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Many models of investor behavior predict that investors prefer assets that they believe to have positively skewed return distributions. We elicit detailed return expectations for a broad index fund and a single stock in a representative sample of the Dutch population. The data show substantial heterogeneity in individuals’ skewness expectations of which only very little is captured by sociodemographics. Across assets, most respondents expect a higher variance and skewness for the individual stock compared to the index fund. Portfolio allocations increase with the skewness of respondents’ return expectations for the respective asset, controlling for other moments of a respondent’s expectations.

Industries differ in the extent to which they can offshore their production. I document that industries with low offshoring potential carry a sizeable risk premium compared with industries with high offshoring potential, suggesting that the possibility to offshore affects industry risk. A two-country dynamic trade model in which offshoring allows firms to secure market share against foreign competitors can rationalize the empirical findings. This paper was accepted by Gustavo Manso, finance. Supplemental Material: The online appendix and data files available at https://doi.org/10.1287/mnsc.2022.4393 .

We classify asset pricing anomalies into those exacerbating mispricing (build-up anomalies) and those resolving it (resolution anomalies). To this end, we estimate the dynamics of price wedges for well-known anomaly portfolios in the factor zoo and map them to firm-level mispricings. We find that several prominent anomalies like momentum and profitability further dislocate prices. Moreover, our estimates yield a novel decomposition of Tobin's q, revealing that q's mispricing component has roughly equal explanatory power for firm investment as its informationally efficient counterpart. Our results suggest that financial intermediaries chasing build-up anomalies negatively affect price efficiency and associated real capital allocation.

We study the equilibrium implications of a multi-asset economy in which asset managers are subject to different benchmarks, and demonstrate how heterogeneous benchmarking generates a mechanism through which fundamental shocks propagate across assets. Fluctuations in asset managers' capital invested for benchmarking purposes, scaled by the size of the economy, induce price pressure that can result in negative spillovers across asset returns. We highlight the economic significance of these benchmarking-induced spillovers by analyzing shock elasticities and cross-elasticities of price-dividend ratios, and characterize a rich structure of asset price comovements within and across benchmarks. Heterogeneous benchmarking also induces return predictability, generating both reversal and momentum.

This publication offers a comprehensive survey on the topic of Low-Power Wide Area Networks (LPWANs). We discuss the scope of LPWAN connectivity and present numerous well-established technologies that can offer such a service. From this presentation, LoRa/LoRaWAN systems emerge as one of the dominant solutions, and hence we offer details about end-to-end LoRa/LoRaWAN-enabled networking. As an area being in its infancy, LPWANs in general and LoRa/LoRaWAN in specific undergo extensive research. We shed light in key contributions, whereas we also provide our findings regarding the main research focus and needs. Simulation studies appear to be a popular choice among researchers, whereas real system implementations are constantly emerging thanks to low deployment and operational cost. For the former, we offer a comparative overview of existing simulators, whereas for the latter we discuss key considerations about the server-side part of a deployed system, which is usually supported by cloud infrastructure. Finally, LPWANs are systems that experience intense data generation, and thus we deliver a presentation of data-related operations which take place in such systems. This presentation revealed a major vertical market that is expected to be served by LPWANs: the Industiral IoT; which embodies concepts like Asset Administration Shell and Digital Twins.

Revaluations of industry peers around horizontal acquisitions are negative when targets are private, but positive when they are public. We posit this “revaluation spread” arises because acquiring managers favor private targets when public firms are overvalued. Targets’ ownership status thus conveys information about industry assets’ misvaluation and triggers predictable revaluations. Supporting this idea, private acquisitions occur when private targets appear “cheaper” than public firms based on valuation multiples or the trading activity of industry insiders. The revaluation spread varies with overall market misvaluation, predicts future industry returns, and is unrelated to peers’ and industries’ fundamentals.

Stable and adequate housing is critical in the midst of a pandemic; without housing, individuals and families cannot shelter in place to prevent the spread of disease. Understanding and combating housing hardships in vulnerable populations is therefore essential to a sound public health response. This study aims to explore the pandemic’s disproportionate impacts on housing-related hardships across racial/ethnic groups in the United States as well as the extent to which these disparities are mediated by households’ broader economic circumstances; namely, their pre-pandemic liquid asset levels and the experience of COVID-19-related job and income losses. Using a national survey of over 4,000 households, we find that Black and Hispanic respondents were more vulnerable to housing-related hardships during the pandemic than White respondents. These impacts were particularly pronounced in lower-income minority households. For Black respondents, who had much lower levels of pre-pandemic liquid assets than other groups, liquid assets acted as a strong mediator of housing hardship disparities between White and Black respondents. On the other hand, neither liquid asset amounts before the pandemic nor employment shocks during the pandemic explain the pandemic’s disproportionate impacts on Hispanic families. Our findings imply that housing became less stable for racial/ethnic minority groups as a result of the pandemic. In particular, the observed disparities, as well as the mechanisms driving them, demonstrate the necessity of policies and practices that target support to these economically marginalized groups.

