Capitalogix

The 2.9 Trillion Dollar Drop

Last Friday was the stock market’s worst day since COVID. 

The media says weak job reports and recessionary fears fueled it.  Geopolitics might have played a part, too.

Over 2.9 Trillion Dollars got wiped out. 

To visualize what happened, here is a market heatmap of the S&P 500 index stocks categorized by sectors and industries.  Size represents market cap.  There was very little green in a sea of red.

 

via FinViz

Last year, I asked if we would see a recession in 2024?  Here is an excerpt from that post:

 

I want to remind people that the U.S. is resilient, and it seems like public sentiment is moving in a positive direction. 

That said, 84% of CEOs and 69% of consumers think we're headed toward a recession. Meanwhile, the Fed is positive we won't ... and banks are almost positive we won't. 

It seems like they know something we don't ... or maybe vice versa. 

To lend some credence to the bullish sentiment, consumer spending is still high despite inflation and interest rates. In addition, Retailers are still posting solid earnings.

Unfortunately, we're increasingly seeing consumers resort to borrowing (including short-term lending options) to pay for goods. Household debt has hit a record high of $17 trillion in March.

— Are We Going To See A Recession In 2024?, October 2023

Yes, last week was potentially alarming.  Even the ordinarily resilient tech giants took a hit. 

With the unemployment rate reaching 4.3% in July, the three-month moving average is at least 0.5 percentage points above the minimum of the previous 12 months’ averages.  This triggers the Sahm rule, which supposedly signals a recession.  According to the rule, reaching the 0.5% threshold indicates a recession.  When the jobless rate rises quickly, it suggests the economy is slumping.

But, even the inventor of the rule, Claudia Sahm, says the doomsday narrative may be overblown. 

I’m not here to tell you that everything is sunshine and roses, but I am here to remind you that no indicator exists in a vacuum.  While the negative performance is real, household income is still growing, and consumer spending and business investment remain resilient.

Not to mention that with graduation, there’s a massive increase in the workforce, which also impacts the numbers. 

Recessions can build slowly - but come quickly - but as they build, there is time to react ... and even better - there is time to not ‘react’ but ‘respond’. 

AI will likely impact the workforce, business, and eventually ... the economy. 

I’ve learned that the market often feels random because you can’t predict events like global pandemics, threats, assassinations, or cybersecurity outages.

Over time, I’ve focussed less on guessing what will happen and more on responding faster and better to what happens.

With that said, I do have an opinion here.  It’s an election year, and I suspect the government will push every button and pull every lever to boost the market leading into November.  Even though markets and the economy are not the same thing, many voters believe they are.  So, I would say this correction is perfectly timed ... and I anticipate a steady ramp-up so that people feel as good as possible about the economy when they vote.

What do you think is going to happen?

2024 Update: What Happens In An Internet Minute

The Internet is both timeless and timely in an interesting way.  While what's popular changes seemingly instantly, and what we're capable of doing on it continues to grow exponentially.  Ultimately, the Internet is the digital town square of a global village, where all types and professions gather. 

In 2011, I first wrote about what happens in 60 seconds on the Internet. 

I've since updated the article a few times.

• 2014
• 2018

Each time I write the article, I'm in awe at the amount of data we create and how much it has grown.  For example, looking back to 2011, I was amazed that users created 600+ new videos and 60 new blog posts each minute.  Those numbers seem quaint today. 

 

via DOMO

Shortly after I started sharing the articles, Data Never Sleeps started standardizing the data, which is helpful. 

Today, the Internet reaches 5.4 billion people.  Most of them also use social media. 

To add some more perspective, 

• In 2008, 1.4 billion people were online; in 2015, we were at 3 billion.  Now, that number has almost doubled again. 
• In 2008, Facebook only had 80 million users, and Twitter (now X) had 2 million users.
• In 2008, there were 250 million smartphones, and now there are almost 7 billion of them!

It is mind-blowing to consider what happens each minute on the Internet today.  For example, the 104,000 hours spent on Zoom represents a significant societal shift ... and the over 500 hours of video uploaded to YouTube highlights the incredible amount of content that's being created to share. 

