In this course, you will start by reviewing the fundamentals of investments, including the trading off of return and risk when forming a portfolio, asset pricing models such as the Capital Asset Pricing Model (CAPM) and the 3-Factor Model, and the efficient market hypothesis. You will be introduced to the two components of stock returns – dividends and capital gains – and will learn how each are taxed and the incentives provided to investors from a realization-based capital gains tax. You will examine the investment decisions (and behavioral biases) of participants in defined-contribution (DC) pension plans like 401(k) plans in the U.S. and will learn about the evidence regarding the performance of individual investors in their stock portfolios. The course concludes by discussing the evidence regarding the performance of actively-managed mutual funds. You will learn about the fees charged to investors by mutual funds and the evidence regarding the relation between fees charged and fund performance. Segments of the portfolios of mutual funds that may be more likely to outperform and examples of strategies designed to “earn alpha” will also be introduced. Learners are welcome to take this course even if they have not completed "Investments I: Fundamentals of Performance Evaluation," as the first module contain a review of investment fundamentals and regression analysis to get everyone up to speed. Also, the course contains several innovative features, including creative out-of-the-studio introductions followed by quick-hitting "Module in 60" countdowns that highlight what will be covered in each module, four "Faculty Focus" interview episodes with leading professors in finance, and a summary of each module done with the help of animations! This course is part of the iMBA offered by the University of Illinois, a flexible, fully-accredited online MBA at an incredibly competitive price. For more information, please see the Resource page in this course and onlinemba.illinois.edu.
4-2.9. Faculty Focus with Professor Jiekun Huang (interview)
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From the course by University of Illinois at Urbana-Champaign
Investments II: Lessons and Applications for Investors
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University of Illinois at Urbana-Champaign
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Course 4 of 7 in the Specialization Financial Management
From the lesson
Module 4: Performance of Mutual Funds and Search for Alpha
In Module 4, we will learn about the evidence regarding the performance of actively-managed mutual funds. We will also discuss a few examples of portfolio strategies designed to “earn alpha” (i.e., yield positive risk-adjusted returns).
Meet the Instructors
Scott WeisbennerWilliam G. Karnes Professor of Finance
Department of Finance, College of Business
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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?
>> Exactly, exactly.
And I measure of a normal customer ratings as
the difference between the average rating in a month and
the average rating in the prior 12 months for the company's products.
>> Okay, so for example, if this company you aggregate across all the products,
the average rating is 2.5 for this month over the past year,
the ratings had averaged two, then this would be a positive 0.5.
It uptick in the ratings for the products of that company.
>> Exactly.
So basically, a positive value in this measure indicates positive surprises and
a negative value indicates negative surprises.
>> And just quickly, what could cause the economics behind why there'd be either
an increase in this abnormal customer rating for a given firm or a decrease?
What could cause that?
>> So I think there could be several reasons for
the changes in abnormal customer rating.
So one is that the company introduces new products
that are perceived differently as compared to its previous or existing products.
And also, there could be changes in the comparative landscape.
So for example, the Garmin GPS could be adversely affected
when smartphones, all have GPS available for free.
And so basically, this can induce a change in consumer's preferences and tastes.
It can also result in changes in abnormal customer ratings.
>> Got it. So now, we have this set for every firm.
You have this measure of change in Amazon ratings across all their products,
let's get to the meat of the matter here.
What are the results in terms of forecasting stock returns?
>> Exactly. So basically,
in each month, I sort stocks into third cells.
Three equals. >> Thirds, yep.
>> Yes, and the stocks in the lowest abnormal ratings will be
the stocks that experience massive shocks to customer ratings.
So these are the T1 portfolio, and
stocks that have experienced high abnormal ratings will be the T3 portfolio.
So these are stocks that have experienced positive shocks in customer ratings.
And if we look at the subsequent month stock return, we can see that T1 stocks
experience an active but insignificant returns in the subsequent models,
whereas T3 stocks experience passive and significant returns.
>> And this isn't we're controlling for the risk here and
setting the benchmark in a four-factor models or controlling for market risk,
the size factor, the value-growth factor, and then also momentum.
