Capitalogix

A Brief Look At Bear Markets

Main Street and Wall Street are often at odds.  Terms like "retail" and "professional" or "smart money" and "dumb money" highlight the difference in perspective and access to tools, processes, and even information.  

The biggest disparities happen at turning points.  Today, many companies are posting record profits, but markets are volatile, gas is expensive, and inflation is high.  So, we're getting some mixed signals.

It may be too soon to say we're in a recession, but we are experiencing a downturn. 

Here is a comparison of recent market corrections showing each decline's intensity and duration.

 

via Reddit (Dow Jones Market Data & the WSJ)

While this chart is a week or two old, it shows some interesting data.  While there are a few shorter drops, most were longer and deeper than where we currently are. 

Thus, we could have further to go ... but it could also be a sign that we're responding better to market issues than in the past. 

via Cascade Financial Strategies

The blue areas represent past bull market durations and returns (total and annualized).  The red areas represent past bear markets.

 

Note: this chart is from 2018 - Nonetheless, it is a good reminder of the bigger picture.

I remain optimistic about the future state of our economy.  That doesn't mean there won't be pain.  Still, I believe that technology continues to increase the size of our potential pie and the capabilities we can leverage as a catalyst to recovery. 

 

As a bonus, if you want to see a flashback to the Great Recession, here are two pieces of my market commentary from the time.  It's interesting to look back and see how my writing has changed. 

• Market Commentary 02/06/09
• Market Commentary 05/01/09

How are you feeling about the markets and our economy?

Origin Story: Warren Buffet

Warren Buffett is a legend for many reasons.  Foremost among them might be that he's one of the few investors who clearly has an edge ... and has for a long time. 

From 1976 to 2017, his Sharpe ratio (excess return relative to risk) was approximately double the overall market.  He even did well in 2021.  Berkshire Hathaway now has almost a trillion in assets (up from $700 billion in 2019) – and is still performing well. 

While many people consider Buffett to be an investor, I also consider him to be an entrepreneur.

At the age of six, he started selling gum door to door.  Obviously, selling gum wasn't the key to his path to riches.  So, how did he make his first million?  Here's a video that explains it.

 

via Coolnimation

He made his first million at age 30 (in 1960).  For context, a million dollars in 1960 would be worth about $8.5 million today.

Buffet has always been honest about his bread-and-butter "trick" ...  he buys quality companies at a discount and holds on to them.

It is fascinating to recognize how much the world has changed – and yet how much it has stayed the same.

For extra viewing: Warren Buffett, Charlie Munger, and Bill Gates recently did a full 2-hour interview with CNBC.  You can watch it here.

The OODA Loop: Making Fast Decisions

I recently came across an interesting technique that fighter pilots use to make fast and accurate decisions in high-stakes situations. 

The Air Force calls it an OODA Loop.

It is an iterative feedback model designed by Colonel John Boyd that serves as a foundation for rational thinking in chaotic situations like dogfights.

It stands for Observe, Orient, Decide, Act. 

via Wikipedia

Why do people use decision models?  Obviously, to make better decisions.  But really, they use models to create a process that avoids many of the mistakes or constraints that prevent good decisions.

You make countless decisions every day - and at a certain point, you reach decision fatigue. It can be harder to make decisions when you are tired, after you've made too many, or when the intensity of the environment distracts or drains you. 

It's one of the reasons I rely on artificial intelligence. Here are some others. 

• Best practice becomes standard practice. 
• It accounts for signal and noise.
• It attempts to quantify or otherwise make objective assessments, comparisons, and choices. 
• And, it often gives you a better perspective by letting you apply and compare different models or decision techniques to achieve the desired outcome.

Nonetheless, many algorithms are dynamic and adaptive automation of processes or strategies that humans have used successfully before.

So, let's take a closer look at the OODA Loop, which stemmed from analyzing many interactions between and among fighter pilots during battle and training. 

Observe

The first step is to observe the situation to build the most accurate and comprehensive picture possible. The goal is to take in the whole of the circumstances and environment. It's not enough to observe and collect information, you must process the data and create useful meaning. 

It's the same with data collection for an AI system. Ingesting or collecting data isn't enough. You have to be able to apply the data for it to become useful. 

