Seeking Alpha

An Equity/Risk Asset Sell-Off Now, Then A Major Trough Is Due In Late April-H1 Of May: Actionable Ideas To Profit From Those Moves

by: Robert P. Balan

On April 2, we called the bottom of the current rally in equities and other risk assets. However, we believe that the current rally is now winding down.

A peak now is followed by further downside consolidation. But a sub-cycle trough follows sometime in late April-early May, followed by new upside phase of the intact equity bull market.

We (PAM) present several actionable ideas to benefit from the forming price peak and a subsequent decline which will finish off the large consolidation phase that started in January 26.

We also suggest several venues how to benefit from the sharp market recovery which we believe will take place after a market bottom in late April-early May.

At the last part of the article, we explain why a study of systemic liquidity should be a part of your market analytical toolkit, and what avenues to explore.

We have been closely tracking the shorter-term moves in equities and other risk assets at Predictive Analytic Models (PAM) because we believe that the current rally is winding down and will be followed by further consolidation to the downside. However, the sub-cycle trough in those assets is not too distant from here.

We called the bottom of the current rally in equities on April 2, the same day the market made the trough, but added further that a significant decline follows thereafter. This is what we said:

  • For equities, the US dollar, high yield, and Bitcoin, expect a small recovery for a week (a two-week rebound is also possible) - a significant decline follows thereafter. But we should see a cyclic low sometime in late April-early May, followed by a new upside phase of the bull market.
  • For bond yields and gold, expect small downticks over the next few days, but this should be followed by significant rallies in gold and in the 10-year yield into early May. Significant declines in the price/yield of both assets follow thereafter.

The two graphs shown above are basically the same graphs shown on April 2, along with the price updates. Illustrative graphs of the other risk assets were also published in that article on April 2 ("Money Flows, Market Structure Project Major Turnings Points In SPX, NDX, 10Y Yield, Gold, U.S. Dollar, High Yield, And Bitcoin,"­ see it here).

I have mentioned in several articles that we are in the process of completing the creation of a trading advisory service at Seeking Alpha, which we call "Predictive Analytic Models (PAM)." While waiting for the signal to initiate the service (which we hope to finally launch early next week), my partners at PAM, Tim Kiser and Seeking Trends, put together a set of actionable ideas. These are also illustrated to show the linkages of very recent and near-term prospective moves to the liquidity flows. Please note that these charts were drawn last weekend, as we prepared for a launch which was delayed.

Here are actionable ideas from Tim Kiser and Seeking Trends:

Timothy R. Kiser's Trading Plan based on Liquidity Models:

Inverse S&P 500

40% is devoted to this trade for a very short duration (low conviction trade)

SPY to hit a minor peak sometime soon, and the trade is to short the index. This is a lower conviction trade, and no leverage will be used. We do expect one more selloff. Sell S&P 500 soon by utilizing SH. SH is an inverse ETF that tracks the S&P 500. It will go up as the S&P 500 goes down on a 1 for 1 ratio, therefore no leverage is involved. Buy SH (within days) which will increase as the S&P 500 decreases. An email will be sent to initiate the trade.

Buy SH when the email arrives.

Make plans to sell SH when the recommendation goes long SPY by late April (see discussion below). We confirm this trade with another email.

TRK's Future trade plans:

S&P 500

70% devoted to this trade split between 30% SPY, 20% SSO, and 20% UPRO. (High conviction trade)

When the S&P 500 bottoms in late April, we will give a sell signal for SH. We will sell SH as we expect the S&P 500 to rise sharply from the trough. This bottom marks the end of the SPX sub-cycle correction and resumption of the bull market for a leg higher. The initial buying will be intense as the market selloff fears subsides or evaporates and the outlook improves. Therefore, we will employ leverage on this move and will use a 1x fund, a 2x fund and a 3x fund.

We will split the trade allocation into three parts and utilize the following funds. The SPY ETF, SSO ETF, which is a 2x long S&P 500 fund, and UPRO which is a 3x long S&P 500 fund.

Leveraged funds should only be used during the primary bull market waves. We expect to go long in late April, after the expected selloff gives the final thrust lower. An email will be sent giving the signal to go long equities.

Seeking Trends' trading plan based on the Oil models:

After crude oil (WTI) dropped sharply on February 2, it has been in consolidation. The consolidation pattern will continue, and this structure creates a short-term opportunity for traders to take advantage of the volatility. For long only investors, I would advise to either "HOLD" their position, or if they are planning on adding on, to be patient for a major drop coming soon for a better entry point.

We will trade oil utilizing UWT and DWT. Emails will be sent when to buy and sell in general alignment with the chart below.

As you see in the chart above, we now have a similar structure as we had a few weeks ago. Triangles could break in either direction. If oil breaks higher from here, we will wait for the "short" trade. If it breaks down, we will go long first.

