From one perspective the move from active, human portfolio decision making to passive, computer-driven investment management mirrors societal trend shifts to various degrees. The impact automated intelligence has on a job function, for instance, transforms the human from a leader to a follower to various degrees. In a human / computer artificial intelligence integration project, computers often frame decisions, provide probability path analysis, and point the human in a direction to make a decision. The same can be seen in how high passive ETF participation are impacting active stock management. A new study from hedge fund portfolio management services firm Symmetric highlights just how deadened key stocks have become to the most important idiosyncratic performance driver of all: earnings.

Stocks with high passive ETF participation simply don't respond to earnings the same as those without ETF influence

A key basis of supply and demand market theory is found in understanding the nature of the performance driver. In a traditional market environment, earnings have been a key driver of demand, with supply being constant.

This obvious performance driver is starting to change the supply and demand landscape, calling into question the impact passive market demand is having on an “efficient market.”

Stocks with high passive ETF participation exhibit a deadened response to earnings, the report noted.

“The analysis confirms a relationship between ETF trading of a stock and the likelihood that a stock will respond to its earnings release,” with increased non-econoimc, passive buying exacerbating the trend. “The more a stock’s volume is driven by ETF trading, the less it is likely to move on earnings day in response to stock specific news.”

Benchmarking the responsiveness of a stock to earnings based on volatility prior to and in trading immediately after earnings announcements, stocks with large ETF participation “may not be trading according to their fundamentals,” the report observed. “In the case of heavy, corresponding ETF volume, they may be trading more in line with the supply and demand for the ETFs that hold the stock than how the company is performing.”

The reason ETFs are deadened to respond to individual stock fundamentals is because the investment decision is based not on one issue, but often times hundreds, if not thousands of stocks. For a stock to get included in this larger basket of demand that is not based on individual performance is negating the importance of corporate returns to a degree.

Looking at the average price change surrounding earnings days bolsters the point. Stocks with low investments from ETFs, under 10%, might move near 6% on an earnings day. Contrast this to stocks with near 90% of their volume trading due to ETFs, and the price change on earnings day is near half that of those with high passive ETF participation.

Non-economic demand dominating a market impairs the price discovery function

A key premise of free market theory and price discovery is that participants setting prices largely have an economic interest in the underlying fundamentals. Eugine Fama’s much-critiqued efficient-market hypothesis assumes that all market participants are rational and have equal access to information. What the Symmetric study reveals is that as markets become more automated, they no longer pay attention to information and may be driven by momentum into the larger investment.

In a supply and demand continuum, both factors are often measured as a percentage of overall market factors. Market demand represented by high passive ETF participation is documented to have increased by more than 6 times in a short period of time, up from $600 billion in 2007 to over $4 trillion today.

There have long been concerns regarding the percentage of non-economic, “mindless” volume in free markets that date back to trend following concerns expressed by regulators in 2010. The market, in the autonomous age, can be better understood by its structural components, its technical factors, as opposed to its fundamentals now more so than at any point in history. This is one reason certain derivatives analysts were able to partly call a flash crash in 2015. With an understanding of demand in the pipeline and the type of economic demand, markets are, themselves, becoming more robotic in their outputs.