Sell in May and go away until end of Oct.
On average, all the returns of the S&P 500 are made during the seasonally six strong months, November to April.
The market shows zero return on average over the seasonally poor months, from May to October. Nobody tells you this, but the odds of winning over the next 5 months are low.
Better have a good trading strategy until October.
Thanks for sharing. Apologies if this is obvious – would you be able to share the rules (syntax) to limit buying in specific months and sell rules for exiting in specific months
Syntax for hedge rules for seasonal investment in SPY from Nov to April:
Go to Cash
enter hedge: (Mod(Month,12)=4 & MonthDay>=23 & MonthDay<=30)
exit hedge: (Mod(Month,12)=10 & MonthDay>=23 & MonthDay<=31)
Syntax for hedge rules for seasonal investment in SPY from May to Oct:
Go to Cash
enter hedge: (Mod(Month,12)=10 & MonthDay>=23 & MonthDay<=31)
exit hedge: (Mod(Month,12)=4 & MonthDay>=23 & MonthDay<=30)
Great info! I modified a micocap simulation to try this idea. In my experiment, the ranker was changed to tilt towards Healthcare, Staple and Util stocks during the May-October (inclusive) months. That’s the only addition. The buy/sell rules remained unchanged. The net effect is to never exclude any sector but to favor defensives buys during the summer/fall months.
Overall, the annualized return went from 33.16% to 35.60%. During the latest three year period, the AR went from 41.53% to 49.94%.
Very nice bump for such a small change.
Walter
Thanks GV!
Walter,
That is the correct strategy. Go defensive from May to October.
Designer Models should be checked for performance during the weak and strong periods. Easy to do with the Go to Cash hedge rules, and designers should publish the respective returns. My bet is that there is not a single model that does better during the weak periods if it does not have market timing in it.
I love this (thank you Georg) but I feel some discomfort (read ‘fear of missing out’) using it with live money as I cannot get my head around the following:
What could be a logical decent reason for such a market behavior?
(I can understand August = holiday = lower liquidity = any bad news might have an oversized adverse effect. Although with the massive rise of bots, I am not sure the holiday argument and lack of humans around still holds)
I recon this is relatively well known (e.g. I think Georg - you might have already mentioned something similar years ago on this forum or on your blog). I also remember reading things of this kind on seeking alpha or elsewhere back in 2009 or so.
So, assuming it is “known” at a minimum by the real pros and by many of us the “passionate amateurs”, why does it persist and will it persist in the future?
Many thanks,
Jerome
Jerome,
I don’t know the reason for this abnormality. That could be a PhD thesis for someone. I think that the reason it persists is that it is not in the interest of the financial community to tell people to get out of the market during the bad periods.
And it is not a recent phenomena either. Just tested this for the S&P500 with dividends from 1950 to now; for 68 years.
Buy&Hold 4/24/1950 to 4/23/2018 CAGR= 10.48%
good 6-mo periods: 10/30/1950 to 4/23/2018 CAGR= 8.45%
bad 6-mo periods: 4/24/1950 to 10/23/2017 CAGR= 1.94%
Adding the two period returns gives 10.39%, so this checks ok.
And average for 44 rolling 25-year periods:
good 6-mo periods: CAGR= 8.30%
bad 6-mo periods: CAGR= 1.57%
So our P123 models should be structured accordingly. Worked for Walter, as per his posting.
If I may, I posit some thoughts on why the seasonality of the market appears to be so. I have been aware of the “sell in May and go away” sentiment for many years and have not followed it, perhaps to my loss. I figured it was a well known effect that would be arbitered away.
Many firms perform budgeting and strategic planning relative to annual financial periods, which are mostly reported by May. So they are beginning a new fiscal year with a new budget and strategic plan. That means likely changes throughout the business in employee count, expense adjustments, capital changes, etcetera in the first and second quarter especially. I believe some investors take a conservative step back to wait until the changes pan out, then move money into businesses that they believe will have good annual reports.
Also, most political action (elections and changes in laws affecting commerce) perhaps happen in the November - April period, although I have not confirmed that. Investors are quick to act on anticipated results. Politicians seem quick to try changes they can label as good for business.
On Quantpedia post on Market Seasonality Effect in World Equity Indexes:
This seemed pretty ridiculous until I found out reverse seasonality exists in the Southern Hemisphere (where seasons are reversed). I sometimes wonder what aliens would assess regarding the human condition. Maybe they might conclude that the Seasonal Affective Disorder is just one of those odd quirks about humanity, like chick flicks, cat memes, or skinny jeans. Oh yeah, and the Kardashians.
The “Seasonal Affective Disorder” explanation is ridiculous.
I checked seasonal effects for Australia (^AORD) and Hong Kong (^HSI). Those two markets exhibit the same seasonal effects as the S&P 500. So it has nothing to do with the Hemisphere you are in. It is simple, world markets follow the US market.
Here are some more stats for the S&P500 (no dividends).
For election years (even years) the absolute return for the 6-month period prior to election (May-Oct):
34 samples excluding best and worst return periods, average return= 0.46%
For non-election years (odd years) average of the absolute returns for the 6-month periods (May-Oct):
34 samples excluding best and worst return periods, average return= 1.95%
So we can look forward to the statistically worst period for the S&P500 until the mid-term election.