While the literature has already widely documented the effects of macroeconomic news announcements on asset prices, as well as their asymmetric impact during good and bad times, we focus on the reaction to news based on the description of the state of the economy as painted by the Federal Open Market Committee (FOMC) statements. We develop a novel FOMC sentiment index using textual analysis techniques, and find that news has a bigger (smaller) effect on equity prices during bad (good) times as described by the FOMC sentiment index. Our analysis suggests that the FOMC sentiment index offers a reading on current and future macroeconomic conditions that will affect the probability of a change in interest rates, and the reaction of equity prices to news depends on the FOMC sentiment index which is one of the best predictors of this probability.

Sentiment should exhibit its strongest effects on asset prices at times when valuations are most subjective. Consistent with this hypothesis, we show that a one-standard-deviation increase in aggregate uncertainty amplifies the predictive ability of sentiment for market returns by two to four times relative to when uncertainty is at its mean. We find similar evidence for the cross-section of returns; the predictive ability of sentiment for returns of test assets expected to be most sensitive to sentiment and for anomaly returns is substantially larger in times of higher uncertainty. The results hold for both daily and monthly proxies for sentiment and for various proxies for uncertainty. The evidence sheds light on one component of time-series variation in mispricing and suggests that the effects of sentiment are greatest in times of higher uncertainty.

Poor-quality remains a pervasive challenge for the delivery of large-scale transport infrastructure projects. Typically, it manifests as nonconformance and often requires rework. If, however, a nonconformance goes unidentified, then an asset's integrity can be jeopardized and put people's lives at risk. Hence, governments should shoulder the responsibility for quality. In practice, however, there has been a proclivity for governments to place fixed-price contracts and high-level quality risks on contractors. Yet, there is a dearth of research that has addressed the issue of poor-quality within large-scale transport infrastructure projects. In this article, we attempt to fill this gap and adopt an illustrative case-study approach to garner an understanding as to why poor-quality manifests during the construction of large-scale transport projects. We examine the issue of poor-quality in three high-profile rail transit projects using the gray literature: 1) Delhi Airport Metro Express (India); 2) Honolulu Rail Transit (United States); and 3) Sydney Skytrain (Australia). Then, drawing on empirical observations from two road projects, a highway corridor and a busway, we bring to the fore the poor-quality issue that pervades practice. To curb poor-quality, we suggest that the public and private sectors work collaboratively and share risks to ensure the benefits and value of an asset can be realized. To facilitate the process of collaboration and sharing of quality risks, we propose a Standard Life-cycle Quality Assessment system that can be used to ensure projects conform to standards and requirements.

This paper estimates a DSGE model to explore how the economy is affected by financial and non-financial shocks. Bayesian methods and data from the US corroborate the results. This paper first illustrates how the financial sector is not only a transfer channel for exogenous shocks to the real economy, but also a contributor to business cycles through banks’ asset reallocation channel and the quality of capital adequacy constraint. The paper then shows that the contribution of financial and non-financial shocks has varied before, during and after the recent financial crisis; housing demand and asset price shocks are the main contributors and the credit shocks are the most persistent. In addition, the paper presents the application of macroprudential tools, along with their impact on the economy in general, and on welfare in particular. The findings illustrate that the tools which control household borrowing ability, such as loan-to-value or debt-to-income ratios, do not impact welfare significantly. However, the impact of policies on the leveraged sector is substantial. The paper proposes macroprudential policies that allow policy makers to stabilize the economy without changing welfare. Such policies, however, should be timely, targeted and temporary, otherwise they may cause disruptions.

We estimate a structural model using data from a novel experiment investigating how investors’ preferred frequency of portfolio evaluations balance the opposing effects of ambiguity and loss aversion. Investors in the experiment face initial ambiguity concerning return distributions for an asset. They observe draws from the true return distribution of the asset, allowing them to reduce their ambiguity through time. We exploit portfolio choices and stated beliefs over possible return distributions to estimate preferences and ambiguity updating rules. We find that 70% of investors would opt for a high frequency of portfolio evaluations, reflecting the dominating effect of ambiguity aversion over loss aversion.

In this paper, we document the importance of memory in machine learning (ML)-based models relying on firm characteristics for asset pricing. We come to three empirical conclusions. First, the pure out-of-sample fit of the models is often poor: we find that most R^2 measures are negative, especially when training samples are short. Second, we show that poor fit does not necessarily matter from an investment standpoint: what actually counts are measures of cross-sectional accuracy, which are seldom reported in the literature. Third, memory is key. The accuracy of models is maximal when both labels and features are highly autocorrelated. Relatedly, we show that investments are the most profitable when they are based on models driven by strong persistence. Average realized returns are the highest when the size of training samples is large and when the horizon of the predicted variable is also long.

