Research articles for the 2020-04-20

A New Capital Structure Theory: The Four-Factor Model
Miglo, Anton
This article presents a new capital structure model based on four factors well documented in literature: asymmetric information, taxes, bankruptcy costs and decision-makers' overconfidence. The model can simultaneously explain several facts about capital structure including those that remain puzzling from existing theories point of view eg. negative correlation between debt and profitability; why firms issue equity etc. Unlike many advanced research on capital structure, a closed-form solution is obtained for most results.

A System Dynamics Model of Bitcoin: Mining as an Efficient Market and the Possibility of "Peak Hash"
Davide Lasi,Lukas Saul

The mining of bitcoin is modeled using system dynamics, showing that the past evolution of the network hash rate can be explained to a large extent by an efficient market hypothesis applied to the mining of blocks. The possibility of a decrease in the network hash rate from the next halving event (May 2020) is exposed, implying that the network may be close to 'peak hash', if the price of bitcoin and the revenues from transaction fees will remain at approximately the present level.

A many-to-many assignment game and stable outcome algorithm to evaluate collaborative Mobility-as-a-Service platforms
Theodoros P. Pantelidis,Joseph Y. J. Chow,Saeid Rasulkhani

As Mobility as a Service (MaaS) systems become increasingly popular, travel is changing from unimodal trips to personalized services offered by a platform of mobility operators. Evaluation of MaaS platforms depends on modeling both user route decisions as well as operator service and pricing decisions. We adopt a new paradigm for traffic assignment in a MaaS network of multiple operators using the concept of stable matching to allocate costs and determine prices offered by operators corresponding to user route choices and operator service choices without resorting to nonconvex bilevel programming formulations. Unlike our prior work, the proposed model allows travelers to make multimodal, multi-operator trips, resulting in stable cost allocations between competing network operators to provide MaaS for users. An algorithm is proposed to efficiently generate stability conditions for the stable outcome model. Extensive computational experiments demonstrate the use of the model to handling pricing responses of MaaS operators in technological and capacity changes, government acquisition, consolidation, and firm entry, using the classic Sioux Falls network. The proposed algorithm replicates the same stability conditions as explicit path enumeration while taking only 17 seconds compared to explicit path enumeration timing out over 2 hours.

A perspective on correlation-based financial networks and entropy measures
Vishwas Kukreti,Hirdesh K. Pharasi,Priya Gupta,Sunil Kumar

In this brief review, we critically examine the recent work done on correlation-based networks in financial systems. The structure of empirical correlation matrices constructed from the financial market data changes as the individual stock prices fluctuate with time, showing interesting evolutionary patterns, especially during critical events such as market crashes, bubbles, etc. We show that the study of correlation-based networks and their evolution with time is useful for extracting important information of the underlying market dynamics. We, also, present our perspective on the use of recently developed entropy measures such as structural entropy and eigen-entropy for continuous monitoring of correlation-based networks.

An arbitrage-free interpolation of class $C^2$ for option prices
Fabien Le Floc'h

This paper presents simple formulae for the local variance gamma model of Carr and Nadtochiy, extended with a piecewise-linear local variance function. The new formulae allow to calibrate the model efficiently to market option quotes. On a small set of quotes, exact calibration is achieved under one millisecond. This effectively results in an arbitrage-free interpolation of class $C^2$. The paper proposes a good regularization when the quotes are noisy. Finally, it puts in evidence an issue of the model at-the-money, which is also present in the related one-step finite difference technique of Andreasen and Huge, and gives two solutions for it.

Asset Pricing with Cyclical Consumption
Mai, Dat,Pukthuanthong, Kuntara
Change in cyclical consumption can explain the market risk premium and the joint equity premium--risk-free rate puzzle with a risk aversion coefficient much lower than any consumption measures. This outperformance is robust across 18 countries. At the cross section, over the full 90-year period, change in cyclical consumption encompasses other consumption measures in explaining the variation of the returns on various portfolios and it is the only consumption measure that passes both the identification test and the GRS-Factor Anderson-Rubin test of the consumption risk premium proposed by Kleibergen and Zhan (2020). The change in cyclical consumption using only services is comparable to the Fama and French (1993) three-factor model in explaining the variation of the 25 size and book-to-market portfolio returns.

