This market analysis examines the recent performance of global financial markets, the role of liquidity in supporting financial markets, and the prospect of currency wars from the viewpoint of an analyst-trader that utilizes fundamental, technical, quantitative, and big data analyses.
It’s All About Liquidity
As discussed ad nauseam, governments relied on loose monetary policies in the aftermath of the 2007-2008 Financial Crises to prop up financial markets as exorbitant debt levels curtailed fiscal remedies and fear of political blame prevented necessary structural changes. Like passing the baton in a relay race (or a game of hot potato), monetary bodies took turns implementing different brands of QE. In approximate order: Fed (QE1, QE2, Op Twist, ZIRP, QE3), BoE (asset purchases), BOJ (asset purchases, ZIRP, NIRP), SNB (asset purchases), ECB (asset purchases, ZIRP, NIRP). But, despite the influx of paper money, developed and many developing economies failed to rise from the mat leading to the next phase of the Financial Crises: currency wars.
Fear of a repeat of the 1989 Tiananmen Square protests, China unilaterally devalued the yuan twice in August (~2.9%) and December (~4.4%) 2015 in an effort to boost exports and jobs, resulting in a significant global sell-off each time (light blue boxes in Charts 1 and 2). Leaders from Washington to Brussels to Tokyo gasped and an undisclosed agreement was made at the Feb 2016 G20 finance ministers and central bankers meeting in Shanghai between the US, China, EU, and Japan (G4) to prevent a future financial market panic. As described by Jim Rickards, China would peg the renminbi to the USD (e.g. CNY6.50-6.30, Nov 2015 to Feb 2016 range) which would weaken, while the yen and euro would strengthen (Charts 2 and 3). But, a funny thing happened on the way to the pagoda. With the US presidential elections approaching and Donald Trump unseemingly holding steady, DXY drifted upwards starting in May 2016 as the markets factored in the remote possibility of a Trump win and a pro-growth platform. Not part of the agreement, China devalued in May-Jul (~3.4%) and again in Oct-Dec (~4.9%), contributing to a minor sell-off in stocks (green boxes in Charts 1 and 2). Now, with Trump in the White House, all bets are off as the greenback reached a 15-year high in Jan (black circle in Chart 2; DXY intraday high 103.82 on Jan 3).
Pushing an “America first agenda,” Pres Trump has threatened to institute import tariffs, declared China a currency manipulator, counteract China’s aggressive posture in the South China Sea, and has pivoted the US away from China and towards Russia. Many of his cabinet appointees support this view. Nevertheless, the Communist Party’s cling to power resides in its ability to provide jobs for the masses and keep the economy growing – no amount of antagonizing is going to change its stance. With problems of its own making, China must battle internal political unrest stemming from Pres Xi’s centralization of power, a massive credit bubble, and capital flight, to name a few. If push comes to shove, China may forego mini-devaluations and opt for a 20-30% maxi-devaluation, which some neocons would surely label as an act of war. The situation could go pear shape in the short future.
My next post will examine the current state of financial markets in detail, discuss the role of complexity theory in analyzing the global financial markets, drawing reference to the Thai butterfly that caused the 1997 Asian Crisis (see Case Study: 1997 Asian Financial Crises, Parts I and II), and apply complexity theory and Bayesian statistics to today’s markets to determine the key drivers that could trigger a global market sell-off. My subsequent post will discuss the role of gold, SDRs, or eDollars in the event of a financial market meltdown that surpasses those of 1987, 1998, 2000, and 2007-2008 and the importance of China having a seat at the table of the next global financial system reset. Ultimately, I shall post on my results of using big data analysis, predictive analytics, and ensembles modeling techniques to produce leading drivers and their assigned probabilities of inducing a major market correction using the conclusions from my next post. Stay tuned!