Human's can't do a lot of things. Honestly, the fact that we're 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 couldn't understand a future where shopping was done online and Kodak didn't think digital cameras would replace good ol' film. Blockbuster couldn't foresee a future where people would want movies in their mailboxes, because "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.
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 A.I. 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).
The leadership in your company is often the difference between a good company and a great company.
Leadership (not just the boss, but the top-level managers as well) can make or break a company.
Am I hands-on or hands-off? Am I encouraging my team to grow? Have I made our company objectives and values inherent?
These are all questions that we – as leaders – need to be asking.
As you answer those questions, you can also be thinking about what leader archetype you follow …
Laissez-Faire leaders are hands-off. They don't directly supervise employees or provide regular feedback. Highly trained employees may benefit from this style, but most employees find this a hindrance.
Transactional leaders show the difference between a leader and a manager. They're worried about tasks and provide reward/punishment based solely on task completion.
Autocratic leaders make decisions without the input of others. They possess total authority and impose their will on their employees. This can benefit low-level employees who require close supervision. This will stifle creative employees.
Participative leaders are democratic. They value the input of their team members but hold ultimate decision-making power. This can cause challenges when there's a time crunch.
Transformational leaders require high levels of communication from their management team. They motivate employees through effective communication and high visibility. This requires more involvement from the various layers of management.
Or you can check out this less serious flowchart to see which fictional boss you are.
We often talk about Artificial Intelligence's applications – meaning, what we use it for – but we often forget to talk about a more crucial question:
How do we use AI effectively?
Many people misuse AI. They think they can simply plug in a dataset, press a button, and poof! Magically, an edge appears.
Most commonly, people lack the infrastructure (or the data literacy) to properly handle even the most basic algorithms and operations.
That doesn't even touch machine learning or deep learning (where you have to understand math and statistics to make sure you use the right tools for the right jobs).
Even though this is the golden age of AI … we are just at the beginning. Awareness leads to focus, which leads to experimentation, which leads to finer distinctions, which leads to wisdom.
Do you remember Maslow's Hierarchy of Needs? Ultimately, self-actualization is the goal … but before you can focus on that, you need food, water, shelter, etc.
In other words, you most likely have to crawl before you can walk, and you have to be able to survive before you can thrive.
Artificial Intelligence and Data Science follow a similar model. Here it is:
First, there's data collection. Do you have the right dataset? Is it complete?
Then, data flow. How is the data going to move through your systems?
Once your data is accessible and manageable you can begin to explore and transform it.
Exploring and transforming is a crucial stage that's often neglected.
One of the biggest challenges we had to overcome at Capitalogix was handling real-time market data.
The data stream from exchanges isn't perfect.
Consequently, using real-time market data as an input for AI is challenging. We have to identify, fix, and re-publish bad ticks or missing ticks as quickly as possible. Think of this like trying to drink muddy stream water (without a filtration process, it isn't always safe).
Once your data is clean, you can then define which metrics you care about, how they all rank in the grand scheme of things … and then begin to train your data.
Compared to just plugging in a data set, there are a lot more steps; but, the results are worth it.
That's the foundation to allow you to start model creation and optimization.
The point is, ultimately, it's more efficient and effective to spend the time on the infrastructure and methodology of your project (rather than to rush the process and get poor results).
If you put garbage into a system, most likely you'll get garbage out.
Today's investors have access to data and information that would have been unheard of 10 years ago … and unfathomable 20 years ago. In the past, investors relied on information and experience from their real lives, from counterparties, and from fastidious attention to CNBC and stock tickers.
While the games, the rules, and the players have all changed, the goal hasn't … more alpha … more money … more reliably.
What's Changed?
Algorithmic trading isn't new, but there is a shift in who's making the algorithms. For example, you can crowdsource development through Quantopian … or let machines do the heavy lifting through A.I.-based firms like Sentient.
But I'd argue that's only true if you look at the same data, the same way.
The Future of Trading
One of the reasons A.I. is a great option for trading is that it takes away the human element of fear, greed, and discretionary mistakes.
Sentient's founder says:
"For me, it's scarier to be relying on those human-based intuitions and justifications than relying on purely what the data and statistics are telling you." – Babak Hodjat
In addition, people tend to get similar results because they do things similarly. As A.I. matures (and more researchers become better versed in what's possible) solutions will evolve.
It won't be a Ph.d. writing an algorithm … it will be machines and code trying unthinkable combinations and finding edges that otherwise would remain invisible and unused.
Currently, most people train their algorithms on markets, or with human intervention, but there are more data sets that can be used to build more robust models.
Alternative Data
Alternative data, to most, means tracking Twitter and Facebook sentiment, but confining your definition to that limits potential alpha.
New sources of data are being mined everywhere, and are letting investors understand trends "before they happen".
For example, mobile devices, low-cost sensors, and a host of new technologies have led to an explosion of new potential data sources to use directly for predictive insight or indirectly to help improve models.