>> Exactly, exactly.
So basically, after controlling for this risk factors,
we still find some alphas, especially for the T3 portfolio.
>> Right, and this is non-trivial in terms of magnitude.
If we just look at the stocks that have the biggest optic in terms of the Amazon
ranks with their products, this alpha 0.5% per month,
that's a little over 6% on an annual basis.
And if you look at the final row, which is the hedge-fund strategy long,
the firms whose products or rating are going up short those that are going down.
This difference almost 0.8% on an annual basis,
almost ten percentage points per year.
So it seems like there's actually some relevant information
in these Amazon reviews in terms of forecasting future stock returns.
>> Right, so the economic [INAUDIBLE] service requires significant here.
>> Okay, so also, you subjected this as any serious study would do.
You always want to look at where should this effect be larger,
where should this effect be smaller?
And I understand you did that as well.
>> Exactly. So I look along three dimensions,
as suggested by previous literature.
In particular, I show that the alpha on these spread portfolio of high
abnormal rating minus lower abnormal rating seems to be stronger among stocks
that have high idiosyncratic volatility, low analyst coverage, and small cap firms.
And in the literature, it has been argued that these measures are proxies for
limits to arbitrage and limits to investor attention.
So it seems to be consistent with the idea that limits to arbitrage and
limits to investor attention drives some of this stock return predictability.
>> So the basic idea is high idiosyncratic volatility,
maybe a lot of uncertainty about the firm.
Low analyst coverage, people aren't following it that much,
small firms as well.
So those might be the firms where you expect there to be more
value relevant information contained in these Amazon ratings just because they're
not known as well.
>> Exactly, yeah.
So it would take sometime for the information to be fully refracted in
a stock price resulting in this predictability pattern.
>> So then, key, I think, to your paper here is I'm looking at,
does this return effect that you show in the subsequent month,
is this a price pressure effect that people are just reading about the ratings,
they're buying the stock?
But the market has already incorporated this news, so
you'd expect that price pressure effect to subside, the return effect to reverse, or
is this a sustainable increase in price that you observe?
What happens beyond this one month period?
>> Yes, so I looked also up to 12 months after the first month post formation of
the portfolios, and I don't see reversal of this stock return predictability.
So it seems that the reviews convey information about the company
rather than some non-information related factors.
>> Interesting.
So it seems like you've presented some pretty compelling evidence that
Amazon reviews are more specifically the change in these amazon reviews,
relative to the prior history, predicts stock returns.
And this prediction, it's effect on stock returns, is it reverse?.
So it suggests something fundamental is at play.
>> Exactly.
>> So do you have any evidence regarding predictability, not just for
stock prices but for future revenue, for future earnings?
Do you have that in your paper as well?
>> Yes, so as part of the paper, I also show that there is a probability
of abnormal customer ratings for subsequent cash flow surprises.
In particular, look at revenue surprises as well as earning surprises.
And show that when abnormal customer rating is high, subsequently,
the company is more likely to experience positive shocks to their revenue as well
as positive shocks to their earnings.
So this is consistent with this cash flow for you.
>> And for the earnings, this is already controlling for
the best guess that the professional analyst estimates.
So these Amazon reviews can predict the earnings beyond what the experts
are saying.
>> Exactly, so it seems to suggest that the average analyst hasn't
uncovered all the information in consumer reviews in his estimate.
So that still,
there is some predictability about the average analyst forecast.
>> So in this module, I presented some research and
looked at searches for stock tickers on Google.
And that seem to be predictive as stock prices in the short-term but
then reversals in the long-term.
So it's consistent with a price pressure effect.
Like the searches on Google for
stock tickers didn't seem to really reflect anything fundamental.
You seem to have a different set of results.
Why is that?
Why the difference when you're looking at the Amazon reviews as opposed to
the Google searches for stock tickers?
>> Yeah, that's a very good observation.
So I think the key difference here is that Google search
volume only captures the quantity of investor's interest.
It doesn't captures the reaction of the underlying information.
In particular, the high search volume does not necessarily indicate good news.
>> It could be a buy or a sell.
