Is a Correction Looming?

October 1st, 2021

The market has been defying gravity since the March, 2009 low and many Wall Street pundits forecast a continuation of the melt up at least until the end of this year. But is this prognostication justified?

Could it be that the continued rise in equities is due to the fact that the Fed has kept interest rates artificially low? Many say that this is the likely case and I have to agree with them. But will this continue to be the case? Maybe for the near future but what happens if inflation keeps rising, as it has started to do? There’s no doubt, at least in my mind, that we could be in for a painful market correction. The reason I draw this conclusion is based in part on the market internals, many of which are near, at, or have exceeded historic levels.

Let’s consider some key internals gathered by over the past 150 years for the large-cap S&P 500 index:

Price/Earnings (P/E)
Current: 33.99 (Third highest in history)
Historic Mean: 15.95
% Above/Below the mean: +213%
Historic Median: 14.86
% Above/Below the mean: +229%

Current: 4.64 (Second highest in history)
Historic Mean: 2.89
% Above/Below the mean: +160%
Historic Median: 2.78
% Above/Below the mean: +167%

Current: 3.13 (Highest in history)
Historic Mean: 1.62
% Above/Below the mean: +193%
Historic Median: 1.52
% Above/Below the mean: +206%

Earnings Yield
Current: 2.94% (Third lowest in history)
Historic Mean: 7.30%
% Above/Below the mean: -60%
Historic Median: 6.73%
% Above/Below the mean: -56%

Dividend Yield
Current: 1.33% (A virtual tie with the 2000 historic low)
Historic Mean: 4.30%
% Above/Below the mean: -69%
Historic Median: 4.26%
% Above/Below the mean: -69%

The above statics indeed show that the market is on very shaky grounds. There is no question that we are well overdue for a reversion to the mean but when exactly that will occur is up to the Fed regarding monetary policy and up to Congress regarding raising the debt ceiling and continuing their drunken spending spree.

Methinks this won’t end well.

The Race to a $Trillion

July 20th, 2018

In his best-selling book called “The Four: The Hidden DNA of Apple, Amazon, Facebook, and Google”, author Scott Galloway ponders the question of which of these and other tech companies (including Microsoft) will be the first to reach the $1 Trillion mark in terms of market capitalization. For new investors, market capitalization (or “market cap” for short) is computed by taking the number of outstanding shares of stock multiplied by the current price of a single share. For example, if a company has 10,000 outstanding shares and its stock price is at $12, then it has a market cap of 10,000 x $12 = $120,000.

Galloway predicts that Apple may be the winner, but his prediction was made in late fall last year prior to publication. Let’s see if his prediction is still on track.

Here are the current market capitalizations of the above tech names including Microsoft:

AAPL $943B

AMZN $883B

MSFT $821B

FB $504B


(For reference, here are the market caps of other large US companies in non-tech sectors: JP Morgan/Chase (JPM) $379B, Exxon/Mobil (XOM) $345B, Berkshire-Hathaway BRK.A $223B.)

So, it does indeed appear that Apple is still the front runner but Amazon and Microsoft are nipping at its heels. Just one bad quarter or a downward revision may be enough to put Apple’s first place position in jeopardy. For Apple’s market cap to fall to that of Amazon’s current market cap ($883B) means that Apple shares would be trading at $179.5.

Today, Apple is trading around $191.5/share so it would only take a 6.2% move to drop it to $179.5. That’s not unrealistic at all considering its share price dropped over 16% in the latter part of January/early February.

Does Apple have something to worry about? If price appreciation is any indication (and that’s a question for fundamentalists), then perhaps it does.

Recently, Apple shares have significantly under-performed both those of Amazon and Microsoft. Since July 20th of last year (2017), Amazon shares are up 76%, Microsoft’s are up 43%, while Apple shares come in a distant third at 27% (which isn’t shabby considering that the S & P gained 13% in that time). But if Apple’s share price keeps on going at this rate, it won’t take long for the others to over take it.

