Market Commentary

  • Who’s The Most Innovative?

    Innovation means a lot of different things. It changes based on where we are in history, the amount of time we're considering, and the scale. 

    Language was an innovation, the piece of plastic on the edge of your shoelaces was an innovation (called the Aglet), changing time signatures in music was an innovation in history, and so is artificial intelligence. 

    Defining and measuring innovation is difficult even in your business … but the Global Innovation Index attempts to do it globally. It does so by measuring several factors, like:

    • Knowledge and Technology Outputs -  patents & high-tech manufacturing
    • Human Capital & Research -  number of researchers & global corporate R&D investment
    • Business & Market Sophistication – knowledge-intensive employment & financing/VCs for startups
    • Creative Output, Institutions, and Infrastructure – trademarks, access to resources, and policy

    Ranked: The Most Innovative Countries in 2023

    via visualcapitalist

    By this metric, Switzerland and Sweden take the top two spots – followed by the U.S. and the U.K.

    Honestly, the list surprised me some. Some names I expected to be on the list – or higher on the list – didn't crack the top ten. Though Switzerland and Sweden have dominated this list for many years. 

    A topic I'm very passionate about right now is patents – and how valuable they can be to your business. Here's a previous article I wrote on the subject, but I'll revisit it soon with new ideas and distinctions. 

  • Skill Versus Luck: A Sustainable Competitive Advantage

    In 2016, I wrote a variation of this article focused on trading … but it's even more relevant today as I spend more time talking with entrepreneurs and AI enthusiasts. 

    There are many lucky people in the business world.  Perhaps they made a good decision at the right time – and are now on top of the world.  There's nothing wrong with luck.  But, the goal is to make sure your success isn't predicated on it.  Why?  Because you might get lucky once, but it's unlikely you'll get lucky every time. 

    Luck favors the prepared … and those who understand the difference between skill and luck. 

    First, let's talk about luck.  Think about a nationwide coin-flipping contest.  Initially, each citizen is paired up with another for a contest.  The winner goes on to the next round.  Think how many rounds you would need to win to be City Champion, State Champion, Regional Champion, etc. 

    At the end, someone would have won many coin-flip contests.  Assuming they didn't cheat, they were lucky … but does the winner have an edge?  If so, what could it be? 

    If you followed the contest from beginning to end, I'm sure you could imagine the finalists doing articles or interviews about how their mindfulness practice gives them an edge … Or, the law of attraction …. Or, how the power of prayer is the difference. 

    Meanwhile, sometimes, the most straightforward rationale provides the best explanation.  Somebody had to win that contest – and luck was the reason.

     

    Premium Photo | Tossing euro coin, heads or tails you decide

    Finding The Edge

    Likewise, just because a product or business makes money doesn't prove it has an edge.  For example, at OpenAI's Developers Conference last week, they announced several new models and internally created tools that cannibalize or obsolete many tools or businesses built on their platform.  Meanwhile, they also announced several new models and tools that will help create new businesses.  But, the app developers who have been made redundant are out of luck. 

    I saw the same thing with the rush of .com companies in the late '90s.  The ones that made it are now the underpinning of a new era, but they climbed out of a sea of failed businesses that might have even been better businesses – they were just unlucky (e.g., Betamax vs. VHS). 

    Simply relying on whether something is profitable NOW means you have both the chance that you have an edge – and also that you got lucky.
     
    If it isn't just a matter of winning, how do we know if we're skillful?  In trading, we would call this alpha.  We are searching for clues to help find systems with an edge … or at least have an edge in certain market conditions.

    Unfortunately, I can't give you the one rule to follow to identify skill vs. luck, but it's much easier to find the answer if you're asking yourself the question. 

    Internally, we've built validation protocols to help filter lucky systems and systems that can't repeat their results on unseen data.  

    It is exciting as we solve more of the bits and pieces of this puzzle.

    What we have learned is that one of the secrets to long-term success is (unsurprisingly) adaptability. 

    What that looks like for us is a library of systems ready to respond to any market condition – and a focus on improving our ability to dynamically select the systems that are "in-phase".  The secret isn't predicting the future, but responding faster – and more reliably – to changing environments. 