SPY trade based on models

We are forming a downside consolidation on SPY or S&P 500. This should be followed by a last leg down for this cycle, after which, the rally should resume. For this trade, we will use SH, which is S&P 500 short to trade the downside.

Copper and FCX

15% is devoted to this trade.

Freeport-McMoRan, Inc. (NYSE:FCX) has been in an uptrend for two years. The uptrend will continue as the reflation trade resumes. In the meantime, there should be one more leg down before the rally resumes. Below, I show the Copper and FCX charts.

Bitcoin Trade based on models

15% is devoted to this trade - split 5% each.

Bitcoin has broken out of its massive downtrend. What we are looking for is a break out and a consolidation flag to go long. That would constitute a buy setup.

Gold trade based on models:

Depending on your trading or investing style, this is how to interpret what the liquidity model is seeing in the spot price. Gold has been in a very long-term consolidation since 2016, and the longer-term outlook does not bode well for Gold. But, in the meantime, this is how we see Gold performing in the short term. We are more interested in the short trade instead of the long.

Favourable impact from liquidity flows is due in late April-early May

As those actionable trade ideas from Tim Kiser and Seeking Trends underline, the lagged impact of a multitude of recent domestic liquidity flows (real money balances) converge during the period encompassing the 3rd week of April and 1st week of May. Therefore, we expect to see the trough of risk asset prices (equities, High Yield, Bitcoin, and the US Dollar) during that period, which should be followed by a new upside phase of the bull market (see graph below).

Forthcoming earnings season will be spectacular

Coincidentally, the equity market also expects the forthcoming earnings season as generally being positive, especially into Amazon (NASDAQ:AMZN)/Google (NASDAQ:GOOG) (NASDAQ:GOOGL) earnings in late April, early May. If the numbers come out as supportive as many equity analysts expect (including us), we could be in for some interesting valuation expansion going into the end of the second quarter. US companies are expected to report earnings growth of 17 percent per share for the first three months of the year, as the recent cut to the corporate tax rate helps deliver what would be the biggest quarterly increase in seven years. Wall Street analysts have forecast broad-based gains in earnings with all 11 sectors of the S&P 500 expected to report growth (Bloomberg). See the relationship of the S&P 500 forward earnings with other metrics that influences it. The outlook in earnings is another evidence for us that a trough in equities is due soon.

When is the next crunch time?

The next barrier may appear sometime in Q3 when the lagged impact of the liquidity waves wanes or will have been replaced by more stringent real cash balance regimes at the Fed and the US Treasury. We have rough ideas when to expect troublesome or favourable periods, but the analytical system is *adaptive*, and so it will require concurrent data to finalize the assessment. It is not enough (for us) to say that we will have 6 points in the calendar when risk assets are a sell, and 5 or 6 points in the year when risk assets are a buy. That statement is generally true, but the hypothesis has to be validated by current data. The current summation of the liquidity vectors will provide the characteristics of the current high-frequency changes taking place, so we take that extra step. We explain the process at the last part of the article.

Background Materials On Systemic Liquidity

How systemic liquidity affects asset prices

We believe that asset price moves are influenced in a significant way by the flows of liquidity (money) in, and out, of the financial system. Our experience and work, indeed, support the thesis that the most reliable correlations (relatively speaking) can be found in the causality, which stems from monetary flows (changes in systemic liquidity) to the changes in asset prices (see graphs above).

Very often, it is the change rate in the nominal values (flows), not the absolute changes in nominal value (the stock), which makes the most impact. Note, however, that the response of asset prices to the impact of those flows is variable; some assets respond more quickly to the flows; for other assets, the impact of those flows come later. There are also several sources of systemic liquidity, and those sources do not necessarily synchronize their activity. While there are elements of seasonality in the liquidity mix, sometimes, there are episodic variations which could skew well-established patterns which have held up for years.

Using liquidity as analytical tool

Those are the most difficult aspects we've met in using the flow of real money balances and predicting their impact in the behaviour of asset prices. Therefore, the key to a successful use of systemic liquidity as bedrock in the analysis of risk asset price dynamics lies in being able to corral that variability into a singular vector which could relate to the behaviour of risk asset price. We discuss this in the latter part of the article.

Fiscal and monetary policy initiatives lead by 5 to 6 quarters

We have discussed this phenomenon before and showed examples at various settings (see that here) - fiscal and monetary initiatives take from 5 to 6 quarters before the peak effect is seen in the changes in growth and activity. In the example below, changes in GDP lag behind changes in fiscal budget outlays by almost 4 quarters. Changes in Core CPI, in turn, lag behind changes in growth by 18 months; likewise, changes in Headline CPI and Commodities lag changes in GDP growth by 18 months. It takes some time for the peak effect of changes in fiscal outlays to appear in the changes in some macro data and in the price changes of many risk assets.

