I’m going to be doing a MarketSci-style review of Larry Connors’ new book High Probability ETF Trading.

By “MarketSci-style” I mean I’ll be putting Larry’s specific rules to the test and showing/discussing the historical results on the type of assets that we focus on (actively-traded mutual funds when possible, but ETFs otherwise, covering broad based indices).

Note that, to protect Larry’s work I won’t be talking about specific rules (for that you’ll have to buy his book), but we will try to give a general idea of what each strategy is doing. Larry does good work, and is one of the very few who take a real quantitative-approach to swing trading sans-hype. I hope that we’re able to send a few book sales his way as a result.

Last note: I did a pretty thorough review of Larry’s TradingMarket.com 10 Trading Rules back in January that I recommend readers take a peek at. There will be some overlap between this analysis and that one.

Happy Trading,
ms

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I have (to my perpetual surprise) never been asked this before, but I’ve heard this logic grenade tossed at others who purport to possess investment expertise, so I’ll assume that at least the thought has been directed at me.

The logic grenade goes something like this: if you’re such a good trader, why are you spending your time here and not sitting on a beach watching your money grow?

Fair enough young tosser.

I’ve talked about my motivation for keeping this blog in the past (which can be summed up as “it keeps my head in the game”), but there’s a far more pragmatic and sinister reason too: it keeps my trading capital trading and not putting food on the table.

[linearly-scaled]

Warning: extreme oversimplification for the sake of making a point ahead!!!

The graph above shows two hypothetical investment “gurus”.

Both of our gurus will start with $1,000,000 in trading equity with annual living expenses of $150,000 (excluding taxes and withdrawn at year’s end), and we’ll make a rather modest assumption of 20% after-tax trading returns per year (these are gurus after all).

Note: The assumptions above are in no way a reflection of me personally. I could actually be an elusive billionaire writing this post from my bat cave, or a homeless guy using the free wireless at the public library. That really misses the point.

There’s just one difference between our two gurus: our retired guru (in grey) is living entirely off of trading capital, while the other (red) is putting food on the table being a public guru rather than a beach-going one.

After 20 years, our retired guru has reached a terminal account value of $10.3 million (12.4% annualized), while our working guru’s account has swelled many times larger to $38.3 million (20.0% annualized).

This example is ridiculously oversimplified (inflation-less, constant trading returns, etc.) but the point doesn’t change and is applicable to all of us, guru or not: the power of compounding benefits the trader who keeps his capital doing what it does best – trading – not putting food on the table.

The logic grenade is a total dud (unless of course said guru purports to consistently make 100% a year, or trades a very, very large account, or eats canned beans every night, in which case, none of the above holds true, and yes, said guru should be sitting on a beach somewhere).

Happy Trading,
ms

P.S. The follow up logic grenade says that if we share our ideas or our strategies in the public domain, that they’ll become less effective. Anyone who makes that argument just doesn’t understand how incredibly large the U.S. market is (or alternatively, just doesn’t understand how small MarketSci is). This grenade, like the first, is a dud.

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Warning: extreme geekiness ahead…

In my previous post I looked at the Russian stock market and showed that like India, but unlike the US, short-term price changes in Russia are momentum, not mean-reversion driven. That’s important for at least two reasons: (a) short-term indicators like RSI(2) will work opposite they way they do in the U.S., and (b) it completely changes the dynamic of whether we should let winners run or take profits quickly, and cut losses early or wait for trades to develop.

This post will be about two oddities in the Russian data that I know are important to the US market, but I’m still wrapping my head around why.

[logarithmically-scaled]

In my previous post, I showed a graph like the one above that assumed a trader went long (at the close) following a close up, and short following a close down, Russia’s RTS Index (denominated in USD), from 1996 frictionless (these results wouldn’t actually be achievable, but just for giggles, that’s a 132% annualized return). Note that based on these results, the Russian market is momentum-driven: up days tend to follow up days, and vice-versa.