    From a business perspective, this looks like being willing to adapt to and adopt new technologies without losing track of a bigger 'why' like we talked about in last week's article. 

    A Practical Example

    When we first wrote on this, one of Capitalogix's advisors wrote back to see if they understood the coin-flipping analogy.

    The odds of flipping a coin and getting heads 25 times in a row is roughly 1-in-33 million. So if we have 33 million flippers and 100 get 25 heads in a row, statistically that is very improbable.  We can deduce that group of 100 is a combination of some lucky flippers, but also that some have a "flipping edge."  We may not be able to say which is which, but as a group our 100 will still consistently provide an edge in future flip-offs.

    Well, that is correct.  If we were developing coin-flipping agents, that would be as far as we could go.  However, we are in luck because our trading "problem" has an extra dimension, which makes it possible to filter out some of the "lucky" trading systems.

    Determining Which are the Best Systems.

    There are several ways to determine whether a trading system has a persistent edge.  For example, we can look at the market returns during the trading period and compare and contrast that with trading results.

    This is significant because many systems have either a long or short bias.  That means even if a system does not have an edge, it would be more likely to turn a profit when its bias aligns with the market. 

    You can try to correct that bias using math and statistical magic to determine whether the system has a predictive edge. 

    It Is a Lot Simpler Than It Sounds.

    Imagine a system that picks trades based on a roulette spin.  Instead of numbers or colors, the wheel is filled with "Go Long" and "Go Short" selections.  As long as the choices are balanced, the system is random.  But what if the roulette wheel had more opportunities for "long" selections than "short" selections?

    3) ROULETTE BALLS 3/8 INCH USED ON ROULETTE WHEEL BRAND NEW - FREE SHIPPING * | eBay

    This random system would appear to be "in-phase" whenever the market is in an uptrend.  But does it have an edge? 

    One Way To Calculate Whether You Have An Edge.

    Let's say that you test a particular trading system on hourly bars of the S&P 500 Index from January 2000 until today. 

    1. The first thing you need is the total net profit of the system for all its trades.  
    2. The second thing you need to calculate is the percentage of time spent long and short during the test period.
    3. Third, you need to generate a reasonably large population of entirely random entries and exits with the same percentage of long/short time as your back-tested results (this step can be done many times to create a range of results). 
    4. Fourth, use statistical inference to calculate the average profit of these random entry tests for that same test period. 
    5. Finally, subtract that amount from the total back-tested net profit from the first step.

    According to the law of large numbers, in the case of the "roulette" system illustrated above, correcting for bias this way, the P&L of random systems would end up close to zero … while systems with real predictive power would be left with significant residual profits after the bias correction.

    While the math isn't difficult … the process is still challenging because it takes significant resources to crunch that many numbers for hundreds of thousands of Bots. 

    The good thing about RAM, CPU cycles, and disk space is that they keep getting cheaper and more powerful.

    Conclusion

    It is relatively easy to measure the wins and losses (and luck versus skill) of trading systems.  It can be complicated, but ultimately, it's just math.  The logic of the example also applies to adopting technology, starting a business, or transforming from a product-based to a platform-based business model, etc. 

    In most situations, the secret is to figure out what data is incumbent to your industry as well as what data you're creating.  Figure out how to analyze it.  Figure out how to do that consistently, autonomously, and efficiently.  Then … test. 

    It's not sexy, and it's not complicated. 

    We live in a ready, fire, aim era.  The speed of innovation is staggering, and the capital and energy needed to create an app or start a business is less than ever before … and a bias for action is powerful. 

    Luck and a bias for action will take you further than most – but it still won't take you far enough. 

    So, I'll leave you with the question… 

    If you're reading this, you've almost certainly been lucky … but have you been skillful?

  • The Difference Between Gen Z And Millennials

    I had friends in town for today's Cowboys game against the Giants. If you care, it was a massive win.

    We discussed the difference between Gen Z and Millennials on our way back from dinner last night. 

    During the conversation, my youngest, Zach (who is 30), called to tell me that his face had been sewn back together after a rugby game. 

    IMG_8003

    Wonderful. 

