A strategy that has been working very well for trading the S&P 500 since the beginning of our current bear market in late 2007 is playing the relative performance between the S&P 500 (SPY) and the energy sector (XLE).
The graph below shows a simple leader/laggard strategy (red) versus the S&P 500 buy & hold (blue) from 10/2007: go long the SPY at today’s close if XLE lagged the SPY today, and go short the SPY if XLE led.
[logarithmically-scaled]
This strategy predicted 60% of days correctly with winners 1.2x larger than losers.
I call this a theme (implying that it’s temporary) because prior to the start of this bear market it didn’t provide any consistent edge. See graph below of the same strategy from 1999.
[logarithmically-scaled]
The theme has been strong enough for the last year that I’ll be tracking it on the State of the Market report beginning tomorrow.
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.
Filed under: Stock Market Sectors, Trading Strategies | 0 Comments
Note: daily signal updates for the adaptive version of this strategy are available in the State of the Market report.
Probably the simplest indicator I’ve ever talked about on this blog is daily follow-through. By “follow-through” I mean if the market is up today how likely is to be up tomorrow, and if it’s down today, how likely is it be down tomorrow?
In my previous study, I showed that this concept has had a consistent impact on the stock market over the last 60 years, but that the direction of the impact has evolved. To illustrate, the following graph shows going long/short the S&P 500 tomorrow (at today’s close) when the market closed up/down today from 1955 (frictionless).
For 40 years, follow-through was consistently positive – down days tended to be followed by down days (and vice-versa). But around 2000, the market made an abrupt change (dotted line) – down days now tend to be followed by up days (and vice-versa).
Adaptive Strategies
The markets are constantly in flux and the themes that work today won’t work tomorrow. And because of that, I’ve made the case that our ultimate goal should be strategies that learn from the markets…that are adaptive.
Below is this same follow-through strategy (blue) overlaid with the adaptive approach (red) plus the abnormal market filter (green) that I use in the State of the Market report.
[logarithmically-scaled]
Note how the adaptive strategy actually trailed the original strategy for quite a few years as it began to detect the change in the market and take less aggressive positions. But as the original strategy began to invert, the adaptive strategy evolved with the change. The graph below of annual returns for both strategies makes this a bit clearer.
Also note that the addition of the Abnormal Market Filter reduced total returns, but increased risk-adjusted returns (see table below) because it reduced portfolio exposure when the market became stretched and (by my theory) unpredictable.
Calculation Notes
For the adaptive strategy, I’ve assumed that the percentage of capital invested each day equaled the 5-year confidence figure that I would have provided in the SOTM report. For the abnormal market filter, I’ve assumed that the trader then reduced that percentage of capital by the SOTM report’s “percentage abnormal” figure.
Note that this is a proof of concept so this study is frictionless (no transaction costs or slippage). Unless you trade leveraged mutual funds from Rydex, ProFunds, or Direxion like I do, trading frictions would impact these results.
Closing Thoughts…
The main points I hope I’ve conveyed in this post are: (1) very simple indicators like this one can be very powerful (remember, we don’t have to be perfect - we just have to compound small quantifiable edges over time), (2) the fundamental characteristics of the market are constantly in flux, (3) static strategies will eventually fail, and (4) adaptive strategies, while not a magic bullet, are a step in the right direction towards permanently wrangling these unruly markets.
Happy Trading,
ms
P.S. some previous posts on designing adaptive strategies: How to Build an Adaptive Strategy, a Simple Example, and Coping with Abnormal Markets.
To stay up to date with what’s happening at the MarketSci Blog, we recommend subscribing to our RSS Feed or Email Feed.
Filed under: Evolving Markets, Follow-Through, Stock Market Mechanics, Trading Strategies | 1 Comment
This is a quick test of a Jack Ablin strategy shared over at the Big Picture. To be fair, I can’t find where Ablin originally discussed this strategy, so I don’t know what context he put it in, but the rules appear to be to go long the S&P 500 when it closes more than 5% above its 200-day moving average and hold until it breaks 5% below its 200-day MA. See graphic from Ablin (click to zoom):
For comparison’s sake, I’ve also included a simpler strategy in this test: buy when the S&P 500 crosses over its 200-day MA and sell when it crosses below.