Orient

This step is less intuitive but very important. When you orient yourself, you're recognizing strength, weakness, opportunity, and threat to identify how changing the dimensionality or perspective alters the outcome. 

It's reconnecting with reality in the context of your cognitive biases, your recent decisions, and more. Have you received new information since starting?

I think of this as carrying a map and pulling out a compass while exploring new lands. Sometimes you need to remember where you started, and sometimes you need to make sure you're going where you think you are. 

Decide

The last two steps provide the foundation for taking action. When there are multiple decisions in front of you, observing and orienting help you choose wisely. 

In business and with AI, you can go through these loops multiple times. 

Act 

The best-made plans mean nothing if you don't act on them. Once you've taken action, you can reobserve, reorient, and keep moving forward. 

Conclusion

Like most good mental models, The OODA loop works in many situations and industries.

Speed is often a crucial competitive advantage. For example, knowing (and taking decisive action) while others are still guessing (and taking tentative action) is something I call time arbitrage. 

Said another way, you make progress faster by walking in the right direction than by running in the wrong direction. 

These processes (and technology) also help us grow more comfortable with uncertainty and uncomfortableness. Markets are only getting more volatile. Uncertainty is increasing. But, when you have the ability to adapt and respond, you can survive and thrive in any climate. 

The Intersection of Fintech, AI, and Analytics

At the beginning of the pandemic, I participated in a series of webinars for IBM. The focus was on building smart and secure financial services. My talk was about advanced computing and the new world of trading.

Challenging times drive advancement - and what better time to talk about advancements in technology (and their applications) than in the midst of a global pandemic. 

You can watch a replay of the Fintech webinar here. There are several interesting presentations.  If you just want to watch my presentation, it starts at the 5:16 mark.

In addition, I've uploaded a different version of just my talk that you can watch directly here.

 

IBM and Capitalogix via YouTube

In the past, trading used to be about people trading with people. Markets represented the collective fear and greed of populations. So, price patterns and other technical analysis measures represented the collective fear and greed of a population. If you could capture that data and figure out certain statistical probabilities, you might have had an edge. The keywords are "might have". 

If you had more information than your competitors - meaning, an information asymmetry - you had an amazing edge. At one time, that was being able to print out reports on stocks from that new-fangled technology called the internet. As time passed, it became harder to gain an asymmetric information advantage (because people had access to more and better data). 

Each generation of traders finds new ways to play the game and generate "Alpha" (the excess return generated by manager skill, rather than luck or excess risk). As soon as enough people adopt a strategy (or figure out a way to combat it), the edge begins to decay.

When computerized data became available, simply understanding how to download and use it generated Alpha. The same could be said for each later evolution – the adoption of complex algorithms, access to massive amounts of clean data, or the adoption of AI strategies.

Each time a new shift happens, traders pivot or fail – it's not that active trading stopped working – it's that the tools, speed, and styles necessary to play that game evolved.

Said another way, the rules, the players, and the game (itself) have all changed. Today, technological asymmetry is a significant factor, and your edges come from things like bigger and faster servers, low latency connections to markets, or the ability to calculate the odds better or faster than others.

In the future, I see those edges combining as artificial intelligence starts to leverage exponential technologies and new data sources (like alternative data and metadata feedback loops). It is easy to imagine a time when information is the "fuel," but your ability to digest and parse that information is the "engine." 

Playing a New Game

Historically, most active traders don't beat the S&P in any given year ... and even less beat it with any semblance of consistency.  But those that do – the ones that have been doing it for long enough that it's not chance ... exercise a willingness (and a skill) to adapt quickly. 

One of Charles Darwin's best-known concepts is: It is not the strongest species that survive, nor the most intelligent, but the ones most responsive to change.

While computers have made information accessible to everyone, they've also created a massive asymmetric information advantage for those who have both the access and the skill to best use the massive amounts of data now available.  This is more complicated than it seems.  You need the information, the technology, the process, and the people.  There is so much data available now that figuring out what to ignore is probably more important than what to use.  Likewise, the ability to ingest, clean, validate and curate the data is a huge hurdle that most can't clear.

