If you could get a copy of next Tuesday’s Wall Street Journal today, what would you do?
If you’re like me, you’d look through its pages, find stocks that are up big from today’s price, bet on them rising, and make a pile of money.
Yes, having “financial foresight” would be an incredible thing. Of course, nobody can know the future.
That said, Louis Navellier has developed a market approach called Project Mastermind, which harnesses the power of data analytics, and it’s about as close to having tomorrow’s Wall Street Journal as you’ll probably get.
This coming Tuesday, he’s holding a special event at 4 p.m. that goes over all the details. This is not something you want to miss.
Now, to better understand what tonight is about, let’s turn to Louis for today’s Digest. In the below essay, Louis explains the extraordinary power of predicative data analytics, and what it can do for your portfolio.
I’ll let Louis take it from here.
Have a good weekend,
As Close as You’ll Get to Crystal Ball Investing
In 1997, a former major league baseball player turned baseball executive named Billy Beane became the general manager of the Oakland Athletics baseball team … and revolutionized the game.
At the time, many baseball scouts and executives depended on gut instincts and unproven beliefs to decide what players to draft and hire.
Beane was different.
As a student of statistics and data analysis, he performed an enormous amount of statistical analysis on players and strategy. You see, Beane didn’t have the big budget that the top-tier teams did. So, he couldn’t hire the star big-leaguers who commanded huge salaries.
So, Beane took a different approach. He wanted to find out what really worked … not what people thought should work.
Beane found that major league executives and scouts believed in a lot of myths and old wives’ tales that didn’t hold up to statistical analysis.
For example, Beane’s analysis found executives placed too much importance on how fast a player could run. But they didn’t place enough importance on how often a batter drew a walk.
Because the league placed a lot of importance on some areas that weren’t actually important … and because it didn’t place enough importance on things that actually were important, Beane found that many players were “overpriced” and many players were “underpriced,” much like companies in the stock market.
These findings gave Beane an “edge” he knew was backed by facts and evidence. He was able to see what others did not.
Beane used this hidden knowledge to sign valuable players his research said could help him win. But because these guys weren’t highly valued by the rest of the league, Beane got them cheaply. At the same time, Beane avoided expensive players he knew weren’t really worth the money.
How did it turn out?
Beane’s emphasis on facts and evidence allowed the Oakland Athletics to become one of the most successful teams in baseball … while also being one of the cheapest teams in baseball.
With one of the league’s smallest payrolls, the Athletics made the playoffs four consecutive years from 2000 – 2003. Plus, Beane payed less money per win than any other executive in the sport.
***Now, you might have heard this story since it went on to become the subject of the hit movie Moneyball which was based off a book of the same name. But this isn’t where the story ends …
You see, what Beane did was remarkable. But he was still constrained by the tiny budget of the Oakland Athletics.
To see what the power of data analytics could do with a bigger payroll, we have to look to Theo Epstein.
But let’s start this part of the story many decades earlier …
On January 3, 1920, the Boston Red Sox completed the sale of legendary baseball player Babe Ruth to the New York Yankees.
The sale of Ruth kicked off one of the most brutal runs of failure in sports history. Thanks to the “Curse of the Bambino,” Boston went 86 years without a championship. Meanwhile, its hated New York rival became the most successful Major League team of the 20th century.
But finally, in 2004, the Red Sox broke the curse … thanks to data analytics.
The general manager who helped build the Red Sox into a championship team was Theo Epstein.
Instead of coming up through pro ball, Epstein worked his way up the front-office ranks and made himself a master of using data analytics to make great baseball team decisions along the way.
Epstein employed Billy Beane’s analytical approach known as “sabermetrics.”
Thanks to data analytics and a big budget, Epstein helped break the Curse of the Bambino. The Red Sox won the 2004 World Series and another one in 2007.
But that’s not the end of it …
Epstein then moved to the Chicago Cubs, where he broke yet another one of sport’s longest championship droughts by winning the World Series in 2016.
