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Panels and Cross-sections 1

Panels and Cross-sections 1. Paul A. Gompers Empirical Topics in Corporate Finance February 19, 2009. Panel and Cross Sectional Data. Today look at panel and cross sectional data. Covers lots of interesting papers and data sets.

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Panels and Cross-sections 1

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  1. Panels and Cross-sections 1 Paul A. Gompers Empirical Topics in Corporate Finance February 19, 2009

  2. Panel and Cross Sectional Data • Today look at panel and cross sectional data. • Covers lots of interesting papers and data sets. • Methodological issues arise in the cross section and we will deal with those in a variety of settings.

  3. Agenda • Look at methodological papers as well as applications of cross sectional and panel data. • Hopefully this examination will provide insights into how to approach many of the most interesting problems in CF.

  4. Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches Mitch Petersen RFS 2009

  5. Papers Contribution • Examines a variety of approaches to estimating standard errors and statistical significance in panel data sets • Interesting look at a variety of papers published from 2001-2004. • Only 42% of papers adjusted standard errors for possible dependence in residuals. • Many different approaches. • Which are correct under what circumstances.

  6. Overview • OLS standard errors are unbiased when residuals are independent and identically distributed. • Residuals in panel data may be correlated by firm-specific effects that are correlated across time. • Firm effect. • Residuals of a given year may be correlated across different firms (cross sectional dependence) • Time effect.

  7. Paper’s Approach • Simulate data that has either firm effect or a time effect. • Test various estimation techniques. • See how they deal with the simulated data. • Then takes regression approaches to actual data and compares them.

  8. Firm Fixed Effects • Assumption of OLS is that cross product matrix has only non-zero numbers on the diagonal. • Figure 1 – Example of a firm effect. • Cluster standard errors by firm.

  9. OLS vs. Clustering by Time vs. FM with Firm Effect • Simulate 5000 samples with 5000 observations. • 500 firms and ten years of observations. • Let the residual and independent variable variance due to the firm effect vary between 0 and 75%. • 500 clusters by firm.

  10. OLS vs. Clustering on Firm vs. FM with Firm Effect • Table 1 • Compare average coefficients, st. dev. of coefficient estimates, % significant, average SE clustered and % significant with clustered SE. • Vary how much of the independent variable variation is due to firm effect and how much of the residual variation is due to firm effect. • Figure 2 – Compare OLS, Clustered by firm, and Fama-McBeth. • Table 2- Fama-McBeth

  11. OLS vs. Clustering by Time vs. FM with Time Effect • Simulate 5000 samples with 5000 observations. • Let the residual and independent variable variance due to the time effect vary between 0 and 75%. • Not this is the situation that FM developed FM for. • Clustering will be by the 10 years.

  12. OLS vs. Clustering by Time vs. FM with Time Effect • Table 3 – Compare OLS and Clustering by time. • OLS does pretty poor job. • Table 4 – Using FM to estimate.

  13. OLS vs. Clustering by Time vs. FM with Firm and Time Effect • In many typical examples, could have both a firm and time effect. • Figure 6, typical structure with both. • Can cluster by firm and time together. • See Samuel Thompson’s 2006 working paper for math.

  14. OLS vs. Clustering by Time vs. FM with Firm and Time Effect • Simulate 5000 samples with 5000 observations. • Let the residual and independent variable variance due to firm and time effect vary • Table 5 – Compare OLS, with and without firm dummies, Clustered by firm and time, GLS, and FM.

  15. Real Data • Table 6 – Look at asset pricing application. • Equity returns on asset tangibility. • Different methods matter. • OLS and firm clusters do poorly. • Time and firm clustering and FM work well. • Seems to say that for returns may be more affected by a time effect.

  16. Recommendations • Think about the structure of the panel data structure. • What is the likely source of dependence. • Comparing different methods may provide additional information about the research question.

  17. Real Data • Table 7 – Capital structure regressions. • OLS, clustering by time, and FM do poorly. • Clustering by firm and clustering by firm and time do well. • Says that within corporate finance a lot of the effects seem to have firm level persistence.

