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 to YK managed accounts: per Scott Daly, you returned a higher -0.4% (gross) for the month. There was a trading error in our tracking account that led to the -3.1% reported here. For consistency, I’m using the lower figure moving forward.

Performance has been tepid the last few months – we’ve been spinning our wheels not going forward or backwards as this market grinds up. I’m bored to no end with recent performance, but confident that all will be good and right in the world as this market settles in.

For a more detailed look at the breakdown in short-term mean-reversion the last few months (something we lean on heavily), refer to our most recent State of Short-term Mean-Reversion report.

One small update: we’re entering a new strategy niche this month with our Enhanced S&P 500 Index:

Most of our strategies target absolute returns, meaning they have little correlation to the broader market and are designed to profit regardless of market conditions.

The Enhanced S&P 500 Index is something very different. This program is designed to move like the market, but to do it with about half the downside risk. Put another way, we’re attempting to build a better, smoother S&P 500.

Specifically, we have four goals over the life of the program: (1) outperform the S&P 500, (2) reduce S&P 500 volatility by half (annualized based on monthly returns), (3) reduce average and peak S&P 500 drawdowns by half, and (4) exhibit greater than 80% correlation to the S&P 500 (based on monthly returns). [read more]

I’m not sure how much I’ll talk about enhanced indexes on this blog – this is really targeted towards institutional money and my readers tend to be high-vol. absolute-return types. But it’s out there and we’ll be updating stats monthly. Note that because this is not an absolute-return program, we will not be including it in our combined performance graph.

* * *

The equity curve below shows our combined real-time monthly performance vs the S&P 500, assuming an equal investment in each family’s flagship program: MarketSci ProFunds (through 08/2009), 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

To stay up to date with what’s happening at the MarketSci Blog, we recommend subscribing to our RSS Feed or Email Feed, or following us on Twitter.

*** CLICK TO ZOOM ***

This is our monthly health check of short-term mean-reversion in the US market.

Why a health check? Because short-term MR (by “short-term”, think for example RSI(2)) is so important to what we index swing-traders are doing right now because at this moment in history, it’s the most effective directional trade.

See the July and August reports for details re: how this report is calculated. In a nutshell, we’re using both daily mean-reversion (the likelihood that down days follow up days, and vice-versa) and RSI(2) as simple proxies for all other short-term strategies. I would never recommend anyone actually trade either as I’ve defined them here, but I think they make a good proxy because this tendency for the market to retrace very recent gains is exactly why all of these short-term indicators work the way they do.

What is the state of short-term mean-reversion?

Very much under the weather.

3-month averages for the two metrics I think best capture short-term MR, “Return vs Vol” and “RSI(2) Stretch” (upper right and lower right graphs), are hovering near their lowest levels since short-term mean-reversion became the play du jour around the turn of the century. Both metrics have reached much lower points in individual months, but have rarely failed this consistently for this long.

Back in the real-world, I’ve seen a number of alt.-investment programs, that I know are trading short-term MR in some form, go off the rails the last few months. We’ve been fortunate that, despite leaning heavily on short-term MR in our own programs, our combined performance has been more or less flat.

Do I think short-term MR will stage a comeback? Yes. I think the breakdown over the last few months is tied to the strong protracted rally, but that this slow grind up will come to an end and that short-term MR will again be the play du jour once we get to the other side. Do I know that with any certainty? Absolutely not.

More to follow.

Happy Trading,
ms

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It’s been eerily quiet around the MarketSci blog the last month or so.

I appreciate all of the emails asking if we’ve given up on this blogging thingamajig or if we got hit by a milk truck. Luckily, it’s nothing that sinister.

I’ve been going to extremes lately.

On one hand I’ve been very focused on some serious for-profit issues that have been on my to-do list for a while. And on the other I’ve been getting a bit of very much not serious R&R in – mostly training the new Lab-mix puppy to be a running/hiking machine (and not eat my shoes).

Unfortunately, that hasn’t left much time for everything in the middle (like this blog), but I’m starting to feel the creative juices flowing again, so expect more geekery in the near future.

P.S. there are now two of us in the investment blogging world: checkout Michael Stokes, President of the Financial Coaching Group [not me]. Damn I hope this guy makes it big so I can ride the coattails of all his Google searches =)

Happy Trading,
ms

To stay up to date with what’s happening at the MarketSci Blog, we recommend subscribing to our RSS Feed or Email Feed, or following us on Twitter.

