Trading the Greek Gods

Saturday, July 6, 2019

Selling CC and CSP Strategy Lessons Learned

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Next, same net data, but with WEEKLY resolution (instead of monthly):

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Obviously, the lower-left / upper-right (LLUR) direction of the equity graphs are the direction that I want to go, so I'm happy with this. Starting capital was around $71,000, and despite some bumpy roads in January 2018 as well as January 2019, my net return is north of 15% CAGR, with all expenses, platform fees, commissions, etc. included. Solid revenue.

What Causes Losses (for me)?

I log EVERY trade. Every one. I've made a total of 586 round-trip trades, and 12 of them were BUYING protective puts. You lose money on a protective put if the stock continues higher. I'm am not using protective puts right now, but they are useful in my arsenal should I need them. Overall, the markets are moving higher, so buying puts is not a key component of my strategy.

The losses in January 2018 -- nearly 17 months ago -- were due to just getting started and being careless. These losses, which resulted in ($2,339) of losses on a $71,000 portfolio forced me to develop key position sizing rules. Here is the fundamental of that rule: with little exception, no position should occupy more than 14% of your portfolio. I usually start in the 2-5% range, and if the trade works out, I'll add to it by selling another put and/or selling a call to straddle an assigned position. It is rare that I sell an initial position that is 14% of my portfolio, but I've done it. I generally attempt to sell a 1/4 or 1/2 position first, then work from there if assigned.

The following chart will highlight something I'm REALLY weak at: Rolling for profit:

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The chart shows that my losses are predominantly from rolling options (79 positions rolled for an average loss of ($114.01) per roll. This has accounted for over $9,007 in portfolio losses. My other loss mechanism is the "sell if the stock hit hits -3% below my entry strike", but you can see that has only occurred 3 times out of all of my trades.

To address this I have greatly curtailed my roll operations. With little exception, I have decided NOT to roll, rather just let the stock get called away and be done with it. I'll still retain discretion to use a roll when it makes sense, but the premise of a roll operation when holding the call is simply not a good one.

This position is further supported in other data that I have. Despite the roll operations being a big negative influence on my performance, positions that I DID roll into have been profitable, but not nearly to the extent necessary to make up for the difference. In fact, I've only recovered $24.65 on average out of the lost ($114.01) due to the roll, so rolling is not a long-term viable strategy.

Key takeaway: minimize rolling positions.

Other Nuances of My Strategy and Trading Performance

The following graph shows which months have been profitable and which have not been so profitable:

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The December-June period has had 2 years of data rolled into the chart, whereas July - November still only have one year of data (July 2018 - November 2018). I know what caused issues for both January months, and looking at trades from September and October 2018, I can see that rolling heavily offset some of my gains. A few positions were put and/or called against my desires, and there is nothing that you can do about early assignment except manage the position. Whatever you do, DON'T PANIC. If your position size is correct, you'll take a hit, but you will not lose your portfolio.

An important graph is this:

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This is a graph of drawdown, which is the amount the portfolio dropped once it hit an equity high. You can see that for the PORTFOLIO, although some positions fell enough to cause heartache, the impact on the PORTFOLIO over the last 19 months has only been a -3% (less than) drop in overall value.

Think about that from a risk management point of view.

Currently, the portfolio is making new highs, as exemplified by the green dots in the equity line:

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The next few graphs help answer whether selling a cash-secured put (CSP, or Sput in the graph), a covered call (CC), or rolling the position (Roll) has resulted in greater gains:

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It's pretty clear that the Ccall graph -- the middle one -- has spent the majority of the time above it's 20d MA. This suggests that CC's have a definite place in my strategy.

It's also pretty clear that the Sput graph -- the top one -- has also spent most of its time in an uptrend. Same comment as CC.

Finally, rolling positions suck, or at least, I'm bad at it. In my defense I think I'm not too bad at it, but rolling the position when it is already moving towards being in-the-money means that you are losing value on the call option -- rolling it just locks in that loss with the "hope" of recovering it later, either through stock appreciation or through collection of higher premium. I'm obviously minimizing my roll activity.

Take away: selling BOTH puts and calls is a good business.

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That's all for now. If you have questions -- ask.

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As with all my ramblings, you are responsible for your own actions and I am not. Nothing I've written here is advice to buy or sell any security, so don't do it unless you absolutely take ownership for your actions.