    But, it was a great chance to hear his opinion about the difference between Gen Z and Millennials. 

    I'm paraphrasing, but he stressed that the main difference was that he lived through a transition of technologies that they didn't experience. 

    For example, he is old enough to remember cassette tapes, floppy discs, boomboxes, and more. His first computer was an old-school Mac with a black-and-white display (how primitive). 

    So, though he didn't see the prior shifts that I did (like the invention of the color TV), he is still aware of the shift between the "old world" and the "new world" … and how radical the difference was. 

    Meanwhile, Gen Zers were raised with the technology we see today as their only reality. 

    As a result, they're much more immune to how awkward or cringy it is to share their entire life online, hopping from instant gratification to instant gratification. 

     

    GenZ-Millenials

    via blackbear

    We hear a lot of doom and gloom from (and about) Gen Z – which isn't new. The younger generations are always derided … in part because they're young.

    Nonetheless, GenZ still believes the future is bright. 

    What do you think about Gen Z? And, what differentiates them from Millennials? I'm curious.

  • 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.

    171220 Lessons From Kodak

    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!

  • Investment Themes Since The 1950s

    I tend to focus on the themes that are impacting industry and the world … but when I started this blog back in 2008, I was much more focused on investment themes … why were markets doing what they were doing, both on the micro and the macro scale? 

    Based on data from Morgan Stanley, visualcapitalist just put together a chart that looks at the key investment themes from each decade since 1950. It's a great retrospective. 

    AC-_-History-of-Investment-Cycles-Oct-25via visualcapitalist

    In the 1950s, we saw a post-war boom in European stocks, followed by a shift into "blue chip stocks."

    When I grew up, my grandparents advocated for blue chip stocks, and they held their investments until the day they died … 

    By the 1990s, when I started paying attention to markets, tech startups were taking over, and stocks weren't primarily held for years and years. Instead, they were getting calculated in weeks and months; people were trying to capitalize on a "quick trend."

    Now, a quick trend can last under a day, and the average holding time for a stock (based on trading volume) is calculated in seconds. 

    Where will investments go in the '20s? We're currently seeing massive investment in tech, specifically the platforms that enable burgeoning tech, like NVIDIA. We also see a disconnect in U.S. equity markets, with 43% of global investment, but 26% of the world's economic output.

    I think that, plus the growth in emerging markets, will result in a massive shift. Time will tell. 

    What do you think? 

  • Riding The Data Wave – Data Is Becoming a New Asset Class

    Data is the fastest-growing commodity, and is today’s “wild west” and the battlefield of today’s tech titans.  We talk about AI as this gold rush, but data is the underpinning of it all. 

    A staggering 328.77 million terabytes of data are created daily, which means around 120 zettabytes of data will be generated this year. 

    Video is still growing rapidly, but so is IoT, with more than 15% annual growth.  There are now almost 20 billion connected devices. 

    Alphabet, Amazon, Apple, Facebook, and Microsoft all have unprecedented amounts of data (and power).

    Rapid growth means little time to create adequate rules. Everyone’s jumping to own more data than the next and to protect that data from prying eyes.

    As a great example of this, I often warn people to keep their intellectual property off of ChatGPT or other hosted language models. 

    I also see it trading, but it’s pervasive in every industry and our personal lives as well. 

    Collecting basic data and using basic analytics used to be enough … but not anymore.  The game is changing. 

    For example, traders used to focus on price data … but there has been an influx of firms using alternative data sets and extraordinary hardware and software investments to find an edge.  If you’re using the same data sources as your competitors and competing on the same set of beliefs, it’s hard to find a sustainable edge. 

    Understanding the game others are playing (and the rules of that game) is important.  However, that’s only table stakes.

    Figuring out where you can find extra insight, or where you can make the invisible visible, creates a moat between you and your competition and lets you play your own game.

    Here is a quick high-level video about Data as fuel for your business.  Check it out.

     

     

    It is interesting to think about what’s driving the new world (of trading, technology, AI, etc.), which often involves identifying what drove the old world.

    History has a way of repeating itself.  Even when it doesn’t repeat itself, it often rhymes.

