An End of Month Big Perspective

Here and in the Trading Psychology Weblog, I typically take a short-term look at markets. At the end of a month, however, let's take a big perspective.

Since 1971 (N = 414 months), we've had 152 months in the Dow Jones Industrial Average that have touched 12-month new highs. This was the case during May. Twelve months later, the Dow was up by an average of 11.74%, with 120 months up and 32 down. That's quite a performance.

During the remainder of the months in the sample (N = 262), the Dow was no slouch, but not nearly so robust. The average gain was 7.01% (172 up, 90 down).

What this tells us is that, historically, strength has begotten strength in equities. It also points out a reality that we're apt to forget in the welter of negative news and scandals: long-term investment in American equities has, on balance, been a favorable proposition.

How about when the Dow has made a 12-month low? We've only had 41 months fit that bill. Twelve months later, the Dow was up by only 4.13% on average (23 up, 18 down), compared with an average gain of 9.25% (269 up, 104 down) for the remainder of the sample.

Buying a twelve month low in the Dow has, on average, led to underperformance.

There is an element of fear and volatility in the market that we haven't encountered for a while. And after we've made a 12-month high, probably any market looks overbought and ripe for decline. Nonetheless, these strong markets have been the ones with the most favorable outlooks 12 months out.

When the VIX Itself Becomes Volatile

Yesterday, we had an enormous daily rise in the VIX of over 30%. To put this into perspective, we have only had one larger VIX spike in percentage terms since 1998, and that was the day we opened after 9/11. A chart in my most recent post on the Trading Psychology Weblog puts this into perspective.

What typically happens after a single day spike in VIX?

Since January, 1998 (N = 2108), we have had 27 occasions in which VIX has moved more than 15% in a day. All but one of these occasions were market declines. The next day, the market (SPY) was up by an average .37% (18 up, 9 down). That is much stronger than the average daily change for the entire sample (.02%; 1092 up, 1016 down).

Just as important, the five days following the VIX spike day showed much higher VIX (and price) volatility than average. The average daily VIX change over the next five days was 7.91%, and the average daily price change was 1.42%. Those compare with the averages for the sample of 4.13% and .89%, respectively.

After a five-day period of VIX volatility averaging 9% per day or more, the next day in SPY averaged a loss of -.18% (11 up, 15 down), but the next five days averaged a gain of .34% (14 up, 12 down). Once again, VIX volatility led price and further VIX volatility. Over the next five days, the average daily price change was 1.39% (vs. .89% for the sample), and the average daily VIX change was 7.14% (vs. 4.13% for the sample).

Overall, when the five-day average VIX change is 6% or greater (N = 287), the next five days in SPY average a gain of .67% (178 up, 109 down). This bullish tendency exists even when VIX levels following the VIX volatility are below 20. Interestingly, there is no edge one day out, however, in any of the data.

In short, volatility in the VIX begets volatility, with bullish implications five days out. That having been said, VIX percentage changes can be misleading, as recently pointed out by Adam Warner in his excellent blog. Nonetheless, look at the time periods with the highest five-day periods of average VIX change: April, 2005; September, 1998; March, 2004; and September, 2001. These were good times to buy stocks for an intermediate-term hold.

Profiting From Market Fear

I'm writing this prior to the market close, so it may not precisely match conditions at the close. Nonetheless, I think the underlying concept may have utility.

Basically, I decided to stay short the market through the day as long as two conditions held: 1) emerging markets (EEM) were underperforming the U.S. market (SPY); and 2) VIX continued to make higher highs through the day. My reasoning was that, as long as the market was trading on fear--avoiding volatility and risk--we were likely to see a flight from equities overall.

Do fearful markets have a short-term bearish bias? I went back to March, 2003 (N = 815 trading days) and found 76 occasions in which SPY was down between 1.0-1.5%. Two days later, SPY was up by an average .26% (39 up, 37 down).

