Course Description This PhD course is aimed to provide students
advanced corporate finance knowledge. Students are expected to
understand empirical issues and methodologies regarding corporate
finance. Topics: capital structure new issues corporate investment
M&A corporate government corporate payout. Students who take
this course are strongly recommended to have undergraduate
background in Finance, Accounting and Econometrics. 2
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Grading and Requirements Mid-term25% Final (take-home)25%
Presentation10% Research paper25% Exerciseand quiz15% 3
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Grading and Requirements This course is processed all in
English. You are required to preview papers, which would be your
assignments for examinations. (It will be great if you could
quickly preview them in the winter break). You are responsible to
present papers in turn in the class (indicated by (S), we will
arrange presenters for papers in the first week). I will cover
papers with * mark. In addition, you need to complete a research
project. To make sure that you can submit your research paper on
time, please submit your proposal before Apr end. An uncreative
proposal or unworkable project would be rejected until I am
satisfied. Two types of exercises are requested for each topic: i)
comments for a working paper, and ii) programing exercises. You
will have an open-book test on basic corporate finance at the first
week. 4
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Financial Databases 5
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Financial databases US data for corporate finance studies:
Compustat, Center for Research in Security Prices (CRSP),
Securities Data Company (SDC), Institutional Brokers' Estimate
System (IBES), Thomson 13f, Thomson insider trading, Corporate
Library, RiskMetrics database and so on. Global data Datastream and
Worldscope Taiwan TEJ 6
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Identification variable in US database CUSIP: Operated by
S&P Capital IQ. A unique corporate ID for all US and non- US
firms. Most of US databases use this one to identify each firm.
GVKEY: Global key, which is assigned by S&P too. PERMNO:
Permanent number, which is assigned by CRSP. GVEKY and PERMNO are
more stable than CUSIP. 7
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Methodologies Part I. Firm valuation Part II. Event study
method Part III. Operating performance 8
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Part I. Firm Valuation 9
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Firm value Tobins Q is most popular measure for firm value
Tobins Q is defined as market value of the firm divided by
replacement value of the firm. Firm value includes value of equity
and debt What is replacement value? 10
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Firm value Finance people usually use this way to proxy Tobins
Q: Tobins Q=(Market value of equity + book value of
liability)/Total book asset Tobins Q reflects the market valuation
on a firm per replacement cost. If Q is greater than one, then the
firm value includes intangible proportion that relates the future
cash inflows. This is also known as the investment opportunity.
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Firm value? Investment opportunity? Tobins Q sometimes
represents the investment opportunities of a firm too. High Q firm
(usually recognized by Q>1 or Q> median) not only indicates a
higher firm value but also implies more investment opportunities.
Another investment opportunity measure is M/B ratio, which is
market value of common equity divided by book common equity.
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Part II. Event Study 13
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Preliminary The purpose of an event study is to detect any
abnormal performance around the corporate event to understand the
economic impact that the event will generate Kothari and Warner
(2005) Abnormal performance around an event provides a measure of
the impact of this type of event on the wealth of firms
claimholders Event studies can serve to test market efficiency In
different fields, accounting: PEAD; law: regulation effect Prior to
90s, most event studies focus on short-horizon stock performance.
After Ritter (1991), more and more research starts to work on
long-run stock returns 14
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Time line If we look at SEO and market reaction at the event
day, and are interested in how investor reacts to the leverage
decreasing SEO and other SEO, then we can plot the time line here:
What would be the time line if no market reaction is involved in
the study? 15
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Short-Run Event Study The idea of short-run event study is to
examine the stock performance within a small window of a corporate
event. The window can be one-day, two-days, three-days, a week, a
month, or even a quarter. 16
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Short-Run: Procedure Identify an event Why this event is
interesting? Sample selection Any contamination with other events?
Any special characteristics? Any clustering? Any abnormal prior
performance? Returns daily, weekly, monthly? Measure raw and
abnormal returns Benchmark market returns, market model, or
matching firm? Significance test parameter test (t-stat)
non-parameter test (Wilcoxon Z-stat) 17
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Short-Run: Measure Abnormal Return Abnormal return (AR)
Cumulative abnormal return (CAR) : abnormal return for security i
at time t : average abnormal return at time t : cumulative abnormal
return from q to s Testing Assume that mean abnormal return (AR) is
independent and stable prior to the event S: standard deviation of
AR estimated prior to event where N is s q +1 18
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Long-Run: Measure Abnormal Return Initial methodology is
introduced by Ritter (1991) and Loughran and Ritter (1995) CAR
Identical to that in short-run. Just enlarge the window
Buy-and-hold return (BHR) : BHR for security i at time t : BHR
abnormal return : average buy-and-hold return One problem: how
about a firm is delisted before time T? BL (1997) recommend BHR,
why? 19
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Long-Run: Why Prefer BHR? CAR does not represent the return
that long-horizon investors will earn (unrealistic investment
strategy). CAR is adding average returns, which is a biased
predictor of long-run returns. CAR implicitly assumes frequent
rebalancing which involves transaction costs and bid-ask spread.
