Investing is much harder than the advertisers of our industry want you to believe.
The numbers bear this out.
About a decade ago, the research shop Longboard studied the total lifetime returns for individual U.S. stocks from 1983 through 2006.
They found that the worst-performing 6,000 stocks — which represented 75% of the stock-universe in the study — collectively had a total return of … 0%.
The best-performing 2,000 stocks — the remaining 25% — accounted for all of the gains.
It gets worse.
While it would be unfortunate to sink your money into a stock that generated nothing (0% returns), the unspoken implication there is that you’d at least walk away with your original investment capital.
Not so much.
The Longboard study found that 18.5% of stocks lost at least 75% of their value.
In other words, nearly one in five stocks didn’t just return nothing, they were double-digit losers that destroyed investment capital.
Other studies have found similar results.
Research from economist and academic Hendrik Bessembinder, which looked at equities from 1926 to 2015, concluded that about 60% of stocks were so bad that their performance was worse than one-month U.S. Treasury notes.
It’s not easy finding a winner and even more difficult to find the big winners. And if you don’t find a big winner, getting a 0% return isn’t the worst potential outcome. Instead, significant loss of your hard-earned money is a very real threat — and it happens with greater frequency than most investors realize.
For you to make money in the stock market, someone else must lose money (or miss out on enjoying those gains with you).
After all, it’s a zero-sum game.
How often do you think about the person on the other side of your stock market wager?
Why are they taking the opposite position from yours? What do they know that you don’t? Why are they confident that they’re going to take make money from you “bad” decision?
For decades, the biggest threat to the Main Street investor was the big investment shops, who employed legions of analysts whose sole job was to gather more market data than you, outthink you, and then take your money.
The average investor did very poorly in this match-up.
Most investors have heard of the Dalbar studies. Each year, they compare the investment performance of the average investor to the major market indexes. And each year, the average investor posts worse returns than the index – usually by a lot.
For example, in 2018, the S&P 500 returned -4.38%. Meanwhile, the average equity mutual fund investor did twice as bad, returning -9.42%.
According to Dalbar, over the 30-year period ending on 12/31/2021, the average equity fund investor made 7.13%. Meanwhile, the S&P returned 10.65%.
While that might not sound like a huge difference, keep in mind that equal starting investments would have resulted in an ending value of $800K for the average investor but over $2M for the S&P.
With this context, here’s the punchline…
For as poorly as the average investor has done in decades past, today, we’re no longer betting against humans, who have their own limitations and investment blindsides. Instead, we’re now wagering against artificial intelligence (AI).
Let’s begin with The Globe and Mail from back in 2017:
AI…surpasses humans in its powers of prediction. It can determine if one stock or bond is likely to perform better than another based on factors ranging from past returns to weather patterns to who uses a company’s products where and when…
Numerous fund companies are already turning to AI in the hope it can deliver better returns than human stock pickers.
The systems identify patterns in pricing data, yield curves, how markets execute trades and much more, and then make predictions based on those patterns.
Bridgewater Associates, the world’s largest hedge fund, said last year that it will replace many of its managers with machines.
Again, that was from back in 2017.
Let’s jump to BuiltIn.com for an update:
Artificial intelligence is a game changer for the stock market.
When Wall Street statisticians realized they could apply AI to many aspects of finance, including investment trading applications, Anthony Antenucci, vice president of global business development at Startek, had insight to share.
“They could effectively crunch millions upon millions of data points in real time and capture information that current statistical models couldn’t,” he told ITPro Today. “Machine learning is evolving at an even quicker pace and financial institutions are one of the first adaptors.”
Of course, Antenucci isn’t the only one to recognize AI’s stock potential. Online trading is expected to reach a market value of approximately $12 billion by 2028. Much of this anticipated growth will be thanks to AI.
While humans remain a big part of the trading equation, AI plays an increasingly significant role. Algorithmic trading accounts for around 60 to 73 percent of U.S. equity trading, according to Wall Street data highlighted in one report.