  18. Market timing and capital structure Malcolm Baker Harvard Business School Jeffrey Wurgler NYU

  19. Research question • Why do similar firms have different capital structures? • Temporary fluctuations in market valuehave a lasting impact on capital structure outcomes

  20. Overview • A new fact: Temporary fluctuations in market value have a lasting impact on capital structure outcomes • Possible explanations: • (1) Trade-off theories • Taxes, costs of financial distress, and agency lead to an optimal leverage ratio • (2) Pecking order • Adverse selection dominates other considerations, leading to a pecking order • Neither appears to explain the new fact • Consistent with the idea that managers are motivated by market-timing

  21. Some motivation • Prevailing market values are the most important empirical determinant of financing decisions… • Equity issues • IPOs: Loughran, Ritter, Rydqvist (1994),Pagano, Panetta, Zingales (1998) et al.SEOs: Asquith and Mullins (1986), Marsh (1982) et al. • Debt issues and repurchases • Debt: Marsh (1982) et al. Repurchases: Ikenberry, Lakonishok, Vermaelen (1995) et al. • … and capital structure is the sum of past financing decisions (accounting identity) • So, capital structure may depend on the historical path of market valuations

  22. Sample • Trace the evolution of capital structure as firms mature • Require a known IPO date • Natural starting point for the historical path of market valuations • And, can trace the determinants of capital structure as firms mature • Capital structure  Et / At • Compustat coverage from 1969 to 1998 • Empirical approaches • IPO time • Calendar time

  23. Capital structure changes • Changes in the equity ratio come in two forms (1) New issues and repurchases • Active change in capital structure (Table 3A) • (2) Retained earnings • Passive change in capital structure (Table 3B) • Examine the determinants of each • Market-to-book, asset tangibility, profitability, and size • Table 3

  24. Measures of M/B • Summarize the historical path of valuations with a single statistic: (1) Maximum market-to-book ratio • The highest year-end value from the IPO through t-1 (2) Weighted average market-to-book ratio • The weights are the amount of external finance (debt plus equity) raised in each year from the IPO through t-1 • Financing events represent a practical opportunity to change capital structure

  25. Temporary fluctuations in M/B and capital structure • Cross-section regressions in Table 7 (1) Include controls x • Fixed assets intensity • Profitability • Firm size (2) b2 captures the impact of temporary fluctuations in market value • Control for endpoints at IPO and t-1

  26. Is M/B a measure of mispricing? • M/B predicts stock returns ... • Basu (1977, 1983)Fama and French (1992)LSV (1994) … partly because of errors in expectations • La Porta (1996)LLSV (1997) • But M/B captures both mispricing and legitimate growth prospects ... • Growth could be correlated with agency, asymmetric information, financial distress costs … so control for the level of M/B • Starting point (IPO), ending point (t-1), both

  27. Some robustness checks (1) Market value capital structure (2) Industry effects IPO year effects (3) Fama and French (2000) Five profitability lags (4) Outliers included Mature firms included • Tobit Table 8.

  28. Economic significance • Figures 1 • Large relative to the other determinants • Asset tangibility and size die • Profitability emerges

  29. Possible explanations (1) Trade-off theories • Taxes, costs of financial distress, and agency lead to an optimal leverage ratio Ancillary prediction: Temporary fluctuations in market-to-book (or anything else) should have a temporary impact (2) Pecking order • Adverse selection dominates other considerations, leading to a pecking order Ancillary prediction: Temporary increases in market-to-book should lead to lower cash balances or higher future investment (3) Market timing • Managers believe they can time the market

  30. Trade-off theories • Taxes, costs of financial distress, and agency lead to an optimal capital structure • Market-to-book could be connected to one or more inputs to the trade-off, and… • Costly financial distress • Debt overhang • Agency • Perhaps tax benefits … may have some persistence, but… • Adjustment costs … temporary fluctuations in market-to-book (or anything else) should have a temporary impact • Table 9.

