Is the S&P 500 Extremely Overbought?

According to this article from Bespoke, S&P 500’s Most Overbought Close in More Than a Year, the S&P 500 is extremely overbought.

The only problem is that I can’t seem to replicate Bespoke’s study.

No matter how I try, I get the S&P trading at approximately 1 Standard Deviation above the mean of the distance from its 50 Day Moving Average. I have converted the distance above/below the 50DMA into percentage terms, but that should not make a difference.

My attempt at replicating Bespoke’s study is below.

Index Indicators also performs similar studies. Their chart is below.

My results aren’t exactly the same as Index Indicator’s results, but they are closer than Bespoke’s.

Does anyone out there understand exactly what Bespoke is measuring?

Based on my study and the graph from Index Indicators, the S&P 500 is overbought, but it is nowhere near the most overbought close in more than a year.

Update: Based on the work Chris posted (see link in comments) replicating Bespoke’s study, the S&P 500 was indeed the most overbought in a year. I just disagree that this presages a pullback.

Read a follow-up post here.

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I’ve been disconnected from the markets over the past week, working on a new project. At times like these, when I don’t have an hour to run different scans and read some blogs to get a feel for what is going on under the market’s hood, I just look at the trendlines and volume.

I noted in the last post that a break above these lines would be bullish. We’ve now broken above both lines, and although we are getting overbought here on declining volume, after a quick pullback, I’m looking for the rally to continue through the holidays. If I had to guess about the next day or so, I’d say to expect a large (1% or more), and then a quick pullback.

If the market continues upward at its current pace, we should end the year out another 3-5% higher.

Let’s take a step back and look at the big picture:

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Back on November 15th, I drew some trendlines on a SPY chart. Despite my leanings towards quantifiable trading, I still believe in the power of the trendline. I checked these pre-drawn trendlines this evening. You can see below that recent action has brought the SPY to close at two areas of resistance.

We have had above average volume on this recent surge, and we have positive seasonality in play. Still, I expect resistance here, if only for a couple of days.

What we want to see is SPY clear resistance, definitively. Should this happen, the indices may be off to the races.

It is increasingly looking like the we may have a strong bull run through the year end. Of course Europe may throw a wrench in things. But technically speaking, the market is attempting to shrug off the European debt issues.

If you follow the horizontal trendline backwards, you see it originates as support before the 2008 market meltdown. When the market broke through this level, the Armageddon trade ensued. If the market can overcome this resistance, I see it as a very bullish development as it represents recovery from the monumental declines of September and October of 2008.

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I am currently working on the compiling the results of many alternative ranking mechanisms for PDS as well as getting the November end-of-month report finished, so please excuse the dearth of posts. Things will get back to normal soon.

In the meantime, I made the mistake of surfing over to Frank’s blog, where he posted another great idea for ranking stocks within a mean-reversion system. It may just work well on PDS picks, and so now I must add it to the ranking tests.

So much to do, so little time. Thanks for your patience!

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We have decided to temporarily disable PDS free trials. From time to time we will again make available the free trial.

While I strive to be transparent and want people to know up front what they are purchasing, I also want to protect the signals that the system generates.

Over the next few days I will take some screen shots of the site and archive them so that anyone who is interested can take a look around. The site is very simple–basically just a database that is updated every evening–so it is easy to take a quick look and understand how it works.

If you have any questions about PDS site, the system, or the service, feel free to email me at woodshedder73 at gmail.

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In the first post we examined a random stock selection method vs. quantifiable ranking methods. Those tests were run over a 10 year period.

In this post we will drill-down to test from 11/18/09 – 11/19/10. This length of time is important because it covers PDS picks since inception and represents in-sample results. During this time period, SPY posted a gain of 8.12% with a max drawdown of -16.09%.

The model used in the test is the 2% risk, 10% stop model. Commissions are included at .01/share. The model is not allowed to hold more than one open position in the same stock and there are no restrictions put on how many new purchases could be made at one time. De-listed, survivor free data was used.