The race to a trillion is heating up and seeing who first crosses the finish line first will be exciting. Just wonder what the odds are in Vegas…

Expectations in Asset Class Returns

September 19th, 2017

Patrick Glenn

We have come to expect that certain asset classes will, in the long run, produce a certain rate of return. When that class fails to produce this return we are disappointed and look for answers and someone or something to blame. But what is the basis for these expectations? This article will attempt to address these issues and provide some answers.

Total Return Profile of Large Cap Stocks
Let’s start with the Large Cap Stock (S&P 500) asset class. We are told that the total return should be about 10%. But where does this number come from? Figure 1 shows the cumulative monthly total return (price + dividends) of the S&P 500 since 1928. Note that the portion of total return due to dividends has historically been in the range from 30% to 60% depending on holding period. Shown on a log axis, where equal percentage changes receive equal weight at all points on the curve, the data is very much visually about a straight line. The actual straight line shown through the data is a best fit exponential regression, Y = a exp (b X), which also displays as a straight line on a log scale and mathematically reports a very high Pearson Correlation Coefficient with this data of 0.99. The annualized rate of return this regression line exhibits, as measured by its slope, is 10.98%.

 Figure 1. Total Return of the S&P 500 since 1928

Based on this you would expect that in the long run one should be able to achieve nearly 11% on an annual total return basis by investing in the S&P 500. But, as you can see, there are extended periods where the return fails to follow this benchmark. For example, from January 2000 through today (August 2017) the average annual total return in the S&P 500 has been only about 5.0%.

Figure 1 can be useful in a number of ways. First, the principle of ‘reversion to the mean’ indicates where the eventual value of this index will be at some time in the future. If the data is currently below the regression line, as it is now, then it is highly likely that it will eventually rise to cross it. If it is above the line, as it was in 2000, then it is equally likely that it will fall to the line and then perhaps cross below it. You can use this line as an aid in determining entry and exit points. If you buy when it is far above the line then you will experience diminished returns as compared to the 11% or better annual return expectation of those who buy when below the regression line and sell when the market is on or above the line.

A possible observation and interpretation from Figure 1 is that unlike the subprime mortgage crisis of 2008 which was a genuine crash the dot-com bubble burst of 2000 was merely an inevitable reversion to the mean.

Total Return Profile of Small Cap Stocks
A very similar result appears for the Small Cap Stock asset class. This asset class mirrors an index such as the the CRSP U.S. Small Cap Index which includes U.S. companies that fall between the bottom 2%-15% by market capitalization. [The Vanguard Small Cap Fund, VSMAX, uses this index as its benchmark.]

Figure 2 below indicates that a long-term annual total return of 14.2% can be expected by investing in a Small Cap stock index fund or similar. Again, this is given by the slope of the regression line and its Pearson Correlation Coefficient is also a robust 0.99.

Figure 2. Total Return of the Small Cap Stock Asset Class

Of course, your actual expected return depends on when you buy and when you sell. You will achieve the expected annual rate of return when you buy and sell directly on the regression line. Better returns can be achieved by buying when the value dips below the line and selling when it rises above it. The above chart indicates that right now is a good time to buy.

Total Return Profile of REITs and International Stocks
Exponential regressions can be performed for the Real- Estate Investment Trust (REIT) and International Stock asset classes. The results are shown in Figures 3 and 4 below. For REITs, the average annual rate of total return is 12.4%. For International Stocks, the value is 10.8%. The correlation coefficients for these above asset classes is 0.99 as well.

Figure 3. Total Return of the REIT Asset Class

Figure 4. Total Return of the International Stock Asset Class

The impact of mean reversion
These figures can be useful in a number of ways. First, the principle of “reversion to the mean“ indicates where the eventual value of an index will be at some time in the future. If the value is currently below its regression line (as it is in all of the above cases), then it is highly likely that it will rise to touch the line or cross it. Conversely, if it is above the regression line, it will eventually fall to the line or below it.