Systemic liquidity from the Treasury and Fed

There is another class of systemic liquidity which influences the high-frequency changes in many risk assets - the periodic, and episodic, infusion and claw-back of systemic liquidity conducted by the Federal Reserve and the US Treasury. To cite one such example: the US Treasury Cash Balances expand and contract in significant degrees at least 6 times a year (perhaps even eight times, if minor episodes are included), see graph below.

There was only one instance in the past 12 years when the cadence of the inflow and outflow of the Treasury's cash balances deviated from historic norms. That was in late Q3 2008, when the Great Financial Crisis was full-blown. The Treasury Cash Balance at that time mushroomed circa 350 times in about two months. That period, indeed, was crunch time, so the Treasury let loose a massive wave of systemic liquidity (see graph below, orange line). This is just one example of liquidity infusion and claw-back from the Treasury. There are others.

The seasonality of the US Treasury Cash Balances (from 2006 to 2017)

Note how "regular" the peaks and troughs of the Treasury Cash Balance data. There was only one anomaly - in 2008, at the onset of the Great Financial Crisis.

The seasonality of the US Treasury Cash Balances (from 2013 to present)

Note the "nesting" of seasonal troughs and peaks of the US Treasury Cash Balances. Balance data for 2018 is just about to make a bottom.

The Federal Reserve has plethora of ways available to them to control systemic liquidity. The central bank may deploy its balance sheet, the bank reserves, the required reserves (although this is more used to sop up liquidity in the narrower sense), reverse repos (take out liquidity from the system, as well).

All these tool, and the various Open Market Operations which the New York Fed conducts on a regular basis (e.g., Temporary OMO Repos), or buying or selling securities, provide the means for the central banks to control the Monetary Base. Of course, the Fed has total control over the size of the Monetary Base (MB). It has become synonymous with the Fed's balance sheet after the central bank's Large-Scale Asset Purchases (LSAP), more popularly known as Quantitative Easing (QE), conducted during the early part of the Great Financial Crisis (GFC).

Interaction of liquidity flows

The liquidity from the Fed's Open Market Operations and the US Treasury interacts with the systemic liquidity stemming from fiscal and monetary policy on cumulative basis. If the disparate waves of liquidity from various sources coincide, the aggregate, subsequent positive impact on risk assets' prices will be huge. The inverse is, of course, also true - the negative impact on safe haven assets will be huge as well. If the aggregated withdrawal of liquidity from various sources coincide, risk asset prices will subsequently tend to crumble (and safe haven asset prices will subsequently outperform). See the example in the graph below.

Liquidity sources are combining to push up bond yields higher in the near term. That is not a good sign for safe haven assets, which adds to our conviction that risk assets will recover soon and will be buoyant over the medium term, as liquidity inflows stoke upwards pressure for equities.

Cumulative process

This highlights the fact that the impact of disparate sources of liquidity is cumulative. The procedure, however, is not a straight-forward summation of the liquidity vectors (as in Fourier wave analysis) - that route does not work too well here. Also, asset prices do not necessarily respond in a coincidental way to the waxing and waning of aggregate liquidity. And very often, it is the change rate in the nominal values (flows), not the absolute changes in nominal value (the stock), which makes the most impact. As mentioned, the response of asset prices to the impact of those flows is variable; some assets respond more quickly to the flows; for other assets, the impact of those flows come later (see graph above and below).

How to handle the chaos

From the point of view of one particular asset, the disparate liquidity flows will be peaking and bottoming at various times (and at different amplitudes). One way to handle the chaos and to make the math work more tractable is to index the data, then find a calibrated match of the resulting vector versus the regressed price of the asset in the time continuum. We are not looking for point solutions in price terms here - just the inflection points in time. Even then, it is laborious - but it is worth the trouble (see graph below).

That roughly describes the fundamental framework and the set-up of the analytical tools that we use at Predictive Analytical Models (PAM).

Post Notes:

We are in the process of completing the creation of a trading advisory service at Seeking Alpha. Called "Predictive Analytic Models (PAM)" the service uses statistical analysis (models) to obtain actionable long or short insights from relationships that are inherent between fundamental data, money flows, and market prices. The lagged response of asset prices to those relationships provide opportunities for profitable trades or investments. Please stay tuned!

Please consider following Tim Kiser, Seeking Trends, and myself as we utilize Predictive Analytic Models and tools to forecast asset price directions. There will be more articles coming, and more models will be presented for consideration of upcoming swing trades for a variety of risk assets. Following us makes sure that you will be immediately notified of any articles or notes coming from the Predictive Analytic Models (PAM) team.

Also, please consider joining our investment/trading advisory service (PAM) at Seeking Alpha once it is out of the gate. Thanks.

For queries, you can reach me here.

Disclosure: I am/we are long U:CLR, U:EOG, U:XOM. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

Additional disclosure: We are looking to BUY ProShares Short S&P500 (SH) within the next 24 trading hours.