This study generalizes a standard heterogeneous firm model with endogenous entry and exit by allowing for asset bubbles. We highlight the selection effect of bubbles that incentivizes low-productivity firms to enter or remain in the market. We show that a rise in the aggregate bubble can boost real economic activities by increasing the number of entrants and decreasing the number of exits. Using firm-level data, we find that an overvalued firm is less likely to exit the market, which supports the novel transmission channel of bubbles. Moreover, we show that the model-implied impulse responses are consistent with those identified in the data. Finally, we demonstrate that a model without bubbles fails to reproduce our empirical findings.

We study the interaction between noisy demand and skewed asset payoffs. In our model, price as a function of quantities is convex in a neighborhood around zero if and only if skewness is positive. The combination of convexity and noise produces the idiosyncratic skewness effect, a documented negative relationship between an asset’s idiosyncratic skewness and its expected return. We further offer an explanation for the idiosyncratic volatility puzzle. Finally, our theory predicts that higher idiosyncratic skewness strengthens the idiosyncratic volatility effect (and vice versa). We find support for this prediction in the cross-section of stock returns.

Bank-created money, shadow-bank money, and Treasury bonds all satisfy investors’ demand for liquidity. We measure the quantity of these forms of liquidity and their corresponding liquidity premium in a sample from 1934 to 2016, estimating the substitutability of these assets and the liquidity per unit delivered by each asset. Treasuries and bank transaction deposits are imperfect substitutes, in contrast to perfect substitutes found by Nagel (2016). Bank and nonbank non-transaction deposits are closer substitutes for Treasuries. Our empirical results inform theories of the monetary transmission mechanism running through shifts in asset supplies and models of the coexistence of the shadow banking and regulated banking system. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

This study examines whether bank lenders have different reactions to various types of real transaction management (RTM) by borrowing firms. Drawing upon Jensen and Meckling’s asset substitution theory (1976) as well as banks’ unique payoff functions and monitoring incentives, we predict and find that banks offer more favorable loan terms (i.e., lower interest spreads, reduced probability for required collateral, reduced number and strictness of financial covenants) for firms displaying higher levels of RTM through reductions in R&D expenditures. In contrast, banks offer less favorable loan terms for firms displaying higher levels of RTM through aggressive price discounts, overproduction of inventories, and abnormal reduction of SG&A expenses. Additional analysis reveals that the favorable effect on loan pricing for R&D outlay reductions is smaller for longer-maturity loans. Overall, our study suggests that banks do not negatively view all forms of RTM.

Consumers are increasingly concerned about pollution and are, at least in part, making their purchases accordingly. In this paper, we consider a supply chain where demand depends on the manufacturer’s environmental reputation, an asset that can only be built over time. We assume that this reputation depends on information emanating from the manufacturer itself about its environmental actions and on its objective environmental record when compared to an industry standard. In this paper, we look at the impact of consumers’ environmental concern on the strategies and outcomes of a supply chain. As the retailer has a direct influence on sales through its pricing policy, it seem more realistic to take a supply chain perspective instead of a single-firm one. Therefore, the contractual arrangement in the supply chain becomes a relevant issue. We model the supply chain as a two-player dynamic game and characterize and compare the equilibrium strategies and outcomes under wholesale-price and revenue-sharing contracts. We analyze the impact of the sharing parameter, the industry pollution standard and consumers’ degree of environmental sensitivity on the results, and in particular, on the region in the parameter space where a revenue-sharing contract is Pareto improving with respect to a wholesale-price contract. Also, we examine whether it is in the best interest of the retailer to behave myopically when setting its price.

Relationally integrated value networks for total asset management (Rivans-Tam) have been recommended by scholars as a vehicle to integrate the stakeholders of project management (PM) and of asset management (Am), covering the entire life cycle of a built asset. This paper presents a scoping and exploratory pilot study to test a further proposition that expands the basic Rivans-Tam concept to encompass portfolio management, to benefit asset-intensive organisations holding a large portfolio of ongoing construction projects and operational built assets. Accordingly, it establishes a strong theoretical underpinning based on previously validated research exercises on Rivans-Tam and previous applications of social network analysis (SNA) for Rivans-Tam and developed from in-depth high-level semi-structured interviews with six industry professionals with relevant expertise and experience. The outcomes broaden the theoretical base of Rivans-Tam, also highlighting its practical potential to empower total portfolio management (TPM). This TPM perspective expands the hitherto ‘single asset’-focused Rivans-Tam concept and related SNA methods to a wider portfolio of both ongoing construction projects and operational built assets. Moreover, the consolidated findings contribute substantially to practice by providing a basis to overcome the main challenges and unlock the potential synergies from ‘overall best value’-focused relational integration of hitherto disconnected diverse stakeholders.

