A large body of work has examined the impact of corporate takeovers on the financial stakeholders (shareholders and bondholders) of the merging firms. Since the late 2000s, empirical research has increasingly highlighted the crucial role played by the non-financial stakeholders (labor, suppliers, customers, government, and communities) in these transactions. It is, therefore, important to understand the interplay between corporate takeovers and the non-financial stakeholders of the firm. Financial economists have long viewed the firm as a nexus of contracts between various stakeholders connected to the firm. Corporate takeovers not only play an important role in redefining the broad boundaries of the firm but also result in major changes to corporate ownership and structure. In the process, takeovers can significantly alter the contractual relationships with non-financial stakeholders. Because the firm’s relationships with these stakeholders are governed by implicit and explicit contracts, circumstances can arise that allow acquiring firms to fully or partially abrogate these contracts and extract rents from non-financial stakeholders after deal completion. In contrast, non-financial stakeholders can also potentially benefit from a takeover if they get to share in any efficiency gains that are generated in the deal. Given this framework, the ex-ante importance of these contractual relationships can have a bearing on the efficacy of takeovers. The ability to alter contractual relationships ex post can affect the propensity of a takeover and merging firms’ shareholders and, in turn, impact non-financial stakeholders. Non-financial stakeholders will be more vested in post-takeover success if they can trust the acquiring firm to not take actions that are detrimental to them. The big picture that emerges from the surveyed literature is that non-financial stakeholder considerations affect takeover decisions and post-takeover outcomes. Moreover, takeovers also have an impact on non-financial stakeholders. The directions of all these effects, however, depend on the economic environment in which the merging firms operate.
Daniel Greene, Omesh Kini, Mo Shen, and Jaideep Shenoy
Hao Liang and Luc Renneboog
Corporate social responsibility (CSR) refers to the incorporation of environmental, social, and governance (ESG) considerations into corporate management, financial decision-making, and investors’ portfolio decisions. Socially responsible firms are expected to internalize the externalities they create (e.g., pollution) and be accountable to shareholders and other stakeholders (employees, customers, suppliers, local communities, etc.). Rating agencies have developed firm-level measures of ESG performance that are widely used in the literature. However, these ratings show inconsistencies that result from the rating agencies’ preferences, weights of the constituting factors, and rating methodology. CSR also deals with sustainable, responsible, and impact investing. The return implications of investing in the stocks of socially responsible firms include the search for an EGS factor and the performance of SRI funds. SRI funds apply negative screening (exclusion of “sin” industries), positive screening, and activism through engagement or proxy voting. In this context, one wonders whether responsible investors are willing to trade off financial returns with a “moral” dividend (the return given up in exchange for an increase in utility driven by the knowledge that an investment is ethical). Related to the analysis of externalities and the ethical dimension of corporate decisions is the literature on green financing (the financing of environmentally friendly investment projects by means of green bonds) and on how to foster economic decarbonization as climate change affects financial markets and investor behavior.
The current discontent with the dominant macroeconomic theory paradigm, known as Dynamic Stochastic General Equilibrium (DSGE) models, calls for an appraisal of the methods and strategies employed in studying and modeling macroeconomic phenomena using aggregate time series data. The appraisal pertains to the effectiveness of these methods and strategies in accomplishing the primary objective of empirical modeling: to learn from data about phenomena of interest. The co-occurring developments in macroeconomics and econometrics since the 1930s provides the backdrop for the appraisal with the Keynes vs. Tinbergen controversy at center stage. The overall appraisal is that the DSGE paradigm gives rise to estimated structural models that are both statistically and substantively misspecified, yielding untrustworthy evidence that contribute very little, if anything, to real learning from data about macroeconomic phenomena. A primary contributor to the untrustworthiness of evidence is the traditional econometric perspective of viewing empirical modeling as curve-fitting (structural models), guided by impromptu error term assumptions, and evaluated on goodness-of-fit grounds. Regrettably, excellent fit is neither necessary nor sufficient for the reliability of inference and the trustworthiness of the ensuing evidence. Recommendations on how to improve the trustworthiness of empirical evidence revolve around a broader model-based (non-curve-fitting) modeling framework, that attributes cardinal roles to both theory and data without undermining the credibleness of either source of information. Two crucial distinctions hold the key to securing the trusworthiness of evidence. The first distinguishes between modeling (specification, misspeification testing, respecification, and inference), and the second between a substantive (structural) and a statistical model (the probabilistic assumptions imposed on the particular data). This enables one to establish statistical adequacy (the validity of these assumptions) before relating it to the structural model and posing questions of interest to the data. The greatest enemy of learning from data about macroeconomic phenomena is not the absence of an alternative and more coherent empirical modeling framework, but the illusion that foisting highly formal structural models on the data can give rise to such learning just because their construction and curve-fitting rely on seemingly sophisticated tools. Regrettably, applying sophisticated tools to a statistically and substantively misspecified DSGE model does nothing to restore the trustworthiness of the evidence stemming from it.
