Category Archives: Trading/Speculating/Investing

Cisco GSX 2018

Cisco GSX (our annual sales meeting) was, as always, an outstanding experience! There was one line spoken on the main stage that got the largest applause I have seen in years – and deservedly so. Gerri Elliott (Cisco’s new sales and marketing boss, and also my boss’s boss’s boss’s boss’s boss’s boss’s boss :/ – wish I was closer to the top! ) pointed out to Chuck Robbins on stage that this meeting marked his 3-year anniversary as Cisco CEO and in that time, Cisco stock price is up… 82%!   That brought the house down, as it should.

82% seemed incredible to me when I heard it. I know the price has risen – but wow, a near doubling? That means under Chuck’s leadership Cisco has added $90B of market cap.  I know that’s small compared to the top 5 stocks, but it is an amazing amount of money in its own right. John Chambers, when he became CEO in 1995 had a $5B company, and left it 20 years later at $130B — $125B of market cap in 20 years.  Chuck has done $90B in just 3 years.

So, how it is possible that Cisco stock has risen so much in 3 years?  From a sales point of view we have not materially grown sales at all in the last 5 years, not even at the rate of inflation, even with new acquisitions.  Here are the numbers for the last 10 years of Cisco earnings:

Quarter Ending revenue ($m) net income ($m) dividends paid Shares outstanding (billions) stock price on Q end market cap ($B)
7/28/18 12,840 3,800 0.33 4.7 47.15 221.605
4/28/18 12,463 2,691 0.33 4.8 42.7 204.96
1/27/18 11,887 2,322 0.29 4.9 44.78 219.422
10/28/17 12,136 2,394 0.29 5 35.88 179.4
7/29/17 12,133 2,424 0.29 5 32.21 161.05
4/29/17 11,940 2,515 0.29 5 31.53 157.65
11,580 2,348 0.26 5 34.18 170.9
10/29/16 12,352 2,322 0.26 5 29.82 149.1
7/30/16 12,638 2,813 0.26 5 31.44 157.2
4/30/16 12,000 2,349 0.26 5 29.05 145.25
1/23/16 11,927 3,147 0.21 5.1 26.18 133.518
10/24/15 12,682 2,430 0.21 5.1 27.25 138.975
7/25/15 12,843 2,319 0.21 5.1 25.88 131.988
4/25/15 12,137 2,437 0.21 5.1 28.83 147.033
1/24/15 11,936 2,397 0.19 5.1 29.51 150.501
10/25/14 12,245 1,828 0.19 5.1 27.64 140.964
7/26/14 12,357 2,247 0.19 5.1 24.99 127.449
4/26/14 11,545 2,181 0.19 5.1 24.62 125.562
1/25/14 11,155 1,429 0.17 5.3 21.8 115.54
12,085 1,996 0.17 5.4 21.25 114.75
7/27/13 12,417 2,270 0.17 5.4 23.31 125.874
4/27/13 12,216 2,478 0.17 5.3 24.12 127.836
1/26/13 12,098 3,143 0.14 5.3 20.86 110.558
10/27/12 11,876 2,092 0.14 5.3 18.91 100.223
7/28/12 11,690 1,917 0.08 5.3 19.08 101.124
4/28/12 11,588 2,165 0.08 5.4 16.33 88.182
1/28/12 11,527 2,182 0.06 5.4 19.88 107.352
10/29/11 11,256 1,777 0.06 5.4 18.64 100.656
7/30/11 11,195 1,232 0.06 5.5 15.67 86.185
4/30/11 10,866 1,807 0.06 5.5 16.8 92.4
1/29/11 10,407 1,521 0 5.5 18.56 102.08
10/30/10 10,750 1,930 0 5.6 19.16 107.296
7/31/10 10,836 1,935 0 5.7 19.99 113.943
5/1/10 10,368 2,192 0 5.7 23.16 132.012
1/23/10 9,815 1,853 0 5.7 24.33 138.681
10/24/09 9,021 1,787 0 5.8 23.4 135.72
7/25/09 8,535 1,081 0 5.8 21.6 125.28
4/25/09 8,162 1,348 0 5.8 18.5 107.3
1/24/09 9,089 1,504 0 5.8 14.57 84.506


What you should take away from the above table:

