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Wednesday, August 12, 2020

Dawn of the investment assembly line


Date: Friday, August 14, 2009
Author: FTAlphaville.com/blog

Machines are taking over the management of equity assets, according a new report issued this week by Tabb group, the research house that drew heavy attention to the proliferation and effects of high frequency trading in US equity markets.

Securities Industries News wrote up the report, saying Tabb believe up to 34 per cent of US equity investments are now managed by a broad set of strategies that can be classified as “quantitative” or automated methods. That compares with 14 per cent in 2000.

Hedge funds, meanwhile, have been actively managing assets through automated means for decades, while mutual funds joined the game in the last five years. Next to come will be ETFs and index futures, Tabb said in the report.
SIN goes on to quote the authors as saying (our emphasis):
What is occurring is “the gradual automation of the entire investment management process across a comprehensive spectrum of investment strategies,’‘ the report, authored by Paul Rowady Jr. and Adam Sussman, said.

Here’s the abstract of the report, which happens also to include observations like there being no such thing as “alpha generation.” Our emphasis throughout:

Despite its popularity in the modern market lexicon, there is no such thing as “alpha generation.”  Investment managers and hedge funds do not produce alpha —as if it were the proverbial special sauce.  Alpha is indirectly generated: It is a symptom of inefficiencies caused by market structure; inadvertently conflicting regulations and regulatory regimes; information distribution and cognitive disparities; and naturally occurring anomalies and behaviors among various types of competing market participants.

Quantitative research is nothing more than a highly systematic approach to pattern recognition, and quantitative trading is simply the application of automation to the detection of these patterns.  To translate this axiom into the overused concept of “alpha”—or risk-adjusted returns in excess of a benchmark—quantitative research is another term for alpha discovery, and quantitative trading—itself a euphemism for many other activities, such as algorithmic trading—is another term for alpha capture.

For many strategies and markets, automation is now a competitive necessity; but over time, more and more manually operated processes and instruments in the investment strategy workflow will fall prey to automation, generally for the greater good.  The changes implemented roughly 30 years ago with a few pioneering physicists have now directly or indirectly infiltrated even the most conservative long-only asset managers. 

One of the strongest indications of the maturity of this era of automation is the growth of industrial-grade tools, which were initially built internally, to manage the full workflow process of strategy development and production—known as the investment assembly line.  Although comprehensive investment strategy platforms are normally the result of years of in-house development, the vendor community is quickly attempting to fill the growing demand for the rapid discovery, development, and deployment of automated strategies.

There are four core steps in the investment strategy process that are independent whether or not the strategy is automated, manual, or somewhere in between: Data management, Alpha discovery, Alpha capture and Feedback loop. Each phase has its own set of market data and analytics and includes major opportunities and challenges.

Although the level of automation across strategies remains quite disparate (compare distressed debt to statistical arbitrage, for example), the processes among those strategies that are particularly well-suited for automation are highly automated, with the exception of alpha discovery. Until software can be programmed to spot new patterns (rather than find known patterns in new datasets), the automation of the alpha discovery process is unlikely to change.