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Even the billionaires have this problem

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From Richard Smith, Founder, TradeStops:

Eric Arthur Blair — better known by his pen name, George Orwell — is the British author who wrote the timeless classics Animal Farm and 1984. Phrases still in use today — like Orwellian, Big Brother, and Thought Police — are a result of those novels.

Orwell was also known for his essays. In 1946, the year after World War II ended, he wrote the following in a piece titled “In Front of Your Nose:”

“We are all capable of believing things which we know to be untrue, and then, when we are finally proved wrong, impudently twisting the facts so as to show that we were right. Intellectually, it is possible to carry on this process for an indefinite time: the only check on it is that sooner or later a false belief bumps up against solid reality, usually on a battlefield.”

Orwell also has a famous quote: “To see what is in front of one’s nose needs a constant struggle.”

Long before the phrase “cognitive bias” gained attention in the 1970s, Orwell and many others (all the way back to the ancient Greeks) knew something basic about human nature.

It can be very hard to see what is “in front of your nose” — in other words, the glaring evidence right in front of you. There are countless ways to be distracted or misled … or focused on the wrong thing … or thrown off balance by emotion … or a dozen other things.

At the same time, every so often and seemingly like clockwork, there’s an example in the news where human judgment fails spectacularly.

Such-and-such person makes a decision (or a string of decisions) so terrible that the age-old question arises: “How could anyone make such an obvious mistake? How in the world did that happen?”

They probably failed to see “in front of their nose.” And it was probably due to cognitive bias.

Cognitive biases help explain the “why” behind certain bizarre quirks of human nature

They can be described as “systemic patterns of deviation” from rational thought.

These biases are “systemic” in the sense they are built into the brain, which means everyone has them as part of the brain’s default setting. They are not glitches or flaws in day-to-day functioning, but a result of the brain’s architecture.

Cognitive biases are literally everywhere. Anyone with a brain is susceptible to them.

But how do cognitive biases make it hard to see something obvious?

Take confirmation bias — one of the better-known biases (there are hundreds of them) — as an example. Confirmation bias is the tendency to filter information in a way that supports a desired belief.

If you deeply want to believe “X,” for example, your brain will seek out and register information that confirms the validity of X. At the same time, your brain downplays or ignores inputs that go against X.

The stronger your desire to believe something, the more powerful this effect becomes.

Confirmation bias can sometimes be so strong that it creates a reality distortion field — where the person in the grip of the bias is no longer able to process reality accurately.

In markets, this can get expensive. The problem is that different cognitive biases can combine and reinforce each other, leading to irrational behaviors that cost a lot of money.

You may have heard a version of this joke:

Q: What do you call a short-term trade that doesn’t work out?

A: long-term investment.

Here’s how that works:

Bob believes a certain stock will have strong earnings and a string of profitable quarters ahead. He buys the stock, assuming the earnings report will be a catalyst for a nice move higher.

Alas — the earnings report is bad. The company failed to meet expectations. The outlook is “meh” and was supposed to be great. The stock goes in the wrong direction. It gaps down and starts drifting lower.

At this point, Bob experiences the cognitive bias known as “loss aversion.” His desire to avoid a loss is stronger than his desire to seek gains. So, he holds onto the losing position.

If he waits a little while, Bob reasons, then maybe the stock will come back. Never mind that his whole thesis for buying the stock in the first place (strong earnings and rising profits) has been trashed.

Now that Bob’s short-term trade is a “long-term investment,” he has a vested interest in seeing the stock go up. This translates to an emotional desire — Bob deeply wants the stock to make a comeback.

Confirmation bias then takes hold: Bob’s brain starts paying attention to articles that are friendly to the idea the stock might come back … while discounting or ignoring evidence that the stock could go lower.

There is so much written about publicly traded stocks, it’s almost always possible to find a bullish article or someone making a weak bullish case somewhere — even if the stock is a total dog. Caught in the grip of confirmation bias, Bob seeks out these bullish articles. The stock keeps falling.

Bob ignores the strongly negative evidence — the warning signs in front of his nose — and stays with the position. A year later, the stock has fallen dramatically … and badly damaged Bob’s portfolio.

This can happen to anyone. It even happens to hedge-fund billionaires.

The tricky thing about cognitive bias is that, much of the time, you don’t even know it’s there. The bias factors play out in the realm of the subconscious.