In discussing mean reversion, the real question is not will it ever revert to the mean, but when? It’s impossible to determine exactly that time in the future when it will revert to the mean (ie., the regression line)—it could be a matter of months or even decades. The real strength of this approach is you can improve your returns just by buying when the value is at or below the line, and selling when the value rises above it. On the other hand, if the value is currently well above the line, you may wish to wait until the value comes down, or at least take a smaller position so you don’t lose out on returns entirely.

From the above charts, you can see that if you had bought right before the 1929 market crash and held until now, your total returns in all of the asset classes would have suffered. The table below shows that your actual returns would be off by 1% – 2%, depending on asset class.

Table 1. Comparison of Expected versus Actual Returns

Historical Variations in Expected Returns
Mathematically astute readers will note that the regression lines shown are based on the data up to the present. What was the slope of the regression line in the past when less data was available? Looking at the data for the Large Cap Stocks in Figure 5, you can see that since the ending of the Great Depression in the early 1940’s the expected return as measured by the slope of the regression line climbed steadily until about 1970 when it leveled off. Since then it has been fairly stable around today’s value. A similar return profile holds for all of the four stock asset classes discussed above.

Figure 5. Historical Expected Returns for the Large Stock Asset Class

It’s interesting to look at the International Stock asset class date line (Figure 4). You will see that it appears to have taken a new and flatter path starting in 1987. Only the future will tell if this is a real effect signifying a fundamental change or whether it will revert to its current historical mean value.

Stocks vs Bonds
These stock asset classes seem to behave fairly well according to the regression line premise. However, the same cannot be said of the bond asset classes. They behave much differently. This is particularly true of the Treasury Bill asset class shown in Figure 6 below. As can be readily seen, the exponential regression line approach doesn’t do very well in representing bond performance.

This is to be expected as short-term interest rates are governed by external market forces (e.g., the Federal Reserve Bank) and not strictly by supply and demand. Being directly responsive to interest rate variations, bond performance has no single long-term trend but instead varies slowly at different rates over long periods of time. Low interest rates (and inflation) are seen through the 1960’s slowly transitioning to higher rates in the 1970’s, 1980’s, and 1990’s with lower rates and returns returning in the beginning of this century. For reference, the slope of the regression line for this bond asset class gives an expected annual return of 4.2%.

Figure 6. Total Return of the Treasury Bill Asset Class

Using regression analysis, the long-term expected returns of free floating asset classes (stocks) can be determined with a high degree of accuracy given a large enough data set. A plot of the regression line over actual return data can indicate to investors when it would be prudent to buy and when it would be prudent to sell in order to maximize portfolio returns.

Note: The graphs and charts presented here were all generated by the Portfolio Preserver (TM), a powerful software tool designed to maximize portfolio returns while minimizing risk.  Find out more at

About the Author:
Patrick Glenn is a former physicist, computer scientist, and business owner. He specializes in mathematical modeling and algorithm development and is the author of the popular CarTest 2000 automobile acceleration simulation and FinanceMaster, a personal finance management program. Today he is an advocate of financial quantitative analysis and is the designer and architect of the Portfolio Preserver software, an innovative approach to portfolio management and risk minimization.

Beware the current market calm!

May 4th, 2017

While the major averages have been treading water for the past week, there’s been a lot of turbulence roiling under the surface. We’ve been noting the recent plummet in metals, and today’s continuation is no exception. Adding to the recent breakdowns in precious metals, base metals finally decided to join them in their misery. Today, both copper (JJC) and nickel (JJN–thinly traded!) broke support levels while gold, silver, and platinum continue to spiral into the depths of despair. Uranium (URA) (which should really be considered an energy play) has been one of the biggest losers with today’s 4.8% drop adding to it’s growing pain. The stock has shed over 30% of its mid-February peak value

Reflecting these losses are the fall in commodity based currencies–the Loonie (FXC) and the Aussie dollar (FXA). Both are sinking below support and their recent downtrends have only been exacerbated by the falloff in energy and metals.

Oil, too, has succumbed to downward pressures. Today, the USO and OIL, both big oil etfs, plunged below support levels. While oil had held steady for a while, it’s downward movement was presaged by the fall in oil explorers (IEO, XOP) and servicers (PXJ, OIH, XES), all of which are off 25-30% since their mid-December highs.

