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Article

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

Sumit Agarwal, Jian Zhang, and Xin Zou

Households are one of the key participants in the economy. Households provide land, labor, and capital to the external economy, in exchange for incomes including rents, wages, interests, and profits; the incomes are then utilized to buy goods and services from the external economy again, rendering an income flow circular. This suggests that households make complicated decisions in almost all areas of economics and finance, which constitute the scope of household finance studies. Specifically, household finance encompasses the following three topics: (a) how households make financial decisions regarding saving, consumption, investment, housing, and borrowing; (b) how organizations provide goods and services to satisfy these financial functions; and (c) how external interventions (from firms, governments, or other parties) such as financial technology (FinTech) affect these financial activities. Despite the important stake in the financial system, it was not until recent decades that household finance became a prosperous research field. For many years, financial studies mostly focused on financial markets, nonfinancial corporations, and financial institutions and intermediaries, with households being delivered as a simplified representative agent. Classical economic models do consider households in the economic system, but mainly focus on their functions in the income flow circular (i.e., the saving or demand for products). Recently, the household finance field received more attention and has produced a large strand of theoretical and empirical studies due to the incremental participation of households in financial markets, the observed consequences of events such as financial crises, the availability of more detailed high-quality granular data, and the regulations and interventions induced by technology innovation.

Article

William Megginson, Herber Farnsworth, and Bing (Violet) Xu

Defined as a single industrial sector, the global production, distribution, and consumption of energy is the world’s largest in terms of annual capital investment (US$1.83 trillion in 2019, the last prepandemic year for which full data are available) and the second largest nonfinancial industry in terms of sales revenue (US$4.51 trillion). Production and consumption of more than 100 million barrels of oil occurs each day—with 70% being traded across borders. Each of the world’s 7.5 billion citizens consumes an average of 3,181 kilowatt-hours per year, although per capita energy consumption varies enormously and is much higher in rich than in poor countries. Properly analyzing the financial economics of the global energy industry requires focusing on both the physical aspects of production and distribution—how, where, and with what type of fuel energy is produced and consumed—and the capital investment required to support each energy segment. The global energy “industry” can be broadly categorized into two main segments: (a) provision of fuels for transportation and production and (b) distribution of electricity for residential and industrial consumption. The fuels sector encompasses the production; processing; and distribution of crude oil and its refined products, mostly gasoline, kerosene (which becomes jet fuel), diesel, gas oil, and residual fuel oil. The electric power sector includes four related businesses: generation, transmission, distribution, and supply. Two imperatives drive the ongoing transformation of the global energy industry. These are (a) meeting rising demand due to population growth and rising wealth and (b) addressing climate change through greener energy policies and massive capital investments by corporations and governments. The pathway to decarbonizing electricity production and distribution by 2050 is fairly straightforward technologically; however, doing so will require both scientific innovations (particularly regarding scalable battery storage) and sustained multitrillion dollar annual investments for the next three decades. Decarbonizing transportation is a far more difficult and expensive proposition, which will require fundamental breakthroughs in multiple technologies, coupled with unusually farsighted policy action. Extant academic research already provides useful guidance for policymakers in many areas, but far more is required to help shape the future policy agenda.

Article

Rongbing Huang and Donghang Zhang

The number of initial public offerings (IPOs) in the United States has been much lower since 2000 than in the preceding two decades, although there was a surge in IPO activity in 2021. The Securities and Exchange Commission (SEC) has attempted to reduce the regulatory and cost burdens of going public. Important new developments in the U.S. IPO market include confidential filings, testing the waters, direct listings, and special purpose acquisition companies (SPACs). Related research sheds light on whether the new developments can help capital formation and lower the costs of going public. Also relevant are the motives for going public, new insights into IPO pricing, institutional investors’ pre-IPO investments, and the consequences of firms’ IPO decisions. In the U.S., the majority of IPOs have used the bookbuilding mechanism, which involves generating and recording investors’ buying interests. Different companies prefer different mechanisms for going public. Bookbuilding is valuable for companies that face uncertainty regarding investor demand. A private firm may prefer selling itself to a publicly traded acquiring firm over an IPO in order to expand more quickly by utilizing the acquirer’s capital and established platform. A high-growth firm may prefer a merger with a SPAC over a traditional bookbuilt IPO due to the ability to use solid forecasts to increase the stock’s valuation. And companies with strong brand recognition or easy-to-understand business models, but no immediate cash needs, may find a direct listing more attractive, especially when their insiders have large diversification or liquidity needs. The SEC and other regulatory agencies should embrace and enhance these alternative mechanisms. Economies-of-scope considerations, globalization, regulatory and disclosure requirements, and the relative costs of public versus private capital all play a part in firms’ decisions to go public. Careful examinations of the benefits and costs are still needed. Possible widespread use of confidential filings and testing-the-waters communications after the regulatory changes initiated by the 2012 JOBS Act can substantially influence information production, IPO decisions, and IPO pricing. Private companies have increasingly used direct listings and SPAC mergers to go public, and emerging evidence suggests these methods will continue to evolve. Several recent papers have examined IPO underpricing across countries, and their findings are consistent with both information-asymmetry-based explanations and those based on issuer–underwriter conflicts. Information asymmetry helps explain the 7% average IPO underpricing in the U.S. during the 1980s. However, explanations based on agency problems, underwriter power, and issuer complacency are instructive for understanding the average underpricing of over 18% for all IPOs since then or over 50% for large subsets that can be identified ex ante. Future research would be useful to shed light on the predictability and magnitude of IPO underpricing, as well as on the effects of regulations and issuer–underwriter conflicts on underpricing. Recent research shows that IPOs have far-reaching effects. They not only influence the financing and investment policies of the issuing companies, but also have spillover effects to other companies, local communities, and labor markets.

