Historic Black Swans in Historical Financial Data: EQT on 13.11. 2018 et al.

On November 13, 2018 the shares of EQT Corporation (NYSE:EQT) fell down by 46%. Yet, as Montley Fool reported, it was just a spinoff of the midstream assets into a separate public company, Equitrans Midstream Corp... FinViz and eoddata.com has completely failed to depict this event properly. Yahoo.Finance and AlphaVantage coped with it but only to some extent. We discuss the problems, caused by such events and sketch some ways to mitigate them.

The case of EQT is although recent but is no way the only one, which we have experienced. Still, since it is probably the most recent one, let us consider it first. We, ourselves, have noticed the anomaly as we checked the performance of the stocks from our 2nd stock list on FinViz. It showed the loss of ca. -50%! Either eoddata.com has demonstrated a full naivity

Date Open High Low Close Volume Open Interest
11/05/2018 33.64 35.49 33.64 35.35 4,344,100 0
11/06/2018 34.91 35.61 34.90 35.02 3,252,100 0
11/07/2018 35.39 35.64 32.94 34.17 5,632,800 0
11/08/2018 33.84 35.35 33.34 34.80 8,448,300 0
11/09/2018 34.69 37.46 34.48 35.90 11,832,500 0
11/12/2018 36.15 36.39 34.49 34.64 69,020,800 0
11/13/18 18.89 21.15 18.37 18.56 35,458,800 0
11/14/18 18.73 19.11 16.63 17.48 20,937,100 0
11/15/18 17.09 17.84 16.91 17.20 12,247,500 0
11/16/18 17.08 17.23 16.29 16.63 11,981,700 0
Table 1: EQT OHLC prices from eoddata.com

Yahoo.Finance was somewhat better but as you can readily see, Yahoo overdid it, since not everything (not only the column Ajdusted) was adjusted.

Date EQT.Open EQT.High EQT.Low EQT.Close EQT.Volume EQT.Adjusted
25/10/2018 20,84377 21,29015 18,2417 19,23789 25022900 19,20686
26/10/2018 18,9276 19,23789 17,63201 17,71911 21553000 17,69053
29/10/2018 17,71911 17,88786 16,61949 16,87534 18931900 16,84812
30/10/2018 16,83179 17,93685 16,79913 17,89331 13236900 17,86445
31/10/2018 18,25803 18,97115 18,06206 18,49211 13681800 18,46228
01/11/2018 18,85139 19,12901 18,61731 18,98748 6560800 18,95686
02/11/2018 18,82417 18,93849 17,58846 17,76266 7729400 17,73401
05/11/2018 18,31247 19,31954 18,31247 19,24333 7980300 19,2123
06/11/2018 19,00381 19,38487 18,99837 19,06369 5974100 19,03294
07/11/2018 19,26511 19,4012 17,93141 18,60098 10347300 18,57098
08/11/2018 18,42134 19,24333 18,14916 18,94393 15519500 18,94393
09/11/2018 18,88405 20,39194 18,76973 19,54273 21735900 19,54273
12/11/2018 19,67882 19,80947 18,77518 18,85683 1,27E+08 18,85683
13/11/2018 18,89 21,15 18,36 18,56 35458800 18,56
14/11/2018 18,73 19,11 16,63 17,48 20937200 17,48
15/11/2018 17,09 17,84 16,91 17,2 12247500 17,2
16/11/2018 17,08 17,23 16,29 16,63 11981700 16,63
Table 2: EQT OHLC prices from Yahoo.Finance


Finally, AlphaVantage was nearly perfect.

timestamp open high low close adjusted_close volume dividend_amount
<date> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
22/10/2018 46.3 46.3 44.1 44.4 24.2 2.88e6 0
23/10/2018 43.5 43.8 41.8 42.5 23.1 3.37e6 0
24/10/2018 42.6 43.1 40.4 40.5 22.0 4.28e6 0
25/10/2018 38.3 39.1 33.5 35.3 19.2 1.36e7 0
26/10/2018 34.8 35.3 32.4 32.6 17.7 1.17e7 0
29/10/2018 32.6 32.9 30.5 31 16.9 1.03e7 0
30/10/2018 30.9 33.0 30.9 32.9 17.9 7.21e6 0
31/10/2018 33.5 34.8 33.2 34.0 18.5 7.45e6 0
01/11/2018 34.6 35.1 34.2 34.9 19.0 3.57e6 0
02/11/2018 34.6 34.8 32.3 32.6 17.8 4.21e6 0
05/11/2018 33.6 35.5 33.6 35.4 19.2 4.34e6 0
06/11/2018 34.9 35.6 34.9 35.0 19.1 3.25e6 0
07/11/2018 35.4 35.6 32.9 34.2 18.6 5.63e6 0
08/11/2018 33.8 35.4 33.3 34.8 18.9 8.45e6 0
09/11/2018 34.7 37.5 34.5 35.9 19.5 1.18e7 0
12/11/2018 36.2 36.4 34.5 34.6 18.9 6.90e7 0
13/11/2018 18.9 21.2 18.4 18.6 18.6 3.55e7 0
14/11/2018 18.7 19.1 16.6 17.5 17.5 2.09e7 0
15/11/2018 17.1 17.8 16.9 17.2 17.2 1.22e7 0
16/11/2018 17.1 17.2 16.3 16.6 16.6 1.20e7 0
Table 3: EQT OHLC prices from AlphaVantage

We say nearly because although both close and adjusted_close values are correct, we have no info on the kind of adjustment. Indeed, the financial time series are usually adjusted for the dividends and splits but their standard format there is no room for such events!. It shall not be a big problem if you get a "fresh" data from AlphaVantage but if you gather it everyday and put them in your database (as we, ourselves, do) you will have neither proper adjusted_close values, nor can you re-calculate them from close!

Ok, you may think, but how often do suchlike cases take place. Well, definitely not every day but probably more frequently than you think. Here is what we can just quickly recall without googling:
1. Split of METRO GROUP to Metro and Ceconomy stocks.
2. Split of E.On to E.On and Uniper.
3. DWS Spin-off from Deutsche Bank .
4. Issue of Bezigsrechte (Warrants to buy the newly-issued stocks) by Commerzbank

But what does it mean for your models? Well, normally a portfolio optimization model is sensitive to big price movements. So if you neglect the data cleansing then a blunder a-la Reinhart–Rogoff might await you!
How can you mitigate this problem? Well:

1. Do use several sources of data. It is easy to automatically detect the differences between the data sources (which is always good check) and then you can have a closer look, in particular google what has happened with the stock on the date in question.

2. Take en effort to visually screen the data. It might seem tedious (and it is) but not impossible because our brain normally process images much faster than symbols. For example, Vasily Nekrasov has visually checked more than 3000 stocks by his study of volatility clustering and predictability.

3. Check the sensitivity and robustness of you model, calibrating it with and without suspicious outliers.

4. Try to cooperate with other researches by sharing your cleansed dataset (in future we will probably offer an infrastructure for such sharing).

Like this post and wanna learn more? Have a look at Knowledge rather than Hope: A Book for Retail Investors and Mathematical Finance Students

FinViz - an advanced stock screener (both for technical and fundamental traders)

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