S&P 500 Sectors – Volume, Correlation and Beta

It has been said that stocks held by the major ETFs have become more correlated over time as the popularity and volume of these ETFs has increased.  As you know each ETF physically holds a basket of stocks in the weighting if the index or asset group that they are designed to emulate.  If enough trading of the ETF occurs then in theory the tail could wag the dog and move the underlying assets that are supposed to be being tracked.

Volume, (with regard to financial securities) refers to the number of trades that have taken place.  This is a useful useful measure but it isn’t relative.  If two stocks both have a daily volume of 100,000 but one is trading at $4 and the other at $400, then a volume comparison does not make sense.  A more useful measure is Trading Volume Value (TVV) which is simply the price times the volume.

SPY is now the worlds most heavily traded ETF but on day 1, January 29th, 1993 its TVV was just $44.1 million; a tiny fraction of the 7.3 trillion for all of the S&P 500 stocks combined.  Fast forward to November 20th, 2008 and the TVV for SPY and the other SPDR sector ETFs was over 50% of the TVV for the entire S&P 500.  We are talking about $70 trillion dollars flowing through the SPDRs in one day!!  If the tail is 52% of the dog then who is waging who?  Here is a chart:

spx-correlation-tvv

To measure the change in correlation for the constituents of the S&P 500 over time, I first took the average 252 day (one trading year) correlation relative to the S&P 500 for each individual stock split by sector (creating 9 sector averages).  Then on the chart ‘Average Correlation To MC S&P 500′ I plotted the average of each sector along with the reading for the sector with the highest and the lowest average correlation.  Below that on the chart ‘TVV – All SPDRs vs Holdings’ you can see the sum of the TVV for SPY and the 9 sector ETFs as a percentage of the TVV for all the S&P 500 stocks.

When the market was falling violently during the first few months of 2009 the average correlation was over 80% and there was very little difference between the most and least correlated sector.  In other words; everything was going down creating a ‘nowhere to hide’ scenario (this pattern is was slightly more pronounced within the sectors themselves).

At the time TVV was at record levels, averaging around 25% but a similar ‘nowhere to hide’ situation was also seen during the bull markets of 2003 and 2004 while TVV at that time was only around 10%.  So we have examples of very high correlation during bull and bear markets, through varying levels of ETF TVV.  So volume on these ETFs certainly has an impact but it is not the only factor.  (see TVV for each sector individually)

What about beta?  Are the S&P 500 stocks becoming more or less volatile in relation to the SPX?
spx-beta

Clearly there has been an upward drift in beta from the S&P 500 constituents over the last 20 years but at all times there has remained a healthy spread between high and low beta sectors.  Within each sector however that spread is much smaller:

spx-sector-beta

 

Conclusion

Trading Volume Value (TVV) has increased dramatically on the SPDRs since SPY was first introduced back in 1993.  This increase can be seen having an impact on the correlation of the S&P 500 constituents but is clearly not the only factor that causes correlation to fluctuate.  I would speculate that automated trading algorithms and qualitative easing by the Fed are also major factors (among others).

During some periods correlation will be to such level that it will be almost impossible for stock pickers or sector selectors to escape the trend of the broad market.  At such times a specific directional bias will be mandatory as a delta neutral strategy would be an exercise in futility.

Over the last 10 years the average correlation to the S&P 500 has been 56% and for each stock to its sector, 63%.  For this reason you would be unwise to ignore the trend of the broad market and utterly foolish to trade a stock against the trend of its sector.  Yes, the trend is your friend and that friendship is developing.

Stock beta has also shown to be drifting higher over time and while the beta spread does fluctuate it has remained fairly wide for the last 20 years.  This means that at any time risk can be adjusted by taking positions in sectors with higher or lower betas.  Take note however; the beta spread within a sector is much more slight and consistent.

More in this series:

This is part of a research series on the S&P 500 and its Sectors utilizing historical constituent data.  Here is proof that our database is accurate.

 

S&P Sector Constituent Database – Garbage In, Garbage Out

We are currently engaging in research utilising 23 years of historical constituent data for the S&P 500 sectors.  But if our database isn’t accurate then our test results will be worthless.  I started writing this post about the processes we went through to ensure that the historical data we used was clean and that our constituent list was accurate.  But then I realised that no one cares how many multiple fail safe cross over checks were made or how difficult the process was.  The only thing people care about (the only thing that matters) is being able to prove that the database is accurate.

So how do we prove that we are working with an accurate database?

Well the second half of our S&P 500 Sector constituent list (Sept 2001 – March 2013) came directory from our insider at State Street; the company that actually issues the Select Sector SPDR ETFs.  With the data for this period coming straight from the horses mouth it is safe to say that the accuracy for this period can be relied upon.  It also contains an abundance of information, enough to reconstruct the ETFs, including:

Company Name, Symbol, Exchange, Shares, Float, Float Shares, Multiplier, Adjusted Shares, Last Sale, Previous Close, Index Weight, Index Market Value, Market Value (Unadjusted Shares), Current Cap, Divisor, Previous Cap, Number of Components, Sum Of Adjusted Shares, Calculated Index, Published Index, # of Stocks, Sum of Adjusted Shares, Capitalization Using Unadjusted Shares, Estimated Weight of Index Components in the S&P 500…

The first half of our S&P 500 Sector constituent list however (Feb 1990 – Aug 2001) was compiled from several sources of varying reliability and only consists of dates and symbols.  Plus most of the stocks had to be classified into their sectors manually.

The best way to prove the accuracy of our database then is to reconstruct the sector indices and compare the correlation coefficient for each of the two periods against the actual indices published by S&P.  If our data is good then we should be able to closely reproduce the Equal Weighted S&P 500 Sector Indices.

In this post there is reference to several different indices.  Here are a number of relevant links:

Index and ETF Link Matrix

Market Capitalization Weighted Index Select Sector Index Select Sector SPDR ETF Equal Weight Index Equal Weighted ETF
S&P 500 SPX/GSPC/INX SPY SPW / SPXEW RSP
Materials S5MATR / SPXM IXB XLB S15 RTM
Energy SPN / SPXE IXE XLE S10 RYE
Industrials S5INDU / SPXI IXI XLI S20 RGI
Financials SPF / SPXF IXM XLF S40 RYF
Cons Staples S5CONS / SPXS IXR XLP S30 RHS
Technology S5INFT / SPXT IXT XLK S45 RYT
Utilities S5UTIL / SPXU IXU XLU S55 RYU
Health Care S5HLTH / SPXA IXV XLV S35 RHY
Cons Discret S5COND / SPXD IXY XLY S25 RCD

 

Now, to keep things simple the ETFHQ constructed indices will be equally weighted on a daily basis rather than quarterly.  For this reason our results won’t be identical to that of the S&P, but this is not an issue.  As long as the level of correlation Feb 1990 – Aug 2001 is not far below the level of correlation Sept 2001 – March 2013 then our hard work and patience has paid off:

Correlation - S&P EW Index vs ETFHQ

(Special thanks to Mr Anonymous for sending us some data that we needed for these tests).  As you can see above, the results are even better than we could have hoped.  In many cases the correlation for the first half of our data is greater than that for the second.  How is this possible when we know that the data from Sept 2001 – March 2013 is from a reliable source?  Because during this period the market has endured some extreme turmoil.  Extreme stock behavior will result in greater index discrepancies when the component weightings are not identical.