Benchmarks in the Spotlight: The Impact on Exchange Traded Markets
Aspris, Angelo,Foley, Sean,O'Neill, Peter
The Fix for precious metals is a global pricing benchmark that provides pricing and liquidity provision for market participants. We exploit the gradual change in the century old auction process to quantify the efficiencies related to more transparent pricing for several precious metals. Our focus is on the market impact of this change on exchange listed products. We find that reforms to the Fix structure have reduced quoted and effective bid-ask spreads for exchange traded futures contracts and improved overall market depth. The results imply a positive spillover effect stemming from more timely and accurate pricing information. The conditions under which we observe the benefits from transparency are related to product liquidity and the degree of market segmentation.

Beveridgean Unemployment Gap
Pascal Michaillat,Emmanuel Saez

This paper proposes a new method to estimate the unemployment gap (the actual unemployment rate minus the efficient rate). While lowering unemployment puts more people into work, it forces firms to post more vacancies and devote more resources to recruiting. This unemployment-vacancy tradeoff, governed by the Beveridge curve, determines the efficient unemployment rate. Accordingly, the unemployment gap can be measured from three sufficient statistics: the elasticity of the Beveridge curve, cost of recruiting, and social cost of unemployment. In the United States the unemployment gap is countercyclical, reaching 1.5--6.5 percentage points in slumps. Thus the US labor market appears inefficient---especially inefficiently slack in slumps.

Competition, Cost Structure, and Labor Leverage: Evidence from the U.S. Airline Industry
Wagner, Konstantin
This paper studies the effect of increasing competition on firm-level labor shares in the U.S. airline industry by exploiting variation in product market contestability. First, I find that increasing competitive pressure leads to an increase in labor shares. This effect can explain the decrease in operating profitability, because rigid labor costs result in higher labor leverage. Second, by exploiting variation in human capital specificity, I show that contestability of product markets can induce contestability of labor markets. While affected firms might experience more stress through higher wages or loss of skilled human capital, more mobile employee groups benefit from labor market contestability through higher labor shares.

Consistent Calibration of Economic Scenario Generators: The Case for Conditional Simulation
Misha van Beek

Economic Scenario Generators (ESGs) simulate economic and financial variables forward in time for risk management and asset allocation purposes. It is often not feasible to calibrate the dynamics of all variables within the ESG to historical data alone. Calibration to forward-information such as future scenarios and return expectations is needed for stress testing and portfolio optimization, but no generally accepted methodology is available. This paper introduces the Conditional Scenario Simulator, which is a framework for consistently calibrating simulations and projections of economic and financial variables both to historical data and forward-looking information. The framework can be viewed as a multi-period, multi-factor generalization of the Black-Litterman model, and can embed a wide array of financial and macroeconomic models. Two practical examples demonstrate this in a frequentist and Bayesian setting.

De Facto Bank Bailouts
Ngo, Phong T. H.,Puente M., Diego
We show that the likelihood a defaulting sovereign is granted an IMF loan is increasing in U.S. banks' exposure to that country. We argue the U.S. government uses its voting power in the IMF to direct IMF funds to countries where U.S. banks stand to lose the most from sovereign default -- a de facto bailout. Consistent with this, we show that (1) U.S. Congressional voting on IMF funding increases is consistent with special (banking) interests; and (2) U.S. bank stocks' market reaction to the announcement of an IMF loan increases with its exposure to the defaulting sovereign.

Dealers' Incentives to Reveal Their Names
Karam, Arzé
This research investigates dealers’ motivation to disclose their names when quoting on the NASDAQ over the years. NASDAQ enables dealers to quote limit orders either anonymously or with a feature that reveals their names. Results are consistent with dealers advertising by revealing their identities so as to develop and maintain their reputation for reliable pricing. Dealers strategically choose to reveal their identities when order flow is profitable. Post-name disclosure analysis further suggests that named quotations are likely to be driven by informational considerations. This research contributes to our understanding of the use of non-anonymity in electronic trading.

Death and Contagious Infectious Diseases: Impact of the COVID-19 Virus on Stock Market Returns
Al-Awadhi, Abdullah
This study investigates whether contagious infectious diseases affect stock market outcomes. As a natural experiment, we use panel data analysis to test the effect of the COVID-19 virus, which is a contagious infectious disease, on the Chinese stock market. The findings indicate that both the daily growth in total confirmed cases and in total cases of death caused by COVID-19 have significant negative effects on stock returns across all companies.

