Trading the Greek Gods

Thursday, March 19, 2020

Where to Find Current Market Discussions

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Hello all!

Paul Duncan here.

As many of you are aware, I lead the Northern Virginia Computer Investing Special Interest Group (CISIG) which is associated with the American Association of Individual Investors (AAII) ( https://www.aaii.com/ ) and the Washington, DC Chapter of AAII ( http://www.aaiidcmetro.com/ ). As such, I'm focusing my time and writing to supporting that group and am not positing here.

If you are interested in becoming better positioned to deal with the stock market, please join our discussion group at the following link:

CISIG+subscribe@groups.io

Because of automated bots and other bad actors, PLEASE send a separate note to me personally at GreekGodTrading [ a t ] gmail [d o t] com (fixing the email address as you know how to do) indicating that you want to join the CISIG group and the email you used to send the request. This will help me keep the noise out of the group.

See you there!

Regards,

Paul Duncan
March 19, 2020

Sunday, October 6, 2019

Oct 6 - Question about Gamma

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Regarding the Greeks ...

A question came in a week or so ago regarding "gamma" and that "short dated options are prone to gamma effect and therefore are not a good idea". "What am I missing?"

First of all, let me make clear that I do not watch "gamma", "theta", or any of the greeks that are associated with options. The only greek that I really care about is delta, and I care about it only because:

it's a rough proxy for the probability of the option hitting expiration and being out of the money (OTM), and
I have set a minimum threshold of delta that I will sell premium (-0.25 or smaller for puts, 0.25 or smaller for calls).

Since I sell options and collect the premium from the sale, I want my options to expire OTM, which means they are worthless. The take away is that if the option is OTM then I pocket all of the money I received when I sold it, and that's that. Smaller deltas --> greater chance of option being OTM.

What follows is a bit technical, and in the big picture of cash-secured puts and covered calls, is really not needed. It shows a bit of mental gymnastics, and the end shows that there MAY be a reason to trade short duration options (e.g. sell them), but the conclusions I make are a reach and note, I don't really pay attention to this. It *could* be one of the factors of why selling premium is successful -- I simply don't know.

That being said, I'm willing to look at this a bit more ... I always like option analysis.

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The stock price, the option price, option delta, and option gamma are all related. While option pricing is beyond the scope of where I want to take this blog entry, let's simply assume that an option price is established by and is fairly valued by the market. An example is a good way to think about this; let's start with the stock price, option price, option Delta, then move into Gamma.

This past Friday INTC fired an alert in the GreekGodTrading Twitter account ( https://twitter.com/GreekGodTrading ) and here is an echo of what went out to the folks who follow me:

INTC:!P_CSPv1.6. INTC 191025P48, MinBid=$0.68, MinPrem=$56.88, Last=$50.88, AROO=21%, Prob=70%, Spread=$0.01, ROO=1.3%, Days2Exp=22, CashReqd=$5020, IVRank=16.1, StrikeInc=$0.50, Delta=-0.2418, ImplVol=0.38, Fisher=2. 10/4/2019 3:28:40 PM

Lots of info there, but there are a couple of things that are important for this discussion:

Last=$50.88: INTC was trading at $50.88 when this alert fired
MinBid=$0.68: The half-way point between the ask and the bid, for the option INTC 191025P48, was $0.68, meaning, one contract would net you $68, less commissions, if you sold the contract when the alert sounded.
Delta=-0.2418: the option INTC 191025P48 had a delta of -0.2418; what does this mean?

Delta is simply how much the option price changes for every dollar change in the stock. If INTC went from $50.88 to $51.88 (an increase of +$1.00), then we would expect that the option INTC 191025P48 would DROP from $0.68 to ($0.68 - 0.2418) ~ $0.44. Note the minus sign -- PUTS have a negative delta, and calls have a positive delta. This is because as the stock price moves away from the put strike (in this case strike = $48), the put becomes less valuable.

The units on Delta are "$ change in option price per $ change in underlying".

When an alert fires, various prices (underlying, option) are fairly stable. Here's a table of INTC and consecutive entries where if the previous line would not have triggered an alert, the following one would have. You can see that INTC fired more/less solidly for about 30 minutes this past Friday:

Price of INTC is in the 2nd column from the left; Delta is in the right column. Bid/Ask, in the middle of the able, show the relative tightness of the spread (typically $0.01). The time span is about 32 minutes.

What should be evident is that everything remains in a tight range, so even if you are late to an alert, the overall conditions are fairly stationary. This is important for you if you desire to echo my trades (not recommended by the way -- this is only for informational purposes).

Here's a chart that shows the same data:

The x-axis is the underlying price of INTC. The option price of INTC 191025P48 is shown on the left axis, and the Delta of the option is shown on the right axis.