You might have missed it, but last month, news broke that JPMorgan is working on developing a ChatGPT-like A.I. service that gives investment advice.
From CNBC:
JPMorgan Chase is developing a ChatGPT-like software service that leans on a disruptive form of artificial intelligence to select investments for customers, CNBC has learned…
IndexGPT will tap “cloud computing software using artificial intelligence” for “analyzing and selecting securities tailored to customer needs,” according to the filing…
The technology has a range of possible uses in finance. Banks including Goldman Sachs and Morgan Stanley have already begun testing it for internal use…
The bank, which employs 1,500 data scientists and machine-learning engineers, is testing “a number of use cases” for GPT technology, said global tech chief Lori Beer.
So, what is the average investor doing in his own portfolio while this army of data scientists and machine-learning engineers is gearing up for battle?
Watching TV.
In 2018, the Bureau of Labor Statistics surveyed how Americans spend their time. After “sleeping,” and “working,” “watching TV” came in as the most time-intensive activity for survey respondents.
That clocked in at 2.84 hours per day.
And how much time, on average, was allocated to personal financial management?
0.03 hours per days…or less than two minutes.
In other words, the average person spends more time enjoying their coffee each morning than they do preparing for their financial future.
How do you think the results of those Dalbar studies will come in now that the average investor is going head-to-head with AI?
You don’t have to bet against AI.
You can put it on your side, reaping the investment reward of this hyperintelligence.
Stepping back a moment, though rarely used by average investors, we’ve had the ability to incorporate computerized algorithmic trading into our investment approaches for decades.
Legendary investor Louis Navellier was an early pioneer in this field, with Forbes even naming him the “King of Quants.” Louis’ use of computers is, in part, how he has amassed one of the most envied long-term investment track records in our industry.
Well, we’re now stepping into a new era of computerized investment thanks to AI. And last week, Louis sat down with Keith Kaplan, the CEO of our corporate partner TradeSmith to introduce an AI investment tool that could be a gamechanger in this zero-sum game of investing.
If you’re not familiar with TradeSmith, they’re an investment quant shop. They’ve spent over $19 million and over 11,000 man-hours developing their market analysis algorithms. They have a staff of 36 people working on developing and maintaining their software and data systems.
To explain what their AI tool is, and why TradeSmith developed it, let’s turn to Keith. In a recent article he wrote, Keith walked through why buy-and-hold is very challenging for investors today. We’ll pick up with him discussing the need for better investment timing:
Given the uncertain, volatile conditions we’ll face in the months to come, the biggest (and perhaps only) profits will go to the folks who know when to buy, when to sell, and how to keep repeating that process.
Like lather, rinse, repeat.
But if that sounds easier said than done, then it’s time to meet “Project An-E.”
Instead of just holding a stock and waiting for things to get better, or sitting on the sidelines because you’re afraid to time an investment, what if you had an AI tool that alerted you to potential optimal times to enter and exit an investment?
Well, you don’t have to wonder for very long.
With incredible computing power and AI at our fingertips, our team embarked on the most important research project in our company’s history… one that could help you make much bigger stock market returns than you’re making now, while taking less risk.
We call this “Project An-E” (pronounced Annie), short for Analytical Engine.
An-E doesn’t have biases.
It’s designed to create its own optimal parameters based purely on getting a desired result: helping folks make money and avoid taking unnecessary risks.
Let’s wrap up this Digest before we get too long. But to get all the details on An-E, you can watch a free replay of Keith’s and Louis’ event from last week right here. You’ll see them provide back-tests showing An-E’s stock price predictions, and you’ll also get its latest price predictions for NVDA, AAPL, and SYM as we look forward today.
Let’s not pretend otherwise.
But we’re entering a new era in which small investors like you and me now have the same ultra-powerful technology as the big investment shops. It’s the ultimate gamechanger.
Investing is evolving – but no longer at the expense of the Main Street investor. This is a step forward that can make a tremendous positive impact on your portfolio.
Have a good evening,
Jeff Remsburg
The post Investing is Hard â This Will Make It Easier appeared first on InvestorPlace.