Hengjie Ai, Murray Z. Frank, and Ali Sanati
The trade-off theory of capital structure says that corporate leverage is determined by balancing the tax-saving benefits of debt against dead-weight costs of bankruptcy. The theory was developed in the early 1970s and despite a number of important challenges, it remains the dominant theory of corporate capital structure. The theory predicts that corporate debt will increase in the risk-free interest rate and if the tax code allows more generous interest rate tax deductions. Debt is decreasing in the deadweight losses in a bankruptcy. The equilibrium price of debt is decreasing in the tax benefits and increasing in the risk-free interest rate. Dynamic trade-off models can be broadly divided into two categories: models that build capital structure into a real options framework with exogenous investments and models with endogeneous investment. These models are relatively flexible, and are generally able to match a range of firm decisions and features of the data, which include the typical leverage ratios of real firms and related data moments. The literature has essentially resolved empirical challenges to the theory based on the low leverage puzzle, profits-leverage puzzle, and speed of target adjustment. As predicted, interest rates and market conditions matter for leverage. There is some evidence of the predicted tax rate and bankruptcy code effects, but it remains challenging to establish tight causal links. Overall, the theory provides a reasonable basis on which to build understanding of capital structure.
Mahendrarajah Nimalendran and Giovanni Petrella
The most important friction studied in the microstructure literature is the adverse selection borne by liquidity providers when facing traders who are better informed, and the bid-ask spread quoted by market makers is one of these frictions in securities markets that has been extensively studied. In the early 1980s, the transparency of U.S. stock markets was limited to post-trade end-of-day transactions prices, and there were no easily available market quotes for researchers and market participants to study the effects of bid-ask spread on the liquidity and quality of markets. This led to models that used the auto-covariance of daily transactions prices to estimate the bid-ask spread. In the early 1990s, the U.S. stock markets (NYSE/AMEX/NASDAQ) provided pre-trade quotes and transaction sizes for researchers and market participants. The increased transparency and access to quotes and trades led to the development of theoretical models and empirical methods to decompose the bid-ask spread into its components: adverse selection, inventory, and order processing. These models and methods can be broadly classified into those that use the serial covariance properties of quotes and transaction prices, and others that use a trade direction indicator and a regression approach to decompose the bid-ask spread. Covariance and trade indicator models are equivalent in structural form, but they differ in parameters’ estimation (reduced form). The basic microstructure model is composed of two equations; the first defines the law of motion of the “true” price, while the second defines the process generating transaction price. From these two equations, an appropriate relation for transaction price changes is derived in terms of observed variables. A crucial point that differentiates the two approaches is the assumption made for estimation purposes relative to the behavior of order arrival, which is the probability of order reversal or continuation. Thus, the specification of the most general models allows for including an additional parameter that accounts for order behavior. The article provides a unified framework to compare the different models with respect to the restrictions that are imposed, and how this affects the relative proportions of the different components of the spread.