I talk about much more in the video but boiling down the main points, ask yourself (in business, in trading, in life) are you separating the "signal" from the "noise?"

A technological advantage doesn't mean anything if you're plugging in inaccurate or biased data into it ... just like with the news. 

But, even with those skills, it's harder than ever to take advantage of inefficiencies (edges) than ever before.  The edges are smaller, more fleeting, and surrounded by more volatility and noise.  It's like finding a needle in a haystack.  That being said - finding a needle in a haystack is easy when you have a metal detector. 

That's where A.I. has come in for us. We use A.I. to develop algorithms, analyze markets, and create meaning where humans can't find any. 

We live in exciting times. 

Onwards!

Here Are Some Links For Your Weekly Reading - May 1st, 2022

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

Lighter Links:

• What is Web 3.0? Here's What the Internet Will Look Like In the Future. (LaptopMag)
• Amazing Video Shows a Tiny Jumping Robot Like Nothing You've Ever Seen. (BGR)
• 103 Bits of Advice I Wish I Had Known. (Kevin Kelly’s Technium)
• Does (Hand) Size Matter? Inside the NFL Draft's Fear of Tiny-Handed QBs. (GQ)
• This $2 Billion Personal Submarine is So Huge that It Can Dwarf a MegaYacht. the Vessel is Almost Three Times Longer Than a Football Field, It Has Lavish VIP Suites, a Helipad and Even an Open Deck Swimming Pool. (LL)

 

Trading Links:

• Bored Ape Metaverse Frenzy Raises Millions, Disrupts Ethereum. (Bloomberg)
• Can Capitalism Survive? 80 Years After Schumpeter's Answer. (AIER)
• Americans are Showing Inflation Fatigue, and Some Companies See a Breaking Point. (FoxBusiness)
• Foreclosure Filings are Up 132% From a Year Prior. Here's What that Means for the Housing Market (And It's Not What You Might Think). (MarketWatch)
• Warren Buffett and Charlie Munger Called Out Stock-Market Gamblers, Dismissed Bitcoin as Worthless, and Underlined the Risks of Inflation. Here are Their 12 Best Quotes from Berkshire Hathaway's Annual Meeting. (Markets)

How I Got Started In Artificial Intelligence

Recently, I've had several people ask about how I got into AI. 

There are a couple of different answers, but I shot a video to go through the main points. 

 

Click here for a transcript

You could argue that I got my start in AI with my most recent company - Capitalogix - which started almost 20 years ago. You could also say that my previous company - IntellAgent Control - was an early AI company, and that's where I got my start.  By today's standards, the technology we used back then was too simple to call AI ... but at the time, we were on the cutting edge.

You could go further back and say it started when I became the first lawyer in my firm to use a computer, and I fell in love with technology. 

As I look back, I've spent my whole life on this path.  My fascination with making better decisions, taking smarter actions, and a commitment to getting better results probably started when I was two years old (because of the incident discussed in the video).

Ultimately, the starting point is irrelevant. Looking back, it seems inevitable. The decisions I made, the people I met, and my experiences ... they all led me here.

However, at any point in the journey, if you asked, "Is this where you thought you'd end up?" I doubt that I'd have said yes. 

I've always been fascinated by what makes people successful and how to become more efficient and effective. In a sense, that's what AI does. It's a capability amplifier. 

When I switched from being a corporate securities lawyer to an entrepreneur, I intended to go down that path. 

Artificial Intelligence happened to be the best vehicle I found to do that. It made sense then, and it makes sense now.

I wouldn't have it any other way. 

Onwards!

A Few Graphs On The State Of AI

Every year, Stanford puts out an AI Index with a massive amount of data attempting to sum up the current state of AI. 

It's 190 pages that detail where research is going and covers current specs, ethics, policy, and more. 

It is super nerdy ... yet, it's probably worth a skim. 

Here are a few things that caught my eye and might help set some high-level context for you. 

Investments In AI 

 

via AI Index 2022

In 2021, private investments in AI totaled over $93 billion - which was double the investments made in 2020. However, fewer companies received investments. The number of companies receiving funding dropped from 1051 in 2019 to 746 in 2021.