In 2023, the world created approximately 120 zettabytes of data ... which breaks down to approximately 337,000 petabytes of data a day.  Broken down even further, it calculates to more than 15 Terabytes of new data created per person. 

The calculations about what happens in an Internet minute will change rapidly again because of AI.  Consider the amount of computing power and data it takes to power all of these new GPTs. Now, imagine the amount of new data that AI is creating.  Then, try to imagine the challenge we'll have figuring out what's real, what's made up, and what is simply wrong or intentionally misleading.

In addition, as more devices and digital WHOs start creating and sharing data, it's hard to fathom the ramifications and sheer increase in data. 

I'm curious about what the next five years have in store for us as we approach the 40th anniversary of the World Wide Web. 

Cognitive Biases & The Consequences of Labeling

“Words can be twisted into any shape. Promises can be made to lull the heart and seduce the soul. In the final analysis, words mean nothing.
They are labels we give things in an effort to wrap our puny little brains around their underlying natures,
when ninety-nine percent of the time the totality of the reality is an entirely different beast.
The wisest man is the silent one. Examine his actions. Judge him by them.”
― Karen Marie Moning

Continuing with the theme of cognitive biases, the upcoming election has me thinking about the consequences of labeling things, creating boxes, and simplifying ideas into news-ready headlines.

With more news sources than ever and less attention span, you see ideas packaged into attention-grabbing parts.  The focus isn't on education or the issues, but on getting the click, making your stay on their page longer, and sending you to a new article utterly unrelated to why you clicked on the page.

Complex issues are simplified – not even into their most basic forms – but instead into their most divisive forms ... because there's no money in the middle.

via Quote Investigator

The amplified voices are those on the fringe of the average constituents' beliefs – precisely because those are the ones who are often the most outspoken.  We might think that because they're the voices we hear, these fringe messages fairly represent what people like us believe or think ... but they rarely do. 

Issues that should be bipartisan have been made "us" versus "them," "liberal" versus "conservative," or "right" versus "wrong." The algorithms many of our information sites use create echo chambers that increase radicalization and decrease comprehension. 

Identity politics have gotten so strong that you see families breaking apart and friend groups disintegrating ... because people can't imagine sharing a room with someone with whom they don't share the same values. 

In psychology, heuristics are mental models that help you make decisions easier.  They're a starting point to save mental bandwidth, allowing you to spend more brain cycles on the important stuff.

That's a great use of "boxes" and "simplification"… but it shouldn't eliminate deeper and more nuanced thought on important issues. 

Most situations are nuanced, and the "correct" answer changes as you change your vantage point.

In an ideal world, we'd consider every angle.  I recognize that's not realistic.

Instead, I encourage you to remember to continue to think and learn ... even about things you already know.  And, if you become familiar with the most common cognitive biases, you can hopefully identify them in your thinking and decision-making.

Confirmation Bias is one of the more common forms of cognitive bias.  Here is an infographic that lists 50 common cognitive biases.  Click to explore further.

 

via VisualCapitalist

Important issues deserve more research.  New insights happen between the boundaries of what we know and don't.  Knowledge comes from truly understanding the border between what you are certain and uncertain about. 

I challenge you to look beyond the headlines, slogans, and talking points you like most.  Look for dissenting opinions and understand what's driving their dissent.  Are they really blind or dumb (or are their value systems just weighted differently)?

Not everything needs to be boxed.  Not everything needs to be simple.  You should explore things and people outside of your comfort zone and look to see things from their point of view ... not your own. 

Recently, I've started using a website and news app called Ground News.  They claim to be a news platform that makes it easy to compare news sources, read between the lines of media bias, and break free from algorithms. 

As discussed above, online news and ad-driven algorithms have made it profitable for news outlets to embrace a position on the bias spectrum to target specific consumers.   That bias in the media affects everything from what events receive coverage to how a news outlet frames those events in their reporting.

As media outlets narrow their perspective and range of coverage, I use Ground to help me get a well-rounded view of important issues and become aware of my blind spots.

Applying This Lesson  

“I am ashamed to think how easily we capitulate to badges and names, to large societies and dead institutions.”
― Ralph Waldo Emerson, Self-Reliance

I love learning a lesson in one space and applying it to other spaces.  It's one of the cool things about AI.  An algorithm can learn rules in the construction space that may help in the medicine or trading space.  Everything's a lesson if you let it be.

