Crypto Market Commentary 

31 October 2019

Doc's Daily Commentary

The 10/30 ReadySetLive session with Doc and Mav is posted below:

Mind Of Mav

The Rise Of Finacial Machine Learning

Hey Siri, can you invest my life savings?

The greatest innovations in finance are unstoppable, but often lead to crises as they find their feet. In the 18th century the joint-stock company created bubbles, before going on to make large-scale business possible in the 19th century. Securitization caused the subprime debacle, but is today an important tool for laying off risk. The broad principles of market regulation are eternal: equal treatment of all customers, equal access to information and the promotion of competition. However, the computing revolution looks as if it will make today’s rules look horribly out of date. Human investors are about to discover that they are no longer the smartest guys in the room.

The old image of brokers rushing round the stock market to place their orders or the trading room filled with traders with a telephone in each ear are now an anachronism.

Traders now require a completely different set of skills, among which is knowing how to write code. Traders must have the ability to develop, understand and adjust an algorithm that can analyze with something more than a gut feeling, intuition or a premonition. Algorithms are increasingly more efficient at carrying out a growing number of analytical tasks needed to make decisions such as risk estimates. For many people, this will mean learning new skills, or simply being replaced with machines people capable understanding them.

As is becoming clear, automation no longer applies just to dull, dirty, dangerous, and demeaning jobs — it now applies to traders on Wall Street.

Image result for financial machine learning

As a result, in the distance I can hear the world’s tiniest piano playing Trois Gymnopédies while Jim Cramer sheds a single, solitary tear.

But in all seriousness, funds run by computers that follow rules set by humans account for 35% of America’s stockmarket, 60% of institutional equity assets and 60% of trading activity.

New artificial-intelligence programs are also writing their own investing rules, in ways their human masters only partly understand. Industries from pizza-delivery to Hollywood are being changed by technology, but finance is unique because it can exert voting power over firms, redistribute wealth and cause mayhem in the economy.

In the past decade computers have graduated to running portfolios. Exchange-traded funds (etfs) and mutual funds automatically track indices of shares and bonds. Last month these vehicles had $4.3trn invested in American equities, exceeding the sums actively run by humans for the first time. A strategy known as smart-beta isolates a statistical characteristic—volatility, say—and loads up on securities that exhibit it.

An elite of quantitative hedge funds, most of them on America’s east coast, uses complex black-box mathematics to invest some $1trn. As machines prove themselves in equities and derivatives, they are growing in debt markets, too.

Until now, the rise of computers has democratised finance by cutting costs. A typical etf charges 0.1% a year, compared with perhaps 1% for an active fund. You can buy etfs on your phone.

An ongoing price war means the cost of trading has collapsed, and markets are usually more liquid than ever before. I often refer to this as the rise of the “Robinhood generation”, as Robinhood is an app targeted to younger users with 0% commission trading.

When the returns on most investments are as low as today’s, it all adds up. Yet the emerging era of machine-dominated finance raises worries, any of which could imperil these benefits.

One is financial stability. Seasoned investors complain that computers can distort asset prices, as lots of algorithms chase securities with a given characteristic and then suddenly ditch them. Regulators worry that liquidity evaporates as markets fall. These claims can be overdone—humans are perfectly capable of causing carnage on their own, and computers can help manage risk.

Nonetheless, a series of “flash-crashes” and spooky incidents have occurred, including a disruption in etf prices in 2010, a crash in sterling in October 2016 and a slump in debt prices in December last year. These dislocations might become more severe and frequent as computers become more powerful.

Another worry is how computerised finance could concentrate wealth. Because performance rests more on processing power and data, those with clout could make a disproportionate amount of money. Quant investors argue that any edge they have is soon competed away. However, some funds are paying to secure exclusive rights to data. Imagine, for example, if Amazon (whose boss, Jeff Bezos, used to work for a quant fund) started trading using its proprietary information on e-commerce, or JPMorgan Chase used its internal data on credit-card flows to trade the Treasury bond market.

These kinds of hypothetical conflicts could soon become real.

A final concern is corporate governance. For decades company boards have been voted in and out of office by fund managers on behalf of their clients. What if those shares are run by computers that are agnostic, or worse, have been programmed to pursue a narrow objective such as getting firms to pay a dividend at all costs? Of course, humans could override this. For example, BlackRock, the biggest etf firm, gives firms guidance on strategy and environmental policy. But that raises its own problem: if assets flow to a few big fund managers with economies of scale, they will have disproportionate voting power over the economy.

For the moment, machine learning cannot accurately predict the functioning of a market. But it can be used to predict many specific behaviors better than humans. Is it necessary to know how to program in order to carry out this type of analysis? No: many sophisticated analytical tools of this type are getting easier to use, and many complex analyzes can be carried out by choosing options in a menu, which makes them more efficient and more likely to reach production status. But the people who use those algorithms now require a new skill set.

If this is happening in the financial markets at the level of professional traders, what about amateur investors who want to manage the purchase and sale of their shares, helped by the race to the bottom in commission-free trades of stocks and exchange-traded funds? This too, will soon be an anachronism: if the professionals can’t beat the market, what chance to mere mortals stand, with their outdated analytical tools? The growth of automated services and passive management is no accident.

In short, it’s the end of an era.

And the beginning of something . . .

Oh, and happy birthday, Bitcoin.

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