Determination of Bayesian optimal warranty length under Type-II unified hybrid censoring scheme
Tanmay Sen,Ritwik Bhattacharya,Biswabrata Pradhan,Yogesh Mani Tripathi

Determination of an appropriate warranty length for the lifetime of the product is an important issue to the manufacturer. In this article, optimal warranty length of the product for the combined free replacement and the pro-rata warranty policy is computed based on the Type-II unified hybrid censored data. A non-linear pro-rata warranty policy is proposed in this context. The optimal warranty length is obtained by maximizing an expected utility function. The expectation is taken with respect to the posterior predictive model for the time-to-failure data. It is observed that the non-linear pro-rata warranty policy gives a larger warranty length with maximum profit as compared to linear warranty policy. Finally, a real-data set is analyzed in order to illustrate the advantage of using non-linear pro-rata warranty policy.

Do Stress Tests Affect Bank Liquidity Creation?
Nguyen, Thach Vu Hong,Ahmed, Shamim,Chevapatrakul, Thanaset,Onali, Enrico
We examine the impact of Federal Reserve stress tests from 2009 to 2016 on U.S. bank liquidity creation. Empirical results show that regulatory stress tests have a negative effect on both on- and off-balance sheet bank liquidity creation and asset-side liquidity creation. As banks enter the stress tests, they reduce their liquidity creation to avoid failing the stress tests. These results are consistent with the hypothesis that banks manage their risk exposures to meet higher capital requirements. The negative effect of stress testing on liquidity creation continues to persist in the quarters after the stress tests. Finally, stress test banks appear to increase liability-side liquidity creation. These findings highlight that the enhanced financial stability from greater regulatory scrutiny may be achieved at the expense of financial intermediation.

Does COVID-19 Affect the Financial Market?
Cheng, Hui-Pei,Yen, Kuang-Chieh
This paper studies the relationship between the COVID-19, an infectious disease, and different financial assets. We find that the change in the newly confirmed cases per thousand people in US rather than that in China can positively predict the S&P 500 return. Moreover, we find that the change in the newly disease confirmed cases per thousand persons in US is negatively correlated with the change in the T-Bill spread. However, we observe that the change of China disease newly confirmed cases per thousand people can positively or negatively affect the change in the T-Bill spread. Furthermore, the change in the newly confirmed cases per thousand people cannot predict the Bitcoin return whatever of US or China.

Does Cross-Border Equity Trading Destabilize the Stock Market: Evidence from Chinese Stock Markets
Bian, Jiangze,Chan, Kalok,Shi, Donghui
We obtain a unique dataset to examine the effect of the Shanghai-Hong Kong Stock Connect program, which allows foreign investors from Hong Kong to buy stocks listed in Shanghai (northbound) and domestic investors from mainland China to buy stocks listed in Hong Kong (southbound). There is a positive (negative) cross-sectional relationship between volatility of connected Hong Kong (Shanghai) stocks and southbound (northbound) trading activity. Lagged values of northbound aggregate net purchase predict returns of Shanghai stocks on both daily and weekly basis, while lagged values of southbound aggregate net purchase predict returns of Hong Kong stocks on daily basis only. Lagged AH premium of dual-listed stocks is positively related to southbound aggregate net purchase but not related to northbound aggregate net purchase.