To explore how speculative trading influences prices in financial markets, we conduct a laboratory market experiment with speculating investors (who do not collect dividends and trade only for capital gains) and dividend-collecting investors. Moreover, we operate markets at two different levels of money supply. We find that in phases with only speculating investors present (i) price deviations from fundamentals are larger; (ii) prices are more volatile; (iii) mispricing increases with the number of transfers until maturity; and (iv) speculative trading pushes prices upward (downward) when the supply of money is high (low). These results suggest that controlling the money supply can help to stabilize asset prices.

Blockholder monitoring is central to corporate governance, but blockholders large enough to exercise significant unilateral influence are rare. Mechanisms that enable moderately sized blockholders to exert collective influence are therefore important. Existing theory suggests that engagement by moderately sized blockholders is unlikely, especially when the blocks are held by delegated asset managers who have limited skin in the game. We present a model in which multiple delegated blockholders engage target management in parallel, that is, “wolf pack activism.” Delegation reduces skin in the game, which decreases incentives for engagement. However, it also induces competition over investor capital (i.e., competition for flow). We show that this increases engagement incentives and helps ameliorate the problem of insufficient engagement, although it can also foster excess engagement. Under competition for flow, the total amount of capital seeking skilled activist managers is relevant to engagement incentives, which helps to predict when and where wolf packs arise. Flow incentives are particularly valuable in incentivizing engagement by packs with smaller members. This paper was accepted by Gustavo Manso, finance.

We apply advances in analysis of mixed frequency and sparse data methods to estimate ``unsmoothed'' private equity (PE) Net Asset Values (NAVs) at the weekly frequency for individual funds. Using simulations and a large sample of buyout and venture funds, we show that our method yields superior estimates of NAVs relative to a simple approach based on comparable public asset and as-reported NAVs. The market beta of an average buyout [venture] fund, around 1.05 [1.20], is notably lower than previous studies suggest. The overall risk of a median buyout [venture] fund is 34% [40%] per year if measured in terms of the standard deviation of total returns, an increase of 10 [13] percentage points relative to the series based on as-reported NAVs. However, we find significant variation in systematic and idiosyncratic risk within and across vintages, and the risk-return profile based on the samples from the 1990s is not representative of currently operating funds. Our method easily accommodates additional data, such as individual holdings details, and related M&A activity. As an example application, we show how the cross-section of appraisal biases and nowcast errors evolved around the 2008 Financial crisis.

Prior research documents that asset growth is negatively associated with future firm performance. In contrast, we show that growth financed by product market stakeholders (i.e., “operating growth”) is positively associated with future firm performance. Investors and security analysts underestimate the positive effects of operating growth on future performance, resulting in return predictability and overly pessimistic earnings forecasts for firms with high operating growth. Future stock returns largely concentrate around subsequent earnings announcements with declining magnitudes, consistent with the error-in-expectation explanation. Results from cross-sectional tests further support the hypothesis that operating growth signals high future performance but investors underreact to it.

We exploit information in sovereign CDS spreads and the international trade network to provide causal evidence of the propagation of global economic shocks. We show that trade links are an important source of shock transmission using the natural experiments of the Japanese tsunami and the COVID-19 lockdown in China. We then confirm more general and gradual information flows along the trade network by showing extensive country-level credit/equity cross-sectional return predictability. News about country fundamentals flows primarily from importers to exporters, depends on both direct and indirect links in the trade network, and is magnified by the exporting country's financial vulnerability.

This paper focuses on the variance risk premium of project returns and its impacts on the real options to expand and to abandon a project. Variance risk premium measures the hedging costs associated with the time-varying variance of the returns. We find that the variance risk premium increases the real option value to expand and to abandon a project. Hence, the variance premium delays both abandoning and expanding the project. Our setting also enables us to study the multi-stage decisions with real options under the variance risk premium. For example, we consider when there is an abandonment or a re-expansion add-on attached to an expansion project, which is a reminiscence of how our framework facilitates modeling compound real option structures in the presence of the variance risk premium. We present two applications of our models that involve Ford Motor company and battery storage investments for electricity distribution networks. These cases demonstrate how the proposed framework in this paper can be applied to real world decisions, and, moreover, they establish a baseline for how much decisions can be improved when variance risk premium is included in the modeling.

In a liability problem, the asset value of aninsolvent firm must be distributed among the creditors and the firm itself, when the firm has some freedom in negotiating with the creditors. We model the negotiations using cooperative game theory and analyze the Shapley value to resolve such liability problems. We establish three main monotonicity properties of the Shapley value. First, creditors can only benefit from the increase in their claims or of the asset value. Second, the firm can only benefit from the increase of a claim but can end up with more or with less if the asset value increases, depending on the configuration of small and large liabilities. Third, creditors with larger claims benefit more from the increase of the asset value.Even though liability games are constant-sum games and we show that the Shapley value can be calculated directly from a liability problem, we prove that calculating the Shapley payoff to the firm is NP-hard.

