Research articles for the 2021-02-22

A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction
Christoph Berninger,Almond Stöcker,David Rügamer

Motivated by the application to German interest rates, we propose a timevarying autoregressive model for short and long term prediction of time series that exhibit a temporary non-stationary behavior but are assumed to mean revert in the long run. We use a Bayesian formulation to incorporate prior assumptions on the mean reverting process in the model and thereby regularize predictions in the far future. We use MCMC-based inference by deriving relevant full conditional distributions and employ a Metropolis-Hastings within Gibbs Sampler approach to sample from the posterior (predictive) distribution. In combining data-driven short term predictions with long term distribution assumptions our model is competitive to the existing methods in the short horizon while yielding reasonable predictions in the long run. We apply our model to interest rate data and contrast the forecasting performance to the one of a 2-Additive-Factor Gaussian model as well as to the predictions of a dynamic Nelson-Siegel model.

Angels and Demons: The Negative Effect of Employees' Angel Investments on Corporate Innovation
Kundu, Santanu,Mueller, Clemens
A firm's economic value of patents decreases by 3.3% - 5% when its employees are investing their personal wealth in start-ups. We refer to such angel investors employed at public corporations as angel employees. We establish causality with matching and instrumental variable regressions, which rely on quasi-exogenous competition in the early-stage financing market. The negative relationship is stronger for angel employees in innovation-related roles, if the linked start-ups are more time consuming, and for exploratory patents. Start-ups financed by angel employees are more likely to successfully exit. Our results indicate that angel employees divert time and effort from their employers to their personal start-up investments. Overall, we highlight unexplored negative effects of angel investors in our economy.

Bond Risk Premia: The Information in Really Long-Maturity Forward Rates
Berardi, Andrea,Brown, Roger,Schaefer, Stephen M.
Long term forward rates contain information that greatly improves the precision with which expectations of future short rates can be distinguished from risk premia in the term structure. Indeed, in affine models, the slope of the term structure of risk premia for long maturities is very closely approximated by the sum of (i) the slope of the forward rate curve and (ii) a term that depends only of yield volatility and maturity ("convexity"), two quantities that can easily be estimated independently of the details of model specification. Key to extracting the risk premium information in long-term forward rates is capturing the dynamics of convexity, which requires a model with time-varying volatility. Using a four-factor ATSM, we find that risk premia on long term bonds are almost entirely driven by volatility. Short rate expectations in our model account for a much larger fraction of the volatility of yields than is typically reported, a result that we show is mainly the result of econometric bias in some estimates of Gaussian models. We also show - using Monte Carlo simulation - that including data on long-term yields in the estimation of term structure models greatly improves the precision of estimated values of short rate expectations, term premia and risk premia. Compared with benchmark estimates that use yield data up to 25 years, excluding data on yields longer than 10 years results in the standard errors of both estimated means and volatilities roughly tripling in size.

Capital Budgeting â€" The Tools for Project Evaluation
Senthilnathan, Samithamby
As the objective of the capital budgeting is to add values to the wealth of an owner of a business, the capital budgeting primarily insists the recovery of investments made in the projects. To improve the owner’s wealth, it is important to evaluate and identify profitable projects using some evaluation tools. Concerning the project evaluation, there are traditional and non-traditional tools in application to evaluate the projects in finance and financial management. In this context, this paper illustrates the project evaluation techniques contextually with specific exhibits as examples to make the reader to understand them.

Climate Change Valuation Adjustment (CCVA) using parameterized climate change impacts
Chris Kenyon,Mourad Berrahoui

We introduce Climate Change Valuation Adjustment (CCVA) to capture climate change impacts on XVA that are currently invisible assuming typical market practice. To discuss such impacts on XVA from changes to instantaneous hazard rates we introduce a flexible and expressive parameterization to capture the path of this impact to climate change endpoints, and transition effects. Finally we provide quantification of examples of typical interest where there is risk of economic stress from sea level change up to 2101, and from transformations of business models. We find that even with the slowest possible uniform approach to a climate change impact in 2101 there can still be significant XVA impacts on interest rate swaps of 20 years or more maturity. Transformation effects on XVA are strongly dependent on timing and duration of business model transformation. Using a parameterized approach enables discussion with stakeholders of economic impacts on XVA, whatever the details behind the climate impact.

