Do Good Days Follow Good Days?


Have you ever wondered whether one good day tends to follow another? I have, and I’ve done a little research.

I’m sure many times you’ve looked at a chart, or seen a list of the day’s top stock winners, and wondered “what if I bought this tomorrow? Would it go up, or would it go down?”

There are of course two opposing logical predictions you could make when you see a nice juicy green bar on today’s chart:

1. “Buyers are excited about this stock. It must be a good one, so it’ll keep going up tomorrow as well. I should buy at the opening bell.” This is a momentum strategy.

2. “Short-sellers will see this as an opportunity when the stock declines back toward its original level. And those who already own it will want to take profits while they can. So I should avoid going long in this stock, as it will likely snap back tomorrow.” This is a mean-reversion strategy.

Neither is wrong. Either one of these can happen, depending on many circumstances. But what’s the probability of one good day leading to another?

Let’s get our hands dirty with some data. But first, some parameters:

• I looked at all the stocks currently in the Russell 3000 index, from Jan 1, 2012 through May 18, 2015. The closing price at the time must have been greater than $2, and the 10-day average volume was greater than 50,000 shares/day. This gave me lots of data but avoided really illiquid stocks.

• I then defined my ‘signal’ day as being any day with a close that was 3% or more above the open. This is not the same as comparing two days’ worth of closing prices. You’re welcome to compare your own data on a close-to-close basis if you’d like!

• For each signal (and there were many thousands of them), I then looked at whether the close of the following day was less than its open. After all, the point is to decide whether we should pile in on the following day. Comparing consecutive closes is problematic from a trading standpoint, as it requires the intraday scanning of many tickers.

So basically, when we get a nice green bar on one day, do we get a nice green bar the following day? Or do we get a down day instead? Also, does the probability get better or worse depending on the size of the ‘signal’ bar? Below I’ve graphed the probability of failure, based on the size of the signal bar.

Screen Shot 2015-05-19 at 9.32.25 AM

Well it’s not good news. Taking no other conditions into consideration, you have a greater than 50% chance of having a loser day after a signal day. And the chances of having a down day (open to close) only increases, as the size of your signal bar increases.

But might there be any conditions that would make a stock more likely to ‘go green’ on that second day? So I decided to see if history tends to repeat itself. I then looked at the same data set, but filtered for stocks that had at least one instance of two days in a row where the close was above the open by 3% (within the last 60 bars).


Screen Shot 2015-05-19 at 9.30.29 AM

First, let me apologize about the scaling. It’s difficult to force two google charts to have the same scaling. But if you look carefully, you can see that the probabilities are pretty similar…until you get to the >16% range. For some reason the failure probability shoots up dramatically when you’ve already had a two-good-day sequence in the past. No idea why that might be.

A question comes o mind: do the number of signals in the past 60 bars make a difference? If a stock is prone to good behavior, will it affect the probability? To find out, I then sorted by the number of these previous two-day ‘bumps’ that occurred in the previous 60 bars. These signals could overlap: three days of 3% would count as two signals. On the chart below I’ve called these signals “bumps”. And this is what I got:

Screen Shot 2015-05-19 at 9.44.07 AM


For the first time, the probability of failure has dipped down below 50% in some cases. Specifically, a stock that has generated 5, 6 or 8 signals in the previous 60 bars is slightly more likely to have an up day after we get a signal.

Yes, I see it too. WTF is going on with the 7-bump set of data? No idea whatsoever. There were over 1000 samples in the 7-bump set, so it’s not due to some outlier phenomenon. Even the 8-bump set had >500 samples. Above that, the data gets patchy so I would ignore the last bar.

So what does this mean? I think it ties in well with my earlier research, in that the recent history of a stock’s performance can be an indicator of future behavior. When designing a trading system, you might consider this aspect for filtering the most likely candidates to trade.

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