You are Horrible at Market Timing

You are horrible at market timing. Don’t even attempt it. I probably can’t convince you how horrible you are, but hopefully some empirical data analysis will show how you and the majority of people are no good at market timing.

Recently a friend came to me lamenting about a recent stock purchase he made, lamenting how the stock has gone down since he’s bought it and how he should have waited to buy it for even cheaper. From this, I was reminded by an anecdote from a professor from my econometrics class. I was taking the class in late 2008, which if you don’t remember, was right in the midst of the major financial collapse, with all the major indices taking a huge nose dive.

Students being students, somebody asked the professor what he thought about the collapse and what he was doing himself in his own personal account. Keep in mind the tone was what a “normal” person does instead what a 1000-person hedge fund does. He referred to a past study that showed that most recoveries in the equities space didn’t come from steady returns but instead were concentrated on a few, infrequently-spaced days. That is, there was no way for you to catch the recoveries unless you were already invested the day before. And, if you were sitting on cash before, saw the move happen, and attempted to then get into the markets, it would have been too late for you.

I decided to (very) roughly replicate this purported study for my friend.

I first went to google to download daily prices for SPY. They provided a nice facility for you to export the data to a csv format.

The data is relatively straightforward.

Date,Open,High,Low,Close,Volume
29-May-12,133.16,133.92,132.75,133.70,32727593
25-May-12,132.48,132.85,131.78,132.10,28450945
24-May-12,132.67,132.84,131.42,132.53,31309748
...

I wrote some R code to read in this data and to trim out days that didn’t have an open, which left me with observation starting in 2000/01/03 and ~3100 data points. Additionally, I created log returns for that day’s open to close, i.e., log(p_{close}) - log(p_{open}).

# Get the data
xx <- read.table(file="~/tmp/spy.data.csv", header=T, sep=",", as.is=T)
names(xx) <- c("date", "open", "high", "low", "close", "vlm")

# Get date in ymd format
xx$ymd <- as.numeric(strftime(as.Date(xx$date, "%d-%b-%y"), "%Y%m%d"))
xx <- xx[, names(xx)[-1]]
xx <- xx[,c(ncol(xx), 1:(ncol(xx)-1))]

# We want to work with complete data
xx <- xx[xx$open != 0,]

# I prefer low dates first than high dates
xx <- xx[order(xx$ymd),]
rownames(xx) <- 1:nrow(xx)

# Getting open to close
xx$o2c <- log(xx$close) - log(xx$open)
xx <- xx[!is.infinite(xx$o2c),]

Getting the top 10 return days is relatively straightforward. Note that finger-in-the-wind, a lot of the top 10 return days came from end of 2008, for which presumably a lot of people decided to put their money into cash out of fear.

> head(xx[order(-xx$o2c),], n=10)
          ymd   open   high    low  close       vlm        o2c
635  20020724  78.14  85.12  77.68  84.72    671400 0.08084961
2202 20081013  93.87 101.35  89.95 101.35   2821800 0.07666903
2213 20081028  87.34  94.24  84.53  93.76  81089900 0.07092978
2225 20081113  86.13  91.73  82.09  91.17 753800996 0.05686811
2234 20081126  84.30  89.19  84.24  88.97 370320441 0.05391737
2019 20080123 127.09 134.19 126.84 133.86  53861000 0.05189898
248  20010103 128.31 136.00 127.66 135.00  17523900 0.05082557
2241 20081205  83.65  88.42  82.24  87.93 471947872 0.04989962
2239 20081203  83.40  87.83  83.14  87.32 520103726 0.04593122
2315 20090323  78.74  82.29  78.31  82.22 420247245 0.04324730

Emphasizing this point more, if you didn't have your cash in equities at the beginning of the day, you would have missed out on the recovery. An additional point we can do is to see what the returns were on the prior day. In other words, is there some in-your-face behavior the prior day that would lead you to believe that huge returns would have come the next day?

> max.ndx <- head(order(-xx$o2c), n=10)
> max.ndx <- as.vector(t(cbind(max.ndx, max.ndx-1)))
> xx[max.ndx,]
          ymd   open   high    low  close       vlm          o2c
635  20020724  78.14  85.12  77.68  84.72    671400  0.080849612
634  20020723  82.55  83.24  78.85  79.95  65806500 -0.032002731
2202 20081013  93.87 101.35  89.95 101.35   2821800  0.076669027
2201 20081010  86.76  93.94  83.58  88.50  90590400  0.019856866
2213 20081028  87.34  94.24  84.53  93.76  81089900  0.070929778
2212 20081027  85.97  89.51  83.70  83.95  62953200 -0.023777015
2225 20081113  86.13  91.73  82.09  91.17 753800996  0.056868113
2224 20081112  88.23  90.15  85.12  85.82 454330554 -0.027694962
2234 20081126  84.30  89.19  84.24  88.97 370320441  0.053917369
2233 20081125  87.30  87.51  83.82  85.66 454188290 -0.018964491
2019 20080123 127.09 134.19 126.84 133.86  53861000  0.051898981
2018 20080122 127.21 132.43 126.00 130.72  75350600  0.027218367
248  20010103 128.31 136.00 127.66 135.00  17523900  0.050825568
247  20010102 132.00 132.16 127.56 128.81   8732200 -0.024463472
2241 20081205  83.65  88.42  82.24  87.93 471947872  0.049899616
2240 20081204  86.06  88.05  83.74  85.30 444341542 -0.008870273
2239 20081203  83.40  87.83  83.14  87.32 520103726  0.045931222
2238 20081202  83.47  85.49  82.04  85.27 469785220  0.021335407
2315 20090323  78.74  82.29  78.31  82.22 420247245  0.043247296
2314 20090320  78.76  78.91  76.53  76.71 371165651 -0.026373176

Looking at the data, we can see that there were both positive and negative returns the day before. However, there weren't any moments of "large return today, I better get in." My perception of this is that, from the perspective of a normal investor saving for retirement, they should just leave their money in and are already hopefully using some variant of dollar cost averaging.

For what it's worth, my professor said he hardly touched his own personal investments, presumably just putting his 401k money in a few indices and forgetting about it. His time was better spent on writing academic papers.

Random Readings 0001 – Investment Related

Kiplinger provided a list of four companies who are similar to Berkshire Hathaway and its chairman Warren Buffet. Specifically, they highlighted, Markel, Fairfax Financial, Loews, and Leucadia National. The common thread of such companies is that they are cash rich businesses from underwriting insurance and need to do something with the cash. At least for “Berkshire Hathaway”-like companies, they leverage the cash in building large stock portfolios and/or acquiring value/distress-based companies.

Continuing the theme of taking insurance premiums and investing it, Greenlight Capital Re is a reinsurer who takes its premiums and invests it in David’s Einhorn’s hedgefund, Greenlight Capital.

Jeffrey was profiling Annaly Capital Management and incidentally highlighted the downside risks of all the high dividend yield REITs we see. Specifically, the strategies typically encompass borrowing low interest rates and investing them in various sorts of mortgage securities, which typically earn a higher rate of return. The risks come from 1) increasing interest rates going forward relative to the all time low interest rates we have now will shrink the yields obtainable and 2) if home owners become able to refinance at the current lower interest rates (although if you’re underwater, it will be difficult to refinance), the yields will shrink.