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David Garrity, CEO of GVA Research, joins BNN for a look at the debate about just who or what is to blame for the volatile trading activity in the markets: man or machine?
With Programmatic Trading Driving Well Over 50% Of Trading Volume, Important To Check Historical Assumptions Embedded In The Algorithms
The extreme volatility in the Mon 2/5 record-setting plunge of U.S. equity markets served to highlight the shortcomings of relying upon quantitative investment strategies and the preprogrammed trading algorithms through which they are executed. The primary weakness is a reliance upon the assumption that past macroeconomic trends such as low inflation and low interest rates will continue indefinitely. While no significant portion of the fund managers overseeing quantitative investment strategies is calling for a recession, the question is instead how hot the economy gets on the back of the recent U.S. tax reform and what that does to yields.
There has been a shift as we have gone from a market used to playing checkers (i.e. rising earnings, low interest rates equals higher prices) to being forced to compete in grand master 3-dimension chess (i.e. worries over growth versus interest rates, equity valuations, and the strength of the U.S. dollar, plus now market structure concerns). With the market regime shift, there are 3 questions fund managers must answer:
A) Has the US made a fiscal (rather than monetary) policy mistake? The recently passed tax reform is both dangerously inflationary and puts unwanted pressure on the US Treasury market. How does this work? By putting hundreds of dollars a month into consumer wallets when the US economy is close to full capacity, this stimulus risks a hockey-stick ramp in price levels.
And by funding that expansion with Federal debt, the tax cuts also add +$300 billion to Treasury issuance in 2018. With the Federal Reserve already a net seller of bonds, that incremental issuance faces a stiff headwind and will need higher yields to find buyers.
B) How do US equity valuations readjust to the end of a +30 year bull market in bonds? Interest rates are structurally on the increase around the world. Not only is the Federal Reserve reducing its portfolio, but the days of quantitative easing also seem numbered in Europe and Japan. Over the long term, this is a positive for capital markets as investors will price risk rather than central banks. In the short term, however, it creates an information vacuum of where rates will settle out. All this forces equity investors to precisely answer a key question: “What is the ‘right’ valuation for equities?” The waters are further muddied by another quandary: “How do I value a one-off increase in earnings power from corporate tax reform?” Is a 16x multiple correct? Or 18x? And how do we know for sure?
C) Where does the US dollar settle out? Consider the following points: As of Fri 2/2, the S&P 500 was still up 3.2% on the year, but the DXY Dollar Index was down 3.2% over the same period. For non-US equity holders, therefore, US stocks are at breakeven YTD. Any further selloff will tip them into the red. Long dated Treasuries were down 5.7% on a price basis in 2018, but adding dollar weakness (as measured by DXY) their aggregate return for foreign owners was negative 8.9%. Oil prices (WTI) were up 9% since the start of the year. While the US is pumping more oil for itself (production now 10mm barrels/day), it still imports +10 mm barrels/day from other non-dollar-denominated sources. Dollar weakness is therefore still relevant to oil prices, which remain largely valued in dollars. The suddenly much weaker dollar is a clear challenge for US financial assets and an inflationary force in terms of oil prices. As with recent volatility in fixed income markets, investors are waiting for the dust to settle before calling a bottom. Until that happens, however, the dollar will be one more uncertainty that equity markets will have to discount.
As Long-run Market Regime Changes, Investing Robots Need To Adapt
As quantitative managers grapple with the poor performance correlation of their trading algorithms with their expected results, we are left with the clear impression that a reliance on statistical modeling based on historical relationships has left market participants in a position where they are not as nimble as they ought to be in reacting to the bend in the macroeconomic road. Algorithms and the artificial intelligence they represent are not at present sufficiently flexible. Despite whatever vaunted capacity they may have as “learning machines”, the present financial market environment is too dynamic and fluid for those fund managers relying on them to stay ahead of the curve. With investor capital put at risk on what are now seen to be inadequate technology platforms, it is quite possible market regulators may need to implement trading curves and restrictions necessary to slow the speed of programmatic trading so that a more nuanced and intelligent human hand can steer market wheel.