Why we do what we do

Why we do what we do

Some of the most pressing problems of the world (and life) would be better understood by recognizing the patterns which produce them. To separate signal from noise, wheat from chaff.

Indeed, life itself is a dance of pattern (hat tip, Alan Watts).  Right down to the level of DNA in cells and up to the level of cosmic supernova there are structures – patterns – which underpin reality.

Survival itself is predicated on success in rapidly recognizing meaningful patterns. Snake! Our peripheral vision and autonomic nervous system (“fight-or-flight”) is wired to respond to threats.  Wild berries!  Our acute frontal vision is wired to locate camouflaged prey or gather nuts and fruits. 

To get the pattern wrong – or to miss it entirely - can be fatal. Don’t eat the red berries.

G2’s aim is to use machine learning to find patterns in the universe of data. We are part of the (bottom up) AI revolution, focused on one of the most challenging problems: how to systematize fundamental investing. In short, how to beat humans at their own game. Our progress through the years is outlined below.

The Future is not what it used to be*

The Future is not what it used to be*

Anyone with grey hair (or no hair) will remember the hype and promise of AI through the 1970’s and 80’s. Recall:  Hal 9000 (2001: A Space Odyssey), SkyNet (Terminator); C-3P0 (StarWars) and Deep Thought (A Hitchhikers Guide to the Galaxy).

Such attempts to replicate human consciousness (known as Strong AI) indeed failed to deliver, leading to an ‘AI Winter’ during the 1980’s. One infamous AI fail was Biblical in proportions: Matthew 26:41 “the spirit is willing but the flesh is weak” machine translated to “the vodka is strong but the meat is soft” when reversed from Russian.

However, in recent decades a quiet revolution has been taking place in Applied AI, led by engineers trying to solve practical problems.

Deux ex machina

Deux ex machina

One thing which strikes us, apart from the whoosh as the years fly by, is how revolutionary recent advances in computing, data science and machine learning are.

Consider the increasing number of ‘high order’ pattern recognition tasks where computers outperform humans: chess, poker, quiz shows, cancer diagnosis, driving, et cetera.

It’s better to be lucky than smart*

It’s better to be lucky than smart*

Since G2’s launch in 2011, markets have delivered a wild ride. Indeed, Mr Market has lived up to his schizophrenic reputation from one year to the next, if not from quarter to quarter.

In 2012, large cap and low volatility stocks dominated globally. In 2013 small cap stocks and the reflation trade was the place to be. While in 2014 high yield stocks and deflation was the better bet. 

Not to be out done, 2015 required a focus on mega cap stocks in developed markets – everything else was … messy.

Warren Buffett 2.0

Warren Buffett 2.0

It is popular – probably too popular – to refer to Buffett every time someone wants to make an anti-efficient market argument. There is a kind of reductio ad Buffettum going on.

Perhaps we fall into this trap here, but we try to avoid it on the way to making a strong point about the need for investment processes to be systematic.

The ‘Superinvestors of Graham-and-Doddsville’ is an article by Warren Buffett promoting value investing, published in 1984.

It famously challenged the idea that equity markets are efficient through a study of nine successful investment funds generating long-term returns above the market index, all of which shared the same patriarch in Ben Graham.

Chasing tails

Chasing tails

The fat tails are obvious. One out of every five stocks is a significant winner or loser – the rest don’t matter.

Key to G2’s philosophy is that stock-selection is a Pareto classification problem. Stock-pickers (or systems like G2’s) must classify stocks into three buckets: companies to own, companies to sell and companies not to bother with.

Crowded wisdom

Crowded wisdom

Successful investing requires taking action only when a great business becomes temporarily undervalued. The market is “mostly efficient” rather than “always efficient”:  the distinction makes all the difference in the world. 

Thus, the task of an investor is to constantly search for mispriced assets and pounce when Mr Market becomes irrational.

If this sounds simple, why isn’t it easy?  Why do most individual investors and active managers fail to outperform market indices?

Life is a dance of pattern

Life is a dance of pattern

A good investment process first needs to find the signal in the noise – persistent patterns which predict company success.  But that’s not enough.  The process then needs to be executed in a way which combines paradoxical qualities, such as:  patience and aggressive action; discipline and adaptability; being aware of the crowd psychology but not swayed by it.

To compound the difficulty, the stock market is a wicked learning environment. Feedback (essential for learning) is ambiguous, delayed and clouded by randomness. To misappropriate Churchill:  the market is a riddle, wrapped in a mystery, inside an enigma.

G2 was established to bring machine intelligence to fundamental investing. To systematize stock selection and redefine investing. In short, to beat humans at their own game.