CoinTossX: An open-source low-latency high-throughput matching engine
Ivan Jericevich,Dharmesh Sing,Tim Gebbie

We deploy and demonstrate the CoinTossX low-latency, high-throughput, open-source matching engine with orders sent using the Julia and Python languages. We show how this can be deployed for small-scale local desk-top testing and discuss a larger scale, but local hosting, with multiple traded instruments managed concurrently and managed by multiple clients. We then demonstrate a cloud based deployment using Microsoft Azure, with large-scale industrial and simulation research use cases in mind. The system is exposed and interacted with via sockets using UDP SBE message protocols and can be monitored using a simple web browser interface using HTTP. We give examples showing how orders can be be sent to the system and market data feeds monitored using the Julia and Python languages. The system is developed in Java with orders submitted as binary encodings (SBE) via UDP protocols using the Aeron Media Driver as the low-latency, high throughput message transport. The system separates the order-generation and simulation environments e.g. agent-based model simulation, from the matching of orders, data-feeds and various modularised components of the order-book system. This ensures a more natural and realistic asynchronicity between events generating orders, and the events associated with order-book dynamics and market data-feeds. We promote the use of Julia as the preferred order submission and simulation environment.

Competition between shared autonomous vehicles and public transit: A case study in Singapore
Baichuan Mo,Zhejing Cao,Hongmou Zhang,Yu Shen,Jinhua Zhao

Emerging autonomous vehicles (AV) can either supplement the public transportation (PT) system or compete with it. This study examines the competitive perspective where both AV and PT operators are profit-oriented with dynamic adjustable supply strategies under five regulatory structures regarding whether the AV operator is allowed to change the fleet size and whether the PT operator is allowed to adjust headway. Four out of the five scenarios are constrained competition while the other one focuses on unconstrained competition to find the Nash Equilibrium. We evaluate the competition process as well as the system performance from the standpoints of four stakeholders -- the AV operator, the PT operator, passengers, and the transport authority. We also examine the impact of PT subsidies on the competition results including both demand-based and supply-based subsidies. A heuristic algorithm is proposed to update supply strategies for AV and PT based on the operators' historical actions and profits. An agent-based simulation model is implemented in the first-mile scenario in Tampines, Singapore. We find that the competition can result in higher profits and higher system efficiency for both operators compared to the status quo. After the supply updates, the PT services are spatially concentrated to shorter routes feeding directly to the subway station and temporally concentrated to peak hours. On average, the competition reduces the travel time of passengers but increases their travel costs. Nonetheless, the generalized travel cost is reduced when incorporating the value of time. With respect to the system efficiency, the bus supply adjustment increases the average vehicle load and reduces the total vehicle kilometer traveled measured by the passenger car equivalent (PCE), while the AV supply adjustment does the opposite.

Conditional Value at Risk and Partial Moments for the Metalog Distributions
Valentyn Khokhlov

The metalog distributions represent a convenient way to approach many practical applications. Their distinctive feature is simple closed-form expressions for quantile functions. This paper contributes to further development of the metalog distributions by deriving the closed-form expressions for the Conditional Value at Risk, a risk measure that is closely related to the tail conditional expectations. It also addressed the derivation of the first-order partial moments and shows that they are convex with respect to the vector of the metalog distribution parameters.

Do Required Minimum Distribution 401(K) Rules Matter, and for Whom? Insights from a Lifecycle Model
Horneff, Vanya,Maurer, Raimond,Mitchell, Olivia S.
Tax-qualified vehicles helped U.S. private-sector workers accumulate $25Tr in retirement assets. An often-overlooked important institutional feature shaping decumulations from these retirement plans is the “Required Minimum Distribution” (RMD) regulation, requiring retirees to withdraw a minimum fraction from their retirement accounts or pay excise taxes on withdrawal shortfalls. Our calibrated lifecycle model measures the impact of RMD rules on financial behavior of heterogeneous households during their worklives and retirement. We show that proposed reforms to delay or eliminate the RMD rules should have little effects on consumption profiles but more impact on withdrawals and tax payments for households with bequest motives.