ODDITY #1: BEND IN THE EQUITY CURVE

Note the bend in the equity curve in the graph above (denoted by red arrow). Right about that time, follow-through in Russia lost a good deal of its steam. It still existed, but was far less potent than the years prior.

That change occurred right around the turn of the millennium, which is of course interesting, because it’s right about the same time that follow-through in the US flipped from being momentum to mean-reversion driven (read more here and here).

So the question is: (a) was this roughly simultaneous change in Russia because of the influence of the US market? Or (b) did both change because of some third outside influence (more on this below)? Or (c) is this purely coincidence?

It would be interesting to do a study of how each market directly influences the other like I did with East Asia (read more). If there is a strong connection with the US (like Hong Kong), then a, b, or c above might be true. If there isn’t a strong connection (like in Shanghai), then only b or c might be true. More to follow on this.

ODDITY #2: INDEX DENOMINATED IN RUBLES

This is the one that really has my goat.

Compare the graph above of follow-through in the Russian index denominated in USD, to the one below of the same strategy applied to the index denominated in Russian rubles…

[logarithmically-scaled]

Note the breakdown in follow-through over the last year or so (red arrow). It’s a little hard to put in perspective because this is a logarithmic-chart, so below I’ve included a graph of the drawdown of this same strategy (i.e. how far the strategy is from its highs at any given moment).

Now the breakdown is a bit easier to see. Since about October of last year, follow-through in Russia has flipped contrarian (and is now like the US).

Is this change permanent? I don’t know, but if it is, it raises a very interesting question:

Do fluctuations in the strength/weakness of a nation’s currency have an impact on short-term momentum vs mean-reversion in the country’s stock market? Remember that the only difference between the first and second Russian tests were that the first was denominated in USD, and the second in Rubles.

We’ve seen the Ruble growing stronger over the last 6 months and a breakdown in short-term momentum in the last 9 – are these connected? We saw stabilization and even appreciation in the Ruble between 2000 and mid-2008, and over the same period, a tempering of short-term momentum – are these connected? Or are these both being driven by some third outside influence?

I don’t know the answer to any of these questions, but it’s uncanny how well the events line up.

I’m going to keep plugging away at this concept. Short-term US price movement is so important to what we do in our own proprietary strategies that anything we can do to model and better understand its ebb and flow (and when it will make its next evolution) is incredibly important.

Happy Trading,
ms

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All of the results below have been independently-audited in real-time by at least one third-party. Visit MarketSci.com for links to third-party audits and to learn more about accessing our strategies via a managed account or subscription.

Note: I’m going to start showing just our four flagship programs in this monthly return table. Things were getting too cluttered with all of the original MarketSci variations; it made it look like those strategies were a bigger part of our business than they actually are. You’ll now find monthly returns for non-flagship programs listed under the table, and of course, in our summary stats.

Hmmm…what to say about June…

I’m not concerned that any single strategy performed poorly this month; real traders take their lumps. But I’m a little miffed that all four families of strategies performed poorly in the same month (despite their low correlation). Having said that, based on the fact that each underperformed during different parts of the month, I don’t think it points to anything bigger than a confluence of unrelated unprofitable trades.

This was our 5th worse combined monthly performance (see graph below) since our independently-audited track record began.

The equity curve below shows our combined real-time monthly performance since inception vs the S&P 500, assuming an equal investment in each family’s flagship program: MarketSci ProFunds, RH S&P 500, YK (A and B), and Scotty.

COMBINED REAL-TIME PERFORMANCE vs S&P 500

SUMMARY OF ALL PORTFOLIOS SINCE INCEPTION
SORTED FROM MOST TO LEAST AGGRESSIVE *

Notes: (1) results account for transaction costs, but not subscription or managed account fees, (2) YK(B) returns reflect performance after addition of “abnormal market filter” in late October, 2008 (read more), (3) table sorted from highest to lowest measured volatility and may not reflect future performance.

* * *

One of the ways I justify spending my time on this blog is it gives me an opportunity once a month to share these independently-audited trading results. I really hope you enjoy my ramblings each week, but developing these proprietary strategies is really what I do, and I invite you to find out more about accessing our strategies via a subscription or managed account.