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.

Apologies for the late report this month…been buried.

August was a very blah month across the board as most of our strategies had little exposure to the market as a result of the abnormal market filter.

RH was the month’s leader, making a bit of headway chipping away at July’s drawdown, and is up again nicely so far this month. YK gave back a bit of last month’s big gains and is still struggling at the moment.

Two Big Changes to Talk About

First, the original MarketSci strategies…

We’ve been alluding to this coming down the pipe for a while: I officially retired the original MarketSci strategies effective the end of August.

These were my first foray into developing programs professionally, and they’ve had an outstanding run over the last 44 months. The flagship ProFunds program was ranked #1 at Theta Research for risk-adjusted performance in its first two years trading, and we were able to do some real good for folks. I’m happy about that.

But I just don’t feel that these strategies represent our best work today, or are as robust as the strategies I talk about on this blog or those we represent through our Timer Seeds program. Though the loss of revenue is always painful, I can’t accept not putting our best foot forward.

Of course, unlike some less savory characters in this business, we will be carrying the original MarketSci programs indefinitely in our summary stats and combined portfolio graph (ending 08/2009), and independently-verified results will always be available.

And second, to RH…

For the uninitiated, RH is a product of our Timer Seeds program and is the only strategy I talk about on this blog that I didn’t directly develop. Because of that, and the fact that I try not to impose my own thoughts on our Timer Seeds (lest they all start to look the same), RH doesn’t include things like the abnormal market filter.

But July made painfully clear that group-think be damned, some of those concepts needed to be applied to RH, and I’ve worked with the developer to do just that.

A new overlay was applied to RH late in August. By overlay I mean that I’m not changing the developer’s underlying signal (long/short/cash) so RH’s success or failure is still squarely on the shoulders of RH. But we’ll be reducing exposure at times to reduce downside volatility (hopefully without sacrificing return). Managed account holders will have already noticed drastically reduced position sizing.

* * *

The equity curve below shows our combined real-time monthly performance vs the S&P 500, assuming an equal investment in each family’s flagship program: MarketSci ProFunds (through 08/2009), 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

To stay up to date with what’s happening at the MarketSci Blog, we recommend subscribing to our RSS Feed or Email Feed, or following us on Twitter.

*** CLICK TO ZOOM ***

This is our monthly health check of short-term mean-reversion in the US market.

Why a health check? Because short-term MR (by “short-term”, think for example RSI(2)) is so important to what we swing-traders are doing right now, because at this moment in history, it’s the most effective directional trade.

See the July report for details re: how this report is calculated, but in a nutshell, we’re using daily mean-reversion as a simple proxy for all other short-term strategies. Put another way, we assume a trader went long the S&P 500 at the close if the market closed down, and short if it closed up, and look at the results each month in terms of (a) average daily return, (b) average return relative to volatility (daily SD), and (c) win percentage.

I would of course never recommend anyone actually trade this strategy, but I think it makes a good proxy, because this tendency for the market to retrace very recent gains is exactly why all of these short-term indicators work the way they do.

A New Addition: RSI(2) Stretch

Note that I’ve also added a fourth metric: RSI(2) Stretch.

The problem with looking at short-term MR in a purely binary sense (up days and down days) is that it doesn’t capture the fact that at this moment in history the more the market becomes stretched, the more likely it is to retrace (like a rubber band).

For example, consider four fictional daily returns: -0.3%, -0.3%, -0.3%, +0.5%.

Binary daily mean-reversion would see that sequence as a failure because it would have been equally long on days 2, 3, and 4, resulting in a negative total return. But an indicator based on the stretch of the market (like scaling in/out of RSI(2)) might not have ratcheted up exposure until day 4 (if at all), meaning that that sequence might have been profitable.

The RSI(2) Stretch graph tries to capture this by looking at the return vs. vol. of a strategy that goes long or short based on how close RSI(2) is to its extremes. An RSI(2) reading of 0 would equal 100% long exposure, a reading of 25 = 50% long exposure, 50 = no exposure, 75 = 50% short exposure, 100 = 100% short exposure, etc (and all points in between).

