Bear on its Back? (Part I)

What drives the stock-market anyway?

On October 13th the S&P 500 dipped deep below the 200-week moving average but quickly regained this level. Bullish divergence between price and the RSI also confirmed on the weekly chart. This was a very bullish setup in classical technical analysis terms and for short-term traders it was a golden opportunity for a snap-back swing-trade. Price has subsequently moved up by 10% and good swing- traders would have banked some profits by now. But what about the long-term investor? Was the 13th of October the bottom of the bear market, or will we see lower lows?

To answer this question, we cannot simply rely on price charts. We will have to look at the market-fundamentals to see if they line up with a bear bottom scenario or not. In this first part of a two-part series, I will describe my mental model of the underlying cause-effect relationships that ultimately drive markets. In Part II, I will look at historical statistics of bear markets, analyze current market sentiment and positioning, and then finally put the whole puzzle together in an attempt to answer the question in the title above.

You can read Part II here:

Bear on its Back? (Part II)

What are the fundamental drivers of the stock-market?

(6 Categories)

Some investors and traders look at the market with only one lens, such as valuation or technical price structure, but I believe that it is better to look through multiple lenses to build sufficient confidence in your strategy. The problem with this multi-lens strategy is, well, “analysis paralysis”. If your analysis is confusing and prevents you from making clear decisions, it defeats the purpose. Therefore, it is imperative that you have a well-defined framework for forming a view on markets which feeds into your investment strategy. My simplified model of what drives markets will be the focus of this article.

I have categorized the market drivers into 6 broad categories, in no particular order. All are important but difficult to rank or weight in terms of impact. Markets are vastly complex systems and the framework described below tries to simplify the complexity into an easily comprehensible model, which means it is fallible. “All models are wrong, some are useful” (Prof George Box). I find this one useful in the sense that it frees me from the dreaded “analysis paralysis”, and it has worked for me (so far anyway).

1. Market liquidity and fund flows

(Monetary & Fiscal policy and Credit conditions)

Market liquidity refers to the availability of money for flowing into assets. This is driven mainly by liquidity injection/draining by central banks and fiscal policy, credit creation by banks and other financial institutions, and cross-border fund flows. ETF index flows, pension fund mandates and stock buybacks are other sources of flows, but those are beyond the scope of this article.

CrossBoarder Capital has developed the Global Liquidity Index (GLI) which measures the major sources of global liquidity and compiles it into one index. This includes 90 central banks across the globe, all large commercial banks and financial institutions and cross-border capital flows.

The chart below shows how global liquidity moves in 5 to 6-year cycles and that liquidity has contracted severely in the past 12 months (As we all know by now). It is important to note that absolute levels of interest rates are only one of the inputs into the index and that the rate of increase in rates as well as central bank balance sheet contraction/expansion plays an equally important role in the availability of liquidity. Although the absolute levels of rates are still way below the long term average, the pace of increase is unprecidented.

Judging by past cycle bottoms, it would seem that we are close to a turning point in global liquidity. Note that even during the 1970’s and 80’s when inflation was high (much higher than today) the GLI turned up at levels similar to now.

If we now look at the rate of change of the GLI and plot the global wealth rate of change on top of it, we can see the very tight correlation between liquidity and asset prices. See chart below. According to CrossBoarder Capital, liquidity is set to tighten further, based on what central banks are signaling. This was again evident in Fed Chair J Powell’s speech yesterday (2nd of November) where he clearly said that the Fed is not done raising rates yet. But be careful of taking that to the bank (pun intended). We know how central bankers can tap-dance (I’m looking at you, Bank of England).

Similarly, there is also a close correlation between the GLI and global business activity:

So, zooming in to equity markets, the S&P 500 to be specific, it is evident that the US Federal Reserve’s liquidity throttle is very closely correlated with US stock prices. The moment that Fed Chair J Powell turned hawkish (tightening-talk) in December 2021, the market started rolling over.

The mere observation of correlations is meaningless unless we can explain the link. In this case it is easy: When money is abundant and cheap, investors are prepared to pay more for assets. In other words, when money supply is high, assets become relatively more scarce and therefore their prices rise. Or if money supply is high, the cost of capital drops (low interest rates) and therefore the future discount rate for determining the value of such assets are lower, thus returning a higher valuation.

