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.
4-2.8. Potential Modern Sources of Information
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来自 University of Illinois at Urbana-Champaign 的课程
Investments II: Lessons and Applications for Investors
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从本节课中
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).
与讲师见面
Scott WeisbennerWilliam G. Karnes Professor of Finance
Department of Finance, College of Business
[MUSIC]
So in this video, we're going to talk about is there any way to maybe get
an informational edge using modern technology, search engines, websites?
You might think it's harder and
harder to get an edge with all the information out there.
In the 1800s and the early 1900s,
you could think of monitoring the number of fully-packed trains passing through
a depot to get a "heads up" as to what are the current economic conditions.
Hey, there seems to be a lot of corn going this way, or
a lot of coal going this way, or a lot of empty cars.
Maybe that's signifying what the economic conditions
are ahead of kind of government reports.
Certainly, the pace of information transmission clearly has increased a lot
over time with the age of the Internet.
So are there any sources of information that others may overlook
that could be useful in forecasting trends,
with a kind of hint, hint on the word trends there at the end?
Let's see what people are thinking about, and how can you do it?
If you're a mind reader, that's easy, but if you're not,
you can almost be a mind reader by looking at things such as Google Trends, okay?
And here, we're looking at, I accessed this in June of 2012,
so we see NBA draft 2012, Tom Cruise, Ann Curry basically getting fired or
demoted at the Today Show are all kind of hot topics.
But I just thought there's really a lot of interesting information here to kind
of look at.
One of the searches I put in was kind of something near and
dear to all of our hearts here at this point, or
maybe familiarity breeds contempt, and we don't want to hear this word any more.
But regardless, CAPM, C-A-P-M.
And when you look here at CAPM,
you actually see some interesting variation in search over time.
There always seems to be this pickup in the spring in searches for CAPM,
then a precipitous decline.
Then a pickup at the end of the year, and
then a sharp decline right at the end of the calendar year.
So what's going on?
What's going on with that?
Can you think what is kind of happening with kind of CAPM to lead to
these searches?
Okay, well, let's actually formalize this here.
I was so excited to talk about these results,
I kind of stole the thunder from my buddy here, Le Penseur.
What explains the robust seasonal pattern in Google searches for CAPM?
Let's think about that, and then I'll give you my take.
So I ask this question to a kind of executive MBA class or MSF class.
Some will kind of give kind of this response right off the bat that I'm about
to show you.
Others will give complicated stories that somehow searches for
CAPM is related to the filing of taxes in April.
Therefore, you have this kind of peak around April in the search for CAPM.
It turns out it's just a very simple answer.
Look at the Google Search for the word final exam.
Okay, you also see a peak around April, and
then you see a very strong peak at the end of the year, with a precipitous decline.
So people are searching for CAPM at the same time they're searching for
final exam.
Okay, so to me, this gives a little credibility check for
the Google Trends data that's provided here.
Okay, so could we get an early sense of economic activity?
And this is just an example of one.
Don't ask me why I seem to know a lot about Coach purse and
refer to it in a lot of examples.
It's a long and sad, sad story here, but
believe me, I know more about Coach purses than I care to let on here.
So here, we're looking at Coach purse, and we're just seeing,
over time, how does their search volume kind of compare?
And you can see the high point seems to be the end of 2006, end of 2007.
So you see this precipitous increase in searches for
Coach purse at the end of the year with a decline.
So I don't think this is associated with CAPM or final exam.
A natural hypothesis is this is people looking at what type of purse do I want to
buy right during the holiday season, Christmas, Hanukkah,
searching, hey, what Coach purse should I get?
Look at 2008, how much the search volume dropped off from December 2007 and
December 2006, and how much it dropped off further December 2009.
Maybe there's some information in here that would be useful to investors in
Coach, in terms of forecasting what their fourth quarter earnings will be in 2008,
2009.
Maybe there's some useful information in this search volume
that isn't already reflected in the stock price and has yet to come out, in terms of
that earnings report, which would be released sometime in the next year.
So maybe there's some kind of nuggets here in seeing what people are searching,
particularly in this focus search, Coach purse, right?
It's higher at the end of the year.
But it seems to be less high at the end of the year in 2008 and 2009,
maybe forecasting lower sales, lower earnings during those holiday seasons.
Okay, just another here, looking at the Google Trends, housing bubble.
Okay, and here on the bottom, this was accessed in 2012,
when Google Trends would just showed you the amount of news coverage,
news references to housing bubble.
So you can see the housing bubble that's coming from just people searching on
Google, this seems to be spiking in 2005.
Housing prices actually reached their peak in 2006.
The news coverage using the term housing bubble only happened after
housing prices are collapsing in 2008 into 2009.
