Category: Books

Books that we read and our thoughts about them.

Book Review: The Technology Trap

In The Technology Trap: Capital, Labor, and Power in the Age of Automation (Amazon,) Carl Benedikt Frey gives us a brief history of technology’s impact on society and how we can better prepare ourselves for the coming AI revolution.

Historically, new technologies got adopted only when it didn’t threaten the status quo of the elites.

For most of history, the politics of progress were such that the ruling classes had little to gain and much to lose from the introduction of labor-replacing technology. They rightly feared that angry workers might rebel against the government.
One reason economic growth was stagnant for millennia is that the world was caught in a technology trap, in which labor-replacing technology was consistently and vigorously resisted for fear of its destabilizing force.

The Technology Trap

Artisans formed guilds and openly lobbied to prevent guild members and outsiders from producing things in new ways. However, as trade increased, competition between trading blocs eroded the power of protectionists. Areas that became more exposed to outside competition invested more in the invention of new technologies.

New technologies can be either labor saving or labor displacing. The problem with the latter is that displaced works see a rapid erosion in their income. So even though technological innovation boosts aggregate incomes over the long term, it is not cost-less at the individual level.

The simple existence of better technology does not inevitably translate into faster economic growth. For that, widespread adoption is required. If you want society to be open to new technologies, you absolutely must have a social safety net and a plan to make sure the displaced workers have the wherewithal to up-skill themselves.

Recommendation: Skim.

Book Review: The Man Who Solved the Market

In The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution (Amazon,) Gregory Zuckerman chronicles the rise of Renaissance Technologies (RenTec) and its founder, Jim Simons.

It is a fascinating book. You should absolutely, without a doubt, read it.

The basic question that Jim Simons asked was: Is it possible to develop a mathematical model of the market and use it to trade profitably?

Turns out that it is a qualified “yes.” There are a handful of people who can do it successfully over a long enough period of time to become billionaires without blowing up. And they are extremely secretive of the methods they use.

Reading through the book, I realized that some of the machine learning models they seemed to be using in early 2000’s is what Google has commercialized now. It looks like they have a 10-15 year head-start in some areas over everybody else in things like sentiment analysis and language translation.

Also, Simons, worried about the capacity of his algorithms, capped the size of his fund a long time ago and kicked out outside investors. Today, the fund is run for and by its employees.

The gains Simons and his colleagues have achieved might suggest there are more inefficiencies in the market than most assume. In truth, there likely are fewer inefficiencies and opportunities for investors than generally presumed. For all the unique data, computer firepower, special talent, and trading and risk-management expertise Renaissance has gathered, the firm only profits on barely more than 50 percent of its trades, a sign of how challenging it is to try to beat the market—and how foolish it is for most investors to try.

The Man Who Solved the Market

Recommendation: Must Read!

Book Review: Narrative Economics

In Narrative Economics: How Stories Go Viral and Drive Major Economic Events (Amazon,) Robert Shiller, Nobel laureate, pitches the importance of incorporating popular narratives in economic and financial models.

The biggest problem I have with the way most research is done in quantitative finance is that whatever data is available gets analyzed to death while data that is hard to get, unorganized or tedious to collate is ignored. And given the adaptive complex dynamic nature of the markets, signals derived from the former attenuate at a much faster rate as time goes on.

When markets break quant models, it is often because the underlying narrative has changed. The word “narrative” is just a fancy word to describe the stories we tell each other. And stories are virus. The spread of a narrative can be modeled like how epidemiologists model the spread of contagions. And the main thrust of the book is that it is high time economists and policy-makers began to incorporate narratives into their models and playbooks.

By 1932, the bottom of the stock market decline, the US stock market had lost over 80% of its 1929 value in less than three years. We have to ask: Why did people value the market at such a low level? A big part of the answer was a narrative that went viral: modern industry could now produce more goods than people would ever want to buy, leading to an inevitable and persistent surplus.

Narrative Economics, Shiller 2019

Investors would do well to take Shiller’s ideas and start to systematically track narratives that could impact their portfolios. Maybe, instead of Risk-Parity, try Narrative-Parity portfolios!

Recommendation: Must Read!

Book Review: What It Takes

What It Takes: Lessons in the Pursuit of Excellence (Amazon,) is Steve Schwarzman’s biography. He is the founder of The Blackstone Group ($BX,) a $60 Billion enterprise.

Sadly, the book is not about private equity. It is about whatever Steve decided to share about himself to a wide audience. So, if you were looking for investing gems, then you would to sourly disappointed.

I have excerpted the interesting bits on my Evernote. You can skip the book and read that instead.

Recommendation: Avoid!

Book Review: Alchemy

In Alchemy: The Dark Art and Curious Science of Creating Magic in Brands, Business, and Life (Amazon,) Rory Sutherland makes a strong point that the pendulum has swung too far to the side of “rationality.” Businesses are so enthralled by scientific thinking that they have stopped taking risks.

The problem that bedevils organisations once they reach a certain size is that narrow, conventional logic is the natural mode of thinking for the risk-averse bureaucrat or executive. There is a simple reason for this: you can never be fired for being logical. If your reasoning is sound and unimaginative, even if you fail, it is unlikely you will attract much blame. It is much easier to be fired for being illogical than it is for being unimaginative.

The fatal issue is that logic always gets you to exactly the same place as your competitors. If you are wholly predictable, people learn to hack you.

For an investor, there are quite a few aha moments. To out-perform, you need to be different from everybody else. But if you are a professional money manager, then being different is very hard to defend if things don’t work out. So the larger your get, the lesser the risks you can take. If you think quantitative models will solve this problem, think again:

The risk with the growing use of cheap computational power is that it encourages us to take a simple, mathematically expressible part of a complicated question, solve it to a high degree of mathematical precision, and assume we have solved the whole problem.

We should also remember that all big data comes from the same place: the past. Yet a single change in context can change human behaviour significantly. For instance, all the behavioural data in 1993 would have predicted a great future for the fax machine.

The book is an insightful, yet easy read.

Recommendation: Must read!