So with this we have definitive proof that our data and constituent list is extremely accurate.  Let the testing begin!

But before we do that, for those that are interested, below you will find charts that display each index; the S&P version vs the ETFHQ version including a rolling 252 day (one trading year) correlation coefficient.

spx-vs-spxew

The chart above actually shows the correlation between the S&P 500 (official) and the S&P 500 Equal Weighted Index (official).  I have included it to illustrate why we didn’t test our results against the standard market cap weighted indices.

Stocks in companies of different sizes can behave very differently at times and for that reason market cap and equally weighted indices perform very differently.  In fact, in this case the two diverged to such an extent that the correlation dropped to -44.52%.  That means that they moved in opposite directions for over a year despite tracking the exact same 500 stocks!

spx-ew-v-etfhq

materials-ew-v-etfhq

energy-ew-v-etfhq

industrials-ew-v-etfhq

financials-ew-v-etfhq

consumer-staples-ew-v-etfhq

technology-ew-v-etfhq

utilities-ew-v-etfhq

health-care-ew-v-etfhq

consumer-discretionary-ew-v-etfhq

S&P 500 Sectors – Historical Holdings Data

S&P 500 Sector ETFs“Diversification is protection against ignorance.  It makes little sense if you know what you are doing.”
– Warren Buffett

Well when it comes to selecting individual companies on the basis of value, I certainly don’t know what I am doing and you know what?  I don’t care to learn.

That is the #1 draw card of ETFs; they provide diversification that protects me from my ignorance.  Furthermore by tracking the average of the stocks in an ETF, the noise found in the data of each individual holding is largely canceled out leaving numbers that are easier to decipher through technical analysis.

BUT, the data from an ETF is NOT the data from the underlying assets.  Yes, an ETFs price changes reflect the net asset value (NAV) of its holdings, but nothing more.  Quality breadth data is difficult to come by and historical breadth data going back more than 5-10 years is almost non-existent.  Access to such data is only a dream for most trading system engineers.

We contacted ‘S&P Dow Jones’ looking for such information and discovered that historical constituent data for the S&P 500 would cost $1,800 USD a year… 20 years would cost $36,000 and to include each of the 9 S&P sectors they would do us a deal; just $120,000.00 bucks…  We do have a budget for data, but…

So as luck would have it I managed to make friends with Mr XXXX from State Street who was kind enough to give me monthly S&P sector constituent data back to 2001.  But a lot has changed over the last 12 years.  Many of the S&P 500 holdings have been de-listed, changed names, ticker codes, have merged, been acquired, broken up etc.  Hunting down the last trading name, ticker code and clean data for these stocks is not a task for the faint of heart (or short of patience).

I could write a book about the difficulty of this task but instead will give you one example:

The old ‘General Motors’ (GM) stock was de-listed in March 2011 following bankruptcy.  What was remaining of the old GM at that time was trading under the name ‘Motors Liquidation Company’ (MTLQQ).  You will not find this name or ticker code in any historical holdings data for the S&P 500 or the S&P Consumer Discretionary Index because GM was removed from these indices in June 2009, before the name change.  However in November 2010 the new ‘General Motors’ was re-listed under the same name and symbol and in June 2013 returned to the S&P 500.  Very confusing!  Hundreds of similar yet different scenarios have faced the constituents of the S&P 500 over the last 23 years so you can imagine how difficult it was reconciling this database.

Anyway, with that hard work done we received some help from Frank Hassler over at Engineering Returns who provided us with fairly clean S&P 500 holdings data back to 1990.  Then the hard work began again and after multiple crossover checks it was a matter of researching several hundred stocks individually (many of which had been de-listed for over 15 years) and classifying them into the corresponding sectors.  Several sources were used for this process including:

http://www.moodys.com
http://en.wikipedia.org
http://www.bloomberg.com
http://www.fundinguniverse.com/company-histories/
http://www.nytimes.com
http://www.nndb.com

We logged about 270 hours on the project and now have a very exciting, quality database to work with (proof the data is good).  Realistically, most people wouldn’t know how to use this database even if they wanted to but I am happy to provide you with a copy at no cost on request.  All I ask is three things or your request will be ignored; 1 Let me know what ideas you want to test, 2 I must agree that these ideas are worth testing, 3 I kindly ask that you share your findings 🙂

Over the coming months we will be publishing a variety of tests using this data including:

  • Correlation, Beta and Volume – Does the tail now wag the dog?  Has there been an increase in the correlation of stocks since the proliferation of ETFs?
  • Momentum – Emulating the results seen in published papers on momentum and looking for new findings.
  • Volume – How can an index’s internal volume best be utilised in a trading system?
  • Breadth Data – What is effective?
  • Identifying The Best – A rising tide lifts all boats but how can one identify the best/worst performers within an asset group?

What kinds of tests would you like to see us perform?  Please leave your suggestions below:

Market Timing Through Market Dominance – TransDow

The market is a dynamic, living and instant measure of the constant battle between Fear and Greed, between Supply and Demand. The participants in this battle are also split into two groups, the Smart Money and the Average Investor; those who profit over the long term due to skill and those from whom the funds originate.

A fool and his money are soon parted – Thomas Tusser (1557)

The Average Investor gets caught up in the emotional flow of the Stock Market.  This causes him to follow the herd who buys when prices and greed are high and then sells when prices are low and fear rules.

The Smart Money on the other hand, won’t let emotion or the herd influence their decisions.  They have their own strategies and follow them religiously.  While there will be times when the market goes against them, they remain confident in the knowledge that sound investing principles will always ring true over time.

Wouldn’t it be great if there was a way you could look over the shoulder of this Smart Money group and simply copy them?  Well by measuring Market Dominance it is possible to do just that.  I am about to share with you a system that I created in January 2005.  Inspiration for this model came from Charles Dow’s ‘Dow Theory‘ (1899), Joseph E. Granville (1976), Don Beasley’s ‘Dominant Market Theory’ (1997) and Norman Fay for introducing me to the work of Mr Beasley.

 

Measuring Risk – A time for Fear and A Time for Greed

The Smart Money becomes fearful when the risk levels are high and moves their funds to more stable areas of the market.  Conversely they become greedy when the risk levels are low and look for investments that will most benefit from a rising market.

Investors should remember that excitement and expenses are their enemies.  And if they insist on trying to time their participation in equities, they should try to be fearful when others are greedy and greedy when others are fearful – Warren Buffet Berkshire Hathaway 2004 Chairman’s Letter

To copy the Smart Money’s interpretation of Risk we need to compare two related yet separate areas of the market; one that is comparatively economically stable Vs one that is comparatively economically sensitive.  In doing so we can reveal the dominant market and know if it is a time for Fear or a time for Greed.