The results for Ablin’s strategy (red) and the simpler version (green) trading the S&P 500 frictionless and with no return on cash from 1951:
[logarithmically-scaled]
And for the number lovers:
With the exception of the very narrow window of time from Ablin’s original graphic the super simple strategy outperformed both Ablin’s variation and the broader market. And this is a good example of the problem with drawing conclusions from just part of the story.
Which one is going to be better tomorrow? I have no idea. But I have a sneaking suspicion that the 5% bands were added because without them the strategy would have entered and exited the market prematurely quite a few times over the last 10 years (and that’s never good for impressing people). But armed with the bigger picture over the last 50+ years, I would guess that some folks would wager that the simpler strategy stands a better chance of prevailing in the future.
Just my $0.02.
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.
Filed under: Trading Strategies | 8 Comments
In this post, inspired by Crossing Wall Street, I’m going to look at the spread between AAA and BAA-rated corporate bonds and show that, generally speaking, high spreads have been bullish for the stock market, except when spreads reach extreme heights (like they have at this very moment).
A little perspective – right now the corporate bond spread is at levels not seen since 1943. See graph below. Note that AAA (blue) and BAA (green) yields are on the left axis and the spread (red) as a percentage of the AAA yield is on the right axis.
All interest rate data from St. Louis FRED Database
Conventional wisdom says that a high spread between the yields on top-rated AAA and less creditworthy BAA corporate bonds is a bearish sign for the stock market. It signals that lenders are risk-averse because they are paying a significant premium (in the form of a lower yield received) for high-quality companies.
But excluding times of severe market stress, this relationship has been contrarian (the stock market outperforms when things look particularly bad). As evidence, in the graph below I’ve divided the weekly spread since 1962 (as a % of the AAA yield) into four quartiles from lowest (purple) to highest (red), and shown the results of trading the Dow the following week.
[logarithmically-scaled, data in monthly-intervals]
Clearly, with the exception of our current crises, high corporate bond spreads have been followed by stock market outperformance.
But as I mentioned previously, in times of severe market stress, this contrarian relationship falls apart. The graph below shows the same 4-quartile test, this time using monthly data from 1928 (because more data is available in monthly intervals) in order to capture those extremely high spreads in the 30’s and 40’s.
[logarithmically-scaled, data in monthly-intervals]
At above-average spreads (green), the conclusion is still the same, the market performs well. But at extreme historic levels (red), the market has bombed. It’s noteworthy that our current bear market slipped into this highest quartile before it began its death-spiral.
Closing thoughts…
I think a similar conclusion could be drawn about a lot of contrarian indicators. To paint a picture – the market is like a little kid playing with a rubber band…stretching it back and forth, and back and forth in a predictable rhythm…until one day it breaks and pokes out his eye.
Knowing the difference between stretching and breaking is really the hardest part of being a contrarian trader.
Happy Trading,
ms
P.S. this post reminds me of my report Rising Credit Spreads and the Stock Market and makes me wonder if the results had been different had pre-1953 data been included.
Geek Note: I calculated the spread as a % of the AAA yield i.e. ((Baa – Aaa)/Aaa) to try to normalize the data and create a conclusion that held true regardless of whether all bonds were high or low at any given time. Usually the absolute spread (Baa – Aaa) is used instead. I tested the absolute version as well and came to basically the same conclusion, although less clearly defined as the normalized version.
To stay up to date with what’s happening at the MarketSci Blog, we recommend subscribing to our RSS Feed or Email Feed.
Filed under: Treasuries & Interest Rates, Uncategorized | 2 Comments
I’ve shared quite a few good trading strategies on this blog. Some of those have been a little complicated and I’ve always known that regardless of how good the strategy was, the average investor would never be able to apply them to their own trading because they just didn’t have the right tools. So…we have a new feature:
The State of the Market Report (also, see link on top navigation bar)
This report will be a snapshot of what some of the strategies we’ve discussed are predicting each day about the following day. I tried to pick a diverse group because I didn’t want to just look at the market through the market’s eyes. The report will also use the VIX, TED spread, Treasuries, SOXX, and time-based indicators.
Adding another layer of über-cool, I’ve programmed two additional concepts into the report: confidence and adaptation (checkout the report for additional information). This is a living document, so some of the future strategies we discuss on this blog will also be added to the report.