Does Modern Information Technology Attenuate Managerial Information Hoarding? Evidence from EDGAR Implementation
Ni, Xiaoran,Wang, Ye (Emma),Yin, David
Exploiting the staggered implementation of the EDGAR system from 1993 to 1996 as quasi-exogenous shocks, we find that the internet dissemination of corporate disclosures encourages managers’ bad news hoarding and thus increases firms’ future stock price crash risk. Supplemental evidence suggests that short-term pressures due to increased stock liquidity and investors’ increased reliance on accounting numbers appear to play a role, and both accrual and real earnings management increase. Taken together, our findings indicate that while modern information technologies increase market efficiency by lowering information acquisition costs, they may have an unintended effect on managers’ incentives to hoard bad news.

Enabling or Hampering? Climate Risk and the Role of Finance in the Low-Carbon Transition
Battiston, Stefano,Monasterolo, Irene,Min, Jihoon,Riahi, Keywan,van Ruijven, Bas
While there is a broad understanding that the financial system can play a major enabling role in achieving the low-carbon transition, it is not well understood under which specific conditions such an orderly transition scenario when finance works as an enabler could occur. Even more importantly, it has not been clarified under which conditions the financial system could instead hamper the low-carbon transition. Overlooking the possible (even temporary) hampering role of the financial system in the low-carbon transition can lead to a large underestimation of climate transition risk. The set of scenarios that financial supervisors recommended to investors to analyse climate transition risk include scenarios labelled as ‘disorderly transition’. Nevertheless, there are important limitations in interpreting such scenarios in terms of a disorderly transition. In particular, since in the climate economic models currently used by financial supervisors, finance plays no role, it is not possible to investigate the conditions for the financial system to act as an enabler or as a barrier for the low-carbon transition. To shed light on this issue, here we analyse how the economic trajectories of these climate economics models are reshaped depending on the timing and the extent by which financial actors assess climate risk.

Equilibrium Price Formation with a Major Player and its Mean Field Limit
Masaaki Fujii,Akihiko Takahashi

In this article, we consider the problem of equilibrium price formation in an incomplete securities market consisting of one major financial firm and a large number of minor firms. They carry out continuous trading via the securities exchange to minimize their cost while facing idiosyncratic and common noises as well as stochastic order flows from their individual clients. The equilibrium price process that balances demand and supply of the securities, including the functional form of the price impact for the major firm, is derived endogenously both in the market of finite population size and in the corresponding mean field limit.

Equity Portfolio Diversification: How Many Stocks Are Enough? Evidence From India
Raju, Rajan,Agarwalla, Sobhesh Kumar
How many stocks are required to reduce unsystematic risk significantly is an important question for investors. While there is a large body of research on the subject in the United States, there is little formal work on this question in India. We show that a 15-20 stock portfolio, the traditional market rule-of-thumb for a diversified portfolio, is likely inadequate to minimize unsystematic risk. We show that an investor could target to reduce diversifiable risk by 90\% with a 90\% confidence with a portfolio of 40-50 stocks. We build a practical framework that serves as a baseline for investors to target a specific reduction in diversifiable unsystematic risk at a chosen confidence level.

Extensions of the Pesaran, Shin and Smith (2001) Bounds Testing Procedure
Bertsatos, Georgios,Sakellaris, Plutarchos,Tsionas, Mike
We replicate the Pesaran, Shin and Smith (2001) bounds testing procedure (BTP), and extend it with 6 new cases, 4 of which involve a quadratic trend. We provide critical values for the BTP of the lagged regressors in levels under the framework of unrestricted error-correction models (UECMs) to account for degenerate cases of co-integration. Further, we extend the BTP with 11 cases for the quantile UECMs of Cho, Kim and Shin, and present critical values for inter-decile and interquartile BTPs for the unrestricted cases. We extend the Shin, Yu and Greenwood-Nimmo methodology to account for non-linear, or asymmetric, responses of the dependent variables to its covariates (NARDL) and for distributional, or location, asymmetry (QARDL of Cho, Kim and Shin; of the dependent variable. We call this quantile non-linear ARDL, or QNARDL. We provide codes that generate sample-specific critical values of the BTPs. We utilize these critical values in an empirical application of a dynamic equity valuation model for the S&P Global Index. We find that mis-specifying a non-linear relationship as linear produces misleading results and policy implications. There is strong evidence of: (i) trading activity based on fundamentals and (ii) the existence of a stable equilibrium relationship for the price-to-book (PB) ratio of the market index and its fundamentals. During periods of high PB relative to its fundamental values, convergence to equilibrium is faster than during periods of relatively low PB. There is also evidence of momentum trading, i.e. of traders that rely on positive feedback.

