Our Collection of the Leading Voices in Autonomous Learning Investment Strategies (ALIS)

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“The Third Wave is for those who think the human story, far from ending, has only just begun.”
-Alvin Toffler, The Third Wave.

As I embark on the next chapter in my life as an investor in what Wired1 magazine recently called the ‘Third Wave’ of investing, I cannot help but agree with renowned author and futurist Alvin Toffler. From my point of view, however, it is the next chapter of the investment story that has only just begun.

The world we are living in today was outlined by Toffler in his book The Third Wave2 published in 1980. This was in the same decade as I started to invest in hedge funds, which were far from the mainstream investment vehicle that they are now.

At the time, Toffler described with eerie accuracy the digital, communication, corporate and technological revolutions that would take place in the post-industrial society, often referred to as the Information or Technetronic Age, the impact of which we are only now starting to grasp.

Toffler suggested that the ‘Third Wave’ will not be a linear extension of the past, but more a quantum jump driven by the rise of new scientific-breakthrough-driven industries and the rapid evolution of the processing power of computers.

In The Third Wave, Toffler also predicted the ‘stateless currency’, the changing nature of media/news consumption, and the rise of the intelligent environment. All of this decades before Bitcoin, social media and WiFi-enabled domestic robots.

Why is this relevant? Just as Toffler saw the signs of a technology-driven future, so too have I realised that we are at a cusp of the next investment process paradigm. This ‘Third Wave’ is the confluence of several unprecedented developments.

I must emphasise that this Third Wave is not just the fact that machine learning, or artificial intelligence, is finally working. But the game changer lies in the fact that there is a ‘mashing together’, as I like to call it, of an enormous growth (and multiple structures) of data; combined with new data science and structuring platforms to parse and classify the data; and on top of this low cost on-demand computing and an overhaul of the regulatory environment.

It is the combination of these five factors that will lead to the collapse in the cost structure of the investment management industry and irreversible changes will start to occur. Think Uber. It is the classic example of what Clayton Christiansen calls in his book The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail3, disruptive innovation. He states that technology starts the disruption, but the innovation typically comes from outside of the industry it is disrupting.

Uber was not a taxi business that became more efficient, but a technology company that redefined and transformed transportation through the mashing together of mobile technology, GPS, a payment system and a rating system.

This is happening in asset management now. Having focused on finding and seeding talent for the last three decades, I allocated to the first wave of discretionary hedge fund managers. They came from prop desks, trading floors and event driven risk-arb firms; not Fidelity.

The second wave, in the 1990s, came from mathematics and physics; not discretionary hedge funds. What have commonly become referred to as ‘quants’ have brought with them a hypothesis-driven quantitative approach to investing.

Autonomous Learning Investment Strategies (ALIS) is the ‘Third Wave’ of investment management. In a similar fashion to the preceding strategies, this new breed of managers is not coming from the hedge fund industry. ALIS managers’ brains are wired differently. Their modus operandi is more aligned to that of hackers and computer gamers, with a similar healthy disrespect for convention.

As I started to allocate to ALIS, I thought it would be useful to put my ‘pre-science’ investment history into a bit of context. After all, not all the investors will come from my current client-base who know about my historical achievements.

My track record includes both my time at Protégé Partners (until I handed the investment reigns to my business partner in 2011) and from January 1989 when I started managing client portfolios. The graph below shows that $1,000 invested in January 1989 would have grown to $17,792 by December 2010.

Growth of $1,000 Invested January 1989 – December 2010

Looking back over more than three decades of investing, predominantly in hedge funds, two things strike me. The first is that even before Protégé Partners, a firm I co-founded in 2002 to allocate to and seed smaller or start up hedge fund managers, my forte has always been in spotting major trends and finding the talent to execute them.

After receiving my M.B.A. from Harvard in 1985, I joined Berkeley Asset Management in California. It was here that I first allocated to managers that are now considered to be brand name hedge funds. Their stellar track records and longevity in an industry that has seen many hedge funds fall by the wayside have resulted in many of those managers being referred to as ‘hedge fund legends’.

The ‘talent-seeking’ facet of my skillset is not, however, only reserved for hedge funds. I have been able to pick award-winning documentary films to finance with some regularity, too. It turns out it is harder to get a film into the Sundance Film Festival than pick a hedge fund manager, and many of my films have won at this festival. But I digress.

What I have observed from my experiences outside of finance is that the best way to benefit from a collapsing cost structure is to continue finding new talent.

Standing in a world where Donald Trump is currently President—I suggested this was a possibility in 2011 (Investor Letter, First Quarter, 2011)—my second thought is that I have often been what others have termed prescient in terms of insights.

While suggesting that Trump might become President was an entertaining anecdote, looking back at these letters shows which investment trends I did spot and how I acted on them ahead of the crowd.

We all know the standard investment disclaimer: “past performance is not an indicator of future performance”. But as I build my next business around allocating to this brand-new area of Autonomous Investment Learning Strategies (ALIS) and highlighting my ‘Red Thread’, as I like to call it, as trend spotter and talent scout it is essential to put into context why my next phase might sound slightly out there at this moment in time.

As an aside, the ‘Red Thread’, which comes from Greek mythology, was the colour of the thread that was given to Theseus by Ariadne to help him escape from the labyrinth of the Minotaur. Ariadne’s thread, as it is also known, is the name given to a specific type computer science algorithm, which sits synchronistically with the algorithmic nature of the third phase of my investment career.