Regards,

Paul

Sunday, May 26, 2019

How Many Dividend Champion Stocks do I need to perform better than the S&P 500

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I trust that everybody is enjoying the long Memorial Day weekend. Let's pause and remember the reason for the holiday ...

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I received a question this week that was tied to last week's entry, specifically asking "How many Dividend Champion stocks should I hold to perform better than the S&P 500?"

This isn't as easy a question as it appears.

First of all -- "perform better". In return? In volatility? Both? Some other metric? The question wasn't specific.

It isn't practical to hold 500 stocks. Commissions would eat you, and management would be a pain in the tail. Contrary to belief, funds that track indexes also do not invest in all the stocks of the index -- they use a subset for this very reason.

Not all stocks in the Dividend Champion list are in the S&P 500, which further complicates the decision tree.

Not all stocks in the S&P 500 pay dividends, further complicating the answer.

Reformulating the question: Using just stocks that pass Paul's Greenfield criteria, pay a dividend, and are on the Dividend Champions list, can I beat or outperform the S&P 500 in return and/or volatility?

So, let's start with the S&P 500 stocks. I'm only interested in

1) the Greenfield-critiera-passing subset,

2) those stocks that pay a dividend and that are on the Dividend Champions list, and

3) have less or equivalent risk than the S&P 500.

Regarding the first two criteria, I've compiled that list for you, and it is here.

This seems to be a lot of trouble: I *could* simply buy the SPY (an exchange traded fund that mirrors the performance of the the S&P 500) and be done with it. If I were to do that, I'd have the expectation of the performance that I stated last week (5.97% return annually at an average volatility of 17.90%) and I'd presently capture a dividend yield of 1.85% (see this link as an example of how the dividend yield is determined).

Hmmmm.... 1.85% dividend yield.

I'll not get into here but suffice to say, if I want a dividend yield of greater than 1.85% on a basket of stocks, then the "average" of all the yields of the subset of stocks that I invest in has to be greater than 1.85%.

Since I don't *know* what stocks I'm looking for (yet), the way to address this is to only look at stocks that have a yield greater than 1.85%. In doing this I am guaranteed, no matter what, to beat the current dividend yield of the SPY.

I've built the subset of stocks of the S&P 500 that 1) pass my greenfield criteria, and 2) have a dividend yield of greater than 1.85% as of 5/24 closing prices. The stocks on the list are further screened against the May 2019 Dividend Champion CCC tab located here. The final text list is here.

It's still a long list. 76 stocks, to be clear. I have no desire to invest in 76 stocks. Still unmanageable.

Enter Portfolio Optimization

One way to select a basket of stocks is to do so such that some parameter is optimized. Typical parameters are things like 1) maximizing Expected Return (ER), 2) minimizing volatility, 3) minimizing correlation, etc.

If you were paying attention last week, you read that I mentioned the Sharpe Ratio (SR). This is a metric which calculates ER and risk, the latter which is often interchanged with volatility. Hence, for a given equivalent ER between two securities, the one with lower volatility has less risk (and corresponding higher SR).

So, a natural method to select stocks is to maximize SR within the entire portfolio.

But, here's a complicating component: correlation between assets. As one move up, the others may move up, or they may move down. It all depends.

Here's an example that you can use to better understand this. Let's take the exchange traded fund SPY, which I said is a proxy for the S&P 500, and the Vanugard mutual fund VFINX, which also is a proxy for the S&P 500. Theoretically, both should move exactly in lock-step with the S&P 500, and hence, they should both be in sync.

The reality is that yes, the SPY and the VFINX are almost 100% in step. See the picture below:

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Calculating the SR's of the SPY and the VXF isn't enough -- the correlation between the two needs to be considered. It would not provide any benefit to me to invest in BOTH -- there is no diversification -- and only expenses would go up. There would be no other benefit that is hidden or not apparent.

So, when we are looking to reduce a basket of stocks, we want to choose stocks that lack correlation ... so, we specifically want to maximize SR but minimize correlation. This becomes a multi-variate problem.

As complicated as this sounds, there is software out there to crunch through the numbers. Excel does a great job at this, and I have a great piece of Excel software to help me do this.

