Results 1  10
of
26
High idiosyncratic volatility and low returns: international and further U.S. evidence
 Journal of Financial Economics, Vol.91 Issue
, 2008
"... international predictability, factor model We thank Kewei Hou and Soeren Hvidjkaer for kindly providing data. Andrew Ang acknowledges support from the NSF. ..."
Abstract

Cited by 77 (2 self)
 Add to MetaCart
international predictability, factor model We thank Kewei Hou and Soeren Hvidjkaer for kindly providing data. Andrew Ang acknowledges support from the NSF.
Employee sentiment and stock option compensation, MIT working paper
, 2004
"... Preliminary and incomplete 3 The use of broad equitybased compensation for employees in the lower ranks of an organization is a puzzle for standard economic theory: any positive incentive effects should be diminished by free rider problems, and undiversified employees should discount company equity ..."
Abstract

Cited by 44 (0 self)
 Add to MetaCart
Preliminary and incomplete 3 The use of broad equitybased compensation for employees in the lower ranks of an organization is a puzzle for standard economic theory: any positive incentive effects should be diminished by free rider problems, and undiversified employees should discount company equity heavily. We point out that employees do not appear to value company stock as prescribed by extant theory. Employees frequently purchase company stock for their 401(k) plans at market prices, and especially so after company stock has performed well, implying that their private valuation must at least equal the market price. We begin by developing a model of optimal compensation policy for a firm faced with employees with positive sentiment. Our goal is to establish the conditions necessary for the firm to compensate its employees with options in equilibrium, while explicitly taking into account that current and potential employees are able to purchase equity in the firm through the stock market. We show that using option compensation under these circumstances is not a puzzle if employees prefer the (nontraded) options offered by the firm to the (traded) equity offered by the market, or if the (traded) equity is overvalued. We then provide empirical evidence confirming that firms use broadbased option compensation when boundedly rational employees
Behavioral corporate finance: a survey
, 2004
"... Research in behavioral corporate finance takes two distinct approaches. The first emphasizes that investors are less than fully rational. It views managerial financing and investment decisions as rational responses to securities market mispricing. The second approach emphasizes that managers are les ..."
Abstract

Cited by 42 (8 self)
 Add to MetaCart
Research in behavioral corporate finance takes two distinct approaches. The first emphasizes that investors are less than fully rational. It views managerial financing and investment decisions as rational responses to securities market mispricing. The second approach emphasizes that managers are less than fully rational. It studies the effect of nonstandard preferences and judgmental biases on managerial decisions. This survey reviews the theory, empirical challenges, and current evidence pertaining to each approach. Overall, the behavioral approaches help to explain a number of important financing and investment patterns. The survey closes with a list of open questions.
How Do Diversity of Opinion and Information Asymmetry Affect Acquirer Returns? Review of Financial Studies 20:2047–78
, 2007
"... We examine the theoretical predictions that link acquirer returns to diversity of opinion and information asymmetry. Theory suggests that acquirer abnormal returns should be negatively related to information asymmetry and diversityofopinion proxies for equity offers but not cash offers. We find th ..."
Abstract

Cited by 32 (1 self)
 Add to MetaCart
We examine the theoretical predictions that link acquirer returns to diversity of opinion and information asymmetry. Theory suggests that acquirer abnormal returns should be negatively related to information asymmetry and diversityofopinion proxies for equity offers but not cash offers. We find that this is the case and that, more strikingly, there is no difference in abnormal returns between cash offers for public firms, equity offers for public firms, and equity offers for private firms after controlling for one of these proxies, idiosyncratic volatility. (JEL G31, G32, G34) This article examines whether variables suggested by diversityofopinion models and information asymmetry models are helpful in understanding the crosssectional variation in acquirer announcement returns using a sample of pure equity offers and pure cash offers for public and private firms from 1980 to 2002.We document that these variables, the uncertainty proxies, explain a significant fraction of the crosssectional variation in acquirer announcement returns. Perhaps most strikingly, after controlling for the uncertainty proxies, there is no difference in abnormal returns
Which firms follow the market? An analysis of corporate investment decisions. Review of Financial Studies 23:1941–1980
, 2010
"... Abstract We test whether stockmarket mispricing or private investor information in stock prices affects corporate investment. We develop an econometric methodology that disentangles stockprice movements that are relevant for investment from those that are not. We combine this decomposition with p ..."