 

The Dominant Market

The Dow Theory looks for the Transportation Average (DJT) to confirm the movements of the Dow Jones (DOW).  The Transportation index is the more economically sensitive of the two, so when it is outperforming the Dow, this is a good indication that risk levels are low.  However to create a simple trading system we need a way to measure this comparative performance in a decisive way.

One effective method is take the end of week (EOW) close price for the Dow Jones Transportation Index and divide by that of the Dow Jones Industrial Average.  The result is a ratio to which we add a 10 week simple moving average (SMA) for signals.  When the ratio is above its SMA, we know that the Transports are Dominant and vice versa.

In theory the dominant index is receiving more attention from the Smart Money based on their assessment of risk.  When the Transports are dominant the risk levels are lower and this is a good time to be greedy for bullish positions.  Alternatively, to keep things really simple, a long position can be taken in IYT (the ETF that tracks the Transportation Index).  Add an EOW stop loss of -4% and you have a complete trading system called TransDow:

TransDow – Performance

TransDow Performance

The dark blue line on the chart above is the result of nothing more that EOW data, a ratio, a simple moving average and a stop loss!  Only exposed to the market 45% of the time it achieved an annualized return during exposure of 17.62% compared to the Buy and Pray annualized return from the DOW and DJT of just 4.55% and 4.24% respectively.

Note, research shows that only 17% of mutual funds beat the market and only 5% beat them by more than 1% per year.  In fact the average actively managed stock mutual fund returns approximately 2% less per year to its shareholders than the stock market in general.

The TransDow could hardly be described as a complicated system.  All the trading rules can be explained to a child in about 150 words.  Yet despite its simplicity it succeeds in doing something that the MBAs running America’s Mutual Funds have failed to do; it outperforms the market and it had done so in a big way over a VERY long time.

So what are the Transports doing during the times when the Smart Money is seeking safety and the Dow is dominant?

During the test period of 83 years DJT advanced 3,033%.  However if you only had your money in DJT during the 50% of the time that the Dow was dominant over the Transports then you would have lost 84% (see red line on chart above).  This is very compelling evidence to back up the theory that when the more economically sensitive Transports are dominant the vast majority of market gains occur and vice versa.

TransDow Stats

The Problem

So we have demonstrated this system working consistently over an 83 year period.  That is nothing insignificant and I challenge you to provide an example of another system that can do the same over such a time frame.  Despite this, we are not happy and do not view this model as being robust enough.  The reason is simple, care to guess as to what it is?  Leave your thoughts in the comments section below.

Bull / Bear Dichotomy Indicator v 1.0 (BBD)

The purpose of a trading or investing model is to move probability in your favour.  One can be considered worthwhile if it can consistently produce risk adjusted returns in excess of the broad market over any period greater than 2 years.  A mechanical trading model can be considered worthwhile if in addition to this it can:

  1. Remove emotion from decisions.
  2. Do the hard work so you have the time freedom to enjoy your profits.

Regardless of the time domain of your preferred trading style, being able to clearly slice the market into high probability bullish and bearish periods is of great advantage.  So far during the Technical Indicator Fight for Supremacy we have identified three very effective and different ways of doing this:

Below I will cover a quick summary of the previous research or you can jump straight to the latest findings.

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The True Golden Cross

EMA Crossover 13/48 EOD Long

Using a simple 13 / 48 Day EMA crossover; 62% of the time the 13 Day EMA was above the 48 Day EMA.  During this time the average trade duration was 93 days and there was an annual return of 10.17% vs 6.32% for the global average during our test period.  During the balance of time there was an annual return of -3.48% (see full tests and research).

Conclusion:

EMA(13) > EMA(48) = Bullish
EMA(13) < EMA(48) = Bearish

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Relative Strength Index

126 Day RSI EOW, Long

Using a 126 Day RSI (with an EMA instead of Wilder’s Smoothing, it would be 63.5 instead of 126 on a standard RSI) and End OF Week (EOW) signals; 63% of the time the RSI was above 50.  During this period the average trade duration was 97 days and there was an annual return of 8.73% vs 6.32% for the global average during our test period.  During the balance of time there was an annual return of -2.77% (see full tests and research).

Conclusion:

RSI(126) > 50 = Bullish
RSI(126) < 50 =Bearish

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Stochastic Oscillator

252 Day Stochastic Oscillator EOD, Long

Using a 252 Day Stochastic Oscillator (SO); 66% of the time the SO was above 50.  During this period the average trade duration was 104 days and there was an annual return of 8.43% vs 6.32% for the global average during our test period.  During the balance of time there was an annual return of -2.05% (see full tests and research).

Conclusion:

SO(252) > 50 = Bullish
SO(252) < 50 = Bearish

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The Bull and Bear Dichotomy v 1.0 (BBD)

The EMA crossover shows us that there is value in measuring shorter term momentum vs longer term momentum.  The RSI shows us that there is value in a measure of declines vs advances.  While the Stochastic Oscillator shows us that there is value in being long a market when it is in the top half of its range.  The trade profile for each indicator is desirable but their signals are often in conflict.

Don Beasley of Trademark Capital has been an inspiration of mine for many years and has been kind enough on several occasions to share his insight and experience.  He likes to combine the RSI and Stochastic Oscillator by taking an average of the two.  This is straight forward because both move between the same 0-100 range.

Including the EMA crossover is not as straight forward however; it is not limited to a scale at all.  But because the percentage distance between the EMA(13) and EMA(48) should be normally distributed we decided to force it into a 0-100 range by using the cumulative distribution of a bell curve.  It has a SD of 2.17% when EMA(13) > EMA(48) and SD of 2.35% when EMA(13) < EMA(48):

Bull / Bear Dichotomy v1 Example

Above you can see the readings from each indicator during a randomly selected 2.5 year period on the Australian All Ordinaries Index.  The thick red line is an equally weighted average of the RSI, SO and MA Cross, smoothed with a EMA(10).  This we are calling the Bull / Bear Dichotomy or BBD Indicator v1.  By combining all three indicators, a greater level of stability and robustness is achieved.  See below the full trade profile:

Bull / Bear Dichotomy (BBD) v1 Trade Profile

In looking at the trade profile the signal stability is clear with an average trade duration of 170 days, an average profit of 21.81% and a probability of profit sitting at 48%!  The other statistics are not dissimilar to the component indicators.  The only thing missing from the BBD is a measure of volume.  We plan to include this in v2 and this will hopefully improve the stability further.

It is highly likely that that we are reaching the upper limits of what is possible, as far as returns, from a long term indicator applied to a blind selection of broad market indices.  To improve on these results it will be necessary to do one or a combination of the following:

  • Have a selection process for the assets to be traded.
  • Apply a secondary trading system specifically designed to perform during the bullish or bearish environments identified by the BBD.

How do think these results could be improved?  What other long term measures would be worth researching for inclusion in v2 of The Bull / Bear Dichotomy Indicator?

Please note: These returns are the result of evenly allocating funds between 16 different global test markets.  If only one market was on a buy signal then only 1/16th of the capital was exposed to the market.  Some markets performed better than others and lifted the returns.  All were profitable and the strategy outperformed on an absolute basis on 15/16 of the test markets.  A further explanation of the methodology can be found here.

