The U.S. housing finance system has been characterized by fixed-rate, long-term, and high maximum loan-to-value ratio mortgage loans, with unique support from secondary market entities Ginnie Mae and the government-sponsored enterprises, Fannie Mae and Freddie Mac. The authors provide a comprehensive review of the U.S. housing finance system, from its structure and evolution to the current continuing policy debate. The “American Mortgage” provides many more options to borrowers than are commonly provided elsewhere: U.S. homebuyers can choose whether to pay a fixed or floating rate of interest; they can lock in their interest rate in between the time they apply for the mortgage and the time they purchase their house; they can choose the time at which the mortgage rate resets; they can choose the term and the amortization period; they can generally prepay without penalty; and they can generally borrow against home equity. They can also obtain insured home mortgages at attractive terms with very low down payments. Perhaps most importantly, in the typical mortgage, payments remain constant throughout the potentially 30-year term of the loan. The unique characteristics of the U.S. mortgage provide substantial benefits for American homeowners and the overall stability of the economy. This article describes the evolution of the housing finance system which has led to the predominant role of this mortgage instrument in the United States.
Article
The American Housing Finance System: Structure, Evolution, and Implications
Yongheng Deng, Susan M. Wachter, and Heejin Yoon
Article
Asset Pricing: Cross-Section Predictability
Paolo Zaffaroni and Guofu Zhou
A fundamental question in finance is the study of why different assets have different expected returns, which is intricately linked to the issue of cross-section prediction in the sense of addressing the question “What explains the cross section of expected returns?” There is vast literature on this topic. There are state-of-the-art methods used to forecast the cross section of stock returns with firm characteristics predictors, and the same methods can be applied to other asset classes, such as corporate bonds and foreign exchange rates, and to managed portfolios such mutual and hedge funds.
First, there are the traditional ordinary least squares and weighted least squares methods, as well as the recently developed various machine learning approaches such as neutral networks and genetic programming. These are the main methods used today in applications. There are three measures that assess how the various methods perform. The first is the Sharpe ratio of a long–short portfolio that longs the assets with the highest predicted return and shorts those with the lowest. This measure provides the economic value for one method versus another. The second measure is an out-of-sample
R
2
that evaluates how the forecasts perform relative to a natural benchmark that is the cross-section mean. This is important as any method that fails to outperform the benchmark is questionable. The third measure is how well the predicted returns explain the realized ones. This provides an overall error assessment cross all the stocks.
Factor models are another tool used to understand cross-section predictability. This sheds light on whether the predictability is due to mispricing or risk exposure. There are three ways to consider these models: First, we can consider how to test traditional factor models and estimate the associated risk premia, where the factors are specified ex ante. Second, we can analyze similar problems for latent factor models. Finally, going beyond the traditional setup, we can consider recent studies on asset-specific risks. This analysis provides the framework to understand the economic driving forces of predictability.
Article
Asset Pricing: Time-Series Predictability
David E. Rapach and Guofu Zhou
Asset returns change with fundamentals and other factors, such as technical information and sentiment over time. In modeling time-varying expected returns, this article focuses on the out-of-sample predictability of the aggregate stock market return via extensions of the conventional predictive regression approach.
The extensions are designed to improve out-of-sample performance in realistic environments characterized by large information sets and noisy data. Large information sets are relevant because there are a plethora of plausible stock return predictors. The information sets include variables typically associated with a rational time-varying market risk premium, as well as variables more likely to reflect market inefficiencies resulting from behavioral influences and information frictions. Noisy data stem from the intrinsically large unpredictable component in stock returns. When forecasting with large information sets and noisy data, it is vital to employ methods that incorporate the relevant information in the large set of predictors in a manner that guards against overfitting the data.
Methods that improve out-of-sample market return prediction include forecast combination, principal component regression, partial least squares, the LASSO and elastic net from machine learning, and a newly developed C-ENet approach that relies on the elastic net to refine the simple combination forecast. Employing these methods, a number of studies provide statistically and economically significant evidence that the aggregate market return is predictable on an out-of-sample basis. Out-of-sample market return predictability based on a rich set of predictors thus appears to be a well-established empirical result in asset pricing.
Article
Behavioral and Social Corporate Finance
Henrik Cronqvist and Désirée-Jessica Pély
Corporate finance is about understanding the determinants and consequences of the investment and financing policies of corporations. In a standard neoclassical profit maximization framework, rational agents, that is, managers, make corporate finance decisions on behalf of rational principals, that is, shareholders. Over the past two decades, there has been a rapidly growing interest in augmenting standard finance frameworks with novel insights from cognitive psychology, and more recently, social psychology and sociology. This emerging subfield in finance research has been dubbed behavioral corporate finance, which differentiates between rational and behavioral agents and principals.
The presence of behavioral shareholders, that is, principals, may lead to market timing and catering behavior by rational managers. Such managers will opportunistically time the market and exploit mispricing by investing capital, issuing securities, or borrowing debt when costs of capital are low and shunning equity, divesting assets, repurchasing securities, and paying back debt when costs of capital are high. Rational managers will also incite mispricing, for example, cater to non-standard preferences of shareholders through earnings management or by transitioning their firms into an in-fashion category to boost the stock’s price.