Abstract

Cited by 24 (1 self)
 Add to MetaCart
Abstract We test whether stockmarket mispricing or private investor information in stock prices affects corporate investment. We develop an econometric methodology that disentangles stockprice movements that are relevant for investment from those that are not. We combine this decomposition with proxies for private information and mispricing to devise unbiased tests for the effects of mispricing and information on investment. We depart from much of the literature by finding that stockmarket mispricing does not affect investment, especially that of large firms and firms subject to mispricing. In contrast, we confirm previous evidence that managers incorporate private investor information when making investment decisions. Keywords: Investment; Stock market; Signal extraction; Errorsinvariables; GMM How does a firm's stock price affect its investment decisions? In a perfect world of symmetric information, efficient capital markets, and no regulatory distortions, this question is uninteresting because movements in asset prices reflect changes in underlying economic fundamentals, and the fundamental value of investment is the market value. However, the question has been of interest at least since Keynes ' (1936) idea that "animal spirits" influence the real economy, precisely because many accept the notion that capital markets are not entirely efficient; that is, that information does not flow freely among investors and firms. The question is also relevant for monetary policy because a link between stock prices and real economic activity opens the door for policy makers to target the stock market. The question is challenging because even an inefficient stock market passively reflects at least some of a firm manager's knowledge about genuine investment opportunities. Therefore, to answer the question one needs to disentangle such managerial knowledge from other sources of stockprice variation, such as private investor information or mispricing. Complicating any such disentanglement is the possibility of feedback from mispricing or from private information embedded in the stock price to the manager's perception of investment opportunities. No single answer to the question has emerged. The numerous papers that tackle this question find conflicting results, and the historical evidence has been similarly mixed. Similarly, the often cited increase in investment during the stockmarket bubble of the late 1990s is small in comparison to the movement in the market. Given this background of scattered anecdotal and formal evidence, this paper takes a step back, identifies the difficulties to overcome in ascertaining whether a firm's stock price affects its investment, and then develops and applies a new econometric methodology that can tackle these difficulties. We examine two related questions: whether investment responds to mispricing or to private information embedded in the stock price. Our innovations take into account important conceptual issues previously ignored by much of the literature. Accordingly, our new approach disputes many previous empirical findings concerning the importance of mispricing for firm investment. However, we confirm previous evidence that private investor information does affect investment. 1 Explaining our empirical approach requires an elaboration of the basic question. On one hand, managers may be better informed about the investment opportunities of their firms than are outside investors. In this case market signals provide no new knowledge to managers, who can, therefore, safely ignore stock market movements. In addition, managers may be reluctant to issue equity to exploit overvaluation of their company's shares because equity issuance can be a negative signal that, in the spirit of We consider two related alternatives to this point of view. First, in This discussion of the mechanisms whereby stock prices affect investment is couched in terms of unobservable quantities such as mispricing and information. Any empirical examination of these issues, therefore, must deal convincingly with biases that inevitably arise in empirical studies that contain unobservables. Our methodology does. It uses a model in which investment is determined primarily, though not solely, by Tobin's q: the market value of the capital stock divided by its replacement value. Because most of the variation in Tobin's q stems from variation in equity, this model is ideal for investigating the effect of the stock market on investment. To isolate the effects of private information and mispricing on investment, we turn to the errorsinvariables remedy in Our technique allows decomposition of the variance of Tobin's q into a component the manager considers relevant for investment and a component the manager considers irrelevant. We use this decomposition to conduct two types of tests. First, if private investor information is reflected in the stock price and if the manager pays attention to this information, the relevant component should be larger. We therefore test whether groups of firms