Stochastic Oscillator (SO) – Test Results

The Stochastic Oscillator (SO) is a widely used momentum indicator.  As part of the Technical Indicator Fight for Supremacy we have put it to the test through 16 different global markets~ (a total of 300 years data) to find out how well it works and what settings produce the best returns.

Download A FREE Spreadsheet With Data, Charts And Results

For all 1,248 Stochastic Oscillator Settings Tested

First of all lets establish how the market performs while the Stochastic Oscillator is in each 10th of its range:

Stochastic Oscillator Range 10

I have highlighted each of the negative results across a Red—>Orange gradient and positive results across a Light Green—>Dark Green gradient (depending on how great the loss or gain).  Clearly most of the market gains occurred while the Stochastic Oscillator was above 50 and the lion’s share when it was above 90.

What this means is that when the market is in the top 50% of its range it has a tendency to go up and when it is in the top 90% of its range it has a strong tendency to go up.  It also tells us that we want to avoid being long when the market is in the bottom 50% of its range.  Over what period do we base this range?  Interestingly, the returns do not change much over the different look back periods although the benefit of a longer look back is less volatility from the signals.

Lets now look at segments of 20-50% when the Stochastic Oscillator is above 50:

Stochastic Oscillator Range Above 50

The table above is colour coded Red—>Yellow—>Green from Lowest—>Middle—>Highest return.  The message coming through loud and clear is that you need to be long when the market is making new highs if you want to make money.  Over a 255 day look back (about 1 year) the difference between going long in the 50-90 range vs the 50-100 range is the difference between making 2.68% or 8.38% a year!!

Lets have a look at the trade profile:

252 Day Stochastic Oscillator EOD, 50-100 Range, Long, Any

The results above show what would have happened if a long position was opened and held any time, any of the 16 test markets had a reading above 50 on their Stochastic Oscillator (meaning that the market was in the top half of its range).  We chose a period of 252 days, not because the returns were the best, but because this look back produced a longer average trade duration and 252 days is the average number of trading days in a year.

The returns are not as good as we have seen from other indicators such as the RSI or the Moving Average Crossover but they are still respectable.  Furthermore an average trade duration of 104 days is advantageous when looking for a long term indication of market direction.

What about the %K signal line?  We did test this but the results were not worth taking the time to publish.  They are included in the results spreadsheet for free download if you wish to review them however.

 

Stochastic Oscillator Conclusion

The Stochastic Oscillator %K line is too volatile and is not worth considering in your trading as originally suggested by Dr. George Lane in the 50’s.  There are better options for short term trading such as the FRAMA.  In fact, there are also better options available for longer-term indications of market direction than the Stochastic Oscillator as presented in this article… So is it worth bothering with at all?

Well… YES and here is why:

The fact illustrated by these tests is that the majority of gains occur when the market is in the top 10% of its range and nearly all of the gains occur when the market is within the top half of its range.

There has been a lot or research published on momentum strategies and they typically involve buying the best performing assets out of a selection and then rotating funds periodically so as to constantly stay with the best.  Many people fear holding markets that are near their highs so by rotating constantly into the current market leaders these fears are be alleviated.

What our tests on the Stochastic Oscillator reveal however is that simply holding an index fund when it is in the top half of its range (over almost any look back period) will capture the majority of the gains while STILL avoiding those much feared ‘bubble burst’ like declines.  Contrary to popular belief; when a market bubble bursts it does not do so over night.  Penny stocks my grow exponentially and then plummet the next day.  On rare occasions large companies may even do so.  But major economies can not turn on a dime.

“In the event of nuclear war, disregard this message.” – Warren Buffett

Therefore the Stochastic Oscillator could be a useful addition to a momentum rotation type strategy.  Another idea worth considering is to change the rules for your trading system based to the Stochastic Oscillator reading.  For instance we know that most gains occur when the market is making new highs, therefore the rules for taking profits on a long position should be different when the Stochastic Oscillator  is above 90 than they are when it is between 50 and 60.

Both of these applications will be included in future tests.

 

More in this series:

We have conducted and continue to conduct extensive tests on a variety of technical indicators.  See how they perform and which reveal themselves as the best in the Technical Indicator Fight for Supremacy.

 

  • ~The data used for these tests is included in the results spreadsheet and more details about our methodology can be found here.
  • No interest was earned while in cash and no allowance has been made for transaction costs or slippage.  Trades were tested using End Of Day (EOD) signals on Daily data.

Relative Strength Index (RSI) – Test Results

The RSI is a staple indicator of the technical analysis community but how good is it, really?  What are the best settings?  What does its trade profile look like?  Ask around and no one can tell you…  Does it not seem strange that so many traders can be using an indicator without solid data on its performance?  Well we are on a mission to change that.  We tested 3800 different RSI settings through 300 years of data across 16 different global markets~ to reveal the facts.

Download A FREE Spreadsheet With Data, Charts

And Results For all 3,800 RSIs Tested

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RSI – Test Results:

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RSI Conclusion

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Our Testing Strategy Explained

noʊtɑ bɛnɛ (Note Well) we use an EMA when calculating the RSI instead of a WS-MA.  This is not just to be difficult, please read more about the RSI for an explanation.  The formula to convert the EMA Look Back period to the identical equivalent WS-MA used by your charting programs when calculating the RSI is (Period + 1)/2.  Below is a table with all the Look Back Periods we tested and how they convert to the original RSI:

RSI Period Conversions

Now there are many different ways that signals can be taken from the RSI but to start with we wanted to see how the market behaved when the RSI was in different ‘zones’.  We also wanted to find out which RSI Look Back period is the most desirable.  But this presents a problem because changing the Look Back period alters the range of an RSI.

For instance during our tests across 16 different markets and 300 years of data the range for the RSI(5) was 89.96 – 10.04 while for the RSI(100) it was 70.77 – 29.23.  Clearly a direct level comparison between two RSIs of different look back periods is not suitable.

To overcome this challenge we identified the range for each RSI across all different look back periods tested. Then divided each range by 10 and advanced from the mid line (50) in 1/10th increments specific for each different RSI.  The normalisations were numbered based on how many 1/10th of their range they were from 50 (with the exception of the final increment at each end which was extended to 100 or 0 respectively).

Here is a table of the normalisations used which will hopefully clarify:

RSI Normalisations

For instance, lets say we wanted to see how the market performed within a 0 – 3 Normalised RSI range on a RSI(15) vs. an RSI(55).  Using the table above as a guide we would test the RSI(15) from 50 – 78.30 and the RSI(55) from 50 – 70.91; in doing so we should be comparing apples with apples.

Next it was necessary to exclude some data because it was taken from a sample too small to be conclusive.  Lets say you were to buy every time that an RSI(35) was in the -4 to -5 RSI range, in our tests your annualized return during exposure was 248377165801.21%… sounds great right?  Yes and no; the average trade did return 1.16% per day… but the average trade duration was only 1 day and you would have only been exposed to the market 3 days a year.  Statistics like this are invalid so we excluded anything that didn’t result in market exposure of at least 6%.