What is evident here is that as price goes up, option price decreases (left axis, blue dots), as we expect, AND, option Delta changes (decreases in magnitude - becomes less negative) as price goes up of the stock.

So what? Well, all this simply shows is that there is some behavior out there that relates stock price (x-axis), put option price (left axis, blue), and option delta (right axis, orange). It isn't overly useful except to say that it is understood.

This next graph shows a general relationship between the delta of an option (0 to -0.3), the days to expiration for the option (8 to 29), and the number of strike intervals from the price (1-4). Options are listed in these intervals ($0.50, $1.00, $2.50, $5.00, etc.) so it is important to know where various option deltas lay vs. days to expiration (DTE) as well as the option chain strike intervals:

The colored part of the graph shows Delta as a function of Days to Expiration as well as the number of intervals we are from a strike price. Anywhere in the orange represents a delta of -0.25 to -0.20, and for the data set shown, this could be 2-3 option chain strike intervals below the current price, especially if we are out 29 days or more, OR, if we are within 8-15 days, it most certainly points to being within 1 strike of the current price of the stock.

In the picture above, Gamma is the slope of the change in delta, and now, you can see that it is a function of Days to Expiration as well as as how far we are away from the stock price.

There are other things that influence Gamma, but think of a marble on the surface of the Delta curve .... as it rolls, does it pick up speed? If so, then Gamma is not constant, e.g., it has some influence on the option price and option delta.

A view-from-the-top of the same graph is shown below, and makes this "where is Delta in the -0.2 to -0.25 range (?) a bit easier to see:

Now, with all of that Delta stuff behind us ...

The original question was about trading Gamma, and a statement that short-duration options have poor Gamma so they should not be traded.

If I take the option prices as shown above in the two graphics, and then take the difference between the Deltas and do some simple manipulations, I can get a picture of the Gamma:

The relationship is kind of "concave" or somewhat parabolic -- for the shortest days to expiration (8), we have virtually no gamma (no change in delta as a function of time duration to expiration), and out beyond 15 days, the influence becomes less and less, to where at 29 days, it is virtually nil.

The arrow I drew points to the 15-day evaluation point, and shows a maximum negative gamma occurs when we are 2-3 strikes away from the underlying price, AND, we are at 15-days to expiration. Note that the X-Y chart above, with the underlying price of INTC, the option price, and the option Delta are all for an option expiring on 10/25, or 18 days from the time this is written. That puts us on the "back wall" of the surface shown above, so Gamma is clearly not zero.

A couple of things are evident to me:

Trades that are within two weeks of expiration show a lessening of gamma -- the change in delta, as a function of distance to the underlying stock price AND the number of days to expiration, gets less and less as we march closer to options expiration. If we choose to care about Gamma as we get closer to option expiration we can care less about it.
Trades that are longer than two weeks from expiration, say 4 weeks, see very little influence of gamma too (evaluated with n = 100 optionable stocks or so). Hence, selling premium out at 4 weeks before OE should see the change in delta remain fairly constant.
I'm not seeing a real problem with a changing Gamma in the bigger picture. Even if I choose a 15-day period to expiration, and I am 3 strikes away from the current stock strike price (the minimum in the surface graph so the maximum influence of Gamma for the data shown), it really doesn't matter to me. At this point I'm seeing a $0.07 change per $1.00 change in Delta, or 7% of an already small number, so the influence on option pricing is pretty insignificant.

In the end I think it boils down to this: if you are selling premium, delta matters more than gamma. Gamma may matter around the 15-day to OE mark, but in general, it isn't driving a major decision.

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I welcome input from those who have studied this more than I.

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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

Saturday, September 21, 2019

Saturday, September 21 Update

I've not updated performance in a while so I thought I would take the time to document the selling cash-secured-put (CSP) and covered call (CC) strategy that I've had in play since December 2017. For those of you who have written and I've not responded, I've been crazy-busy in my professional life so it's been a bit of a challenge to find time to do more than just the minimums of day-to-day portfolio management.

It is my intent to use this entry to give a high-level update of the strategy.

Strategy Overview

I sell premium on options. I generally start with the put option ("cash secured put", or CSP), as this has lower market exposure risk than starting with a covered call (CC), but if I see a CC that I like I will also buy the stock and sell the appropriate call in the same transaction.

My timeframe is generally on the order of a week out to about 5 weeks out. I sell both weekly options (my preference) as well as monthlies. When I see an alert form on my weeklies I almost always take it -- monthlies, not so much. Simply personal preference.