The bearish action in energy and metals has put a damper on the Commodity Index etf, DBC. This tracking stock opened the day below $14.50 support and has shed 2.25% since then. (This is a fairly big drop for this issue.) The stock appears that it could very well re-test major support at $14. Unfortunately, the commodity carnage does not appear to be over yet, folks!

The threat of high interest rates are starting to take their toll on interest rate sensitive issues. REITs and bonds have lately been experiencing outflows, although the market seems to be firming up a bit for them today. However, I wouldn’t hold my breath that this signals the beginning of a major rebound. Best to stay on the sidelines for now.

The biggest winners of late (besides tech which has been well publicized so I won’t rehash what everyone else has been saying) include bitcoin and global-based stock funds, especially European ones. One bitcoin fund, GBTC, has rallied 30% this week, but today’s topping tail suggests that the buying pressure may be drying up, at least in the short term. I still haven’t been able to wrap my brain around cryptocurrency funds (I question their transparency), and if you’re interested in playing these, I’d recommend taking small positions.

Turning to Europe, the country of funds of Italy (EWI), Spain (EWP), France (EWQ), Belgium (EWK), Germany (EWG), Austria (EWO), Sweden (EWD), and Ireland (EIRL) have all gained between 20-43% since their mid-November/early December lows. Even Great Britain (EWU) has rallied 13% which is surprising considering that many pundits felt that Brexit would doom the country.

So, while the major averages are trying to decide which way they care to continue, the action in commodities and global markets has made itself quite clear.

Tra-la or ruh-roh?

May 9th, 2016

I know it may sound unthinkable, but there are times when even market technicians can’t predict the future direction of the S&P. One of those times just so happens to be right now. There are two moving average cross-overs currently flashing conflicting messages–one bullish and one extremely bearish.

Let’s take a quick look at both.

SPX Golden Cross

In the bullish corner we have the recent formation of a Golden Cross in the S&P’s daily chart. For those of you unfamiliar with the concept, it is when the 50 day moving average (50 dma) moves above the 200 dma on a daily chart. Historically, it has shown to be a fairly reliable indicator of a change in market direction from bearish to bullish. From the below chart we can see that a Golden Cross formed around April 26.

In the bearish corner, we have what I’m going to term a Correction Cross because no one else has yet named it. This is when the 50 dma moves below the 100 dma on a weekly chart, not a daily one. A Correction Cross has formed only twice in recent history. The first was at the end of beginning of April, 2001 which heralded the tech correction. The second time was in late June, 2008 at the beginning stages of the mortgage crisis. Here’s what the chart looks like today:

SPX Correction Cross

The above chart shows that it may be a bit too early to call a Correction Cross, but it’s ominously close. What does this all mean? Well, the only thing I know for sure is that right now I don’t know anything for sure. For this reason, I would recommend that sitting on the sidelines may be the most prudent place for cautious investors , at least until market direction is confirmed, one way or the other.

Sugar: Short-Term Shock, But Long-Term Outlook Is Sweet

May 6th, 2016

The Sugar etf  (SGG) suffered one of its largest one day losses in years yesterday (5/5/16). There was nothing in the news to suggest the reason for this massive sell-off (at least that I could find), so I asked my friend, Edgard Cabanillas–a commodities trader specializing in agricultural commodities, if he might have an explanation.

Here is his response sent via email:

“Fund selling to an extreme with upcoming NFP and stronger USD in the last few days. Long-term things to consider are that Wilmar and other cash players (along with funds) are bullish and taking appropriate position in the futures and physical markets. Outlook for tighter balance sheets into 2017 are still present so I am still long the spreads in sugar and will do so. USDA releasing a special sugar outlook next week should help to shed light. They say that funds are almost record long sugar, that to me is supportive to being long myself.”

