Corporate Finance Introduction by Yanzhi Wang 1. Course Description This PhD course is aimed to...

Corporate Finance Introduction by Yanzhi Wang 1

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

Grading and Requirements Mid-term25% Final (take-home)25% Presentation10% Research paper25% Exerciseand quiz15% 3

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

Financial Databases 5

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

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

Methodologies Part I. Firm valuation Part II. Event study method Part III. Operating performance 8

Part I. Firm Valuation 9

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

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. 11

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. 12

Part II. Event Study 13

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

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

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

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

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

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

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. 20

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

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

Long-Run: Simulation on Benchmarks 23

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

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 25

Long-Run: Bootstrapping 26

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

Long-Run: Power of Bootstrapping 28

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

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

Venture vs Non-Venture IPOs (BG 1997) Full sample Low BM Medium BM High BM VC/EW0.29% (0.15) -0.09% (-0.36) 0.26% (0.89) -0.07% (-0.23) NonVC/EW-0.51% (-2.90) -0/42% (-1.61) -0.55% (-2.60) -0.54% (-2.33) VC/VW0.12% (0.42) 0.36% (1.09) 0.29% (0.86) -0.30% (-1.01) NonVC/VW-0.12% (-0.59) 0.21% (0.66) -0.15% (-0.71) -0.39% (-1.81) 32

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

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

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

Solution for Long-Run Performance Using monthly returns Calendar-time portfolio Monthly abnormal returns 36

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 37

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

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

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

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

Part III. Operating performance 42

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))

44 Quarterly Earnings Announcement Returns

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

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?

47 Abnormal Operating Performance Abnormal operating performance Barber and Lyon (1996), and Lie (2001) Abnormal operating performance for share repurchases Grullon and Michaely (2004)

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..

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)

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.

51 Tests BL(1996) Statistical tests for abnormal operating performance Parametric t-test Non-parametric Wilcoxon signed rank test

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)

53 Simulation (size control) BL(1996)

54 Simulation (size control) BL(1996)

55 Simulation (power) BL(1996)

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

Part IV. Topics in this Course 57

Capital structure New issue Corporate investment and innovation M&A Financial constraint and corporate liquidity Corporate governance Corporate Payout 58

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

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