    With that said, the key to unlocking the pathway to the new world often comes from a new or alternative data set that lets you approach the problem, challenge, or opportunity from a different perspective.

    Before e-mails, fax machines were amazing.  Before cars, people were happy with horses and buggies.

    These comparisons help explain the importance of data in today’s new world economics.

     

    New World Economics Data Is A Precious Commodity_GapingVoid

    gapingvoid

    Data as the New Oil

    Petroleum has played a pivotal role in human advancement since the Industrial Revolution.  It fueled (and still fuels) our creativity, technology advancements, and a variety of derivative byproducts.  There are direct competitors to fossil fuels that are gaining steam, but I think it’s more interesting to compare petroleum to data due to their parallels in effect on innovation.

    Pumping crude oil out of the ground and transforming it into a finished product is not a simple process.  Yet, it is relatively easy for someone to understand the process at a high level.  You have to locate a reservoir, drill, capture the resource, and then refine it to the desired product – heating oil, gasoline, asphalt, plastics, etc. 

    The same is true for data.

    You've got to figure out what data you might have, how it might be useful, you have to figure out how to refine it, clean it, fix it, curate it, transform it into something useful, and then how to deliver it to the people that need it in their business. And even if you've done this, you then have to make people aware that it's there, that it's changing, or how they might use it. For people who do it well, it's an incredible edge. – Howard Getson

    In a sense, data fuels the information economy much like oil fuels the industrial economy.  The amount of power someone has can be correlated to their control of and access to these resources.  Likewise, things that diminish or constrain access or use of these resources can lead to extreme consequences.

    Why Data Is Better Than Oil

    The analogy works, but it’s just that, an analogy, and the more you analyze it, the more it falls apart.  Unlike the finite resource that is oil, data is all around us and increasing at an exponential rate, so the game is a little different:

    • Data is a renewable resource. It’s durable, it’s reusable, and it’s being produced faster than we can process it.
    • Because it’s not a scarce resource, there’s no urge to hoard it – you can use it, transform it, and share it knowing that it won’t diminish.
    • Data becomes more valuable the more you use it.
    • As the world’s oil reserves dwindle, and renewable resources grow in popularity and effectiveness, the relative value of oil drops. It’s unlikely that will happen to data.
    • Also, while data transport is important, it’s not expensive the way oil is.  It can be transported and replicated at light speed.

    Using alternative data gives traders an advantage, but it doesn’t always have to be confidential or hard-to-find information.  Traders now have access to vast amounts of structured and unstructured data. A significant source that many overlook is the data produced through their own process or the metadata from their own trades or transactions.

    In the very near future, I expect these systems to be able to go out and search for different sources of information. It's almost like the algorithm becomes an omnivore. Instead of simply looking at market data or transactional data, or even metadata, it starts to look for connections or feedback loops that are profitable in sources of data that the human would never have thought of. – Howard Getson

    In a word of caution, there are two common mistakes people make when making data-driven decisions.  First, people often become slaves to the data, losing focus on the bigger picture. It’s the same mistake people make with AI.  Both are tools, not the end goal.  Second, even the most insightful data can’t predict black swans. It’s important to exercise caution.  Prepare for the unexpected. 

    The future of data is bright, but it’s also littered with potential challenges.  Privacy concerns and data misuse are hot-button topics, as are fake news and the ability of systems to generate misleading data.  In addition, as we gain access to more data, our ability to separate signal from noise becomes more important.

    I think one of the biggest problems facing our youth – and really all of us – is how much information is thrust at us every waking moment of the day.  No previous generation has had this much access to data.  As a result, many are actually less informed than in the past.  Soundbites become the entire news story, and nuance gets lost in the echo chambers. 

    The question becomes, how do you capitalize on data without becoming a victim of it? 

    Food for thought!

  • The Power of One Data Point

    I love statistics.  But I also recognize how easy it is to be tricked by data.

    Here is an example illustrating how factually accurate statistics can be misleading without proper context. 

    Take a quick look at this chart showing Robotics funding in July 2023.

      

    1694121538381

     

    If you look at that chart, you might conclude that Pittsburgh is a Mecca of innovation in robotics.  Carnegie Mellon is there.  That makes sense, right?