sorted by measures of private information have higher relevant components. Second, to ascertain whether these components depend on mispricing, we regress Tobin's q on proxies for mispricing and collect the residual, thereby removing variation from Tobin's q. We then test whether this variation has been removed from the part of Tobin's q that is relevant for investment or the part that is irrelevant for investment. To distinguish these two alternatives, our tests compare the sizes of the relevant and irrelevant components before and after we regress Tobin's q on the mispricing proxies. We structure our tests so that noise in our proxies does not affect test consistency. Finally, we use our technique to identify characteristics of firms that exploit stockmarket mispricing, focusing on access to external finance and the level of mispricing. Because our technique is new, and because a skeptic may also find our econometric model and some of our assumptions questionable, we demonstrate the accuracy of our tests in finite samples in Monte Carlo experiments, and we go to great lengths to check the robustness of our results. To put this method in perspective, we examine the rest of the literature, which can be divided into two strands, the first of which examines the effects of mispricing on investment. In support of this idea, The second strand examines whether external information in the stock price affects investment. Chen, Our paper brings many of these results together by isolating specific mechanisms through which the stock market influences investment. We do find limited evidence that firms invest after issuing overpriced equity in order to relieve a binding finance constraint. However, we find much stronger evidence that many other groups of firms ignore mispricing. Finally, we find that the portion of the variation in Tobin's q that is relevant for investment rises with the amount of private investor information in the stock price. Why do our results depart from those in the literature? The difference stems in part from more accurate identification of firms that face financial constraints. A more important difference, however, arises from the improved ability of our technique to produce unbiased tests. Many of the papers surveyed above include proxies for mispricing, information, and fundamentals in regressions of investment on Tobin's q. Because Tobin's q is itself only a proxy for investment opportunities, such regressions contain more than one proxy. As such, the coefficients on any other proxies are therefore also biased, but, as explained in However, everything else held constant, investmentq sensitivity can also be high in the absence of mispricing or private information if the price fully reflects investment opportunities. Finally, both physical and financial frictions affect investmentq sensitivity. Given these difficulties, one goal in this paper is to determine in which instances previous approaches have been misleading. The paper is organized as follows. Section 1 presents our econometric model and testing strategy. Section 2 summarizes the data, Section 3 presents the results, and Section 4 concludes. The Appendix describes the estimators a Monte Carlo experiment that evaluates their performance. Methodology This section describes our methodology. First, we outline our econometric model and describe our tests. Because our methods are somewhat unusual, we demonstrate in several ways that the results produced by these methods are credible. In this section we address this issue on an intuitive level by discussing the applicability of the underlying empirical model. In later sections we take a more quantitative approach by performing specification tests, conducting robustness checks, and running Monte Carlo experiments designed to assess possible finitesample bias in our tests. Econometric Model Our testing strategy starts with the estimators in in which y i is the ratio of investment to assets for firm i, χ i is the true incentive to invest (true q), x i is an estimate of its true q, and z i is a row vector of perfectly measured regressors, whose first entry is one. The regression error, u i , and the measurement error, ε i , are assumed to be independent of each other and of (z i , χ i ), and the observations within a cross section are assumed i.i.d. The intercept in (2) allows for bias in the measurement of true q . Using the third and higher order moments of (x i , y i ), the Erickson and Whited estimators provide consistent estimates of the slope coefficients, α and β, as well as of the variances of the unobservable variables (χ i , u i , ε i ). These estimators are identified only if β 6 = 0 and χ i is nonnormally distributed. Erickson and Whited To explain the intuition behind these estimators, we consider a simple example based only on thirdorder moments, in which γ and α have been set to zero. This estimator has a familiar instrumental variables representation, which we demonstrate as follows. First, substitute (2) into (1), and set γ = α = 0 to obtain This regression clearly suffers from a correlated error