When we split this sample in half, however, based on VIX changes, a pattern becomes clear. When VIX rises strongly during the SPY decline (as has happened today), the next two days in SPY average a subnormal gain of .01% (15 up, 23 down). When VIX only rises modestly--or falls--during a decline in SPY, the next two days in SPY average a gain of .52% (24 up, 14 down).

It appears that the bearishness which leads to an extreme bidding up of options premiums during a decline tends to carry over in the short run, producing subnormal returns. When the market declines by an equivalent amount but options players do not bid up premiums aggressively, we're much more likely to get a reversal.

The willingness of market participants to take on risk may be an important variable in determining the likelihood of continuation of market moves.

Thoughts at the Edge of a Perfect Storm

Can the financial models of hedge funds and investment banks adequately account for what will happen when everyone makes their decisions from similar models?

I'm not sure American investors recognize how jammed the exits became when Mumbai's Sensex recorded its worst-ever point decline.

Or when investors took $8-9 bln USD out of Turkey's market--almost as much as they had attracted in capital last year.

Russia's stocks lost a quarter of their value in nine days.

In all, the recent losses in emerging markets were the greatest since 1998.

It's relevant to American investors, because daily changes in the emerging markets correlate about .74 with daily changes in the S&P 500 Index.

Banks are key to the contagion of financial crises.

Which is why the health of emerging market banks is of great concern in the face of growth problems.

Though South Africa was also caught in the downdraft, it remains confident that growth in China and India will continue to prop up its own and world economies.

There is hope that this is merely a correction brought on by rising U.S. interest rates and that the emerging market story has not come to an end.

No, the decline has not caused any aversion to investing in Asia.

Just witness the stampede for China's bank IPOs, which have attracted the participation of one in every five or six Hong Kong adults.

These are banks with untested risk management systems and a history of corruption.

And they propose to ramp up their loan issuance, even as China raises downpayments required for real estate transactions in an effort to dampen the explosion of (bad) loans.

For now, though, we're told there's no need for alarm in the Philippines and other Asian markets.

But, if I recall correctly, it was after the storm--and a sigh of relief--that the levees broke in New Orleans, in the face of multiple danger signs.

When everyone is drawing upon similar models, is it just a matter of time before they all head for the exits and create a Category 5 storm?

Note: More intermarket perspectives will be posted to the Trading Psychology Weblog later tonight.

Asia's Enron

When you think of hot markets and rapid growth, what comes to mind but China?

But almost alone of emerging markets, Shanghai trades below its levels of 2001 and 2004.

Could the relative weakness stem from a glut of loans that threatens an already fragile banking system?

Could it be because many of those loans are non-performing and used for political purposes...whoops, no they're not.

Or maybe the market discounts those IPOs that have investors salivating and questions investor funds going to a banking organization that has no centralized computer system.

Alternatively, the market underperformance could reflect an awareness of a lack of intellectual property rights, which weakens the competitiveness of mainland firms.

Or maybe the problem is in the countryside, where rural loans have burdened the nation's second largest lender...

Or in Shanghai, where real estate prices are falling and controls on new mortgages are tightening.

Whatever the reason for market weakness, it doesn't seem to affect investors literally lining up to get shares in the new bank IPOs.

I don't think, however, that we'll see housing slaves line up for those shares.

Nor do I think that markets will be fooled in the long run when bad loans are moved from one state enterprise to another.

It didn't work in Houston, and I doubt it can work in Shanghai.

When Emerging Markets Submerge: Another Way Markets Confound Human Nature

This past week, we saw the Emerging Markets index (EEM) record a sobering 16% drop on a 10-day basis. I was surprised to find that the correlation between ten-day returns on the EEM and on the Standard & Poors 500 Index (SPY) was a hefty .74. That led me to wonder if drops in EEM might represent the kind of fear among speculative traders that you would see at market bottoms.

