That’s the title of a song made famous by Harry Belafonte on his Calypso album released in 1956. I became well acquainted with it after hearing it at countless Grateful Dead shows in the 1980’s and 1990’s. The song popped into my head last week when I read an article about a study done on the different success rates of male and female traders. The study was conducted by Professor Peter Swan of the University of South Wales, working with researchers Joakim Westerholm and Wei Lu.

Unlike a trader study that I mentioned in a previous blog entry that drew conclusions from a sample size of 18 traders, this was a study of almost 1 million traders in Finland over a 17 year period. Those numbers definitely add a sense of validity to the results. And the results were that women make the better traders.

I’m not sure if the results were released to nearly coincide with International Women’s Day, but it certainly was a good coincidence if not. With a 15 year old daughter in the house, the confluence of these two things made me pay attention. Being a male former trader, I couldn’t help but be drawn in. This is not the first study to draw this conclusion, but it is probably the broadest in the sense of sample size and time. So let’s examine….

According to the results, the women outperformed the men across the board. One conclusion was that the women traded more as contrarians then men. This style makes sense as very often the most profitable trades are also the hardest to make. Selling a security, in this case stocks, as it’s running to its highs, or buying as momentum drives it lower is very counterintuitive to me. I trade momentum. Buy a new high, short a new low. The women didn’t peg those absolute highs and lows, so they did experience some short-term losses. But they held those positions and beat the men across the board.

Why? A few reasons mentioned as well as some of my own conclusions. They traded less. The females seemed to zero in on the best stocks to buy or sell. They didn’t just trade to trade. The author mentions that they didn’t diversify as much. They had their targets and stuck to them. The women also profited more when using their own funds in a personal portfolio. I would conclude that this is because they were better able to choose exactly what they wanted to trade whereas an institutional trader is often handed a “universe” to watch and trade. This was an additional indicator of the better focus of the women over all.

Having traded in futures pits as well as within a “prop-trading” firm, I was usually surrounded by men. There is no doubt that historically males have dominated our trading venues as well as the industry as a whole. And it makes you think how much better the industry would have performed if this study’s results could somehow have been available decades ago. No doubt there would be fewer glass ceilings remaining that still need to be shattered.

The futures pits were easily over 95% male. We justified it as it was too physical for those delicate women to endure. It was also an environment that as a dad, I would not want my own daughter to have to exist in. Upstairs, the trading desks were much the same. Not so physical, but almost as chauvinistic and crude. The industry at that time carried the unequal treatment of women as far as it could, and always found some sort of justification. This study just makes us look dumb, for if our true intent was to just make money, we should have invited and embraced women sooner to accomplish that objective.

It seems from the study that women had patience the men did not possess. They held fewer positions and held them longer. The machismo of the trading pits was not there. The need to prove something to others in the pit or on the desk or anywhere else probably was not a part of decision making. The ability to stick to the singular goal of a positive P&L over the long term was the driver. It’s interesting as men often try to brag about buying the low and selling the high, yet the women came closer to doing just that over time than the men in the study. We (males) talk a big game, the women just win.

I also realized that the one place I have been able to accomplish more of what the women did, that is zeroing in on the correct position to take and letting it ride to long term profit, has been in my researching, testing and implementing of automated trading models and strategies. And this makes sense. The women did better analysis. They had patience and the good sense to only trade the best opportunities. Emotion was not the driver of their decisions at any point, just like my computer models.

This is what optimizing and testing trading ideas is all about as well. Don’t be driven by emotions. Analyze actual test results and listen to them in order to improve the strategy over time. Don’t get out of the trade just because there’s a profit. Get out because the research has led to a successful exit strategy. Trade the signals. Don’t just trade. And make sure that the research that went into all these real time decisions is valid. Patience in the amount of time it takes to do proper research pays off with more successful results. It seems to me that these practices are what the women were doing in a subconscious way without the necessity for all the computer programming I’ve had to depend on.

I love this study. I learned a great deal from a very short article. It opened my eyes to why I still have the belief in well researched automated trading strategies. The research is done before the positions are ever put on. No ‘skin in the game’ to lead to emotional, and wrong, decisions. Just the ability to analyze the market in a rational way and only then act on the decisions your research has led to.

We can all learn from this study. And it encourages me that we have more proof of women having the ability to have at least as much success as men in the financial community. The females I taught and mentored while at Bloomberg were additional proof of this every day. So keep shattering those glass ceilings. Every shard on the floor brings us all closer to having more success at whatever it is we are striving to accomplish.