As highlighted earlier, this Red Thread has always been to spot trends and talent. In my career, I became adept at distinguishing wannabes from the real thing. This is perhaps why it was obvious to me to call Fannie Mae the world’s largest and most leveraged hedge fund as early as 2003 (Investor Letter, Third Quarter, 2003), even though it was not a description given by the mainstream media at the time.

In a similar fashion, pointing out the credit bubble—in the same 2003 Investor Letter—was like the child pointing out the obvious in Hans Christian Anderson’s The Emperor’s New Clothes. The problem with spotting trends early is that not everyone sees what you see and, more importantly, when you see it.

“If we believe that the combination of low interest rates and tight credit spreads has created a credit bubble, we must also pay close attention to how the fastest growing financial product (CDS) might influence the very risk it was created to mitigate,” (Investor Letter, Third Quarter, 2004).

So, when I highlighted a brewing storm in the credit default swap market I raised a few eye brows. Once again being a contrarian has never frightened me. As a matter of fact, my most contrarian calls have been some of my most profitable.

Another area where I was adamant about problems on the horizon was liquidity. In fact, Theory of Liquidity Refraction is when I called the impending liquidity crisis publicly (Investor Letter, First Quarter, 2006).

But perhaps one of the more interesting investment stories that I have been involved with was what Gregory Zuckerman went on to write about in his book The Greatest Trade Ever4. The story of John Paulson’s short of subprime mortgage backed securities is well known, both thanks to the book and later the film The Big Short.

In July 2006, I was not only the first to invest in Paulson’s Credit Opportunities Fund (Investor Letter, Second Quarter, 2006), but on a train-ride in August 2006, John and I had a pivotal conversation that resulted in the ‘greatest trade ever’.

“Who’s holding the bag on this stuff?”, I asked John, specifically regarding the banks on the other side of his trade. Because of this question, the real kicker of the trade was conceived: shorting financials. I put this tactical trade on directly in July 2007 via put options and credit default swaps (Investor Letter, Third Quarter, 2007).

“Hindsight is 20/20” usually refers to learning from mistakes and there were obviously calls that did not always go our way. But a retrospective look through these Quarterly Investor Letters has highlighted to me quite how many of my seemingly contrarian views did work out.

I might be biased because I wrote them, but these Quarterly Investor Letters, extracts of which follow, are a fascinating stroll through my views on investment history. These letters are full of gems; calls and trades we executed via our managers.

Even after I handed over the investment reins to my business partner in 2011, I continued to write the investment letters for a full year after. What few realise is that change to an investment process can often take a year or two to take effect. It was during this year that I called the student debt crisis and the troubled relationship between the UK and Europe (Investor Letter Fourth Quarter, 2011).

One Investor Letter for the Second Quarter, 2007, however, sticks in my mind today as I set out on the next leg of my investment journey. In this letter, I outlined the top 10 attributes that success in asset management requires:

1. Hard work
2. Fundamental analysis
3. Peer interaction
4. Independent thinking
5. Knowledge of cycles
6. Anticipating when the rules of the game are changing
7. Respect for risk
8. No fear of risk
9. Patience
10. Sense of humor

I have always loved point 10. In Investor Letter Third Quarter, 2007, I wrote about Tragic Comedy of Man vs. Machine.

“We are frequently asked about the characteristics we look for in money managers. As you might have noticed on the list above, a sense of humor is an important trait. We have found those who can freely associate disparate ideas and synthesize those ideas to mentally connect-the-dots display an intelligence that few possess.

These same people usually can express this talent through their humor. Investment organizations that are fun places to work often handle adversity better than others. Without the ability to survive adversity, individuals or organizations cannot deserve our (your) investment.

In contrast, a computer model or machine can optimize decision algorithms but cannot look a human in the eye and determine truth or deceit. We have yet to meet a computer with a sense of humor, although we have learned about recent attempts to train computers to understand simple jokes.

Considering the tantamount importance of human interaction in the investment world and the companies in which managers back, we prefer to invest with those whose human judgment we respect, and especially those who know how to connect-the-dots.”

Today, as I look to the future, one in which even Alvin Toffler saw artificial intelligence as a worthy ally, I am invoking number six: ‘Anticipating when the rules of the game are changing’. Since I wrote that 2007 letter, AlphaGo has beaten the world’s best human Go player, Lee Sedol; and this year, a decade later, Libratus, a Carnegie Mellon-designed program, defeated four human players in a grueling, 20-day no-limit Texas Hold’ em poker tournament.

On my first day at Harvard Business School, my professor claimed, “I am going to teach you how to make decisions under conditions of uncertainty with incomplete information.” Until the start of 2017, machine learning had not made decisions under conditions of uncertainty and incomplete information. Now that has changed.

What few grasp is that with this ‘artificial’ poker win, we have hit yet another artificial intelligence milestone and have crossed the Rubicon. For now, a machine has successfully worked within the framework of an incomplete informational picture—its opponents’ unseen cards—in contrast to board games where all strategic playing pieces are visible.

To me this is a significant signal that the next iteration of investment management is here. As I mentioned earlier on, it is not just that artificial intelligence has become ‘cleverer’, it is that AI combined with data, data platforms, computing power and regulatory changes have all come together to transform the asset management industry.

I hope you enjoy reading the letters of my past investments as much as I enjoyed writing them. I am looking forward to this next tech and data-driven leg of the investment journey via my new firm Mov37. You read it here in the Second Quarter, 2017 that Autonomous Learning Investment Strategies (ALIS)5 are the new face of investment management for the intelligent investor.

Jeffrey Tarrant, July 2017