Happy Trading,
ms

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It’s been a while since I’ve talked about the day-after options expiration, but this particular oddity might be a changin’.

When is options expiration day?

Equity and index options expire after the close of trading on the third Friday of every month. When the third Friday is a holiday, they expire on Thursday. The “day-after options expiration” then is usually the following Monday.

My previous posts here and here on the topic could be summed up as: the day-after options expiration (Monday) has historically been very bearish when expiration day (Friday) was down, but neutral when expiration day was up. To illustrate…

[Growth of $10,000, logarithmically-scaled]

The graph above shows the results of three “strategies” trading the S&P 500 index from 1970.

The first (blue) made twelve one-day trades per year, from the close of each expiration day (Friday) until the close of the day-after (Monday). This is our benchmark day-after options expiration day. The second strategy (green) only took that position when expiration day was up, and the third (red), only when expiration day was down.

Geek note: this is a proof of concept so these results are frictionless (i.e. do not account for transaction costs and slippage) and ignore return on cash.

Clearly, from 1970, the day-after options expiration has been very bearish when the market fell on expiration day (red), but only neutral when it rose (green).

But notice what has happened more recently towards the right hand of the graph above – the difference between the red and green lines has all but disappeared. The next graph shows the same results as the first from 2000…

[Growth of $10,000, logarithmically-scaled]

In more recent history, there’s been little difference in day-after options expiration returns based on expiration day – they’ve both been about equally bad.

In fact, in the last year or so, the observation has actually reversed with positive expiration days leading to the most bearish returns (but I think that that’s too few observations to take seriously…yet).

In Summary…

For most of the market’s history, negative monthly expiration day returns beget negative returns the following day, and positive expiration days beget neutral returns. But more recently, that difference has disappeared and both are now about equally bearish.

Three random closing thoughts: (1) Perhaps this is related to the evolution in daily follow-through (read more here and here) which seems to have occurred at roughly the same time? (2) This could very well just be a fluke in the data – note other periods (ex. mid-80’s) where positive and negative expiration days were also about even. And (3) this is another example of why adaptive approaches to trading are so important…the State of the Market report (which includes the day-after options expiration) caught on to this change and adjusted its predictions long before I snapped to it.

Happy Trading,
ms

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This is the fourth contribution to the MarketSci Blog from Andrey S. of Russia (along with the trading nuggets here, here, and here).

In this post, Andrey shows that, like India, day-to-day changes in the Russian stock market behave opposite of the US: they are momentum, not contrarian driven. This has huge consequences for all short-term indicators, such as blogosphere fav RSI(2).

A simple illustration…

[logarithmically-scaled]

The graph above shows the results of two trading strategies: one going long Russia’s RTS Index at today’s close if the market closed up today (green) and the other if it closed down (red), from 1996.

Geek note: this is a proof of concept, so these results are frictionless (i.e. do not account for transaction fees or slippage). Also, I’ve used the version of the index denominated in US Dollars (more on this in a bit).

Obviously, the Russian market demonstrates tremendous daily follow-through; up days tend to be followed by up days, and vice-versa. But recall that this is the exact opposite of what we see in the U.S. today (read more here and here) where short-term movement is very much contrarian; up days tend to be followed by down days, and vice-versa.

A different view…

[logarithmically-scaled]

The graph above shows the same data in a single “portfolio”, assuming we traded long following a close up, but short following a close down (frictionless).

There are two oddities about the results above: (1) the bend in the equity curve where I’ve placed the red arrow, and (2), very different results over the last year or so when viewing the index denominated in Rubles (rather than USD). More on both of these oddities in part 2 of this post.

Happy Trading,
ms

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Just a little housekeeping. This is a roundup of our recent posts talking about the Golden Cross (aka 50/200-day moving average crossovers).

>> Moving Average Crossovers Debunked?

Our original (long ago) post on 50/200-day (and 10/50-day) moving average crossovers.

Conclusion: longer-term MA crossover strategies like these aren’t particularly great for producing outsized returns, but they have done a fairly good job at protecting investors from protracted downturns.