  1. Revenue per quarter now ($12.3B over FY18) is identical to revenue per quarter in FY15 (also $12.3B).

commentary: Even with a paltry 2% inflation over 3 years that $12.3B should have grown to $13B.  On top of that, Cisco has acquired 25 companies over the last 3 years. That alone should add another $500M of revenue.  So at 0% real growth, Cisco revenue for FY18 should have been $13.5B, but it was not — only $12.3B — in real terms that’s a contraction of 9%.  Yuck!

commentary 2: In Q2 of FY18 Cisco actually reported a net loss of $8.8B — we took a one time charge associated with repatriating overseas capital for $11.1B.  I have not added in that $11.1B into the table above, otherwise the P/Es would be negative.

So based on #1 alone, Cisco’s stock price should have gone down 10% in the last 3 years, not up 82%.   Let’s move on…


2. Net Income per year is up slightly in three years, maybe. Take the above table and compute yearly numbers. Here are the year over year numbers for trailing 4 quarters net income:

FY18 has grown to $11.2B from FY15’s $9B (25%), but that’s not entirely accurate.  See the note about the Q2 charge. With that included, Cisco earned $0 for 2018.

So the company is not selling more, but it is making more money on the same volume of stuff sold. Good!  Let’s say that should increase the stock price, 10-20%.  So where do we get 82%?  Read on…


3. Shares are contracting.  The company continues to use profits to retire shares, going from about 6B shares a decade ago to under 5B shares now.  That is big. Same income spread across fewer shares yields higher EPS.  The reduction in shares from FY15 (5.1B to FY18 4.7B) is about 8%, so there is another 10% of that 82% we are looking for


4.  Multiple expansion. This is the real generator of wealth.  In FY15 Cisco had a P/E of 14.7 trailing (12.3 on a forward basis).  That was, just simply way too low.  Now we get a 19.8 trailing P/E and a forward estimate of about 18 — which is in line with the market. I personally think given the opportunity and space Cisco plays in we should get a rich premium over market multiples — not back to 2001’s 120x, but to 30x? Sure! The Internet of Things is a big opportunity and we are poised to capture it.

That expansion from 14.7 to 19.8 is 35% — so that’s 35% of the 82%.  Or is it?   Look more closely:

1.25 * 1.08 * 1.35 = 1.82

Did you see it? There is your 82%.

Increase earnings 25%

Decrease shares outstanding 8%

and expand the whole multiple 35%

— its a multiplicative effect — the net is 25% + 8% + 35% = 82% — it is geometric — the multiple applies not only to the new income, but to all income.  That’s the magic.

So in a real sense the Trump tax cuts are what has Cisco up 82% in 3 years. As the animal spirits come out and the multiples expand there is a real wealth effect generated.   Again, this is all in the backdrop of real revenue down 10% in 3 years.

How does the future set up?  Very well.  If the new revenues get to ~$14B per year, that’s 20% more than now. If the shares keep dwindling at a similar rate. that’s another 10%, and if the political backdrop remains the same and multiples continue to expand to 25 for Cisco — then that (1.2 * 1.1 * 1.25) = 65% — which would be $82.5 per share, which would tie the all-time high for the company set on 28 March 2000.






True Bid/Ask spreads on 3xETF, 9+ months out, out-of-the money calls

Most people like to sell premium and collect money.  Me, I like to buy premium in anticipation of a melt-up.

Today I placed an order to buy TQQQ 76.67C, Jan 2019, and another order to sell the same call option. I did this to see what the true market bid/ask spreads are.

In the morning, Schwab was publishing bid@1.40, midpoint@2.60 and ask at 3.80.   There had been no volume on this contract for several days, and the market has been up over the last few days/weeks.

I started placing orders to buy at $1.40, going up in 20c increments until the bid dropped again when I removed my order. The marketmaker bids rose and stayed elevated to $2.50, at which point my bid became best when my order was in, and the bid dropped to $2.50 when I removed my order.  I got filled at $3.50. I bought 10 contracts.

Then I sold  1 contract. Started at $3.50, got filled at $3.30.

So the real spread was $3.30-$3.50 (about 6%), and not the $1.40-$3.80 (46%)  that the platform said at the beginning of the day.