This concept of correlation is important in stock selection. It is best to select stocks that are uncorrelated. Stocks that are perfectly correlated have a correlation coefficient of "1". Stocks that are perfectly INVERSELY correlated have a coefficient of "-1". Stocks that are UNCORRELATED have a correlation coefficient of "0". We want a basket of stocks with as low as a correlation coefficient as possible (close to zero as possible).

Let's take the 76 candidate stocks and calculate the correlation to the S&P 500, the Russell 2000 index, and each other. The following figure is a small slice of that 76-stock universe:

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^GSPC is Yahoo's symbol for the S&P 500. ^RUT is the symbol for the Russell 2000 index. Other stocks are listed as shown.

The green "1"s that form a diagonal are due to the fact that a stock has a perfect correlation with itself.

I've color-coded the figure to show that the closer the correlation, the greater the amount of green. The more neutral, or uncorrelated, we see orange, and the more inversely correlated, we see red.

The S&P 500 (^GSPC) has a 0.908 correlation with the Russell 2000 (^RUT). Both are darker green, so as one moves up, the other also moves up in almost the same manner.

Conversely, you see that WELL is more negatively correlated with the other stocks shown (see the negative numbers in RED).

As I wrote above, this isn't important in the big picture, but it shows you how stock prices move with each other. The goal in portfolio selection is to maximize diversification through as few selections as possible, and this is one way to get there. We want a basket of stocks that has an average correlation as close to "0" as possible".

If you are looking for a slightly more in-depth treatment of the math behind this, go here or here.

Portfolio Component Selection

How to choose which stocks? How to ignore some, but select others? How to weight each stock so to maximize some value? The paper here gets into the gory details. What is important here is that there is a trade off between a risk-free investment, a risk-free + risky investment, and a number of risky investments. The goal is to figure out how to get as risk-less as possible but maximize return -- hence optimization of the Sharpe Ratio, while minimizing individual stock correlation.

Tying this back to the correlation thing above, when I crunch through the numbers, here is the correlation matrix for the "solution":

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Of significance is that there is NOT a great deal of green on the matrix -- most of these stocks are uncorrelated with each other, or probably better-aptly described, do not have a tight correlation. The average correlation to each other is 0.341, which isn't bad. 0.000 would be ideal.

Given that this is the list of stocks with the lowest portfolio correlation, the next challenge is optimizing SR. There is a large number of possible weightings of stocks to give us a return, and when we do that, we play with volatility. It's possible to maximize Expected Return but have terrible volatility; conversely, it's possible to have extremely low volatility but not maximize Expected Return.

Everything has been leading up to this -- the calculations. Don't worry, I won't go through the details. Simply put, the Efficient Frontier and ER/Volatility points for all the stocks shown is here in the following graph:

Click on the image to enlarge.

If you are not familiar with Efficient Frontier go watch this (you REALLY need to watch that video if this is new to you). In the example I've set the S&P 500 expected return to 5.97% (recall that this is the average annual performance of the S&P 500 over history, not including dividends). Any stock below this value is an underperformer in terms of gain; and any stock above this is an overperformer in terms of gain. Further, recall that the S&P 500 volatility over history is about 17.90%, on average, and when you look at the graph above, you see that the majority of stocks have greater volatility than the S&P 500.

There is a unique condition in this process where two volatilities of greater than some number, when combined in the process, actually reduce the overall volatility of the combined set. This means that it is possible to select stocks with higher ER (y-axis) and higher volatility (x-axis) yet come up with a solution that has LOWER volatility but higher ER. This is the Efficient Frontier blue line in the picture.

The red line is the tangent line to the Efficient Frontier. The slope of this line is determined by the risk-free interest rate -- currently a value of 2.37% and zero volatility. The intersection of the straight red line and the blue line is the best the portfolio can be expected to do, given a risk free rate of 2.37% (go here). It is shown as the purple triangle at the intersecting points.

So, provided you watched the video above and understand the construct, using weights between 0% and 100%, ALL of those stocks define portfolios that are boundaried by the blue line, and because the red line and the blue line are tangent (intersect at 1 point only), there is a theoretical solution to a unique portfolio that provides the optimum ER at the lowest volatility.

That is what I'm seeking. Maximize ER while minimizing volatility.

For a reference point, the S&P 500 has a historical Sharpe Ratio of 0.3335, produced from an average yearly return of 5.97% and an average volatility of 17.90% (see last week's blog entry).

