The Burden Of The Bullish-Bearish Meme: Unleash The Total Power Of Compounding And Large Numbers Laws, Instead
Summary
- Many investors are unnecessarily locked in an onerous bullish-bearish mindset, which severely constrains the earning power of their investment capital trapped in rigid buy-and-hold strategies.
- There's another way: trade according to the seasonality of systemic liquidity flows, especially during an era when the primary driver of risk asset prices is largess from the Fed/Treasury.
- Systemic liquidity levels create the base market conditions, but daily newsflow could dictate high-frequency moves; in the absence of compelling newsflow, the market defaults to liquidity imperatives.
- The very distinct seasonality of liquidity flows provides a basic framework for PAM's so-called "swing strategies." These basically follow the 8 major liquidity flow swings in a year, which tend to tow asset prices along their wake. Even the COVID-19 pandemic hasn't altered the tight linkage of risk asset prices to monetary flows from the Fed and Treasury.
- Setting investments long or short, depending on the seasonality of liquidity flow, plus judicious hedging and short-term trading in the direction of the liquidity seasonality flow (the "4-D Method"), unleashes the total power of compounding law. Utilizing these methods, PAM expects to deliver super-excellent 2020 year-to-date performance, now running at 1,485.92% (as of May 30).
- Looking for a helping hand in the market? Members of Predictive Analytic Models get exclusive ideas and guidance to navigate any climate. Get started today »
These are the primary sources of the US financial system's liquidity: The Fed's balance sheet, bank reserves (excess and required), treasury cash balances, and commercial bank loans.
Some definitions:
Fed's Balance Sheet - The Fed's balance sheet is a weekly report presenting a consolidated balance sheet for all 12 reserve banks that lists factors supplying reserves into the banking system and factors absorbing reserves from the system. The report is officially named Factors Affecting Reserve Balances, otherwise known as the "H.4.1" report.
Bank Reserves (Excess and Required) - bank reserves for commercial banks are held in part as a credit balance in an account for commercial banks at regional Federal Reserve banks. This credit balance used to be separated into separate "required reserves" and "excess reserves" accounts. But that distinction stopped after the Fed started to pay interest on all bank reserves. The total amount of FRB credits (bank reserves) held in all FRB accounts for all commercial banks, together with all currency and vault cash, form the M0 monetary base.
Treasury Cash Balances - The Daily Treasury Statement summarizes the US Treasury's cash and debt operations for the Federal Government on a modified cash basis. Deposits are reported as received and withdrawals are reported as processed. This account is maintained at the Federal Reserve. The US treasury pays all non-bank transactions through this account.
Commercial Bank Loans - These are the loans and leases granted by commercial banks. The deposits created as the aftermath of such loans and leases is "new money" without countervailing liabilities. These deposits are major components of M2 Money Supply.
The dynamic between these liquidity sources and risk asset prices
For the most part, risk assets respond mostly to changes in the Fed's Balance Sheet (RBC), the bank reserves at the Fed (BRB), and to Treasury Cash Balances (TCB). Credit creation (black, dashed line, CBC) is also important as a lead indicator of impending liquidity changes via the M2 Money Supply (brown line); see chart below.
Required Reserves (RRs) have been subsumed into the rubric of bank reserves, and so we do not focus on it anymore. The modelling work that we have done has been focused mainly on the Fed's balance sheet, bank reserves, and the Treasury Cash Balance. Nonetheless, RRs are a potent addition to the liquidity tool kit as a means of obtaining future TCB trends (see chart below).
Note, in the chart above, the prevailing regime in the Fed's balance sheet, bank reserves, and TCB has been very stable since 2015. No large changes. That has been beneficial with regards to modelling because the parameters do not have to change every year since 2015.
The basic framework of the models has shown very stable seasonality among all the major sources of liquidity since 2015.
Even the COVID-19 pandemic hasn't altered the tight linkage of risk asset prices to monetary flows from the Fed and Treasury, and Commercial Banks' credit creation, as illustrated by the chart below.