We tested all combinations of increment ranges:

Range of 1 = -5 to -4, -4 to -3 … 3 to 4, 4 to 5

Range of 2 = -5 to -3, -4 to -2 … 2 to 4, 3 to 5

Range of 3 = -5 to -2, -4 to -1 … 1 to 4, 2 to 5

Range of 4 = -5 to -1, -4 to -0 … 1 to 4, 2 to 5

Range of 5 = -5 to 0, -4 to 1 … -1 to 4, 0 to 5

The key findings are published below, to see all the results download the full results spreadsheet.

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RSI: ANY, Range = 1

First, to see how the market behaved in each increment; a long position was taken ANY time the test market was in the corresponding RSI range:

RSI, ANY, Increment Range 1, Annualized Return During Exposure, Long

Above 0 (50 on the RSI) and the returns are positive, below zero and the returns were negative, you don’t often see such a clear edge over the market as that (see the results when going Short).  The blank cells, (if you were wondering) are where data was excluded because the market exposure < 6%.

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RSI: ANY > 0, Range = 1, 2, 3, 4, 5

So we now know that RSI > 50 = Good and RSI < 50 = Bad.  Lets now look at how far above 50 (the 0 increment) we can go and capture the best profits.  A Long position was open ANY time the test market was in the corresponding RSI range:

RSI ANY - Long, Increment Range 1, 2, 3, 4, 5 > 0

From the above table we can see that the most gains on the Long side occur when the RSI is between the 0 and 4 increment (see results going short).  The Look Back period makes surprisingly little difference although around 55 days we see the most gains captured over all:

55 Day RSI EOW, 50 - 77.88 Range, Long Any

Above are the results from an RSI(55) with a open Long position any time that the RSI was in in the 50 – 77.88 range (0 – 4 increment).  The positions were only opened and closed at the End Of the Week (EOW) because switching from EOD to EOW almost doubled the average trade duration and the probability of profit (see the results EOD).  While the trade profile is quite good, the MA Crossover or FRAMA are still both more desirable.

Note – our RSI(55) using an EMA is equivalent to an RSI(28) in your charting programs which use Wilder’s Smoothing.

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RSI, ENTRY > 0, Range = 1, 2, 3, 4, 5

What if we only opened a Long position when the RSI was rising?  In these tests a position was only initiated when the RSI went from being below 50 (the 0 increment) to above 50.  It was then held as long as the RSI remained in the corresponding range:

RSI ENTRY - Long, Increment Range 1, 2, 3, 4, 5 > 0

By introducing entry criteria to the RSI trades the market exposure decreased and with it the returns in most areas (see results going short).  One area that does stands out however; the Annualized Return During Exposure when the RSI(5) moves through the 0 – 1 increment.  Lets take a look at the trade profile:

5-day-rsi-eod-0-1-l-entry

Above are the results from an RSI(5) with a position opened Long only when the RSI raised above 50.  The position was then held until the RSI moved above 55.99 or back below 50 (the 0 – 1 increment).  The resulting trade profile doesn’t suit my style but I will entertain the idea because it may suit yours…

You don’t have to look far in the quant blogosphere to find examples of systems based on holding a position for only one day following an fed announcement when the VIX is above a certain level etc.  Anyway, be this a practical system or not, it does have a rather smooth looking equity curve and a high probability of profit. Just for fun, lets look at what happens if we add 4X leverage and only go Long when the 13 / 48 MA Crossover is confirming the RSI signal:

13 EMA > 48 EMA + 5 Day RSI, EOD, 50 - 60 Range, Long + Entry, 4X

You must admit, once you crank up the leverage and remove the bear markets by confirming the signals with the 13 / 48 MA Crossover; this is an impressive looking equity curve.  The best part is that you are only exposed to the market 7% of the time!  Realistic in the real market?  Questionable…

Perhaps with the use of futures this could be a workable strategy.  It was profitable on 15/16 global indices we tested and showed a 54% probability of profit through 3837 trades (a nice large sample).  What do you think?

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RSI Conclusion

Never before have I seen such a dichotomy of profitable and unprofitable trades when an indicator is above or below a level as is the case with the RSI being above or below 50.  This proves that momentum is a strong and valuable predictor of market direction and the theory behind the RSI is sound.  For this reason it would be worth testing your system with entry signals confirmed by the RSI(55) being on the appropriate side of 50.  (Remember to use the conversion table; our RSI(55) will be an RSI(28) in your charting program.)

While the RSI clearly provides valuable information, unfortunately we are yet to identify a method of use that presents a more desirable trade profile than the simple effectiveness of the MA Crossover or the FRAMA.

We also tried using an EMA signal line on the RSI but the results where not worth writing about (download all the results in a spreadsheet to see for yourself.)  However I feel that there will be other worthwhile ways to test the RSI.  Perhaps it could be used as a breadth indicator where the number of higher highs from the RSI is compared to the number of higher highs from the stocks within an ETF?

How would you like to see the RSI tested?  Ideas?

 

More in this series:

We have conducted and continue to conduct extensive tests on a variety of technical indicators.  See how they perform and which reveal themselves as the best in the Technical Indicator Fight for Supremacy.

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  • ~The data used for these tests is included in the results spreadsheet and more details about our methodology can be found here.
  • No interest was earned while in cash and no allowance has been made for transaction costs or slippage.  Trades were tested using End Of Day (EOD) signals on Daily data except where otherwise noted.

Top 7 Technical Analysts of All Time Share Their Secrets

My first brush with Technical Analysis was not a good one and I was left asking the question “Does Technical Analysis work?”.  There was plenty of evidence to suggest Fundamental Analysis worked (Warren Buffett has Billions of evidence).  But Fundamental Analysis really doesn’t suit my personality so what were the other options?

Does Technical Analysis Work?Everywhere you go online there is another guru selling the latest TA system accompanied with confusing looking charts.  I decided that if there wasn’t a long list of very rich Technical Analysts out there then I had lost enough money using TA and was ready to quit.  To my delight I discovered many successful traders and investors who had the track record to prove that Technical Analysis does work.  Here is a list of the traders I found particularly noteworthy:

 

The Worlds Best TA Traders:

Marty Schwartz

Best Technical Analyst Marty SchwartzOriginally a stock analyst but got sick of having to write bullish investment advice on overpriced companies.  He developed and combined several technical indicators in an effort to determine lower risk entry points for his trades.  Schwartz found success when he shifted to technical analysis and focused on mathematical probabilities.

He ran his account up from $40,000 to $20 Million and also won the U.S. Investing Championship in 1984.  When asked if Technical Analysis works he replied “I used fundamentals for nine years and got rich as a technician”.  A big advocate of moving averages, Schwartz identifies healthy stocks by looking for positive divergences in price action over the broad market.

They (traders) would rather lose money than admit they’re wrong…  I became a winning trader when I was able to say, “To hell with my ego, making money is more important” – Marty Schwartz

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Mark D. Cook

Top Technical Analyst Mark D. CookLost all his capital several times while learning to trade including one occasion when he lost more than his entire net worth.  In 1982 he sold naked calls on Cities Service that expired deep in the money.  His account dropped from $165,000 to a deficit of $350,000 in a matter of days; a total loss of $815,000 when taking into account for the money that he lost in his family’s accounts.