As with the previous tests, I used the primary ranking method, the inverse of the primary, an alternate ranking method, and the inverse of the alternate method. I then ran 31 tests where stock picks generated by PDS were randomly selected.

A * denotes that the test selected ALKS. ALKS was a very large losing trade for the system in October 2010 after the FDA blindsided the company.

Highlights-

• The biggest affect on performance was whether or not the system was holding ALKS.
• Random28 traded ALKS and still generated an annual return of 24.11%
• The best performance, with a gain of 37.23%, was from the inverse of the alternate ranking method.
• The average of the annual return from the random selection tests was 14.82% with a standard deviation of 8.06%
• 26 (84%) of the random selection tests beat the performance of SPY over the same time period.
• Only 5 (16%) of the random selection tests performed worse than SPY over the same time period and 3 of those 5 underperformed by less than -2%.
• 13 (42%) of the random selection tests doubled (or better) the performance of SPY over the same time period.

Implications of the Results-

Without ALKS, the performance of the primary ranking method would be 16.26%, putting performance slightly better than the average return of the random selection methods. However, 16.26% is considerably less than the inverse ranking results. One possible explanation for this is that trading the system based on the primary ranking method traded out some of the performance. For one year, subscribers have (theoretically) traded the system based on the published rankings. It is certainly possible that this has reduced the edge for the highest ranked stocks.

To reduce this effect in the future, on PDS site, I will be detailing other methods for ranking the picks. It is obvious that the system can outperform the market with a random selection method, but I do not want to encourage subscribers to choose randomly as I believe they will introduce their own biases. Instead I will implement alternative ranking methods. Ultimately we want to spread buying out over a larger number of stocks to avoid a reduction of the edge.

Another explanation for why the primary ranking method has generated an average performance has to do with small sample sizes, both with the number of tests I ran and with the number of trades taken overall. Statistically speaking, we expect that after running more tests with random rankings and having more actual trades taken, eventually the results will approximate the results from the tests generated with 10 years worth of trades.

We must rank stocks, if only to track performance. I have to be able to replicate the strategy. I do not believe it is honest or transparent to use random rankings to demonstrate the performance of the system. From time to time a particular ranking method will be in sync with the market. Other times it may be out-of-sync. Over time, results will likely be smoothed.

The most important element is to follow a ranking method, period. If not, selection bias may become a significant problem.

Reading the Report–

• RAR = Risk Adjusted Return (CAR/Exposure);
• CAR = Compound Annual Return;
• DVR = R² of the Equity Curve * Sharpe Ratio
• SR = Sharpe Ratio
• R2 = A statistical measure of how close an equity curve is to a straight line plotted on a logarithmic graph. A   value of 0 indicates a jagged line and a value of 1.0 represents a straight line. A fixed percentage investment that compounded and paid daily would have a straight line equity curve and an R Squared value of 1.0.
• %W = Percentage of Winning Trades
• %Avg = The Average Trade in Percentage Terms
• MaxDD = The Maximum Drawdown
• TT = Total Trades

About the Ranking Mechanism:

• PrimaryRank = The Default Ranking Mechanism used by The Power Dip System.
• Inv_Primary = The Default Ranking Mechanism in reverse. High ranked picks become low ranked and vice versa.
• Alt.Rank = An Alternative Ranking Mechanism
• Inv_Alt.Rank = The Alternative Ranking Mechanism in reverse. High ranked picks become low ranked and vice versa.
• Random = Picks are Selected at Random with no Ranking

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Inevitably, the active trader will find him or herself in the following predicament: More stock picks than he or she has cash to purchase. When this happens, release the biases! If the trader does not have a quantifiable method of ranking the stocks so that he or she spends the cash on the best picks, there exists the distinct possibility that the stocks that are selected are chosen due to the bias of the trader, and not because they are the best setups.

Now certainly there are some traders among us who are more gifted than others. I do not count myself in the gifted group. The vast majority of traders are not able to look at a particular chart or setup and not introduce biases. Instead of selecting the best stock for the particular trade he or she wants to execute, the stock he or she selects is the one he or she likes the most. There is a difference. Instead of seeing the stock or setup for what it is, he or she sees AMZN and remembers how frustrating it was when the book was delivered two weeks late. Or, they see XOM and recall something about an oil spill in the Gulf of Mexico.