Compare to QQQ options for the same date (Jan 2019) — the same % out of the money (7% for QQQ, 21% for TQQQ) is 190 strike. Before starting, bid is $3.28 to ask of $3.35  (2%) . Using the same methodology buying 32 contracts and selling 2 I got filled buying at $3.29 and selling at $3.28 (0.3%).

Above is the view before starting QQQ trade

Above is the view after completing both QQQ trades (buying and selling).  Notice I am all of the volume. Started at 405, I bought 32 and sold 2, ending volume is 439.


So, the final analysis is as following:



My IRA option, 2017

2017 was a great year for stocks.   I want to use this column to detail one particular trade I made in 2017 — my call option on TQQQ.

So first, why options?  I have been trading stocks since in my early twenties, but trading options are new to me, something I have only done for a couple years now. Back in 2016 I spent a lot of time watching Robert Shiller’s OpenYale class on Financial Markets. In that class he talks about the development of options and futures markets. Options and futures are labeled as derivatives markets, but when you step back and think about it the options market IS the real market. Think about a soybean farmer in Texas. On December 1 she will not particularly care about the spot price of soybeans on that day- her land is all harvested and ready for next year’s planting. But she would be very interested in the price of soybeans in October of the following year, when she could bring a crop to market if she decides to plant it now. Same for a company: Delta Airlines does not care so much about the spot price of jet fuel today, but they do care very much about what it will be 1-24 months from now, and getting a predictable price so they can plan their capital expenditures and fare prices accordingly. In a lot of ways the options market is what drives real business and spot prices are not nearly as important. So from that view options are not merely gambling.

I trade almost exclusively in an IRA account, and you can trade options there — just no margin, which is fine for me.  In my IRA I am granted level 1 options access, which means I can buy calls and puts. I have been comfortable with owning QQQ (Nasdaq 100) for 10+ years, one day in about 2013 I saw a ticker “TQQQ” pass on the bottom of the CNBC screen. I looked it up and it was 3x the QQQ return. I knew interest rates were ridiculously low then and I naively thought this fund achieved 3x by borrowing cash at low rates and using that to actually buy things like Apple and Cisco. So I went in – and it has done fantastically, returning about 1000% over that period.

In 2017 I decided to try TQQQ as an option. I’m not even sure that an option should be allowed on such a product as it is an option to being with. Sort of an option on an option.  The thought was buy an out of the money call. My goal was to buy an instrument that worked as follows — if the market went up 20-30% in 2007 this option would return +700%. That is a significant return on a sizable investment that can potentially be somewhat life-changing. At least enough to buy a new car or along those lines. If the market went up less, say 10%, this option would return -100%. Fortunately I have an overall portfolio where I can stand to lose a few % if the option did not pan out and I was comfortable defining my risk this way so I decided to pull the trigger.

On 3/31/17 the TQQQ option book looked like this:

The underlying spot price was 88.21. I knew I wanted to give myself some time – to me a short term option is more like gambling but a longer term option is a call on the market. So I picked January 2018. (The actual expiration is 1/19/2018). I also knew I wanted an out-of-the-money call. The only real question was how out of the money? I felt there was a chance the Nasdaq could return 25%, which would imply a TQQQ 1 year return of about 75%. How strong was my conviction? A 75% return on an 88.21 price is 154.3 Doing analysis with those number you get the following:

All the numbers above assume an initial investment of $10,000.

If I picked a strike of 130 I would make 1,250%. A strike of 90 would result in a profit of 400%. In either case, if the market was down for 2017 I would have lost -100%.

In the end I debated hard between the 100 and 120 strikes. I really wanted to pull the trigger on the 120 strikes, but I felt there was too much risk there. For example, if the market had returned 10% last year the returns would have looked like this:

It would have been a good year, The market would have been up for most, but that 120 strike option would have returned -100%. A total wipeout in an excellent year for stocks was too painful for me to contemplate, so I pulled the trigger on the 100 strikes.

I bought 16 calls of TQQQ strike 100 / 1/19/2018 on April 11, 2017. The purchase price was $6.00 each contract.