    However, there's an immediate red flag … it's only for the month of July 2023. 

    So the question becomes … why? 

    Turns out, that entire number is essentially the result of a single check to Stack AV to recapitulate what was Argo.  Argo is a Ford and VW-backed autonomous vehicle startup, and Stack AV is the founders' new self-driving startup. 

    One significant move skewed the scale so strongly that it trumped major countries' expenditures that month. 

    There's often an issue about not having enough data to be statistically significant.  Another common issue is confusing coincidence with causality.

    This isn't meant to undermine the effect of one data point on a chart.  For example, think about Taylor Swift's impact on the economy.  Taylor's Eras Tour has already netted more than $100M but also reportedly has had a $5B impact on the economy.

    Cincinnati reported that Taylor Swift's Concert Tour brought $90M to their city in two days.  Her 60,000 attendees pushed the city's hotels to 98% occupancy rates.  Beyond that, her concert-goers also consumed the city's restaurants, bars, tourism, and retail. 

    Here is a different example of accurate data leading to an unusual conclusion, At a Genius Network meeting this week, the creator of OsteoStrong and the X3 bar spoke about people's misconceptions about fitness and workouts.  One point, in particular, caught my attention.  He claimed that most people only get stronger as a direct result of their workouts about ten times in their lives.  This isn't true of competitive athletes or weightlifters – but the average gym goer.  Why? His logic was you only get stronger when you take your muscle to failure, past its previous limits.  Most people rarely work out to exhaustion and don't keep track of their best.  They often stop one rep – or even half a rep – before there's a meaningful improvement. 

    A good lesson for life. 

    As entrepreneurs, we've all seen people get the "one big break" or the "one domino" that led to success.  The goal is often to be good enough that you only have to get lucky once.

    While one data point can ruin a statistic, it can also change your life. The power of an inflection point. 

    Hope that helps.

  • Here Are Some Links For Your Weekly Reading – October 8th, 2023

    Reports of bed bug infestations in Paris are so bad the government is deploying sniffer dogs to inspect trains.

     

    278912

    Here are some of the posts that caught my eye recently. Hope you find something interesting.

    Lighter Links:

    Trading Links:

  • The Most Popular Spotify Artists

    Time and technology march forward relentlessly. 

    It is easy to keep up – until you pause or slow down.

    Being an Early Adopter was a big part of my identity.  At this point in my life, I am still early with respect to new technologies, but I feel like I'm losing touch with a lot of today's culture. 

    Perhaps this started over a decade ago?  I remember finding my sons' slang and music off-putting. 

    As an aside, my youngest son, Zach, went through a phase where it felt like he used the verbal tic … "Dude" in every other sentence.  Parenting trick – I broke his habit by screaming "FOOPDEEDOO!!" every time he said it, regardless of when it happened, where we were, or who we were with. 

    If it's crazy and it works … it's not crazy.  He certainly stopped saying "dude". 

    OK, back to the point.  I realize that the Top 40 is basically a list of 40 songs that I don't know (and feel like I only randomly know some of the artists).  Meanwhile, my staff laughingly refer to my favorite stations on SiriusXM radio as old-man music.

    To make the point further, my research assistant asked me if I knew about Bad Bunny.  To me, it sounded like a Disney cartoon for Halloween.  But, apparently, he is a Grammy-winning recording artist who won "Album of the Year" for music that I had never heard.

    It didn't take long to get to the list of top Spotify artists.  For the record, I do know most of those artists – but admittedly few of their songs. 

    Here is the list.  How did you do?

    Ezgif.com-webp-to-jpg

    via StatsPanda

     

    But as I said, listening to the Top 40 is getting harder for me.  Where's the rock (or songs with discernable melodies)?! 

    Meanwhile, I'm about to start a new art exhibit. I call it "Jen Sleeps At Pop Concerts" 

    IMG_0263

    So far, we've got Taylor Swift, Coldplay, Beyoncé, Ariana Grande, Bob Seger, the Eagles, and the Rolling Stones. In case you're curious, she did not fall asleep at John Legend, Queen, or Ed Sheeran. 

    Times are changing …