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Trading the Night Effect
The dream of making money while you sleep
Yesterday, the Financial Times published an articleabout the Night Effect in stocks.
We wanted to follow up on their article and give you access to a simple Excel-based model which you can download here to test the Night Effect for all sorts of securities. We’ll use this model further down in this post to analyze two overnight trading systems.
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The Night Effect, also known as the overnight return effect, refers to a recurring pattern observed in financial markets where the returns of stocks or other financial assets tend to be higher during overnight periods compared to intraday trading sessions. This effect has been observed in various stock markets around the world and contradicts the notion of market efficiency, which suggests that asset prices should fully reflect all available information.
The overnight anomaly is not a brand new topic and has been the focus of several academic papers here, here, here, and here as well as a 2018 New York Times article. There’s also a Nightshares ETF designed to capture the effect.
Shown below is the Night Effect for SPY since 1993. The red line depicts the cumulative overnight return and the black line the cumulative intraday return. Adding both lines together creates the SPY return.
More than 88% of the SPY’s overall return is due to overnight returns! However, the overnight performance is not a straight line and the Night Effect wouldn’t have worked well during crises (see Covid, GFC, tech bubble). The overnight performance peaked in January 2022 and has deteriorated since then, maybe because the effect is now widely known and exploited.
Apropos, it is not entirely clear why the Night Effect exists. Some explanations propose that the effect could be a result of overnight news releases that could not be fully incorporated into market prices during the previous trading day. Others suggest that the excess return is a compensation for liquidity provision, especially at the end of down-days in equity markets. Someone will take the overnight gap risk for a discounted price at today’s close and, if nothing happens, the discount should close overnight. Moreover, when market makers accumulate excess long inventory on down-days and/or when there’s strong selling pressure into the closing auction, they will likely demand compensation for their liquidity provision by buying at a lower-than-fair-market price. And some suggest that the effect is “caused by systematic market manipulation on a Herculean scaleby some quantitative hedge funds” as mentioned by the Financial Times here.
Before we start with our model, please note that neither the authors nor Takahē Capital or Twoquants® maintain any positions in the securities mentioned below. We don’t trade the Night Effect, and we explain why at the end of this post.
System #1 - Buy MOC, Sell MOO Every Day
Order size: 100 shares
System rules: Buy market on close, MOC. Sell market on open, MOO on the immediately following trading day
Transaction costs per trade: $0 to $3
The red line in the following chart assumes $0 transaction costs. While certain brokers offer $0 commission trading today, they didn’t offer it in earlier years, say in 1995. Operating in the markets is never free of charge, which is why the red line is merely a dreamy baseline.
When transaction costs are increased to $1 per trade, shown in blue, the system still makes money but looks less appealing. At $3, shown in black, the system produces a loss.
Note that our model assumes that costs are limited to brokerage commissions, which isn’t true. Every trade, even the smallest one, has market impact. This is the nature of EVERY transaction as each trade causes changes to the order/auction book. The question is: what “footprint” does your trade leave, and what is the corresponding cost?
Imagine you are a hedge fund and wanted to exploit the SPY Night Effect in size, i.e., trade much more than 100 shares MOC. You’ll need a market impact model to ascertain to what extent your order will likely move the price of the closing auction on that particular day. Additionally, your commission won’t be $0. The people running the Nightshares ETF have probably put some thought into this…
Using S&P e-mini futures also doesn’t solve this issue. There’s no auction in the futures and you’d have to time and manage the execution around the cash close. That’s where slippage comes in. Furthermore, a large futures order will impact the SPY (and vice versa) as both products are essentially tied to the hip.
System #2 - Buy LOC on Down-Days, Sell MOO
Now, we combine the Night Effect with a simple buy-the-dip (mean reversion) system.
Order size: 100 shares
System rules: Buy limit on close, LOC, if today is a down-day. Sell market MOO on the immediately following trading day
Transaction costs per trade: $0 to $3
Since we know yesterday’s closing price, we can place a LOC order today whose limit price equals yesterday’s closing price. Such order will be executed if today is a down-day, and lapse if today is an up-day.
Compared to System #1, the performance of System #2 looks somewhat better and even the version which uses $3 transaction costs wouldn’t have lost money in the past.
You may change cell H2 in the Excel spreadsheet to condition the system on more severe down-days, e.g., only buy if the down-day is worse than -1%. See what happens… and if you like it.
Conclusion: We believe capturing the Night Effect is tougher than most people think. Moreover, it seems the market has put an appropriate price tag on the Night Effect, demanding adequate compensation for carrying excess inventory and assuming overnight event and gap risks. Historically, the Night Effect has produced a negatively skewed return distribution as it exposes the trader to the left-tailed equity risks. We aim to do the exact opposite at Takahē Capital, i.e., take advantage of outlier moves and thrive in the tails, which is why we are not interested in trading strategies such as the ones presented in this post.
If you are interested in how we trade at Takahē Capital, please contact us here.
Disclaimer: No investment advice. Please do your own research before making any investment decisions. Past performance is not indicative of future results.