Since late April, 2003 (N = 768 trading days), we've had 40 occasions in which EEM has been down by more than 5% on a 10-day basis. Ten days later, SPY has been up by an average 1.03% (30 up, 10 down)--much stronger than the average ten-day change for the sample overall (.43%; 459 up, 309 down). Big drops in emerging markets have thus, on balance, been bullish for American stocks in the near term.

How about when emerging markets are strong? We've had 114 occasions in which EEM has risen by more than 5% in a 10-day period. Ten days later, SPY was up by an average of only .08% (60 up, 54 down)--much weaker than its average performance. Strong emerging markets have thus led to underperformance among American stocks in the near term.

Readers of this blog will recall that, a while ago, I posted some findings regarding speculative vs. non-speculative stocks and the tendency of the former to lead the latter. EEM is one worthy proxy for those speculative issues. When the most speculative participants in the market are most bearish, it's best to step up to the plate. When those speculators are most bullish, it's time to take a few chips off the table.

It's another way in which markets confound human nature. We tend to extrapolate from the recent past to the near future, whether in predicting sports events, people's moods, or weather. Markets are one of the only spheres of life in which the recent past is an excellent predictor of the near-term future--in reverse!

What We Can Learn From Relative Sector Performance

I have begun a large-scale study of the intercorrelations among equity sectors and asset classes; so far, the study appears promising. I will describe the study in greater detail later today when I post to the Trader Performance page. The gist of my findings is that market inefficiencies--and hence trading edges--are greatest when sectors and asset classes deviate from their normal relationships with one another. This can then yield directional trade ideas for particular sectors/asset classes or can generate spread ideas which profit from a normalization of the relationship among the sectors/asset classes.

Readers of this blog will recognize that this line of research is designed to mirror the ways in which I have found highly successful market professionals to operate.

Let's begin with a simple example. Using ETFs, I divided the equity market into seven sectors: energy, finance, technology, consumer, health care, utilities, and raw materials. I then computed monthly correlations between each of the sectors on a moving basis and calculated an average correlation among all the sectors. This overall average correlation since March, 2003 (N = 807 trading days) has been .48.

Since March, 2003, we have had 97 days in which SPY has risen 2% or more on a five-day basis. Five days later, the average gain in SPY has been .25% (58 up, 39 down). This is not different from the average gain in SPY for the entire sample (.28%; 465 up, 342 down).

When, however, SPY has risen 2% or more *and* we have a high intercorrelation among the sectors (N = 48), the next five days in SPY averages a strong gain of .42% (34 up, 14 down). When SPY has risen 2% or more but the intercorrelation is low (N = 49), the next five days in SPY averages a gain of only .08% (24 up, 25 down).

It thus appears that strong rises are most likely to continue when the sectors are moving in concert. Returns are subnormal after strong rises when the sectors diverge relative to one another.

When it comes to declines, however, it's a different story. Since March, 2003, we've had 105 days in which SPY has dropped 1.5% or more in a five-day period. Five days later, SPY has averaged a gain of .99% (72 up, 33 down)--much stronger than the average gain for the entire sample as noted above.

When SPY has dropped 1.5% or more in a five-day period *and* the intercorrelation among sectors is high (N = 53), the next five days in SPY average a very strong gain of 1.44% (40 up, 13 down). When SPY has dropped sharply and the intercorrelation among sectors is low (N = 52), the next five days in SPY average a more modest gain of .54% (32 up, 20 down).

The bottom line is that when sectors are moving in unison, near-term results are bullish. Large gains tend to follow through with further strength (momentum effect), but large declines tend to reverse and produce large gains (reversal effect).

It does, indeed, appear that meaningful market edges occur when sectors deviate from their normal relationships with one another. More studies will follow.

How Pessimistic Are Traders When Markets Fall?

On the heels of my last post, which found that rises were more likely to reverse if they were not accompanied by an expansion of equity call option volume, I decided to look at falling markets and equity put option volume.