Investment funds that claim to focus on socially responsible stocks have proliferated in recent times. In this paper, we verify whether ESG mutual funds actually invest in firms that have stakeholder-friendly track records. Using a comprehensive sample of self-labelled ESG mutual funds (as identified by Morningstar) in the United States from 2010 to 2018, we find that these funds hold portfolio firms with worse track records for compliance with labor and environmental laws, relative to portfolio firms held by non-ESG funds managed by the same financial institutions in the same years. Relative to other funds offered by the same asset managers in the same years, ESG funds hold stocks that are more likely to voluntarily disclose carbon emissions performance but also stocks with higher carbon emissions per unit of revenue. Despite these findings, ESG funds hold portfolio firms with higher average ESG scores. We show that ESG scores are correlated with the quantity of voluntary ESG-related disclosures but not with firms’ compliance records or actual levels of carbon emissions. Finally, ESG funds appear to underperform financially relative to other funds within the same asset manager and year, and to charge higher fees. Our findings suggest that socially responsible funds do not appear to follow through on proclamations of concerns for stakeholders.

This paper investigates the effects of economic policy uncertainty (EPU) and monetary policy uncertainty (MPU) in the US and China on oil-stock and gold-stock correlations. A quantile regression approach is employed to analyze the heterogeneous impacts under different market correlation regimes. Our findings suggest that the "US impact" prevails across all market correlations in the sample, while "China impact" is found for oil-stock correlations. Furthermore, the impacts of EPU and MPU on correlations of different asset pairs exhibit heterogeneity in direction and in different correlation regimes. EPU and MPU have homogenously negative effects on gold-stock correlations across various correlation regimes. Differently, in terms of oil-stock correlations, they exhibit more significant and stronger positive impacts in the medium and high correlation regime than in the low correlation regime. Gold can provide a better diversification for stock market risks than crude oil during the period of high level of economic uncertainty.

Well known oil recovery factor estimation techniques such as analogy, volumetric calculations, material balance, decline curve analysis, hydrodynamic simulations have certain limitations. Those techniques are time-consuming, require specific data and expert knowledge. Besides, though uncertainty estimation is highly desirable for this problem, the methods above do not include this by default. In this work, we present a data-driven technique for oil recovery factor (limited to water flooding) estimation using reservoir parameters and representative statistics. We apply advanced machine learning methods to historical worldwide oilfields datasets (more than 2000 oil reservoirs). The data-driven model might be used as a general tool for rapid and completely objective estimation of the oil recovery factor. In addition, it includes the ability to work with partial input data and to estimate the prediction interval of the oil recovery factor. We perform the evaluation in terms of accuracy and prediction intervals coverage for several tree-based machine learning techniques in application to the following two cases: (1) using parameters only related to geometry, geology, transport, storage and fluid properties, (2) using an extended set of parameters including development and production data. For both cases model proved itself to be robust and reliable. We conclude that the proposed data-driven approach overcomes several limitations of the traditional methods and is suitable for rapid, reliable and objective estimation of oil recovery factor for hydrocarbon reservoir.

We develop the construct of global dynamic managerial capability (GDMC) and identify its underlying sources: (1) international human capital, (2) international social capital, and (3) international managerial cognitions. Consistent with dynamic capabilities view and upper echelons theory, we suggest that GDMC leads to global asset orchestration, which in turn, results in superior company performance in a two-stage process. First, GDMC leads companies to adopt global strategies that spread the risk of internationalizations across different entry modes and geographic regions. Second, the (re)configuration of global assets positively influences subsequent firm performance and thus mediates the relationship between GDMC and performance. Recognizing the triad of factors that form global dynamic managerial capability is crucial when selecting future managers of multinational corporations. Hence, firms with increasing international exposure – both domestically via foreign competition and internationally via foreign market presence – may seek to compose their upper echelons with a suitable combination of international human and social capital as well as internationally diverse cognitions in the pursuit of sustained competitive advantage.

How does the health of creditors affect the pass-through of monetary policy to households? In a financial crisis, asset losses among creditors can either dampen or amplify the effects of monetary policy on lending, depending on how these losses and policies interact with financial frictions. Frictions such as leverage constraints my hinder creditor responses, however easing may instead alleviate frictions that would otherwise constrain lending. Using data on the universe of US credit unions, I document that asset losses increase the sensitivity of consumer credit to monetary policy. Identification exploits plausibly exogenous variation in asset losses and high frequency identification of monetary policy shocks. I find that a one standard deviation asset loss increases the response of credit union lending to a 10 basis point fall in the two-year Treasury rate from a 0.86 to 1.15 percentage point increase. The estimates imply that constraints on monetary policy become more costly in a financial crisis characterized by creditor asset losses and that an additional benefit of monetary easing is that it weakens the causal, contractionary effect of asset losses.