At no point did Bob (our hypothetical investor) realize that he was acting irrationally as the stock kept going down. In the real world, Bill Ackman (a billionaire) did the same with Valeant Pharmaceuticals.

But cognitive biases can be trickier still — because they still distort things even when you spot them.

When a bias is strong enough, it creates a form of “cognitive illusion.” This causes you to see something that isn’t there, or to wrongly perceive an irrational course of action as rational. And even if you recognize what’s happening, the illusion persists.

We can use a visual cognitive illusion to demonstrate the point.

The “Müller-Lyer Illusion” was developed in 1889 by Franz Carl Müller-Lyer, a German sociologist. A version of it from Daniel Kahneman’s book, Thinking Fast and Slow, appears below.

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As you look at the illusion, ask yourself: Which of the two horizontal lines is longer?

The correct answer is: It’s a trick question. Both of the horizontal lines are exactly the same length. (You could prove this by taking a measurement.)

The illusion persists even after you know the correct answer. The bottom line still looks longer, even when you know the truth. This is due to a quirk of how the brain works and how certain shapes are interpreted relative to depth perception.

Certain types of cognitive bias can create a similar type of illusion. But these illusions are patterns of thought or beliefs rather than visual brain teasers. That makes them far more dangerous.

In the world of investing, persistent cognitive illusions (created by built-in cognitive biases) can lead to irrational decision making, which winds up costing investors money. Sometimes a LOT of money.

And the real challenge is, you can study up on the various biases … and be fully aware that you have them (just like everyone else) … and still fall victim to them anyway.

This is another benefit of using a set of rules that exists outside your brain … while interpreting market data with the help of software that informs and guides decision making.

A well-designed software algorithm doesn’t see with human eyes. Technically speaking, it doesn’t “see” at all. It just crunches a vast stream of ones and zeroes. That helps make the algorithm a reliable interpreter — an impartial judge, if you will — when programmed correctly.

And this, again, is why software can be such a help to investors.

You won’t always know when your cognitive biases (which all investors share) are negatively impacting your investing decisions. And sometimes those biases will distort your perception … even if you are well aware of them and know they exist.

But well-designed investment software can help you see “in front of your nose” in terms of making rational decisions with the help of data — thus increasing the likelihood of investment success.

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Richard

Crux note: As if the the challenge of savvy investing wasn’t hard enough, you have to fight against your own subconscious… But that’s why Richard created TradeStops.

His philosophy is to cut your losses and let your winners ride… And the results speak for themselves.

You can discover why one satisfied investor called TradeStops his “safety net” right here.


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Why artificial intelligence is the future of investing

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From Richard Smith, Founder, TradeStops:

Artificial intelligence — and “deep learning,” a specific form of artificial intelligence — is changing the face of investing.

This is not something that will happen in the future. It is already happening. The changes are widespread and they will only intensify in the coming years.

This could be seen as good or bad news, depending on how you look at it. Artificial intelligence, or AI, is a powerful form of technology that is brand new to most people. That makes it frightening as well as exciting.

There is also a lot of hype surrounding AI, including overblown predictions of what artificial intelligence will do (and what science will be able to achieve). Sometimes it’s hard to separate the reality from the fantasy — and some have a vested interest in drumming up fear and worry.

The good news is that AI, through a technique known as “deep learning,” will be an incredible resource for individual investors. The widespread availability of low-cost computing power and increasingly powerful software programs means that the benefits of AI will ultimately flow to Main Street, and not just Wall Street or Silicon Valley.

And yet, when you hear about artificial intelligence in the news these days, it is often attached to a big prediction or a slightly ominous sounding breakthrough. For example, a London-based artificial intelligence company called DeepMind — which is owned by Alphabet, the parent company of Google — recently announced an “intuition” breakthrough in one of its game-playing AI programs.

AlphaZero, a descendant of DeepMind’s AlphaGo, is an AI machine that shows human-like creativity in games like chess and shogi (Japanese chess).

Human grandmasters, like the world-famous Garry Kasparov, have confirmed the surprising degree of “creativity” in AlphaZero’s style of play, with a willingness to take risks and make bold, unconventional moves that feel more human than machine-like.