So it's interesting that the Google searches for
housing bubble actually seemed to happen pretty close to the high prices,
and seemed then to be predictive of the housing decline, much more so
than newspaper coverage of kind of housing bubble.
So you could see that news coverage happens clearly after the housing bubble
had already popped.
The Google searches seem to precede the housing bubble.
So at least in this one case,
some predictability there from the searches on Google.
Now, it's always useful to do placebo tests.
Think of something where there might be kind of no information, and
let's do a Google Trend search on that, and see if nothing comes up.
So I'm sorry, I was a little vain here.
Let's search for Scott Weisbenner, and I have to be honest with you guys,
we built this strong relationship over these two courses.
Do not have enough search volume to show graphs, so
a kind of very sad, very sad ending here.
So I would just say, can we please fix this travesty?
Courserians, unite, start to search for my name, #getScotttrending.
I don't have a Twitter account, but still, we could create the #getScotttrending.
Let's fix this travesty if possible.
So we did a few fun searches on Google Trends.
We looked at CAPM, final exam, Coach purse, and then a humiliating examination
of Scott Weisbenner, but we're going to fix that, right, in #getScotttrending.
I look forward to seeing that happen.
Da, Engelberg, and Gao did a more systematic analysis,
looking at Google Trends and examined whether it predicts markets.
So in particular, they look at the frequency of searches in Google
using Google Trends for stock tickers, like "MSFT" or "AAPL".
And they looked at the search volume of these stock tickers.
Okay, now why are we using stock tickers?
Well, Apple is a great example.
People could be searching for apples because, hey, I want to make apple pie for
Thanksgiving.
So it's better to look at the stock ticker, "AAPL",
as indicating maybe people are maybe interested in buying the stock, okay?
So the authors look at the search volume for a stock ticker in a given week.
And then they compare that to the median search volume for
this ticker over the prior 8 weeks.
And then they calculate this "Abnormal Search Volume Index", or ASVI.
So they're looking at searches this week, and then they benchmark it relative to
the median of what the searches had been over the prior eight weeks, okay?
So then the authors examine does this ASVI,
this "Abnormal Search Volume Index" predict future stock returns, okay?
So you could think of kind of two hypotheses here.
So maybe we see these searches on Google for a stock's ticker,
maybe it's predictive of short-term buying for that stock, okay?
And that results in shortward, upward price pressure, but
it's not really based on any good information.
So if we see a bunch of searches on Google, but
it's not based on good information, we would predicted this liquidity effect to
have a short-term effect on prices, but not a long-term effect, okay?
On the other hand, are a bunch of searches on Google for a stock's
ticker motivated by good information some investors may have about that stock?
Well, if that's the case, this revelation of good information that comes out in
the future should predict both short and long-term returns.
We shouldn't see the return reversal.
Okay, so let's look at the results here that the authors have in this study.
They're looking at the Abnormal Search Volume Index
from a Google search on a stock ticker.
And then they're looking at kind of future returns,
okay, so here we look and we see if there is.
We're looking at the sample period, January 2004 to June of 2008.
Okay, the dependent variable is a stock return for
the firm relative to this Daniel, Grinblatt, Titman, and Wermers benchmark.
So we haven't really talked about that, but just think, if we're looking
at the returns, risk-adjusted with this kind of benchmark here, okay, and
the returns are reported in basis points, so 20 basis points would be 0.2%.
The regression coefficients can be interpreted as the impact
of a one standard deviation change, okay?
So remember, we're looking at search volume on a given week.
What does that predict about returns 1 week out, 2 weeks out,
3 weeks out, 4 weeks out, up to weeks 5 to 52 here?
Okay, so we're going, what does search volume,
a big increase in search in a particular week
predict about the stock's performance of that firm going up to a full year?
Okay, so first let's look in the short term.
So if we have a one standard deviation in abnormal search for
a stock ticker, the stock for that firm increases in value 19 plus 15 here.
We're talking about 33, 34 basis points,
about 0.3% increase in the stock price over the next 2 weeks.
So there is some short-term increase in the price.
Now the question is, does that sustain, or does that gradually reverse over time?
If you add up these coefficients here, these effects on return
from a one standard deviation in increase in search volume, 19, 15.
That's roughly 34, kind of 38, but
then minus 30 here when you add these two in.
If you add these coefficients across all the columns,
the net coefficient is about 0, about 0.
So basically, a year later, the stock is at the same place where it started.
So the searches for stock tickers,
they're predictive of short-term increase in the stock price in weeks 1 and
2 after the searches, consistent with this liquidity effect, but
not a long-term increase in the stock price, suggesting these searches on
Google by stock ticker aren't predictive of long-term prospects of the firm,
aren't predictive of good information, okay?