Often when you have a big winner you sell too early. I did a that this time, but I don’t regret it. My goal was to let the $10k bet ride to Jan 18, 2018 and take what it was worth then. Instead after the position doubled (which turned out to be 1 month later on 5/11/2017) I sold half the position. I sold 8 contracts at $12.60, getting back my $10k investment. I would then let the rest of it ride to expiration.

Except I didn’t. On 12/4/2017 I sold another 4 of the contracts, knowing the end of the option period was near and I did not want to lose all my profit. I sold that lot at $36 each.

I let my 4 remaining options ride to the end. Since they were now deep in the money calls near expiration, they trade at almost the exact difference between the underlying price and option strike price – (Delta of 1). I sold those for whatever the market would bear on market open on 1/17/2018, which turned out to be $63 each.

So my profit as it stands today is:
debit of $6 * 16 * 100 = -$9,600
credit of $12.60 * 8 * 100 = +$10,080
credit of $36 * 4 * 100 = +$14,400
credit of $63 * 4 * 100 = $25,200
Total of $40,080

So a return of 417% in 9 months. That will do pig, that’ll do.

What if I had waited and not sold at all? Based on the absolute final trade of TQQQ on 1/19/2018 expiration (167.94) – that would have been 1032%.

What if I had done the 120 strike? There the return becomes a fantastic 2200%. The calculations to here have been using the ask price of the options, not the midpoint. Another problem with these options are the spreads. Look at the 120 strike- $2.8 to buy and $1.3 to sell. That means as soon as you execute your order your $10,000 position gets cut in half. Even though that would have worked wonders in 2017 it is still a hard call to pull that trigger.

What’s next?
So fo 2018 I am going to keep 10% of my portfolio in options. My thinking is to ladder 4 options, with strikes of 3,6, 9 and 12 months and $5,000 position each. As for the underlying I am sticking with TQQQ – If we are in the last throes of a bull market, that one will go parabolic before crashing — I would not be surprised to see NASDAQ 8600 by the end of March 2018.  If that were to happen, that implies TQQQ would be at $260 / share.   If you took a position in March 16, 2018 call @ 200 strike which you can buy now for $2.00 each and you could sell them for $60 each, that’s a return of 2,900%.  Insane? Yes. Improbable? Yes. Impossible?  We’ll know in 54 trading days.

I’ll detail how that works out in 2019.

IBD “Distribution Days” backtesting

So I have been going to the Investor Business Daily meetup in OKC. for the last few months. We usually have about 5 people in attendance and we talk stocks for a couple hours. The host, Raylon Rogers, reads a chapter from an IBD book and then we look at the charts to get our investment ideas.  This last week he talked about “Redemption Days”.  This is an IBD term to look for selling with volume.  IBD is pretty much all a technical analysis method (I perfer fundamentals).  If at the end of one trading day the price action was a lower close and the volume rose from yesterday than this is a distribution day.  Their sell signal is 5 distribution days out of the last 15 trading days.

How often should a redemption day occur?  If it is all truly random, you would expect 3.75 distribution days over any rolling 15 day period (volume can either go up or down, price can either go up or down — therefore a .25 probability that both price moves down and volume moves up on any given day).


So I decided to backtest.  I wrote a script in PERL and am hosting it on github if you would like to download and try yourself. It uses data from API to get daily stock data.

What I follow, and what we talked about in class was the NASDAQ index.  Using the tool there have been a total of 4,484 trading days since Jan 1, 2000  (where the alphavantage data stops– I would really like to test this for 1997-1999, since I think that will be the best model for how this particular bull market ends).    Of those 4,484 days I can use 4,408 of them for backtesting — I can’t use Jan 1-15, 2000 (since I can’t compute the redemption days between Dec 15-31, 1999), and I can’t use anything after July 26th, 2017 (as I can’t compute the 90 day return after that date (yet)). I use 90 days because that gets a single earning release cycle and I think that is a good number to judge — oh yeah, the market tanked, and you can tell over the last 90 days.

So we are analyzing about ~4,400 days.  In that period for the NASDAQ there were 932 times we had 5 or more redemption days out of the last 15 trading days.   Of those 932 times, 90 days later the index was down 355 times (38%).