Since March, 2003 (N = 811 trading days), we've had 176 days in which SPY has declined by at least -.50%. Normally, such a large one day decline leads to favorable expectations in the near term. One day after SPY declines half a percent or more, the market averages a gain of .17% (109 up, 67 down). That is quite a bit stronger than the average gain for the rest of the sample of .02% (341 up, 294 down).

When SPY has dropped more than half a percent in a single day and equity put option volume declines on the day (N = 47; showing less bearish speculative interest among option participants), the next day averages a whopping gain of .40% (33 up, 14 down). When SPY has dropped by more than a half percent but put option volume increases (N = 129), the next day averages a gain of .09% (76 up, 53 down).

It thus appears that declines are more likely to reverse in the short run if they are not accompanied by expanded bearish activity among equity put option participants. It does seem as though large single day rises and declines should be viewed with special skepticism if speculative interest among option traders does not accompany the moves. I will be following and refining this research on the Weblog.

How Optimistic Are Traders When Markets Rise?

Thursday's market rose steadily through the day with solid buying, as noted in the Weblog. As an aside, I should note that that's not necessarily a positive thing--or at least hasn't been recently. Since March, 2003 (N = 811 trading days), we've had 204 days in which SPY has risen by .50% or more in a single day. Two days later, SPY has averaged a gain of only .03% (102 up, 102 down)--no upside edge at all. On the remainder of trading occasions, SPY has averaged a two-day gain of .13% (337 up, 270 down).

But what happens when the market rises by .50% or more and traders lose their optimism? I measure this very simply by tracking equity call volume changes from day to day. When SPY has risen strongly in a single day and call volume has expanded (N = 149), the next day averages a gain of .02% (86 up, 63 down). But when SPY has risen strongly in a single day and call volume has dropped (N = 55)--as happened on Thursday--the next day averages a loss of -.09% (22 up, 33 down).

Markets have tended to correct strong one-day gains, but this tendency is particularly pronounced when those gains have not been accompanied by commensurate speculative interest, as demonstrated by the actions of equity options traders. In my next post, we'll look at trader pessimism as markets decline.

Finding Solutions to Your Trading Problems

Note: Well, it's been inconvenient: stuck in Toronto overnight when Chicago's airports shut down last night. I will resume TraderFeed analytics tomorrow and will update the Trading Psychology Weblog tonight. Sorry for the delays in posting.

My talk at the Annual Meeting of the American Psychiatric Association in Toronto yesterday afternoon concerned solution-focused brief therapy (SFBT). I continue to find it one of the most applicable short-term change technique to trading problems. It has also been used commonly and successfully with relationship and parenting concerns, as well as with overcoming personal barriers to reaching goals.

What makes the solution-focused approach unique is that it starts with the assumption that many problems that emotionally healthy people encounter are the results of their becoming too problem focused. In other words, once people become convinced that they have a problem, their subsequent actions unwittingly reinforce this problem, creating a damaging, circular pattern.

The classic example is the person who doesn't sleep well for a couple of evenings. He becomes convinced that he has an insomnia problem, and now starts worrying about his problem and taking extra steps to *make* himself sleep. All of these simply interfere with normal sleep processes and prolong the original concern.

This occurs with traders as well. Just as there can be chance streaks of wins, there are random losing streaks. Once traders define these as "slumps", they alter their trading, becoming overly cautious at times and overly aggressive to regain losses at other times. These changes to normal, good, planful trading only make the slump worse, creating the self-reinforcing problem.

The solution-focused approach gets away from this problem immersion and, instead, emphasizes *exceptions* to problem patterns. These are potential solutions.

For instance, it is very common that traders even in bad slumps make some good trades and have some good days and weeks. The solution-focused approach takes a hard look at these and pushes traders to identify what they're doing right when they're making money. These exceptions to slumps become positive behaviors that can then be codified as trading rules and mentally rehearsed before each day's trade. Pretty soon, the trader is doing more of what works and no longer feels in a slump.