Financial Intermediaries’ Assetâ€"Liability Dependency and Low-Interest-Rate Environment: Evidence from EU Life Insurers
Curcio, Domenico,Borri, Nicola,Cerrone, Rosaria,Cocozza, Rosa
This research studies the relationships between the two sides of life insurers' balance sheet and investigates whether and how they changed during recent past years, when European Central Bank monetary policy drove market rates to unprecedented low levels. By using a canonical correlation analysis, we study the internal structure of the links within and between the asset and liability sides of 24 major European Union (EU) life insurers' balance sheets over the 2007â€" 2015 time horizon.We find strong and substantial evidence that life insurers' assets and liabilities have become more independent over time. We argue that the declining trend of market interest rates has contributed to the generalized reduction in the linkage between the asset side and the liability side of EU life insurers, and has made insurance companies more exposed to ALM-related risks relative to the period before the financial crisis.

Gender Inequality in Research Productivity During the COVID-19 Pandemic
Ruomeng Cui,Hao Ding,Feng Zhu

We study the disproportionate impact of the lockdown as a result of the COVID-19 outbreak on female and male academics' research productivity in social science. The lockdown has caused substantial disruptions to academic activities, requiring people to work from home. How this disruption affects productivity and the related gender equity is an important operations and societal question. We collect data from the largest open-access preprint repository for social science on 41,858 research preprints in 18 disciplines produced by 76,832 authors across 25 countries over a span of two years. We use a difference-in-differences approach leveraging the exogenous pandemic shock. Our results indicate that, in the 10 weeks after the lockdown in the United States, although the total research productivity increased by 35%, female academics' productivity dropped by 13.9% relative to that of male academics. We also show that several disciplines drive such gender inequality. Finally, we find that this intensified productivity gap is more pronounced for academics in top-ranked universities, and the effect exists in six other countries. Our work points out the fairness issue in productivity caused by the lockdown, a finding that universities will find helpful when evaluating faculty productivity. It also helps organizations realize the potential unintended consequences that can arise from telecommuting.

Global Equity Market Co-Movement During Crisis Periods
Heil, Thomas,Peter, Franziska J.,Prange, Philipp
We analyze global equity market co-movement during the last 25 years using a dynamic spatial model. Based on a generalized autoregressive score model, we analyze the co-movement among global, European, American, and Asian equity markets during various crises including the Asian, the financial, the European sovereign debt crisis, as well as the economic turmoil due to the current COVID-19 pandemic. Our results show an increase in the co-movement prior to the onset of the global financial crisis in 2008, a generally higher co-movement among European countries compared to American and Asian-Pacific countries, while the highest level of co-movement is reached during the current pandemic. When accounting for time-varying variances, however, we find evidence for global contagion only during the COVID-19 pandemic which induces an economic downturn in the first quarter of 2020.

Green Bonds and Carbon Emissions: Exploring the Case for a Rating System at the Firm Level
Ehlers, Torsten,Mojon, Benoît,Packer , Frank
Green bonds are debt instruments whose proceeds finance projects with various environmental benefits - including climate change mitigation. So far, however, green bond projects have not necessarily translated into comparatively low or falling carbon emissions at the firm level. We discuss the potential benefits of a firm-level rating based on carbon intensity (emissions relative to revenue) to complement existing project-based green labels. We argue that such a rating system could provide a useful signal to investors and encourage firms to reduce their carbon footprint.

Heterogeneous effects of waste pricing policies
Marica Valente

Using machine learning methods in a quasi-experimental setting, I study the heterogeneous effects of introducing waste prices -- unit prices on household unsorted waste disposal -- on waste demands and social welfare. First, using a unique panel of Italian municipalities with large variation in prices and observables, I show that waste demands are nonlinear. I find evidence of nudge effects at low prices, and increasing elasticities at high prices driven by income effects and waste habits before policy. Second, I estimate policy impacts on pollution and municipal management costs, and compute the overall social cost savings for each municipality. Social welfare effects become positive for most municipalities after three years of adoption, when waste prices cause significant waste avoidance.

