What A.I. Investing Can Do for Your Portfolio

By TradeSmith Research Team

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By now, you’ve heard the incredible news.

It’s spreading around the world like wildfire.

In December 2022, the revolutionary artificial intelligence program called “ChatGPT” made its public debut – and it became a worldwide sensation.

ChatGPT is a hyperintelligent computer program that can write novels, screenplays, Ph.D. dissertations, and research papers – all in a way that is indistinguishable from humans.

Microsoft founder Bill Gates says ChatGPT is as significant as the invention of the Internet and will “change our world.”

Google CEO Sundar Pichai has said A.I. is “one of the most profound things we’re working on as humanity. It’s more profound than fire or electricity.”

Then in March 2023, not long after ChatGPT stunned the business community, a new version, Chat GPT-4, debuted and was even more powerful than the previous version.

The uses of ChatGPT – and its implications – are beyond enormous, which explains why ChatGPT was adopted by over one million people in just five days… the fastest rate of technological adoption in history.

As a point of reference, it took Facebook 10 months to reach one million users, and it took over a year for Twitter.

Let that sink in. It took Facebook 10 months to reach 1 million users. ChatGPT did it in just five days.

The world is scrambling to grasp the implications here.

Google executives called a Code Red meeting.

Bank of America equity strategist Haim Israel advised clients that we are at an inflection point in history, “If data is the new oil, then A.I. is the new electricity,” he wrote.

And the mainstream media is rushing to cover the story.

While ChatGPT has caught the mainstream media and most stock market personalities off guard, none of this came as a surprise to TradeSmith executives or our research team.

TradeSmith is one of the world’s leading investment data analytics firms. We have a staff of 36 data scientists, software engineers, and investment analysts working on developing and maintaining our investment software and market analysis algorithms. Our team literally has hundreds of years of collective experience in the software development and data science fields.

So, it’s no surprise we developed deep artificial intelligence knowledge years ago. We’ve been using it for years to design strategies that help ourselves and our customers beat the market.

Our market-crushing, world-beating, mega-hit options analysis programs? Those employ A.I.

Put differently, we were A.I. experts before A.I. was cool.

Now that A.I. is cool in the eyes of the media, we’re seeing a lot of people writing about it as if they are experts.

People who can barely turn on a computer are claiming to be A.I. experts. Many of these people don’t know artificial intelligence from artificial flavors.

As a company that has integrated A.I. into its systems for years, we can tell you that the hype around A.I. and next-generation data analytics is justified.

As mentioned above, ChatGPT and other A.I.-powered programs can write novels, screenplays, Ph.D. dissertations, and research papers… all in a way that is indistinguishable from humans. But the list of ways A.I. has changed the way we live is longer than you might think.

  • A.I.-powered data analytics is revolutionizing pro sports. When you watch an NFL game, they have probabilities and stats all over the place. The Boston Red Sox finally won the World Series in 2004 because they used data analytics to field a winning team. The Chicago Cubs broke a long World Series drought with data analytics as well.
  • In 1997, the A.I. program Deep Blue defeated the world’s best chess player.
  • In 2016, an A.I. program defeated a world-class Go player. (Go is much more complicated than chess, so the victory was a major milestone in computer history)
  • A.I. can now beat the best cancer doctors at spotting cancer on X-rays.
  • Uber uses A.I. to dispatch drivers and link them with customers.
  • Amazon uses A.I. to recommend potential purchases to you.
  • Facebook uses A.I. to arrange and customize its news feeds.
  • Dating sites like Match.com use A.I. to help people find potential soulmates.
  • Healthcare companies are using A.I. to scan DNA, blood, and other test results to spot problems with greater accuracy than human experts.
  • Recruiting firms are using A.I. to sift through resumes and job applications and recommend the best candidates. No humans needed.

Right now, many people are saying “The A.I. revolution is here.”

We say, “You’re right – it’s been here for a long time. You just weren’t aware of it.”

How A.I. Can Help You Increase Returns While Lowering Risk

Data analytics helped Jim Simons become the most successful, highest-performing hedge fund manager of all time. It allowed the Boston Red Sox and Chicago Cubs to break long World Series droughts. It has allowed A.I. programs to defeat the world’s best chess and “Jeopardy” players.

For those of us steeped in the data science world, all that is familiar history. However, the relatively new development in data science is something called “machine learning.”

Machine learning is changing the world as you read this… and will change it even more in the years ahead.

And for our purposes as investors, it can greatly improve your investing results. In fact, it can massively increase your returns while decreasing the risks you take.

Here’s how machine learning shaped our massive research project, Project An-E.

The way traditional investment data analytics worked a decade ago, a human would think of a set of parameters he or she would like to test and then enter those parameters into a computer. There were predefined data rules to generate an output.

The computer would then perform a “test” of those parameters over past financial market data and analyze the results. If the results are great, you might implement the investment strategy in real life.

For example, you might want to “test” what kind of returns you’d have earned in the past by buying when the stock market trades at a cheap 12 times earnings.

Or, you could test what happens if you only own a stock index like the S&P 500 when it trades above its 200-day moving average.