The solution-focused approach suggests that sometimes the problem is not a deeply rooted psychological conflict. Sometimes we become too problem focused for our own good. No one ever succeeded merely by reducing or eliminating negatives. Our strengths are all we have to build upon.

Note: The Articles page of my personal site has free articles related to solution-focused techniques and trading.

Intraday Participation

Today gave us a nice opportunity to see how the concept of participation applies to intraday time frames as well.

The May 24th entry in the Trading Psychology Weblog offers a picture of market participation on a 3 minute basis leading up to the market's swan dive late in the afternoon. This chart tracks the number of advancing minus declining NYSE stocks. We began the day with strength, but advancers waned through the day *prior* to the large drop. Just as Monday's weakness could not extend to a majority of issues, Tuesday's rising tide could not lift all boats. That's a clear negative for this market and will have me looking once more for downside participation on Wednesday.

The Importance of Participation

One of the market variables that has served me the best in determining the likelihood that moves will either continue in their direction or reverse is the degree of participation in that move. When a market makes new lows, for example--as happened with the S&P today--I look to see how many individual stocks and sectors participated in that move. This indicates whether the move was dominated by a few highly weighted stocks within the index or was part of a broader market weakness.

The picture was mixed today, as we saw 4 stocks making new 52 week highs and 17 new lows for the session. Among smallcaps, it was 0 new highs and 16 new lows and among midcaps it was 1 high and 20 lows. All three of these do not represent an expansion of new lows from last week's worst levels. My Demand/Supply index is 43/50, which also does not indicate broad downside momentum.

Until we see broader downside participation, I am not selling lows here. I'll wait for participation to tip its hand before making that move. Falling tides, like rising ones, should affect all boats.

The Market is Rigged Against Human Nature

Suppose you had two traders. One--a momentum trader--became optimistic and bought the S&P 500 Index (SPY) every time it rose in price for the day; the other--a contrarian--became optimistic and bought the S&P 500 Index (SPY) every time it declined in price for the day. Each trader held positions three days. What would be their return?

Since January, 1996 (N = 2584 trading days), the momentum trader would have an average return per trade of .01% (725 up, 617 down). The contrarian's average return would be .20% (679 up, 563 down). The trader who bought at the end of *every* day and held three days would have had a return of .10%.

In other words, simply by buying after a down day, the contrarian would have doubled the average return in the market.

By buying after an up day, the momentum trader severely underperformed the market.

Now you know why overconfidence in trading is the greatest pitfall of all. The market is rigged against human nature: getting excited after a rise and discouraged after a decline ensures that traders are on the wrong side of markets.

We Gotta Hot One Today!

You always know when Randy Jackson, American Idol judge, is excited about a performance. "America," he shouts, "we gotta a hot one tonight!"

Well, how do we know when we got a hot trading day today? Hot for the trader means volatility. "I'll trade anything," one successful trader recently told me, "as long as it moves."

Since March, 2003 (N = 812 trading days), we've had 95 occasions in which the S&P 500 Index (SPY) has opened with a gain or loss exceeding .50%. That same day, the high-low range has averaged 1.46%, quite a bit higher than the 1.01% range for the rest of the sample.

Since 2005, we've only had 14 days in which SPY has opened with a move of .50% or more. The average range that day has been 1.20%, wider than the .90% average range for the rest of the sample. In current S&P point terms, that translates to a range difference of close to 4 points.

For similar reasons, since March, 2003, when VIX opens 4% or more from its previous closing level (N = 143), that day's high-low range averages 1.20%. The average range for the remainder of the sample has been 1.04%.

But how about when SPY opens with a move of .60% or greater *and* VIX opens with a move of more than 5%? Then, America, we got a hot one today! The average range (N = 15) has been 1.87%, which means that we have almost 90% more movement than the average market day. Even Simon might like that performance.

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Later this AM, I will post the update to the Trading Psychology Weblog.

The Most Common Trading Problem

At a gathering of investment bank trainees in suburban New York last night, I was asked my opinion of the most common problem among traders. My answer was neither fear nor greed. It was overconfidence.