In that vein, the lesson on labeling also applies to yourself and your business.  Don't get me wrong - naming things is powerful.  It can help make the intangible tangible.  However, don't let the label (or your perception of the label) stop you from achieving something greater. 

Many things are true because we believe them to be, but when we let go of past beliefs, the impossible becomes possible, and the invisible becomes visible.  

Hope that helps. 

“Words can be twisted into any shape. Promises can be made to lull the heart and seduce the soul. In the final analysis, words mean nothing.
They are labels we give things in an effort to wrap our puny little brains around their underlying natures,
when ninety-nine percent of the time the totality of the reality is an entirely different beast.
The wisest man is the silent one. Examine his actions. Judge him by them.”
― Karen Marie Moning

Continuing with the theme of cognitive biases, the upcoming election has me thinking about the consequences of labeling things, creating boxes, and simplifying ideas into news-ready headlines.

With more news sources than ever and less attention span, you see ideas packaged into attention-grabbing parts.  The focus isn't on education or the issues, but on getting the click, making your stay on their page longer, and sending you to a new article utterly unrelated to why you clicked on the page.

Complex issues are simplified – not even into their most basic forms – but instead into their most divisive forms ... because there's no money in the middle.

via Quote Investigator

The amplified voices are those on the fringe of the average constituents' beliefs – precisely because those are the ones who are often the most outspoken.  We might think that because they're the voices we hear, these fringe messages fairly represent what people like us believe or think ... but they rarely do. 

Issues that should be bipartisan have been made "us" versus "them," "liberal" versus "conservative," or "right" versus "wrong." The algorithms many of our information sites use create echo chambers that increase radicalization and decrease comprehension. 

Identity politics have gotten so strong that you see families breaking apart and friend groups disintegrating ... because people can't imagine sharing a room with someone with whom they don't share the same values. 

In psychology, heuristics are mental models that help you make decisions easier.  They're a starting point to save mental bandwidth, allowing you to spend more brain cycles on the important stuff.

That's a great use of "boxes" and "simplification"… but it shouldn't eliminate deeper and more nuanced thought on important issues. 

Most situations are nuanced, and the "correct" answer changes as you change your vantage point.

In an ideal world, we'd consider every angle.  I recognize that's not realistic.

Instead, I encourage you to remember to continue to think and learn ... even about things you already know.  And, if you become familiar with the most common cognitive biases, you can hopefully identify them in your thinking and decision-making.

Confirmation Bias is one of the more common forms of cognitive bias.  Here is an infographic that lists 50 common cognitive biases.  Click to explore further.

 

via VisualCapitalist

Important issues deserve more research.  New insights happen between the boundaries of what we know and don't.  Knowledge comes from truly understanding the border between what you are certain and uncertain about. 

I challenge you to look beyond the headlines, slogans, and talking points you like most.  Look for dissenting opinions and understand what's driving their dissent.  Are they really blind or dumb (or are their value systems just weighted differently)?

Not everything needs to be boxed.  Not everything needs to be simple.  You should explore things and people outside of your comfort zone and look to see things from their point of view ... not your own. 

Recently, I've started using a website and news app called Ground News.  They claim to be a news platform that makes it easy to compare news sources, read between the lines of media bias, and break free from algorithms. 

As discussed above, online news and ad-driven algorithms have made it profitable for news outlets to embrace a position on the bias spectrum to target specific consumers.   That bias in the media affects everything from what events receive coverage to how a news outlet frames those events in their reporting.

As media outlets narrow their perspective and range of coverage, I use Ground to help me get a well-rounded view of important issues and become aware of my blind spots.

Applying This Lesson  

“I am ashamed to think how easily we capitulate to badges and names, to large societies and dead institutions.”
― Ralph Waldo Emerson, Self-Reliance

I love learning a lesson in one space and applying it to other spaces.  It's one of the cool things about AI.  An algorithm can learn rules in the construction space that may help in the medicine or trading space.  Everything's a lesson if you let it be.

In that vein, the lesson on labeling also applies to yourself and your business.  Don't get me wrong - naming things is powerful.  It can help make the intangible tangible.  However, don't let the label (or your perception of the label) stop you from achieving something greater. 

