This AI Technology Is Worth Its Weight in Gold

Maximize your returns and avoid market pitfalls... SPECIAL OPPORTUNITIES [The Oxford Club Special Opportunities]( Note From Publisher Rachel Gearhart: The big day is nearing... We're getting closer to Tuesday, December 19, when we'll be airing [The Predictive AI Profits Event](. If you haven't reserved your spot yet, you can do so by simply [clicking here](. During the event, we'll be unveiling a breakthrough predictive artificial intelligence (AI) stock forecasting system. AI is proving to be a retirement game changer that can maximize your returns while helping you avoid pitfalls in the markets. Check out Chief Income Strategist Marc Lichtenfeld's article below about why AI is becoming the next big thing in investing and how it can help you down your path to wealth. --------------------------------------------------------------- The Heart of This New Project Marc Lichtenfeld, Chief Income Strategist, The Oxford Club [Marc Lichtenfeld] As my friend and colleague Chief Investment Strategist Alexander Green covered in yesterday's essay, 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 - Me to become 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 Alex 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 was trading at a cheap 12 times earnings. Or you could test what would happen if you owned a stock index like the S&P 500 only when it traded above its 200-day moving average. Or you could test what would happen if you bought when the stock market traded down to 12 times earnings and was 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 that 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" - and then the machine crunches trillions of data points to determine whether 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]( TradeSmith loaded over 100 distinct variables into the machine learning program. TradeSmith CEO Keith Kaplan and his team of 36 data scientists, software engineers and investment analysts wanted to create a system that had 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. They just gave the machine a desired outcome (find stocks poised to rise over the short term) - and let it do the rest. They 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 AI's ability to analyze millions of possible outcomes, the moves only make sense. They seem bizarre only compared with 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 Garry 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 and his team at TradeSmith were 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 superintelligent computer. [Keith and his 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 momentum stocks. With the human element removed, the system freely ranks based on the data analysis, regardless of where it leads. And what Keith and his team have found is a strong, statistically significant set of results. They 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 the financial equivalent of closely guarded recipes, like those of Coca-Cola and Heinz ketchup. The team at TradeSmith doesn't want someone replicating its strategy and "front running" its trades, so I can't tell you the exact makeup of the program. But Keith will talk more about how the system works and all the factors considered at [The Predictive AI Profits Event](. If you haven't already reserved your spot for that free, LIVE event on Tuesday, December 19, at 8 p.m. ET, you can do so by simply [clicking here](. If you can't attend on the 19th, don't worry. Once you [register for the event]( we'll send you the rebroadcast as soon as it's ready. See you on Tuesday, Marc OPPORTUNITIES OF INTEREST - [For Free? Click Here to Get the Names and Ticker Symbols of the Top Dividend Stocks in the Market!]( - [Alexander Green Invested $100,000 Into 1 Stock! Discover Where He's Invested $100K+ Right Now.]( - [Two Recent Ultra-Cheap (Under $5) Penny Options Made 131% and 115% in Under 49 Days. Discover How You Can Get the Next Recommendation Here.]( SPONSORED [Half of Americans Have One of the World's WORST Savings Accounts... Are You One of Them??]( [Burning Money]( Americans lost out on $603 BILLION since 2014 by keeping money with one of the five big banks. 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