But be careful what you wish for. Yes, a Fed pivot in a benign environment, will pump stocks immediately, but if a pivot is forced by a bond market freeze or some other financial system melt-down, there might be a time lag of several months — months of severe pain for those who were expecting a pivot to make them rich immediately. See the table below.

I think we are in such an environment now, where a pivot will not be a good thing. A pivot will mean that there are serious issues with the economy which will not be good for stocks. This is because inflation is high and the Fed is committed to bringing it down even if markets tumble and the economy goes into recession. They will not be easily convinced to open the spigots again.

2. Economic outlook

(GDP growth, inflation & employment)

These economic indicators are important because the Fed looks at them to determine their monetary policy which, as discussed above, is a key driver of market direction. Unfortunately, these are all lagging indicators and by the time they confirm a trend, it is too late to react. This is true for investors, but also for the policy-makers, and is, arguably, the reason why the Fed tends to overshoot on tightening and easing and why they fail to smooth out the business cycle. Some even believe that they exacerbate the cycle with their policies. I’ll elaborate on this a bit more when discussing yield curve inversion later.

Furthermore, the economic cycle (needless to say) plays an important role in company performance and consumer behaviour.

Before we get to the interesting stuff, let’s quickly review the boring lagging indicators.

2.1. The lagging indicators

(5 indicators)

2.1.1. Real GDP & Manufacturing output

The bar-chart below shows that US GDP contracted (in real terms) for the first two quarters of 2022, and that it grew by 2.6% (annualized) in Q3.

Although the rule of thumb condition for recession was met in Q1 & Q2 (2 consecutive quarters of negative real GDP), the NBER (National Bureau of Economic Research) looks for a more widespread downturn before declaring an official recession. They want to see a downturn in manufacturing output, consumer demand and incomes as well as an increase in unemployment to satisfy their recession criteria.

The chart below shows that although manufacturing output has lost significant momentum, it has not yet gone negative. The grey bars on the chart indicate recessions as declared by the NBER and one can see that in each instance, manufacturing output was negative, although not in each instance where output went negative, was a recession declared. This is because the decline was short and shallow, and most probably because the other indicators were still positive.

2.1.2. Consumer demand

Below is a chart of real household consumption expenditure growth (YoY% ch), which is a good (but lagging) indicator of consumer demand. Much as in the case of manufacturing output growth, in all but one of the 7 recessions on the chart, household consumption growth dropped below zero, but is currently still positive.

2.1.3. Real Personal income and corporate earnings

From the chart below, it seems that there is less of a correlation between real personal income growth and recessions, than between corporate earnings growth and recessions. Note that real personal income growth has entered negative territory while corporate earnings growth has decelerated but remains slightly positive. The anomaly on this chart, is the deep corporate earnings decline in 2018, while a recession was not declared — probably because economic output and real personal income did not decline.

So, if Real disposable personal Income is negative, how can real household consumption expenditure (See chart under 2.1.2 above) be positive? Well, it could be financed by a decline in savings and by credit card spending — See chart below. But is this sustainable?

The last time we saw such a divergence between consumer confidence and credit card debt growth was just before the 2008/09 Great Financial Crisis.

2.1.4. Unemployment

The chart below clearly shows that a sharp spike in unemployment has coincided with each and every past recession. Remember that an official recession is not an objectively verifiable event. It is “declared” by an organisation (NBER) run by subjective humans who decide whether conditions are bad enough to call it a recession. From this chart, one could infer that the unemployment rate carries the most weight in this decision. It is clear that unemployment is at historic lows and there are no signs of a spike — yet.

Given the abovementioned 4 lagging economic indicators, it is no surprise that a recession was not declared for Q1 & Q2 2022.

2.1.5. Inflation

The chart below shows that every large spike in the inflation rate since 1955 led to a recession. Now, we have another spike (the largest since the 1980’s), but the problem is that we have no way to tell how high it will go before conditions deteriorate enough for a recession to ensue.

Looking at the monthly CPI year-on-year change figures, it seems that headline inflation might have peaked in July 2022, but core CPI remains on an upward trajectory. Note that core inflation is also the longest lagging component of inflation.

The above charts and observations are not very useful in terms of what to expect next. They simply sketch the picture in the rear-view mirror. For investors to get value from economic data, it is important to be able to anticipate the trends and inflection points in the data well in advance. To this end, I will now turn to leading indicators and their implications for the future.