Article

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

Gianluca Cubadda and Alain Hecq

Reduced rank regression (RRR) has been extensively employed for modelling economic and financial time series. The main goals of RRR are to specify and estimate models that are capable of reproducing the presence of common dynamics among variables such as the serial correlation common feature and the multivariate autoregressive index models. Although cointegration analysis is likely the most prominent example of the use of RRR in econometrics, a large body of research is aimed at detecting and modelling co-movements in time series that are stationary or that have been stationarized after proper transformations. The motivations for the use of RRR in time series econometrics include dimension reductions, which simplify complex dynamics and thus make interpretations easier, as well as the pursuit of efficiency gains in both estimation and prediction. Via the final equation representation, RRR also makes the nexus between multivariate time series and parsimonious marginal ARIMA (autoregressive integrated moving average) models. RRR’s drawback, which is common to all of the dimension reduction techniques, is that the underlying restrictions may or may not be present in the data.

Article

International transactions are riskier than domestic transactions for several reasons, including, but not limited to, geographical distance, longer shipping times, greater informational frictions, contract enforcement, and dispute resolution problems. Such risks stem, fundamentally, from a timing mismatch between payment and delivery in business transactions. Trade finance plays a critical role in bridging the gap, thereby overcoming greater risks inherent in international trade. It is thus even described as the lifeline of international trade, because more than 90% of international transactions involve some form of credit, insurance, or guarantee. Despite its importance in international trade, however, it was not until the great trade collapse in 2008–2009 that trade finance came to the attention of academic researchers. An emerging literature on trade finance has contributed to providing answers to questions such as: Who is responsible for financing transactions, and, hence, who would need liquidity support most to sustain international trade? This is particularly relevant in developing countries, where the lack of trade finance is often identified as the main hindrance to trade, and in times of financial crisis, when the overall drying up of trade finance could lead to a global collapse in trade.

Article

The interrelationships between upstream supplier firms and downstream customer firms—popularly referred to as supply-chain relationships—constitute one of the most important linkages in the economy. Suppliers not only provide production inputs for their customers but, increasingly, also engage in R&D and innovation activity that is beneficial to the customers. Yet, the high degree of relationship specificity that such activities involve, and the difficulty of writing complete contracts, expose suppliers to potential hold-up problems. Mechanisms that mitigate opportunism have implications for the origins of such relationships, firm boundary, and organizational structure. Smaller supplier firms benefit from relationships with large customer firms in many ways, such as knowledge sharing, operational efficiency, insulation from competition, and reputation in capital markets. However, customer bargaining power, undiversified customer base, and innovation strategy also expose suppliers to disruption risk. Relationship specificity of investment, customer bargaining power, and customer concentration associated with a less diversified customer base have important consequences for financing decisions of suppliers and customers, such as capital structure choice and the provision and role of trade credit. Changes in the risk of disruption (e.g., bankruptcy filings, takeover activity, and credit market shocks) have spillover effects along the supply chain. The correlation of economic fundamentals of suppliers and customers and the co-attention that they receive from market participants translate to return predictability (with implications for trading strategies), information diffusion along the supply chain, and stock-price informativeness of supply-chain partners.

Article

Rajesh P. Narayanan and Jonathan Pritchett

Financial economics reveals that slaves were profitable investments and that the rate of return from owning slaves was at least as high as the return on comparable investments. The profitability of slavery depended on both the productivity and the market valuation of slaves. Owners increased the productivity of slaves by developing better strains of cotton, employing more efficient systems of production (gang labor), and using force and coercion (whippings). Efficient markets facilitated the interregional transfer of labor, and selective sales devastated slave families. Market studies show that slave prices reflected the capitalized value of labor and that they varied based on labor productivity. The profitability of slaves and the availability of efficient markets made slaves attractive investment vehicles for storing wealth. Their attractiveness as investments, however, may have had some other costs. Several studies argue and provide evidence that investment in slaves supplanted investment in other forms of physical and human capital, much to the detriment of southern industrialization and development. Besides serving as investment vehicles, slaves also facilitated financing. A growing body of work provides evidence that slaves were pledged as collateral to obtain credit.

Article

Facundo Abraham, Juan J. Cortina, and Sergio L. Schmukler

There has been substantial debate about the expansion of global non-financial corporate debt after the global financial crisis (GFC) of 2008–2009. But the main facts and policy challenges discussed in the literature are yet to be uncovered and summarized. Understanding the trends and issues can help readers gauge how large the growth of this type of financing has been, as well as the risks that more non-financial corporate debt might entail. Non-financial corporate debt steadily increased after the GFC, especially in emerging economies. Between 2008 and 2018, corporate debt rose from 56 to 96% of gross domestic product (GDP) in emerging economies whereas it grew at the same rate as GDP in developed economies. Non-financial corporate debt after the crisis was mainly issued through bond markets, and its growth can be largely attributed to accommodative monetary policies in developed economies. Although the growth in debt financing has some positive aspects for emerging market firms in terms of expanding financing and diversifying financing sources, it also amplified solvency risks and firms’ exposure to changes in market conditions. Slower global economic growth worldwide as a result of the COVID-19 pandemic could impose significant costs to emerging market firms that increased reliance on debt financing. Policy makers in emerging economies face challenges to mitigate overall risks and to contain corporate vulnerability in the non-financial sector. Because capital markets have an important role in the expansion of financial activity and are not as regulated as banks, policy makers have limited tools to alleviate the risks of growing non-financial corporate debt.