When SPY is up > 1% before the Fed Day announcement

The market is off to a strong start to the day, with SPX up over 1% at around noon EST. I decided to look back at all times the market was up at least 1% at 2pm on a Fed Day (the typical time for a statement release). Below is the full list of instances and their 2pm – 4pm EST performance to finish the day.

SPX up > 1% pre-Fed announcement.

Returns here are mixed, and don’t suggest a strong directional edge either way. There have been some strong moves in both directions after the announcement. I also filtered for times SPY was in a long-term uptrend (above its 200-day moving average). Those results are below.

up 1% pre-Fed with 200ma filter

These results are a bit more encouraging. Six of the ten instances saw further gains. One finished flat vs 2pm. The other three saw only mild declines, but still finished the day overall positive. Should be an interesting finish to the day. The good start seems to be a potential positive.

Want research like this delivered directly to your inbox on a timely basis? Sign up for the Quantifiable Edges Email List.

How about a free trial to the Quantifiable Edges Gold subscription?

SPX Declines Into A Fed Day

Fed Days often generate compelling studies to consider and share in the nightly subscriber letter.  I have also covered them in the blog many times over the years. A Fed Day is one of eight days per year that the Federal Reserve concludes one of their scheduled meetings and makes a policy announcement.  Wednesday is a Fed Day.  Historically, Fed Days have had a bullish inclination.  That inclination has been even stronger when there has been selling heading into the Fed Day. The study below examines other times that SPX was in a long-term uptrend, but closed down for at least the third day in a row going into the Fed Day.

$SPX 3 down into a Fed Day has been bullish over the next 1-5 days

These are some very encouraging numbers for the bulls.  Below is the list of instances.

$SPX 3 down days into Fed Day - list of instances

The setup has certainly been potent over a long period of time.  There has not been a loser for the 4-day holding period since 1986.  And every instance back to 1982 has closed higher than the entry price at some point in the next 4 days. Of course anything can happen when it comes to the market, but evidence here suggests a substantial upside edge over the next few days.

Want research like this delivered directly to your inbox on a timely basis? Sign up for the Quantifiable Edges Email List.

How about a free trial to the Quantifiable Edges Gold subscription?

Monday’s Strong Selling & New Lows Triggered This Historically Bullish Setup

Many studies identified by the Quantifnder Monday afternoon showed the strong selling and closing lows to be potentially bullish. And Turnaround Tuesday is typically the best day for a bounce to begin. The study below considered the long-term uptrend, intermediate-term low, and strong selling on Monday.

SPX big drop to a low clos on a Monday has led to consistent bounces during uptrends.

The only instance that did not close higher 4 days later was the one that triggered on 2/5/18. But it did close higher on day 5. In fact the last 18 instances all closed higher 5 days later. The last 5-day loser was in 1981. There are certainly dangers right now, but this suggests a high probability of a bounce in the coming days.

Want research like this delivered directly to your inbox on a timely basis? Sign up for the Quantifiable Edges Email List.

How about a free trial to the Quantifiable Edges Gold subscription?

Weakness Prior to the “Weakest Week”

For years I have written about the “weakest week”. It is the week following the 3rd Friday in September, and it has been the worst week seasonally when measured over many time periods. I wrote about it in some detail recently in ProActive Advisor Magazine.

But this year we are seeing two weeks of selling before we even get to the “Weakest Week”. So did the weakness arrive early this year, and are we more likely to see a move higher next week because of this? This is something I examined last year. Below I have updated the table showing all instances of 2 week selloffs heading into the Weakest Week, and then the Weakest Week return.

Weakest week returns were bad even with 2 weeks of selling prior

There have only been 11 instances. But 10 of 11 saw further selling in the Weakest Week. The lone winner (1989) only managed a 0.58% return. The average week of the sample saw a 1.41% loss. This is not encouraging for people hoping for a bounce next week.

Low in the 5-Day Range but High in the 10-Day Range

Tuesday’s action caused SPY to close in an interesting position. Traders could look at the chart and say it is “short-term oversold” due to the fact that it closed at a 5-day low. They might also say it is “short-term overbought” since it closed above its 10-day moving average. I have found that edges often arise when something is short-term overdone in one timeframe, but overdone in another direction in another timeframe. The study below looks at the current discrepancy.