Studies in behavioral finance find that about 3/4 of all traders rate their prowess as "above average", despite the obvious reality that only half of us are better than the other half. This overconfidence, moreover, affects actual trading performance. Research by Terence Odean and colleagues finds that overconfidence affects frequency of trading, which in turn contributes to poor trading results. In one study of online traders, the group of traders favored high beta (volatile) small cap companies and tended to not diversify their portfolios. Their actual trading results slightly beat the small cap index, but after trading costs were factored in, they significantly underperformed the index. The most frequent traders were the ones who underperformed the index by the greatest margin.

One of my favorite studies of overconfidence came from the London Business School. Traders were shown price patterns and asked to figure out the market's next direction and indicate their confidence in their prediction. The price patterns were generated entirely randomly. The traders with the highest confidence in their predictions traded the most frequently and incurred the greatest losses.

A completely random trader--50% right, 50% wrong--who trades once a day will have runs of five consecutive winners about six times during a year. It is difficult to not think you have the hot hand after such a string, become overconfident, and raise your trading size. Of course, the random trader will have an equal number of strings of losers. That is likely to burst confidence and lead the trader to cut trading size--assuming, of course, that he's still in the game at that point.

Is it any wonder that traders seek help from coaches and psychologists? Few of those coaches and psychologists, however, will tell the trader the truth: You're trading random patterns and your problem is overconfidence in them.

The Market is at an Important Juncture

After attempting a recovery this AM, the market fell in late trading, making new lows for this recent decline. As a counterpoint to the late bearishness today, allow me to point out a few perspectives:

* Across the broad universe of operating company stocks, we made 2026 new 20-day lows today--fewer than the day before.

* My Demand/Supply Index, which tracks stocks with very strong and very weak momentum, shows greatly reduced downside momentum. Demand was 42 today; Supply was 37.

* Today we had 13 new 52-week lows in the S&P 500, down from 23 the day before. We had 13 new lows in the S&P 600 small cap issues, down from 18 yesterday. We also had 12 new lows in the S&P 400 midcaps, down from 14 yesterday.

* Finally, a total of 15% of NYSE issues are trading above their 20 day moving average. The last times we've had readings below 20% have been October, 2005; April, 2005; August, 2004; and May, 2004. Those were excellent intermediate-term buying opportunities.

Yes, this may be the start of a new bear market. If so, we should see an *expansion* of new lows and *increasing* downside momentum. If we do not see a continuation of the late weakness tomorrow and continue to see new lows and downside momentum dry up, the odds of a market bounce will be greatly enhanced.

I will be following the new highs/lows and momentum studies carefully on the Trading Psychology Weblog in my next post Saturday.

What the Relative Range is Telling Us About This Decline

Recall that the relative range is a measure that compares the range of the last day or week with the median range for the past 20 days or weeks. The logic behind this is that people's psychological responses are to relative events, not absolute ones. They will, for instance, react differently to a 40 degree afternoon in summer than to a 40 degree day in mid January.

Yesterday, we had dropped 4.3% on SPY for the past five trading sessions. The five-day relative range was 2.39, which means that the range was more than twice the median five-day range for the past twenty days.

Since March, 1996 (N = 2551), when SPY has been down 4% or more for a five-day period (N = 120), the next five days in SPY have averaged a gain of 1.03% (75 up, 45 down). When we look at occasions when SPY was down more than 4% *and* the relative range was twice or more its median (N = 36), the next five days in SPY averaged a gain of 2.31% (25 up, 11 down). Conversely, when SPY was down more than 4% and the relative range was less than twice its median (N = 84), the next five days in SPY averaged a gain of only .48% (50 up, 34 down).

What this suggests is that periods such as the current one, in which a steep drop is accompanied by a sizable expansion in the relative range, yields superior returns in the near term. Perhaps this is because the large drop on a large range represents panic selling of short-term traders, creating value for longer timeframe market participants.