and regressor. However, the product of x i and y i can serve as a valid instrument for x i because the independence of u i , ε i , and χ i implies that this instrument is orthogonal to the composite error Premultiplying both sides of (3) by y i x i , taking expectations, and rearranging produces The moments This technique produces an estimate of our parameter of interest, which is the population R 2 of 6 equation From a purely econometric point of view, a value of τ 2 close to one implies that the proxy is quite informative about variation in χ i . Conversely, a value close to zero implies that the proxy is nearly worthless. We discuss the economic interpretation of τ 2 below. Because these estimators can be applied only to samples that are arguably i.i.d., we obtain our estimates in two steps. First, we estimate τ 2 for each cross section of our unbalanced panel. Second, we pool these estimates via the procedure in Test Description Before describing our tests we need to interpret τ 2 in economic rather than econometric terms. To begin we note that equations Our tests combine the two most common methods for dealing with unobservables in empirical work: the use of proxies and the imposition of structure on the econometric model. We have already described our structure. An estimate of τ 2 measures the ratio of the distance between points a and b to the distance between points a and c. However, although estimating τ 2 can separate these components, this estimation cannot by itself provide information on whether either component contains any mispricing. To complete our identification strategy, we combine estimation of τ 2 with a more common method for econometrically estimating the effects of unobservables: the use of proxies, in particular, proxies for market mispricing. Use of proxies typically results in biased regression coefficients and misleading tests. However, we structure our tests in such a way that the use of possibly noisy proxies 8 does not produce bias and only lowers the power of our tests. Specifically, we perform a firststage regression of Tobin's q on each of these proxies and then make the observation that the variation thus removed has to be either relevant for investment (i.e., lie in the interval a to b) or irrelevant for investment (i.e., lie in the interval b to c). Consider first the former case, in which market mispricing is relevant for managerial investment decisions, and which is depicted in Panel B of Tobin's q in To describe the testing strategy more formally, let ω i be a proxy for mispricing, and letδω i be the fitted value from regressing x i on ω i . Next, rewrite (2) as in which χ * i and v i are defined in terms of the null and alternative hypotheses below. In this framework, the null hypothesis that ω i has no effect on Tobin's q, x i , can be written as H 0 :δ = 0. With reference to the original measurement equation, (2), ifδ = 0, then χ * i = χ i and v i = ε i ; and, therefore, τ 2 m = τ 2 . The first alternative joint hypothesis is that ω i affects x i and that the manager pays attention to ω i . This hypothesis can be written as H 1 :δ 6 = 0, χ i = χ * i +δω i , and ε i = v i . Under this first alternative τ 2 m < τ 2 . The second alternative joint hypothesis that ω i affects x i and that the manager ignores ω i can be written as H 2 :δ 6 = 0, χ i = χ * i , and ε i = v i +δω i . Under this second alternative τ 2 m > τ 2 . To examine the significance of τ 2 m , we first estimate (1) and (2) using x i . We then reestimate (1) 9 and (2) using x i −δω i in place of x i , thus producing estimates of τ 2 m . We then form the difference τ 2 m − τ 2 and test whether this difference is significantly greater or less than zero. In this framework, our null hypothesis is τ 2 m − τ 2 = 0. Our first alternative hypothesis that firms react to mispricing can be expressed as τ 2 m − τ 2 < 0. Our second alternative hypothesis that firms ignore mispricing can be expressed as τ 2 m − τ 2 > 0. We next discuss intermediate cases in which we cannot reject our null hypothesis. An obvious scenario that leads to a failure to reject is the absence of mispricing. However, our data analysis reveals thatδ = 0 for only one of the subsamples of firms we investigate. Because ω i is a proxy, its slope coefficient is biased toward zero. Therefore, our findings of nonzero slopes make this scenario unlikely. A second reason for a failure to reject is managerial attention to a portion of mispricing combined with managerial inattention to the rest. We deal with this possibility in the robustness section below. A final scenario that can lead to a failure to reject the null is noise in our imperfect proxies for mispricing. As shown in a Monte Carlo simulation in the Appendix, however, the presence of measurement error in these proxies only lowers the power of our tests relative to a situation in which we use (hypothetical) perfect measures. It does not bias the tests. These Monte Carlo experiments also show that even the diminished power of our tests is still quite effective in detecting the alternative hypotheses that τ 