Overnight and Intraday SPX returns

Posted on May 05, 2020 by Robot James
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One of the things I’ve noticed from staring at the screen all day for the last few months is that most of the large negative returns in US stock indexes have come overnight.

What do you mean by “overnight”?

The core stock trading session for US stocks is between 9:30 am and 4 pm Eastern Time.

That’s when most stock market transactions take place. When we look at daily OHLC (Open High Low Close) stock data, the open price is the first trade of the core 9:30 am session, and the close price is the price of the auction at the end of the 4 pm core trading session.

However, stocks also trade in the “pre-open” or “early trading session” which starts at 6:30 am and in the “late trading session” which goes until 8 pm. Futures on stock indexes also trade most of the day.

I’m interested to see how overnight returns (the jump from the close to the open) differ from intraday returns – and how that relationship may have changed recently.

Intuitively, we’d probably expect to see higher average returns overnight when the market is closed – because it’s much more difficult to hedge and manage our exposures when the cash market is closed, so we might expect to get paid a premium, on average, for taking that risk.

Let’s have a look…

Getting Data

First, we need some daily OHLC data. (Open, High, Low, Close).

Let’s use the SPY ETF, which is an exchange-traded fund which tracks the S&P 500 index.

If you want daily price data and don’t need to pull too much data at a time, then there are a number of free online sources for this, including:

  • AlphaVantage
  • Tiingo

We can use the alphavantager and riingo packages to pull data from Alpha Vantage and Tiingo respectively.

Let’s use alphavantage. Go here and sign up for a free API key.

We’re going to use alphavantager to pull daily adjusted time series data for SPY and hold it in an R data frame called SPY.

To make this work you’ll need to tell alphavantager about your API key running the command: av_api_key(MY_API_KEY). You’ll need to replace AV_API_KEY with your actual API key.

av_api_key(MY_API_KEY) 
SPY <- av_get(symbol = 'SPY', av_fun = 'TIME_SERIES_DAILY_ADJUSTED', outputsize = 'full')

Here’s what our data looks like:

SPY %>% 
  head(20) %>% 
  kable() %>% 
  kable_styling(full_width = FALSE, position = 'center') %>% scroll_box(width = '800px', height = '300px')

 

timestamp open high low close adjusted_close volume dividend_amount split_coefficient
2000-04-10 151.7500 153.1093 150.3125 150.8437 103.3035 9624200 0 1
2000-04-11 150.0000 151.6250 148.3750 150.4062 103.0038 9006400 0 1
2000-04-12 150.3750 151.1562 146.1562 146.2812 100.1789 10779200 0 1
2000-04-13 147.4687 148.1562 143.7812 144.2500 98.7878 12225800 0 1
2000-04-14 142.6250 142.8125 133.5000 136.0000 93.1379 29604000 0 1
2000-04-17 135.1875 140.7500 134.6875 140.7500 96.3909 23918200 0 1
2000-04-18 140.5625 144.4687 139.7812 144.4687 98.9376 11069200 0 1
2000-04-19 144.5000 145.1250 142.5312 143.1250 98.0174 6553700 0 1
2000-04-20 143.5625 143.9375 142.3750 143.8125 98.4882 8537600 0 1
2000-04-24 141.5000 143.3125 140.5000 142.2500 97.4182 12893100 0 1
2000-04-25 144.6250 148.1562 144.4375 148.1562 101.4630 14102000 0 1
2000-04-26 147.9687 148.7500 146.0000 146.4843 100.3180 7711100 0 1
2000-04-27 143.0000 147.3437 143.0000 146.0000 99.9863 15595300 0 1
2000-04-28 147.0000 147.8593 145.0625 145.0937 99.3656 8743400 0 1
2000-05-01 146.5625 148.4843 145.8437 147.0625 100.7139 7328300 0 1
2000-05-02 145.5000 147.1250 144.1250 144.1250 98.7022 9411900 0 1
2000-05-03 144.0000 144.0000 139.7812 140.7500 96.3909 12630700 0 1
2000-05-04 142.0000 142.3593 140.7500 141.8125 97.1185 5963600 0 1
2000-05-05 141.0625 144.0000 140.9375 143.5312 98.2956 7862400 0 1
2000-05-08 142.7500 143.3750 141.8437 142.4531 97.5572 5064100 0 1

Calculate overnight and intraday returns

Now we calculate:

  • overnight returns as the % difference between the close price and the previous open
  • intraday returns as the % difference between the open and the close.

(I’ve also calculated close to close returns, which don’t get used in this analysis.)