2.2. Leading indicators

(10 Indicators)

2.2.1. Business and consumer sentiment surveys

Business optimism and consumer confidence both turned down sharply long before the 2008/2009 recession, but they were less useful for predicting the 1991 & 2001 recessions. That said, it is hard to ignore the extreme lows of current sentiment readings. See charts below.

While business and consumer confidence cannot predict recession, it does play a part in shaping the future. An environment in which businesses and households do not have confidence to invest or spend, will by definition dampen activity and growth.

2.2.2. ISM Manufacturing PMI

The chart below shows that over the past 25 years, markets led PMI by a few months, while PMI declined and approached zero before each of the 3 recessions. So, are Equities cheap right now, or did the market sniff out the looming recession once again?

2.2.3. Unemployment

In the previous section, I listed unemployment as a lagging indicator, because retrenchment is the last resort for companies to cut costs and batten down the hatchets to weather a recession storm. However, when we zoom in and look past the lagging unemployment spikes, we can see that the troughs in the chart reveals some interesting patterns. Note that in 9 out of the past 11 recessions, unemployment dipped below 5% and suddenly turned up sharply just before the start of each recession. A sudden up-tick from current levels will certainly be a high-fidelity signal to watch out for.

2.2.4. S&P Global new orders

The Latest S&P Global US PMI data shows that slowing new orders have already resulted in slower jobs gains. See below.

2.2.5. NAHB housing market Index

Some economists say that “the US housing market is the business cycle”. This makes intuitive sense when considering that housing and related industries make up 20% of US GDP while consumer spending, which is also impacted by housing, makes up 70% of the US economy. A downturn in the housing market impacts consumer confidence and all activities related to home building, furnishing and decoration. The chart below clearly shows the strong link between the housing market and unemployment. But note that the NHAB Index leads unemployment by +- 12 months and it has turned down (Index is inverted on the chart) sharply in recent months.

The last time we saw a 30 percentage-point drop in the 3-month annualized growth rate of home prices was in the 2008 financial crisis. But back then the Fed was cutting rates aggressively. Now they are still planning on raising rates for at least the next three months.

2.2.6. The 2-Year Treasury Yield

The 2-year US Treasury yield is a very reliable leading indicator for the terminal Fed Funds Rate (the level where the Fed stops raising rates.). The chart below indicates that historically, whenever the EFFR (Effective Fed Funds Rate) reached or exceeded the 2-year Treasury yield, that was the point where the Fed paused or started cutting.

Given the spread between the two rates at present (4.72–3.08 = 1.64%) and the fact that the 2-year has not shown any signs of slowing down, we can assume that a pause or pivot by the Fed is not imminent, unless something really bad happens. The vast distance between the inflation rate and Effective Fed Funds (8.3–3.08 = 5.22%), as shown in the chart below, further supports this view. This spread between CPI and EFFR is similar to the situation in 1973 when the Fed was far behind the curve and had to tighten into a recession. (Assuming that we are in or entering into, a recession soon)

2.2.7. Financial Conditions

The term financial conditions refers to the ease with which households and businesses can gain access to liquidity and credit. Inputs include interest rates, bank lending criteria, incomes, consumer prices, producer prices and more.

The chart below shows that the last 5 recessions were all preceded by tightening financial conditions (NFCI) at or above the orange line (level = zero). Currently the NFCI is again approaching this line. Historically, and now, steep increases in this index have coincided with stock market declines.

Looking at the rate of change in financial conditions, we see that over the past 20 years, only during the 2020 Covid pandemic and the 2008 Great Recession, did financial conditions deteriorate faster than the current rate of decline.

2.2.8. Supply chain indicators.

The most watched supply chain indicators are suggesting that pressures are easing. This could be because of supply constraints being alleviated as pandemic and lockdown disruptions get worked out, or because the economy might be slowing. I think it is probably a bit of both. This is good news for inflation which should come down as supply and demand balances out.

2.2.9. Yield curve inversion

The chart below shows that a yield curve inversion, where the 2-year government bond yield went higher than the 10-year yield, occurred 6–18 months before each of the past 6 recessions since 1976.

