Capitalogix

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?

How Long Do You Have Left?

We only have a limited time on this earth - and a lot of it is taken by frivolous activities. 

How much time do you think the average millennial spends on their phones ... or a baby-boomer sits in front of the TV?

The answer is a lot.

via dailyinfographic

Nine years in front of entertainment devices - another 10.5 years spent working.  You get the idea.

If you have goals you want to accomplish, aspirational travel, and lifestyle plans - this really puts the idea of finding and living your passion in perspective. 

Do you have the time to waste it?

VisualCapitalist put together a chart projecting longevity based on 2020 mortality rates.

 

via visualcapitalist
 

I turned 60 this July, so I only have 20 years left, according to this calculator.   I expect more!

There are some interesting statistical facts in this; for example, an average American baby boy can expect to live until 74 ... but if that boy turns 21, his life expectancy jumps to over 75. 

While these numbers seem pretty high, there are two things to consider. First, COVID heavily reduced these numbers because mortality rates went up. That likely won't continue (though it will likely continue to affect your insurance premiums and pension benefits). 

Also, remember that these numbers are based on 2020 averages, which likely differ from you (specifically considering your race, income, location, etc.). These numbers also don't take into account expected medical and technology advances, etc. 

Finally, I think Purpose is one of the most important catalysts of longevity. People often die when they retire ... not because they're done working, but because they're done striving. 

If you're not growing, you're dying!

Revenge Of The Nerds: 5 Years later

AI is Hot!

In 2018, the local news did a brief story about Capitalogix - centered around finding tech talent ... and how hard it can be. 

It has only become harder since then. In part because of the growing demand for tech talent ... and in part because success today requires a higher level of mathematical, statistical, and innovative problem-solving talent than ever before.

And that's only part of the reason that I'm proud of our team!

The robots aren't coming for our jobs.  We're creating the robots, the AI, and the automation. 

The secret to great AI is that it still has a heartbeat. 

Here's the article. You can watch the video below. 

via NBC DFW

We are always hiring.

It's not enough to invest in the right ideas or technologies. You have to invest in the right people as well. 

"Standing still is moving backward ... so you don't only need new technology, you need a new level of data scientists –  a new level of professional that can think about what's possible, rather than how to do what we want to do right now."

Even though we've got an incredible edge now. I recognize that edges decay faster than ever. The trick is to stay ahead.  

I can predict that the future is bright ... And I know that the best way to predict the future is to create it.

Onwards!

The Cost Of Thinking Linearly In Today's Age

Humans can’t do a lot of things.

Honestly, the fact that we’re at the top of the food chain is pretty miraculous. 

We’re slow, we’re weak, and we’re famously bad at understanding large numbers and exponential growth. 

Our brains are hardwired to think locally and linearly.

It’s a monumental task for us to fathom exponential growth … let alone its implications. 

Think how many companies have failed due to that inability … RadioShack didn’t foresee a future where shopping was done online.  Kodak didn’t think digital cameras would replace good ol’ film.  Blockbuster dismissed a future where people would want movies in their mailboxes because they were anchored to the belief that “part of the joy is seeing all your options!” They didn’t even make it long enough to see “Netflix and Chill” become a thing. 

 

via Diamandis

Human perception is linear.  Technological growth is exponential.

There are many examples.  Here is one Diamandis calls “The Kodak Moment.”

In 1996, Kodak was at the top of its game, with a market cap of over $28 billion and 140,000 employees.

Few people know that 20 years earlier, in 1976, Kodak had invented the digital camera.  It had the patents and the first-mover advantage.

But that first digital camera was a baby that only its inventor could love and appreciate.

That first camera took .01 megapixel photos, took 23 seconds to record the image to a tape drive, and only shot in black and white.

Not surprisingly, Kodak ignored the technology and its implications.

Fast forward to 2012, when Kodak filed for bankruptcy – disrupted by the very technology that they invented and subsequently ignored.

via Diamandis

Innovation is a reminder that you can’t be medium-obsessed.  Kodak’s goal was to preserve memories.  It wasn’t to sell film.  Blockbuster’s goal wasn’t to get people in their stores; it was to get movies in homes.  

Henry Ford famously said: “If I had asked people what they wanted, they would have said faster horses.” Steve Jobs was famous for spending all his time with customers but never asking them what they wanted.

Two of our greatest innovators realized something that many never do.  Being conscientious of your consumers doesn’t necessarily mean listening to them.  It means thinking about and anticipating their wants and future needs.

Tech and AI are creating tectonic forces throughout industry and the world.  It is time to embrace and leverage what that makes possible.  History has many prior examples of Creative Destruction (and what gets left in the dust).

Opportunity or Chaos …  You get to decide.

Onward!