COVID-19, the causative agent of which is a new type of coronavirus called SARS-CoV-2, has caused the most severe pandemic in the last 100 years. The condition is mainly respiratory, and up to 5% of patients develop critical illness, a situation that has put enormous pressure on the health systems of affected countries. A high demand for care has mainly been observed in intensive care units and critical care resources, which is why the need to redistribute resources in critical medicine emerged, with an emphasis on distributive justice, which establishes the provision of care to the largest number of people and saving the largest number of lives. One principle lies in allocating resources to patients with higher life expectancy. Mechanical ventilator has been assumed to be an indivisible asset; however, simultaneous mechanical ventilation to more than one patient with COVID-19 is technically possible. Ventilator sharing is not without risks, but the principles of beneficence, non-maleficence and justice prevail. According to distributive justice, being a divisible resource, mechanical ventilator can be shared; however, we should ask ourselves if this action is ethically correct.

Several recent papers introduce different mechanisms to explain why asset bubbles are observed in periods of larger growth. These papers share common assumptions, heterogeneity among traders and credit market imperfection , but differ in the role of the bubble, used to provide liquidities or as collateral in a borrowing constraint. In this paper, we introduce heterogeneous traders by considering an overlapping generations model with households living three periods. Young households cannot invest in capital, while adults have access to investment and face a borrowing constraint. Introducing bubbles in a quite general way, encompassing the different roles they have in the existing literature, we show that the bubble may enhance growth when the borrowing constraint is binding. More significantly, our results do not depend on the-liquidity or collateral-role attributed to the bubble. We finally extend our analysis to a stochas-tic bubble, which may burst with a positive probability. Because credit and bubble are no more perfectly substitutable assets, the liquidity and collateral roles of the bubble are not equivalent. Growth is larger when bubbles play the liquidity role, because the burst of a bubble used for liquidity is less damaging to agents who invest in capital.

The funding policy for defined benefit pension plans covering government employees represents an important decision for government entities sponsoring those plans. In recent years, a number of state and local governments have experienced extreme funding shortfalls (e.g., New Jersey, Illinois, and Detroit), raising concerns about whether government entities are contributing enough to their pensions. Governmental Accounting Standards Board Statements Number 67/68 (hereafter, “GASB 67/68”) fundamentally alter the financial reporting of pension liabilities, by (i) requiring pension liabilities to be estimated using a potentially lower discount rate (which increases estimated liabilities and any funding deficits), and (ii) mandating recognition of funding deficits (surpluses), which were previously only disclosed in footnotes, as a liability (asset) on governmental balance sheets. Although GASB 67/68 only changes financial reporting requirements and acknowledges specifically that funding decisions are outside its scope, we find, for a sample of 100 large state-administered plans, that governments increase pension contributions upon applying GASB 67/68, over time as well as compared to corporate pension plans as a control group in difference-in-differences tests. In cross-sectional tests within governmental plans, the funding response is primarily observed from governments expecting a larger dollar impact to applying GASB 67/68, and from governments facing more adverse economic or political consequences from financial statement recognition. Government plans that increase funding are also more likely to have passed benefit cuts, suggesting that taxpayers and public employees share the costs. Overall, these responses suggest that governmental entities are willing to take actions with cash flow consequences to avoid recognizing large liabilities on-balance sheet; purely accounting changes, therefore, can have “real” effects on governmental pension policy.

Using an asset pricing model of a multisector production economy including pandemic disaster, we explain the average stock price boom and significant cross-sectional variation of stock returns in the United States and Japan during the COVID-19 pandemic recession. We find that two features of the pandemic, namely ambiguity and sector-specific shocks, are critical determinants of the unusual asset price dynamics observed. Extending the model, we analyze the welfare effects of lockdown policy during pandemics for heterogeneous households. We theoretically show that enforcing a lockdown improves the welfare of asset holders and households working in sectors with positive sector-specific shocks. Consequently, a Pareto-optimal lockdown policy controls for the tightness of lockdown to maximize the welfare of households working in sectors with negative sector-specific shocks.

We study a two-agent equilibrium model with two goods where we interpret the agents as countries. We analyze the effect of an endogenous habit specification where each country benchmarks its consumption decision against the decision of the other country. We show that endogenous habits can generate high equity premia and low interest rates. Our framework allows to study setups where agents cooperate or do not cooperate. The optimality conditions of the non-cooperative setting are similar to models that consider agents with exogenous habit levels. Although the size of the equity premia and the risk-free rate are of the same order of magnitude in both settings, the consumption allocations are more even in the cooperative situation.