The gameplay, however, is just a means of testing capabilities and generating publicity. The goal of DeepMind is not to design an ever-more-impressive roster of game-playing machines, but to tackle tricky and lucrative real-world problems, like the development of pharmaceutical drugs.

In the pharma world, for example, predicting the three-dimensional structure of proteins — also known as “protein folding” — is an important area where DeepMind hopes to apply deep learning techniques.

Protein structures are at the core of many life-saving drugs. If you count up all the seconds that have ticked by since the universe began, there are more potential protein-folding combinations than that very large number.

That makes the design of new protein-based drugs very challenging, and an area where an AI program — like a pharmaceutical version of AlphaZero — could accelerate the process by orders of magnitude.

It’s important, though, to clarify what is not going to happen.

AlphaZero is not close to “awareness” or “consciousness” or anything resembling human brain activity. And in fact, this particular area of AI is widely hyped and overblown.

Broadly speaking there are two types of artificial intelligence: narrow and general.

“Narrow AI” is focused on a very specific task. It is far more like a software program than anything else. The much more ambitious “general AI” is the notion of computer code achieving something like human consciousness.

Narrow AI is already here. You see it in all kinds of places, and likely make use of it multiple times without realizing it on any given day.

Narrow AI does things like auto-complete the text you type into your smartphone, translate a foreign language web page, or give suggestions for restaurants or coffee shops based on your GPS location. It does very specific things, typically by parsing large amounts of data in real-time.

General AI only exists in Hollywood movies. It is the computer that is supposedly smarter than a million humans, or the army of terminator droids trying to wipe out humanity.

A handful of experts think General AI could arrive in a decade. But those are extreme outliers. A far higher number of experts think General AI — a sort of computer-based consciousness, or a program smart enough to have awareness — could take 50 to 100 years, or in fact may never arrive at all.

In spite of the fact that smart speakers like Alexa can mimic a conversation, conscious AI is nowhere close to being achieved, and may not even be possible.

The current AI techniques being deployed aren’t in the same ballpark as General AI. They aren’t even the same sport. We may actually be closer to reaching Mars, or even colonizing Mars, than we are to any kind of substantial General AI breakthrough. That’s how big the Narrow vs. General gap is.

So, even though DeepMind as a company talks about its AI having “intuition,” we shouldn’t confuse that with any kind of march toward human consciousness. It is possible for an AI program to show what appears to be creativity, and to be useful in all kinds of powerful ways, without being conscious at all.

This is what “deep learning,” a specific type of artificial intelligence, is all about — helping human researchers (and investors) become more powerful in various ways.

Deep learning relies on “neural networks.” Because of this, the claim is that deep learning, as a technique, mimics the structure of the human brain. This is far from true.

It sounds sexy to suggest that AI mimics the human brain, because it implies that, if you go along this path far enough, you get consciousness.

The reality is far more basic, but still fascinating. It is possible to exploit the power of “neural networks” without trying to copy the human brain at all, except in a super-abstract way, and that is what deep learning does.

The “neural” part means storing information as a network of nodes, with recognition capabilities — the program’s version of awareness — distributed across multiple nodes, rather than residing in any one place like a text file.

To understand how deep learning works, imagine you come from a far-off land where there are no housecats. You have never seen a housecat before, or a cat of any kind.

Traveling to the United States, your host wants to teach you what a cat is. But instead of describing a housecat, or introducing you to a live one, they show you pictures of housecats.

After a while, in order to test your knowledge, your host starts showing you thousands of pictures, some of them with housecats and many of them without.

You make guesses as to which picture contains a housecat and which doesn’t. With each guess you get feedback — “correct” or “incorrect” — and over time your guesses improve.

Eventually, sticking at this for long enough, you have a pretty good sense of what a housecat looks like, thanks to huge volumes of trial and error.

Deep learning as an AI technique essentially does the same thing.

A computer program is taught to identify a pattern — like, say, the shape of a cat in a picture.

The program tries to “guess” at the pattern, over and over, getting feedback each time. After hundreds of thousands or even millions of guesses, the program has a pretty good sense of what a cat looks like.

This methodology requires huge volumes of data for the program to train itself. That is why giant tech companies like Google, Amazon and Alibaba have such an edge in these areas — nobody else has access to oceans of data like they do.

But again, this brute-force means of recognizing patterns within patterns is nowhere near human consciousness. It is a million miles away from it.

