How AI Can Help You Increase Returns While Lowering Risk Dear Reader, As I covered in yesterday’s article, using advanced computers to analyze huge data sets is not a brand-new thing.
Data analytics allowed: - Jim Simons to become the most successful, highest performing hedge fund manager of all time.
- The Boston Red Sox and Chicago Cubs to break long World Series droughts.
- AI programs to defeat the world’s best chess and Jeopardy players.
- And me to become one of America’s top money managers, a guide to millions of retail investors, and personally wealthy.
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 the massive research project I mentioned yesterday.
The way traditional investment data analytics worked a decade ago, you would think of a set of parameters you 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 “test” those parameters over past financial market data and analyze the results. If the results were 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 person selected the strategy or “parameters” that were tested.
Machine learning flips this script in a powerful way. Instead of having a person 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.”
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, InvestorPlace’s partners at TradeSmith loaded over 100 distinct variables into the machine learning program.
TradeSmith CEO Keith Kaplan and his team 0f 36 data scientists, software engineers, and investment analysts wanted 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.
They also included fundamental data like profit margins and price-to-sales ratios and technical data like relative price strength and moving averages.
My friend Keith tells me that they 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 I’ll show you in a moment – are fantastic.
But first, we need to quickly discuss a fascinating aspect of machine learning and how it creates brand-new ways of thinking about the stock market… Investing From Another Dimension When designers of AI-powered chess-playing programs started evaluating their systems years ago, they noticed something peculiar about the strategies their programs employed.
The AI programs tended to employ seemingly bizarre strategies.
These were 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 they believe 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, AI chess programs often make sacrifices that seem bizarre and nonsensical. AI chess programs create wild and complex strategies humans would never think of. These AI-created chess strategies have been called “alien” and “chess from another dimension.”
And they end up crushing human players.
AI chess programs make seemingly bizarre moves because they have the computational firepower to “see” much further into the future than a human can.
AI programs can analyze millions of potential outcomes and create multi-move contingency plans for each outcome… all in less than the time it takes you to take a sip of water.
The chess strategies that AI 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 0.0001% of the computational ability an AI chess program has. It’s not even a contest.
Knowing this fascinating aspect of AI, Keith’s team at TradeSmith was not surprised to see that their AI-powered stock market data analysis produced a specific type of trading strategy that most people would be very surprised by.
As I mentioned, they gave the computer a huge variety of data sets to work with… - Macroeconomic data.
- Company-specific fundamental data.
- Technical analysis data.
They expected to find a telling indicator – something that would matter more than the other factors.
Maybe it would be momentum.
Maybe stock fundamentals.
But as I said, 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.
Keith’s team at TradeSmith 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. An-E 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.
And what they’ve found is a strong, statistically significant set of results. Keith and his team believe it can provide you with a big edge in the markets.
Proprietary trading algorithms like the one TradeSmith has developed can be worth their weight in gold. They are like the financial equivalent of closely guarded recipes like Coca-Cola and Heinz ketchup.
The team at TradeSmith don’t want someone replicating their strategy and “front running” their trades, so I can’t tell you the exact make up of their program.
But TradeSmith CEO Keith Kaplan will talk more about how the system works and all the factors considered at the AI Predictive Project.
We’ll be hosting this event LIVE on Tuesday, June 20, at 8 p.m. Eastern time. Click here to reserve your spot now .
Tomorrow, I’ll explain why this kind of technology isn’t only desirable, but, frankly, necessary as we move into an AI-dominated future.
Again, our AI Predictive Project event will take place on Tuesday, June 20, at 8 p.m. Eastern time. Register today so you don’t miss out! Sincerely, |
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