2 m − τ 2 is either greater than or less than zero. Three features of our testing strategy are important. First, we can quantify the extent to which the market influences investment, which is a calculation that cannot be made using previously formulated approaches. In particular, we can calculate an upper bound on the percent of the variation in χ i that is due to ω i if τ 2 m − τ 2 < 0. To obtain this bound, we substitute (5) into the expression for the R 2 from regressing x i on ω i , which we denote as R 2 xω ≡ var If ω i explains none of the variance of ε i , then Second, because our test is formulated as a difference between coefficients of determination, it is 10 robust to misspecification of the basic investmentq regression (1). For example, in Abel and Eberly (1994) the investmentq relationship can be nonlinear because a wedge between the purchase and sale prices of capital causes the level of q to affect the response of investment to q. For this problem to affect our tests, however, the source of nonlinearity needs to be correlated with our mispricing proxies because nonlinearity affects both the regression (1) and the version of (1) in which x i −δω i has been substituted in for x i . We view this possibility as unlikely. Third, the structure of our tests differs dramatically from those in previous studies, all of which are based on the null hypothesis that firms ignore the market. In contrast, this null is one of our two alternative hypotheses. Therefore, although previous findings that firms do not follow the market can be critiqued as resulting from low test power, any such findings on our part cannot. Applicability of the Model Is a linear errorsinvariables model appropriate for studying the effect of stock prices on investment? No econometric model ever represents reality perfectly, so the real question is whether this model captures the relevant features of the data. Our answer focuses the interpretation of the measurement error, ε i , because if factors other than mispricing influence ε i , and if our proxies for mispricing are correlated with these factors, our tests may simply pick up variation in these other factors. To organize our discussion, we start with a candidate definition of fundamental investment opportunities as marginal qthe manager's expectation of the future marginal product of capital. As discussed in 11 We view this correlation as unlikely because this source of error primarily arises from technological considerations, whereas the proxies for mispricing and information depend on investor behavior. The next link between fundamental investment opportunities and an observable proxy is the equality of average q and Tobin's q, which is the financial markets' valuation of average q. A discrepancy between these two quantities arises if stock market inefficiencies create variation in the stock price that is irrelevant for investment. This component of ε i is the one on which we focus. The third link arises because researchers estimate Tobin's q from accounting data that do not adequately represent market and replacement values. These wellknown mundane measurement issues admit a further interpretation of ε i as literal data recording error. Nonetheless, we view this interpretation as unimportant, given the evidence in Erickson and Whited A further complication is the existence of two different ways to calculate Tobin's q. The first is the markettobook ratio, which is the market value of assets divided by their book value. The second is what we call macro q, which is the sum of the market values of debt and equity less the value of current assets, all divided by the capital stock. The use of macro q dates back to In sum, although a series of links joins Tobin's q (x i ) to true investment opportunities (χ i ), the link most likely to be broken is the one due to stock market inefficiencies. Further, other possible sources of variation in ε i are unlikely to be correlated with our proxies for mispricing or information. Therefore, our testing strategy based on a signal extraction exercise is indeed appropriate. 12 Data and Summary Statistics This section describes our data sources. It then explains how we construct measures of financial constraints, mispricing, and information. It concludes by presenting summary statistics. Data and Variable Construction The data come from several sources. The first is the combined annual, research, and full coverage 2005 Standard and Poor's Compustat industrial files. We select the sample by first deleting any firmyear observations with missing data. Next, we delete any observations for which total assets, the gross capital stock, or sales are either zero or negative. Then for each firm we select the longest consecutive times series of data in which it did not undertake a merger greater than 25% of the book value of assets. We exclude firms with only one observation. Finally, we omit all firms whose primary SIC classification is between 4900 and 4999, between 6000 and 6999, or greater than 9000, because our model is inappropriate for regulated, financial, or