Event Study - Trading on Negative News & Blog Article Sentiment

Am I reading it correctly, that the mean drift is -0.01 with a standard deviation of +/- 0.1?

Simon,

Yeah, since the sample size is so large it gets hard to conclude a single, concrete result. It's common in doing event studies - there are differences in volatility even year-over-year (e.g. 2012 versus 2014).

My guess is that the standard deviation becomes much more contained as you, say, limit the study to stocks within a specific sector and so forth.

What are your thoughts on that?

Seong

Disclaimer

Simon Thornington

I'll believe it when I see it :)

Good point.

I definitely encourage people to expand, disprove, dissect the research. I plan on tackling Simon's questions soon as well.

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Thomas Wiecki

The alphaone_abnormal_returns DataFrame has a colum for each day in the time-frame. Does that mean there was one (and only one) data point each day?

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joe lee

I added fundamentals filtering but getting a starange error, any idea ? :

ValueError: Bad response: 500 Internal Server Error
Internal Server Error
The server encountered an internal error and was unable to complete your request. Either the server is overloaded or there is an error in the application.

Josh Payne

joe, I'm unable to duplicate the behavior right now. I cloned the notebook and ran it successfully. I suspect you just got unlucky with a bad connection or some temporary blip. We didn't see anything in our logs or utilization patterns that showed something systematically wrong (as would be suggested by that error message).

But if it continues to happen, let us know or submit a ticket to Help.

Thanks,
Josh

Disclaimer

Thanks for the questions guys.

Thomas - looks like even when there multiple events per day, we only ended up looking at one event per day. That was an error in the code. We're fixing it now and it looks like the results have changed with the fix. I'll update with new information soon. Thanks for waiting!

For those interested in learning more about news sentiment - how it's built, ways to use it in trading strategies, and more - join our webinar next Thurs. Oct 22 at 1PM EST. We'll be joined by Kumesh from Accern and Josh Payne from Quantopian.

You can register for the webinar here: https://attendee.gotowebinar.com/register/5430134256751399938

Seong

Disclaimer

Hey all,

So Thomas pointed out a pretty glaring flaw in the original event study. It was only looking at the last article sentiment for a given date versus the average of all sentiments for a given date. I've fixed that and updated the notebook accordingly.

Long-story short, it looks like negative sentiment articles could signal a stock's price reversal. Please take a look at the attached notebook. Looking forward to your feedback.

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Thomas Wiecki

Oct 17, 2015

Now that looks much more interesting! Did the standard deviation decrease?

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Grant Kiehne

Oct 17, 2015

Seong,

Is the asof_date the date of the negative news release? If so, how do you explain the decline in returns starting 5 days prior? Also, are your days calendar days, or trading days?

Grant

joe lee

Oct 17, 2015

How would you explain the fact that when running the notebook with the Event free data , then the results are totally different (e.g. returns go up after the events).
In addition I noticed that the notebook brings positive sentiments although (e.g. for INTEL as example) although the filter is for negative only ?

sorry if I am missing something here.

Oct 19, 2015

Thanks for the questions. We're hosting a webinar this Thursday, October 22 at 1PM EST and if you'd like your questions answered in person, please register here: https://attendee.gotowebinar.com/register/5430134256751399938

Joe,

In the original notebook I also saw that returns went up after the event - which matches what you found in your research. In this case though, we're looking at the ENTIRE dataset versus the limited set that comes with the unpaid. I also chose only negative sentiment news articles because Paul Tetlock's paper stated "that stock market prices incorporate the information embedded in negative words with a slight delay." I sought to see if there was a similar behavior with Accern's sentiment data. It doesn't look like there was - rather, there it was more than people generally overreacted to negative news which causes the reversal in price/increase in return stream after the event.

Grant,

I'm looking at trading days, not calendar days - and the decrease in price you see can be explained by a few factors. (1) The asof_date is the release of the sentiment score versus the article. However, the article release time and sentiment release time could happen on the same day. (2) The decrease in price could be related to the fact that information is readily priced in prior to the release of a news article (i.e. asymmetric access to information). I'm still learning about this and would love your thoughts on this.