Or, you could test what happens when you buy when the stock market trades down to 12 times earnings and is above its 200-day moving average.

Over the years, people have tested hundreds of thousands of indicators and combinations of indicators.

The key here is a human selected the strategy or “parameters” that were tested.

Machine learning flips this traditional script in a powerful way.

Instead of having a human select a set of parameters to test, machine learning asks a hyperintelligent computer program to select the parameters. The machine doesn’t require any predefined rules to generate a selected outcome.

Instead of telling the machine what to test, the human suggests a desired outcome – like “find a reliable stock-picking method that does well with 30-day holding periods” – and then the machine crunches trillions of data points to determine if it can create a useful system.

The machine analyzes single indicators. It analyzes two-indicator combinations, three-indicator combinations, and even multi-hundred-indicator combinations. The combinations a machine can test are essentially endless.

For Project An-E, we loaded more than 100 distinct variables into the machine learning program. Our goal was to create a system that has strong predictive ability over the short-term (around 30 days). These data sets included macroeconomic data such as interest rates and inflation figures. We also included fundamental data like profit margins and price-to-sales ratios. We included technical data like relative price strength and moving averages.

And again, we brought no preconceived notions or biases to the project. There wasn’t a fanatical fundamental investor on the team rooting for his strategy. There wasn’t a dedicated technical analyst rooting for her strategy.

We just gave the machine a desired outcome (find stocks poised to rise over the short term) – and let it do the rest. We didn’t teach the program anything. It taught itself.

The results – which we’ll get to in a moment – are fantastic.

But at this point in the story, we need to quickly discuss a fascinating aspect of machine learning and how it creates brand-new ways of thinking about the stock market.

When designers of A.I.-powered chess-playing programs started testing their systems years ago, they noticed something peculiar about the strategies their programs employed. The A.I. programs tended to employ seemingly bizarre strategies – strategies that human players would never come up with and, in many cases, would ridicule if they came from another human player.

For example, in chess, a player can “sacrifice” a key piece if he or she believes that sacrifice will lead to ultimate victory. Sacrificing pieces in the pursuit of ultimate victory has been a strategy in chess for centuries.

However, to the surprise of human players, A.I. chess programs often make sacrifices that seem bizarre and nonsensical. A.I. chess programs create wild and complex strategies humans would never think of. These A.I.-created chess strategies have been called “alien” and “chess from another dimension.”

And they end up crushing human players.

A.I. chess programs make seemingly bizarre moves because they have the computational firepower to “see” much further into the future than a human can. A.I. programs can analyze millions of potential outcomes and create multi-move contingency plans for each outcome… all in less than the time it takes a human to take a sip of water.

The chess strategies that A.I. produces aren’t bizarre. With its ability to analyze millions of possible outcomes, the moves only make sense. They only seem bizarre compared to the primitive and unimaginative strategies that the feeble human brain with its poor computational ability makes.

Even a chess super genius, such as the legendary Gary Kasparov, has less than .0001% of the computational ability an A.I. chess program has. It’s not even a contest.

Knowing this fascinating aspect of A.I., our team was not surprised to see that our A.I.-powered stock market data analysis produced a specific type of trading strategy that most people would be very surprised by.

We gave the computer a huge variety of data sets to work with, including macroeconomic data, company-specific fundamental data, and technical analysis data.

We expected to find a telling indicator – something that would matter more than the other factors.

Maybe it would be momentum.

Maybe stock fundamentals.

But sometimes the moves can seem bizarre to the human mind.

And it so clearly demonstrates the futility of picking stocks with the human brain instead of with a super-intelligent computer.

We found that while some factors matter more than others, An-E doesn’t stick to one generalized course over time. 

Sometimes the best-performing stocks over a 30-day period have strong momentum.

Sometimes the best are severely oversold.

Sometimes the best are boosted by shifting macroeconomic indicators.

To the computer, there are no biases based on previous successful strategies. It simply analyzes the data and produces the prediction for the best outcome.

There is no chess player with favorite moves. No stock analyst who picks based on fundamentals, or who might favor only momentum stocks.

With the human element removed, the system freely ranks based on the data analysis regardless of where it leads.

What Our Results Look Like

After the machine created its strategy by analyzing 15 years of historical data, we analyzed the results it would have produced in various time frames in different market environments.

Our program analyzes hundreds of stocks and ranks them every day on its expectation of what the stock will do over the next 21 trading sessions.

Stocks with a high likelihood of going up are ranked in the top 10%, while stocks with a high likelihood of going down are ranked in the lowest 10% – and all others fall in between.

Over a 5-month period, the stocks in that top 10% – the ones predicted to go up the most – showed an average return of 6.5% with a win rate of 68.7%

This is a strong, statistically significant set of results. We believe it can provide you with a big edge in the markets.

Proprietary trading algorithms like the one we’ve developed can be worth their weight in gold. They are like the financial equivalent of closely guarded recipes like Coca-Cola and Heinz ketchup.

That is why this kind of technology isn’t only desirable, but, frankly, necessary as we move into an A.I.-dominated future.

P.S. To see TradeSmith’s urgent presentation showcasing how An-E is generating winning predictions over and over again, click here now.