The purpose of this paper is to show a novel approach to automatically generating Probabilistic Causal Models (Bayesian Networks (BN)) by applying Natural Language Processing (NLP) techniques to a corpus of millions of digitally published news articles in which views by different authors are expressed on the future states of economic and financial variables, and geopolitical events. The BNs that we will show how to derive will represent the wisdom-of-the-crowds forward-looking point-in-time views on various variables of interest and their dependencies. These Bayesian Networks are likely to be of interest to asset managers and to economists who want to gain a better understanding of the current drivers of an economy based upon a rigorous probabilistic methodology. Additionally, in an asset allocation context, the BNs we derive can be fed to an optimization engine to construct a forward-looking optimal portfolio given the constraints of the asset manager (e.g., budget, short constraints etc.). We demonstrate various automatically derived Bayesian Networks in a financial context

Intertemporal choices are affected by both discount rate and utility curvature. We investigate how the two aspects of time preference are affected by the size of the total budget using an intertemporal allocation task. At the aggregate level as well as at the individual level, we find magnitude effects both on the discount rate and on intertemporal substitutability (i.e., utility curvature). Individuals are more patient when dealing with larger budgets and also regard larger budgets to be more fungible. The latter effect suggests that the degree of asset integration is increasing in the stake. Nederlandse Organisatie voor Wetenschappelijk Onderzoek http://dx.doi.org/10.13039/501100003246 Deutsche Forschungsgemeinschaft Humboldt-Universität zu Berlin (1034)

This paper uses risk-shifting models to analyze some potential policy responses to asset price booms and bubbles. We argue that the presence of risk shifting can generate many of the features of such booms and so is a reasonable framework to explore these issues. Our analysis offers several insights. First, we find that determining whether there is indeed a bubble in asset markets is unimportant, since risk-shifting leads to the same inefficiencies regardless of whether it gives rise to a bubble or not. Second, while risk shifting offers a reason for intervention, we find the leading proposals for interventions against booms have ambiguous welfare implications in our model. Specifically, we show tighter monetary policy may exacerbate some inefficiencies due to risk shifting even as it mitigates others, and that leverage restrictions may fan asset prices and exacerbate excessive leverage rather than curb it.

Along with the deepening of the power system reform, the input and output evaluation and analysis of asset became more and more important. Arounding the goal of comprehensive optimization of safety, efficiency and cost throughout the life cycle of assets, this paper constructed a quantitative evaluation model for asset input–output analysis, and put forward an input–output analysis method combining the boston matrix method and the equivalent analysis method. Actual substations’ input cost and output efficiency analysis verified the method feasibility, which could provide decision-making support for optimizing power grid asset management strategies and enhancing asset value operation benefits.

We provide an estimated DSGE model of a small open economy with both domestic and international financial market frictions. Firms face credit constraints and trade an intrinsically useless asset. Low foreign interest rates are conducive to bubble formation. An asset bubble provides liquidity and relaxes credit constraints. It provides a powerful amplification and propagation mechanism. Our estimated model based on Bayesian methods explains the high volatilities of consumption and stock prices relative to output, countercyclical trade balance, and procyclical stock prices observed in the Mexican data over the period 1990Q1-2011Q4.

Revisiting the two-factor valuation of futures contracts, we propose a new pricing model for financial futures and their derivatives. The linkage between the money market funding rate and the underlying asset price is stochastic and state-dependent, in compliance with investors’ arbitrage strategies. The model explicitly captures the impact of interest rate expectations in the marking-to-market feature of futures, as predicted by Cox, Ingersoll, and Ross (1981) theory. The backwardation vs. contango regime of financial futures depends on a new parameter, the contango factor, which paves the way for future empirical studies. Akin to the implied volatility of option contracts, the contango factor provides market participants with a universal gauge of futures contracts’ level of contango, consistent across futures markets and maturities. Our numerical simulations show significant deviations from the traditional cost-of-carry model of futures prices, with price deviations above 1% even for short-term futures contracts.

The replication of any European contingent claim by a static portfolio of calls and puts with strikes forming a continuum, formally proven by Carr and Madan (1998), extends to "standard dispersion" options written on the Euclidean norm of a vector of n asset performances. With the help of integral equation techniques we derive replicating portfolios for calls, puts and indeed any claim contingent on standard dispersion using vanilla basket calls whose basket weights span an n-dimensional continuum. Consequently multi-asset standard dispersion options admit a model-free price enforced by arbitrage, just as single-asset European claims do.