The portfolio shown above has an optimal Expected Return of 5.94% yet the volatility has dropped to 13.97%. This is an improved SR that is 0.4252. The method has the potential to produce the same behavior as the S&P 500 yet a a lower volatility, by specifically weighting each stock between 0% and 100%.

What is that weighting? Here you go:

This is a big list of stocks, but you can see that the weights (next to the symbols in light yellow) get smaller and smaller as you go from top to bottom. This is the "perfect solution", e.g., the one where the Efficient Frontier (blue line) and the risk-free interest rate line (red line) intersect.

Next to the weights are the closing prices for each of the stocks, as of the 5/24 close.

To the right of the closing prices are the latest quarterly dividends that were paid for the stock. Remember, our initial criteria specified that we had to pick stocks with a dividend yield of at least 1.85%.

To the right of the dividend levels are the number of shares, given a $1,000,000 starting level, rounded to the lowest whole number. Note that the portfolio size is $1,000,000, and yet EQIX, at a close on 5/24 of $496.52, would only have you purchasing 9 shares. You can see that some of these stocks will not be purchased if the starting level is much lower (as is the case with me).

Next to the number of shares are the yearly dividend payments, if everything remains constant. Remember -- these are Dividend Champions -- so we have an expectation of dividend growth. At a minimum, we can expect to see $29,644 over the next year.

The right column shows the actual dollar value committed for each stock.

At the bottom of the table, you can see that we have $29K in expected dividends as well as $999K committed in total portfolio, giving us a 2.97% dividend yield. Note how much better this is than the 1.85% dividend yield cut off. We will certainly beat the S&P 500 in terms of dividends received.

Impact of Starting Smaller

It is highly probable that most of us do not have $1,000,000 to throw at a new strategy.

What if we start with $10,000? What happens then?

Here's the table:

The first thing that should be evident is that there is simply not enough cash to fund all of the positions.

The next thing that is evident is that the share purchases of 1 share probably are not worth the cost of commissions. In fact, I think the lowest number is probably around $200 per stock.

As an example, round-trip costs for me (to buy and to sell a security) are $1.00 each way with TradeStation, so I pay $2.00 round trip. If the stock moves 1% upward on a starting position size of $200 then I'm at break even. If you take a look at the right column you can see that many of these position sizes are below $200, so it probably is not worth attempting to enter this portfolio with $10,000.

Commissions will eat into the portfolio, so it important that you understand the impact of round-trip fees.

If you use Fidelity, Schwab, or E-Trade the commissions are higher and you would be worse off. I would simply buy the SPY and be done with it.

Impact of Starting at $100,000

If $1,000,000 is out of reach, and $10,000 doesn't make sense, what about starting at $100,000?

Here's the table:

This scenario isn't far off the mark.

You could argue that any position that is 2% or greater in size (invested capital ~ $2,000 minimum per position), the impact of commissions could be negligible.

If we choose not to invest in some positions, we throw off the weighting for each stock, and we also have cash left over. The key is to deploy ALL our cash.

So, let's only pick those position sizes above greater than 2%. Here's what the new portfolio looks like:

This portfolio contains 20 positions.

The starting level is $100,000.

You can see that you should receive nearly $3K in dividends the first year if you are invested in this portfolio and hold for the duration (obviously presumes that no company cuts their stock dividend).

The new calculated volatility of the portfolio has increased from 13.97% to 14.06%. Not terrible, but not ideal. Expected Return is constant at 5.94%.

You can also see that some of the position sizes jumped -- and a few others dropped. Nevertheless, this should be an achievable portfolio if you have $100K or more to start.

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Summary

1) We started with the stocks comprising the S&P 500.

2) We downselected those stocks to choose only the dividend payers that were yielding greater than 1.85%, the present yield of the exchange traded fund SPY.

3) We further downselected stocks to make sure they were part of the Dividend Champions list.

4) We selected a smaller group of stocks that had the lowest correlation to each other.

5) We further selected weights of stocks to maximize the Sharpe Ratio of a portfolio.

6) We looked at investing $1M, $10K, and $100K in that basket of stocks. It is probably better to simply buy the SPY if you only have $10K to invest.

7) We chose only the stocks of the original optimal SR solution that had a position size of 2% or greater (about $2,000), then we re-optimized. SR dropped a bit on the re-optimization, as you would expect, since we used a subset of the previous optimized universe.