While a 100% hit-rate over a 65-year period should not be ignorred, absent a “good explanation” of why this phenominon occurrs before a recession, it remains a useless correlation with no causation. There are many theories that try to explain this link, such as the Pure Expectations Theory and the Liquidity Preference Theory. Some explanations get very technical and confusing, and in my opinion, they try too hard to highlight one specific driver and to discount others. There are many factors that play into the inversion, but I think that the market mechanism captures most of these in the price of long bonds (10-year +) which leaves us with a fairly simple and logical explanation (I hope you’ll agree):

Let’s first define some bond basics:

1. Bond prices and bond yields have an inverse relationship. Why? Because the future cash-flows of a bond (the coupon and principal) are fixed by contract. If you pay more for the bond than its issue price, your yield will necessarily be lower than the coupon, and vice versa. This relationship is mechanical and unbreakable as long as the contract terms are upheld.
2. Under “normal” circumstances (if that even exists anymore), the yield curve should have a positive slope. i.e. long term yields (interest rates) should be higher than short term ones. This is to compensate investors for taking duration risk, inflation risk and their opportunity cost for having their capital tied up.
3. The Fed determines short term rates via their policy rate (Fed Funds Rate — FFR).
4. The market determines long term rates by bidding up or selling down long term bond prices. (Although lately Central Banks have been entering this market as well through “open market operations”, thereby distorting the price signal)

So, why would short term rates ever be higher than long term rates? Let’s look at the long and short end separately. (“long” and “short” in this context, refers to bond duration and not to buying and selling)

The long end of the curve (i.e. long duration bonds: 10y — 30 year) can only move down if bond prices move up, i.e. investors want to buy more bonds than what is available for sale at the current price and therefore prices rise and yields fall. There are many possible reasons for investors wanting to buy or sell long bonds. I will delve deeper into this in a later article, but for now, let’s just say in times of uncertainty, investors prefer to buy safe assets with low yields rather than more risky assets with higher yields.

At the short end of the bond market, rates are determined by the Fed Funds Rate which is determined by the FOMC (Federal Open Market Committee). This rate is set by Fed officials, based on their mandates and perception of the economy and financial market conditions. The rate is therefore reflective of what the Fed officials are thinking, and not what is really happening in the economy or what the market thinks will happen. The Fed has a triple mandate

• To maintain full employment (Keep unemployment below5%),
• To maintain price stability (Keep inflation below 2%) and
• To ensure that the bond market functions properly (Ensure adequate levels of liquidity in bond markets).

Therefore economic data that impacts these 3 things and the Fed’s interpretation of that data will ultimately determine what short term rates will be.

Now for my explanation of the link between yield curve inversion and recession:

If long bond yields are low (due to investors bidding up their prices) it means that investors prefer to own safe assets at lower yields and that their risk apatite is low. No matter what the Fed thinks they should be doing, if they are raising short term rates, they are draining liquidity from the financial system, thereby further discouraging market participants to take on risk. By deffinition, this slows down the economy, and pushes even more investors into long bonds. All is well and good while the spread between long and short bonds is positive. But when it inverts, it means that investors are not enticed by the higher short term rates and that they don’t believe these rates will stay higher than long term rates into the future. They think the Fed will soon have to lower short rates due to economic weakness on the horizon, which will benefit the value of long bonds much more than those of the short bonds. This is because long bond prices are much more sensitive to falling rates than short-term bond prices. Furthermore, when those short bonds mature (expire), the investors will then have to roll into lower yielding bonds, should rates fall.

So in summary, an inverted yield curve is a signal that the market thinks the investment opportunities ahead are so bad, that they will rather sit with low yielding long bonds, rather than switching into higher yielding short duration bonds (Remember the default risk on both types of bonds are equally low), because the little bit of additional yield those short term bonds offer them over that of their long bonds, do not compensate them enough for the gains they will lose out on, when long bonds increase in value, once the rates start to fall. In one sentence: An inverted yield curve is simply the market disagreeing with the Fed about the level of rates that the economy and financial system can withstand. In the past, the market has always been right.

One can even make the case that recessions are caused by the Fed’s overtightening (raising interest rates too high) before noticing in their lagging unemployment and CPI indicators, that they’ve gone too far. But then it is too late and a recession has already begun. The chart below clearly indicates that unemployment is at its lowest at the same time that the yield curve inverts and each time a recession followed shortly thereafter.

It is important to note that the high hit-rate of the inversion itself is meaningless if the same underlying drivers are not present. In other words, the underlying drivers of the inversion as described above is what will lead to recession, not the inversion itself. If other drivers lead to an inversion, then the outcome might be different. Beware of the “problem of induction” as described by philosopher Karl Popper. I will discuss this problem and how it relates to markets in Part II of this article series.