Florian Exler and Michèle Tertilt
Consumer debt is an important means for consumption smoothing. In the United States, 70% of households own a credit card, and 40% borrow on it. When borrowers cannot (or do not want to) repay their debts, they can declare bankruptcy, which provides additional insurance in tough times. Since the 2000s, up to 1.5% of households declared bankruptcy per year. Clearly, the option to default affects borrowing interest rates in equilibrium. Consequently, when assessing (welfare) consequences of different bankruptcy regimes or providing policy recommendations, structural models with equilibrium default and endogenous interest rates are needed. At the same time, many questions are quantitative in nature: the benefits of a certain bankruptcy regime critically depend on the nature and amount of risk that households bear. Hence, models for normative or positive analysis should quantitatively match some important data moments. Four important empirical patterns are identified: First, since 1950, consumer debt has risen constantly, and it amounted to 25% of disposable income by 2016. Defaults have risen since the 1980s. Interestingly, interest rates remained roughly constant over the same time period. Second, borrowing and default clearly depend on age: both measures exhibit a distinct hump, peaking around 50 years of age. Third, ownership of credit cards and borrowing clearly depend on income: high-income households are more likely to own a credit card and to use it for borrowing. However, this pattern was stronger in the 1980s than in the 2010s. Finally, interest rates became more dispersed over time: the number of observed interest rates more than quadrupled between 1983 and 2016. These data have clear implications for theory: First, considering the importance of age, life cycle models seem most appropriate when modeling consumer debt and default. Second, bankruptcy must be costly to support any debt in equilibrium. While many types of costs are theoretically possible, only partial repayment requirements are able to quantitatively match the data on filings, debt levels, and interest rates simultaneously. Third, to account for the long-run trends in debts, defaults, and interest rates, several quantitative theory models identify a credit expansion along the intensive and extensive margin as the most likely source. This expansion is a consequence of technological advancements. Many of the quantitative macroeconomic models in this literature assess welfare effects of proposed reforms or of granting bankruptcy at all. These welfare consequences critically hinge on the types of risk that households face—because households incur unforeseen expenditures, not-too-stringent bankruptcy laws are typically found to be welfare superior to banning bankruptcy (or making it extremely costly) but also to extremely lax bankruptcy rules. There are very promising opportunities for future research related to consumer debt and default. Newly available data in the United States and internationally, more powerful computational resources allowing for more complex modeling of household balance sheets, and new loan products are just some of many promising avenues.
Murray Z. Frank, Vidhan Goyal, and Tao Shen
The pecking order theory of corporate capital structure developed by states that issuing securities is subject to an adverse selection problem. Managers endowed with private information have incentives to issue overpriced risky securities. But they also understand that issuing such securities will result in a negative price reaction because rational investors, who are at an information disadvantage, will discount the prices of any risky securities the firm issues. Consequently, firms follow a pecking order: use internal resources when possible; if internal funds are inadequate, obtain external debt; external equity is the last resort. Large firms rely significantly on internal finance to meet their needs. External net debt issues finance the minor deficits that remain. Equity is not a significant source of financing for large firms. By contrast, small firms lack sufficient internal resources and obtain external finance. Although much of it is equity, there are substantial issues of debt by small firms. Firms are sorted into three portfolios based on whether they have a surplus or a deficit. About 15% of firm-year observations are in the surplus group. Firms primarily use surpluses to pay down debt. About 56% of firm-year observations are in the balance group. These firms generate internal cash flows that are just about enough to meet their investment and dividend needs. They issue debt, which is just enough to meet their debt repayments. They are relatively inactive in equity markets. About 29% of firm-year observations are in the deficit group. Deficits arise because of a combination of negative profitability and significant investments in both real and financial assets. Some financing patterns in the data are consistent with a pecking order: firms with moderate deficits favor debt issues; firms with very high deficits rely much more on equity than debt. Others are not: many equity-issuing firms do not seem to have entirely used up the debt capacity; some with a surplus issue equity. The theory suggests a sharp discontinuity in financing methods between surplus firms and deficit firms, and another at debt capacity. The literature provides little support for the predicted threshold effects. The theoretical work has shown that adverse selection does not necessarily lead to pecking order behavior. The pecking order is obtained only under special conditions. With both risky debt and equity being issued, there is often scope for many equilibria, and there is no clear basis for selecting among them. A pecking order may or may not emerge from the theory. Several articles show that the adverse selection problem can be solved by certain financing strategies or properly designed managerial contracts and can even disappear in dynamic models. Although adverse selection can generate a pecking order, it can also be caused by agency considerations, transaction costs, tax consideration, or behavioral decision-making considerations. Under standard tests in the literature, these alternative underlying motivations are commonly observationally equivalent.