If you wish further information, please contact Edgard directly via the contact info below. He publishes a weekly newsletter along with his trade recommendations so that you can either trade along with him or he’ll do it for you. Last year his fund did very well and for those who are looking to diversify some of their portfolio into commodities, you may wish to consider his fund. And no, I am not receiving any compensation for mentioning him except for the occasional newsletter.

Edgard Cabanillas
President, Alpine Trading LLC
Tel: + 1 949 357 4948 U.S.
+ 41 76 785 4487 CH

The Myth of Diversification

December 15th, 2015

***We will be running the “Best Of” articles from previous years.***  This article originally appeared here on 7/11/14 and has been republished elsewhere.

Everyone assumes that broad asset class diversification in an investment portfolio is advantageous. The major benefit is to reduce the risk associated with events that can trigger a decline in any one asset class. By holding a variety of asset classes that are mostly uncorrelated with one another, the investor hopes to avoid those catastrophic occurrences that completely wipes out years of gains such as what happened during the credit crisis of 2008. Further, diversification makes financial planning more reliable and predictable by reducing the variations in portfolio performance from year to year.

Simply put, diversification is a sound investment practice.

But exactly how much risk reduction, in actual numbers, is obtained through application of this philosophy? That was the question I was pondering and was wondering if, indeed, asset class diversification is all that it’s cracked up to be.

Let’s find out.

[Disclaimer: First of all, nothing that follows is an attempt to challenge the precept of broad diversification as an indispensable investment tool, so don’t get scared. Consider this analysis to be an exercise in quantitatively determining the relevance of just how much risk can be reduced by adding more asset classes to one’s portfolio.]


The ideal tool for performing the analysis in this article is Modern Portfolio Theory (MPT). This Nobel Prize winning approach utilizes complex mathematics to tell you how to best allocate your funds among various asset classes to minimize risk.

Risk can be looked at as fluctuations in portfolio returns. In MPT, risk is measured by a statistical term called the standard deviation. It is this quantity that MPT seeks to minimize in recommending portfolio allocations. [The software used in the analyses conducted here is the SMC Analyzer. Click here for more info.]

The portfolios considered here used monthly total return data taken from January 1928 through May 2011 for each of the following ten asset classes:

  1. Large-cap U.S. Stocks
  2. Small U.S. Stocks
  3. Long-Term Corporate Bonds
  4. Long-Term Government Bonds
  5. Intermediate-Term Government Bonds
  6. 30-day U.S. Treasury Bills
  7. Real Estate Investment Trusts (REITs)
  8. International Stocks
  9. International Bonds
  10. Gold

Each of these asset classes are themselves composed of a broad diversification of assets within that class. This article does not address that need, only the benefits of diversification among various classes.


The methodology used in this analysis was to first establish a baseline return/risk table using all ten candidate asset classes (Table 1 below). You’ll see that the table contains certain measures of risk defined as follows:

  1. Standard Deviation – statistical measure of portfolio return fluctuation around the target return
  2. Probability of Loss – chance of that portfolio losing value in any one year
  3. Sharpe Ratio – a measure of risk versus reward with larger numbers being better

This baseline data is shown in Table 1 along with the current ten asset class portfolio allocations (through May 2011.)

Table 1. Baseline portfolio incorporating all ten candidate asset classes. Click to Enlarge

(Click to enlarge.)


The next step was to remove each asset class one by one in each of successive rounds and to assess its effect on the measures of risk. At the end of each round we chose to eliminate that asset class that increased the measures of risk the least [sentence corrected]. This was repeated for eight rounds until only two asset classes remained. Eliminations required examination of 52 separate portfolios (10+9+8+7+6+5+4+3).

By using the above measures of risk as our benchmark, asset classes were eliminated from consideration in each successive round in the following order:

  1. International Bonds
  2. Long-Term Government Bonds
  3. Real Estate Investment Trusts
  4. Gold
  5. International Stocks
  6. 30-day U.S. Treasury Bills
  7. Large U.S. Stocks
  8. Either Intermediate-Term Government Bonds or Long-Term Corporate Bonds depending on target return


Tables 2a and 2b show the measures of risk using only two asset classes in the MPT analysis. There are two tables because different asset class combinations are preferable for the most conservative portfolios (target returns up to and including 7%) and the more aggressive ones (target returns 8% and above).