So what is the current state of short-term mean-reversion?

Depending on how you play it, it’s been under the weather the last two months.

July performed well in a binary sense, but very poorly in terms of stretch. Interestingly, I saw this same disparity in my own strategies. YK approaches daily MR in a more binary sense and performed very well last month (+13.0%), while Scotty looks at daily MR in a more stretched sense and performed poorly (-1.4%).

This month, it was binary MR that performed poorly, while stretched MR was neutral. And more importantly, 1-year averages for the metrics that matter most have fallen very close to 5-year averages.

Short-term MR has clearly lost some of its steam relatively to its peak about a year ago. I’m not going to get too excited about these results just yet, but it’ll be very interesting to see if this trend continues over the coming months.

Happy Trading,
ms

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I’ve covered my five reasons for trading leveraged mutual funds before. A refresher: (a) high daily correlation to the underlying index (unlike ETFs, futures, etc.), (b) no per-trade transaction costs, (c) identical results regardless of account size (i.e. perfect scalability…to a point), (d) selective leverage, and (e) the ability to fire and forget and do fun things in life besides working.

Obviously these vehicles aren’t as popular as their intraday cousins ETFs, so I guess it’s natural that some folks have some grossly inaccurate misconceptions about how they work. A little re-edumacation …

Misconception #1: Day Trading

Leveraged MFs are in most ways just like regular MFs. Like regular MFs, they’re only priced once per day at the close of trading (except for Rydex which offers two prices per day on some of their funds). So no, leveraged MFs can’t be day traded.

This of course brings up the most significant drawback of leveraged MFs (or any MF for that matter) – no intraday orders, but that’s a larger topic for another day.

Misconception #2: “Gaming” the Fund

These are open-end funds (vs closed-end) meaning NAV doesn’t drift from the value of the underlying assets, so no amount of activity is going to game the NAV itself. Could huge inflows/outflows game the underlying contracts the firm is using to replicate the indices? I guess if the fund got big enough it’d be theoretically possible.

But at the end of the day, this is all a moot conversation. A simple test: make a spreadsheet with 3 columns of quotes: (1) the S&P 500 index, (2) the S&P 500 ETF SPY, and (3) a Rydex or ProFunds MF tracking the S&P 500. Calculate the daily % change of each and then look at the r-square between the daily changes of the ETF vs the index, and the MF vs the index.

Notice how ETF r-square drifts from 1.0? Notice how MF r-square is almost a perfect 1.0? Conclusion is plain: these funds are a near-perfect proxy (less annual expense ratio and occasional snafu) for the underlying indices, and therefore, implicitly are not being gamed.

Misconception #3: Capacity

This one is partially misconception and partially a real tangible issue that I hope we’ll be forced to face one day.

If you look at daily total assets (here I’ll use Rydex H Share Class) you’ll notice that they swing wildly from day-to-day. For example, the S&P 500 2X long fund has swung from a low of $94 million to a high of $360 million in August (and from $110 to $348 million for the 2x short version).

Of course, that’s because they’re open-end funds (meaning the number of shares aren’t fixed) that are meant to be timed, and investors are doing just that.

We’ve spoken with trading heads at both firms to get an idea of what would be the maximum we could swing on a daily basis before they’d start limiting our trades. My best guesstimate, assuming we were trading the maximum at both firms (plus Direxion, which we rarely trade at now) is a number pretty far north of what we currently manage. So for now, we’re safe.

If we hit that number, we’ll have to move into more liquid intraday vehicles (ETFs, futures, etc). Would that negatively impact our performance? On the surface, because of trading frictions and tracking error, yes, but it’d also give us an opportunity to do more with entry/exit pricing, so maybe not. Maybe it would actually be a boon. I don’t know.

To be honest I haven’t put too much thought into this potential problem. My line of thinking has always been that if we were to hit the magic number it’d mean we had a pretty big chunk of change under management, and would have all the resources in the world to make the problem go away.

Of course, this also means that we’ll never manage a billion dollar fund with our trading approach, but that isn’t even remotely close to what I want to be doing (no monkey suits for this kid).

Happy Trading,
ms

P.S. When I talk about leveraged mutual funds, it is in no way an attempt to convince anyone to trade the way I trade, only to explain why I trade the way I trade. All in this zero-sum game have to find their own trading niche. This one is mine. 