In addition, private company performance, logistics data, and satellite imagery are becoming popular data sets in a data scientist's alpha creation toolbox.
There are often concerns about the cost and completeness of these datasets, but as we get better at creating and using them, both will improve.
Finding more ways to train algorithms on new data can help traders once again find an edge on their competition.
The thing about "sustainable alpha" is that while one might be able to achieve it, you can't expect to have it doing the same thing everyone else, or that you've always done.
Markets change, and what worked yesterday won't necessarily work today or tomorrow. Trading is a zero-sum game, and as we move toward the future, this only gets more apparent.
Behavioral Game Theory shows that human choices don't necessarily reflect the benefits they expect to receive. That's no longer the case with algorithms.
For more on Big Data and its potential, here's access to the full panel discussion I participated in recently at The Trading Show in New York.
This post seems like it is about football … but it is really a playbook of things we can do in business.
What Can Business Learn From Football Teams?
If you get a chance to watch an NFL practice … I highly recommend it. It is an awesome experience and opportunity for a businessperson.
Each time I've watched a practice session I've come away impressed by the amount of preparation, effort, and skill displayed.
The Cowboys' coach is Jason Garrett. He is detail-oriented and intellectual. His pedigree … he is a Princeton graduate who played quarterback in the NFL.
During practice, there's a scheduled agenda. Practice is broken into chunks, and each chunk has a designed purpose and a desired intensity. There's a rhythm, even to the breaks.
Every minute was scripted. You could tell there was a long-term plan … but, there was also a focus on the short-term details (many details).
They alternate between individual and group drills. Moreover, the drills run fast … but for shorter time periods than you'd guess. It is bang-bang-bang – never longer than a millennial's attention span. And they move from drill to drill – working not just on plays, but the skillsets as well (where are you looking, which foot do you plant, how do you best use your hands, etc.).
They use advanced technology (including advanced player monitoring, bio-metric tracking, and medical recovery devices … but also things like robotic tackling dummies and virtual reality headsets).
They don't just film games, they film the practices … and each individual drill. Coaches and players get a cut of the film on their tablet as soon as they leave. It is a process of constant feedback, constant improvement, or constant renewal.
How you do one thing is how you do everything. So, they try to do everything right.
Pro football is one thing. College football is another. But, even in high school, the coaches have a game plan. There are team practices and individual drills. They have a depth chart, which lists the first, second, and third choice to fill certain roles.
The focus is not just internal, on the team. They focus on the competition as well. Before a game, the coaches prepare a game plan and have the team watch tape of their opponent in order to understand the tendencies and mentally prepare for what's going to happen.
During the game, changes in personnel groups and schemes keep competitors on their toes and allow the team to identify coverages and predict plays. Coaches from different hierarchies work in tandem to respond faster to new problems.
After the game, the film is reviewed in detail. Each person gets a grade on each play, and the coaches make notes for each person about what they did well and what they could do better.
Think about it … everyone knows what game they are playing … and for the most part, everybody understands the rules, and how to keep score (and even where they are in the standings).
Imagine how easy that would be to do in business. Imagine how much better things could be if you did those things.
Genius Network is a business group that also serves as an advisory board, counselor's office, and idea factory.
It brings brilliant minds and industry transformers together in a forum focused on innovation, creative disruption, and possibility.
Peer groups, like this, help you set (and raise) standards.
They help bring new capabilities, but also new possibilities, new found energies and a reconnection to your purpose, mission, and values.
This is a great place to meet extraordinary people.
Buck Joffrey is one of them. He is a doctor, an international best selling author, member of Genius Network, and host of WealthFormula (which is a podcast where he educates professionals on how to build lasting wealth).
Buck recently interviewed me for WealthFormula. We talked about old-world trading versus new-world trading … and where I think A.I and Machine Learning have the best opportunities to add alpha and help investors make and keep more money.
You can listen to it below (or subscribe to his podcast on iTunes, Android, or RSS).
I'm getting cynical, I understand planned obsolescence … but has it occurred to you that Apple could make their phones act sluggish just before the launch of a new version?
My phone has been driving me nuts. So, (as I write this) I'm up at 2 am to place my order for the new iPhone X.
On one hand, it satisfies my desire for the new and shiny … but, on the other hand, it makes it harder for my wife.
Buying gifts is often hard. But it gets harder when the giftee already has everything (or buys it himself).
Every year since 1959, Neiman Marcus has published a Christmas Book. Primarily comprised of normal Neiman Marcus offerings … the book also contains pretty amazing "fantasy" gifts. For example, who doesn't want a rose-gold Cobalt Valkyrie-X private plane (worth $1.5 million) …
Neiman Marcus
I don't know about you, but it's a little feminine for me.
Or, there is a private Submarine (worth $20,000,000).
Neiman Marcus
But that is only good if you don't have one already.
You can check out NM's 2017 Fantasy Gift List, here, and get a personal trip to Champagne, France or a pair of specially commissioned His and Hers Rolls Royces.
Let me know if you have any good gift ideas. I'm always looking for them.