Da, Engelberg and Gao, they also study how searches are related to the first-day
return and subsequent performance of initial public offerings, IPOs.
So they're looking at the frequency of searches in Google using the company name.
If the firm hasn't had an IPO yet, it doesn't have a stock ticker, so
they have to look at the company's name, like Facebook before Facebook had its IPO,
or Twitter before Twitter had its IPO.
So they can look at the search volume to how the IPO performs.
Okay, and we're going to look at what's the first-day return
the stock trades after it's IPO.
And then we're also going to look at benchmark-adjusted longer term returns.
So for example, returns 4 to 12 months after the IPO,
to see if there's any evidence of return reversals happening.
Okay, so what do we see when we look at the data?
So let's break firms into two categories,
those that had low pre-IPO searches, those that had high pre-IPO searches.
So those where there's a lot of searches on Google
before the IPO are the bars on the left.
The bars on the right, they had a lot of searches before the IPO.
So the black bars here are the average IPO first-day return,
the gray bars are the medium.
For those firms where there wasn't much searching on Google, the company name,
before the IPO, they have lower first-day returns than the companies
where there was a lot of search prior to the IPO on the company name,
suggesting this kind of interest that was shown in Google searching for the name
transferred over to the public markets, leading to bidding up of the stock.
Okay, so this was looking at the pre-IPO search volume and
relating it to the average first-day return.
Now lets look at this pre-IPO search volume and
relate it to cumulative returns 4 to 12 months after the IPO.
So we're looking at, did this initial search that
caused an increase in the price of the firm right after they had their IPO and
were publicly traded, was that increase in price sustained, or
was it reversed once we go 4 to 12 months after the IPO?
So we have two firms here.
Again, the black is the average return, the gray is the median return.
Group one, they had a high first-day return on their IPO,
but they had low search.
Group two also had high first-day returns, but
they had high search volume before the IPO.
So maybe the thought process of the authors, if we look at the second group on
the right, maybe one of the reasons they had this high first-day return
is they had all these individual investors searching for the company.
They were very aggressive in buying the stock.
That drove up the price temporarily because of a liquidity effect.
But since it didn't affect firm fundamentals, we should see that price
ultimately reverse, and see negative returns over the period month 4
to month 12, once we adjust here for the industry performance here.
This is exactly what we find.
Those firms that had high first-day returns
accompanied by this high search volume on Google before the IPO,
those are firms that did particularly poorly in months 4 to 12,
as this initial buoy-up of the price due to all these individuals buying the stock.
We saw they are interested in it because they searched for it,
they bid aggressively, drove the stock up the first day it was publicly traded.
But that enthusiasm wanes, that liquidity effect gradually gets reversed.
Long term, these type of IPOs underperform their industry benchmark.
So again, no predictability of the individual investor's searches on Google.
It does affect prices in the short term, but
those short-term effects are ultimately reversed in the long term.
So we just looked at some research whether searches on Google, particularly searches
for stock ticker predict returns in the short term and the long term.
In the short term, there seems to be some evidence that you see a bunch
of Google searches for a particular stock ticker.
There's maybe a buying effect,
kind of some liquidity effect that drive up prices in the next week or two.
But then that's reversed over the long term, and
we saw that in the IPO market as well.
Google is one thing we can look at.
Where's another place we can look at, maybe,
to get a sense of what are customers liking?
Does the customer know best?
Is there any way to get a sense of what products customers like and
which they don't before this information is fully reflected in stock price?
And so obviously, we know what products customers like after the fact, ex-post.
News flash, a lot of people seem to like iPhones, okay, but the key thing
is can we identify people are liking these iPhones early on and invest in Apple stock
before the market as a whole fully processes this information, okay?
So a colleague of mine, Jiekun Huang,
actually examines whether Amazon product ratings provide useful information.
Do changes in Amazon review ratings predict future stock returns and
revenue earnings surprises?
So this could be the point where I kind of go over to the tablet and
talk about his result, but I had an idea, kind of an epiphany here.
Why don't we make this a Faculty Focus episode?
I love seeing this logo here, but this'll be the last time we show it.
This'll be our fourth and final Faculty Focus episode.
I'll be interviewing Jiekun here, talking about his Amazon-related research.
You know it's an important interview when you see the rare purple shirt and
purple tie combination.
You only bring that out when it's going to be the final Faculty Focus episode.
And what we'll talk about in this, we'll have a conversation with Jiekun,
how useful are changes in Amazon product ratings in predicting future
stock returns and fundamental information about the firm?
So stay tuned for that, I'm sure you'll enjoy it.