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Time to (re)focus.

This is the updated short list of things on the drawing board I’m most excited about. Some are carryovers from my previous list (humor me, this is more for me than anyone else), some new, and some done.

Disaster Overlay [New]

I’ve mentioned this before on the blog – the concept is to use options to add a simple “disaster overlay” to each of our portfolios to protect against big market moves over short periods of time (a’la Oct. 1987 or Sep. 2001).

After not giving the concept the time it deserves for way too long, I’ve done the only sensible thing and turned it all over to another developer I know and trust (and you probably do too) to design and implement. I think it’s going to be feasible to roll this out to both subscribers and managed accounts. Expect more this year.

Taking the Pulse of Short-Term Mean-Reversion [Done]

We issued the first monthly report of what I expect to be an ongoing series. The format isn’t set in stone and feedback is welcomed.

RH Modification [Done]

July made painfully clear that, group-think be damned, some of the concepts I talk about like the abnormal market filter needed to be applied to Timer Seed RH. I’ve worked with the developer and we have a solution that’ll be rolled out with Monday’s trade. I’ll be describing the mod in more detail in the monthly performance update.

Combining Strategies [From Previous List]

So given a group of successful individual strategies from many different developers, how do we intelligently dynamically allocate funds between them? This will of course come into the play with the performance-based product being launched. I’m working on a framework that goes beyond the traditional MPT approach to allocation, and should have a very high-level description out shortly.

Timing the Strategy [From Previous List]

This is the concept that’s been getting a lot of blogosphere chatter recently re: taking an effective strategy and then timing that strategy itself (level 2 timing or whatever we want to call it). I had considered dropping this one from my list because I don’t think there’s a one-size-fits-all answer, but I think it at least deserves an example. Whatever that example is will of course respect this critique.

Interesting Strategy Ideas

Two that have the wheels turning: (a) predicting growth vs value outperformance in order to trade the broader market, see What Makes the Market Go Zoom, Growth or Value?, and (b) using either just the overnight market or just the daytime market as an input into a trading strategy, see RE: Why Daytrading Stocks in the U.S. is an Increasingly Limited Proposition.

Back to work.

Happy Trading,
ms

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A little history. I wrote this post re: leaving the blog aggregators because I didn’t want to contribute to a place that was promoting less-than-savory characters.

As should have been expected, The Fly writes this compelling piece and my email and twitter and comment section blows up with such well thought out rebuttals as “Fly is God”, “Fuck you”, and “You’re racist against Asians” (that last one is hilarious for anyone that knows me…I wonder if I should let my Japanese Grandmother and Chinese/Taiwanese fiancé know).

But then Mr. John Lee, whom I probably too-crassly labeled “that guy”, writes this.

Ignoring all the chest pounding and fifth grade antics for a second, I gotta’ say I’m a little impressed.

Not necessarily impressed with the track record (yet) mind you, it’s still short to judge performance relative to claimed performance, but damn it’s a step in the right direction.

You see, you get to pound your chest and be a big shot when you can prove your returns, and over this very short period of time, Mr. Lee did just that. That’s a beautiful thing. That’s what this industry needs.

Next step?

Keep maintaining that track record (and if it’s not too much to ask, post all the track records). Make them front and center in what you do (don’t wait for some third-tier blogger to call you out on them). Don’t let them conveniently disappear when times get tough. Let investors monitor your performance good or bad as it happens. I speak from experience when I say that at the end of the day, it might drop your status as an untouchable trading god just a bit, but it will raise your status in the eyes of investors and financial professionals who drive real money.

Well done Mr. Lee.

Happy Trading,
ms

P.S. Mr. Lee’s post brings up good questions about Timer Trac – I’ve asked TT repeatedly to clean up that verbiage because we’ve had similar questions from third-parties we work with. Mr. Lee forgets that Timer Trac tracks leveraged mutual fund traders like us so as long as the email, phone call, whatever is time stamped prior to the fund cutoff time, you know exactly when the order is executed (i.e. this isn’t intraday trading). I would suggest contacting TT directly if any of that is in doubt.

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I make a pretty big stink about the need for real-time independently-verified results.