Lastly, remember, mechanical strategies are simply projecting future probabilities based on past results. Even if we knew those future probabilities with absolute certainty, we would still be wrong (often). As I wrote recently, we don’t need to be perfect to win this game – we just have to find enough quantifiable edges to be right a bit more often that we’re wrong. And that’s what this report is about.
I’m excited about this new feature…it should be fun to watch her grow. At some point this may become a subscription service for a small pittance just to justify the time it takes my person to put it together each day. But for now, it’s just another value-added service of being a MarketSci Blog reader.
I’ll leave you with a few recent reports just to give you a feel for how the report moves (click to zoom).
Prediction for Thursday, 11/13 (actual return: +6.9%)
Prediction for Friday, 11/14 (actual return: -4.2%)
Prediction for Monday, 11/17 (actual return: unknown, but based on the overnight market as of the time I’m writing this, more bearish than bullish)
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.
Filed under: Random Stuff | 5 Comments
Edit: daily signals for this strategy are available in the State of the Market report.
In my last post I tested two long-term strategies from Mark Boucher’s “The Hedge Fund Edge” that were based on measuring the attractiveness of bond yields in order to trade the S&P 500. In this post, I want to offer an alternative (and more effective) short-term trading strategy that exploits this same idea.
The premise of this strategy is that stocks and bonds are always in competition for investors. As bond yields become more attractive, investors move away from stocks and into bonds, which is of course, bearish for stocks.
The graph below shows the S&P 500 (blue), the following strategy (green), and for comparison’s sake, the inverse of the strategy (red) from 1962:
[logarithmically-scaled]
Strategy Rules: Go long the S&P 500 at today’s close if the 5-day exponential moving average (EMA) of 10-year treasury yields is falling. Close the position and move to money market when the EMA is rising. This test is frictionless, and to compare it accurately to Boucher’s strategies, I’ve assumed money market returns equal to half of the nearest 3-month US Treasury bill.
In a nutshell, the strategy is bullish on stocks when treasury yields are falling relative to very recent history, and neutral/bearish when yields are rising.
The strategy did a good job outperforming the market in all periods with the exception of the late 90’s and mid 2000’s when, one could argue based on the subsequent bear markets that, equities became overinflated relative to treasuries. Drawdowns in all periods were managed well.
For the number-lovers, below are stats for this strategy relative to both the S&P 500 and strategy #1 from my previous post:
CLOSING THOUGHTS
A lot of improvements could be made to this strategy (think 5-day EMA relative to a longer-term EMA, measuring the EMA’s ROC, etc.) but I think I can say that this shorter-term strategy as it looks now is “better” than the longer-term version - and not just because of its historical performance.
In my opinion, short-term strategies (when trading assets that minimize/negate transaction costs, such as leveraged funds), are better than long-term ones because: (a) there are more historical trades to study, reducing the degree of curve-fitting, and increasing the probability the strategy will work in real-time, (b) it’s much easier to determine if a short-term trading strategy has stopped working – reviewing the results of say ten real-time trades would take about 2 months for the short-term strategy, but about 16 months for the long-term version, and (c) I’ve found that long-term market inefficiencies are much more unstable and likely to be consumed by the market.
Regardless of your opinion on the above, I think we can agree that strong treasury yields have been bad medicine for equities, even in very short timeframes.
Happy Trading,
ms
Geek Note: There are two generally accepted ways to calculate an EMA that produce slightly different results. Here I’ve used the ((1/Period)*2) method. If your charting program uses the (2 / (Period + 1)) method, simply reduce my period by one. For example, if I’ve used a 5 period EMA, the alternate EMA would be a 4 period EMA.
To stay up to date with what’s happening at the MarketSci Blog, we recommend subscribing to our RSS Feed or Email Feed.
Filed under: Trading Strategies, Treasuries & Interest Rates, Uncategorized | 12 Comments
This is a test of two of Mark Boucher’s strategies from his book “The Hedge Fund Edge”. I have to rat myself out here – I usually can’t sit still long enough to read whole books (hence the reason I read short and concise blogs) – so I’m basing this test on a description from a great blog, Trader’s Narrative.
Both strategies are based on the idea that stocks and bonds compete for investors, and when bond yields are attractive enough, investors will begin to move away from stocks and towards bonds (a bad omen for stocks).
