580 likes | 802 Vues
Corporate Finance. Introduction by Yanzhi Wang. 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
E N D
Corporate Finance Introduction by Yanzhi Wang
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.
Grading and Requirements • Mid-term 25% • Final 25% • Presentation 10% • Research paper 25% • Exercise and quiz 15%
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) a comment for a working paper, and ii) a proposal for a given project title. • You will have an open-book test on basic corporate finance at the first week.
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
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.
Methodologies Part I. Firm valuation Part II. Event study method Part III. Operating performance
Firm value • Tobin’s Q is most popular measure for firm value • Tobin’s 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?
Firm value • Finance people usually use this way to proxy Tobin’s Q: • Tobin’s Q=(Market value of equity + book value of liability)/Total book asset • Tobin’s 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.
Firm value? Investment opportunity? • Tobin’s 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.
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
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?
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.
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)
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
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?
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.
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
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
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 it’s compounded not cumulative • Skewness and clustering bias the estimation of standard deviation
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
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
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))
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
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
Problem: Bad Model Problem • The under- (out-) performance is due to the misspecification of expected returns • The bad model problem is unavoidable, but it’s 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
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))
Solution for Long-Run Performance • Using monthly returns • Calendar-time portfolio • Monthly abnormal returns
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
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
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
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 investor’s experience • Con: skewness, cross-sectional dependence, exaggerate abnormal performance • Perform the bootstrapping • Robustness • Try different benchmarks • Try different time periods • Out-of-sample tests
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
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))
Financial Analysts’ Forecast • Financial analyst forecast includes • Analysts forecast revision • EPS forecast revision • Forecast on other accounting number • Forecast revision =(Predicted earningst-predicted eatningst-1)/stock price • Analysts forecast error • Forecast error =(Actual earnings-predicted earnings)/stock price
Operating Performance • Operating performance • Return on assets • Return on sales • What’s accounting return? • EBITDA • EBIT • Net income • Abnormal operating performance =OP-median (OP) • Is it well controlled?
Abnormal Operating Performance • Abnormal operating performance –Barber and Lyon (1996), and Lie (2001) • Abnormal operating performance for share repurchases –Grullon and Michaely (2004)
Barber and Lyon (1996) • Why is ROA employed as a main measure of the operating performance? • It’s clearer and meaningful in explaining the productivity of operating assets. • .
Expected performance BL(1996) • Expected performance • Generally, firms in the sample are compared to firms with the same • 2-digit SIC code • 4-digit SIC code • 2-digit SIC code and similar size • 2-digit SIC code and similar pre-event performance (i.e., 4 matching criteria)
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) • Minimize |Pt-1-PIt-1| around 80%~120% of Pt-1 with the same 2-digit SIC code. • Minimize |Pt-1-PIt-1| around 80%~120% of Pt-1 with the same 1-digit SIC code if it’s not found at step 1. • Minimize |Pt-1-PIt-1| around 80%~120% of Pt-1 without industry requirement if it’s still not found at step 2. • Minimize |Pt-1-PIt-1| without industry and filter requirement for all remaining sample.