>> Right, exactly.
So one recent example would be the emission scandal at Volkswagen.
I'm sure that when the scandal erupted,
there was a lot of searches for the company, for VW.
However, this indicates, actually, bad news for the company.
On the other hand, the Amazon reviews
provide us clear indication regarding the direction of the underlying information.
If the ratings are abnormally high, then it suggests good news for
the company's cash flows, whereas if it's inactive, then it's bad news.
>> So the key thing with the Amazon ratings,
you actually have this quantifiable measure like, is this good or is it bad?
Is the rating five, is it one?
>> Exactly.
>> So the sophisticated investors to the extent at which you can study this,
like hedge funds, do they anticipate this good news in the Amazon ratings?
Or they more seem to react to the good news revealed by the Amazon ratings?
>> That's a very good question.
So I, indeed, look into the relationship between hedge fund's rating and
abnormal customer ratings.
I don't find evidence that hedge funds can anticipate abnormal customer ratings.
However, they do react to abnormal customer ratings in their decisions.
So basically, I'll show that following an increase in abnormal customer ratings,
hedge funds tend to buy more of the stock.
>> So you don't see hedge fund buying, let's say, Microsoft right before there's
great Amazon ratings for Microsoft products, but you do see after the great
ratings for Microsoft products on Amazon, hedge funds are more likely to buy.
>> Exactly.
>> And this is from the 13F institutional filings, so
you can see their long positions in the stock.
>> Exactly. >> Okay, so one final question here.
When you're looking at Amazon, it seems they have this great wealth of
information, some of which you've documented the predictability,
how that can be used to predict future firm performance.
Should Amazon have a hedge fund or venture capital division based on all this
great data they have on future trends in the economy?
>> Yeah, that's a great idea.
So actually, you were not the first one to think of that.
>> Story of my life here.
Yeah, it won't be the first time that happened.
>> Yes, so a article in Harvard Business Review
actually commented on the vast amount of data that Amazon has collected.
So basically, they argue that Amazon is
extraordinarily well-positioned to invest in specific companies and
sector because of the information advantage that they may have.
>> Wow, this is really great stuff, Jiekun.
I think our viewers are going to see why we're so
excited to hire you a few years ago in our department.
Before we wrap up, I wonder if you'd be game for,
there's a favorite segment we have on the show called Awkward Moment.
So would you be up for it?
>> Yes, let's do that.
>> All right, excellent.
So let's take a quick break, and then we'll come back for
the Awkward Moment with Jiekun Huang.
The end is definitely near for this interview with Professor Jiekun Huang for
the Faculty Focus series for Module 4 and the course as well.
But we're not done yet.
Please see my upcoming videos.
They will feature a discussion of the economics of the mutual fund industry
as well as providing international perspective on the type of mutual funds,
and the fees associated with those funds from across the world.
And of course, you don't want to miss the course conclusion.
Now, more with Jiekun.
[SOUND] Jiekun, this is actually more of an awkward moment for me.
Given all the Amazon packages that come to my house,
it seems like we should probably have a distribution center in Champagne.
I was wondering if you could use your great data analytics ability and
actually see, if I give you the list of our purchase, does I have any
predictive power for anything, except just to fall on our family's bank account?
>> Yes, so well, that's an excellent question.
So my household has also contributed a lot to add-on sales.
So I think there could be incremental information contained
in the actual purchases of consumers.
So it would be great if our viewers all send me their purchase records on Amazon
so that I can infer the actions of the call to see whether there is incremental
information contained in this aggregated actions by the crowd of consumers.
>> Okay, so just a word of advice,
you might want to get IRB approval before you collect that data.
>> [LAUGH] >> But I'll be sure to at least give you
our household's data, you could see if it predicts anything.
Thanks so much, Jiekun.
>> Thank you.
>> This is a lot of fun, thanks for joining us in our Faculty Focus segment.
>> Thank you very much.
It's a pleasure to be here.
>> Excellent, thanks.
And remember, in Module 4, we actually have two Faculty Focus segments.
Please see my interview with Professor Josh Palette earlier in the module.
And as always, thanks for watching.
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