8) The answer to the question is 20 -- you need 20 stocks from the Dividend Champions list in order to perform better than the historical average of the S&P 500. Stock selection matters.

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Next week I'll get into the rules of buying/selling and how to use options to get into some of the positions with a lot size of greater than 100 shares.

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That's all for now. If you have questions -- ask.

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As with all my ramblings, you are responsible for your own actions and I am not. Nothing I've written here is advice to buy or sell any security, so don't do it unless you absolutely take ownership for your actions.

Regards,

Paul

Sunday, May 19, 2019

May 19 Dividend Champions Weekend Update

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Happy May 19th Weekend Folks.

It's been over half a year since I've updated this blog -- no real reason except "life". Much as occurred in my world (all good), and my time for writing my thoughts and results seems to be stabilizing. Hence, here I am again.

Today's scribbles will provide context on a strategy that many of you know I've been using / iterating / improving for years -- my Greenfield Stock strategy. There are many entries on this so I'll just hit the highlights today, and feel free to post questions (if you have a question I'm sure others will too). I prefer that you use the box at the bottom of this entry, but of course, you can send an email to GreekGodTrading [a t] g m a i l [d ot] c o m (intentionally made difficult for the bots to read and spam me -- so fix it as your intuition tells you).

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Greenfield Stocks Foundation

The premise of the Greenfield strategy is that I will only transact in stocks if the meet the following basic, minimum requirements:

1) Revenues must be increasing on a year-over-year (YoY), quarter-over-quarter (QoQ) compared to the same quarter a year ago, and trailing 12-months (TTM) must be positive growth compared to the one-year-ago TTM.
2) Earnings must be increasing on a year-over-year (YoY), quarter-over-quarter (QoQ) compared to the same quarter a year ago, and trailing 12-months (TTM) must be positive growth compared to the one-year-ago TTM.
3) Free-cash-flow (FCF) must be positive.

These three components are loosely tied to my adaptation of William O'Neil, Mark Minervini, and others of the IBD genre. I've done extensive backtesting and forwardtesting of these (and other related parameters) and my belief is that it all starts with proper stock selection.

Note that REV + EPS + FCF are linked in an "AND" statement -- all must be true or the stock is rejected.

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Applying the Greenfield Stocks in a Real Strategy

Many of you are aware that I use TradeStation as my platform and that I have a number of "scanners" that run on a daily basis, internally producing the Greenfield lists for me.

The large majority of you do not have these scanners, so there is another path that you can use -- the U.S. Dividend Champions List that is updated monthly and can be found here:

https://www.dripinvesting.org/tools/tools.asp

This is a free list, updated at the end of the month, and it produces a master list of all stocks meeting a certain dividend criteria, as well as individual lists that break down into the following names / criteria:

1) Dividend Champions: U.S. Companies with 25+ Straight Years Higher Dividends
2) Dividend Contenders: U.S. Companies with 10 to 24 Straight Years of Higher Dividends
3) Dividend Challengers: U.S. Companies with 5 to 9 Straight Years of Higher Dividends

If a company cuts their dividend then the stock is removed from the list. It's that simple.

The premise of the list is that if you are desiring self-funding through dividend replacement, that over a long period of time you can grow your nest egg using both the capital appreciation of this list as well as the dividend-reinvestment potential from this list.

The latest list has 879 stocks between the three categories, and this is quite overwhelming to pick/choose where to start.

Over the years I've had numerous discussions with many of you and there are as many ways to invest in this list as their are conversations. There is no "right" answer, so don't expect one here. This being stated, if I take the lists and apply my "Greenfield Strategy" to each of the lists, we can start to drop the numbers of potential stocks from 879 to something more manageable.

1) Dividend Champions: 37 stocks remain, list is here.
2) Dividend Contenders: 79 stocks remain, list is here.
3) Dividend Challengers: 186 stocks remain; list is here.

This process culls the 879 list down to 302. Still a large number, but slightly more manageable.

These 302 stocks have two characteristics that I think is relatively important: performance and volatility.