Results here suggest a solid edge over the next 1-5 days. Below is the 5-day profit curve.

The strong and persistent upslope is impressive, and serves as some confirmation of the bullish edge suggested by the numbers. Traders may want to keep this in mind when setting their bias for the next few days.

Want research like this delivered directly to your inbox on a timely basis? Sign up for the Quantifiable Edges Email List.

How about a free trial to the Quantifiable Edges Gold subscription?

Back to Back Outside Days for $SPY

Notable about Tuesday’s action is that it marked the 2nd day in a row that SPY posted an outside day. (An outside day is a day where the security or index makes a higher high and a lower low than the day before.) I last discussed back-to-back outside days in the 7/28/16 letter. I have updated those results below.

$SPY back to back outside days bullish

The numbers look very impressive. Most of the upside edge has been realized in the 1st 4 days. Below is a profit curve using a 4-day holding period.

profit curve for back to back outside days - bullish

The move up is impressive and encouraging for the bullish case.

Want research like this delivered directly to your inbox on a timely basis? Sign up for the Quantifiable Edges Email List.

How about a free trial to the Quantifiable Edges Gold subscription?

When DJI Rallies From 50-day Low to 50-day High Very Quickly

The SPX, Dow, and NASDQ all closed at all-time highs on Friday. Just 10 days ago the Dow closed at a 50-day low. That quick of a move from low to high is quite an accomplishment. Over the last 101 years this just the 21st time the Dow has managed to move from a 50-day closing low to a 50-day closing high within 10 days.  Below are all the other instances, along with the $DJI performance in the following days and weeks.

$DJI Rallies from 50-low to 50-high in 10 days

The Dow has generally seen the upward momentum continue. Those are some impressive gains over the first 1-5 days, and even out through 20 days. Traders may want to keep this in mind when formulating their bias.

Want research like this delivered directly to your inbox on a timely basis? Sign up for the Quantifiable Edges Email List.

How about a free trial to the Quantifiable Edges Gold subscription?

First Day Performance of Each Month

Since the late 80s there has been a tendency for the market to rally on the first day of the month.  One theory on why this occurs is that there are often 401k inflows that are put to work on the 1st of the month.   I examined this tendency and broke it down by month here on the blog a few times over the years.  I decided to update the study again today.

$SPX perf by month

The only month that comes even close to July from a Win % and Avg Trade standpoint is February.  I’ll also note that August is the worst performer of any month.

Want research like this delivered directly to your inbox on a timely basis? Sign up for the Quantifiable Edges Email List.

How about a free trial to the Quantifiable Edges Gold subscription?

When Persistent Higher Highs Don’t Suggest a Pullback

SPX managed to make an intraday high for the 5th day in a row on Friday. An interesting study from the Quantifinder looked at the possible impact of 5 higher highs occurring. The studies examined the impact of the position of the market when the 5 higher highs occurred. I broke it down again over the weekend. I wanted to see all times the 5 higher highs were accompanied by a 50-day high versus times they weren’t. First let’s look at times where 5 higher highs occur without a 50-day high.

Stats over the 1st few days suggest a possible mild downside edge. After 5 higher highs the market will sometimes need a breather.

But what of times (like now) when a strong uptrend exists, and the market is also making a 50-day high? Those stats can be found below.

Interestingly, the number of instances has been nearly the same. But with an intermediate-term rally also occurring the tendency to pull back no longer exists. So the 5 higher highs are really of no concern in situations like the current one.

Want research like this delivered directly to your inbox on a timely basis? Sign up for the Quantifiable Edges Email List.

How about a free trial to the Quantifiable Edges Gold subscription?

June Seasonality for $MID

I have begun sharing one of the Quantifiable Edges Seasonality Calendars each month. This month I decided to show the S&P Midcap 400.

The Quantifiable Edges Seasonality Calendar uses multiple systems to measure historical performance on similar days to those on the upcoming calendar. The systems look at filters like time of week, month, year and so forth. Over the long run, staying out of the market on days that do not appear in green, would have been beneficial. To appear in green the date needs to show a historical Win% of 50% or more and a profit factor of 1.0 or more.

We see some solid seasonal numbers to start off the month. Then it is a mixed bunch for a few weeks. The strongest numbers in June appear in the last 2-3 days. Of course there will be many other factors impacting market action. But historically, avoiding days that do not appear in green has proven beneficial across all the markets we track. This is discussed in the “50/1 Calendar Research Paper”, which is available on the Seasonality page in the client section of Quantifiable Edges. It is available to all subscribers, including those on a free trial.