Hi guys.
Maybe a simple explanation might be that towards the end of the year, the next year and 2-years-out-earnings come visible. And the estimates roll forward another year. And the valuation metrics suddenly look better making stocks appear more attractive than they have been during the summer. This may lead to investors pay more and thereby driving up market prices.
That would also be my explanation, forward P/E`s are based on this years earnings until october/november where it switches to next years earnings. And i think the effect is stronger in europe because of the dividend dates. In the DAX for example nearly all dividends are paid in April/May so people that want to sell wait for the dividend, even if this is a losing proposition. (because of taxes)
Whatever it may be, there is no doubt that stocks perform worse on average for the six months May to Oct.
I just launched this DM: “5 Dual Ranked Stocks of the S&P500 - no trading Jul & Aug” which profits from this anomaly.
https://www.portfolio123.com/app/r2g/summary?id=1531855
This model’s holdings are 5 S&P 500 stocks. It trades for 10 months, from end of August to end of June, only. There is no trading during the summer months of July and August when the model switches to ETF SHY (also ok would be cash or defensive ETFs XLV and XLP.)
During the seasonably weak months, from May to June, it only selects stocks from the Sectors: Staple, Healthcare, Energy, and Discretionary. This increases return and reduces maximum drawdown.
You got me hooked (again) on seasonality. I did a lot of it 20 years ago on the DJIA using calendar months, option months, and moon cycles (!).
Using just the P123 screener and its backtester, this is the best I’ve found so far, which just alternates between three ETFs:
showVar(@1, 0)
showVar(@1, @1 + 10 ^ 0 * (Ticker("TLT") AND InSet(Month,5,6,7,8,9)))
showVar(@1, @1 + 10 ^ 1 * (Ticker("GLD") AND InSet(Month,1,2)))
showVar(@1, @1 + 10 ^ 2 * (Ticker("XLB") AND InSet(Month,3,4,10,11,12)))
@1 > 0That gives a CAGR of 23.46% since the start of 1999.
I started looking at that because the five ETFs (“XLP,XLV,XLU,VIG,IEI”) you held from May thru Sept only had a CAGR of 1.11% as a group. That just didn’t seem very good. GLD (3.4%), TLT (5.4%), and IEI (2.5%) did better on their own, but I wasn’t using a filter to choose the best of the five each for each year’s selection since I didn’t know your criteria.
And, in my testing, it appears Jan and Feb were not good months for most ETFs either, so I was looking for something with little correlation with stocks for those months as well. I even started looking at some country ETFs.
I also got some interesting results doing tests on stocks using SECTOR= criteria, and then choosing the best five from the “sectors of the month”. For example, one rule looks like like:
showVar(@1, @1 + 10 ^ 7 * (Sector=MATERIALS AND InSet(Month,3,4,11,12)))I didn’t limit the exchanges or stock sizes, but I did add an easily changeable liquidity filter which makes sure at least $2M worth of the stock traded every day for the past 50 days:
LoopMin("Vol(CTR) * Close(CTR)", 50) > 2000000 // Liquidity check=======================================================================
As for the seasonality tests I did 20 years ago, see here for a postmortem I did in 2009 of the “systems” I developed in 1998. The backtesting and forward results did well on the “OC3” system, which had these rules:
[list=1]
[]If month is Mar, Apr, May, Nov, or Dec, then “In” the market
[]If day of option month is -7 or -8 or 1 (7th and 8th before expiration, and day after), then “Out” of the market
[]If month is Sep, then “Out” of the market
[]If any other trading day, then “In” the market[/list=1]
Not sure what I’d do with “option month” criteria today, since the most heavily traded stocks now have weekly options.
Klaus (pfrommert),
I am not surprised that your seasonal DAX strategy works.
The DAX without dividends from 12/31/1987 to 4/23/2018 shows a CAGR= 8.69% (local currency)
Only invested in the DAX from Nov-Apr (inclusive), the good periods, from 12/31/1987 to 4/23/2018 and cash from May-Oct provided CAGR= 10.30%
So for the bad periods the cumulative return shows a negative CAGR= -1.5%.
I imagine there’s also a spike in dividend increases and stock buybacks at end of year as a way to distribute standing cash at year end. Also things like mergers & acquisitions are typically done before end of the calendar year for tax and budgeting reasons. Had a friend who worked in M&A for a major financial institution, and he had to celebrate Christmas every year in January because he was always working through November & December.
Pretty good write up on the seasonality of sectors using 75 years of data (Conclusion: It definitely exists, but we don’t have a good explanation why so it’s hard to bet on it out of sample with confidence).
https://blog.thinknewfound.com/2018/06/a-season-for-sectors/
Sell in May and Go Away model now open.
https://www.portfolio123.com/app/r2g/summary?id=1531420
The anomaly, that the S&P 500 performs best from November to April and significantly worse from May to October during most years, has also held for the most recent 12 months. Performance from May to October during election years (even years) appears to be particularly poor.