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Long-Run: Benchmarks See Ritter (1991), ILV (1995), BL (1997)
Equal-weighted index return Value-weighted index return Reference
portfolio, control by Size Size and book-to-market Size and
industry Matching firm Fama-French factor model 21
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Long-Run: Which Benchmark? Three biases of using reference
portfolios (BL (1997)) New listing bias Issuing firms have lower
returns (Loughran and Ritter (1995), so if reference portfolio
contains these firms, its return will be lower Rebalancing bias
Reference portfolio rebalances periodically, but not for sample
firms Skewness bias Long-run returns are positively skewed,
especially for the sample Matching firm approach does not have
these biases BL (1997) recommend to use size and book-to-market
matching firms 22
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Long-Run: Simulation on Benchmarks 23
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Long-Run: Significance Test of BHR Can we use t-statistics?
Why? (ILV (1995)) Need to estimate standard deviation of annual
returns, but firms generally do not have long history Return
distribution may not be stable over long time Hard to estimate the
standard deviation of long-horizon return since its compounded not
cumulative Skewness and clustering bias the estimation of standard
deviation 24
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Long-Run Significance Test: Bootstrapping Bootstrapping is a
simulation procedure to generate the empirical distribution of
abnormal return (also called empirical p- value approach) For each
sample firm, randomly select a non-event firm with similar size and
book-to-market at the time of the event. Repeat this until all
sample firms are replaced by these random firms Compute long-run
abnormal return for this pseudo portfolio. Treat this as one data
point for BHR Repeat all the above process for 1000 times, generate
1000 data points, and empirical distribution of long-run abnormal
returns Compare the sample firms abnormal return with the empirical
distribution. The empirical p-value = number of pseudo portfolios
with abnormal return larger than that of sample firms / 1000
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Long-Run: Bootstrapping 26
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Long-Run: Bootstrapping Using empirical p-value has higher
power than using t-statistics Empirical p-value method performs
well in the random sample However, in the non-random sample (sample
firms have poor prior performance, or industry clustering), even
empirical p-value approach is biased 27
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Long-Run: Power of Bootstrapping 28
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Problems in Long-Run Performance Preliminary (Fama, JFE, 1998)
Long-run performance studies have become a rich literature (IPOs,
SEOs, mergers, repurchases, splits, spinoffs, proxy fights, new
listing, dividends, earnings) Why there are over-reactions
sometimes, while under-reactions at other times? Sensitive to how
to get average return (BG (1997)) Sensitive to the benchmark (BGG
(2000)) Bad model problem and pure luck (Fama (1998)) Difficulty in
statistical tests for BHR (Fama (1998)) 29
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Problem: Sensitive to Different Method Equal-weighted average
(EW) Value-weighted average (VW) Non-venture-backed IPOs
underperform in the long-run, however, only by EW; the
underperformance goes away by VW Small, growth non-venture-backed
IPOs severely underperform, however, this is not a new issue puzzle
but a small growth effect. That is, small growth nonissuers also
perform poorly 31
Problem: Sensitive to Benchmark Poor long-run returns following
equity offerings are not due to new issues, but rather a common
return pattern for small growth firms How well to specify the
returns of small growth firms will greatly affect whether or not
finding underperformance Control size/BM is necessary; adding
momentum factors is useful for SEOs; carefully control the BM
effect in very small firms will well-specify the return pattern in
stock market 33
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Problem: Bad Model Problem The under- (out-) performance is due
to the misspecification of expected returns The bad model problem
is unavoidable, but its more serious for long-run returns Finding
abnormal performance in the existing literature is pure luck since
there are half of the events with underperformance, but the other
half with outperformance 34
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Problem: Difficulty in Statistical Tests The normality
assumption is seemingly correct for short-run returns, such as a
month, but not appropriate for BHR which suffers skewness problem
BHR exaggerates the mispricing, i.e., the abnormal performance will
continue to grow in long-horizon even it happens only in the first
year BHR suffers the cross-sectional dependence which may increase
the test statistics (Brav (2000)) 35
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Solution for Long-Run Performance Using monthly returns
Calendar-time portfolio Monthly abnormal returns 36
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Solution: Calendar-Time Portfolio If the horizon of interests
is 5 years, for each calendar month, get the return for each stock
that had an event in past 5 years. Form a portfolio based on these
stocks. The portfolio return is the average (either EW or VW)
return of all stocks with event in the past. Run the time series
regressions of portfolio returns against Fama-French factors The
time-series variation of monthly portfolio returns accurately
captures the effects of the correlation of returns across event
stocks. This approach can also solve the heteroskedasticity problem
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Solution : Monthly abnormal returns For each calendar month,
get the abnormal return for each stock that had an event in the
last 5 years. Form a portfolio based on these stocks and get the
average return over time Test whether the average of these time-
series abnormal monthly return is zero 38