2.2.10. US Conference Board top 10 Leading Indicators.

The Conference Board Leading Economic Index (LEI) for the US is an index that includes 10 leading indicators compressed into one. The chart below shows how reliable this indicator has been in predicting the direction of the business cycle. It typically leads the coincident economic data by 7 months, and it has now triggered the recession signal (in red on the chart). I will not go into possible explanations here, as the same logic applies as with all the leading indicators discussed above.

In conclusion of my discussion of leading indicators for recession, the above analysis has convinced me of a high probability that a recession in the US has started or will start in the next 1 to 3 quarters. I think most market participants expect a recession by now, and it is now only a question of how deep. This has implications for the next drivers of stock market returns, discussed below.

3. Company earnings outlook

(Revenues, profit margins and EPS)

As mentioned above, I’m factoring in a high probability of recession in the coming months. Recession, by definition, means that company revenues will decline. Declining revenue does not necessarily mean profits will decline because theoretically companies can cut costs to off-set the decline in revenue. But in practice this is rarely the case. Overheads are difficult to cut, and if you did, it would be difficult to increase overhead capacity once the recession is over. Furthermore, it takes time to renegotiate contractual obligations such as rent and wages. Therefore recessions are almost by default earnings killers. However, the effect on different industries will be varied. Some companies have pricing power and products or services with inelastic demand and therefore inelastic price too, while others will see dramatic reduction in the demand for their products or services. Profit margins are therefore cyclical, just as GDP growth is cyclical.

The chart below shows how profit margins declined in the past two recessions and that they are now again at all-time-highs. If a recession is coming, we can expect declining profit margins as well.

Inflation plays an important role in profit margins because in an inflationary environment, companies can raise their prices immediately while their wage bills lag behind until annual wage negotiations kick in. The inverse is true in a falling-inflation environment. If inflation did indeed peak in July 2022 (as discussed under 2.1.5 above) and is about to cool off further in the coming months, while wages remain sticky high, margins will decline.

It seems that company pricing power is already fading. See chart below.

The chart below shows that S&P 500 12-month forward earnings estimates have started to come down, albeit only slightly. I expect estimates to come down further, but stock prices have already corrected 27%. So, the question is how much will earnings come down further and has this been priced in? More on this later when we discuss price-earnings multiples.

Negative earnings revisions began in August and is continuing

But we are still very early in this earnings recession:

As discussed above (under leading indicators) financial conditions is a reliable leading indicator for inflation. For the same reasons, it also leads earnings. Financial conditions, represented in the chart below by the G5 credit impulse (propriety indicator by @macro_alf) shows how tight financial conditions have become, but earnings has yet to react. According to Alf, there is a 4 quarters lag between the credit impulse and earnings.

Another way of looking at the same concept is to plot financial conditions on top of the earnings yield of equities. In the chart below, financial conditions are represented by the median global central bank policy rate and the earnings yield is basically the inverse of the price/earnings ratio. So as stock prices decline, the earnings yield goes up, but as earnings fall, yields also fall. The tight correlation between the two metrics implies that if policy rates are still going up, and earnings are to fall, then stock prices still need to come down further.

4. Risk apatite

(PE Multiple expansion/contraction &Equity Risk Premium )

Historically it seems that during significant bear markets (such as in 2000–2002 and 2008–2009) the current PE ratio (Price/Earnings ratio calculated on the most recent historical annual earnings) of the S&P 500, enters into a highly volatile period and the lows in current P/E do not correspond with the lows in the market cycle.

This is because a PE ratio, based on current earnings, can spike due to a sudden increase in equity prices or due to a sharp drop in earnings. This was the case in 2008 when the PE spiked off the chart during the Great Recession as earnings plummeted close to zero. The current PE ratio, therefore, offers little signal.

To overcome this problem, one can look at the Shiller PE or CAPE ratio (Cyclically Adjusted Price Earnings ratio) which uses the current market price divided by the average earnings over the past 10 years. This drastically reduces the importance of what recent earnings were or what earnings will be in the next few quarters, as it will not have a material effect on the 10-year average. See chart below. According to this metric, there is a lot of room for stock prices to fall further.