Thomas,

Here's a version of the notebook that centers all returns around t=0. This way I got the standard deviation of post-event returns starting from after the event date. It looks like the standard deviation did decrease from the previous version of the notebook - hovering around +/- 7.0% (still quite high) on an approximate average drift of ~ + 1.0%. I think it's worth noting that the previous notebook was looking at cumulative standard deviation from t-20, versus t=0 here.

You can see the std deviation attached here.

Disclaimer

Alpha One

Oct 19, 2015

Advice:
Change...

  # To try out the free sample  
from quantopian.interactive.data.accern import alphaone_free



# To try out the free sample  
from quantopian.interactive.data.accern import alphaone_free as alphaone

Keeps the rest of code intact

Oct 22, 2015

We just finished up the webinar on this research. For those who want to see us and Kumesh from Accern walk through this research you can view the recorded session here: https://www.youtube.com/watch?v=GlEV3WtquK4&feature=youtu.be

Disclaimer

Grant Kiehne

Oct 23, 2015

Hi Seong,

The asof_date is the release of the sentiment score versus the article.

Is the asof_date the new release date or the date when Accern published the sentiment assessment? Is there any reason to think that the news release and sentiment dates would be different?

From your plot above, it appears that non-public negative news takes 5 days to be absorbed by the market, at which time (t = 0), the negative news is reported, and then the returns recover? This still doesn't make sense.

Or are you saying that the public announcement, along with the sentiment publication are coincident with the onset of the returns decline (t = -5), and then by t = 0, the returns recover and start going up again (basically, an over-reaction to bad news that has a 5-day lifetime)?

In other words, how should I interpret t = 0? Naturally, one would think it is the time at which negative news & sentiment were published, but is it?

Oct 23, 2015

Hi Grant,

Thanks for asking. For this alphaone dataset, the asof_date is when Accern published the rolled-up sentiment assessment. So for example, let's say two articles about AAPL came out on October 1st, 2014. The first at 9:45AM and the second at 10:45AM. A single datapoint would exist for AAPL on 10/1/2014 which would be a rolled-up analysis of both the articles.

Or are you saying that the public announcement, along with the sentiment publication are coincident with the onset of the returns decline (t = -5), and then by t = 0, the returns recover and start going up again (basically, an over-reaction to bad news that has a 5-day lifetime)?

This was what I was hoping to get across. I have a hunch that much of the information we see in news articles are already incorporated into stock prices before the articles get published (asymmetric distribution of information) - Paul Tetlock talks about it here: https://www0.gsb.columbia.edu/faculty/ptetlock/papers/Tetlock%2004%2010%20News%20and%20Asymmetric%20Information.pdf

In other words, how should I interpret t = 0? Naturally, one would think it is the time at which negative news & sentiment were published, but is it?

Pretty much in 95% of all cases you can assume that the asof_date is symmetric to the publish of a news article. I think the returns decline (starting from t=-5) is more a phenomenon of asymmetric news versus a discrepancy between dates in news article publish & sentiment publish.

Disclaimer

Simon Thornington

Oct 23, 2015

"Sell the rumor, buy the news"

Grant Kiehne

Oct 24, 2015

Thanks Seong,

Interesting. One has to wonder how the "rumor" process works. Do you know anything about the nature of the articles that are published? Are they generally commentary on news releases by the companies themselves ("Acme Corporation announced today that it will no longer sell dynamite to coyotes, as part of its class action suit settlement, which was brought on by a group of road runners.")? Or are the articles independent investigative reporting? My guess is that we are talking about corporate announcements/news releases, primarily, but I could be wrong.

Also, who is acting on the rumors? Are mutual funds/ETFs/pension funds/etc.--big players--shifting allocations based on rumors? Or some other market players? And how are they getting the information?

Seems like there could be some illegal activity here, too. If the "rumor" is knowledge of the news release, and trades are made based on that information, then I ain't no lawyer, but it would seem like trading on material non-public information. It sure seems like we are talking about a form of insider trading here, no?

Muhammad Asad Aslam

Oct 24, 2015

A suggestion:
How about filtering the articles and blogs based on some category for example the viewership, or number of shares of the article as well as the profile and authenticity of the article/blog so that we may get a better estimate ?

Regards
Asad