The best way to handle this dilemma is to quantify the setup and then rank the picks. Of course there must be a way to backtest the ranking method to ensure that it is better than random selection, and when backtesting, you want to be careful not to curve fit the ranking method.

And so after a year of real-time trading of PDS, it is time to examine the ranking mechanism to see how it has performed vs. random selection and an alternate method of ranking.

First I will examine the ranking mechanism by testing it from 1/1/2001 – 11/19/10. Then I will look at how the ranking mechanism has performed since inception. From these results I will draw some conclusions.

It should be noted that PDS is a positive expectancy system, meaning that any given pick, no matter how low it is ranked, can be expected to generate a positive return. Thus, random selection doesn’t necessarily mean the system will fail. In fact, if it is truly a positive expectancy system, we would not expect the system to fail under most circumstances–we would predict that random selection will mean that it will not perform as well as when using a ranking mechanism.

Ranking Tests:

(all tests include .01/share commissions)

Reading the Report-(Thanks to Frank at Engineering Returns for the code that snips these metrics to the clipboard, making for easy recording of multiple tests).

• RAR = Risk Adjusted Return (CAR/Exposure);
• CAR = Compound Annual Return;
• DVR = R² of the Equity Curve * Sharpe Ratio (as popularized by the great folks at CSS Analytics)
• SR = Sharpe Ratio
• R2 = A statistical measure of how close an equity curve is to a straight line plotted on a logarithmic graph. A   value of 0 indicates a jagged line and a value of 1.0 represents a straight line. A fixed percentage investment that compounded and paid daily would have a straight line equity curve and an R Squared value of 1.0.
• %W = Percentage of Winning Trades
• %Avg = The Average Trade in Percentage Terms
• MaxDD = The Maximum Drawdown
• TT = Total Trades

About the Ranking Mechanism:

• PrimaryRank = The Default Ranking Mechanism used by PDS.
• Inv_Primary = The Default Ranking Mechanism in reverse. High ranked picks become low ranked and vice versa.
• Alt.Rank = An Alternative Ranking Mechanism
• Inv_Alt.Rank = The Alternative Ranking Mechanism in reverse. High ranked picks become low ranked and vice versa.
• Random = Picks are Selected at Random with no Ranking

Interpreting the Results:

• The PrimaryRank and Alt.Rank generate the best returns, with the PrimaryRank easily beating a random selection. This is positive and demonstrates the power of ranking stock picks. The really interesting result is that the inverses of the ranking mechanisms also tend to beat a routine of random selection.

• The random selection method also generated good returns. The caveat is that I only ran ten tests with random selection. I would feel better if I ran 30 or so tests, but these things take time. There are two reasons why it is hard for the system to show significance degradation of performance, even using a random selection routine.

1. Some of the time, there are only a few picks, and the system has enough cash that it takes all of the picks. In these cases, ranking doesn’t matter as all picks will be bought.
2. The system has a high percentage of winners, so it would take a very unlucky series of randomly selected stocks for it hit a long losing streak. Because there are over 2,000 trades represented, the system has plenty of time to hit a losing streak and then recover. Over a shorter time frame, we expect that a random selection routine will show more variation. (In fact I think we’ll see this when I run these tests only since inception).

Summary:

The ranking mechanism beats a method of random selection. The inverse of the PrimaryRank generally performed worse than a random selection. From this we can extrapolate that the ranking method is better than a random method. (Previous caveat about sample size applies).

Perhaps the most important implication of these tests is that even when subscribers to PDS do not religiously trade ONLY the top-ranked picks, they may still expect great performance from the system. Yes, it is possible for someone to have either very strong biases or very bad luck and perform worse than a random selection routine. This selection bias may be magnified over a short time frame. The next post will take a look at the ranking method over a short time frame. I will be examining the past year of real-time results from PDS.

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