The stability and tight linkage of risk asset prices to the seasonality of systemic liquidity have enabled us to predict with success that there will be no "Sell In May" event, and that risk assets will be surging through to a peak in late May-early June. We expected a "Sell In June" phenom and a "Buy in July" event. We discussed these projections in detail in this recent Seeking Alpha article ("Piling On Gold And Equities For A Possible Peak In Late May-Early June: Sell In June, Buy In July").
This is how the basic framework looks like every year since 2015.
This is the Basic Framework:
The very distinct seasonality of liquidity flows provides a basic framework for PAM's so-called "swing strategies." These basically follow the 8 major liquidity flow swings in a year, which tend to tow asset prices along their wake see chart below).
"Tactical (Swing) Strategies" from systemic liquidity framework
How did the risk assets perform vs. the stylized price profiles provided by the models?
We start with bond yields and show how the 10yr yield has performed vs. the stylized liquidity flows over the past few years.
This is how 10Yr Yields performed against the Liquidity Models every year since 2015:
2020 10yr Yield vs. Liquidity Models
Despite the COVID-19 pandemic and the extraordinary measures taken by the Federal Reserve and US Treasury (at the direction of the Federal government), the 10y long bond yield is still hewing to the imperatives of liquidity flows.
2019 10yr Yield vs. Liquidity Models
2018 10yr Yield vs. Liquidity Models
2017 10yr Yield vs. Liquidity Models
2016 10yr Yield vs. Liquidity Models
2015 10yr Yield vs. Liquidity Models
This is the performance of SPX against the models every year since 2015:
2020 SPX vs. Liquidity Models
2019 SPX vs. Liquidity Models
2018 SPX vs. Liquidity Models
2017 SPX vs. Liquidity Models
2016 SPX vs. Liquidity Models
2015 SPX vs. Liquidity Models
Summary:
Case in point
Many investors are unnecessarily locked in an onerous bullish-bearish mindset, which severely constrains the earning power of their investment capital trapped in rigid buy-and-hold strategies.
There's another way: trade according to the seasonality of systemic liquidity flows, especially during an era when the primary driver of risk asset prices is largess from the Fed/Treasury (see chart below).
That "manna from heaven" is definitely being cut back by the Fed, something we at PAM noticed as early as three months ago. The tip-off point shown by our liquidity models was the period of May month-end and first week of June. As earlier pointed out, market action did not hold portent for a "sell-in-May" event, but instead shifted that possibility for June: ("Piling On Gold And Equities For a Possible Peak In Late May-Early June: Sell In June, Buy in July.") We will write the sequel for this Seeking Alpha article in a day or so.
Finally, the commercial banks are catching up to the fact of diminishing largess from the Fed/Treasury combo, and its likely deleterious effect on asset prices. This recent article from ZeroHedge says it all: "BofA: The "Weak" Inherited May... But Sell In June As Fed Bazooka Fades."
With the seasonality being highly directional, with a known length of duration, more than half of your analytical burden is solved. You can devote most of your time honing the tools to time your entry and exit in short-term trades. You can further improve the winning percentages by trading along the direction of the seasonality of the liquidity flows (which we do). This spreadsheet shows how PAM implemented that trading strategy with 95 trades in equity indices in 5 weeks and made $8,088,729.15 with 93 trades won and 2 lost.
The predictability of the seasonality of liquidity flows
US financial system liquidity flows are highly seasonal and tend to recur during the same date every year. As we can see from the illustrations, a large percentage of risk assets prices can be attributed to the influence of liquidity flows. The other segments of price movements very likely stem from daily news flow.
As we have explained before, systemic liquidity levels create the base market conditions, but daily newsflow could dictate high-frequency moves; nonetheless, in the absence of compelling newsflow, the market defaults to liquidity imperatives.
We can use the covariance of risk assets price with liquidity flows by noting the periods when liquidity conditions start to change. That also requires that we anticipate the investing behavior of investment banks and other large investors who are primary recipients of liquidity flows from the Fed and the US Treasury. We invest according to the seasonal trend in liquidity flows, confident in the fact that liquidity infusions to the Master of the Universe (Primary Dealers, investment banks and big Hedge Funds) or the MOTUs will find their way to the risk asset markets in due time.