Many things are true because we believe them to be, but when we let go of past beliefs, the impossible becomes possible, and the invisible becomes visible.  

Hope that helps. 

Making News Beautiful Again

My mother watches the news religiously.  To her credit, she watches a variety of sources and creates her own takeaways based on them.  Regardless, there's a common theme in all the sources she watched – they focus on fear or shock-inducing stories with a negative bias.  As you might guess, I hear it when I talk with her.

While I value being informed, I also value things that nourish or make you stronger (as opposed to things that make you weak or less hopeful).

Negativity Sells. 

Sure, news sources throw in the occasional feel-good story as a pattern interrupt ... but their focus skews negative.  History shows that stories about improvement or the things that work simply don't grab eyeballs, attention, or ratings as consistently as negativity-focused stories do.

The reality is that negativity sells.  If everything were great all the time, people wouldn't need to buy as many products, they wouldn't need to watch the news, and this cycle wouldn't continue.

It's worth acknowledging and understanding the perils our society is facing, but it's also worth focusing on the ways humanity is expanding and improving.

As a brief respite from the seemingly unending stream of doom and gloom, Information Is Beautiful has a section focused on "Beautiful News".  It's a collection of visualizations highlighting positive trends, uplifting statistics, and creative solutions.  It's updated daily and can be sorted by topic.  I suggest you check it out.

 

Beautiful News via Information Is Beautiful

If you're looking for more "good news," here's a list of 10 sources focusing on good news. 

Let me know if you have a site you'd like to share.

Have a great week!

Some Timeless Wisdom From Socrates

Small distinctions separate wise men from fools ... Perhaps most important among them is what the wise man deems consequential. 

This post discusses Socrates' Triple Filter Test, which involves checking information for truth, goodness, and usefulness.  It also explores how this concept applies to decision-making in business and life by focusing on important information and filtering out the rest.  The key to making better choices and staying focused is to avoid damaging or irrelevant information.

Socrates' Triple Filter

In ancient Greece, Socrates was reputed to hold knowledge in high esteem.  One day an acquaintance met the great philosopher and said, "Do you know what I just heard about your friend?"

"Hold on a minute," Socrates replied. "Before telling me anything, I'd like you to pass a little test. It's called the Triple Filter Test."

"Triple filter?"

"That's right," Socrates continued.  "Before you talk to me about my friend, it might be a good idea to take a moment and filter what you're going to say. That's why I call it the triple filter test.

The first filter is Truth.  Have you made absolutely sure that what you are about to tell me is true?"

"No," the man said, "Actually I just heard about it and…"

"All right," said Socrates. "So you don't really know if it's true or not. Now let's try the second filter, the filter of Goodness.  Is what you are about to tell me about my friend something good?"

"No, on the contrary…"

"So," Socrates continued, "You want to tell me something bad about him, but you're not certain it's true.  You may still pass the test though, because there's one filter left.  The third filter is Usefulness.  Is what you want to tell me about my friend going to be useful to me?"

"No, not really."

"Well," concluded Socrates, "If what you want to tell me is neither true, nor good, nor even useful … then why tell it to me at all?"

With all the divisiveness in both media and in our everyday conversations with friends, family, and strangers ... this is a good filter for what you say, what you post, and even how you evaluate markets, the economy, or a business opportunity. 

How Does That Apply to Me or Trading?

The concept of Socrates' Triple Filter applies to markets as well.

When I was a technical trader, rather than looking at fundamental data and scouring the news daily, I focused on developing dynamic and adaptive systems and processes to look at the universe of trading algorithms to identify which were in phase and likely to perform well in the current market environment.

That focus has become more concentrated as we've transitioned to using advanced mathematics and AI to understand markets. 

Filter Out What Isn't Good For You.

In contrast, there are too many ways that the media (meaning the techniques, graphics, music, etc.), the people reporting it, and even the news itself appeal to the fear and greed of human nature.

Likewise, I don't watch the news on TV anymore.  It seems like story after story is about terrible things.  For example, during a recent visit with my mother, I listened to her watch the news.  There was a constant stream of "oh no," or "oh my," and "that's terrible".  You don't even have to watch the news to know what it says.