  31. Long-term impact • Temporary fluctuations in market-to-book have a lasting impact on capital structure • The half-life of the initial effect is at least ten years (Table 9) • Hatched bars are the five percent lower bound

  32. Pecking order • Adverse selection dominates other considerations, leading to a pecking order • Market-to-book is related to investment opportunities, but… • High market-to-book means investment opportunities exceed internally generated funds and debt capacity … extra equity raised should be spent or at least be earmarked for future investment • So, temporary increases in market-to-book should lead to lower cash balances or higher future investment

  33. Cash balances • Temporary fluctuations in market-to-book also have a lasting impact on cash • Increases in market-to-book have a permanent impact on cash balances (Table 10) • No lasting impact on investment (Table 11)

  34. Market timing • A variant of Myers and Majluf (1984) Like Myers-Majluf: • Managers have the incentive to try to time the market because they care more about existing shareholders • Investors react to financing decisions, and this adverse selection dominates other considerations, so… … there is no optimal capital structure Unlike Myers-Majluf: • Managers think that they can successfully time the market, believing (1) Shares are occasionally under or overvalued (2) Investors underreact to new issues

  35. Other evidence of timing • Managers admit to timing the market • Graham and Harvey (2000) • It looks like they’re trying • Marsh (1982)Pagano, Panetta, and Zingales (1998) • Although investors recognize it ... • Asquith and Mullins (1986) … they underreact • Ritter (1991), Loughran and Ritter (1995) • Baker and Wurgler (2000) • Is it actually successful? • A separate debate • Market-timing attempts affect capital structure

  36. Conclusions • Managers try to time their equity issues and this influences capital structure outcomes: (1) Low leverage firms raised external financewhen valuations were comparatively high High leverage firms raised external financewhen valuations were comparatively low (2) Temporary fluctuations in market-to-booklead to lasting changes in capitalstructure and cash balances (3) Trade-off theories and pecking order do notappear to explain the results (4) Market timing fits the new fact and old facts

  37. Testing Trade-Off and Pecking Order Predictions about Dividends and Debt Fama and French RFS 2002

  38. Agenda • Look at two competing theories of capital structure and dividends. • Pecking order • Trade-off theory • Utilize Fama-McBeth techniques. • Very important technique to understand.

  39. Motivation • Most previous studies are pure cross section or small panels. • Results can be wildly overstated. • Cross correlation can reduce standard errors. • Correlation of the residuals across firms are ignored. • Auto correlation can reduce standard errors. • Panels can have residuals correlated across years. • Fama-McBeth gives robust standard errors in these types of situations. • Particularly when there are multiple observations on the same firm and you have unbalanced panels potentially.

  40. Methodology • Run a series of cross sectional regressions. • Can be run annually, monthly, daily, etc. • Only depends upon number of unique observations you have. • Report the average coefficient and test significance by using the standard deviation of the time series of coefficients. • Also can report number of positive and negative coefficients. • Recognize that there may be firm persistence. • Arbitrarily argue that there is a need to increase t-statistics critical value by 2.5x, i.e., 5.00 to get significance.

  41. Pecking Order • Dividends • Less attractive for less profitable firms, large current and expected investments, high leverage. • Leverage • Depends if one period or care about future financing. • Lower leverage for firms with large future investments. • Volatility. • Low dividends and low leverage.

  42. Trade-off Model • Bankruptcy costs • Taxes • Agency costs • Adjustment costs.

  43. Independent Variables • ET/A – Pretax earnings to assets • V/A = Market value to book value (future investments) • RD/A – R&D to assets • Dp/A – Depreciation over assets • Ln(A) – log assets as proxy for volatility.

  44. Dependent Variables • Dividends • D/A – Dividends over assets • Leverage • Market leverage – L/V • Book leverage – L/A

  45. Regressions • Data from 1965-1999. • Table 1 – Dividend payout ratio • Use target leverage from leverage regression in Table 4. • Table 3 – Level of leverage. • Strong evidence of pecking order. • More profits yields less debt.

  46. Sorting the sorts • Table 5 • Sort firms based on dividend paying or not and leverage. • Find that low leverage nonpaying firms have better investment opportunities and more equity issuances. • At odds with pecking order.

  47. Conclusion • Neither theory wins out. • Perhaps a third theory is at work. • Perhaps both have merits. • Interesting techniques.

  48. Conclusion • Interesting paper. • Highly technical with attention to detail. • Firms don’t take full advantage of potential tax benefit savings from debt.

  49. Understanding the Determinants of Managerial Ownership and the Link Between Ownership and Performance Himmelberg, Hubbard, and Palia JFE 1999

  50. Motivation • Lots of studies show non-linear relationship between firm value and inside ownership. • Mork, Shleifer, and Vishny (1988). • McConnell and Servaes (1990). • Hermalin and Weisbach (1991). • Problem is that ownership may be endogenous. • All these studies are large cross-sections.

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