At extremes, putting greater resources in fewer hands increases the danger of monopolies.  But we are early in the game, and it is safe to interpret this consolidation as separating the wheat from the chaff. As these companies become more mature, you're seeing a drop-off similar to when the web began its exponential growth. 

With investment increasing, and the number of companies consolidating, you can expect to see massive improvements in the state of AI over the next few years.

We knew that already - but following the money is a great way to identify a trend. 

Increased regulation is another trend you should expect as AI matures and proliferates.

Ethical AI 

via AI Index 2022

Research on the ethics of AI is becoming much more widespread - while the research influences papers, it is also a catalyst for new laws.

AI's academic and philosophical implications are being taken much more seriously across the board. Many people recognize that AI has the potential to impact the world in unprecedented ways.  As a result, its promise and peril are under constant scrutiny.

The adoption of AI might seem slow ... but like electricity (or the internet), it only seems slow until it's suddenly ubiquitous.

As you find AI in more domains, the ethics of its use becomes a more pressing concern. There is a lot of potential for abuse of technologies like facial recognition and deepfakes.  Likewise, people worry about mistakes, judgment, and who's liable for errors in technologies like self-driving cars.

Luckily, you have many of the world's greatest minds working on the subject - including the Hastings Center.  

Many factors contribute to the speed of AI's maturation and adoption.  Here are three of the obvious reasons. First, hardware and software are getting better.  Second, we have access to more and better data than ever before.  And third, more people are actively seeking to leverage these capabilities for their benefit.

Technical Improvements

via AI Index 2022

Top-performing hardware systems can reach baseline levels of performance in task categories like recommendation, light-weight objection detection, image classification, and language processing in under a minute.

Not only that, but the cost to train systems is also decreasing. By one measure, training costs for image classification systems have dropped by a factor of 223 since 2017. 

When people think of advancements in AI, they often think of the humanization of technology. While that may eventually happen, most of the progress in AI comes from more practical improvements and applications. Think of these as discrete capabilities (like individual Lego blocks) that help you do something better than before.  These capabilities are easily stacked to create prototypes that do more.  Prototypes mature into products when the capabilities are robust and reliable enough to allow new users to achieve desired results.  The next stage happens when the capabilities mature to the point that people use them as the foundation or platform to do a whole new class of things.

We're past the trough of disillusionment and are on the slope to enlightenment.

Practical use cases abound.  Meaning, these technologies aren't only for giant companies anymore.

AI is ready for you to use.

If I think of a seasonal metaphor, it is "springtime" for AI (a time of rapid growth).  But not for you unless you plant the seeds, water them, and start to build your capabilities to understand and use what sprouts.

As a reminder, it isn't really about the AI ... it is about understanding the results you want, the competitive advantages you need, and the data you're feeding it (or getting from it) so that you know whether something is working.

You've probably heard the phrase "garbage-in-garbage-out."  This is especially true with AI. Top results across technical benchmarks have increasingly relied on extra training data for combinatorial and dimensional reasons. Another reason this is important is to compound insights to continue learning and growing.  As of 2021, 9 state-of-the-art AI systems out of the 10 benchmarks in this report are trained with extra data. 

To read more of my thoughts about these topics, you can check out this article on data and this article on alternative datasets. 

Conclusion

Artificial Intelligence capabilities are becoming much more robust and more able to transfer their learnings to new domains. They're taking in broader data sets and producing better results (while taking less investment to do so). 

It isn't a question of "If" ... it is a question of "when." 

AI is exciting and inevitable!

Let me know if you have questions or comments.

Will Robots Take Your Job?

The fear of a robot-dominated future is mounting ... But, is there a basis for that fear?

It's a common trope in film, but as we all know, media is meant to capture attention - not emulate reality. 

Michael Osborne and Carl Frey, from Oxford University, calculated how susceptible various jobs are to automation. They based their results on nine key skills:

• social perceptiveness
• negotiation
• persuasion
• assisting and caring for others
• originality
• fine arts
• finger dexterity
• manual dexterity
• and the need to work in a cramped area

via Michael Osborne & Carl Frey (Click For A Comprehensive Infographic)

There are various statistics about the rate of change for robots taking jobs. Many expect that ~50% of current jobs will be automated by 2035.  Turns out, that statistic is from Michael and Carl, and the numbers were 47% by 2034¹. 