quasipublic firms. Data variables from Compustat are defined as follows: book assets is Item 6; the gross capital stock is Item 7; capital expenditures is Item 128; R&D is item 46; cash flow is the sum of Items 18 and 14; net equity issuance is Item 108 minus Item 115; total longterm debt is Item 9 plus Item 34; total dividends is Item 19 plus Item 21; cash is Item 1; research and development costs are Item 46; inventories is Item 3; and sales is Item 12. The debt overhang correction represents the current value of lenders' rights to recoveries in default and is computed following Our monthly and daily return data are from the 2005 CRSP tapes, and our data on analysts' earnings forecasts are from I/B/E/S. After merging the CRSP and I/B/E/S data with the Compustat data and after deleting the top and bottom 1% of our regression variables, we are left with a sample that contains between 2,684 and 3,891 observations per year, with a sample period that runs from 1991 to 2004. We obtain data on one of our measures of information from Duarte and Young Measures of Mispricing We use three measures of mispricing. Our use of multiple proxies is important, given that mispricing is difficult to measure. Our first proxy is a measure of belief heterogeneity. Denoted SDEV , this proxy is defined as the standard deviation of analysts' earningspershare forecasts. As argued in We obtain the analysts' forecast data from the Summary History file from I/B/E/S. The Summary History file is potentially less accurate than the Detail History file because of the presence of stale forecasts and coding errors. However, 14 Because mispricing is transitory, a necessary (but not sufficient) condition for SDEV to be a good proxy is the existence of low returns for high SDEV firms. We find this pattern in our sample. The firms in the highest SDEV quartile have on average negative returns in months 5 through 12 after the measurement of SDEV . This result has the further implication that mispricing, although transitory, persists long enough to open the avenue for firm investment to respond. In contrast, firms in other SDEV quartiles exhibit no pronounced pattern of returns in either direction. Our second measure of mispricing is the analysts' consensus estimate of earnings per share minus the realized level of earnings per share, which we denote ES. issue that arises in measuring ES is timing. The earnings announcement cannot occur before the time at which Tobin's q is measured because the earnings announcement releases information. The ensuing market reaction then ameliorates any mispricing. We therefore consider the first earnings announcement that occurs from 1 to 5 months after the beginning of the fiscal year, which is when we measure Tobin's q. In results not reported we also examine longer time windows. For lengths of up to one year our results are robust. For lengths longer than one year our results are insignificant. Our final measure of mispricing is the cumulative abnormal stock return from the beginning of the fiscal year to the end. This proxy in part follows We reject several candidate measures of mispricing. For example, Measures of Information We examine two measures of private investor information, one measure of managerial private information, and one measure of public information. Our first proxy for private investor information is from from the regression of firmspecific weekly returns on valueweighted market and valueweighted industry indices. The industry is defined at the threedigit SICcode level. We hereafter refer to Ψ as price nonsynchronicity. Chen, Goldstein, and Jiang (2007) provide a detailed survey of the literature that supports the idea that high idiosyncratic volatility is related to the existence of private investor information. They also survey several papers that argue and show that stockprice comovement is related to a lack of private information in the stock price. Our second measure of private information is a variant of the probability of informed trading, or Our measure of managerial private information is from Chen, Goldstein, and Jiang and is based on insider trading activities. This measure is constructed as the total number of insider stock transactions for the year divided by the total year's transactions. The intuition is that managers are more likely to trade the more private information they possess. We measure this activity using both buys and sells. Isolating buys produces almost identical results in the tests that follow, whereas isolating sells produces insignificant results, possibly because managers can also be motivated to sell for liquidity or diversification reasons. 