STRATEGY #1: 1-YEAR CHANGE IN 10-YEAR TREASURY YIELDS
Strategy in green and S&P 500 buy & hold in blue from 1963:
[logarithmic-scale, return on cash = half of nearest 13-week UST]
Rules: Go long the S&P 500 at today’s close when 10-year US Treasury yields have increased less than 9% over the last 12 months. Close the position and move to money market when yields have increased by more than 9%.
Note that when I say 9%, I mean as a percentage of the previous rate. So a move from 5.0% to 5.5% would be an increase of 10% (not 0.5%). Also note that Boucher originally used 30-year treasuries, but more data is available for the 10-year flavor and the test results are basically the same.
Since 1963, this strategy averaged 7.6 trades per year and was long 70% of the time. In the 38 years prior to 2000, this strategy consistently matched or outperformed the market, and sidestepped all major drawdowns. That’s the good news. The bad news is that since 2000, the strategy has done poorly. Treasuries yields have mostly fallen over that time, but unfortunately, so have stocks.
Stats for the number-lovers are included below. I’ll share my thoughts about this strategy after I discuss strategy #2.
STRATEGY #2: 10-YEAR vs 3-MONTH TREASURY YIELDS
Strategy in green and S&P 500 buy & hold in blue from 1962:
[logarithmic-scale, return on cash = half of nearest 13-week UST]
Rules: Go long the S&P 500 at today’s close when the ratio of 10-year to 3-month US Treasury yields is greater than 1.15. Close the position and move to money market when the ratio is less than 1.15.
Since 1962, this strategy averaged 7.1 trades per year and was long 66% of the time. Results were similar to the previous strategy, although this version was long during the 1987 crash (probably more coincidence than a statement about the strategy) and underperformed the market a good deal during the bull run of the late 90’s.
And for the number-lovers:
MY THOUGHTS
The results show that these particular approaches to measuring the attractiveness of bonds versus stocks worked well in the distant past, but not so well in the last decade or so.
I am a pretty active trader, usually holding positions between 1 and 5 days. I’m very wary of systems like this one that take such long-term positions for two reasons: (a) there are so few historical trades to study that the risk of curve-fitting is increased and (b) because there are so few trades, we usually have to test a very long period of time, and as we’ve shown multiple times on this blog, the markets are constantly evolving. I think the failure of this strategy to outperform in the last decade can be attributed to these points.
In my next post, I’m going to demonstrate a system that takes a much shorter-term, and I think much more effective, approach to trading the bond vs stock relationship. More to follow.
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.
Filed under: Trading Strategies, Treasuries & Interest Rates | 11 Comments
When Does This Ride End?
Interesting graph reproduced from a post by Macro Man showing daily returns on the Dow since late 1928 (blue) along with bands two standard deviations around zero (1-year lookback). Click to zoom.
A few non-surprises here. (1) Volatility from 2004 through most of 2007 was close to historical lows. (2) Current volatility is at levels only seen a few times in market’s history: the late 1920’s, various points in the 1930’s, and 1987. And (3) the market exceeds two standard deviations far more often than it would if it followed a random walk (an assumption of modern finance that is fatally flawed).
The reason that this chart caught my eye was how sustained volatility was in 1920’s and 30’s. We went years and years experiencing the kind of gut wrenching gyrations we’re seeing now. I know it’s dangerous to compare this market to that one, but if we allow history to be our guide here, this ride could be a long way from being over.
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.
Filed under: VIX & Volatility | 3 Comments
Edit: daily signals for this strategy are available in the State of the Market report.
This post will be more complicated than most I write, but I think folks interested in the VIX and volatility and what they tell us about the markets will find this to be a new (and I hope useful) concept. Unfamiliar with the VIX? Read VIX & More’s primer.
As I discussed in The VIX isn’t Magical, the VIX is usually pretty predictable. Even though the VIX is meant to be forward-looking, it tends to just reflect recent past market volatility. To illustrate, the graph below shows the VIX (red) relative to the rolling 20-day volatility of the S&P 500 (blue) YTD.
Note how generally speaking the two (one looking backwards, the other supposedly forward) follow each other closely. Occasionally however, the two diverge. This was seen in late-Oct./early-Nov. when the VIX fell sharply without a similar change in historical volatility (red arrow).
Bill Luby of VIX & More has been looking at how the stock market responds when divergences such as these occur. The strategy I’m about to share is a contribution to that discussion. On the surface, my conclusions contradict Bill’s but that’s because we’re looking at different timeframes (I’ll discuss further at the end of this post).