OK, seems decent. If I have a formula to keep me protected 38% of the time that might be worth something.  Is it? Let’s see the number of days out of those 4,400 when the return was negative 90 days out:  we get 1,609  (36%).  So in that light 38% is not statistically significant. I can simply use a rule that says always stay out of the market and I will avoid having negative 90 days returns 36% of the time.  Seems about as good as saying stay out when you have 5 redemption days over the last 15 and you will avoid negative returns 38% of the time.

OK, what about total return if you follow redemption day theory? Average return 90 days out from our 932 days of 5/15 redemption days is 1.89%.   This compares to an average return 90 days out from all 4,404 analyzed days of 1.25%    So what this is saying is you are worse than the market (note both returns are positive).  You would be better off buying on 5/15 redemption days.


So how about trying 6 or more redemption days out of 15 instead of just 5/15:

times with 6+ redemption days = 320 
times with 7+ redemption days = 106
times with 8+ redemption days = 14

8 is the most, no time since 2000 has there been 9 or more redemption days out of 15 in the IXIC (Nasdaq).  So analyze those 14 times with 8/15 redemption days. Average 90 day return is highly positive +7.17%: Here are the 14 times it happened and the 90 day forward return:

2000-04-18 = 7.9%
2001-09-24 = 30.8%
2001-09-25 = 31.6%
2004-03-15  = 2.3%
2009-11-06  = 3.1%
2010-06-03  = -3.0%
2010-06-04 = -0.5%
2012-04-18 = -3.5%
2012-04-23 = -3.9%
2012-07-27  = 0.6%
2016-01-22 = 6.5%
2016-01-25 = 7.6%
2016-05-05  = 10.5%
2016-05-06  = 10.3%

Of interest, not only is the 90 day return super positive, you are only saved from -3% moves down three times, and two times you miss 90 day moves of 30%. I definitely want the other side of this trade.  Also, following this theory you are not protected against the 2008 financial crisis or the 2000 dot com bubble.

So, safe to say this theory is totally busted as it relates to the NASDAQ index. How about individual stocks?  Let’s try a few:

distribution days = 960 days (21%)
  average 90 day return = +1.72% 
total days = totals 4,408 days
  average 90 day return = +1.02%
distribution days = 849 days (19%)
  average 90 day return = +1.56% 
total days = totals 4,408 days
  average 90 day return = -0.12%
distribution days = 766 days (17%)
  average 90 day return = +6.7% 
total days = totals 4,408 days
  average 90 day return = +7.08%

note – dividends are considered here for TGT and CSCO, we are using TIME_SERIES_DAILY_ADJUSTED, not TIME_SERIES_DAILY.

Again, this distribution day theory is busted.  In fact, you want the other side of this trade, if anything (buy after a series of distribution days, don’t sell).


Want me to try it for your stock?  Let me know what symbol in the comments and I’ll post the results here.




Calculate implied move in a stock from earnings based on options prices

Have you ever listened to CNBC on a marquee company earnings day and heard something along the lines of “options are pricing in a 4% move in this stock post-earning, either up or down”  and wondered how that was calculated?  Here is how I do it:

Yesterday (5-2-2017) Apple (AAPL) reported Q1 earnings of $2.10 per share.  It was one of those scenarios where earnings beat expectation, but guidance was weak, so the stock opened the next day down 1.49%.  How close did that 1.49% on Wednesday match what the options market told you on Tuesday?

First, start with the shortest term option available.  Options always expire on a Friday, so for AAPL you need to look at the options that expire on 5-5-2017. Prior to close on Tuesday AAPL was trading at $147.63 per share.  Look at at-the-money calls and puts – so look at the $148 call. That option was trading on 5-2 at $2.41

Screen Shot 2017-05-02 at 2.29.25 PM


So that means a bullish speculator (3 day options is definitely speculation, not investing!) believes AAPL will get to at least $150.41 by Friday.  There are only two things at play between Tuesday and Friday — 1 is the earnings call (obviously) and 2 is the overall market volatility on Wednesday through Friday.  So a speculator believes those 2 forces will equal a total move of 1.88% (the difference between 150.41 and 147.63).

Now look at the way the options were trading on Wednesday morning.  With the stock now at $145.31 a call for $146 on Friday expiration was trading at $0.80.  Again this implies a break even price of $146.80, or a 1.02% move.  The only thing from Wednesday to Friday is normal overall market volatility, so this tells you that Wed-Fri market vol accounts for 1.02%.