These concepts also apply to what you feed your algorithms.  Garbage in, garbage out.  Just because you can plug in more data doesn't mean that data will add value.  Deciding what "not to do" and "what not to listen to" is equally as important as deciding what to do. 

Artificial intelligence is exciting, but artificial stupidity is terrifying. 

What's The Purpose of News for You?

My purpose changes what I'm looking for and how much attention I pay to different types of information.  Am I reading or watching the news for entertainment, to learn something new, or to find something relevant and actionable?

 

 

One of my favorite activities is looking for new insights and interesting articles to share with you and my team.  If you aren't getting my weekly reading list on Fridays - you're missing out.  You can sign up here. 

By the way, I recently found a site, Ground News, that makes it easy to compare news sources, read between the lines of media bias, and break free from the blinders the algorithms put on what we see.  I'd love to hear about tools or sites you think are worth sharing.

Getting back to Socrates' three filters and business, I often ask myself: is it important, does it affect our edge, or can I use it as a catalyst for getting what we want?

There's a lot of noise out there competing for your attention.  Stay focused. 

Onwards!

The Evolution of Michael Jackson

This lighthearted post has something to do with artificial ...  but nothing to do with artificial intelligence.

While doing my weekly reading and web browsing (which is how I pick those links you probably think an algorithm selects), I happened upon a post about Michael Jackson on Twitter (now called X), and I enjoyed it (or at least was drawn to click and watch it). 

I grew up a huge Michael Jackson fan. As a kid, I watched the Jackson 5 Saturday morning cartoon show. His albums were the soundtrack to my college years. Later, my first wife and I saw him in concert several times. We shared that love with our youngest son, Zach. 

It's funny to look back on, but Zach used to dance to Michael Jackson's songs on stage at his Elementary School talent shows or at random restaurants. There was no choreography ... but lots of movement. I still smile when I think about it. 

You might smile (or shake your head) while watching this short video chronicling the evolution of  Michael Jackson's face changes from birth to death. 

 

via MikeBeast

It's a staggering difference. I won't pretend to know what led him to make the changes, but they're substantial. 

That being said, his music is both timely and timeless - which is very rare. He managed to make music in each era that fit in with the times but still felt very Michael Jackson. 

 

via MikeBeast

 

Gone too soon!

March Is Always Madness ...

March Madness is in full swing and will have the world's attention for a few more days.  As you can guess, almost no one has a perfect bracket anymore.  Yale beat Auburn, James Madison beat Wisconsin, Michigan State beat Mississippi State, and by the end of day 1, only 2,000 brackets remained intact.  That's .008% of all brackets submitted. 

Before 24/7 sports channels, people watched the weekly show "The Wide World of Sports."  Its opening theme promised "The thrill of victory and the agony of defeat!" and "The human drama of athletic competition." That defines March Madness.

The holy grail is mighty elusive in March Madness (as in most things).  For example, the odds of getting the perfect bracket are 1 in 9,223,372,036,854,775,808 (2.4 trillion based on a Duke Mathematician's formula that takes into account rank).  It's easier to win back-to-back lotteries than picking a perfect bracket.  Nonetheless, I bet you felt pretty good when you filled out your bracket.

 

via Duke University

Here's some more crazy March Madness Stats: 

• 80 million tournament brackets were completed
• Companies expect $4 Billion of corporate losses due to employees tracking games
• The highest-paid NCAA coach, Kansas's Bill Self, makes a salary of $9.6 million
• In 2018, it was estimated that March Madness generated $10 Billion in gambling (twice as much as the Super Bowl)

 

Feeding the Madness

"Not only is there more to life than basketball, there's a lot more to basketball than basketball." - Phil Jackson

In 2017, I highlighted three people who were (semi) successful at predicting March Madness: a 13-year-old who used a mix of guesswork and preferences, a 47-year-old English woman who used algorithms and data science (despite not knowing the game), and a 70-year-old bookie who had his finger on the pulse of the betting world.  None of them had the same success even a year later.

Finding an edge is hard - Maintaining an edge is even harder.

That's not to say there aren't edges to be found. 