And yet this deep learning technique is extremely valuable, because AI-powered software can:

  • Detect data patterns that are very subtle and complex
  • Sift through huge mountains of data (find needles in a haystack)
  • Get better at pattern-spotting through testing and feedback
  • Identify useful or valuable patterns instantly and in real-time

And that, in turn, explains why deep learning is the future of investing.

In the hands of regular investors, artificial intelligence tools can scan vast quantities of data to identify useful and important patterns via deep learning techniques. Investors can then use those patterns to make better investment choices.

A key point here is that, from an investing perspective at least, the human being does not get replaced. Human behavior still plays a key role. Human decision-making and human emotion are still big factors.

But software with AI-like capabilities, enabled by powerful deep learning algorithms, can serve as an investor’s eyes and ears.

The software can scan thousands of stocks in real time — something a human can’t do. The software can also look for subtle patterns in the investor’s own behavior and investment record, and make possible suggestions for improvement. These capabilities, and more, make the investor more capable and powerful — and potentially more successful.

Those ideas just scratch the surface. The point is that, while artificial intelligence is a big, complex topic that is both exciting and a little frightening, the dawn of AI — via deep learning — is opening up a world of new possibilities for individual investors.

And this AI investment revolution, so to speak, is truly democratic because the barriers to entry are low and falling. With each passing month, if not each passing day, chips get cheaper and investment software gets more powerful.

That means you won’t have to be a Silicon Valley tech titan or a rich hedge fund mogul to utilize AI-powered software. We can be certain about this because our mission and vision, as a software company, is to empower individual investors.

It’s our bread and butter, and we can’t wait to show you what’s in store for 2019.

Richard

Crux note: More than 25,000 investors are using TradeStops to help manage risk in their portfolios. Try it for yourself today with a one-month free trial.  


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Cramer’s charts signal a potential bottom for the major averages

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The stock market’s recent swings might not be as bad as investors think, especially when stacked against their historical performance, technical expert Rob Moreno told CNBC’s Jim Cramer on Tuesday.

After consulting the charts of the major averages, Moreno, the publisher of RightViewTrading.com and Cramer’s colleague at RealMoney.com, concluded that stocks are in a consolidation phase, trying to digest the gains from a multi-year bull market.

And “the charts, as interpreted by Rob Moreno, suggest that the averages are trying to bottom here in preparation for a nice rebound,” Cramer, host of “Mad Money,” told investors.

Cramer pointed out that since the end of the financial crisis, the averages have climbed steadily higher. The Dow Jones Industrial Average, for one, bottomed at roughly 6,500 in March 2009 and has since traded above 24,000.

But this consolidation period is atypical, Cramer admitted. Normally, times like these “tend to be sedate [and] fairly limited” when it comes to trading; this one has been much more volatile, or prone to big swings, he said.

According to Moreno, “the best way to navigate your way through it is by taking a wider view of the landscape, because that’s the only way you can get enough … perspective that you won’t panic,” Cramer said. “Sure, the decline’s been brutal, but he says we’ve been through previous consolidation periods that were even more volatile and they didn’t derail the bull.”

First, Cramer turned to Moreno’s logarithmic chart of the Nasdaq Composite index. Technicians use logarithmic charts, which measure percentage moves rather than basis points, to compare market action over long periods of time.

“Moreno points out that in 2010, 2011 [and] 2015, there were periods of consolidation that had even wider ranges than the one we’re experiencing now,” Cramer said. “Even though the Nasdaq’s lost a lot of points here, on a percentage basis, the 16 percent decline [from its October highs to its November lows] is smaller than 2010, 2011 or 2015, and each of those times, the market ultimately rebounded phenomenally.”

Moreno also noted the major averages’ floors of support and ceilings of resistance, key levels that technicians watch to know when a given index or stock might change course.

The weekly chart of the S&P 500 showed a ceiling of resistance at 2,800 and a floor of support between 2,550 and 2,600, a range in which that index has been stuck all year. The Dow’s weekly chart had a ceiling at 26,000 and a floor between 23,500 and 24,000, not far below where the Dow was trading on Tuesday.

“In Moreno’s eyes, it looks like the Dow and the S&P are both trying to hammer out a bottom,” Cramer said. As for the Nasdaq, which is trading in the middle of its range, “Moreno wants to see what’s known as a hammer candle, where … the Nasdaq rallies and closes on Friday near its highs for the week. That would send a signal that the bottom will hold and perhaps we could rebound back to the high end of the range.”