That strategy is repeated 8 times in a year, swinging from buying to selling and then buying the risk assets again, following trend of the liquidity flows.
But the real key is capital compounding by using the "4-D Method"
Here is how Investopedia defines the Compounding Law:
Compounding is the process in which an asset's earnings, from either capital gains or interest, are reinvested to generate additional earnings over time. This growth, calculated using exponential functions, occurs because the investment will generate earnings from both its initial principal and the accumulated earnings from preceding periods. Compounding, therefore, differs from linear growth, where only the principal earns interest each period.
From its inception in May 2018, PAM has used the so-called "4-D Method" of trading style which is used by many proprietary traders in investment banks (myself included, in more than 40 years of trading). This trading strategy uses the Compounding Law and the Law of Large Numbers as core operating principles. The "4-D Method" has four key components:
1. Trade according to the direction of the seasonality of liquidity flows. Buying risk assets (and selling bonds) when the liquidity trend is on the upswing and selling those risk assets (and buying bonds) when the seasonality flows go on the downswing. We use the Treasury Cash Balance as proxy to illustrate the predictable seasonality of aggregate systemic liquidity flows (see chart below):
2. Hedge errant trades, instead of using stoplosses. Trading success with a winning edge (provided by knowledge of seasonality flows and its impact on risk assets) requires a period of time for the Law of Large Numbers to take effect. You have to make a large number of individual bets for the winning edge to become inevitable and, thereby, deliver the optimal mathematical outcome.
It so happens that the Compounding Law operates on the same premise of a long process period to deliver optimal results - therefore, the larger the number of trade turn-overs happening, and the longer this process takes place, makes the inevitability of the Law of Large Numbers and the Compounding Law to become total.
But mathematical inevitability cannot take place if you lose your capital through mindless stoplosses. If you are bankrupt, there is no wherewithal for the Law of Large Numbers and the Compounding Law to make you rich.
See examples of how frequent, successful trade turnovers grow a $1 million-plus trading capital into $18 million fund valuation in five months.
See those trades here. And here.
3. We know that since liquidity seasonality makes market mean revert, hedged errant trades will become viable again because the market will eventually come close to their levels again during the next downswing in liquidity seasonality. In fact, we routinely overhedge our errant trades because we frequently make money out of the overhedged situation. In fact, our biggest single profitable trades have come from unwound overhedges.
4. Implement short-term trades in the direction of the liquidity flow seasonality. We try to deploy and chop up these trades in accordance with the five-wave impulsive price action of the market as characterized by the Elliott Wave Principle (EWP) and skipping the correction phases. We deploy the most trading capital in what EWP calls as "Wave 3" when market sentiment and fundamental factors come together as a singular market narrative.
The resulting price action during this market phase tends to be the most rapid and strongest in momentum terms during the price discovery process. This provides the most substantial grist for the Law of Compounding, and so profits from these frequent successful trades build trading capital rapidly on compounded scale.
Here is how PAM implements that fourth component of the trading strategy in equities:
PAM loads to the gills with long equity futures (ES, NQ, YM, RTY) and ETFs (TQQQs, SPXLs, SPYs; which we call "Seasonals") for a seasonal run-up in liquidity. That done, we execute long scalpers (short term trades) for the initial run up from the seasonal trough.
Then we take initial profits after a satisfactory rise - those profits then serve as "trading capital" for the next set of long scalpers. If done properly, you will be playing with OPM, "other people's money", for the next set of scalpers. You have augmented your capital quickly, and on top of that, you are now financing the next set of risks from someone else's previous capital.
This initial run up is invariably followed by a "test of the bottom" - a last gasp from the bears, seeing another chance to sell on.
We reinstate the long scalpers at that bottom, in size, for a sharp impulsive run of the third wave kind (the holy grail of investment bank proprietary trading style). You may be able to do this "scalping" procedure three times in a major equity market rally.
At the top of the third, short-term trading cycle, the destination of the long scalpers and long Seasonal trades converge. What follows is pretty much is just mopping up, clearing out the scalpers and Seasonal long trades as profitable as you can time the exit.