Table 2a. Two asset class allocations and risk measures for conservative portfolios

Required Standard Probability Sharpe Small Medium-Term
Annual Deviation Of Ratio Company Government
Return Loss Stocks Bonds
5.5% 4.6% 0.12 1.19 1.8% 98.2%
6.0% 5.3% 0.13 1.14 10.1% 89.9%
7.0% 8.7% 0.21 0.81 25.0% 75.0%


Table 2b. Two asset class allocations and risk measures for more aggressive portfolios

Required Standard Probability Sharpe Small Long-Term
Annual Deviation Of Ratio Company Corporate
Return Loss Stocks Bonds
8.0% 12.8% 0.27 0.62 34.3% 65.7%
9.0% 17.3% 0.30 0.52 50.4% 49.6%
10.0% 22.4% 0.33 0.45 66.3% 33.7%
11.0% 27.8% 0.35 0.40 82.1% 17.9%
12.0% 33.6% 0.36 0.36 97.8% 2.2%
12.1% 34.4% 0.36 0.35 100.0%

You can see from the tables that returns are best realized by small-cap stocks and medium-term government bonds in conservative portfolios, and by small-cap stocks and long-term corporate bonds (investment grade) in the more aggressive ones. The inclusion of small-cap stocks especially in the more aggressive portfolios should come as no surprise because it is this asset class that is capable of generating the highest returns over the long haul. In fact, it is because of using only small-cap stocks to generate returns in the two candidate model that returns below 5.5% are completely unachievable (but so are very low levels of risk).

Now let’s see how these results compare to the classical model of using ten asset classes.

Comparison of results

Table 3 presents the risk differences associated with reducing ten asset classes to only two.

Table 3. Risk difference between two and ten asset classes

Required Standard Probability Sharpe
Annual Deviation Of Ratio
Return Loss
6.0% 0.2% 0.01 -0.04
7.0% 0.4% 0.01 -0.03
8.0% 0.4% 0.01 -0.03
9.0% 0.5% 0.00 -0.02
10.0% 0.6% 0.01 -0.01
11.0% 0.7% 0.01 -0.01
12.0% 0.2% 0.00 0.00


What this analysis shows is truly astonishing and surprising. You can see that the reduction in the number of asset classes has a relatively insignificant effect on risk. I’m betting few folks would have expected this result!

To summarize all this into one number, you are increasing your overall level of portfolio risk by only about 1 part in 20 by decreasing the number of candidate asset classes from ten all the way down to two. This is based on the general numerical increase in the values of the risk measures as measured from their baselines.

An historical comparison

Looking at this from a historical perspective, let’s see how well a portfolio with only two asset classes would have fared against a traditional portfolio with all ten. Table 4 shows the results of actually following the recommended MPT allocations–with monthly rebalancing–from January of 1928 through May of 2011.

Table 4. Comparison of actual returns achieved utilizing ten asset classes versus two asset classes.

Required Ten Asset Classes Two Asset Classes Differences
Return Return SD Return SD Return SD
6.0% 7.1% 7.3% 8.0% 8.9% 0.9% 1.6%
7.0% 8.2% 10.1% 9.1% 11.1% 0.9% 1.0%
8.0% 8.6% 12.6% 10.2% 13.5% 1.6% 0.9%
9.0% 9.1% 14.5% 10.9% 15.7% 1.8% 1.2%
10.0% 10.1% 16.7% 11.4% 17.6% 1.3% 0.9%
11.0% 10.9% 18.8% 11.7% 19.2% 0.8% 0.4%
12.0% 11.8% 19.9% 11.8% 20.5% 0.0% 0.6%

 [To read this table, the Return under each model is the actual return the portfolio would have realized at the required return level given in the first column, and SD is the risk defined by the standard deviation.]