Bracket-choosing mimics the way investors pick trades or allocate assets.  Some people use gut feelings, some base their decisions on current and historical performance, and some use predictive models.  You've got different inputs, weights, and miscellaneous factors influencing your decision.  That makes you feel powerful.  But knowing the history, their ranks, etc., can help make an educated guess, and they can also lead you astray. 

The allure of March Madness is the same as gambling or trading.  As sports fans, it's easy to believe we know something the layman doesn't.  We want the bragging rights of that sleeper pick, of our alma mater winning, of the big upset. 

You'd think an NCAA analyst might have a better shot at a perfect bracket than your grandma or musical-loving co-worker.

In reality, several of the highest-ranked brackets every year are guesses. 

The commonality in all decisions is that we are biased.  Bias is inherent to the process because there isn't a clear-cut answer.  We don't know who will win or what makes a perfect prediction. 

Think about it from a market efficiency standpoint.  People make decisions based on many factors — sometimes irrational ones — which can create inefficiencies and complexities.  It can be hard to find those inefficiencies and capitalize on them, but they're there to be found. 

In trading, AI and advanced math help remove biases and identify inefficiencies humans miss.

Can machine learning also help in March Madness?

“The greater the uncertainty, the bigger the gap between what you can measure and what matters, the more you should watch out for overfitting - that is, the more you should prefer simplicity” - Tom Griffiths

The data is there.  Over 100,000 NCAA regular-season games were played over the last 25+ years, and we generally have plenty of statistics about the teams for each season.  There are plenty of questions to be asked about that data that may add an extra edge. 

That being said, people have tried before with mediocre success.  It's hard to overcome the intangibles of sports - hustle, the crowd, momentum - and it's hard to overcome 1 in 9.2 quintillion odds. 

Two lessons can be learned from this:

1. People aren't as good at prediction as they predict they are.
2. Machine Learning isn't a one-size-fits-all answer to all your problems.

Something to think about.

 

Feeding the Madness

"Not only is there more to life than basketball, there's a lot more to basketball than basketball." - Phil Jackson

In 2017, I highlighted three people who were (semi) successful at predicting March Madness: a 13-year-old who used a mix of guesswork and preferences, a 47-year-old English woman who used algorithms and data science (despite not knowing the game), and a 70-year-old bookie who had his finger on the pulse of the betting world.  None of them had the same success even a year later.

Finding an edge is hard - Maintaining an edge is even harder.

That's not to say there aren't edges to be found. 

Bracket-choosing mimics the way investors pick trades or allocate assets.  Some people use gut feelings, some base their decisions on current and historical performance, and some use predictive models.  You've got different inputs, weights, and miscellaneous factors influencing your decision.  That makes you feel powerful.  But knowing the history, their ranks, etc., can help make an educated guess, and they can also lead you astray. 

The allure of March Madness is the same as gambling or trading.  As sports fans, it's easy to believe we know something the layman doesn't.  We want the bragging rights of that sleeper pick, of our alma mater winning, of the big upset. 

You'd think an NCAA analyst might have a better shot at a perfect bracket than your grandma or musical-loving co-worker.

In reality, several of the highest-ranked brackets every year are guesses. 

The commonality in all decisions is that we are biased.  Bias is inherent to the process because there isn't a clear-cut answer.  We don't know who will win or what makes a perfect prediction. 

Think about it from a market efficiency standpoint.  People make decisions based on many factors — sometimes irrational ones — which can create inefficiencies and complexities.  It can be hard to find those inefficiencies and capitalize on them, but they're there to be found. 

In trading, AI and advanced math help remove biases and identify inefficiencies humans miss.

Can machine learning also help in March Madness?

“The greater the uncertainty, the bigger the gap between what you can measure and what matters, the more you should watch out for overfitting - that is, the more you should prefer simplicity” - Tom Griffiths

The data is there.  Over 100,000 NCAA regular-season games were played over the last 25+ years, and we generally have plenty of statistics about the teams for each season.  There are plenty of questions to be asked about that data that may add an extra edge. 

That being said, people have tried before with mediocre success.  It's hard to overcome the intangibles of sports - hustle, the crowd, momentum - and it's hard to overcome 1 in 9.2 quintillion odds. 