Now obviously not everybody requires verified results. I have huge respect for all the folks on my blogroll, but very few of them are verified in any way. So be it. Those folks aren’t marketing their super-duper trading prowess, they’re conveying ideas, thoughts, historical studies, whatever.

That’s what 95% of the content on this blog is about. But things change when you start talking about your uber-returns and marketing your abilities as a trader.

An illustration…

[logarithmically-scaled]

I’m using our own YK Strategy as an example because of the epic upchuck we experienced in October of last year. My regular readers have heard this story a hundred times, but for the uninitiated, see the strategy details.

In grey are results prior to the addition of the abnormal market filter, and red everything after (through the end of last month). These are the same results you’ll find in our independently-verified track record, and the same results we will carry (indefinitely) in all of our summary and combined stats.

October was gut wrenching, but despite it, the program has returned 88.6% annualized in 16 months of trading. Not bad.

Now, just to prove my point, let’s say we could magically make the bottom 5% of all of our daily trades disappear. That shouldn’t be too hard if I didn’t have a third-party looking over my shoulder right? Just 1 out of every 20 trades I conveniently gloss over, or pretend they were but a small bit of my portfolio, or that I had hedged them with blah, blah, blah.

The results…

[logarithmically-scaled]

Poof! 420% annualized and an equity curve as smooth as Woodshedder’s mullet. I am now a trading god.

Wait, there’s more…

Verification in our case is simpler than most because we just trade a handful of leveraged mutual funds and we’re very specific about the % of our portfolio we’re allocating to each. There are no per-transaction costs or slippage, and there is only one price per day to consider.

But imagine if all of those nasties were also a factor. Imagine the games I could play.

Happy Trading,
ms

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In my new attempt to be a more vertical blogger, this is a follow up to Dr. Brett’s post: Why Daytrading Stocks in the U.S. is an Increasingly Limited Proposition.

Dr. Brett shows that in very recent history, the market has begun shifting away from making big moves in the daytime market (open-to-close) and towards making them overnight (close-to-open), in theory, making it more difficult for daytraders to capitalize on daytime movement.

Daytime vs Overnight: Two Very Disparate Markets

We covered the disparity in performance between the daytime and overnight markets before in another Dr. Brett-inspired post: The Markets are Nocturnal.

[logarithmically-scaled]

Above is a graph of two “strategies” trading the SPY from 02/1993 to the present. The red line represents only going long in the daytime from the open to the close each day, and the blue line in the overnight from the close to the open. This is a proof of concept, so these results are frictionless (i.e. do not account for transaction costs or slippage).

Clearly over the last 15+ years, bullish periods have tended to play out in the overnight market (blue) and bearish periods in the daytime (red). Further, daytime moves have been on average about 70% more volatile than overnight ones.

Put another way, over the long-term, daytime movements have tended to be much larger, but not really go anywhere (except maybe down).

But Are Daytime Moves Waning?

Dr. Brett’s point was that the size of daytime relative to overnight moves in 2009 has been waning compared to 2007-08.

The graph below shows a 3-month rolling average of the % of days where the daytime move was larger (in absolute terms) than the preceding overnight move.

The graph shows that daytime moves this year have exceeded the size of overnight moves a bit less frequently than average, and are now near historical lows. So I agree with that very specific point from the good doctor, but I think there’s one more part to this story.

The graph above simply shows a rolling 3-month average of the standard deviation of daily (log) changes in the SPY daytime (red) and overnight (blue) markets.

Even though the size of daytime moves relative to the overnight has been waning, there’s still just as much daytime movement today as 2007-08.

In Short…

I agree with Dr. Brett that if this tendency for daytime moves to become smaller than overnight ones continues, it’s an indication of the rise in importance of foreign equity markets as a driver of the US, and that if that was to happen, it would put a dampener on the daytraders’ parade.

That’s all possibly on the horizon, but as long as the size of daytime moves remain inline with historical norms, I think it’s premature to say that the rainy days have come quite yet.

Happy Trading,
ms

Edit: response to a very good reader comment. Nissim postured that comparing the high/low range (instead of open to close) relative to the overnight move, is a better indicator of daytime movement for daytraders. I had the same thought but didn’t include it for simplicity. Using high/low instead yields basically the same result: (a) the high/low range relative to the overnight move is near an all-time low, but (b) the high/low range is still above historical norms.

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