The best part — at least for investors who believe Moreno’s call that this isn’t a bear market, but a consolidation phase — was that even if the averages fall below their floors of support, this is likely still a buying opportunity, Cramer said.

“If you have conviction, you might want to do some buying, seeing as the major averages are all pretty close to their floors of support, and even if these floors are violated, Moreno doesn’t think we’ll have a whole lot more downside,” he said.

Moreno added that buyers should watch for what’s called an eveningstar candle pattern to make their move: in short, it’s a three-day phenomenon when the market rallies for a day, maintains a tight trading range the next day, and then sees an unusually negative trading session.

All in all, “Moreno thinks that the situation might not be as bad as it seems,” Cramer said. “This all sounds a little too sanguine for me given everything that’s going on, but you know what? I think it’s heartening to put these declines in a more constructive perspective.”



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FAANG stocks turn red: What to buy when the market goes nuts

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From Jeff Clark, Editor, Delta Report

According to the efficient market hypothesis, there’s nothing insane going on in the stock market. Stock prices reflect all of the currently available information and investors’ analysis of that information.

Hogwash.

In the long term, I’ll agree that markets are efficient. Investors are rational. And stock prices generally end up where they are logically supposed to.

In the short term, though, the market is nuts.

In the short term, stock prices react to emotion, not logic. Fear and greed are more powerful in the short term than a thoughtful analysis of balance sheets and income statements. That’s why crazy things sometimes happen in the stock market.

Back in 2000, for example, any rational person could see the dot-com bubble inflating. Stocks with no earnings, no revenue, and no hope of either were pressing higher nearly every day. Meanwhile, traditional businesses – with long track records of earnings growth, stable dividends, and long-term business prospects – couldn’t catch a bid.

In 2000, I did not own a single dot-com stock. Instead, my largest holding was Cooper Tire & Rubber (CTB). At the time, CTB was an 87-year-old company. When I started buying the stock in early 2000 at $9 per share, CTB traded at six times earnings and paid a better than 5% dividend.

CTB promptly dropped to $6 per share.

The stock lost 33% of its value in early 2000, at a time when the average dot-com stock was racing to the moon.

As you might imagine, the clients at my brokerage firm were frustrated. Their friends and neighbors were bragging about the piles of money they were making in this.com and that.com. Meanwhile, my clients were stuck in a dusty, old, tire and rubber stock that just seemed to fall every… single… day.

All I could do to console my customers was to tell them that sometimes the markets do screwy things. Sometimes, logic takes a vacation. Stocks that shouldn’t go up, do. Stocks that should rally, don’t.

And it is during those times that investors who have the ability to curtail their emotions also have the ability to make outsized gains. But, you can only make those gains by going against the emotions of fear and greed… and betting on logic instead.

I lost several clients in early 2000. I refused to buy the dot-com stocks. I stuck with buying old, time-tested companies trading at steep discounts to their historic valuations.

By early 2002, most of the dot-com stocks had crashed and burned. The customers who stuck with me were cashing out their Cooper Tire & Rubber trade for a 150% gain.

Here’s my point…

While the focus in my Delta Report trading service is on shorter-term trading – where we attempt to get into a position one day and get out of it a few days later – there are times in the market where it can be far more profitable to take a slightly longer-term view.

Investors’ short-term emotions have decoupled from longer-term logic. So, asset prices have, for lack of a more sophisticated term, gotten out of whack.

It is during these times that it can be HUGELY profitable to take a BIG SWING.

Find a stock that is grossly undervalued by almost every fundamental metric. Find a stock that just can’t seem to get off the mat. Find a stock that nobody likes… a stock that if you mention it at a cocktail party, you end up drinking alone.

Then, buy that stock and hold it for a few months.

You won’t be disappointed.

Best regards and good trading,

Jeff Clark

P.S. Like I mentioned above, the focus in my Delta Report trading service is on shorter-term trading. It’s all about how to maximize your profits while still reducing risk.

The Wall Street players lose a lot of money because they’re always looking for that big swing moment. Though those moments do exist, they aren’t common. My risk-reducing methods have proven that you can make a lot of money while not betting the farm on every trade.

You can read more about my proven trading philosophy right here.


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