The interaction of behavioral managers, that is, agents, with rational shareholders can also lead to distortions in corporate decision making. For example, managers may perceive fundamental values differently and systematically diverge from optimal decisions. Several personal traits, for example, overconfidence or narcissism, and environmental factors, for example, fatal natural disasters, shape behavioral managers’ preferences and beliefs, short or long term. These factors may bias the value perception by managers and thus lead to inferior decision making.
An extension of behavioral corporate finance is social corporate finance, where agents and principals do not make decisions in a vacuum but rather are embedded in a dynamic social environment. Since managers and shareholders take a social position within and across markets, social psychology and sociology can be useful to understand how social traits, states, and activities shape corporate decision making if an individual’s psychology is not directly observable.
Article
Behavioral Corporate Finance: The Life Cycle of a CEO Career
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.
Article
Bid-Ask Spread: Theory and Empirical Evidence
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.
Article
Central Bank Monetary Policy and Consumer Credit Markets
Xudong An, Larry Cordell, Raluca A. Roman, and Calvin Zhang
Central banks around the world use monetary policy tools to promote economic growth and stability; for example, in the United States, the Federal Reserve (Fed) uses federal funds rate adjustments, quantitative easing (QE) or tightening, forward guidance, and other tools “to promote effectively the goals of maximum employment, stable prices, and moderate long-term interest rates.” Changes in monetary policy affect both businesses and consumers. For consumers, changes in monetary policy affect bank credit supply, refinancing activity, and home purchases, which in turn affect household consumption and thus economic growth and price stability. The U.S. Fed rate cuts and QE programs during COVID-19 led to historically low interest rates, which spurred a huge wave of refinancings. However, the pass-through of rate savings in the mortgage market declined during the pandemic. The weaker pass-through can be linked to the extraordinary growth of shadow bank mortgage lenders during the COVID-19 pandemic: Shadow bank mortgage lenders charged mortgage borrowers higher rates and fees; therefore, a higher market share of them means a smaller overall pass-through of rate savings to mortgage borrowers. It is important to note that these shadow banks did provide convenience to consumers, and they originated loans faster than banks. The convenience and speed could be valuable to borrowers and important in transmitting monetary policy in a timelier way, especially during a crisis.
Article
Consumer Debt and Default: A Macro Perspective
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.
Article
Corporate Credit Derivatives
George Batta and Fan Yu
Corporate credit derivatives are over-the-counter (OTC) contracts whose payoffs are determined by a single corporate credit event or a portfolio of such events. Credit derivatives became popular in the late 1990s and early 2000s as a way for financial institutions to reduce their regulatory capital requirement, and early research treated them as redundant securities whose pricing is tied to the underlying corporate bonds and equities, with liquidity and counterparty risk factors playing supplementary roles. Research in the 2010s and beyond, however, increasingly focused on the effects of market frictions on the pricing of CDSs, how CDS trading has impacted corporate behaviors and outcomes as well as the price efficiency and liquidity of other related markets, and the microstructure of the CDS market itself. This was made possible by the availability of market statistics and more granular trade and quote data as a result of the broad movement of the OTC derivatives market toward central clearing.
Article
Corporate Governance Implications of the Growth in Indexing
Alon Brav, Andrey Malenko, and Nadya Malenko
Passively managed (index) funds have grown to become among the largest shareholders in many publicly traded companies. Their large ownership stakes and voting power have attracted the attention of market participants, academics, and regulators and have sparked an active debate about their corporate governance role. While many studies explore the governance implications of passive fund growth, they often come to conflicting conclusions.
To understand how the growth in indexing can affect governance, it is important to understand fund managers’ incentives to be engaged shareholders. These incentives depend on fund managers’ compensation contracts, ownership stakes, assets under management, and costs of engagement. Major passive asset managers, such as the Big Three (BlackRock, State Street, and Vanguard), may have incentives to be engaged even though they track the indices and their engagement efforts benefit all other funds that track the same indices. This is because such funds’ substantial ownership stakes in multiple firms can both increase the effectiveness of their engagement and create relatively large financial benefits from engagement despite the low fees they collect. However, there is a difference between large and small index fund families: the incentives of the latter are likely to be substantially smaller, and the empirical evidence appears to be consistent with this distinction.
The governance effects of passive fund growth also depend on where flows to passive funds come from, which investors are replaced by passive funds in firms’ ownership structures, how passive funds interact with other shareholders, and how their growth affects other asset managers’ compensation structures. Considering such aggregate effects and interactions can help reconcile the seemingly conflicting findings in the empirical literature. It also suggests that policymakers should be careful in using the existing studies to understand the aggregate governance effects of passive fund growth over the past decades.
Overall, the literature has made important progress in understanding and quantifying passive funds’ incentives to engage, their monitoring activities and voting practices, and their interactions with other shareholders. Based on the findings in the literature, there is yet no clear answer to whether passive fund growth has been beneficial or detrimental for governance, and there are many open questions remaining. These open questions suggest several important directions for future research in this area.