How Pandemics Affect International Investment: Evidence from M&As during SARS-CoV-1 in China
Zhang, Chi,Kandilov, Ivan T.,Walker, Mark D.
We evaluate the impact of SARS-CoV-1 infection rates on inbound cross-border mergers & acquisitions (M&As) in China. Using transaction level data on cross-border M&A from 2002 to 2005, we show that Chinese provinces which experienced high level of SARS-CoV-1 infection rates, also suffered a significant decline in cross-border M&A activity, both in terms of number of transactions and overall volume. The overall negative impact is entirely driven by decline in deals for non-state-owned targets. The large, negative effect was short-lived but lost deals were not recouped. The negative effects were larger and longer lasting for provinces with less-affected neighbors suggesting that the presence of good substitutes exacerbated the negative effects.

Inequality in the Time of COVID-19: Evidence from Mortgage Delinquency and Forbearance
An, Xudong,Cordell, Larry,Geng, Liang,Lee, Keyoung
Using a novel database that combines mortgage servicing records and loan application information, we show that lower-income and minority borrowers have significantly higher nonpayment rates during the COVID-19 pandemic, even after controlling for conventional risk factors. A difference-in-differences analysis shows that the pandemic has exacerbated income and racial inequalities in delinquencies. We then find that government and private-sector forbearance programs have partly alleviated these inequalities in the near term, as lower-income and minority borrowers have taken up the short-term debt relief at higher rates. Finally, we examine longer-term solutions for identifying and resolving an estimated 2.8 million mortgage loans with forbearance terms expiring soon.

Inference of Binary Regime Models with Jump Discontinuities
Milan Kumar Das,Anindya Goswami,Sharan Rajani

Identifying the instances of jumps in a discrete time series sample of a jump diffusion model is a challenging task. We have developed a novel statistical technique for jump detection and volatility estimation in a return time series data using a threshold method. Since we derive the threshold and the volatility estimator simultaneously by solving an implicit equation, we obtain unprecedented accuracy across a wide range of parameter values. Using this method, the increments attributed to jumps have been removed from a large collection of historical data of Indian sectoral indices. Subsequently, we test the presence of regime switching dynamics in the volatility coefficient using a new discriminating statistics. The statistics is shown to be sensitive to the transition kernel of the regime switching model. We perform the testing using bootstrap method and find a clear indication of presence of multiple regimes of volatility in the data.

International Diversification: Much Ado About Nothing?
Raju, Rajan,Baruah, Rajiv
A look at diversification between Indian and the US Equity markets as represented by the S&P BSE 100 TR and the S&P 500 TR indexes.

Learning Epidemiology by Doing: The Empirical Implications of a Spatial-SIR Model with Behavioral Responses
Alberto Bisin,Andrea Moro

We simulate a spatial behavioral model of the diffusion of an infection to understand the role of geographic characteristics: the number and distribution of outbreaks, population size, density, and agents' movements. We show that several invariance properties of the SIR model concerning these variables do not hold when agents interact with neighbors in a (two dimensional) geographical space. Indeed, the spatial model's local interactions generate matching frictions and local herd immunity effects, which play a fundamental role in the infection dynamics. We also show that geographical factors affect how behavioral responses affect the epidemics. We derive relevant implications for estimating the effects of the epidemics and policy interventions that use panel data from several geographical units.

M\'etodos Emp\'iricos Aplicados \`a An\'alise Econ\^omica do Direito
Thomas V. Conti

Nas \'ultimas d\'ecadas houve forte mudan\c{c}a no perfil das publica\c{c}\~oes em an\'alise econ\^omica do direito e nos m\'etodos emp\'iricos mais utilizados. Por\'em, nos pr\'oximos anos a mudan\c{c}a pode ser maior e mais r\'apida, exigindo adapta\c{c}\~ao dos pesquisadores da \'area. Neste cap\'itulo analiso algumas tend\^encias recentes de mudan\c{c}a e oportunidades futuras que se avizinham a partir avan\c{c}os nas bases de dados, estat\'istica, computa\c{c}\~ao e no arcabou\c{c}o regulat\'orio dos pa\'ises. Avan\c{c}o a hip\'otese de que expans\~ao de objetos e m\'etodos favorecer\'a equipes de pesquisa maiores e interdisciplinares e apresento evid\^encias circunstanciais a partir de dados bibliom\'etricos de que isso j\'a vem acontecendo no Journal of Law and Economics.