Historically, if we ignore dividends, the S&P 500 has returned 5.97% annually at an average volatility of 17.90%. There is a metric for this -- it's called the Sharpe Ratio (SR), and it is simply a ratio of the return that we receive vs. the risk taken. Higher numbers are better. The historical, long-term SR of the market is 0.3335. This is our benchmark. If a given portfolio that we are evaluating has a higher SR, then it probably is a better investment to go with the new portfolio. If lower, it is better to simply invest in an ETF that invests in the S&P 500, as over the longer term, the reward/risk ratio favors that the ETF will do better.

I have software that helps me build portfolios and determine the SR for a given portfolio. The software ignores dividends, so this these numbers are based purely on capital appreciation.

If I take the Greenfield lists above, and I crank them through the software, here is what I get in terms of historical performance and volatility:

1) Dividend Champions: 4.80% annual return, 11.45% annual volatility, SR = 0.21
2) Dividend Contenders: 5.16% annual return, 12.53% annual volatility, SR = 0.22
3) Dividend Challengers: 5.34% annual return, 12.77% annual volatility, SR = 0.23

Something should be obvious to you: the stocks that have been around the longest -- the Dividend Champions -- are generally big companies and do not have a large annual average return over their life; they also have lower volatility. Contrasting, the stocks that have been around the shortest time who have been paying constant dividends, the Dividend Challengers (5-9 years), have higher annual return but also higher volatility.

Probably the most important -- note the relative stability of the reward/risk. There isn't a great deal of difference in the reward received vs. the risk returned between any of the three classifications, hence, the use of the the SR metric points to "doesn't matter" but since the return is bigger with Dividend Challengers, on a year-over-year basis, you are much further ahead in using the Dividend Challengers list than any other, simply because there is a bias upward that is larger than the Champions.

If nothing else, I personally focus on stocks that are on the Dividend Challengers list, only because they are more in a growth phase than those on the Champions list.

I can hear some of you right now: "Wait, the S&P 500 has returned 5.97% annually and yet you want me to invest in a large basket of companies that only pay for 5.34% on average?"

This is the argument about why over the long haul, most fund managers cannot beat the market average. The question here is whether you think you have a strategy that is better than the market, e.g. is more adaptable and can navigate in and out as necessary. The short answer is "yes", you should be able to beat the S&P 500 long-term averages, and the sections below start to give you a glimpse of how to accomplish this.

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Including Dividends

Note that above the analysis excludes dividends. This is because my software doesn't consider dividends -- it becomes the "value add" of investing in dividend-paying stocks.

The calculator link I provided above gives you the ability to include dividends. If you do this, the average return of the S&P 500 jumps:

Average annual return: 10.69%
Average annual volatility: 18.54%
SR: 0.5766

This presumes that each dividend was reinvested back into the stock, and those reinvestments compound over time.

Note the SR of the S&P 500 over a LONG time when we include dividends: 0.5766. Remember, SR is a measure of reward:risk, so if it is larger than some other comparison, we want the model that produced the higher SR value (more return per unit of risk).

Hence, you want stocks that pay constant to increasing dividends, which is not factored into the S&P 500 benchmark values above. The S&P 500 benchmark above presumes that you have bought and held every stock, good or bad for the duration of time, and this isn't what you will be doing in the Greenfield Dividend Contenders. These are above average dividend stocks, and they also are above average in terms of capital appreciation (share price growth).

To illustrate this, I pulled two different lists from May 2017 to show performance and dividend payments. The system presumes equal balancing into each position, with a quarterly rebalance, and the initial starting amount was $100,000.

The period of these graphs is June 2016 to May 2019.

In the top graph I have shown Annual Returns.

For the 6 months of 2016, both dividend portfolios were positive and the S&P 500 was also up. The numbers are roughly 15% Portfolio 1, 22% Portfolio 2, and 8% S&P 500.
Portfolio 1, shown in blue, had a great 2017 but poor 2018. 2019 is good. Contrasting, Portfolio 2, shown in red, had a great 2016, 2017, and 2018, and is doing well into May 2019. The differences between the two portfolios are only stock list creation and nothing else, so are quite arbitrary.

The bottom graph is Portfolio Income, and you can see that for a $100,000 portfolio, June - Dec 2016 returned about 1.3% dividends in income, and if we double, it would be about 2.6% or so. This was better than the risk free rate back then which was around 1% or less. I love my dividends.

You can see that Portfolio Income moved upward year over year, showing the impact of selection of stocks that pay constant to gaining dividends on a yearly basis. For the 5 months of 2019 we're at 1.25%; I have no idea if we'll beat 2018 or not.