Then we initiate short scalper trades, and when the new seasonality direction (down) is confirmed, we start accumulating seasonal short positions. And we repeat the previous process, but this time towards the downside.
This process is illustrated by a graphic from my free book, Elliott Wave Principle Applied To The Foreign Exchange Markets (which you may download here or elsewhere in the internet), see graphic below.
You will be pleasantly surprised, if done properly, how quickly your trading capital will grow if done properly. It did for us (much better than we expected, due to the volatility) and we were indeed pleasantly surprised with the results.
PAM members benefited the most: community member Scotty007 said after the latest results were published: "There are no poor members of PAM", comments which are still resonating within the PAM investment community, as we initiate short equity trades in another Seasonal foray, this time to the downside.
This article is an update of a write up at the PAM blog on November 15, 2019, which has not been previously published at Seeking Alpha. We do write articles like this, as well as frequent market updates at the PAM blog (recent examples: here and here), which should be a reason why you should follow us so you will be notified of new publications at the PAM blog.
In the first five months of 2020, PAM delivered phenomenal real-money trading performance, the best at Seeking Alpha:
PAM Swing Portfolio, year-to-date (May 30) delivered $17,387.123.25 profit on $1,172,813 capital. Year-to-date performance: 1,485.92%, on 481-69 win-loss trades. Spreadsheet here, and here.
Algo Portfolio, year-to-date (April 24) delivered $17,475,948,95 profit on $1,116,075 capital. Year-to-date performance: 1,565.84% on 483-69 win-loss trades. Spreadsheet here, and here.
In 2019, delivered 97.08% annualized profit, the best at Seeking Alpha.
Try us for two-weeks, free. Go here.
This article was written by
Robert P. Balan runs Predictive Analytic Models, #1-rated trading unit at Seeking Alpha. PAM trades Swiss HF funds using Federal Reserve, US Treasury, and term (money) market liquidity data flows as basis for trading decisions. He is domiciled in Zurich, Switzerland.
Robert Balan has 5 decades of experience in the financial markets. Education in Mining Engineering, Computer Science & Engineering, M.S in Quantitative Finance, and training in Economics led to a commodity analysis career during the commodity boom of the early 1970s. Robert made a switch to global macro focus in the early 1980 when the commodity bull market waned, with specialization in foreign exchange. Robert wrote a very high profile daily FX analysis while Geneva-based (Lloyds Bank Int'l) in the mid-1980s (the first FX commentary with a real global readership, "most accessed" in the Reuters and Telerate networks from 1988 to 1994).
He worked for Swiss Bank Corp and Union Bank of Switzerland (precursors of today's new UBS) as head of technical research in various finance centers (London, New York, and subsequently, head of prop trading at SBC in Toronto ) from the late 1980s to mid-1990s. A stint at Bank of America as head of global technical research followed in late 1990s to the early 2000s.
Robert returned to Switzerland in 2004 as head of technical research and strategy, and FX market analyst for Swiss Life Asset Management in Zurich. Robert wrote FX analysis and capital markets commentary for Saxo Bank (Denmark) in the early 2000s. He joined Diapason Commodities Management (CH) in Lausanne in 2008 as senior market strategist, and subsequently Chief Market Strategist, utilizing fundamental macroeconomic drivers and structural/technical data in modeling asset price and sector movements.
Robert wrote a book on the Elliott Wave Principle in 1988, which has been hailed by the London Society of Technical Analysts as best ever book written on the subject. Robert is a member of the National Association for Business Economics (NABE), U.S.A.
Analyst’s Disclosure: I am/we are short EQUITY FUTURES. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.
PAM is short NQM0, ESM0, RTYM0, YMM0, and long GCQO and UGLD
Seeking Alpha's Disclosure: Past performance is no guarantee of future results. No recommendation or advice is being given as to whether any investment is suitable for a particular investor. Any views or opinions expressed above may not reflect those of Seeking Alpha as a whole. Seeking Alpha is not a licensed securities dealer, broker or US investment adviser or investment bank. Our analysts are third party authors that include both professional investors and individual investors who may not be licensed or certified by any institute or regulatory body.
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