Want research like this delivered directly to your inbox on a timely basis? Sign up for the Quantifiable Edges Email List.

How about a free trial to the Quantifiable Edges Gold subscription?

$SPX Loves Tax Day

In the 4/12/19 blog I showed a study about US tax day (normally April 15th). The reason tax day may be important is that it is the last day that people can make IRA contributions to count for the previous tax year. This can create a last-minute rush and you will often have an inflow of funds heading into the market right around and on the day taxes are due. Fund managers will often put this money to work immediately and it creates a positive bias for the market. Below I have updated the study.

Tax Day Performance

Futures are struggling in the overnight. Let’s see if this seasonal tendency can turn it around by the close today.

Want research like this delivered directly to your inbox on a timely basis? Sign up for the Quantifiable Edges Email List.

How about a free trial to the Quantifiable Edges Gold subscription?

The GLD Seasonality Calendar for May Looks Lousy

I have begun sharing one of the Quantifiable Edges Seasonality Calendars each month. Last month, we looked at the NASDAQ. This month the calendar that caught my eye was GLD, the Gold ETF. It looks lousy (technical term term for “sucky”).

The Quantifiable Edges Seasonality Calendar uses multiple systems to measure historical performance on similar days to those on the upcoming calendar. The systems look at filters like time of week, month, year and so forth. Over the long run, staying out of the market on days that do not appear in green, would have been beneficial. To appear in green the date needs to show a historical Win% of 50% or more and a profit factor of 1.0 or more.

GLD May Seasonality Calendar

Obviously, what stood out with regards to GLD is how bearish it appears. Only 3 “green” days all month is the worst for any of the 10 indices/securities we publish. The Baseline hurdles are fairly low, and they are rarely exceeded in the May calendar. Seasonality is just one factor, but I have found paying attention to it to be worthwhile. A couple of months ago, I published a research paper that showed the value of avoiding gold on days that did not show favorable seasonality odds. That paper is available to all subscribers, including trial subscribers, on the Quantifiable Edges Seasonality page. It is titled “Gold 50/1 Calendar Model Research”.

Want research like this delivered directly to your inbox on a timely basis? Sign up for the Quantifiable Edges Email List.

How about a free trial to the Quantifiable Edges Gold subscription?

Webinar: A Long-Term Look at Markets & Opportunity with Capital Advisors 360

I am going to hold a webinar for Quantifiable Edges followers on Monday after the market close. I will be joined by Jeff Pietsch, who is the principal of Capital Advisors 360, which is the investment advisory firm I work with. We will be discussing some long-term outlooks for the stock and bond markets based on a few different models. We will also share ideas on ways to enhance traditional portfolio allocations with alternative strategies. To register for the webinar, you may use the link below. Note…all registrants will receive a copy of the recording, so no worries if the time does not work for you.

https://quantifiableedges.com/subscribers/signup/CA360

Webinar Info:

Date/Time: Monday, 4/26/2021 4:15pm Eastern Time

Topic: A Long-Term Look at Markets & Opportunity with Capital Advisors 360

Online Meeting Link: https://join.freeconferencecall.com/robh60

April Seasonality Calendar for the NASDAQ

I have begun sharing one of the Quantifiable Edges Seasonality Calendars each month. Last month, we looked at long-term treasuries. This month the calendar that caught my eye was the NASDAQ Composite Index.

The Quantifiable Edges Seasonality Calendar uses multiple systems to measure historical performance on similar days to those on the upcoming calendar. The systems look at filters like time of week, month, year and so forth. Over the long run, staying out of the market on days that do not appear in green, would have been beneficial. To appear in green the date needs to show a historical Win% of 50% or more and a profit factor of 1.0 or more.

Obviously the NASDAQ stood out because its Calendar is almost all green. But not only do we see mostly green this month, I’ll also note that almost every day up until April 23rd we see numbers above the “baseline”. The baseline is simply NASDAQ stats over about the last 10 years. So the next 3 weeks we see that NASDAQ seasonality is both positive, and mostly better than average. Traders may want to keep this in mind along with other factors they consider as they establish their market outlook over the next few weeks.