Does Temporary Mortgage Assistance for Unemployed Homeowners Reduce Longer Term Mortgage Default? An Analysis of the Hardest Hit Fund Program
Moulton, Stephanie,Chun, Yung,Pierce, Stephanie,Holtzen, Holly ,Quercia, Roberto,Riley, Sarah
The substantial costs of foreclosures to individuals and society motivated nearly $40 billion in government subsidies to homeowners during the Great Recession. Most of these subsidies were in the form of permanent loan modifications with mixed evidence of effectiveness. This paper estimates the loan outcomes of an alternative form of mortgage subsidy that provided unemployed homeowners with temporary mortgage payment assistance, through the U.S. Department of Treasury’s Hardest Hit Fund (HHF). Our primary empirical strategy exploits the fact that some states were not eligible to offer an HHF program and that certain Metropolitan Statistical Areas (MSAs) encompass jurisdictions in both HHF and non-HHF states. We match HHF-assisted homeowners to otherwise similar non-assisted homeowners who lived in the same MSA but were not eligible for HHF assistance because they lived in a non-HHF state. By 48 months after the start of assistance, receipt of HHF is associated with a 28 percentage point reduction in the probability of default, which is a 49 percent reduction in the average default rate of 57 percent. In support of the liquidity hypothesis, we find that the HHF effect is not driven by a reduction in mortgage balance, which only occurs for about 10 percent of HHF borrowers. Further, the effect is larger for borrowers who were underwater on their mortgages at the time of assistance.

Doing More With Less: The Catalytic Function of IMF Lending and the Role of Program Size
Krahnke, Tobias
Financial assistance provided by the International Monetary Fund (IMF) is supposed to unlock other financing, acting as a catalyst for private capital flows. The empirical evidence of the presence of such a catalytic effect has, however, been mixed. This paper shows that a possible explanation for the rather inconclusive empirical evidence to date is the neglect of the size of an IMF program. Applying a novel identification strategy to account for endogenous selection into (large) adjustment programs, and using a comprehensive data set spanning the years 1990-2018, we show that the catalytic effect of IMF financial assistance is weakened - and potentially reversed - if the size of a program exceeds a certain level. We argue that large IMF financial assistance coupled with the IMF's preferred creditor status can lead to a crowding-out of private investors by increasing their loss in the event of default. Our findings add to the debate on the optimal size of Fund-supported programs and can also inform the broader policy discussions on the adequacy of IMF resources.

Dynamic Programming with State-Dependent Discounting
John Stachurski,Junnan Zhang

This paper extends the core results of discrete time infinite horizon dynamic programming theory to the case of state-dependent discounting. We replace the constant discount factor from the standard theory with a discount factor process and obtain a natural analog to the traditional condition that the discount factor is strictly less than one. We show that, when this condition holds for the discount factor process, all of the standard optimality results can be recovered. We also show that the condition cannot be significantly weakened. The dynamic programming framework considered in the paper is general enough to contain features such as recursive preferences. Several applications are discussed.

Education and Innovation: The Long Shadow of the Cultural Revolution
Huang, Zhangkai,Phillips, Gordon M.,Jialun, Yang,Zhang, Cherry Yi
The Cultural Revolution deprived Chinese students of the opportunity to receive higher education for 10 years when colleges and universities were closed from 1966-1976. We examine the human capital cost of this loss of education on subsequent innovation by firms, and ask if it impacted firms more than 30 years later. We examine the innovation of firms with CEOs who turned 18 during the Cultural Revolution, which sharply reduced their chances of attending college. Using multiple approaches to control for selection and endogeneity, including an instrument based on whether the CEO turned 18 during the Cultural Revolution, we show that Chinese firms led by CEOs without a college degree spend less on R&D, generate fewer patents, and receive fewer citations to these patents.

Effects of the COVID-19 Pandemic on Population Mobility under Mild Policies: Causal Evidence from Sweden
Matz Dahlberg,Per-Anders Edin,Erik Grönqvist,Johan Lyhagen,John Östh,Alexey Siretskiy,Marina Toger

Sweden has adopted far less restrictive social distancing policies than most countries following the COVID-19 pandemic. This paper uses data on all mobile phone users, from one major Swedish mobile phone network, to examine the impact of the Coronavirus outbreak under the Swedish mild recommendations and restrictions regime on individual mobility and if changes in geographical mobility vary over different socio-economic strata. Having access to data for January-March in both 2019 and 2020 enables the estimation of causal effects of the COVID-19 outbreak by adopting a Difference-in-Differences research design. The paper reaches four main conclusions: (i) The daytime population in residential areas increased significantly (64 percent average increase); (ii) The daytime presence in industrial and commercial areas decreased significantly (33 percent average decrease); (iii) The distance individuals move from their homes during a day was substantially reduced (38 percent decrease in the maximum distance moved and 36 percent increase in share of individuals who move less than one kilometer from home); (iv) Similar reductions in mobility were found for residents in areas with different socioeconomic and demographic characteristics. These results show that mild government policies can compel people to adopt social distancing behavior.