Alerts are automatically formed by some software that I wrote that runs within the TradeStation platform. The alerts are echo'd in real time to Twitter, and you can follow them for free simply by going to https://twitter.com/GreekGodTrading and following my account. All my trades are echo'd there too, so nothing is hidden. You get to see the good, (rarely) bad, and (never) ugly of my actual performance.

The alerts have a number of criteria but one of the important ones is that I selected a minimum threshold of Annualized Return on Option (AROO) before the alert will fire. Hence, if I sell the premium and hold it to the expiration, I'm guaranteed to get at least that AROO on the position. Right now that number is 18%, and this provides a solid, relatively straight-line performance. More on that later.

Once I've sold the position, I generally attempt to buy it back at $0.05. Not only does this accelerate the annualization calculations (instead of 100% profit over 4 weeks perhaps I get 95% of the profit in 2 weeks), it also returns the money to my account and lets me adapt to market changing conditions. This is why I like weekly options over monthly options.

Stock selection is important. I use my "Greenfield Stocks" selection criteria, which I've written about extensively in this blog and you can go find past references for yourself. It has been a staple of my investing for well over a decade and I'm a firm believer that it all starts with proper stock selection.

I trade in three accounts: a Traditional IRA, a SEP-IRA, and a margin account. I use the funds of the tax-sheltered accounts first, then generally only trade monthlies in the margin account. I try not to use margin for this strategy, although there is no reason why it would not work. I just don't want the headache of accounting.

I keep perfect logs of my trades. Every trade is logged, and every expired worthless trade is also logged. You'll see some of the graphs below. If you don't measure it, you can't fix it, and that will be evident in my pictures.

Strategy Performance

Let's start with the monthly net performance chart:

The "red" probably catches your eye first; these are the January months, and they are associated with market pullbacks and me rolling positions, mostly call positions. Each January has been a loss, so I am watching carefully as we approach December of 2019 to not have a repeat. Here's TradeStation's reporting of the same time period:

TradeStation has an error in how it reports some trades, so this is only approximate in numbers, but you see that the trend is there and intact.

Here's what happens when I bucket by month. Again, all that matters is the relative shape in each month; the overall values are less in TradeStation because they do not log profits due to expired (worthless) contracts or called positions:

So, October could present a challenge (historically), as may January. I'm on the watch.

On a weekly basis, here's what it looks like:

You can see some weeks with really thin trades -- if the market is acting poorly, my system keeps me out of the market (but there is a lag -- it is not predictive).

All of this suggests just north of 30% annual performance, net of all fees, commissions, kitchen sink charges, etc.

.... and I don't do a lot of work to get that 30%. It's largely on autopilot.

This figure provides the view of history -- it really gives me a view of how TradeStation is records transactions. Note, these are round-trip, closed transactions -- those that expired worthless (which is an ideal situation) are ignored by the TradeStation platform. Note that the 19.57% gain over 589 significantly under represents the actual IRR performance of 32.2%.

The last line in the figure is the historical performance, to the beginning of September 2019. Basically, for every dollar that I trade, I make back $2.55. This is based on 589 round-trip trades, with 83% of them profitable. Expectation of profit on each trade is currently about $49, net.

This last picture shows the drawdown the account has experienced, which is an indicator of how well risk is managed in the strategy:

Worse-case, I've lived through 3% drawdown from the previous equity high, since December 2017. I can live with that.

Summary

Using the "Rule of 72", I'm basically on track to double the account in 2.5 years, using historical performance.

I'm not inclined to "tweak" the strategy at this point -- it works well enough, and my time per day to service the strategy is less than 15-minutes or so. If I miss a day due to travel no big deal -- I simply use the previous day's stock list and most likely no new trades (on the sell premium side) are executed. Trades related to buy-to-close at the $0.05 are GTC orders and will execute at any time, up to options expiration.

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.

~~~~~~~~~

Regards,

Paul

edit: updated the actual performance using the daily account balance and presuming that I "cash out" account value on 9/21/2019. IRR updates to 32.2%, net of everything: all fees, all commissions, subscriptions for data feeds, platform fees, etc.

Saturday, July 6, 2019

Selling CC and CSP Strategy Lessons Learned

Click on the image to enlarge

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

Click on the image to enlarge

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:

Click on the image to enlarge

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:

Click on the image to enlarge

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:

Click on the image to enlarge

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:

Click on the image to enlarge

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:

Click on the image to enlarge

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:

Click on the image to enlarge

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:

Click on the image to enlarge

^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":

Click on the image to enlarge

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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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

Trading the Greek Gods

Thursday, March 19, 2020

Where to Find Current Market Discussions

Sunday, October 6, 2019

Oct 6 - Question about Gamma

Saturday, September 21, 2019

Saturday, September 21 Update

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

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