>> Which Golden Cross is the Best Golden Cross?

A look at EMA vs SMA golden crosses (two different flavors of moving averages).

Conclusion: the SMA variation has performed a small bit better historically, but the difference isn’t significant enough to get worked up over.

>> Testing the Rare Downtrending Golden Cross

A look at golden crosses that occur when the 200-day SMA is still falling.

Conclusion: downtrending (falling 200-day SMA) golden crosses have historically been about as bullish as any other.

>> The Golden Cross and Dow Jones Transport Confirmation (Trading DJIA 30)

How the golden cross has performed with and without confirmation by the transportation index.

Conclusion: waiting for confirmation from transports hasn’t hurt or helped performance of the golden cross much, but has significantly reduced exposure (time in market) – an inherently good thing.

>> The Golden Cross and Dow Jones Transport Confirmation (Trading S&P 500)

Same test as the previous report, but trading the S&P 500 rather than the DJIA 30.

Conclusion: same conclusion as well…more or less the same performance, with a lot less exposure to the market.

Happy Trading,
ms

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Last post on the subject of golden crosses (aka 50/200-day moving average crossovers). Unfamiliar with the golden cross? Read our take on the cross, about its two flavors (SMA and EMA) and crosses in up vs downtrending markets.

In our previous post we looked at trading the golden cross with and without confirmation by the Dow Jones Transportation Index (DJT). In that post we tested a strategy trading the Dow Jones Industrial Average (DJIA), because it was a nice match for the transport index.

But as I wrote in that post, I’m not a fan of trading the DJIA (it’s just too concentrated in too few names), so in this post we’ll rerun the same test trading the S&P 500 index.

[logarithmically-scaled]

The chart above shows the results of two strategies trading the S&P 500 from 1951 (blue) to present. The first (red) is the straight version, going long at today’s close if the 50-day SMA of the S&P 500 crossed above the 200-day today. The second (green) is looking for DJT confirmation – it will only go long if both the first condition is met AND the 50-day SMA of the DJT index is trading above its 200-day.

Geek notes: these results are frictionless (i.e. do not account for transaction costs or slippage), and I’ve included a return on cash when not invested of half the nearest 13-week Treasury bill.

And for the number lovers…

Same conclusion as the previous DJIA test: though performance wasn’t significantly improved, the benefit of requiring DJT confirmation has been a sizeable reduction in exposure (time in market).

And accomplishing (more or less) the same end goal with less exposure to the market is an inherently good thing because it reduces the risk of getting caught looking the wrong way on a massive unpredictable black-swan’ish day (a’la Oct. 1987).

What do these results say about this market?

The 50-day SMA of the S&P 500 closed over its 200-day SMA at Tuesday’s close (06/23), meaning by the traditional definition, the market is now bullish. Side note: the 200-day SMA is still falling, but as we showed here, that hasn’t historically been a negative.

But the 50-day SMA of the Dow Jones Transportation Index is still below its 200-day SMA, meaning, based on this test, this golden cross is not yet so bullish (rather, neutral).

What’s my take?

In short, I don’t have one – I don’t know what the broader trend is (and don’t really care all that much).

Readers know, I’m not a fan of trend-following – I think investors are much better suited using shorter-term more active strategies. This series has really been for the benefit of folks (a lot) less hyperactive than us.

[Edit: click for a summary of all posts in this series on trading the Golden Cross]

Happy Trading,
ms

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A lot of chatter about the stock market’s upcoming golden cross has focused on whether the Dow Jones Transportation Index (DJT) is confirming a bullish trend.

Unfamiliar with the golden cross? Read our take on the cross, about its two flavors (SMA and EMA) and crosses in up vs downtrending markets.

As the theory goes, to qualify as bullish, the DJT (an index of U.S. companies that move stuff around like airlines, railroads, ocean freight, etc) must also confirm the market’s golden cross. I’m not a Dow Theory proponent, but if I remember correctly, this concept originated there.