Want research like this delivered directly to your inbox on a timely basis? Sign up for the Quantifiable Edges Email List.

How about a free trial to the Quantifiable Edges Gold subscription?

A Simple Machine-Based Day of Month Trading Model

In this writeup I am going to explain how to build a simple calendar model that uses machine learning to determine the days with the best odds to trade. The I will show how the calendar model does versus “Buy and Hold” over a long time period.  The approach here is similar to how the Quantifiable Edges Seasonality Calendars are produced each month. Of course they are a bit more complex, and they are also enhanced by the fact that rather than using just one model, they use an ensemble of models to generate the statistics. But as you will see, even a very simple calendar model can produce surprisingly effective results.

Below are the steps to build the model.

I’ll go through these briefly one-by-one.

  1. Data is sorted by calendar day of month (1 through 31) – Here we are simply establishing groupings. In this model, the grouping are extremely simple. What day of the month is it? You could also use trade day of month, or normalize the number of days, or segment many other ways. But the idea is to determine whether certain groupings provide an edge vs others. Here we are just doing “day of month”.
  2. Performance is measured over a rolling 10-year period – To see what groups provide an edge, you need to measure the performance of each group over a specified time period. In this case, I chose 10 years. With 31 groups and 252ish trading days per year, this means each group will have about 81-82 instances, though a little less for the 29th – 31st. Performance can be measured multiple ways. For instance, Win %, Avg Gain, Profit Factor, or by any other performance measure you deem important. The time that is measured can be whatever you determine appropriate. I used 10 years as a nice, round number that is long enough to typically include both bull and bear market phases. You can use much longer or much shorter.
  3. Stats calculated and tracked for each period – Once ten years (or whatever length you choose) of data is available, the performance stats can be generated. They should then be rolled forward continuously. This will allow the machine to adapt to changing market conditions, and for the old data to roll off, no longer including it in forward decisions.
  4. Stats for the upcoming day are used to determine whether to be in or out of the market on that day – So if tomorrow is the 5th of the month, the model will look back at performance of the 5th of the month over the last 10 years to determine whether to be long or flat at the close today. In the results I am going to share, I required a Win % of at least 50% and a Profit Factor of at least 1.0. So we are looking for the stats to be neutral or positive in order to have a long position. Otherwise, we get flat.
  5. This is a machine-learning approach. We don’t tell the model the best days. The model finds them itself – Again – nowhere in the code do we specify that we view the 1st or the 5th or any other day as bullish. Bullish/neutral/bearish are evaluated on a rolling basis by the code.

These results are fairly remarkable. The model is only exposed to the market about 52% of the time, and yet the annual return beats the market by 1.35% per year. When it is out of the market, it has earned interest at the rate equal to the 30-day Fed Funds rate. (I used that rate since 1) it is generally the lowest published rate, and 2) I had data back to 1957. Prior to 1957, 0% interest was earned by the model.) Over a long period of time, the 8.63% CAGR vs the 7.28% CAGR makes a very big difference. Also interesting about this is the fact that it is 100% in or 100% out. There is no ability to leverage in this model. So it cannot outperform when the market is rising. It can only outperform by side-stepping unfavorable days. And since it did outperform, you would think it did a decent job of reducing drawdowns. Below is the drawdown chart.

The model is represented by the red line and the SPX by the gold line. Interestingly, in almost every major drawdown over the 81 years, the model managed to avoid a portion of the drawdown. And that is how the performance difference became so large over time.

As I stated earlier, this is similar to the approach used by the QE Seasonality Calendar models. I also thought it was a decent example of machine modeling. (Note: This approach can be used with any kind of data. It does not need to be calendar-based.) I also thought people would find it interesting how a simple day-of-month filter could be so effective over such a long time period. Day-of-month seasonality IS a thing. And based on this, it appears worth paying attention to. I hope you found the above exercise thought provoking.

If you’d like to learn more about the QE Seasonality Calendars, feel free to check out the Intro Video or the Quantifying Seasonality webinar on the QE Youtube page. You can also take a free trial of Quantifiable Edges, where you can download white papers and more information in the Seasonality section of the subscriber area.

Want research like this delivered directly to your inbox on a timely basis? Sign up for the Quantifiable Edges Email List.

How about a free trial to the Quantifiable Edges Gold subscription?