Not one to give up, after five years Mark had totally recovered from the losses but vowed never to sell another naked option.  He attributes his turn around in success to the development of what he calls the ‘Cumulative Tick Indicator’.

There is a widely used indicator called the ‘Tick’ that measures the number of NYSE stocks whose last trade was an uptick minus the number whose last trade was a downtick.  When the ‘tick’ indicator is above or below a neutral band the ‘cumulative tick indicator’ starts to add or subtract the ticks from a cumulative total.  This works as an over brought and over sold indicator.  When it reaches extremes of bullish or bearish readings the market tends to reverse direction.

In 1989 Cook finished second in the US Investing Championship trading stocks and in 1992 after shifting to options he won the championship with a return of 563%.  Now he trades options holding them 3-30 days and day trades S&P 500 and NASDAQ futures.

To succeed as a trader, one needs complete commitment… Those seeking shortcuts are doomed to failure.  And even if you do everything right, you should still expect to, lose money during the first five years…  These are cold, hard facts that many would-be traders prefer not to hear or believe, but ignoring them doesn’t change the reality. – Mark D. Cook

 

Victor Sperandeo

Successful Technical Analyst Victor SperandeoAn options trader and technical analyst who had a string of 18 profitable years clocking an average return of 72%.  His first loss was in 1990 with a 35% drawdown.

He described his style as only taking risks when the odds are in his favor.  After an extensive two year study he identified ‘life expectancy’ profiles for market moves.  For example he noticed that an intermediate swing on the Dow during a bull market is typically 20%.  After that 20% has been realized the odds of further advances are diminished significantly.

Understanding this makes a big difference he says, like when a life insurance policy is written the risk profile of an 80 year old is very different from that of a 20 year old.  Sperandeo believes that the most common reason for failure with technical analysts is that they apply their strategies to the market with no allowance for the life expectancy of the bullish or bearish move.

Theses days Victor is the President and CEO of Alpha Financial Technologies which is widely known for its trend-following, futures-based indices: The Diversified Trends Indicator, The Commodity Trends Indicator, and The Financial Trends Indicator.

The key to trading success is emotional discipline.  Making money has nothing to do with intelligence.  To be a successful trader, you have to be able to admit mistakes.  People who are very bright don’t make very many mistakes.  Besides trading, there is probably no other profession where you have to admit when you’re wrong.  In trading, you can’t hide your failures. – Victor Sperandeo

 

Ed Seykota

Rich Technical Analyst Ed SeykotaTHE pioneer when it comes to computerized trading systems.  Inspired by the work of Richard Donchian he began developing futures trading systems in the 1970s.  Seykota tested and implemented his ideas using an IBM 360.  This was well before the days of online stock trading, back then such computers were the size of a large room and were programmed using punch cards.

Originally he wrote trend following systems with some pattern recognition and money management rules.  By 1988 one of his clients’ accounts was up 250,000% on a cash-on-cash basis.  Today it is reported that his daily trading efforts consist of the few minutes it takes him to run his computer programs and generate the new signals.

Ed attributes his success to good money management, his ability to cut losses and the technical analysis based systems he created.  He refers to fundamentals as “funny-mentals” explaining that the market discounts all publicly available information making it of little use.

There are old traders and there are bold traders, but there are very few old, bold traders. – Ed Seykota

 

Worlds Richest TA Traders:

I was very happy to discover that the Forbes Rich List was scattered with investors and hedge fund managers who have profited handsomely despite giving fundamentals a back seat.  Here are my favourites from the 2012 list:

 

2012 Forbes – #82 James Simons – 11.0 Billion

Best Technical Analyst James SimonsSometimes referred to as the “Quant King” he is also a maths guru and a very smart cookie who studied maths at MIT and got a Ph.D. from UC, Berkeley.  Simons deciphered codes for U.S. department of defence during Vietnam and went on to found Renaissance Technologies in 1982 and at the start of 2013 was managing over 15 billion.

He Co-authored Cherns-Simons theory in 1974; a geometry based formula now used by mathematicians to distinguish between distortions of ordinary space that exist according to Einstein’s theory of relativity.  In addition to this it had been used to help explain parts of the string theory.

Renaissance Technologies is a quantitative hedge fund that uses complex computer models to analyze and trade securities.  A $10,000 investment with them in 1990 would have been worth over $4 million by 2007.

We are a research organization… We hire people to make mathematical models of the markets in which we invest… We look for people capable of doing good science, on the research side, or they are excellent computer scientists in architecting good programs. – James Simons

The flag ship Medallion Fund trades everything from Pork Bellies to Russian Bonds.  In 2008 the fund forged ahead another 80% even after the 5% management and 44% performance fee.  More recently 9.9% returns were seen net of fees through the end of July 2012.  Unfortunately the Medallion fund is now only open to employees, family and friends.

The key to the success of Renaissance Technologies has much to do with the people they hire; PhDs and not MBAs. About a third of their 275 employees have PhDs.  Those on the payroll include code breakers and engineers, people who have worked in computer programming, astrophysics and language recognition.

They also look for people with creativity.  Simons says that creativity is about discovering something new and you don’t do that by reading books or looking in the library, you need ideas.

Everything’s tested in historical markets.  The past is a pretty good predictor of the future.  It’s not perfect.  But human beings drive markets, and human beings don’t change their stripes overnight.  So to the extent that one can understand the past, there’s a good likelihood you’ll have some insight into the future. – James Simons

 

Forbes 2012 #88 – Ray Dalio – 10 Billion

Rich Technical Analyst Ray DalioPlaced his first trade at the age of just 12, studied finance at Long Island University and got and MBA from Harvard in 1973.  Dalio traded futures early in his career and founded Bridgewater Associates in 1975 when he was just 25.  From the moment he started managing money Dalio kept notes in a trading diary with the hope that his ideas could later be back tested.

Now king of the rich hedge fund industry, Dalio controls the world’s biggest hedge fund Bridgewater Associates which has about $130 billion in assets.  His flag ship fund ‘Pure Alpha’ has had an average annual return of 15% from 1992 – 2010 and has never suffered a loss over 2%.  Big bets on U.S. and German government bonds saw his funds surge about 20% in 2011; a year where most hedge funds struggled.

Dalio focuses heavily on understanding the processes that govern the way the financial markets work.  By studying and dissecting the fundamental reasons and outcomes from historical financial events he has been able to translate this insight into computer algorithms that scan the world in search of opportunities.  He says by doing this research it provides “a virtual experience of what it would be like to trade through each scenario”.

Ray is particularly interesting because he does not believe in an approach devoid of understanding fundamental cause-effect relationships.  He has however been able to use technical analysis to identify mispriced assets based on fundamental information.  So to say that Ray gives fundamentals analysis the back seat to technical analysis would not be entirely accurate.

Well defined systems, processes and principles are his key when is comes to making investing decisions.  All strategies are back tested and stress tested across different time periods and different market around the world to ensure that they are timeless and universal.  The strategies are all about looking at the probabilities and extreme caution is exercised; for a hedge fund Bridgewater uses relatively low leverage of 4 to 1.