We examine the diversification benefits of cryptocurrency asset categories. To mitigate the effects of estimation risk, we employ the Bayes-Stein model with no short-selling and variance-based constraints. We estimate the inputs using lasso regression and elastic net regression, employing the shrunk Wishart stochastic volatility model and Gaussian random projection. We consider nine cryptocurrency asset categories, and find that all but two provide significant out-of-sample diversification benefits. The lower is investor risk aversion, the more beneficial are cryptocurrencies as portfolio diversifiers. During uncertain economic environments, such as the post-COVID-19 period, cryptocurrencies provide the same diversification benefits as in more stable environments. Our results are robust to different portfolio benchmarks, regression technique, transaction cost, portfolio constraints, higher moments and Black-Litterman models.

In the backdrop of rapid urbanisation trends, the slum population has increased significantly in the fast-growing cities like Dhaka (the capital of Bangladesh). The increasing rural-to-urban migration, housing shortages, and the limited access to affordable housing, are recognised as some of the key underlying drivers of the same. Within the slum pockets, the households possess a varying level of livelihood assets which further determine their housing conditions. To establish a precise understanding of the linkages between the livelihood assets possessed by the slum households and their housing conditions, this study specifically focuses on the case of Dhaka. Building on the framework of sustainable livelihood analysis, and in reference to the World Bank’s Urban Informal Settlements Survey 2016, a specific set of indicators are developed to assess the livelihood assets in different slum pockets of Dhaka and their linkage with the slum housing. Thereafter, correlation analysis of all household (secondary) data is conducted to derive broader findings, and an in-depth comparative analysis is conducted for two contrasting slum groups. Although the overall results do not reveal any direct linkage between the specific asset capitals and the housing conditions, the financial and natural capital are found to be the key factors in determining the level of livelihood assets. By comparing the locational characteristics, it is further found that the slums with highest level of livelihood assets are more located on the periphery of Dhaka city, while the contrasting slum groups are situated more in the central areas. Also, the comparison of housing conditions reveals that the households with higher level of livelihood assets have better access to durable and permanent alternatives for their housing structure. Although there is a huge scope of further research, it is hoped that the study findings will pave the pathway for enhancing slum housing conditions in Dhaka.

This paper investigates the implication of correlation ambiguity to investor behavior, asset pricing and issuers' listing choices from a market microstructure perspective. We introduce two markets to a multi-asset model: Market A is transparent and Market B is opaque, or Market A with low and Market B with high ambiguity perceived by investors. Each firm can choose only a unique market to list its stock. Due to ambiguity aversion, a naive investor's demand function demonstrates piecewise linear. Such trading behavior may lead to different equilibria and hence different market-clearing prices for each listing outcome. Rational entrepreneurs make their optimal listing decisions depending on asset prices on alternative markets. We further demonstrate that a crafted design of specific features of the microstructure, such as listing standards and disclosure requirements, can effectively reduce the perceived ambiguity and induce more naive investors' participation to improve market liquidity, maintain greater volume, and lower the cost of capital, so as to attract more new listings.

We propose an optimal combining strategy to mitigate estimation risk for the popular mean-variance portfolio choice problem in the case without a risk-free asset. We find that our strategy performs well in general, and it can be applied to known estimated rules and the resulting new rules outperform the original ones. We further obtain the exact distribution of the out-of-sample returns and explicit expressions of the expected out-of-sample utilities of the combining strategy, providing not only a fast and accurate way of evaluating the performance, but also analytical insights into the portfolio construction. This paper was accepted by Tyler Shumway, finance.

This paper empirically evaluates how different firm life cycle stages influence managers while making firms' input and output innovation decisions under asset liquidity constraints, idiosyncratic risk, and cash flow risk. We find that firms make more investments in input innovation activities during the introduction, growth, and decline stages in the presence of more asset liquidity; in contrast, less investment is made during the mature stage. Concerning the output innovation, the introduction and decline (growth and mature) stages show a negative (positive) relationship for more asset liquidity in place. Our findings also predict that firms are more likely to invest in input innovation activities during the introduction, growth, and decline stages under more idiosyncratic and cash flow volatilities; however, such investment is less prospective during the mature stage. Both volatilities support reciprocal linkages for firm output innovation. Aside from moderating effects, the firms' asset liquidity positively affects output innovation opportunities during current and future periods, while idiosyncratic and cash flow volatilities negatively contribute to both firms' input and output innovation. These findings not only assist managers in making firms' innovation decisions but also enhance investors' understanding of making investment decisions through the different firm life cycle stages.

We identify fixed-income mutual funds as an important contributor to the unusually high selling pressure in liquid asset markets during the COVID-19 crisis. We show that mutual funds experienced pronounced investor outflows amplified by their liquidity transformation. In meeting redemptions, funds followed a pecking order by first selling their liquid assets, including Treasuries and high-quality corporate bonds, which generated the most concentrated selling pressure in these markets. Overall, the estimated price impact of mutual funds was sizable at a third of the increase in Treasury yields and a quarter of the increase in corporate bond yields during the COVID-19 crisis.