Doing the math on the only other force at play (earning announcement) means that the earnings announcement was 1.88% – 1.02% = 0.86%


Let’s do it now for the downside using at-the-money puts.  The Tuesday $147 put cost $2.27 implying a break even price of $144.73 or a move down of 1.96%.

On Wednesday the $145 put cost $1.34 implying break even at $143.66 or a move down of 0.92% between Wednesday and Friday.

Screen Shot 2017-05-03 at 8.30.50 AM

Doing the math, 1.96%-0.92% = 1.04%

So the options told you to expect a 0.86% move up or a 1.04% move down in AAPL post-earnings.


So really the options did an okay job of predicting the move.  The predicted downside move was 1.04% and the actual was 1.49%.  Kinda close, but not spot on.









Volatility is much lower today than it was when I was born…

This was so non-intutive to me that I deemed it worthy of a quick blog post.  Take historical prices from yahoo finance (or another website) and look at the daily high and low quoted prices of the S&P 500 during any handful of days from 1972 and compare them to a handful of days from 2014.

For all 1972-1975 the lowest daily volatility was 1.05%  (October 9, 1972). The highest was 6.47%  (interestingly October 9 again, this time 1974) and the median was 1.85%. An average day during that period would be Feb 26, 1973 where the S&P500 high was 113.26 and the low was 111.15 for a daily volatility of ~1.86%.

Contrast that to 2014. Over the last 90 days the lowest volatility has been 0.31% (April 23) and the median has been ~ 0.7%.   In fact, 80% of the trading days had volatility below 1%, where as pointed out in the paragraph above zero trading days from 1972 to 1975 were below 1%

Conclusion: Even with all the internet trading and cheap commissions, daily volatility has gone down significantly over the past 40 years.  You would think the free-er flow of capital would increase volatility, but it has not.

Backtesting the “January effect” theory

If you trade stocks you have no doubt heard of the January effect. It goes something like this:

“The stock market for the year does the same that it does in January. If the month of January is up, then stocks will be up for the full year. If the month of January is down, then stocks will be down for the full year. Furthermore, the 1st trading day in January is a predictor for the month of January and thereby a predictor for the full year”

Well, I thought it would be a good exercise to start the year by testing this theory,  As good as last year’s 30% gains were, I sure would not want to go through 30% losses this year :(

Specifically I wanted to test the 1st day theory and test it with the NASDAQ index.  Most of my mutual fund holdings today are in QQQ or TQQQ – which is the NASDAQ 100 ETF.

I wanted to go back through at least my lifetime (from 1980 on), since I remember the NASDAQ even back then was talked about as the “tech index”.  Here are the numbers. Two rows for each year, which show the NASDAQ close on 31 Dec and 2 Jan for each year:

year    NASDAQ closing price         Adj Close    Percentage Gain for year    Percentage Gain for 1st trading day of year    Verdict on Swami…
31-Dec-80        202.34
2-Jan-81        203.55
1981    31-Dec-81        195.84    -4%    0.60%    Incorrect Positive Prediction
4-Jan-82        195.53
1982    31-Dec-82        232.41    19%    -0.16%    Incorrect Negative Prediction
3-Jan-83        230.59
1983    30-Dec-83        278.6    21%    -0.78%    Incorrect Negative Prediction
3-Jan-84        277.63
1984    31-Dec-84        247.1    -11%    -0.35%    Correct Negative Prediction
2-Jan-85        245.9
1985    31-Dec-85        324.9    32%    -0.49%    Incorrect Negative Prediction
2-Jan-86        325
1986    31-Dec-86        348.8    7%    0.03%    Correct Positive Prediction
2-Jan-87        353.2
1987    31-Dec-87        330.5    -6%    1.26%    Incorrect Positive Prediction
4-Jan-88        338.5
1988    30-Dec-88        381.4    13%    2.42%    Correct Positive Prediction
3-Jan-89        378.6
1989    29-Dec-89        454.8    20%    -0.73%    Incorrect Negative Prediction
2-Jan-90        459.3
1990    31-Dec-90        373.8    -19%    0.99%    Incorrect Positive Prediction
2-Jan-91        372.2
1991    31-Dec-91        586.34    58%    -0.43%    Incorrect Negative Prediction
2-Jan-92        586.45
1992    31-Dec-92        676.95    15%    0.02%    Correct Positive Prediction
4-Jan-93        671.8
1993    31-Dec-93        776.8    16%    -0.76%    Incorrect Negative Prediction
3-Jan-94        770.76
1994    30-Dec-94        751.96    -2%    -0.78%    Correct Negative Prediction
3-Jan-95        743.58
1995    29-Dec-95        1,052.13    41%    -1.11%    Incorrect Negative Prediction
2-Jan-96        1,058.65
1996    31-Dec-96        1,291.03    22%    0.62%    Correct Positive Prediction
2-Jan-97        1,280.70
1997    31-Dec-97        1,570.35    23%    -0.80%    Incorrect Negative Prediction
2-Jan-98        1,581.53
1998    31-Dec-98        2,192.69    39%    0.71%    Correct Positive Prediction
4-Jan-99        2,208.05
1999    31-Dec-99        4,069.31    84%    0.70%    Correct Positive Prediction
3-Jan-00        4,131.15
2000    29-Dec-00        2,470.52    -40%    1.52%    Incorrect Positive Prediction
2-Jan-01        2,291.86
2001    31-Dec-01        1,950.40    -15%    -7.23%    Correct Negative Prediction
2-Jan-02        1,979.25
2002    31-Dec-02        1,335.51    -33%    1.48%    Incorrect Positive Prediction
2-Jan-03        1,384.85
2003    31-Dec-03        2,003.37    45%    3.69%    Correct Positive Prediction
2-Jan-04        2,006.68
2004    31-Dec-04        2,175.44    8%    0.17%    Correct Positive Prediction
3-Jan-05        2,152.15
2005    30-Dec-05        2,205.32    2%    -1.07%    Incorrect Negative Prediction
3-Jan-06        2,243.74
2006    29-Dec-06        2,415.29    8%    1.74%    Correct Positive Prediction
3-Jan-07        2,423.16
2007    31-Dec-07        2,652.28    9%    0.33%    Correct Positive Prediction
2-Jan-08        2,609.63
2008    31-Dec-08        1,577.03    -40%    -1.61%    Correct Negative Prediction
2-Jan-09        1,632.21
2009    31-Dec-09        2,269.15    39%    3.50%    Correct Positive Prediction
4-Jan-10        2,308.42
2010    31-Dec-10        2,652.87    15%    1.73%    Correct Positive Prediction
3-Jan-11        2,691.52
2011    30-Dec-11        2,605.15    -3%    1.46%    Incorrect Positive Prediction
3-Jan-12        2,648.72
2012    31-Dec-12        3,019.51    14%    1.67%    Correct Positive Prediction
2-Jan-13        3,112.26
2013    31-Dec-13        4,176.59    34%    3.07%    Correct Positive Prediction
2-Jan-14        4,143.07
2014                ???    -0.80%

All total that’s 33 years.

23 up years and 10 down years (up 70% of the years)

14 correct positive predictions
4 correct negative predictions
6 Incorrect Positive Predictions
9 Incorrect Negative Predictions
In total, that 18 correct predictions and 15 incorrect predictions  (55%)

Some interesting things fall out of this table.  For instance, do you remember that Jan 2 2001 was down -7.23% for the 1st trading day of January! Whew!

At first glance you may say that something that predicts with 50+% accuracy is a good predictor. That is not correct. You have to remember the stock market is not a random coin flip (where 55% prediction accuracy would be great!) but something that tends to rise over time. So a predictor that says “Any year beginning with the digit 1 or 2 will be an up year” would be right 70% of the time. In that light, I’d need a January effect predictor that would need to be something much more accurate than 70% for me to be interested.

More interesting still, if trying to use the data to your trading advantage.

If you bought the NASDAQ on Jan 2, 1980 and held it to Dec 31, 2013- your gain would be 1952% (4176-203)/203.   If instead you watched the 1st trading day of Jan and went 100% in the market in years where that 1st trading day was up and stayed out of the market for the whole year when the 1st day was down, you would have a 33 year total return of 1945%, virtually identical to buy and hold.

Therefore, in proper Mythbuster’s fashion, the 1st trading day of January theory is *BUSTED*