While the hedge fund industry as a whole has an average correlation to the S&P 500 of 75% Dalio claims to have discovered 15 uncorrelated investment vehicles.  Bridgewater focuses mostly in the currency and fixed income markets but uses powerful computers to identify mispriced assets on dozens of markets all over the world.  To find so many different uncorrelated investments requires stepping well beyond the realm of the stock exchange.

I learned to be especially wary about data mining – to not go looking for what would have worked in the past, which will lead me to have an incorrect perspective.  Having a sound fundamental basis for making a trade, and an excellent perspective concerning what to expect from that trade, are the building blocks that have to be combined into a strategy. – Ray Dalio

 

2012 Forbes – #106 Steven Cohen – $8.8 Billion

Top Technical Analyst Steven CohenNow a well know force on Wall Street due to his world class performance and high volume of trading which accounts for about 2% of the daily volume on the New York Stock Exchange.  Steven started trading options in 1978 and made $8,000 on his first day.

He founded hedge fund SAC Capital in 1992 with $25 million in assets.  By the end of 2012 SAC had about $13 billion under management across 9 funds and had averaged 36% net return annually.  It is reported however that SAC suffered a loss of approximately 15% in 2008.  Its flagship fund was up 8% in 2011, a year in which the average hedge fund was down 5% and up again in 2012 8% through to August.

Steven keeps his activities very secretive but his style is understood to be high volume hair-trigger stock and options trading.

The old guard wasn’t crazy about me, I used to hear it all the time… Most of the old-school had no belief in anything that wasn’t based on fundamental analysis… We were trading more than investing, and people frowned on it, they looked at it and didn’t want to partake.  Finally, they said, ‘Shoot.  He’s making money.’ And they started copying me. – Steven Cohen

He believes that 40% of a stocks price fluctuations are due to the market, 30% to the sector and 30% to the stock itself.

Despite the great performance of SAC Capital their best trader makes a profit on 63% of their trades while most of the traders are profitable 50-55% of the time.  Interestingly 5% of their trades account for virtually all their profits.  Something to keep in mind the next time you get a spam email claiming that your can buy a 95% accurate ‘Stock Trading Robot’.

Steven attributes the success of SAC to the breath of experience and skills found in the people working for the firm.  They look for traders who have the confidence to take risks, those who wait for someone to tell them what to do never succeed.

You have to know what you are, and not try to be what you’re not.  If you are a day trader, day trade.  If you are an investor, then be an investor.  It’s like a comedian who gets up onstage and starts singing.  What’s he singing for?  He’s a comedian. – Steven Cohen

 

Forbes 2012 #330 – Paul Tudor Jones II – 3.6 Billion

Successful Technical Analyst Paul Tudor Jones, IIBoth a discretionary and systems trader who had his early success trading cotton futures.  Jones majored in economics at the University of Virginia in 1976 and got a job working for the cotton speculator Eli Tullis not long after graduating.  The greatest lesson that he learnt from Eli was emotional control but was later fired for falling asleep on the job after a big night out on the town with his friends.

In 1983 Jones began the hedge fund Tudor Investment Corp with $300,000 under management.  At the end of 1012 the fund was estimated to be managing $12 billion and had achieved an average annual return of 24%.  His firm’s flagship fund, BVI Global saw a gain of 2% in 2011 and 3.8% net of fees through to August 2012.

Much of his fame came from predicting the 1987 stock market crash from which he pulled a 200% return or roughly $100 million.  Jones claims that predicting the crash was possible because he understood how derivatives were being used at the time to insure positions and how selling pressure on an over priced market would set off a chain reaction.  He says that you need a core competency and understanding of the asset class you are trading.

He attributes his success to a deep thirst for knowledge and strong risk management.  Jones is a swing trader, trend follower and contrarian investor who also uses Elliot Wave principles.  Most of his profits have been made picking the tops and bottoms of the market while often missing the ‘meat in the middle’.  Jones believes that prices move first and fundamentals come second.

A self professed conservative investor who hates losing money.  He tries to identify opportunities where the risk/reward ratio is strongly skewed in his favor and does not use a lot of leverage.  In his eyes a good trader is someone who can deliver an annual return of 2-3 times their largest draw down.

Don’t be a hero.  Don’t have an ego.  Always question yourself and your ability.  Don’t ever feel that you are very good.  The second you do, you are dead… my guiding philosophy is playing great defense.  If you make a good trade, don’t think it is because you have some uncanny foresight.  Always maintain your sense of confidence, but keep it in check. – Paul Tudor Jones II

 

Top Traders Secrets

It is clear that Technical Analysis has worked in the past and continues to work for many successful traders and investors today.  But what are the common aspects that are being were used by these successful market technicians?

Unfortunately due to the extreme secrecy surrounding nearly all of these traders, the specific methods that they use are not known.  However I did uncover the following:

Common Themes

  • Mechanical trading models were used my many of the most successful.
  • They all used clearly defined systems and stuck to their rules.
  • Many of them back tested their ideas before implementing them in the real market.
  • Most of them surrounded themselves with exceptional people who had the expertise they needed.
  • Many of them lost money for the first few years before hitting their stride.
  • Each trading system suited their personality.

 

Common Personality Traits

  • Low Emotional Reactivity – Staying calm; experiencing neither major highs nor lows.
  • Detached – Understanding the market does what it does that they have no control over it.
  • Humble – With little ego they have no challenge taking losses or letting profits run.
  • Decisive – They reach decisions quickly and take action without second guessing.
  • Conscientious – Self-controlled, disciplined, consistent, and plan-driven, they persevere.
  • Confident – They have faith in their system and their ability to implement it.

 

It is undeniable that Technical Analysis does work so ignore all those who try and tell you otherwise.  The next step is to make Technical Analysis work for you and that first requires identifying or creating a system that suits your personality.

What has your experience been with Technical Analysis? Did I leave anyone off the list?  Let me know in the comments section below. (Also I realize that I listed 8 traders not 7 :))

 

Related Posts

MACD – Test Results

The MACD is one of the most widely used technical indicators in the world and is included in every charting program worth owning.  Unfortunately however, reliable data on its performance is almost non-existent.  Are the standard settings of 26, 12, and 9 the best?  To reveal the answer we tested 2000 different combinations through 300 years of data across 16 different global markets~. Stand by for the results below…

 

Download A FREE Spreadsheet With Data, Charts

And Results For all 2,000 MACDs Tested

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MACD – Test Results:

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MACD Conclusion

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Our Testing Strategy Explained

Because there are so many different possible settings for a MACD we started by testing a broad range with the hope this would reveal the areas to focus on more closely.  To cast our testing range wide but strategically, we progressed in a liner fashion through the Fast Moving Averages (FC) and set the Slow Moving Averages (SC) as of multiple of the FC:

Fast Moving Averages (FC) = 10, 20, 30, 40, 50
Slow Moving Averages (SC) = 2 * FC, 3 * FC, 4 * FC, 5 * FC, 6 * FC

So each of the five FC settings were tested against five SC settings based on a multiple of the FC. e.g  A SC of 50 would be tested against a FC of 100, 150, 200, 250, 300 as these are equal to 50 multiplied by 2, 3, 4, 5 and 6.