A couple of important points:

Dividend income is just that -- income. Share price does not matter. If you are retired, this can be a nice generator.
None of these portfolios were "managed" -- I simply picked two lists from the past and forward tested. In reality, if a stock fails to maintain the Greenfield criteria (REV, EPS, FCF) I sell it, and if it is taken off the monthly U.S. Dividend Champions list because they cut the dividend, I sell it.

There simply is no reason to hold a dividend stock if they cut the dividend, and there is no reason to hold a Greenfield Stock if the fundamentals of the company change, causing a blurp in REV, EPS, or FCF.

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So, what have we done here? I assert the following:

Consider only dividend paying stocks.
Consider only Greenfield-Screened dividend paying stocks.
Sell the stock if the company cuts the dividend.
Sell the stock if the company has an earnings report that shows negative growth REV, EPS, or FCF on a QoQ, YoY, or TTM basis.

For those of you wondering, I combine the above with selling cash-secured puts (CSPs) and buying a covered call (CC). Doing so can add to your overall gains. It all adds up.

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That's all for now. If you have questions -- ask.

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Regards,

Paul

Friday, October 26, 2018

Premarket Friday, October 26

My Crystal Ball

If you feel you must dabble, then do so knowing risk is high. Quite high. Stick to your trading plan and if you don't have a trading plan, especially one that addresses risk management or position sizing, don't play in this market.

The $TIKUS shows constructive, but only 1-day positive behavior:

Click on the image to enlarge.

Thursday saw steady buying out of the starting blocks and this continued, more/less, all day long. This is good.

We also had over 700 stocks making new lows while only 43 made new 52-week highs. This is bad.

We also have some distance to go to get the cumulative tick to reverse and start moving upward.

Again, if you feel you must participate, then do so with caution.

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CSP Lists are updated and we may generate some weekly and monthly alerts today. Ensure that you look at earnings, as I do not suppress alerts based upon ER.

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As with all my ramblings, you are responsible for your own investment/trading decisions and I am not. Please do your own diligence, and please take ownership for your actions. Please read and acknowledge the disclaimer that is listed on the left on the web site page.

Regards,

Paul

Wednesday, October 24, 2018

Premarket Wednesday, October 24th

If you prefer to be long, here's what I'm thinking about today...

Many folks like to buy the dip (BTD), but the danger in this when the dip is still dipping is that we may all be dipshits... meaning... don't buy the dip until we have some indication of stability.

As of the close of markets last night we have no indication that there is stability in the market.

My tool to sample this is the Cumulative Tick:

Click on the image to enlarge.

This Cumulative Tick (CT) plot is of $TIKUS, which is a composite of all exchanges in the U.S. and shows what the broad market is doing. Others, such as the NYSE ($TICK), the Russell 2000 ($TIKRL), and other larger groupings of stocks all show more/less the same picture, so I'll stick with the $TIKUS.

Yesterday's action is located on the right side of the figure. The top plot is the 52w New Highs (green, almost flat), 52w New Lows (red, dominating), and the net between the two (yellow, very negative). When red is dominating we are in a contracting market -- prices are dropping, and many stocks are making new 52-week lows. I don't invest in new lows (I'm not a bottom fisher), so while you can go that route, I suggest that you seriously diversify before you put all your eggs in that strategy. It doesn't work over the long haul.

The middle plot is a filter, and it requires the element of time to move the red trace up or down. If buying/selling pressure is present, the red line will tick up (buying) or tick down (selling). If it is sustained you'll see continued movement in one direction or another.

You can see that for most of the morning yesterday that the markets were bearish, and then after lunch, we started a push that more/less recovered for the day. At the end we had some selling, as evidenced by the downward movement, but as we know, the day finished well off the lows of the day.

The crystal ball portion of the CT is in the lower pane. The white line is the instantaneous CT -- no filter, and it basically shows that there was an attempt to move upwards after the 10:30 am ET lows. One day does not make a reversal, and we will need to see continued progress of this white line, eventually crossing ALL the downward trend lines and pulling them upward. This often takes several days and is related to volume and conviction of the markets to step in and "buy the dip".

So, we're not seeing any indication that yesterday's action was a stability action -- we may continue downward quite easily from here -- or not.

Your crystal ball is as good as mine.