Two lessons can be learned from this:

1. People aren't as good at prediction as they predict they are.
2. Machine Learning isn't a one-size-fits-all answer to all your problems.

Something to think about.

Checking Back In with Dr. Doom

In 2018, while in New York for work, I was invited to a party that ended up being a pretty unique experience. 

The rules were that for the first hour it would be first names only, no discussion of what you do, etc. Part of the fun was figuring out who was there and why they were special. And, there were a lot of pretty impressive people in the room. As I was wondering who was able to bring all these experts and thought leaders into one home for a house party, I found it was Nouriel Roubini - the infamous Harvard economist known as Dr. Doom. 

Nouriel Roubini's predictions have earned him the nicknames "Dr. Doom" and "PermaBear" in the media. He predicted the housing bubble crash in 2007-2008, and has extensively studied the collapse of emerging economies. 

So, after a tumultuous few years for the global economy, I thought I'd check back in and see what he was saying. 

It turns out he's less pessimistic than you would guess. He's pretty optimistic about 2024 growth and not particularly worried about a recession—though he is expecting a downturn. 

He also thinks there's a possibility that growth remains above potential, and inflation remains sticky. That would be good news for the economy, but bad news for markets – as the Fed likely wouldn't cut as much or as soon as people are hoping for. 

Now, Nouriel has been wrong before, and I don't trust any singular pundit. My mindset is to listen to voices that don't already believe what I do. I tend to be optimistic as a rule, and I've been optimistic on things like blockchain, whereas Nouriel has been staunchly negative.

But, he's a smart and educated voice who can justify his opinions. And I end up more educated - and often modifying my stance a little bit - based on the context he's able to give. 

By the way, as I was editing this post, I saw that Chase CEO Jamie Dimon and billionaire hedge fund founder Ray Dalio admit they got warnings on the US economy wrong — for now.

Are you listening to voices outside of your preferred channels?

An Early Look At The Upcoming Election

You know it's election season when Jon Stewart steps back in to host The Daily Show. 

Unsurprisingly, the antagonism and hatred from both sides of the fence are still in full force - now with extra boosts from fake news and AI's influence. 

Surprisingly (to me), the frontrunners are still Biden and Trump, despite them being 81 and 77, respectively ... or despite Trump's several cases against him and Biden's horrible approval ratings ... or ... (it seems like we could keep finding reasons to be surprised).

Truthfully, I am surprised neither party is running someone "better" in this election. 

It feels like almost everyone I talk with is voting for the "lesser of two evils," even though they know it may not serve the greater good.

Here is a chart showing third-year approval ratings of first-term Presidents since WWII.

 

I thought the last Presidential election was pretty interesting, but this one's shaping up to be even more so. If it were fiction, no one would believe it.

The most popular issue on people's minds is the economy, but we also have controversial issues around trust in the government, immigration, abortion, foreign policy, and even the fate of Democracy in America itself. 

For all the economic woes and complaints, the unemployment rate remains low, GDP growth is strong, and the S&P 500 recently hit record highs. Yet, Americans are still feeling it in their pockets as the price of goods has gone up. 

It is a good reminder that the "facts" are often less important than how it feels. 

It is still early, but what do you think will happen?

Predictions for 2024

At the beginning of 2022, I shared VisualCapitalist's predictions for the year, and then at the end of the year, we checked to see how well they did. 

Since they posted a 2024 version, I figured, why not do it again? 

via VisualCapitalist.

You can find forecasts anywhere ... but since they're basically guesswork and I don't know what data they are using or excluding, I don't spend much time on them.

That said, I found this infographic easy to look at and think about. So, it's as good a place to start as any. Also, VC draws from a database of 700 expert forecasts to make this chart. 

Some are pretty easy to predict - like GenAI's shift to text-to-video. Or, an increase in lawsuits and regulations around AI. Others are questionable. In the past few months, we've seen TikTok investing heavily in the growth of its e-commerce business. Insiders have stated they believe they're trying to compete with Amazon. We'll see how that goes?

On a positive note, it seems like most are predicting positive effects on the market & economy, including a decrease in inflation and interest rates, bonds on the way back, and record highs in the S&P 500.

I'd love to hear your predictions. What do you think?