pulling data from Google Finance and Yahoo finance into a spreadsheet on Google Drive. The data you can pull from Google Finance is fairly rudimentary, but once you have the data in your spreadsheet you can search and manipulate it the same way you would any spreadsheet data. Here is a quick idea of what you can do using Google Finance, and here is an even better version using Yahoo finance.
If you have enough time and desire to build your own screener, I would definitely recommend it, but just be prepared to do a lot of work on it. Currently I use Google Drive for monitoring my trades and maintaining records as the data is so easy to manipulate and filter, but I use standalone sites for normal stock-screening.