Marius Guenzel and Ulrike Malmendier
One of the fastest-growing areas of finance research is the study of managerial biases and their implications for firm outcomes. Since the mid-2000s, this strand of behavioral corporate finance has provided theoretical and empirical evidence on the influence of biases in the corporate realm, such as overconfidence, experience effects, and the sunk-cost fallacy. The field has been a leading force in dismantling the argument that traditional economic mechanisms—selection, learning, and market discipline—would suffice to uphold the rational-manager paradigm. Instead, the evidence reveals that behavioral forces exert a significant influence at every stage of a chief executive officer’s (CEO’s) career. First, at the appointment stage, selection does not impede the promotion of behavioral managers. Instead, competitive environments oftentimes promote their advancement, even under value-maximizing selection mechanisms. Second, while at the helm of the company, learning opportunities are limited, since many managerial decisions occur at low frequency, and their causal effects are clouded by self-attribution bias and difficult to disentangle from those of concurrent events. Third, at the dismissal stage, market discipline does not ensure the firing of biased decision-makers as board members themselves are subject to biases in their evaluation of CEOs. By documenting how biases affect even the most educated and influential decision-makers, such as CEOs, the field has generated important insights into the hard-wiring of biases. Biases do not simply stem from a lack of education, nor are they restricted to low-ability agents. Instead, biases are significant elements of human decision-making at the highest levels of organizations. An important question for future research is how to limit, in each CEO career phase, the adverse effects of managerial biases. Potential approaches include refining selection mechanisms, designing and implementing corporate repairs, and reshaping corporate governance to account not only for incentive misalignments, but also for biased decision-making.
African financial history is often neglected in research on the history of global financial systems, and in its turn research on African financial systems in the past often fails to explore links with the rest of the world. However, African economies and financial systems have been linked to the rest of the world since ancient times. Sub-Saharan Africa was a key supplier of gold used to underpin the monetary systems of Europe and the North from the medieval period through the 19th century. It was West African gold rather than slaves that first brought Europeans to the Atlantic coast of Africa during the early modern period. Within sub-Saharan Africa, currency and credit systems reflected both internal economic and political structures as well as international links. Before the colonial period, indigenous currencies were often tied to particular trades or trade routes. These systems did not immediately cease to exist with the introduction of territorial currencies by colonial governments. Rather, both systems coexisted, often leading to shocks and localized crises during periods of global financial uncertainty. At independence, African governments had to contend with a legacy of financial underdevelopment left from the colonial period. Their efforts to address this have, however, been shaped by global economic trends. Despite recent expansion and innovation, limited financial development remains a hindrance to economic growth.
Miles Livingston and Lei Zhou
Credit rating agencies have developed as an information intermediary in the credit market because there are very large numbers of bonds outstanding with many different features. The Securities Industry and Financial Markets Association reports over $20 trillion of corporate bonds, mortgaged-backed securities, and asset-backed securities in the United States. The vast size of the bond markets, the number of different bond issues, and the complexity of these securities result in a massive amount of information for potential investors to evaluate. The magnitude of the information creates the need for independent companies to provide objective evaluations of the ability of bond issuers to pay their contractually binding obligations. The result is credit rating agencies (CRAs), private companies that monitor debt securities/issuers and provide information to investors about the potential default risk of individual bond issues and issuing firms. Rating agencies provide ratings for many types of debt instruments including corporate bonds, debt instruments backed by assets such as mortgages (mortgage-backed securities), short-term debt of corporations, municipal government debt, and debt issued by central governments (sovereign bonds). The three largest rating agencies are Moody’s, Standard & Poor’s, and Fitch. These agencies provide ratings that are indicators of the relative probability of default. Bonds with the highest rating of AAA have very low probabilities of default and consequently the yields on these bonds are relatively low. As the ratings decline, the probability of default increases and the bond yields increase. Ratings are important to institutional investors such as insurance companies, pension funds, and mutual funds. These large investors are often restricted to purchasing exclusively or primarily bonds in the highest rating categories. Consequently, the highest ratings are usually called investment grade. The lower ratings are usually designated as high-yield or “junk bonds.” There is a controversy about the possibility of inflated ratings. Since issuers pay rating agencies for providing ratings, there may be an incentive for the rating agencies to provide inflated ratings in exchange for fees. In the U.S. corporate bond market, at least two and often three agencies provide ratings. Multiple ratings make it difficult for one rating agency to provide inflated ratings. Rating agencies are regulated by the Securities and Exchange Commission to ensure that agencies follow reasonable procedures.