Ideas

  • The Time Value of Time

    Einstein_1979_USSR_StampIn many senses time is relative. 

    You don't have to be a rocket scientist to understand this quote from Einstein.

    "When you are courting a nice girl, an hour seems like a second.  When you sit on a red-hot cinder, a second seems like an hour.  That's relativity."

    It is about more than perception. 

    Here is something that highlights the relative value of time. 

     

    The Value Of Time:

    • To understand the value of a year, talk to a student who has failed an important exam.
    • To understand the value of a month, talk to a mother who has given birth to a baby a month prematurely.
    • To understand the value of a week, talk to the publisher of a weekly newspaper.
    • To understand the value of an hour, talk to a couple in love who are separated and want only to be together again.
    • To understand the value of a minute, talk to someone who has just missed their train or plane flight.
    • To understand the value of a second, talk to someone who has lost a loved one in an accident.
    • And to understand the value of a millisecond, talk to someone who won the silver medal at the Olympic Games.

    Time waits for no one.  So it is important to remember to make the best use of the time you have.

     

    That Doesn't Mean Time Is Scarce Or Has To Be A Constraint:

    090614 time Time is often thought of as a constraint or a scarce resource. There are lots of phrases that highlight this type of thinking.  For example: I don't have enough time; I'm running late; I'm up against a deadline; There are only 24-hours in a day; or, I’m going as fast as I can. As you might guess, that list goes on further.  Yet, time does not have to be that way … it can be a tool instead.

    So, I started to think about how I used time. 

    Was I making the most of it … or taking it for granted?  It didn’t take much introspection to notice a few of the ruts I fell into.  I'm going to talk about one of them, here, because a small shift can have a massive impact. 

    To start, let's talk about pace.

     

    A Change of Pace:

    When I jog, the beginning and the end are the hardest for me. Yet, after I find that initial pace and I settle into a comfortable rhythm, the majority of the run is relatively painless. My mind and body switch to a nearly automatic mode and I have time to think about many things.

    Work is similar in many respects. Once a team gets into a rhythm, work and progress are somewhat automatic. Breaking inertia is a challenge; but, people recognize that it's a challenge. The more insidious problem is to fail to recognize that the work rhythm that's comfortable, and which produces progress, is still a rut. It doesn't stretch and challenge the team to strive for more. Yet, this stretching is what drives innovation. It's the thought we haven't had yet … and a new perspective that changes everything.

    Changing your pace can be an incredible catalyst to make that happen for you. For example, imagine that we put together a new portfolio in two weeks, on a wholly new tech platform, with new markets, and using new techniques. Then we tested, re-balanced and rebuilt that portfolio in one week. What we did, or the time in which we did it, wasn’t important. The important part is that it caused the team to work at a radically different pace than before. It was a sprint.

    Moreover, this sprint caused us to re-think what we do, and more importantly, how we do it.  Many of the innovations and new distinctions that we discovered through this process will work their way into other areas of our work (and will act as a catalyst for us to re-evaluate the way we do things).

     

    A Challenge For You:

    I challenge you to consciously change the pace of something that you are already comfortable doing a certain way. The pace can be faster, or the pace can be slower … it doesn't matter.  Then notice what comes up for you, and what new opportunities and possibilities you discover.

    Time is a valuable resource. Take this opportunity to re-examine how you can best view and use time to make the most of it.

  • Thoughts on Ten Years of Marriage …

    I met Jennifer in April of 2004.  We got married in January of 2008.  We celebrated our 10th Wedding Anniversary this week.

      

    180107 HMG JBR 10-Year Anniversary

    Wow, how time flies!

    On one hand, it seems like just yesterday.  On the other hand, portions of that decade seemed to take forever.

    As an entrepreneur, I live in a weird "tense".  For me, the future and present are often blended.  Meaning, I imagine the future I want – and then I find the path to create (or manifest) that destiny.  Not surprisingly, some of the things that were easy to imagine were hard to bring into reality (in a reproducible, efficient, and effective manner or process) … And these things seem to take forever.  

    Other things (like relationships or the growth and maturation of my kids) seemed to happen in the blink of an eye.

    I am consciously trying to be more mindful and grateful for the progress (and even the minor moments, wins, or curiosities) before me.  The truth is that if you fail to notice them, you don't experience them.

    Here is to experiencing all that you need or want … and I hope the rest serves as raw material, learning, or fuel to get you there faster.

    Onwards!

  • Here Are Some Links For Your Weekly Reading – December 22nd, 2017

    12222017 Tis the season

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

    Lighter Links:

    Trading Links:

  • Can You Afford To Think Linearly in this Exponential Age?

    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. 

       

    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

    2x2

    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).

    Opportunity or Chaos …  You get to decide.

    Onward!

  • What Type of Leader Are You?

    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. 

     10112017 fictional boss

    via FastCompany

     

  • Creating An Artificial Intelligence Methodology: AI’s Hierarchy of Needs

    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:

      1252017 AI Hierarchy of NeedsMonica Rogati via hackernoon

    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. 

    Slower sometimes means faster.

    Onwards.

  • Here Are Some Links For Your Weekly Reading – December 3rd, 2017

    1232017 NDT

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

    Lighter Links:

    Trading Links:

  • Alternative Data Streams: Noise or Alpha

    There's a paradigm shift happening in trading.

    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. 

    However, fundamental discretionary traders account for just 10% of today's trading volume. Quantitative investing based on machine intelligence and algorithms is the new normal.

    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.

    Some argue that artificial intelligence is unable to generate significantly different results because "analyzing more and more data results in increasingly similar strategies". 

    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. 

    Here is a chart of alternative data sources.

     

    via CBInsights

    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. 

     

      via YouTube

     Let me know if you have questions or comments.  Thanks.

  • Here Are Some Links For Your Weekly Reading – November 12th, 2017

      1182017 Hysterectomy

     

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

     

    Lighter Links:

    Trading Links:

     

     

  • Before and After The Snap: What Can Business Learn From Football?

     
    Nonetheless, I am impressed by the product NFL teams put on the field week-after-week and year-after-year.
     
    Recently, I took some friends to The Star, which is the Cowboys' new world headquarters and training facility  (and, as a side note, cost more to build than the Cowboys' stadium).
     
     
    Fullsizeoutput_47b4
     

    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.

     

    171111 HMG and Jason Garrett 2

     

    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.

    Challenge accepted.