What to Expect After a Big Drop on a Big Relative Range

Today's market, as measured by SPY, dropped about 1.9% on a range that was over 2.3 times its average daily range from the past twenty sessions.

Since March, 1996 (N = 2553 trading days), when SPY has dropped 1.5% or more in a single day with a daily range of twice or more its 20-day average, the next day in SPY has averaged a gain of .55% (30 up, 19 down). When SPY has dropped 1.5% or more in a day with a range of less than twice its average, the next day in SPY has averaged a gain of .15% (99 up, 81 down). It thus appears that a single day drop on a large range tends to bounce the following day.

Finally, combining this analysis with my last one, I found 24 occasions in which we had a one-day drop of over 1.5%, a five-day decline of over 3%, *and* a one-day relative range greater than twice its twenty-day average. Two days later, SPY was up by an average 1.02% (17 up, 7 down)--much stronger than the average two-day gain of .07% (1360 up, 1193 down) for the sample overall.

In all, it appears that we tend to get a bounce following a large decline during a weak week when the daily range is extended.

I will post details of the market drop later tonight in the Trading Psychology Weblog.

What To Expect After a Weak Day and a Weak Week

Today's very weak market has given us more than enough material for an enlightening historical look at edges going forward. The edges are always greatest following extreme events, and the recent market drop--especially in the context of a market that had been low in volatility--qualifies as extreme.

I will post an initial analysis here and further analyses tonight and tomorrow AM. Tonight's Trading Psychology Weblog will also contain a large amount of material on the recent market weakness and what it means for the market's near-term course.

First off, we had a drop in SPY of approximately 1.9% today, with the last five sessions giving us a decline of 4.3%. Since March, 1996 (N = 2553 trading sessions), we've had 229 days in which SPY has dropped by 1.5% or more. Two days later, SPY has been up by an average of .43% (126 up, 103 down). That is stronger than the average two-day gain of .07% (1360 up, 1193 down) for the sample overall.

When we divide the weak S&P days in half based on the performance of the past five trading sessions, we see a clear pattern. When S&P is down by 1.5% or more *and* the five-day S&P is strong, the next two days in SPY average a loss of -.06% (53 up, 61 down). When S&P is down by 1.5% *and* the five-day S&P is also weak (as at present), the next two days in SPY average a gain of .91% (73 up, 42 down).

That suggests that a very weak S&P day only has obtained a bullish edge if it occurs in the context of a weak five-day period.

Stay tuned. I will be spitting out further studies later tonight.

A Momentum Pattern That Seems to Work: The Relative Range

Here's an interesting application of the relative measurement concept that I've been toying with during the past several posts:

Suppose we look at the day's trading range as a function of the median daily trading range for the past 20 days. This relative range statistic tells us when a market move is very large or small compared to recent volatility norms.

I focused especially on large relative range days, because those also had the potential to be breakout days and perhaps lead to some momentum follow through. I also focused on the relatively low volatility market we've had since January, 2004 (N = 595 trading days); later explorations will look at a wider timeframe.

Since January, 2004, we've had 32 days in which the two day relative range in SPY exceeded 1.70 (i.e., today's range exceeded the median two day range by 70% or more). This was the case at the end of last week with the large drop. Two days later, SPY was down by an average -.10% (13 up, 19 down), which is weaker than the average two-day gain of .05% (320 up, 275 down).

Here is the interesting part, however. When the second day of the large two-day range was up from open to close (N = 11), the market was higher two days later on 10 of those occasions. When the second day of the large two-day range was down from open to close (N = 21), the market was down two days later on 18 of those occasions.

It thus appears that a large relative range leads to short-term price continuation in the direction of the large range move. I will need to explore this over a longer time frame and under different market conditions to determine whether this is a general or local pattern.

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An observation from the Weblog: Energy stocks are actually below their levels from last January. Could it be that the fortunes of major oil companies will increasingly diverge from oil prices as countries nationalize their reserves?