Let’s look at an example. To achieve a 10% required compounded average annual return, a ten candidate asset class portfolio would have achieved its goal and would have actually returned 10.1% at a risk of 16.7%. That same 10% targeted return attempted using only two asset classes would have actually produced a greater return, 11.4%, with only a slightly higher standard deviation of 17.6%.

It is interesting to note that the portfolios composed of only two asset classes exceeded their targets more so than their ten asset class counterparts. This is due to the fewer choices available to the mathematics of MPT in its attempts to achieve, at least, the required return while also minimizing the level of risk. But in so doing, the level of resultant risk is commensurately slightly higher.


The takeaway from this article should be to note that it doesn’t take broad asset class diversification to adequately achieve one’s investment goals with a reasonable level of reward versus risk. So all of you lazy Lisas and Larrys out there can sleep easier knowing that your nest egg needn’t be diversified among more than the two carefully selected asset classes discussed above for you to realize your desired long-term return at minimum risk.

There are also some practical advantages of choosing the two asset class approach over the ten asset class model. One is the amount of time and inconvenience it may take to rebalance many asset classes every month. The other, and possibly more important advantage, is the amount of coin you might save in trading fees. That alone could well justify the small increase in risk!

Pat Glenn contributed to this article.

Fashion statement: Mixed markets ahead!

July 27th, 2015

emma-summerton-collection[1]There have been a few theories that expound a correlation between fashion and the stock market. For example, there’s a theory that the stock market rises along with hemlines, and while that may be true, I believe there are more telling features, such as:

Primary Factor: Color

Secondary Factor: Print

During up markets, colors tend to be bright, and prints tend to be diverse (think floral, small patterns, etc).

During down markets, colors tend to be muted (black/grey/white/brown), and prints are either non-existent or are blocked (in larger, geometric patterns).

What the fall 2015 couture collection is showing: Clashing prints in heavy tweeds that aren’t congruent with either a male or female form. Perhaps it’s a reflection of the rising LGBT culture.

This is a new wrinkle in the fabric of stock trading and as such, I’m not sure what it means. Perhaps the market doesn’t know, either.

Final Analysis: We’re in for a completely new paradigm. One way to be prepared for a bumpy ride is to book profits in “old” companies and sit in cash on the sidelines and wait for the newbies to appear.

Uranium & Steel Stocks rise on nuclear build-out

April 23rd, 2015

Whoa, Nellie! Lotsa’ action in today’s market–where to start…

1. Are the bulls ready to take a rest? The S&P 500 (SPX) made another run at a new high but a late day sell-off put the kibosh on that. As Scarlett O’Hara famously said: “Tomorrow is another day,” but contrarian market internals (low volatility, rising Trin, low negative VWAPs) are suggesting that tomorrow’s market may not see the bullish action that it enjoyed today. I wouldn’t be surprised if Friday’s market ends in the red.

2. Steel stocks are heating up. The Steel etf (SLX, $34.78) has been rising off a double bottom put in a month ago. The stock gained nearly 5% on heavy volume today but that wasn’t close to how some of its constituent components fared. Many gained in the neighborhood of 4%-9% on massive volume. Managing to break resistance levels today were the following issues: Reliance Steel (RS, $62.69), Companhia Siderugica (SID, $2.31), and Usinas Siderurgicas de Minas (USNZY, $2.03). Close to breaking out are the following (by symbol only): SID, PKX, X, MT, NUE. This industry group has been suffering for a while and a follow-through in today’s rally might be a great entry point for potential big gains.

3. Today’s rally in steel stocks could be a by-product (at least in part) of the surge in uranium and nuclear energy stocks. According to a recent article appearing in Forbes, India, China, and Japan are building out their nuclear power infrastructures and this is bullish for both uranium and steel (as steel is a major building component of nuclear power plants). The Uranium etf (URA, $11.95; Dividend Yield = 4%) has jumped in the past two days on extremely heavy volume. It’s bumping up against $12 resistance and judging from the recent buying strength, it probably will have little trouble pushing above it. Four days ago (on 4/20) I alerted my subscribers to the breakout in Uranium Energy Corp (UEC, $2.41). Since then, it’s jumped nearly 30%. Other players in this area are Energy Fuels (UUUU, $5.42), UR Energy (URG, $1.03), Uranerz Energy (URZ, $1.29), and the elephant in the room Cameco (CCJ, $17.27).