Epidemic control via stochastic optimal control
Andrew Lesniewski

We study the problem of optimal control of the stochastic SIR model. Models of this type are used in mathematical epidemiology to capture the time evolution of highly infectious diseases such as COVID-19. Our approach relies on reformulating the Hamilton-Jacobi-Bellman equation as a stochastic minimum principle. This results in a system of forward backward stochastic differential equations, which is amenable to numerical solution via Monte Carlo simulations. We present a number of numerical solutions of the system under a variety of scenarios.

Ethics, Culture and Higher Purpose in Banking: Post-Crisis Governance Developments
Thakor, Anjan V.
This paper examines the roles of ethics, culture, and higher purpose in the conduct of corporate governance in banking. Developments in these areas since the financial crisis are discussed. A framework for diagnosing bank culture is discussed and the theoretical and empirical research on bank culture is reviewed. The roles of executive compensation and better market discipline from bank equity are examined in the context of bank corporate governance. The conclusion of the paper is that we need to strengthen capital ratios and equity governance in banking to improve ethics and culture, and de-emphasize liquidity regulation. The embrace of authentic organizational higher purpose in bankingâ€"through dialog between regulators and banks rather than regulationâ€"is advocated as a way to enhance the effectiveness of corporate governance and prudential regulation to achieve greater financial stability and economic growth.

Factor Performance 2010-2019: A Lost Decade?
Blitz, David
The factors in the widely used Fama-French model experienced a negative average return over the 2010-2019 period. Perhaps surprisingly, such a lost decade is not unprecedented in history, as factor performance in the 2010s is, in fact, remarkably similar to factor performance in the 1990s. By contrast, many other factors did deliver a positive premium over the past decade. These factors include low risk, price momentum, earnings momentum, analyst revisions, seasonals, and short-term reversal. Thus, there appears to be a clear dichotomy in recent factor performance: while generally accepted factors struggled, various factors that are considered to be inferior or redundant remained effective.

Financial Constraints and Likelihood of Product Innovation: Firm-level Evidence from China
Sun, Sizhong
This paper investigates how financial constraints affect a firm’s decision on whether to conduct product innovation. Theoretically, we show that a firm's optimal decision on product innovation is a function of its past decision, financial constraints and a set of control variables. Through influencing investment, financial constraints increase a firm's marginal cost of production, which eventually reduces the firm's probability of conducting product innovation. The theoretical modelling implies a set of population moments, which we fit with firm-level data of China's manufacturing sector from 2005 to 2007. Our estimations find that the more financially constrained a firm is, the less likely it will conduct product innovation. For a firm that innovates previously, a one unit increase in financial constraints results in around 0.9 per cent drop in the probability of innovation. In contrast, it leads to around 0.14 per cent decrease in the probability if a firm does not innovate previously.

Hedging longevity risk in defined contribution pension schemes
Ankush Agarwal,Christian-Oliver Ewald,Yongjie Wang

Pension schemes all over the world are under increasing pressure to efficiently hedge the longevity risk posed by ageing populations. In this work, we study an optimal investment problem for a defined contribution pension scheme which decides to hedge the longevity risk using a mortality-linked security, typically a longevity bond. The pension scheme invests in the risky assets available in the market, including the longevity bond, by using the contributions from a representative scheme member to ensure a minimum guarantee such that the member is able to purchase a lifetime annuity upon retirement. We transform this constrained optimal investment problem into an unconstrained problem by replicating a self-financing portfolio of future contributions from the member and the minimum guarantee provided by the scheme. We solve the resulting optimisation problem using the dynamic programming principle and through a series of numerical studies reveal that the longevity risk has an important impact on the performance of investment strategies. Our results provide mathematical evidence supporting the use of mortality-linked securities for efficient hedging of the longevity risk.

Hedging with Neural Networks
Johannes Ruf,Weiguan Wang

We study neural networks as nonparametric estimation tools for the hedging of options. To this end, we design a network, named HedgeNet, that directly outputs a hedging strategy. This network is trained to minimise the hedging error instead of the pricing error. Applied to end-of-day and tick prices of S&P 500 and Euro Stoxx 50 options, the network is able to reduce the mean squared hedging error of the Black-Scholes benchmark significantly. We illustrate, however, that a similar benefit arises by simple linear regressions that incorporate the leverage effect. Finally, we show how a faulty training/test data split, possibly along with an additional 'tagging' of data, leads to a significant overestimation of the outperformance of neural networks.