Realize that statistic actually refers to the risk of them being automated. That number doesn't take into account the realities of cost, regulation, politics, social pressure, preference, or the actual work and progress necessary to automate something – so it's unlikely the full 47% will be realized. 

 

via The Economist

Nonetheless, many use that quote to point toward a dystopian future of joblessness and an increasing lack of middle-class mobility.  

Mr. Frey isn't a proponent of that belief … and neither am I.  

Automation and innovation free us to focus on what matters most (or what can create the most value). The goal is not to have machines let us be fat, dumb, and lazy … it is to free us to focus on bigger and better things.

Industrialization created short-term strife – but vastly increased the economic pie over the long term. So did electricity or the internet. It's likely that future automation will have similar effects, but it's possible to minimize the pain and potential negative impacts if we learn from previous iterations of this cycle. The fact that we're so far along technologically in comparison to previous revolutions means we're in a better position to proactively handle the transition periods.

New tech comes with both “promise” and “peril”. We must manage the short-term consequences of the new tech – because it is inevitable. With that said, by embracing innovation, we can make sure it is a boon to the middle-class (and all of society) and not the bane of their existence.

Throughout history, technology has always created more jobs than it has destroyed.

Progress means the restructuring of society’s norms … not the destruction of society.

When we first started using technology, that progress allowed humans to stop acting like robots (think farming and manufacturing). As technology improved, we have "robots" that seem to act more like humans. They can play chess, or shoot a basketball, etc.

The truth is that humans didn’t act like robots. They did what they had to to survive. As technology improved, we look back and have trouble imagining a time when humans had to do those things. Technology often focuses on the most pressing “constraint” or “pain." It isn’t getting more human, it is simply more capable … which frees us to ascend as well.
There are many aspects of humanity that robots can't yet replace. But as we move forward, technology will continue to free us to be more human (which I assume means to be more creative, more caring, more empathetic, and more original).

Doom and gloom sell. It's much easier to convince people something's going to be painful than amazing (because we're creatures of habit, and our monkey brains fear pain much more than they enjoy pleasure).

Our attitudes and actions play a pivotal role in how the world impacts us.

We are positioned not only to survive the revolution but to take advantage of it.

AI is a gold rush, but you don't have to be a miner to strike it rich. You can provide the picks and shovels, the amenities, or a map that helps people find treasures.

Onwards!

_________________

^[1] Frey, Carl & Osborne, Michael. (2013). The Future of Employment: How Susceptible Are Jobs to Computerisation?

Changing the Course of History

A little over a week ago, a deepfake of Ukrainian President Volodymyr Zelenskyy was used to try and convince Ukraine's soldiers to lay down their arms and surrender against Russia.  On top of being shared on social media, hackers got it onto news sites and a TV ticker as well. 

While it's not explicitly known that Russia did this – there's a long history of Russian cyberwarfare, including many instances of media manipulation. 

Luckily, while the lip-sync was okay in this video, several cues helped us know it was fake. 

Unfortunately, this is only the tip of the iceberg.  Many deepfakes aren't as easy to discern.  Consequently, as we fight wars (both physical and cultural), manipulated videos will increasingly alter both perceptions and reality. 

Even when proven to be fake, the damage can persist.  Some people might believe it anyway ... while others may begin distrusting all videos from leaders as potential misinformation. 

That being said, not all deepfakes are malicious, and the potential for the technology is attractive.  Production companies are already using it to splice in actors who might have aged or died into scenes in movies.  Deepfake technology can also be used to allow a celebrity to sell their likeness without having to waste their time doing all the filming necessary to produce the intended finished product. 

Deepfake technology also allows us to create glimpses into potential pasts or futures.  For example, On July 20th, 1969, Neil Armstrong and Buzz Aldrin landed safely on the moon.  They then returned to Earth safely as well.   What if they didn't?  MIT recently created a deepfake of a speech Nixon's speechwriter William Safire wrote during the Apollo 11 mission in case of disaster.  The whole video is worth watching, but the "fake history" speech starts around the 4:20 mark. 