Street Signs

March 21st, 2015

Street Signs

Is the market poised for a correction?

It’s been March Madness for Mr. Market–one week the trend is down, the next week it’s up. So, where’s it heading now?

Judging by the bullish movement in the major averages, it appears as if we’re entering a new leg up. The fact that the small-cap Russell 2000 and the tech laden Nasdaq popped to new highs on Friday reflects the fact that it’s been precisely those stocks which have advanced the most. The problem is that this advance hasn’t been mirrored in the larger-cap indices. Sure, the S&P and the Dow are close to retesting their recent highs and the market-leading Dow Transports is trying its best to rise above its quadruple top at 920 (9200 on some charts). If this doesn’t happen very soon, we could be in for a sharp sell-off.

Why? Today, a couple of technical signs indicate that a correction may be in the cards. The first involves volatility. The Volatility Index (VIX) has dropped precipitously over the last four trading sessions and is now nearing contrarian levels. It closed Friday at the 13 mark. Should it fall to 12 (or beneath it especially), that would be a strong sign that there’s too much complacency and that a market reversal is in the cards.

The next sign is the fact that soft (agricultural) and hard (oil and metal) commodities reversed their extended downtrends and abruptly moved up. One big standout in the softs was the Wheat etf (WEAT). The stock popped out of a one month base rising above $11 resistance. It ended the day up 3% on heavier than normal volume.

In metals, the Copper etf (JJC) jumped nearly 4% out of a two month base. This is a big deal considering that copper is associated with being the metal of choice for countries expanding their infrastructures, most notably China. And if copper is rising, could Chinese stocks also be poised for gains?

Looking globally, most country and foreign currency exchange-traded funds rallied on Friday in direct contrast to the fall in the greenback. The Long US dollar fund (UUP) has been in strong rally mode since its September breakout and was overdue for consolidation.

Going global

However, many Wall Street analysts believe that the dollar will be strong for the next several years. What this means is that foreign companies will become more attractive to investors at the expense of large-cap US stocks (hence the reason why the S&P and the Dow Industrials are lagging). Many feel that China, Japan, and Europe in particular will be the biggest beneficiaries of a strong dollar.

One way to play the global market is through the International Small-cap etf (GWX, $29.11). This fund covers a broad spectrum of industry groups and is a "market capitalization weighted index designed to define and measure the investable universe of publicly traded small-cap companies domiciled in developed countries outside the United States." The fund has been trending up since it’s December 19th ex-dividend date (of $3.56) and broke resistance at $29 on Friday. One way to buy the stock would be to sell a cash-secured put at $29. The drawback is that open interest is virtually nil but it could be feasible using a limit order.

If you’re more interested in building your own basket of international stocks, take a look under the hood of the following which all broke out of bases on Friday:
1. Indesit (IDEXY, $16.39): Note that Whirlpool owns the majority of voting stock in this Italian appliance maker.
2. CSL (CSLLY, $37.39): This Australian bio-pharma specializes in vaccines and plasma products.
3. Marks & Spencer (MAKSY, $15.97): British apparel retailer affectionately known as Marks & Sparks popped out of a one year base on 28x normal volume on rumors that a Middle Eastern group will be making an offer for the company 30% above its current share price (around $19.50).
4. Julius Baer (JBAXY, $10.20): Shares of this Swiss investment bank broke out of a one year base on no news. Credit Suisse (CS, $26.47) also broke through resistance on twice normal volume (but it has a ways to go before testing its previous high at $33).
5. Canon (CAJ, $34.84): Technically, the stock of this Japanese camera maker broke out on Wednesday but it jumped again on Friday. Yes, the digital camera space is essentially dead but Canon is refocusing (no pun intended) on the surveillance camera market.