Right Direction, Wrong Trade: The Earnings Trap

Even when traders correctly predict a stock’s direction after earnings, option trades can still lose money due to volatility shifts and pricing expectations. In this episode, Jeff Praissman sits down with Market Chameleons’ Dmitry Pargamanik and Will McBride to explore how implied volatility, magnitude of moves, and historical data shape earnings strategies.

Summary – IBKR Podcasts Ep. 362

The following is a summary of a live audio recording and may contain errors in spelling or grammar. Although IBKR has edited for clarity no material changes have been made.

Jeff Praissman

Hi everyone. This is Jeff Praissman, Interactive Brokers Podcast. It’s my pleasure to welcome to the IBKR Podcast Studio Market Chameleon’s Will McBride and Dmitry Pargamanik. Hey guys, how are you? 

Dmitry Pargamanik

Hey Jeff. Thanks for having us. 

Will McBride

Hey Jeff. Always a pleasure. Thanks so much for having us again. 

Jeff Praissman

My pleasure. And you guys just finished up a great webinar on analyzing historical data earnings datas using option strategies. And as we always do, we have you guys pop into the podcast studio right afterwards to sort of, I don’t wanna say a deeper dive, but I guess a different dive. And so I kind of wanted to jump right in. So, Dmitry and Will, why are earnings events so different from regular trading? What makes them such a big deal that we need to analyze them different than other setups? 

Dmitry Pargamanik

Yeah. So one of the things about earnings is that it’s a known scheduled catalyst event. What I mean by that is we have earnings typically four times a year, four quarters. The company usually releases the date of the earnings release ahead of time, so we’ll know the date and when they’re gonna release it. So because of that, we could strategize, organize, plan, and use risk management around those unique catalyst dates. And we know that there are certain behaviors around earnings that we observe differently than normal non-earnings states. So those have become very important catalyst dates for traders. 

Jeff Praissman

And something with earnings there, really anything really, right? But with these strategies, right, a trader could be completely right on direction, but they can still lose money with the trade. Could you give us an example of how that could happen? Because it sounds, to a lot of people, pretty counterintuitive. 

Dmitry Pargamanik

And when we look at earnings, there are a couple things that options price into earnings. Besides direction, it’s the magnitude of the move. And you can be right in the direction, but if the magnitude of the move was overpriced, for example in a call, that call could still lose value. Or in a put, if the magnitude of the move was overpriced and there is a down move, for example after post earnings. Because that magnitude of the move was priced in and the options were overpriced, the options will reprice going forward, taking out that uncertainty now that the earnings are out. We’re released and we’re going back to normal volatility regime. 

Jeff Praissman

So really just kind of maybe phrase it another way. Even if the call or put becomes basically in the money after the move, if the premium was priced so much more, then they would still lose money on what they paid for that premium. Well, they paid the premium on the option even if the option does finish with some intrinsic value. 

Dmitry Pargamanik

Yeah, right. Or maybe not even depending on the call you are looking at. Or if you’re an out-of-the-money call, it may have not reached that point either. But it can still lose value because that risk premium is also removed post earnings. 

Jeff Praissman

Right. And so there’s really probably three main forces that are driving these earnings outcomes, right? It’s the actual price move, the implied volatility expansion, then the implied volatility crush afterwards. 

Dmitry Pargamanik

Right. 

Jeff Praissman

So is there one of these that traders kind of most commonly underestimate, do you think? 

Dmitry Pargamanik

I think that a common factor that may not be fully analyzed is that implied volatility crush post earnings, or maybe even the implied volatility move prior to earnings depending on the strategy. Where you can end up with what we’re talking about, where the stock, for example, goes up after earnings, but you have this out-of-the-money call which implied volatility crush will still make it come down even though the direction, the delta, is the right way. The stock is moving in the right direction, but once you take that implied volatility out, it could lose value. You know that the stock moved in the same direction and that could happen in time spreads where people look at time spreads and maybe are looking at a time spread where they’re doing a spread based on implied volatility, like selling a higher implied volatility and buying a lower implied volatility. But you have to understand that there’s a different vega to them. 

And once it corrects and kind of gets back to normal, you’ll see the implied volatility move down perhaps even on both, and even one more than the other. But the vega of one option is much higher than the vega of the other. So they might be surprised to see, well why did this time spread lose value when the implied volatility moved down, both moved down? 

Jeff Praissman

And another item that’s really critical too is entry. Entry timing, right? So someone entering two weeks before earnings versus the day before, it’s a completely different risk profile. Could you break down how vega exposure and then like the of decay change across these different entry windows? And which timing do you think produces maybe the most favorable historical results? 

Dmitry Pargamanik

Yeah. So that brings up a good question because when we look at strategies around earnings, you could use strategies by using different combinations of options. You could do a straddle, you could do a vertical spread, a butterfly. But picking entry points and exit points is a strategy in itself where you can look at a strategy where you start maybe two weeks prior to earnings and try to strategize around the run up to the earnings and exit it right before earnings, which is a strategy based on anticipated move of the stock heading into earnings and the high volatility heading into earnings. 

And that itself, or even post earnings where you wait for the earnings to be released, then the next day the earnings are out, the options open up. You could enter in the strategy at that point and end of day the same day. So that itself is a strategy based on some kind of anticipation or your outlook that might be different than where the options are priced now. 

Jeff Praissman

And you could you walk me through, like let’s just say I’m sitting down to research a trade tonight, right And hypothetically Apple has earnings coming up. What’s my actual workflow here? Like where do I even start? What am I looking for? 