Each of these were tested against 10 different Signal Line settings:

Signal Line (SL) = 2, 4, 6, 8, 10, 12, 14, 16, 18, 20

Trading Rules:

An entry signal to go Long for each MACD tested was generated when the MACD Line was above zero AND above the Signal Line.  The position was closed when the MACD Line moved below zero OR below the Signal Line (vice versa when going short)^.

If what you have read so far does not make much sense, please read more about the MACD before continuing 🙂

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MACD Test Sets – Broad

MACD, EOD Long - Annualized Return During Exposure

Above you can see the annualized return during the time each MACD was exposed Long to the market.  Clearly the Signal Line setting is far more influential than the ‘Fast’ and ‘Slow’ Moving Averages (MACD Line).  To my surprise having the Signal Line as fast as just 2 days produced the the best results and even more surprising is that the trades produced are not prohibitively short (8 – 27 days on average from the table above, see spreadsheet for full stats).

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MACD Test Sets – Short Term Trading

MACD EOD, Long - Annualized Return During Exposure

After refining the tests down several times we uncovered some interesting findings.  Firstly the most efficient returns came from a MACD with a ‘Fast’ Moving Average of 1, which isn’t actually a MACD at all (a Moving Average with a period of 1 is equal to the price itself).  So the best results come from measuring the Convergence and Divergence between an MA and the price, with the addition of a Signal Line.  What is really exciting however is this also works exceptionally well on the Short side of the market:

MACD EOD, Short - Annualized Return During Exposure

For me, when assessing a trading system, I am more interested in the return during exposure than the return overall.  A system may have you exposed to the market for 30%, 70% or 99% of the time but the more time you are exposed to the market the more time your money is at risk.  While my money is at risk, if it is not working hard, if I am not getting a high return, then I would rather sit in cash!

The Short side of the market is often not worth trading because decent returns during exposure are difficult to get from a mechanical system.  What we see with the MACD however are returns during exposure then exceed even the best that the FRAMA could produce when Long….  Sooooo, what is the catch?  Lets have a closer look:

MACD EOD 1, 56 Long and Short, Sig 2

The pink line on the chart above is the performance, taking signals both Long and Short from a MACD with a ‘Fast’ MA of 1 (price), a ‘Slow’ MA of 56 and a Signal Line of 2.  I have included the results from the best FRAMA for comparison.

The impressiveness of this MACD can’t be doubted; consider the fact that it achieves these returns while only being exposed to the market 56% of the time and delivers a 42% probability of profit for each trade.  But can you see the problems?  With an average return of just 0.25% and an average trade duration of 4 days, a MACD with these settings is limited in its practical applications.

Firstly you would need near frictionless trading, such as that offered by some index mutual funds (e.g. Rydex, ProFunds, or Direxion)

Secondly you would need to gain exposure to several diverse equity index funds.  Part of the reason for the success of this strategy is the fact that it spreads the risk across 16 different global markets, some of which performed better than others in our tests.  In the real world frictionless trading is not accessible to such a variety of indices.

Thirdly between 2003 and 2007 while the Global Average was experiencing a very strong bull market the MACD underperformed quite significantly.

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MACD Test Sets – Practical Trades: Long

MACD EOD, Long - Annualized Return During Exposure

The table above is the result of a search for some more practical MACD settings.  As you can see the best returns localise around the the 21/81 mark area:

MACD EOD 21, 81 Long, 2

Above we are looking at the performance of a MACD going Long with a ‘Fast’ Moving Average of 21, a ‘Slow’ Moving Average of 81 and a signal line of 2 compared to the best FRAMA (also notice the poor performance from the standard MACD of 12, 26, 9 – See Full Stats).

Now when comparing the 21, 81, 2 MACD to the FRAMA it must be taken into consideration that the MACD is only exposed to the market 35% of the time while the FRAMA is exposed 57% of the time.  So a side by side, total return comparison is not really fair.  What is good to see however is the consistency and stability from the MACD during market declines.  What I don’t like though is the familiar under-performance during the strong bull market between 2003 and 2007.

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MACD Test Sets – Practical Trades Short

MACD EOD, Short - Annualized Return During Exposure
The Short side of the market behaves differently to the Long so it is not surprising to see that a more reactive MACD performs better, and the top returns were found around 16/97:

MACD EOD 16, 97 Short, 2

Above we are looking at the performance of a MACD going Short with a ‘Fast’ Moving Average of 16, a ‘Slow’ Moving Average of 97 and a signal line of 2 compared to the best FRAMA (also included is the standard MACD of 12, 26, 9 – See Full Stats).

The 16, 97, 2 MACD is quite exceptional, managing to match the returns from the FRAMA with 2/3 the market exposure and a higher probability of profit.  These results are very exciting.  It would appear as though the MACD’s true strength is in its ability to go Short.

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MACD Conclusion

I have been a big fan of the MACD for a long time and had high expectations for these test results.  But reality has been harsh on the MACD and in many ways the Emperor has no clothes.

In an attempt to limit the length of this article we only published results from trades off the Signal Line when the MACD line was above zero (when Long) or below zero (when short).  Please note however that trying the trade the MACD when it is on the wrong side of zero will lead to an unhappy bank account, an unhappy wife and an unhappy life.

As a tool for long term trading the MACD fails and can’t compete with its less evolved relative the Moving Average Crossover.

As a tool for short term trading (4 days on average) the MACD is very powerful in theory but with such a small average return the practical applications are limited.

As a tool for medium term trading the MACD should not be your first choice on the Long side of the market BUT on the Short side the MACD is simply outstanding!  Using a ‘Fast’ Moving Average of 16, a ‘Slow’ Moving Average of 97 and a signal line of 2 you have a powerful indicator for taming the bear.

 

More in this series:

We have conducted and continue to conduct extensive tests on a variety of technical indicators. See how they perform and which reveal themselves as the best in the Technical Indicator Fight for Supremacy.

 

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  • ~The data used for these tests is included in the results spreadsheet and more details about our methodology can be found here.
  • ^ No interest was earned while in cash and no allowance has been made for transaction costs or slippage.  Trades were tested using End Of Day (EOD) signals on Daily data.  All Moving Averages (MA) in these tests were Exponential (EMA).

Oscillator Classification

  • Absolute Price Oscillator (APO) deals with moving averages of actual prices such as the MACD.
  • Percentage Price Oscillator (PPO) computes the difference between two moving averages on a normalized basis by percentage.  There are several different methods for this including:
    1. Taking calculations on price percentage changes.
    2. Taking the difference between two moving averages and dividing them by the longer moving average value.
    3. Taking the difference between two moving averages and dividing them by the Average True Range (ATR)

All of the PPO methods produce the same signals however they allow you to compare securities of different prices or the same security during different time periods.  Dividing by the ATR is particularly useful when comparing different asset classes or securities of vastly different volatility.