Recently I had a chance to talk with Josh Elledge on his Thoughtful Entrepreneur podcast. We talked about AI's inevitable influence on trading as well as my experience as an entrepreneur.
Despite mis-spelling Capital Logix ... it's Capitalogix ... the conversation we had is worth a listen.
In the past, I've talked about where America gets its oil, and how data is more than just "the new oil".
During the worst of the pandemic, oil prices dropped almost 40%. They've now risen 216% from that pandemic low. With gas prices skyrocketing, it's probably time to look at historical oil prices.
Visual Capitalist put together a simple infographic.
So why are prices so high? Unfortunately, large portions of the world's oil reserves are in regions prone to political conflict.
Currently, the Russia-Ukraine conflict is leading to supply uncertainty and rising prices.
Meanwhile, oil companies are bringing in record profits.
Oil prices are a complicated topic because supply and demand aren't particularly responsive to price changes in the short term. Regardless of price, people need to drive. And, regardless of demand, it takes time and money to drill new oil wells.
As a result, you can see mismatches, like today, where uncertainty has raised prices, but oil companies are doing tremendously well.
The question now becomes ... where does it end? Does it take a resolution to the current conflict? Or, will prices reach a ceiling and fall back down based on other factors?
Neural networks creating images from text isn't new. I wrote about it in 2019 when AI self-portraits were going viral.
Mauro Martino via YouTube
Just like VR is getting a new lease on life, despite its age, AI-generated art is getting another 15-minutes of fame.
This past week, a new model called Dall-E Mini went viral. It creates images based on the text prompts you give it – and it's surprisingly good. You even can give Dall-E absurd prompts, and it will do its best to hybridize them (for example, a kangaroo made of cheese).
Unfortunately, like our current reality, Dall-E may not be able to produce cheap gas prices. Nonetheless, it is fun to try. Click the image to enter the concepts you want Dall-E to attempt to represent.
via Dall-E mini
While the images themselves aren't fantastic, the tool's goal is to understand and translate text into a coherent graphic response. The capabilities of tools like this are growing exponentially (and reflect a massive improvement since I last talked about AI-generated images).
Part of the improvement is organic (better hardware, software, algorithmic evolution, etc.), while another part comes from stacking. For example, Dall-E's use of GPT-3 has vastly increased its ability to process language.
However, the algorithms still don't "understand" the meaning of the images the way we do ... they are guessing based on what they've "seen" before. That means it's biased by the data it was fed and can easily get stumped. The Dall-E website's "Bias and Limitations" section acknowledges that it was trained on unfiltered internet data, which means it has a known, but unintended, bias to be offensive or stereotypical against minority groups.
It's not the first time, and it won't be the last, that an internet-trained AI will be offensive.
Currently, most AI is essentially a brute force application of math masquerading as intelligence and computer science. Fortunately, it provides a lot of value even in that regard.
The uses continue to get more elegant and complex as time passes ... but we're still coding the elegance.
Onwards!
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
How are you feeling about the markets and our economy?
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.
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.
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.
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.
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.
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.
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.
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."
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 of the posts that caught my eye. Hope you find something interesting.
Companies With The Most Patents in 2021
Intellectual Property is an important asset class in exponential industries.
Why? Because I.P. is both a property right (that increases the owner's tangible and intangible value) and a form of protection.
They say good fences make good neighbors. But you are also more willing to work to build an asset if you know that your right to use and profit from it is protected.
As a result of that thinking, Capitalogix has numerous patents - and we're developing a patent strategy that goes far into the future. So, it's a topic that's front of mind for me.
Consequently, this visualization of which companies got the most patents last year caught my eye. In 2021, the U.S. granted over 327,000 patents. Here is who got them.
While IBM isn't the public-facing industry leader they once were, they've been topping the list for most patents for the past three decades. Their patents this past year cover everything from climate change to energies, high-performance computing, and A.I..
What ideas and processes do you have that are worth patenting? And, what processes are worth not patenting - to keep from prying eyes?
Food for thought ... Onwards!
Posted at 08:13 PM in Business, Current Affairs, Gadgets, Ideas, Market Commentary, Science, Trading, Trading Tools, Web/Tech | Permalink | Comments (0)
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