The whole philosophy of using machine learning and artificial intelligence in investing is that machines can make trades quickly and without being burdened by emotions.
It’s the last bit that I’ll talk about today. Emotions — like fear and greed — skew the decision-making processes of the investor, causing him or her to but too high or sell to low based purely on feelings. A good example: you would probably pay more for food if you’re hungry than if you just had a big dinner.
Therefore — and this conclusion is inescapable — machines that do not have emotions can trade better.
Except, this logic is totally wrong. Well, not wrong. But it forgets the most important variable. The market and the economy runs on emotions. Even if every investor was an emotion-less cyborg, the rest of the economy, which underlies all stocks and commodities, runs on desires and emotions.
A successful investment system can not possibly rule out emotions. In fact, the most successful investment system would totally understand emotions, but not ruled by them. This system would understand how desire for something quickly turns to the desire to not lose something — fear. Likewise, it would be able to sense when the momentum of fear turns into a buying opportunity. It could sense that the greed in the real estate market was becoming too frothy, or that the fear of a recession was starting to bottom out.
In short, the best investment system understands emotions. It just isn’t ruled by them.
Our blog talks a lot about using artificial intelligence — or, AI — as a tool for investing and trading.
Trading firms and hedge funds are using AI every single minute of every single trading day to place investments and make trades. Billions and billions of dollars — not to sound too Carl Sagany — are riding on how well AI or machine learning trading schemes work.
We’re also seeing artificial intelligence revolutionize other industries. Pick up an iPhone and ask Siri about AI. She might lead you to several stories on the web about how AI is being used in everything from telecommunications to health care.
But, the perception among many is that we are still stranded in the depths of AI winter — that’s the term for the years of little progress in artificial intelligence. Some experts are saying that this is a misperception. AI has made huge gains in the last few decades, but when compared to the expectations, this progress seems minuscule.
If the perception is that AI is hopeless — and the reality is that AI developers are making great strides in creating artificial intelligence- and machine learning-based products, then wouldn’t placing bets in AI companies add up to a possibly lucrative, albeit contrarian investment idea?
Over the past few years, we have seen forms of artificial intelligence begin to quietly creep into our everyday lives and become something on which we depend. A very simple form would be something like Microsoft Corporation’s (MSFT) spelling and grammar check in its Word software or the method that Facebook, Inc. (FB) uses to suggest people that you might know.
Each day, machines, not humans, are plowing through statistics and crunching numbers trying to devise trading algorithms to make money in stock markets, futures markets, commodities markets, and any other place that traders can turn a dime.
They’ve been successful.
But not totally successful. And not successful for long stretches.
The stories of trading strategies failing are just as common as stories of amazing machine-learning success in the market.
Why is that?
I’ll take a stab. I believe that most machine learning and artificial intelligence programs are essentially created short-sighted. To build an automated trading system, you “train” the program to understand data. This data can be technical or fundamental, or a range of other data sets. The program learns the relationships between the data and the market. When factor x goes up, the market reacts with a y, let’s say.
But, this data is not an object, per se, but is really a shadow of currents in a broader economy. So, the program ends up not be predictive at all. It is reacting to a reactive set of data.
I first connected with Nick at Becoming Capitalist through this blog. When I read his intro, I liked the way Nick thought. He sees artificial intelligence and automated-algorithmic trading as a way to tap technology for personal financial freedom. We call that the Wealth Singularity. I’ve been reading Nick’s blog and communicating via email. I’m pretty impressed and thought you might want to see an Expert Profile on what Nick is up to and how he approaches automated trading.
What attracted you to automated or algorithmic trading? Why is this so important to you?
The attraction for me to automated trading was driven by the same desires I had getting into IT and software development. When I see a problem or a set of work, I look for ways to automate it and streamline it. And then of course I have a computer do it. This really provides endless satisfaction. Those who build or create anything understand the feeling of joy when there creation is finally complete.
For the programmer, this joy can be ongoing. Unlike a machine, once you’ve coded something it will execute perfectly again and again; never again requiring your input. I suppose the objective in trading then is for your AI to become a version of you without your bad qualities. No emotion, never tires, hungers, or needs a break, and can multi-task in ways you could only dream of.
As someone who is interested in artificial intelligence, especially how AI will work in a financial environment, there’s a lot of information out there.
And that information doesn’t always agree. In fact, there’s a chasm of difference between the camps of AI advocates and critics. There’s also a difference in opinions about the potential for AI to change things–or mess things up, depending on that outlook.
That’ what makes an article in H+ Magazine so interesting. The article, based on Eric Drexler’s Engines of Creation, just looks at the effect a little AI will have on the world when mixed with nanotechnology. We’re not even talking about massive AI here.
Artificial Intelligence, or machine learning, or simply AI, drives more and more financial systems. The big boys –from hedge funds to mutual funds, and everyone in between–use AI.
The types of AI that serve as the foundation of these financial application are wide ranging is size and scope. The programming varies, too. Genetic programming, neural networks, and a host of other applications are used as underpinnings for the strategies and algorithms.
Since most independent traders don’t get a chance to invest in hedge funds, Online Investing AI, we want to create trading systems and strategies for independent traders and investors that are based on the performance of the best AI technology.
It’s a pretty audacious task. That’s why we need you.
In 1997 chess grandmaster Gary Kasparov met the Singularity. And the Singularity won.
In 1985 Kasparov easily beat a chess-playing computer, even though he resorted to a trick to out-Kasparov the machine’s Kasparov program. Eleven years later, though, the chess legend struggled with Deep Blue, a computer with even more powerful processing power. But even Kasparov couldn’t compete with Deep Blue once its development team doubled the processing power a few years later.
Kasparov was beat, but he drew new lessons from the run-in that he details in New York Review of Books. These lessons might help you become a smarter–and less anxious–trader.
According to Kasparov, the exponential gain in technology didn’t ruin the game; it actually had some surprising aftershocks.
Genetic Algorithms are a specific area of Artificial Intelligence that has the power to change the world. Why? Because they allow us to solve very difficult problems that have no known solutions. These problems include:
How do you design a jet engine to optimize efficiency?
How do you optimize the components and connections in a circuit board or chip?
How helpful is a movie recommendation from one person for another person?
These perplexing problems have no simple solutions, and often the details of the question are unclear. Genetic Algorithms are flexible enough to give us high quality solutions to these difficult problems.
Another fear real traders have is that Automated Trading will turn them into idiots. By relying on technology to complete their trades, they’ll lose their knack for picking winners. Or so the thinking goes. Maybe they’re afraid the instincts that they have honed over years in the market tranches will fade. Or, some of this knowledge will just disappear.
If you don’t use it, you’ll lose it.
I believe this fear is completely ungrounded. If the Automated Trading system or systems are use correctly, they can be study aids for the trader. And, at the very least give the trader time to study and implement his or her own position moves.