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Covered Call / Cash Secured Put Strategy

Only 4 stocks made the list this morning, meaning, they are showing enough relative strength and option action to warrant possible entry for selling a cash-secured put (CSP). Follow my Twitter feed ( https://goo.gl/UoGgBg ) if you want to see any generated alerts on these stocks and of course, you need to conduct further diligence on the underlying.

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As with all my ramblings, you are responsible for your own investment/trading decisions and I am not. Please do your own diligence, and please take ownership for your actions. Please read and acknowledge the disclaimer that is listed on the left on the web site page.

Regards,

Paul

Monday, October 22, 2018

CSP Scan List for Monday, October 22

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CSP Source List for Monday

The lists today are incredibly thin, which is an outcome of my screening processes. The lists are published here at https://goo.gl/XZKgwY

Going forward, the lists will expand/contract in relation to four primary criteria:

Does the stock have increasing revenues, earnings per share, and free cash flow on a QoQ, YoY, and TTM basis? Only select stocks where the answer is yes.
Is the stock price above the 50d MA?
Are the 50d MA > 150d MA > 200d MA?
Is the individual candidate equity emerging into a new uptrend on the daily and/or weekly time frame?

The first item is simply my definition of a quality stock. Substitute your own definition as necessary. For those of you who have been with me over the years, this is the "Greenfield Criteria".

The next two items are simple filters that really do have a material change on long-term performance. I've tested the heck out of these two conditions and I am 100% convinced that independent of the numbers (e.g. 50d, 150d, 200d), these two conditions reduce drawdown and provide a natural block to entering the markets in stocks that are bottom swimmers (bottom swimmers are terrible CSP candidates).

The last criteria is relatively new and was developed as a result of wanting to have fewer stocks assigned to me. Basically, having a stock in an uptrend is not sufficient -- the longer the uptrend, the more sensitive it becomes to any market weakness, causing the price to drop (sometimes rapidly drop). Hence, my intention is to enter into positions that are newly emerging and that have defined risk points below the entry level.

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Market Climate

From the CSP perspective, the broader market is still in a downtrend, and even if Monday is a majorly up day, several things need to change in order to have confidence to move into long positions (or sell CSPs).

My favorite crystal ball is the Cumulative Tick. If you are not familiar, this is a good starting point:

https://traderfeed.blogspot.com/2006/09/cumulative-nyse-tick-valuable-measure.html

I've modified the presentation, but the approach is the same:

Click on the image to enlarge

Three plot areas are shown; here is what you are looking at:

Top plots. Daily 52-week New High/New Lows. Red is the New Lows, and as you can see by my circles, far more stocks are making 52-week new lows relative to new highs (green). This is not an expanding market -- it is contracting. Contracting markets are not conducive to selling CSPs.
Middle plots: Daily "sustained filter" of the real time cumulative tick. The filter here is that the cumulative tick must be ticking higher or lower, on a net basis, minute-over-minute in the same direction in order to move higher or lower. When the line is flat there is not buying/selling pressure. When the line trends up (like last Tuesday), there is strong buying pressure. When the line trends down (like last Thursday), there is strong selling pressure. As you can see, Friday was net down, but not overly strong. It started slightly bullish in the morning and became weaker as positions were unwound, most likely to reduce risk into the weekend.
Bottom plots. Real-time cumulative tick and moving averages. The RT CT is white, the solid heavy red line is about a 10-day moving average. When both are trending down we haven't hit the floor, so going long when this is occurring is really risky. Selling CSPs before this bottoms is also really risky.

So, the lack of deep lists to alert on, and the downward push of the CT tells me that sitting on the sidelines today is not a bad risk-mitigation approach.

I do have some existing positions that I need to manage so you will see that activity through the Twitter feed.

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Regards,

Paul

Trading the Greek Gods

Saturday, July 6, 2019

Selling CC and CSP Strategy Lessons Learned

Sunday, May 26, 2019

How Many Dividend Champion Stocks do I need to perform better than the S&P 500

Sunday, May 19, 2019

May 19 Dividend Champions Weekend Update

Friday, October 26, 2018

Premarket Friday, October 26

Wednesday, October 24, 2018

Premarket Wednesday, October 24th

Monday, October 22, 2018

CSP Scan List for Monday, October 22

Update on Twitter CSP Alerts

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