Implications of Negative Interest Rates For the Net Interest Margin and Lending of Euro Area Banks
Klein, Melanie
This paper explores the impact of low (but) positive and negative market interest rates on euro area banks' net interest margin (NIM) and its components, retail lending and retail deposit rates. Using two proprietary bank-level data sets, I find a positive impact of the level of the short-term rate on the NIM, which increases substantially at negative market rates. As low profitability could hamper the ability of banks to expand lending, I also investigate the impact of the NIM on new lending to the non-financial private sector. In general, the NIM is positively related to lending: When lending is less profitable, banks cut lending. However, at negative rates this effect vanishes. This finding suggests that banks adjusted their business practices when servicing new loans, thereby contributing to higher new lending in the euro area since 2014.

Independent Audit Committee, Risk Management Committee, and Audit Fees
larasati, Dyah Ayu,Ratri, Melinda Cahyaning,Nasih, Mohammad,Harymawan, Iman
This study aims to analyze the role of an independent audit committee on the relationship between the Risk Management Committee (RMC) and audit fees. We use 510 observations from 216 different companies indexed on the Indonesia Stock Exchange for 2014â€"2016. This study uses ordinary least square analysis to prove our hypotheses. We find that participation of the independent commissioner as an audit committee member will strengthen the relationship between RMC and audit fee. Consistent with the demand side of audit theory, our study shows that the existence of a stand-alone risk management committee and a more independent commissioner sitting on the audit committee will demand higher audit coverage. As a result, it will increase the audit fee. It occurs since the existence of a more independent audit committees could be able to objectively assess the risk as recommended by the RMC and respond to it by increasing the demand on audit coverage for higher audit quality, and hence increase the fees paid to the external auditor. These findings could contribute to the regulatory bodies in Indonesia in terms of providing empirical evidence on the relationship between board governance structure and audit pricing within non-financial industries companies.

Information flow networks of Chinese stock market sectors
Peng Yue,Qing Cai,Wanfeng Yan,Wei-Xing Zhou

Transfer entropy measures the strength and direction of information flow between different time series. We study the information flow networks of the Chinese stock market and identify important sectors and information flow paths. This paper uses the daily closing price data of the 28 level-1 sectors from Shenyin \& Wanguo Securities ranging from 2000 to 2017 to study the information transmission between different sectors. We construct information flow networks with the sectors as the nodes and the transfer entropy between them as the corresponding edges. Then we adopt the maximum spanning arborescence (MSA) to extracting important information flows and the hierarchical structure of the networks. We find that, during the whole sample period, the \textit{composite} sector is an information source of the whole stock market, while the \textit{non-bank financial} sector is the information sink. We also find that the \textit{non-bank finance}, \textit{bank}, \textit{computer}, \textit{media}, \textit{real estate}, \textit{medical biology} and \textit{non-ferrous metals} sectors appear as high-degree root nodes in the outgoing and incoming information flow MSAs. Especially, the \textit{non-bank finance} and \textit{bank} sectors have significantly high degrees after 2008 in the outgoing information flow networks. We uncover how stock market turmoils affect the structure of the MSAs. Finally, we reveal the specificity of information source and sink sectors and make a conclusion that the root node sector as the information sink of the incoming information flow networks. Overall, our analyses show that the structure of information flow networks changes with time and the market exhibits a sector rotation phenomenon. Our work has important implications for market participants and policy makers in managing market risks and controlling the contagion of risks.

International Capital Flows at the Security Level - Evidence from the Ecb's Asset Purchase Programme
Bergant, Katharina,Fidora, Michael,Schmitz, Martin
We analyse euro area investors' portfolio rebalancing during the ECB's Asset PurchaseProgramme at the security level. Our empirical analysis shows that euro area investors (inparticular investment funds and households) actively rebalanced away from securitiestargeted under the Public Sector Purchase Programme and other euro-denominated debtsecurities, towards foreign debt instruments, including `closest substitutes', i.e. certainsovereign debt securities issued by non-euro area advanced countries. This rebalancingwas particularly strong during the first six quarters of the programme. Our analysis alsoreveals marked differences across sectors as well as country groups within the euro area,suggesting that quantitative easing has induced heterogeneous portfolio shifts.

