Capitalogix

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

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

Lighter Links:

• The Seven Powers of Gratitude. (LeadershipFreak)
• The Top 18 Destinations for 2018. (ThePointsGuy)
• Lab-Grown "Clean Meat" is Almost Here. Will You Eat It? (BigThink)
• The White House Wants to Ban Cellphones, Citing Security Risk. (TechCrunch)
• The 12 Best Business Books of the Year, According to Stanford Business School. (BusinessInsider)

Trading Links:

• CryptoCurrencies and Blockchains. (Inference-Review)
• Investors Beware: 'Sentiment' is Not an Exact Market-Timing Tool. (Forbes)
• Institutions Shift More Assets Into Smart Beta. (FT)
• 'Stealth Hedging' Shows Investors Not So Complacent. (Reuters)
• Entrepreneurs Eye the Early Exits as M&A Activity for Young Companies Rises. (CityAM)

A Look at What Makes a Top Tech Company a "Top Tech" Company

The Top 20 Tech companies are pretty easily recognized – tech giants like Apple, Facebook, and Alphabet top the list. 

via Visual Capitalist

There are a lot of commonalities between successful tech companies.  Pretty much all of them attempt to leverage software to do jobs that would be too expensive, too boring, or virtually impossible to do otherwise.

Obviously, technology is a huge force factor impacting the success of a company.  As a result, there is an arms race to get to "next" ...

via Bloomberg

Research and Development has always been a key in growing companies.  Now, that is truer than ever.   As tech improves faster and more dramatically, technologies become relevant and irrelevant faster than ever. 

Challenging ... sure, but a great problem to have.

What a great time to be an entrepreneur!

Good luck with your 2018 plans. I'd love to hear what you are focusing on next year.

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

 

 

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

 

Lighter Links:

• Many Strategies Fail Because They're Not Actually Strategies. (HBR)
• The Purpose of Sleep? to Forget, Scientists Say. (CS)
• What is Unique Ability? (StrategicCoach)
• UPS Drivers Don't Turn Leftand It Saves Them 10 Million Gallons of Gas a Year. (Qz)
• Eugenics 2.0: We're at the Dawn of Choosing Embryos by Health, Height, and More. (TechReview)

Trading Links:

• Why Investors Should Trade Futures. (TheStreet)
• Saudi Arabia Arrests 11 Princes, Including Billionaire Alwaleed Bin Talal. (NYT)
• Investment Queen: A Look at Elizabeth II's Fortune. (AP News)
• Amazon CEO Jeff Bezos Just Sold $1.1 Billion Worth of His Stock. (CNN)
• Bill Gates, Jeff Bezos and Warren Buffett Now Have More Money Than Literally Half of America Combined. (Inc)

 

 

Wealth Formula - Howard Getson and Buck Joffrey Talk The Future of Trading

I'm excited ... because I've been at Joe Polish's Genius Network Annual Event this weekend.

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).

WFP-077-Howard_Getson

 

How Much Debt Does Your Country Have Per Person? An Exercise in Understanding Large Numbers

Debt often enables governments to run smoothly. But sometimes you can have too much of a good thing.

The US recently passed $20 trillion in debt for the first time.  To put that in perspective, I am going to reference an article I wrote in 2011 ... when the debt was approaching $15 trillion. 

Here is what the $15 trillion U.S. debt looked like when it is piled up in stacks of $100 bills.

To put it in context, do you see the tiny tractor trailer?  Squint, and you can see the barely visible human in front of it. 

via WTFNOWAY

The United States has a net wealth of approximately $300 trillion.  Nonetheless, the statistics are sobering. 

Take a look at the list of country debt as a percentage of GDP.   Apparently, Japan has embraced its inner borrower. 

OECD via World Economic Forum 

According to the World Economic Forum, if the Japanese wanted to pay off their national debt, each individual would owe approximately $90,345. For comparison, US citizens would owe $61,539 a person. 

It's also worth noting that lower debt levels don't translate to safety on a global scale. Yugoslavia had very low government debt until its breakup. 

If you want to see an updated, interactive version of the U.S. Debt Clock, just click here.  It is worth spending a little time to watch the pace the numbers turn.

Here Are Some Links For Your Weekly Reading - October 15th, 2017

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

 

 

Lighter Links:

• Radical Candor Will Help Managers Balance Accountability and Empathy. (SmallBizTrends)
• Pretending to Be Batman Helps Kids Stay on Task. (Digest)
• The Secrets of Disneyland: a Company Vet Explains How the Magic Happens. (FastCompany)
• What Actually Happens When You Pull a Muscle? (Popular Science)
• Now that Anyone Can Hack BiologyShould We Be Afraid? (SingularityHub)

Trading Links:

• How to Destroy Our Economy. (Forbes)
• Bulldozers Can Show You Where the Economy's Going before the Official Data Do. (Bloomberg)
• JPMORGAN: Here's What Could Cause the next Financial Crisis. (BusinessInsider)
• Goldman Sachs to Explore Starting Bitcoin Trading Venture. (Bloomberg)
• AI Will Eliminate 1.8M Jobs but Create 2.3M by 2020, Claims Gartner. (TechRepublic)

Here Are Some Links For Your Weekly Reading - October 8th, 2017

 Old people and their joints ... it's becoming an issue. 