Below I’ve calculated what the S&P 500 price will be for every 10th percentile between 40% above and 40% below the mean (CAPE value of 20):

Some observations:

• The S&P 500 price will be at 2700 if the CAPE ratio returns to the mean of 20.
• Should the CAPE bottom out at 36% below the mean as in 2009, then the S&P 500 will fall to a price of 1728 — a 64% drawdown from the top in Jan 2022. Yikes!
• The most recent low of October 13, at 3490 was at 30% above the mean.

Equity risk premium (ERP) is the premium over and above the risk-free rate that equity investors demand for taking on the additional risk associated with equities. In other words, investors demand compensation for taking on higher risk by investing in equities rather than in risk-free government bonds. ERP is calculated as equity earnings yield MINUS the risk-free rate (usually the US 10y Treasury yield). ERP is a similar valuation metric to the PE ratio, but it also takes into account the risk-free rate which is important in determining equity valuations relative to less risky investments.

When the ERP is very low, as it was during the tech bubble of the late 1990’s, it means that investors are overpaying for their equities and underestimating the risks they are taking. To me, ERP is the ultimate indicator of over confidence and hubris amongst equity investors. Although the current ERP is not in extremely low territory, it has also not spiked higher as it tends to do at the bottom of bear markets. The current ERP of 2.5% is substantially lower than the 100-year average of 4%. This tells us that the equity market is not pricing in an earnings recession yet.

The chart below shows what the magnitude of the spike in ERP has been in prior downturns. We haven’t seen anything comparable yet.

5. External Risk Environment

(Geopolitics, natural disasters, systemic risks)

I would group external risks in three general buckets:

• Black swans are unknowable events. They strike without warning, and nobody anticipates them. Examples are terrorist attacks (9–11 comes to mind) and natural disasters like earthquakes and tsunami’s (e.g. Fukushima). The 2008 GFC and the Russia-Ukraine war, for example, were not black swans.
• Grey swans are knowable events that very few people anticipate. Examples include the 1998 LTCM blow-up, the 2000 tech bubble, and the 2008 GFC. These events had severe consequences for markets but were foreseen by a hand-full of specialists. Some who made fortunes trading their anticipation. Sure, it is very difficult to “predict” these events and there is always luck involved in getting it right. But the point is that warning signs do appear before they strike.
• White swans. These are the everyday risks that market participants have to constantly assess and anticipate. This includes the Fed’s next move, the politicians next tax policy, the next CPI print, the next employment number, etc, etc. While these are not external shocks per se, when a white swan is unexpectedly extreme or in an unanticipated direction, it can become a grey swan.

The risk of black swans are ever-present and unquantifiable. Because they are by definition unknowable events, it is impossible to account for them. One can only be aware that anything could happen and therefore never have exposures in sizes that can wipe you out in the unlikely event of such a swan appearing. Historically, true black swans have not had lasting impacts on markets. Markets have always recovered fairly quickly, but the nature of the beast is such that the next one might just be the one that changes history…

Historically, the real wrecking balls have been those dreaded grey swans. But how are we supposed to deal with these? My method is to constantly assess the external risk environment and to adjust my offensive vs defensive stance accordingly. I’ve addressed this topic in much detail in a previous article and you can read it here:

From Pandemic to Putin to your Portfolio

For purposes of this article, suffice to say that we are in an environment of heightened external risk and high volatility. Being on the defence is more appropriate than swinging for the fences. The two charts below show the risks that fund managers currently have on their mind, and all of them are at historically high levels.

6. Market positioning, investor sentiment and technical structure

This is the last and equally critical piece of the puzzle. In this final category, we have to decide how much of the above analysis is already priced into the market. Getting this step wrong could mean the difference between success and failure. Because it is impossible to know for certain, risk management is the most important part of investing and trading.

This article is already more than 5500 words and probably longer than what most people find acceptable. So, I will deal with this last section of the market-driver model in Part II of the series. In Part II, I will look at historical statistics of bear markets and deal with the “Problem of Induction” when extrapolating future probability distributions from historical observations. Then I will sketch my base case for what to expect in the next 6 to 12 months and finally I will attempt to answer the question in the title of this article.

You can read Part II here:

Bear on its Back? (Part II)

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Good luck out there

Disclaimer

The views expressed in this article are the views of Johan Kirsten and are subject to change at any time based on market and other conditions. This is not financial or investment advice, nor a solicitation for investment funds and should not be construed as such. References to specific securities and issuers are for illustrative purposes only and are not intended to be, and should not be interpreted as, recommendations to purchase or sell such securities.