The bottom line? Data analytics works!
***Is it possible to find the “sabermetrics” of the investment world?
You probably know by now that I’m a math and data guy. I’ve always been fascinated with numbers. When I was a grad student at Cal State Hayward some 40 years ago, my professor gave me an assignment that would change my life: Create a model that would mimic the S&P 500.
My professor wanted to prove that indexing was the best way to invest. Using Wells Fargo’s powerful mainframe computers, I completed my assignment — but there was one little problem:
I didn’t just mirror the S&P’s performance, I crushed it … and I never looked back.
You see, there’s no bigger, more lucrative set of data than the history of American stock prices and corporate financial statements.
All this information would fill libraries if it was printed on paper.
This is why I’m so thankful that the speed and power of computers has exploded more than 100,000-fold since my time at Cal State Hayward.
We can use today’s super computers to find gold in all that data.
Thanks to today’s cheap super computers, an analytical project that took one month to complete a few decades ago can be performed today in less than one minute … and at less than 1% of the cost.
Hugely expensive analytical projects that used to be only in the realm of governments and large corporations can now be performed by a teenager in his parent’s basement for less than $10.
The massive increase in computing power we’ve seen over the past few decades is allowing us to gather, record, and monitor trillions of data points, signs, and clues … and then determine exactly what they mean.
We’re using this new technology to uncover hidden correlations, secret relationships, and signals in the data — in essence, a “sabermetric” model for investing.
We’re finding meaningful “cause and effect” relationships where in the past, we only found meaningless noise.
Now that I have space age computing power and speed at my fingertips, I’ve embarked on the most important research project of my career … one that could help you make much bigger stock market returns than you’re making now, while taking less risk.
I call this project “Mastermind.”
With the help of incredibly powerful computers, we conducted our Mastermind study with a simple goal in mind …
Determine the attributes of stocks that are most likely to go up in the near future.
We’re not talking about buy-and-hold for years. Project Mastermind sends us the signals for stocks poised to skyrocket in the near future — 12-24 months.
This can be a retirement game-changer. A chance to make up for lost time and money.
So, how does it work exactly?
As you know, every public company has hundreds of data points related to its business and its stock.
There are annual earnings, quarterly earnings, net profits, gross profits, sales, P/E, return on equity, tangible assets, stock price momentum, trading volume, and relative strength just to name a few.
But which of those data points are proven — beyond a shadow of a doubt — to have real-world, profit-producing predictive power?
Is a low P/E the best predictor of future stock returns?
Is blazing sales growth the best predictor of future stock returns?
Is positive stock price momentum the best predictor of future stock returns?
Is it a combination of factors?
Those are the questions we set out to answer.
We wanted to find what truly works in the stock market.
We brought no preconceived notions to the project. No biases … no wishful thinking … no egos to defend … no past stances to justify.
We just let the numbers do the talking.
We just let the numbers answer the questions …
Just as Billy Beane and Theo Epstein wanted to know which factors had the best predictive power for winning baseball teams, we asked a similar question …
What stock factors have the most predictive power for huge market gains?
What type of stock picking system will give us the greatest profit-producing edge?
Now, let me be crystal clear. I’m NOT talking about predicting the future.
I’ll be the first to tell you that it’s impossible to predict the future. At the Mastermind project, we didn’t chase the impossible dream of predicting the future.
What we did was look for the closest thing to it.
We looked for an “edge” that we could exploit over and over and over and over.
What we’ve found is incredible … and could help you amass stunning, life-changing wealth over the next few years.
***I’ll be providing all the details of Project Mastermind this Tuesday at 4 p.m. Eastern time
I believe this could change everything about the way you invest.
Join us and let me show you how data analytics can enable you to achieve returns that could be multiples greater than what you’re getting right now — without any additional risk.
Even better, with my system, these gains come at an even faster pace than average market gains.
Click here to reserve your seat for Tuesday’s event. Just for joining, I’ll give you my #1 Project Mastermind stock.
See you there,