TRADING STRATEGY
First, let’s look at the graph above in a different way. The graph below shows the difference between the VIX and 20-day historical vol. in percentage terms in blue. Bill refers to this as the “VIX Spread”. In red is a 50-day moving average of this spread.
Note that generally the VIX runs hotter than historical volatility (i.e. the blue line is greater than zero), but occasionally (such as now), it dips below historical vol.
Next, let’s look at the results of trading strategies that go long the S&P 500 tomorrow when the VIX Spread today is below (red) or above (green) the 50-day moving average. Geek note: this test is frictionless.
[logarithmically-scaled]
The graph shows that historically the market has been stronger when the spread between the VIX and historical volatility has been lower, or put another way, when the VIX foresees less future volatility than it usually does relative to past volatility.
This approach stood up very well against the bear market of the early-2000’s and reduced volatility and average drawdowns in our hypothetical portfolio by about 30% and 70% respectively over the entire test, while still matching market returns.
Note however that the test above only extended to May of 2007. This relationship fell apart (reversed actually) in the bear market that began late in 2007. The graph below shows the same strategy traded to the present.
[logarithmically-scaled]
The question becomes: has this VIX Spread trading strategy temporarily or permanently changed course?
It’s hard to say. Most of the losses came in October of this year and as I’ve discussed before, most contrarian indicators completely fell apart in October. Savvy readers will note that this strategy is also contrarian in a roundabout way. High volatility tends to accompany down markets (which Bill touched on here) so buying when the VIX is “relatively” low compared to past volatility could be interpreted as trading against downward pressure.
Note that these results appear to contradict Bill’s conclusions about the VIX Spread (namely, that high spreads are bullish), but I think that’s because Bill’s results are looking at returns further out (over the following couple of weeks) vs the next day. I’m going to continue poking and prodding at this strategy to include Bill’s more long-term oriented observations as well.
More to follow.
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.
Filed under: Trading Strategies, VIX & Volatility | 15 Comments
Roundup of my posts over the last week discussing the TED spread and it’s relation to the US stock market. Now I’m free to move on to other geekery.
TED Spread as an (Inconsistent) Lagging Indicator
Summary: since 1995, the relationship between concurrent changes in the TED spread and the stock market has been inconsistent and/or non-existent, but might be rearing its head again with the current credit crises.
TED Spread as a (Pretty Good) Leading Indicator
Summary: interpreted correctly, the TED spread has done a pretty good job over the last 20 years predicting monthly moves in the stock market.
Summary: additional stats and graphs for the TED-based trading strategy shared in the previous post.
TED Spread is Very Predictable
The post that never was. I didn’t write this post up because I think very few people could make use of the observation, but in a nutshell, the TED spread has been very predictable. It exhibits a strong tendency to reverse in both daily and monthly timeframes, or put another way, up days tend to follow down days (and vice-versa) and up months tend to follow down months (and vice-versa). This observation has been consistent over the entire last 20 years with the exception of the bull market in the late 1990’s.
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.
Filed under: Treasuries & Interest Rates | 2 Comments
Recent Entries
- Bear Market Theme: S&P 500 vs Energy Sector Strategy
- The Simple Made Powerful with Adaptation
- Test of Ablin Trend Following Strategy
- Corporate Bond Spreads: Bullish Until They’re Bearish
- New Feature: The State of the Market Report
- Trading Strategy: My Spin on Using Treasury Yields to Trade the S&P 500
- Test of Boucher Strategies: Using Treasury Yields to Trade the S&P 500
- When Does This Ride End?
- Trading Strategy: the VIX Spread and the Stock Market
- Roundup: TED Spread and the Stock Market
- Right a Bit More than We’re Wrong
Categories
- Evolving Markets (14)
- Follow-Through (13)
- Gold & Precious Metals (3)
- International Markets (6)
- My Performance (5)
- Options-Expiration (3)
- Put-to-Call Ratio (3)
- Random Stuff (27)
- Stock Market Mechanics (14)
- Stock Market Sectors (5)
- Time-based (4)
- Trading Strategies (28)
- Treasuries & Interest Rates (12)
- Uncategorized (2)
- US Dollar & Currencies (5)
- VIX & Volatility (11)