On Classifying the Effects of Policy Announcements on Volatility
Giampiero M. Gallo,Demetrio Lacava,Edoardo Otranto

The financial turmoil surrounding the Great Recession called for unprecedented intervention by Central Banks: unconventional policies affected various areas in the economy, including stock market volatility. In order to evaluate such effects, by including Markov Switching dynamics within a recent Multiplicative Error Model, we propose a model--based classification of the dates of a Central Bank's announcements to distinguish the cases where the announcement implies an increase or a decrease in volatility, or no effect. In detail, we propose two smoothed probability--based classification methods, obtained as a by--product of the model estimation, which provide very similar results to those coming from a classical k--means clustering procedure. The application on four Eurozone market volatility series shows a successful classification of 144 European Central Bank announcements.

Optimal Transport of Information
Semyon Malamud,Anna Cieslak,Andreas Schrimpf

We study the general problem of optimal information design with continuous actions and continuous state space in arbitrary dimensions. First, we show that with a finite signal space, the optimal information design is always given by a partition. Second, we take the limit of an infinite signal space and characterize the solution in terms of a Monge-Kantorovich optimal transport problem with an endogenous information transport cost. We use our novel approach to: 1. Derive necessary and sufficient conditions for optimality based on Bregman divergences for non-convex functions. 2. Compute exact bounds for the Hausdorff dimension of the support of an optimal policy. 3. Derive a non-linear, second-order partial differential equation whose solutions correspond to regular optimal policies.

Persuading Large Investors
Alonso, Ricardo,Zachariadis, Konstantinos E.
A regulator who designs a public stress test to elicit private investment in a distressed bank must account for large investors' private information on the bank's state. We provide conditions for crowding-in (crowding-out) so that the regulator offers more (less) information to better-informed investors. Crowding-in obtains if investors' private information is not too discriminating of the state. We show that the region of the common prior is consequential: if crowding-in occurs for ex-ante optimistic investors then crowding-out follows if they were instead pessimistic. Investors' value from more precise private signals may come from the effect on the public test's precision.

Policy choices can help keep 4G and 5G universal broadband affordable
Edward J Oughton,Niccolò Comini,Vivien Foster,Jim W Hall

The United Nations Broadband Commission has committed the international community to accelerate universal broadband. However, the cost of meeting this objective, and the feasibility of doing so on a commercially viable basis, are not well understood. Using scenario analysis, this paper compares the global cost-effectiveness of different infrastructure strategies for the developing world to achieve universal 4G or 5G mobile broadband. Utilizing remote sensing and demand forecasting, least-cost network designs are developed for eight representative low and middle-income countries (Malawi, Uganda, Kenya, Senegal, Pakistan, Albania, Peru and Mexico), the results from which form the basis for aggregation to the global level. The cost of meeting a minimum 10 Mbps per user is estimated at USD 1.7 trillion using 5G Non-Standalone, approximately 0.6% of annual GDP for the developing world over the next decade. However, by creating a favorable regulatory environment, governments can bring down these costs by as much as three quarters, to USD 0.5 trillion (approximately 0.2% of annual GDP), and avoid the need for public subsidy. Providing governments make judicious choices, adopting fiscal and regulatory regimes conducive to lowering costs, universal broadband may be within reach of most developing countries over the next decade.

Relative Arbitrage: Sharp Time Horizons and Motion by Curvature
Martin Larsson,Johannes Ruf

We characterize the minimal time horizon over which any equity market with $d \geq 2$ stocks and sufficient intrinsic volatility admits relative arbitrage with respect to the market portfolio. If $d \in \{2,3\}$, the minimal time horizon can be computed explicitly, its value being zero if $d=2$ and $\sqrt{3}/(2\pi)$ if $d=3$. If $d \geq 4$, the minimal time horizon can be characterized via the arrival time function of a geometric flow of the unit simplex in $\mathbb R^d$ that we call the minimum curvature flow.

Gahng, Minmo,Ritter, Jay R.,Zhang, Donghang
Special Purpose Acquisition Company (SPAC) IPOs boomed starting in 2020. While SPAC IPO investors have earned 9.3% per year, returns for investors in merged companies are more complex. Depending on weighting methods, they have earned -4.0% to -15.6% in the first year on common shares but 15.6% to 44.3% on warrants. We rationalize why certain companies go public via a SPAC merger despite their high costs by identifying the economic roles of SPAC sponsors and investors. Sponsors transfer more than 30% of their compensation to other investors as inducements to complete mergers. SPACs are evolving towards a more sustainable equilibrium.