In this post, we’ll look at how off-the-shelf golden crossovers, versus those confirmed by the DJT, have performed historically.

[logarithmically-scaled]

The chart above shows the results of two strategies trading the Dow Jones Industrial Average (DJIA) from 1931 (blue) to present. The first (red) is the straight version, going long at today’s close if the 50-day SMA crossed above the 200-day today. The second (green) is looking for DJT confirmation – it will only go long if both the first condition is met AND the 50-day SMA of the DJT index is trading above its 200-day.

Geek notes: these results are frictionless (i.e. do not account for transaction costs or slippage), and I’ve included a return on cash when not invested of half the nearest 13-week Treasury bill.

And for the number lovers…

Requiring DJT confirmation, improved performance over the life of the test, but only slightly. The strategy has gone through long periods where it hasn’t really helped or hurt.

What is more impressive to me is that the confirmation strategy maintained performance, while drastically reducing exposure (time invested in the market).

I’ve written about this before: accomplishing the same end goal with less exposure to the market is an inherently good thing because it reduces the risk of getting caught looking the wrong way on a massive unpredictable black-swan’ish day (a’la Oct. 1987 or Sep. 11, 2001).

Do I think DJT confirmation, as I’ve defined it here, is a huge step up in the strategy? No. Do I think it’s a nice little tweak that should at least be in the back of the mind for trend-following types? Sure.

Last comment: I’m not a fan of trading the DJ Industrial Average. I used it here because it was a nice match for the DJT, but my personal opinion is that it’s too driven by too few names (in other words, it’s not sufficiently diluted) to be easily traded.

I personally would much rather trade something like the S&P 500, so in a follow up post, I’ll rerun this same golden crossover test on the S&P 500, but still using the DJT for confirmation. More to follow.

[Edit: click for a summary of all posts in this series on trading the Golden Cross]

Happy Trading,
ms

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The stock market’s upcoming golden cross (AKA 50/200-day MA crossover) looks like it will be of the rarer variety: the downtrending cross.

Unfamiliar with the golden cross? Read our take on the strategy and its two flavors (SMA and EMA).

Because of the nature of how moving averages are calculated, the golden cross usually occurs in an uptrend, when prices are pushing both averages up, but pushing the 50-day average up faster (leading to the crossover).

But because of the length and severity of this bear market, this golden cross may occur over a 200-day moving average that is still very much in a downtrend. To illustrate, the graph below shows the S&P 500 with its 50-day (red) and 200-day (blue) MAs, since the current bear market began on 10/15/2007.

In this post, I want to look at how the golden crossover strategy has performed following one of these rarer downtrending crosses.

The graph below shows three competing golden cross strategies, applied to the S&P 500 index from 1960 to present (geek note: we’re using the SMA variation, not EMA).

[logarithmically-scaled]

The first (red) has been run straight – it trades every crossover. The second (green) doesn’t initiate the trade until both the 50 and 200-day MA are rising, but is otherwise straight. The third and most drastic (purple), never initiates a trade if the golden cross originally occurred in a downtrend, even if the 50 and 200-day MA eventually turn up.

Geek notes: these results are frictionless (i.e. do not account for transaction costs or slippage). I’ve included return on cash when not invested of half the nearest 13-week Treasury bill. Note that my previous test of MA crossovers assumed the full treasury yield (instead of half), so these results will be just a bit different.

And for the number lovers…

Clearly, variation three, which ignored downtrending golden crosses forever and always, has been a bust. Interestingly, variation two, which simply waited for the 50/200-day averages to turn up, slightly hurt performance.

It would seem that variation one, taking the crossover when it occurred regardless of the direction of the MAs, has been the most effective approach.

Having said all of that, the difference between variations #1 and #2 has been so small (only 129 trading days over nearly 50 years), that like the SMA vs EMA discussion, I don’t think it warrants getting too worked up over.

If the upcoming cross occurs over a downtrending 200-day MA, it deserves the same level of significance as any other.

[Edit: click for a summary of all posts in this series on trading the Golden Cross]

Happy Trading,
ms

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