Dmitry Pargamanik

So when we’re looking from a historical standpoint, your starting point is, well, you picked the stock, which was good. And you would want to come up with a timeframe. But if you’re looking at the option markets now, what you’re trying to compare is how they are priced now. You need reference points to kind of give you a historical perspective. If I compare them now to history, are they on the high end or low end of the historical range? How did the market dynamics and the option impact historically look with dispersion of returns and your max losses versus your max gains, your average returns, the standard deviations of those returns? Those will start helping you shape a potential strategy based on your unique outlook. But the common factors that you’re looking at are that you’re trying to analyze the current markets, right? That’s where you’re looking at these markets and have a certain outlook. Do the options right now, where they’re priced, where the premiums are, where the implied volatilities are, offer a favorable or potential risk-return ratio based on my outlook and based on benchmarks that I could look at historically to help me kind of put that into context? 

Jeff Praissman

So let’s talk about outliers then. How should traders think about a strategy that shows a great average return but really had one or two extreme quarters driving those results versus consistency? What questions should they be asking besides whether this tail risk is acceptable? 

Dmitry Pargamanik

Yeah. And that’s a good question. When we’re looking at analysis and we’re looking at historical data, it’s important to know that markets do change their dynamic. We could be in a different type of environment today than history suggested. So if you are in that different environment, you have to now evaluate that environment and decide if it needs more of a premium or less of a premium than history. But taking that away, when we analyze numbers, sometimes you could get a very good average return, right? But you’re averaging these numbers. If you look at the data and say, well wait a minute, out of the last 12 quarters there’s this huge outlier where the stock was up 40%. It was this special situation where the company announced that they won some kind of lawsuit or they did something so unexpected that you don’t anticipate that going forward. 

So how do you remove that data point from your analysis? If you do, you have 12 observations and you remove that. What does the new analysis tell you? You could go from, hey, buying a call returned an average 25%. But if I remove this one data point where a call was up 5000%, all of a sudden I’m negative. So then it makes you think about your risk and reward differently when you’re trying to apply that strategy. But you can’t discount it either because you’re like, okay, but I do have this tail risk. If I include the tail risk, do I cap it? Maybe my tail risk is an 85,000% return. But if I wanted to run a model and try to create a risk-reward analysis, I can maybe cap it at a similar tail risk, say 500%. What if I rerun the numbers? How does that look? So it’s important to look deeper into the data. You get this high number to look at for your valuation, but you look deeper into the data to see, well, are there any outliers? What happened? And how do I account for those going forward? 

Jeff Praissman

Yeah. And it kind of leads me to my next question. We’re talking about historical data, right? This is going to be a little tricky because even without these outliers, even if everything’s pretty steady, just because it worked in the past doesn’t actually mean it’ll work tomorrow. So along those same lines, how does a trader use that old data without really falling into that trap of thinking this is just going to be a layup and it’s going to repeat itself no matter what? 

Dmitry Pargamanik

Yeah. Well, the data itself helps kind of put things into context and it helps us assess whether the markets right now are in this very euphoric stage or whether they are within the historical range. If I use them, where are we on that scale? Are we on the very low end of the historical range? Are we on the very high end of the historical range? Or are we so far away that we might have to see a stock move that we haven’t seen historically? Where the risk here is much higher than history suggests. Maybe it’s justified, but we would have to justify that. We would have to say, well, what is different now than before that can justify such a move? Like you said, sometimes when you’re seeing the markets moving up with lots of players, you don’t want to miss out. You think you might be missing out. But you have to put it into perspective and say, well, how far did the markets get? Were they driven to the point where I have some serious risk here if it doesn’t go my way? 

It puts things more into context where now it helps you understand your risk. You might still want to take that risk, but at least you understand it from a historical perspective. You might say, well hey, these premiums that everybody is paying or pushing up are three times more than the highest that we’ve ever seen. We haven’t seen these types of situations before. I have to really understand that going forward. Is it justified? You’d have to figure that part out as well. 

Jeff Praissman

Yeah. Because trading in general can get pretty emotional, especially earnings seasons, right? Like you mentioned, fear of missing out or FOMO and chasing moves. It seems like historical data can really at least help the trader stay calm and potentially not do something irrational. Can’t guarantee that though, right? 

Dmitry Pargamanik

Yeah. 

Jeff Praissman

So Dmitry, Will, are there any final thoughts you’d like to leave the listeners with? 

Dmitry Pargamanik

Yeah. I think that just on that note, helping evaluate that risk assessment may allow you to strategize a different way where you might want to hedge off some of the risk. You could always hedge a call by doing a call spread or a time spread so you can hedge off risk when you feel like you have this extra risk but want to do it in a different way. But my final thoughts are that earnings themselves present a different type of opportunity or trading regime. Those who do trade it have a different type of data set to analyze where you have this catalyst event. You now have the magnitude of the move, the implied volatility, and the implied volatility after the move. Those are important dynamics and factors that play into strategies, and you have to take all of them into consideration. 

Jeff Praissman

Will and Dmitry, this has been great. For our listeners, you can catch more from Will and Dmitri at Market Chameleon’s YouTube show every morning. What is it, nine o’clock, I believe? 

Will McBride

Yeah. 

Jeff Praissman

Yep. Or you can go on their website, marketchameleon.com. Also our webinars—we are lucky enough to host a webinar with them every second Tuesday of every month. Guys, until next month, this has been great. Thanks again. 

Dmitry Pargamanik

Thanks, Jeff.