Can retail option traders find an edge?

By , CEO of Volcube.

This question gets asked a lot. And quite rightly. There is no point in trading anything unless you believe you have good reason to do so. Retail option traders can chase their tails with arguments about whether option markets are simply zero-sum environments since every option that is bought and sold will eventually vanish at expiration. They wonder if it is possible for them to have any advantage over the professionals (often viewed as the ‘opponents’) who seem to have superior knowledge, expertise and technology. So in this article, we are going to consider whether retail option traders can find an edge. Firstly, that means trying to define ‘an edge’. Then we look at whether the option markets are necessarily zero-sum and perhaps therefore edge-less. Next, we look at what a successful option strategy might look like and, perhaps more importantly, what it will not look like.

What is a trading edge?

A trading edge can be defined in a couple of different ways. Some traders like the mathematical idea of a positive expected return. An easy way to think of this is the average return expected from winning and losing trades combined. So if we think a trade has a p% chance of succeeding with a return in this case of r1, then it must have a 1-p% chance of losing with say a return of -r2. The average,expected return is therefore :

E = p(r1)+(1-p)(r2)

For example, suppose we estimate a trade will make a 20% return if it succeeds and that the chance of success is 50%. We also estimate the loss from the trade turning sour is 15%. By definition, this possibility must also have a likelihood of occurring of 50%, assuming that the outcome is binary (win/lose).  Plug the numbers in and we get an expected return for this trade of 0.5*0.2 + 0.5*-0.15 = 0.025 = 2.5%. So this strategy has an expected return of 2.5%, which of course is positive. Traders may argue that this is an essential prerequisite for any trading strategy; the return is expected to be positive.

This approach makes intuitive sense. There seems little point in entering into a strategy which is expected to lose money. There are several weaknesses however which mean that the ‘positive expected return’ condition should not simply be equated with ‘an edge’. Firstly, there is an issue around estimating the probabilities and the returns. Usually the latter are easier to predict than the former. Nevertheless, this can make it tricky to ascertain whether or not a strategy has a statistical edge or not. Secondly, the formula says very little about the variance of the returns. Suppose a strategy pays out $1 million with a 0.000002% probability of success. It loses $1 with 99.999998% probability. This ‘strategy’ has a positive expected return (of $1). However the weakness of this assessment is obvious. Unless the trader has the money to (potentially) spin the wheel 1 million times, he cannot expect to achieve the return. This also points to the third weakness. The formula relates to an average and averages can take a long time to be realised. The lower the chance of success, the longer the potential losing streak could be before the expected return is realised.

So whilst it makes sense to say that a strategy has an edge because it has a positive expected return, we might argue that more than this is required to give the strategy a meaningful edge. A meaningful edge is one that can be acted upon and can bear fruit in a realistic time frame. It does not require the wheel to be spun a million times. So let’s take this as a guide. A successful option trading strategy should indeed have a positive expected return. It’s chances of success and failure and its possible returns should, ideally be computable (or at least reasonably guessable). This often leads to an obvious next question: is it possible to find such an edge or are the option markets simply zero sum?

To be continued…

Are the option markets zero-sum?

Coming soon

Successful option trading strategies

Coming soon


Have a question for the author? Happy to help! Email [email protected].

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