5 These data have only enough observations for us to run Finally, our measure of public information is the number of analysts covering a firm measured in the year preceding the measurement of Tobin's q. To the extent that analysts transfer information from managers to investors, high analyst coverage should indicate a small discrepancy between managerial and market expectations about investment opportunities. Measures of Financial Constraints Because financial constraints are endogenously determined with investment, we need an instrument. We use firm size, because small firms tend to be young, and young firms tend to face frictions in obtaining external capital. Indeed, Several authors have used these logit coefficients on data from a broad sample of firms to construct a "synthetic KZ index" to measure financial constraints. It is constructed as −1.001909CF + 3.139193T LT D − 39.36780T DIV − 1.314759CASH + 0.2826389Q, in which CF is the ratio of cash flow to book assets, T LT D is the ratio of total longterm debt to book assets, T DIV is the ratio of total dividends to book assets, CASH is the ratio of the stock of cash to book assets, and Q is the markettobook ratio. As argued in Baker, Stein, and Wurgler Summary Statistics Summary statistics for the sample stratified into quartiles by size and the KZ index are in The first panel contains the sort on size. Small firms clearly do not finance with debt, and they issue
Behavioral Finance in Corporate Governance: Economics and Ethics of the Devil’s Advocate
 Journal of Management and Governance
, 2008
"... Abstract The Common Law, parliamentary democracy, and academia all institutionalize dissent to check undue obedience to authority; and corporate governance reformers advocate the same in boardrooms. Many corporate governance disasters could be averted if directors asked hard questions, demanded cle ..."
Abstract

Cited by 7 (3 self)
 Add to MetaCart
Abstract The Common Law, parliamentary democracy, and academia all institutionalize dissent to check undue obedience to authority; and corporate governance reformers advocate the same in boardrooms. Many corporate governance disasters could be averted if directors asked hard questions, demanded clear answers, and blew whistles. Work by Milgram suggests humans have an innate predisposition to obey authority. This excessive subservience of agent to principal, here dubbed a ‘‘type II agency problem’’, explains directors ’ eerie submission. Rational explanations are reviewed, but behavioral explanations appear more complete. Experimental work shows this predisposition disrupted by dissenting peers, conflicting authorities, and distant authorities. Thus, independent directors, chairs, and committees excluding CEOs might induce greater rationality and more considered ethics in corporate governance. Empirical evidence of this is scant—perhaps reflecting problems identifying genuinely independent directors.
Common flaws in empirical capital structure research. Working paper
, 2007
"... This paper critiques three issues that commonly arise in empirical capital structure research. 1. Capital Structure Proxies: The financialdebttoasset ratio is flawed as a measure of leverage, because the converse of financial debt is not equity. Depending on specification, the debttoasset rati ..."
Abstract

Cited by 6 (0 self)
 Add to MetaCart
This paper critiques three issues that commonly arise in empirical capital structure research. 1. Capital Structure Proxies: The financialdebttoasset ratio is flawed as a measure of leverage, because the converse of financial debt is not equity. Depending on specification, the debttoasset ratio can explain only about 1050 % of the variation in the equitytoasset ratio. This is because most of the opposite of the financialdebttoasset ratio is the nonfinancialliabilitiestoasset ratio. This problem is easy to remedy— researchers should use a debttocapital ratio or a liabilitiestoasset ratio. The converse of either is an equity ratio. 2. Nonlinearity: The intrinsic nonlinearity of leverage ratios can render standard linear regressions even with perfect independent variables seemingly powerless. Fortunately, researchers can easily test whether variables have a linear or nonlinear influence on equity value changes, debt value changes, or leverage ratios. 3. Selection Issues: There are large survivorship biases in the CRSP/Compustat data bases. About 10 % of firms appear and 10 % disappear in a single year. These birth and death rates are themselves functions of capital structure and other firm characteristics. This selection makes studying longterm capital structure changes difficult. Unfortunately, this problem is difficult to remedy. The paper does not claim that these three issues drive results in the existing literature. It does however claim that they are not so small as to allow ignoring them a priori. The paper also clarifies some theoretical issues, most of which are not new, but which are sufficiently often muddled that a clarification is useful. First the paper distinguishes between capital structure mechanisms and causes. Second, when it comes to causes, it clarifies that there is no dichotomy between the pecking order theory and the tradeoff theory. A pecking order arises in a tradeoff theory in which issuing more junior securities is relatively more expensive, or possibly prohibitively expensive. A pecking order is not synonymous with adverse selection, financial slack, or a financing pyramid, either. This draft is early. Comments are welcome.