This week S3, a part of Amazon Web Services (AWS), had an outage that affected many large internet companies that we all use in our regular routine. Netflix, Spotify, and Pinterest to name a few, had problems related to their use of the Amazon cloud services. Things we’ve started taking for granted over just the last few years were suddenly removed from us, or at least moved slower. Amazon soon announced that this wasn’t a computer glitch, but actually human error. It wasn’t that long ago that everything on the internet moved slowly. Dial up modems have given way to high speed data movement that allows us to not only post pictures quickly and easily, but to even watch movies without a glitch in front of our computers, or with an iPad in our hands, and this has become the norm.

The use of computers in our daily routine, or for use in very critical parts of our lives, is something we not only take for granted but depend on with increasing importance and breadth. We all know that our cars will soon be self-driven, and the new wave of investment management is known as robo-investing. Not a niche, but something offered by major investment houses. And all those robo-investing actions are the result of human programming.

This trend interests me greatly, as I’ve been both an active trader as well as having back tested thousands of automated trading ideas to build robust automated strategies. I recently read a piece on LinkedIn about the lack of gray hair on trading desks across the securities industry. The trend is toward hiring young ‘quants’ and programmers to create computer models that make money by analyzing huge amounts of data and deciding what to buy or sell and when. Don’t get me wrong, I started using computers myself to do this over 20 years ago. Not as a quant, but as someone who believed a human only has the capacity to monitor a very small amount of markets or securities on their own.

So is there a problem with this? Well, let’s go back to those cars for a moment. In the 2004 movie “I, Robot” a police detective is saved in a car crash at the expense of a 12 year old girl. The robot behind this action decided whose life was more valuable. And our self-driving cars will at some point need to make just those types of decisions. But maybe that 12 year old girl that is lost in our real world was going to cure cancer, figure out space travel through worm holes. At the time of the accident she’s just a kid getting ready for the awkwardness of adolescence. Who knows? The next Bill Gates. The next Steve Jobs.

And this has what to do with your robo-investments? One more movie reference and I’ll tell you. I watched the movie “Sully” yesterday. In the movie, Tom Hanks has a line that prompted me to write this blog entry: “Everything is unprecedented until it happens for the first time.” This is not only true in our lives, it’s true in the investment world. We call them Black Swan events. In quant investments, people are using statistical analysis to find the best odds of mean reversion. The place where things are so out of whack that they have to return to normal.

This has led to major profits for those that can program this math in ways that analyzes all that data. But, it’s also led to spectacular losses. In 1998, Long Term Capital Management (LTCM) used analysis techniques developed by Myron Scholes and Robert Merton to put on large positions across security classes around the globe. These were Nobel winning smart people. And the hedge fund had outstanding returns. Until it all blew up and we the taxpayers bailed them out. Heard that before? What happened? That unprecedented event.

I’ve mentioned an early student of mine, a physics major, who was amazed at the frequency of six sigma moves in commodities. Six standard deviations away from normal market action. The standard bell curve doesn’t even go out that far. They’re generally drawn to 3 standard deviations because in statistics, that includes 99.7% of the probable outcomes.

Those points all the way to the right and left are the tails. They just don’t happen. Until they do. Like Sully noted, they happen for the first time. And this goes on with more regularity in the markets than we realize. It can be something financial like the implosion of LTCM due to a foreign currency crises. Or a political event, like Brexit or the unpredicted election of Donald Trump as President.

The point is, the programmers often don’t take this into account because it hasn’t happened…yet. Or maybe just a human mistake slips in, like this week with S3. All the computer models, even those involved in robo-investing for the average investor designed to level the playing field and supposedly take out the human factor of your chosen advisor are programmed by people.  And all of those people have human thoughts and biases that go into those models. It can’t be avoided. There are forks in the decision process that must be taken. Not like Yogi Berra, “When you come to a fork in the road, take it,” but one direction or the other. And as time goes on, the models may learn and get ‘smarter,’ but they still start somewhere, programmed by someone.

And the lack of gray hairs is what gives me pause. How many Black Swans have the programmers experienced? We can’t plan for everything, and that’s when the unplanned event usually occurs. Experience has always been of great value in the investment world. The more unexpected events an advisor or trader has lived through, the more they will take this into account in their decisions. Remember, risk/reward, in that order. And if all you’ve experienced is a bull market over the last 8 years, do you really take into account what happened at LTCM? Do you even think about the internet bubble that burst in 2000? The mortgage crisis is the most recent large Black Swan, and many of these investment and trading strategy programmers were kids. Are they thinking about auto debt risk? Student loans? Whatever might be the next event trigger.

I’m not trying to imply that automation of trading decisions doesn’t work. I had success doing it both for myself and clients. It broadens the ways you can allocate money in just recently unforeseen ways. What I am saying is be careful. Because tail events happen. There are Black Swans swimming all the time. We just need to be sure to look out for them.