SPY_returns <- SPY %>% 
  mutate(adjfactor = adjusted_close / close) %>%
  mutate(open = adjfactor * (open - close) + adjusted_close,
         high = adjfactor * (high - close) + adjusted_close,
         low = adjfactor * (low - close) + adjusted_close,
         close = close * adjfactor) %>%
  mutate(c2c = close / lag(close) - 1) %>%
  mutate(intraday = close/open - 1) %>% 
  mutate(overnight = lead(open)/close - 1) %>% 
  mutate(overnightpremium = overnight - intraday) %>%
  na.omit()

Cumulative overnight and intraday returns

Now let’s plot the cumulative performance of two strategies:

  • “intraday” goes long at the open, and holds until the end of the day, and is flat overnight
  • “overnight” goes long at the close, holds overnight, and closes on the open the next day
SPY_returns %>% 
  pivot_longer(c(intraday, overnight), names_to = 'period', values_to = 'returns') %>% 
  group_by(period) %>% 
  mutate(cumreturns = cumprod(1+returns)) %>% 
  ggplot(aes(x=timestamp, y=cumreturns, color=period)) + 
    geom_line() + 
    ggtitle('Intraday and Overnight Cumulative SPY Returns')

 

What do we see?

We see that most of our returns over the full cycle come from holding stock exposure overnight (the green line). In fact, the total return of SPY intraday since 2000 has actually been negative. This quite remarkable.

We also see that most of the recent losses in the last two weeks have come overnight.

And that most of the recent gains in the last few weeks have been intraday.

Rolling average difference in intraday and overnight returns

Let’s plot the rolling difference between overnight and intraday returns.

We use the slide_dbl function from the slider package to achieve this.

SPY_returns %>% 
  mutate(diff = intraday - overnight) %>% 
  mutate(diff20 = slide_dbl(diff, mean, .before = 20, .complete = TRUE)) %>% 
  na.omit() %>% 
  ggplot(aes(x=timestamp, y=diff20)) + 
    geom_line() + 
    ggtitle('20 day moving average of difference between SPX intraday and overnight returns')

 

You can see how historically anomalous the recent behaviour has been.

Would we bet on that continuing for a while? I probably wouldn’t. I’d just expect this behaviour to revert to normal.

Maybe we can get a little more insight by looking at the 5-day average since 2019:

SPY_returns %>% 
  mutate(diff = intraday - overnight) %>% 
  mutate(diff5 = slide_dbl(diff, mean, .before = 5, .complete = TRUE)) %>% 
  filter(timestamp >= '2019-01-01') %>% 
  na.omit() %>% 
  ggplot(aes(x=timestamp, y=diff5)) + 
    geom_line() + 
    ggtitle('5 day moving average of difference between SPX intraday and overnight returns')

 

And that’s exactly what we seem to see. When viewed over a shorter window length, the average difference in overnight and intraday returns does seem to be reverting to its mean.

The overnight drift

This paper looks at the returns from equity index futures and suggests that nearly 100% of those returns have come in one hour between 2 am and 3 am.

This is an insane result, and something well worth looking into…

(10) Comments

May 6, 2020 at 5:27 pm

The overnight drift paper looks very interesting. I wonder if other markets in Asia have these 2 am to 3 am (eastern time) effects too.

nayan savla
May 7, 2020 at 9:04 pm

is an insightful article…thanks

May 9, 2020 at 3:59 am

This is awesome, I just did see it and loved it.

So I started to dive into it, with all the skills you did teach on the FX intrady bootcamp.

Thank you James.

May 25, 2020 at 3:32 pm

Nice one! Great to hear you’re getting stuck in, Jan. Glad to hear the Bootcamp was helpful too.

May 9, 2020 at 7:35 am

Hello James, I’ve written a couple of posts with similar considerations too! It’s really huge the fall of the overnight returns in last March! http://www.nightlypatterns.blog

Terry
May 22, 2020 at 4:35 am

Hi Marco, I just saw this page and was going to tell you about it….but I see you beat me to it! I’ve seen a few sites that attempt to apply your pattern trading concepts here, some rather successfully, but they cost a lot of money.

May 25, 2020 at 3:38 pm

Nice on Marco! Thanks for sharing your blog. Really interesting work.

Tony
May 30, 2020 at 4:57 pm

Your analysis is wrong because you need to use adjusted prices.
For example, if you run your program on GASL, you will see a dramatic divergence of two lines, but it’s actually not the case.

July 15, 2020 at 10:12 am

If adjusted prices were used, wouldn’t adjusted open prices need to be used as well as adjusted close prices? If that is the case, the adjusted open and adjusted close prices would be adjusted using the same factor, so the analysis should persist.

July 15, 2020 at 3:50 pm

Yeah, that’s right… This analysis does actually use dividend-adjusted prices – but you see this effect whether you account for dividends or not. In fact, not adjusting for dividends would have the impact of actually reducing overnight returns.

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