 

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

Lighter Links:

• Why Culture Doesn't Just Beat Strategy, It Must Be the Strategy. (Inc)
• Google Reveals the Top Things People Want to Find out How To' Do. (TechCrunch)
• Pain Leads to Empathy and Self-preservation: Should We Make Robots Feel' It? (Aeon)
• Gartner's Hype Cycle for Emerging Technologies Adds 5G and Deep Learning. (Forbes)
• Hostile Terrain: Tank Traps, Fake Towns & Secret Tunnels of the Korean Borderlands. (99PercentInvisible)

Trading Links:

• Seventy-Eight Billion Reasons Why Bitcoin Is the New Gold. (Globe&Mail)
• Amazon Wants to Open a $5 Billion Second HQ in North America. (CNN)
• Has Disruption from E-commerce Run Its Course? (Latest)
• Who Vladimir Putin Thinks Will Rule the World. (CNN)
• Employees at the World's Largest Hedge Fund Use iPads to Rate Each Other's Performance in Real-Time – See How It Works. (Insider)

Gartner's 2017 Hype Cycle for Emerging Technologies Identifies Innovations Worth Monitoring

There's a lot of "hype" these days.  Social media and tools like Kickstarter would have you believe that every new idea is the "next big new thing".

In contrast, Gartner's Hype Cycle Report is a considered analysis of market excitement, maturity, and the benefit of various technologies.  It aggregates data and distills more than 2,000 technologies into a succinct, contextually understandable, snapshot of where various emerging technologies sit on the hype cycle.

Understanding this hype cycle framework enables you to ask important questions like "How will these technologies impact my business?" and  "Which technologies can I trust to stay relevant in 5 years?"

 

What's a "Hype Cycle"?

 

As technology advances, it is human-nature to get excited about the possibilities we imagine ... and then to get disappointed when those expectations aren't met. 

At its core, the Hype Cycle tells us where in the product's timeline we are, and how long it will take the technology to hit maturity. 

At this point in time, Machine Learning/Deep Learning and Connected Homes are at the peak of their hype cycles ... Meanwhile, Augmented Reality and Blockchain have matured past the hype phase (with Self-Driving Cars following close behind).  

This year, according to Gartner, there are three overarching "mega-trends" to watch. 

• AI Everywhere shows the transition towards a ubiquitous AI experience, from self-driving cars, to machine learning, and to smart robots.  Consider the impact on traffic/accidents with the adoption of autonomous vehicles, or the ability of machine learning to process more data faster. 
• Transparently Immersive Experiences shows our transition towards human-centric contextual and fluid technological experiences.  - like Connected Homes or Virtual Reality. Consider the impact of Augmented Reality on advertisements or gamification. 
• Digital Platforms shows the transition of emergent platforms into adoption. Platforms like Blockchain, IoT,  and Quantum Computing. Consider the effects of bitcoin and other blockchain initiatives, as well as the opportunity for new business models centered around these platforms.

 

Here is the chart. You can click the image to see it larger. 

 

via Gartner

For comparison, here is the chart from last year; and you can check out my article on 2015's hype cycle here.

via Gartner

The hype cycle gives us an idea of which of these technologies will likely survive the market hype and have a potential to become a part of our daily life. 

Here are the five regions of Gartner's Hype Cycle framework:

1. Innovation Trigger (potential technology breakthrough kicks off),
2. Peak of Inflated Expectations (Success stories through early publicity),
3. Trough of Disillusionment (waning interest),
4. Slope of Enlightenment (2nd & 3rd generation products appear), and
5. Plateau of Productivity (Mainstream adoption starts). 

  

Which technologies do you think are over-hyped ... and which ones might survive the hype?

I find this stuff fascinating.  Consider some of the interesting technologies just starting their hype cycle:

• Human Augmentation has the potential to enhance our bodies and minds using electrical currents, chips, or exoskeletons, but also raises ethical and legal questions. 
• Quantum Computing is still in its infancy, but could theoretically solve certain problems far faster than classical computers. 
• Smart Dust opens up the possibility of monitoring essentially everything by creating a vast network of minuscule sensors that can detect various inputs. 
• 4D Printing would theoretically allow us to print objects that reshape or self-assemble over time. 

We live in amazing times! 

Onwards. 

Here Are Some Links For Your Weekly Reading - September 25th, 2017

Interesting week in the Twit-o-Sphere as Steph Curry and Donald Trump feud, and Ted Cruz is "caught" liking porn on Twitter. 

Who would have guessed Twitter would play such a strong a role in today's political climate?

I believe that everyone deserves the right to my opinion (as much as anyone) ... Still, sometimes silence is the best policy.

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

Lighter Links:

• This Company Wants to Grow A.I. by Using Blockchain. (CNBC)
• New Neuroscience Reveals 4 Rituals that Will Make You Happy. (TheLadders)
• Google Wants You to Know What Data It Has on You. (Fortune)
• Marriage vs. the Single Life: Who Has It Better? (PsychologyToday)
• Hackers Could Program Sex Robots to Kill. (NYPost)

Trading Links:

• One-Tenth of Global GDP is Now Held in Offshore Tax Havens. (ZeroHedge)
• Goldman Sachs Thinks Fintech Has As Much Potential as Trading. (Quartz)
• Cowboys Are World's Most Valuable Team at $4.8 Billion. (AP News)
• Social Finance: Where Wealth and Values Meet. (Forbes)
• Obama Goes from White House to Wall Street in Less Than One Year. (Bloomberg)