Tax-Aware Portfolio Construction via Convex Optimization
Nicholas Moehle,Mykel J. Kochenderfer,Stephen Boyd,Andrew Ang

We describe an optimization-based tax-aware portfolio construction method that adds tax liability to standard Markowitz-based portfolio construction. Our method produces a trade list that specifies the number of shares to buy of each asset and the number of shares to sell from each tax lot held. To avoid wash sales (in which some realized capital losses are disallowed), we assume that we trade monthly, and cannot simultaneously buy and sell the same asset.

The tax-aware portfolio construction problem is not convex, but it becomes convex when we specify, for each asset, whether we buy or sell it. It can be solved using standard mixed-integer convex optimization methods at the cost of very long solve times for some problem instances. We present a custom convex relaxation of the problem that borrows curvature from the risk model. This relaxation can provide a good approximation of the true tax liability, while greatly enhancing computational tractability. This method requires the solution of only two convex optimization problems: the first determines whether we buy or sell each asset, and the second generates the final trade list. In our numerical experiments, our method almost always solves the nonconvex problem to optimality, and when it does not, it produces a trade list very close to optimal. Backtests show that the performance of our method is indistinguishable from that obtained using a globally optimal solution, but with significantly reduced computational effort.

The Association of Opening K-12 Schools and Colleges with the Spread of COVID-19 in the United States: County-Level Panel Data Analysis
Victor Chernozhukov,Hiroyuki Kasahara,Paul Schrimpf

This paper empirically examines how the opening of K-12 schools and colleges is associated with the spread of COVID-19 using county-level panel data in the United States. Using data on foot traffic and K-12 school opening plans, we analyze how an increase in visits to schools and opening schools with different teaching methods (in-person, hybrid, and remote) is related to the 2-weeks forward growth rate of confirmed COVID-19 cases. Our debiased panel data regression analysis with a set of county dummies, interactions of state and week dummies, and other controls shows that an increase in visits to both K-12 schools and colleges is associated with a subsequent increase in case growth rates. The estimates indicate that fully opening K-12 schools with in-person learning is associated with a 5 (SE = 2) percentage points increase in the growth rate of cases. We also find that the positive association of K-12 school visits or in-person school openings with case growth is stronger for counties that do not require staff to wear masks at schools. These results have a causal interpretation in a structural model with unobserved county and time confounders. Sensitivity analysis shows that the baseline results are robust to timing assumptions and alternative specifications.

The Cost of Financial Misconduct in Non-Profits
Joukov, Artem
I explore the impact of financial crime on non-profit fundamentals, showing that service revenue and employee compensation, the largest sources of non-profit revenue and expenditures, fall in the years following financial misconduct and its disclosure. However, I observe no declines in volunteerism or employment after financial crime. These results do not align well with the reputational damage hypothesis proposed by prior scholarship. Assuming a competitive free market for services and labor, my findings suggest that afflicted non-profits may experience declines as a result of either operational disruptions that follow the discovery and disclosure of crime or some other non-reputational consequences.

The Effect of Information Asymmetry on the Method of Payment and Post-M&A Involuntary Delisting
Thompson, Ephraim Kwashie,Kim, Changki
Purpose - This paper shows an unexplored area related to involuntary delisting. Specifically, this research investigates the effect of target firm information asymmetry on the likelihood that the acquirer or newly merged firm will be forcibly delisted post-merger.Design/methodology/approach - The research uses a sample gathered on local US mergers and acquisitions from the Thomson Reuters Securities Data Company (SDC) Platinum Mergers and Acquisitions database. It applies the logistic regression with industry and year effects and corrects the error term using clustering at the industry level. The research also matches the forced delisted firms to control firms based on industry, acquisition completion year, and firm size and then employs a matched sample analysis.Findings - Findings show that M&As between firms where the target firm is opaque and burdened with high information asymmetry issues are likely to be paid for using majority stock and that M&As involving such opaque targets also have a higher likelihood of getting delisted post-merger.Research implications or Originality - Our results are relevant given the very nature of M&As which involve two players: the acquirer and target who both may have different incentives. Acquirers especially have the tendency to suffer losses and even get delisted if they over-pay for or get merged to a poor target which conceals its poor performance evidenced by higher accruals quality.