Mortgage Convexity ∗
, 2012
"... Most home mortgages in the United States are fixedrate loans with an embedded prepayment option. When longterm rates decline, the effective duration of mortgagebacked securities (MBS) falls due to heightened refinancing expectations. I show that these changes in aggregate MBS duration function as ..."
Abstract

Cited by 5 (1 self)
 Add to MetaCart
Most home mortgages in the United States are fixedrate loans with an embedded prepayment option. When longterm rates decline, the effective duration of mortgagebacked securities (MBS) falls due to heightened refinancing expectations. I show that these changes in aggregate MBS duration function as largescale shocks to the quantity of interest rate risk that must be borne by professional bond investors. I develop a simple model in which the risk tolerance of bond investors is limited in the short run, so these fluctuations in MBS duration generate significant variation in bond risk premia. Specifically, bond risk premia are high when aggregate MBS duration is high. The model offers an explanation for why longterm rates may appear to be “excessively sensitive”to movements in short rates and explains how changes in MBS duration act as a positivefeedback mechanism that amplifies interest rate volatility. I find strong support for these predictions in the time series of US government bond returns. I am grateful to John Campbell, Robin Greenwood, Erik Stafford, Jeremy Stein, Larry Summers, and
Cosmetic mergers: The effect of style investing on the market for corporate control
 Journal of Financial Economics, Volume 93, Issue
, 2009
"... We study the impact of style investing on the market for corporate control. We argue that a firm may choose to boost its market value by merging with a firm that belongs to a style that is more favored by the market. By using data on the flows in mutual funds, we construct a measure of neglectedness ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
We study the impact of style investing on the market for corporate control. We argue that a firm may choose to boost its market value by merging with a firm that belongs to a style that is more favored by the market. By using data on the flows in mutual funds, we construct a measure of neglectedness, which relies directly on the identification of sentimentinduced investor demand, rather than being a direct transformation of stock market data. We show that bidders tend to pair with targets that are relatively less neglected. The merger with a less neglected target generates a halo effect from the target to the bidder that induces the market to evaluate the assets of the more neglected bidder at the (inflated) market value of the less neglected target. Both bidder and target premia are positively related to the difference in neglectedness between bidder and target. However, the target’s ability to appropriate the gain is reduced by the fact that its bargaining position is weaker when the bidder’s potential for asset appreciation is higher. We document a better mediumterm performance of more neglected firms taking over less neglected firms. The bidder managers engaging in these cosmetic mergers take advantage of the window of opportunity created by the higher stock price induced by the M&A deal to reduce their stake in the firm under convenient conditions. JEL Classification: G34; G23; G32
How to Make Better Decisions? Lessons Learned from Behavioral Corporate Finance
"... This article reviews the literature in the field of Behavioral Corporate Finance. For reasons of simplicity, we distinguish between two approaches. The first approach focuses on the analysis of irrational behavior of managers in the context of efficient financial markets. Many empirical studies disc ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
This article reviews the literature in the field of Behavioral Corporate Finance. For reasons of simplicity, we distinguish between two approaches. The first approach focuses on the analysis of irrational behavior of managers in the context of efficient financial markets. Many empirical studies discover systematic irrational managerial behavior. The second approach regards rational manager decisions in the context of inefficient markets. The analysis focuses on situations where investors are systematically irrational taking rational and wellinformed managers as given. Interestingly, Behavioral Corporate Finance is able to explain many empirical observations that cannot be explained by traditional Corporate Finance. In reality, both managers and investors act to some extent irrationally. Therefore, we make recommendations to both groups in order to improve their decision making. In contrast to other papers, we give specific recommendations for both managers and investors. With the help of these recommendations, managers and investors are able to improve decisionmaking to their mutual advantage.