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And now, Faculty Focus with Scott Weisbenner.
In this module, the focus is on Jiekun Huang.
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Jiekun Huang is an assistant professor of finance at the University of Illinois
having moved here from National University of Singapore.
Jiekun teaches corporate finance for us and
most as research that is the behavior and influence of institutional investors.
But Jiekun also has a recent paper examining whether customer opinions
expressed through Amazon ratings predict future firm performance.
In fact, this was highlighted in July of 2016 in the Wall Street Journal.
Rather than me talk about this interesting work, I had an epiphany.
Why not ask Jiekun himself to talk about it?
Welcome, and thanks for joining us, Jiekun.
>> Thank you, Scott.
I'm really glad to have the opportunity to talk about my research on Amazon reviews.
>> I'm sure the pleasure is all ours and our listeners.
In this module, the course, we've talked about inattention on the part of investors
and how this could potentially lead to some predictability in stock returns.
We also discussed some potential modern sources of information
to get a sense of what are people thinking about or
at least searching for Google, looking at Google trend data.
You're looking at products on Amazon, looking at their reviews,
and in particular, change in reviews, and
what the means maybe for future stock returns of profitability.
What was your motivation?
What was the idea behind this piece of research?.
>> Right, so basically, I had the idea when I was doing online shopping.
So basically, it occurs to me that when I search for a product,
I always look at the customer reviews.
And basically, I thought if such information is valuable to me,
does it also provide value [INAUDIBLE] information to the financial market?
And that's why I started to collect the data on Amazon reviews,
and to do an investigation of the investment value of customer reviews.
So the idea is that basically, if customer reviews provide new information
to the financial markets, then it should predict subsequent stock returns, and
also, it should predict the fundamentals of the company.
>> So how did you get all of this review data?
It couldn't have been easy.
>> Absolutely. So I ask some help from several RAs who
were specialists, who are experts in web scraping, and
they helped collect the data from Amazon.com.
And also, it required other data work, including to match
the Information from Amazon to publicly traded firms in the US.
So in all, it's a lot of data effort.
>> So reviews that I posted or my wife posted could be in your dataset?
>> Absolutely, if it's for products purchased by public companies.
>> That's right, that's right.
So it'd be interesting to get a sense of what you found, and I think to help us
very prepared, you have some figures and some tables that we could look at.
>> Yes, I've had a few of tables and figures here,
I can show you these figures.
>> So it's great to illustrate something with a key figure, and right after that,
something near and dear to a lot of people after filing taxes in the US is TurboTax.
You have an interesting chart of intuit and
its stock price shortly after the TurboTax release.
>> Yes, exactly.
So this concerns customer reviews of TurboTax 2002.
So it was released on November 1st, 2002 on Amazon.com,
and on November 27th of 2002, customers started
to flood Amazon.com with nasty reviews about a product.
So basically, consumers complained about an anti-piracy feature of the software.
>> I see.
>> The feature require that the customer can only print cash returns and
electronically fill cash returns on the first computer on which the software
was installed.
Not all of the computers that the customer installed the software.
And so as you can see, following the first inactive review of the software,
the stock price has dropped quite significantly from
about $27 at the time of the first review to about $22 two months later.
So it seems to be quite dramatic change in terms of the shareholder value for
the company.
And perhaps, not surprisingly, the company lowered it's earning's expectations for
the fiscal year because of weaker sales in March 2003.
So that, in this case, we see that customers as a whole,
it seems to posses information about the company's cash flows as well
as the subsequent stock returns of the company.
>> I see, so at least or this one anecdote,
the negative turn in the Amazon reviews predicts future stock price
changes as well as fundamentals with the earnings.
But this is one anecdote.
How do you go about systematically defining these abnormal customer
ratings across all these many firms?
>> Yes, so in order to capture abnormal customer ratings, were in other words,
these surprises in customer ratings, I first compute the simple average star
rating of all customer reviews posted for company's products in each month.
>> So this would be the average across all the products across a given company?
All the products for 3M, for example?