The Rise and Breakup of the Commodity Exchange Membership: An Analysis of CBOT Seat Prices
Emm, Ekaterina E.,Gay, Gerald D.,Ma, Han,Ren, Honglin
We conduct a clinical analysis of the CBOT full membership that provides holders with rights to trade any of the exchange’s contracts using a unique database of seat information from the period 1897-2020. We examine microstructure and asset pricing properties of seats including during periods before and following the CBOT’s transition from trading primarily agriculture futures to financial futures as well as periods before and following its demutualization at which time members experienced a separation in their ownership and trading rights. Our analysis adds an interesting dimension to the risk management literature and provides added insight into the market for exchange seats including their returns, turnover, and sensitivities to asset factor premia and to exchange volume.

The Role of Technology and Network Externalities in the Long-Term Performance of ICOs
Karkkainen, Tatja
This study examines the impact of technology and network externalities on exchange-listed Initial Coin Offerings (ICOs). ICOs are a new fundraising instrument used to finance technological innovation against a digital voucher or receipt. They diffuse the ownership of claim for a digital good. Utilising an online database comprising of self-reported ICO characteristics, measures of post-ICO performance, along with information on business social networks, higher fundraising figures are found to contribute to the ICO long-term success, but this impact is multiplied by six times when fundraising is conducted to existing, proprietary blockchain. This is explained by the network effect. The modified information ratio measure is used to approximate the quality signalling of ICO organisations using price timeseries and benchmarking these to already functioning blockchain technology, e.g. ethereum in the long-term. The ICO sample’s mean trading period on exchanges is 1.5 years and is used for long-period asset analysis. Additionally, the cointegration to the market technology benchmark is found to have a large, significant negative effect on long-term ICO organisational success as this indicates lower ICO intrinsic value.

The Value of Manager Partisanship
Jiao, Anqi,Ren, Honglin
We investigate the role and value of manager partisanship to U.S. corporations. Using party-specific natural experiments, we find that firms gain greater abnormal returns when shocks favor political party with which firm managers align. To shed lights on potential channels, we show that politicians have favorable views on firms sharing similar political values, which is reflected in that politicians are more likely to join such firms as directors and personally invest in those firms. In addition, firms with manager partisanship leaning toward the political party of the U.S. president gain easier access to the White House, get more federal procurements, and receive less unfavorable regulatory actions. Our results show that manager partisanship affects how firms connect in the U.S. two-party politics and is of significant value to firms.

The relations of Choquet Integral and G-Expectation
Ju Hong Kim

In incomplete financial markets, there exists a set of equivalent martingale measures (or risk-neutral probabilities) in an arbitrage-free pricing of the contingent claims. Minimax expectation is closely related to the $g$-expectation which is the solution of a certain stochastic differential equation. We show that Choquet expectation and minimax expectation are equal in pricing European type options, whose payoff is a monotone function of the terminal stock price $S_T$.

V-, U-, L-, or W-shaped economic recovery after COVID: Insights from an Agent Based Model
Dhruv Sharma,Jean-Philippe Bouchaud,Stanislao Gualdi,Marco Tarzia,Francesco Zamponi

We discuss the impact of a Covid-19--like shock on a simple model economy, described by the previously developed Mark-0 Agent-Based Model. We consider a mixed supply and demand shock, and show that depending on the shock parameters (amplitude and duration), our model economy can display V-shaped, U-shaped or W-shaped recoveries, and even an L-shaped output curve with permanent output loss. This is due to the economy getting trapped in a self-sustained "bad" state. We then discuss two policies that attempt to moderate the impact of the shock: giving easy credit to firms, and the so-called helicopter money, i.e. injecting new money into the households savings. We find that both policies are effective if strong enough. We highlight the potential danger of terminating these policies too early, although inflation is substantially increased by lax access to credit. Finally, we consider the impact of a second lockdown. While we only discuss a limited number of scenarios, our model is flexible and versatile enough to accommodate a wide variety of situations, thus serving as a useful exploratory tool for a qualitative, scenario-based understanding of post-Covid recovery. The corresponding code is available on-line.