Most retail traders lose money?
“95% lose money”?
You may have heard the statement that says “95% of retail traders lose money”. For someone who is just about to start a journey on forex trading as a day trader, the number doesn’t sound very encouraging. But how accurate is this statement? I’m not saying it’s a complete lie, but by working as a data analyst I acquired a habit of being sceptical about a statement with quoted numbers, especially when the source of the data is unclear. So with the above statement, there are tons of questions looming in my head, such as “what’s the definition of retail traders in the statement?”, “is this worldwide?”, “does this include all possible assets and instruments?”, “what’s the time frame?”, etc.
I have tried hard to find the source of this statement, but it was almost impossible to track down any research that proves this statement. Even though it’s not the source for the specific statement quoting 95%, luckily there are some numbers readily available thanks to ESMA (European Securities and Markets Authority)’s product intervention at least within Europe.
Risk Warning (ESMA product intervention)
If you are residing in Europe, you might have seen a Forex broker advertisement with a statement that reads “~% of retail investor accounts lose money when trading CFDs with this provider”. This percentage varies from broker to broker, but there is a certain range. To me, this seems like a more accurate number on how much % of retail traders lose money than the statement almost became the urban legend “95% lose money”.
With all the above information, even if the numbers are not as bad as 95%, but it is quite clear that odds are not in my favour.
Forex has 50:50 chance?
I understand that forex trading is very complicated and also needs a lot of study and experience to be successful. That’s also why I am studying and writing as I learn. But the underlying logic of forex trading is very simple in its nature.
There are only two kinds of orders you can make: buy or sell. The price movement of a pair also only has two outcomes: price up, price down (there is also a possibility that price stays the same, but given a time frame of day trading even scalping, it is highly unlikely that the price stays exactly the same down to the last digit of decimal points).
Then now this sounds quite similar to what they call Bernoulli trial in statistics. In simpler terms, coin flips. You predict whether it will be head or tail, then the outcome will also be either head or tail. It’s not difficult to see that these events will ultimately have 50:50 chances in the long run given many trials.
I know it may be an oversimplified way of looking at forex trading, but the core essence of it is not very different from a coin flip. Then why the majority of retail traders lose money? Shouldn’t the average percentage of losing retail accounts converge somewhere around 50%?
I found my answer in a theory called Prospect Theory, which is an important concept in behavioural economics.
Expected Utility Theory
In order to talk about Prospect theory, it is helpful to first understand its predecessor Expected utility theory. Actually, it is more precise to say that Prospect theory is a theory built with Expected utility theory as a base logic but added human cognitive bias to better explain why humans sometimes make seemingly irrational decisions.
The Expected utility theory is initiated by a Swiss mathematician, Daniel Bernoulli in 1738. If you have taken a statics course in school, the chances are you might have heard of his last name at least once. The Bernoulli trial I briefly mentioned in the above section is named after Jacob Bernoulli (Daniel Bernoulli’s uncle). Not only these two, but the Bernoulli family has turned out eight prominent mathematicians in history.
In a nutshell, Expected utility theory is about making rational decision under conditions of uncertainty. In order to make rational decisions, we need to measure possible outcomes of actions correctly. How do we do that? The theory says that the true value of an outcome should be weighted according to the probability that the act will lead to that outcome.
For example, let’s say we have two choices.
Choice 1. 50% chance of winning $1000
Choice 2. 100% chance of winning $500
According to Expected utility theory, these two choices have same expected utility (1000 x 0.5 = 500 x 1), thus should be treated as equal when making decisions. But do we consider these two choices equal?
I bet most of us will choose Choice 2. Winning $500 for sure sounds like a better deal than gambling for the 50% chance of winning $1000. Then, is Bernoulli’s theory fundamentally flawed, and it is completely useless? No, not really. After all, it explains pretty well how we “should” make decisions, but it doesn’t explain well how we “do” make decisions. That’s why the theory is considered as a normative theory.
As we have seen from the above example of decision making, when offered two choices of the same expected utility, Expected utility theory alone was not enough to explain real choices people “do” make in this situation.
The underlying assumption of Prospect theory is that people do not always make rational decisions. Why? Kahnemand and Tversky say because we value gains and losses differently.
Let’s go back to the two choices of possible gains.
Choice 1. 50% chance of winning $1000
Choice 2. 100% chance of winning $500
The tendency to choose the latter over the former is called risk-aversion. Probably most people will choose the second option.
Now, let’s put the same numbers in a different context.
Choice 1. 50% chance of losing $1000
Choice 2. 100% chance of losing $500
What will you choose now? Will you still choose the second option? Most people in this situation will choose the first option (50% chance of losing $1000) over the second. This is the opposite attitude compared to the choice mostly taken in the possible gain situation. Now, most people become risk-seeking in this possible loss scenario.
We dislike losing more than we like winning (risk-averse), except when faced with bad outcomes (risk-seeking).
The above Prospect theory fits beautifully in trading situations. It has also provided a crucial theoretical base to the famous Disposition effect. What will happen when we (who is risk-averse in gains, and risk-seeking in losses) enter into trading or investment with this cognitive bias?
Hersh Shefrin and Meir Statman who coined the term disposition effect in their paper “The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence” say exactly what they say in their title of the paper.
This is the trap that most of us fall into when trading. It is not difficult to see what will happen when tradings are done with disposition effect. As far as I can see, it is never possible to have 100% winning trades all the time. Sometimes you win, sometimes you lose. You just have to accept it as a fact. Of course the reason why I’m studying is to increase the winning ratio higher than the chance of random coin flips. But even very successful traders will have losing trades.
If we fall into the trap of disposition effect, we will win small when we win, and lose big when we lose. This will eventually lead to losing of the whole amount you had in your account or a margin call in worse cases.
In order to avoid this, we constantly need to make a conscious effort by looking for an entry position where the prospect of gain is higher than that of loss.
Let’s say when you win you win $20, and when you lose you lose $10. If you trade 100 times with a winning rate of only 40%, you will still end up winning $200. But on the other hand, if you win $10 when you win, and lose $20 when you lose, even with a 60% winning rate, you will still end up losing $200.
In this post, I have studied some useful theories when building a trading strategy. It might not be directly related to trading, but I strongly believe the right mindset and an effort to fight our own flaws will play a crucial role in becoming a successful trader.
Thank you for reading. In the next post, I will take a look at candlestick chart, the very basic of forex trading.