"Fate has ordained that the men who went to the moon to explore in peace will stay on the moon to rest in peace." - Nixon's Apollo 11 Disaster Speech

 

MIT via In Event Of Moon Disaster

Conclusion

“Every record has been destroyed or falsified, every book rewritten, every picture has been repainted, every statue and street building has been renamed, every date has been altered. And the process is continuing day by day and minute by minute. History has stopped.“ - Orwell, 1984

In an ideal world, history would be objective; facts about what happened, unencumbered by the bias of society, or the victor, the narrator, etc.  On some level, however, history is written by the winners.  Think about it ... perceived "truth" is shaped by the bias and perspectives of the chronicler.

Consequently, history (as we know it) is subjective.  The narrative shifts to support the needs of the society that's reporting it. 

The Cold War with the Soviet Union was a great example.  During the war, immediately thereafter, and even today, the interpretation of what transpired has repeatedly changed (both here and there).  The truth is that we are uncertain about what we are certain about.

But while that was one example, to a certain degree, we can see this type of phenomenon everywhere.  Yes, we're even seeing it again with Russia.

But it runs deeper than cyber-warfare.  News stations color the story told based on whether they're red or blue, and the internet is quick to jump on a bandwagon even if the information is hearsay.  The goal is attention rather than truth.

Media disinformation is more dangerous than ever.  Alternative history can only be called that when it's discernible from the truth ... and unfortunately, we're prone to look for information that already fits our biases. 

As deepfakes get better, we'll likely get better at detecting them.  But it's a cat-and-mouse game with no end in sight.  Signaling Theory posits that signalers evolve to become better at manipulating receivers, while receivers become more resistant to manipulation. 

I'm excited about the possibilities of technology, even though new capabilities present us with both promise and peril. 

Meanwhile, "Change" and "Human Nature" remain constant.

And so we go.

A Look at Codebases

When I was a child, NASA got to the Moon with computers much less sophisticated than those we now keep in our pockets.

At that time, when somebody used the term "computers," they were probably referring to people doing math.  The idea that businesses or individuals would use computing devices, as we do now, was far-fetched science fiction.

Recently, I shared an article on the growing "compute" calculations used in machine learning.  We showed that the amount of compute used in machine learning has doubled every six months since 2010, with today's largest models using datasets up to 1,900,000,000,000 points.

This week, I want to take a look at lines of code.  Think of that as a loose proxy showing how sophisticated software is becoming.

 

via informationisbeautiful

As you go through the chart, you'll see that in the early 2000's we had software with up to approximately twenty-five million lines of code.  Meanwhile, today, the average car uses one hundred million, and Google uses two billion lines of code across their internet services. 

For context, if you count DNA as code, the human genome has 3.3 billion lines of code.  So, while technology has increased massively - we're still not close to emulating the complexity of humanity. 

Another thing to consider is that when computers had tighter memory constraints, coders had to be deliberate about how they used each line of code or variable.  They found hacks and workarounds to make a lot out of a little.

However, with an abundance of memory and processing power, software can get bloated as lazy (or lesser) programmers get by with inefficient code.  Consequently, not all the increase in size results from increasing complexity - some of it is the result of lackadaisical programming or more forgiving development platforms.

In better-managed products, they consider whether the code is working as intended as well as reasonable resource usage. 

In our internal development, we look to build modular code that allows us to re-use equations, techniques, and resources.  We look at our platform as a collection of evolving components. 

As the cost and practicality of bigger systems become more manageable, we can use our intellectual property assets differently than before. 

For example, a "trading system" doesn't have to trade profitably to be valuable anymore.  It can be used as a "sensor" that generates useful information for other parts of the system. It helps us move closer to what I like to call, digital omniscience. 

As a result of increased capabilities and capacities, we can use older and less capable components to inform better decision-making.

In the past, computing constraints limited us to use only our most recent system at the highest layer of our framework.

We now have more ways to win.

But, bigger isn't always better - and applying constraints can encourage creativity.

Nonetheless, as technology continues to skyrocket, so will the applications and our expectations about what they can do for us.

We live in exciting times ... Onwards!