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Problem of Solutions Sensitive to time period and selection
criterion of sample (Ritter and Welch (2002)) FF factor model : low
power test for long-run returns (Loughran and Ritter (2000))
Weighting firms equally is better than weighting time period
equally if there are time-varying misvaluations that firms want to
capture, and thus there are time clustering for some events If
misvaluations are larger for small firms, weighting firms based on
market cap. will underestimate the abnormal returns If the
benchmark is contaminated with many of the sample firms, the test
will tilt toward finding no abnormal returns 39
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Any Suggestions? Calendar-time portfolio regression with FF 3
factor Pro: better in statistical tests, easy to control cross
correlation and heteroskedasticity, less bad model problem Con:
least powerful model to detect abnormal performance Purge the
sample firms (especially IPOs, SEOs) from the benchmark portfolio
BHR Pro: better in simulating long-horizon investors experience
Con: skewness, cross-sectional dependence, exaggerate abnormal
performance Perform the bootstrapping Robustness Try different
benchmarks Try different time periods Out-of-sample tests 40
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Caveat! All methods and theories are developed in 10 years ago
even further earlier. New methods and models should be updated. But
you can digest new things easier by standing on the shoulders of
giants. New developments that I knew Liquidity model Real
investment, Investment friction and Q-theory Sentiment 41
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Part III. Operating performance 42
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43 Other than Long-Run Returns Quarterly earnings announcement
return (Chan, Ikenberry and Lee (2003)) Financial analysts
forecasts (Brous and Kini (1993), Rajan and Servaes (1997), and
GM(2004)) Operating performance (Lie (2001), and GM (2004)) Others
such as insider trading (Lee (1997)) and research coverage (Cliff
and Denis (2004))
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44 Quarterly Earnings Announcement Returns
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45 Financial Analysts Forecast Financial analyst forecast
includes Analysts forecast revision EPS forecast revision Forecast
on other accounting number Forecast revision =(Predicted earnings t
-predicted eatnings t-1 )/stock price Analysts forecast error
Forecast error =(Actual earnings-predicted earnings)/stock
price
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46 Operating Performance Operating performance Return on assets
Return on sales Whats accounting return? EBITDA EBIT Net income
Abnormal operating performance =OP-median (OP) Is it well
controlled?
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47 Abnormal Operating Performance Abnormal operating
performance Barber and Lyon (1996), and Lie (2001) Abnormal
operating performance for share repurchases Grullon and Michaely
(2004)
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48 Barber and Lyon (1996) Why is ROA employed as a main measure
of the operating performance? Its clearer and meaningful in
explaining the productivity of operating assets..
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49 Expected performance BL(1996) Expected performance
Generally, firms in the sample are compared to firms with the same
1. 2-digit SIC code 2. 4-digit SIC code 3. 2-digit SIC code and
similar size 4. 2-digit SIC code and similar pre-event performance
(i.e., 4 matching criteria)
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50 Matching firms selection BL(1996) Take the example with
model 8 (lag performance, performance change of matching firms with
the same 2-digit SIC code) 1. Minimize |P t-1 -PI t-1 | around
80%~120% of P t-1 with the same 2- digit SIC code. 2. Minimize |P
t-1 -PI t-1 | around 80%~120% of P t-1 with the same 1- digit SIC
code if its not found at step 1. 3. Minimize |P t-1 -PI t-1 |
around 80%~120% of P t-1 without industry requirement if its still
not found at step 2. 4. Minimize |P t-1 -PI t-1 | without industry
and filter requirement for all remaining sample.
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51 Tests BL(1996) Statistical tests for abnormal operating
performance Parametric t-test Non-parametric Wilcoxon signed rank
test
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52 Simulation BL(1996) Draw 1,000 random samples of 50 firm-
years (using 50 observations to get a mean (or a median) of AP)
Then, we have 1,000 APs. Count the number of APs out of 1%, 5% and
10% significant levels. If the way of AP is specified well, it is
supposed insignificant (i.e., control the size of the statistics
well: P(H1|H0) is lower than the confident level)
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53 Simulation (size control) BL(1996)
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54 Simulation (size control) BL(1996)
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55 Simulation (power) BL(1996)
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56 Lie (2001) Matches pre-event performance, change of per-
event performance and pre-event B/M ratio. He suggests this way
more powerful than the approach of Barber and Lyon (1996) He also
argues this way being more robust in sub-sample
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Part IV. Topics in this Course 57
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Capital structure New issue Corporate investment and innovation
M&A Financial constraint and corporate liquidity Corporate
governance Corporate Payout 58
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Miscellaneous issues in corporate finance Other corporate event
studies (stock split, private placement, ECB issue, and so on)
Top-executives compensation Labors and employees Product market
competition Institutional ownership and trading Financial analysts:
forecast, recommendation and error Diversification Geographic
economics Bankruptcy and financial distress Supply chain CEO
overconfidence and myopia Venture capital Insider trading Earnings
management and financial fraud More. 59