Long memory in select stock returns using an alternative wavelet log-scale alignment approach
Avishek Bhandari,Bandi Kamaiah

This study investigates the efficiency of some select stock markets. Using an improved wavelet estimator of long range dependence, we show evidence of long memory in the stock returns of some emerging Asian economies. However, developed markets of Europe and the United States did not exhibit long memory thereby confirming the efficiency of developed stock markets. On the other hand, emerging Asian markets are found to be less efficient as long memory is more pronounced in these markets.

M&As, Employee Costs and Labor Reallocation
Lagaras, Spyridon
I study the impact of M&As on labor market outcomes of employees in target firms. Using a matched employer-employee dataset linked with hand-collected data on M&A activity in Brazil, I estimate the magnitudes of employee costs and document the underlying sources of earnings changes in the post-merger period. I find that M&As are associated with large and persistent earnings declines for employees in target rms, which are largely explained by employment losses from displacement and wage losses from reallocation. Declines in employer wage premiums in the post-merger period account for the majority of wage losses, with 47% of displaced employees transitioning to lower-wage firms. Finally, I provide evidence that the effects are concentrated on employees with lower skills, and increase with the level of firm-specific human capital. Taken together, my findings imply that the personal costs of M&As to employees are large and long-lasting, and highlight the role of displacement and loss of firm-specific wage premiums as the primary contributing factors of earnings declines in the post-merger period.

Macroeconomic Vulnerabilities and Its Effect on Nonperforming Loans in Indian Commercial Banks
Syed, Aamir,Tripathi, Ravindra
This study explores the panel data of 27 public sector banks, 21 private sector banks, 5 SBI & associates banks along with 49 foreign banks covering the period from 2000-2018. The main objective of this paper is to investigate the impact of macroeconomic variables on nonperforming loans by categorizing the Indian schedules banks into four categories namely public, private, foreign and SBI associate banks. Altogether five macroeconomic variables are taken focusing on economic growth, unemployment, interest rates, inflation and exchange rate vulnerabilities. Using GMM model the findings shows that macro-economic variables differ differently for all the categories of banks as for public sector banks including state banks and its associates all the variables are significant whereas for private banks inflation, growth rate and interest rate are significant factor, contrary foreign banks are more effected by exchange rate fluctuations apart from other macroeconomic variables. Findings of the study provides an insight that how this relationship between macroeconomic variables and NPA’s changes among the different categories of banks on the basis of ownership, thus assisting bankers and policy makers in taking precautionary measures while drafting banking and monetary policies as ownership of the banks plays a key role in banking overall management, which we can also see from the analysis that over the years private and foreign banks have considerable reduced their share of nonperforming loans from the overall share of NPA’s in Indian commercial banks.

Mean Field Game Approach to Bitcoin Mining
Charles Bertucci,Louis Bertucci,Jean-Michel Lasry,Pierre-Louis Lions

We present an analysis of the Proof-of-Work consensus algorithm, used on the Bitcoin blockchain, using a Mean Field Game framework. Using a master equation, we provide an equilibrium characterization of the total computational power devoted to mining the blockchain (hashrate). From a simple setting we show how the master equation approach allows us to enrich the model by relaxing most of the simplifying assumptions. The essential structure of the game is preserved across all the enrichments. In deterministic settings, the hashrate ultimately reaches a steady state in which it increases at the rate of technological progress. In stochastic settings, there exists a target for the hashrate for every possible random state. As a consequence, we show that in equilibrium the security of the underlying blockchain is either $i)$ constant, or $ii)$ increases with the demand for the underlying cryptocurrency.

On Adjusting the One-sided Hodrick-Prescott Filter
Wolf, Elias,Mokinski, Frieder,Schüler, Yves S.
We show that one should not use the one-sided Hodrick-Prescott filter (HP-1s) as the real-time version of the two-sided Hodrick-Prescott filter (HP-2s): First, in terms of the extracted cyclical component, HP-1s fails to remove low-frequency fluctuations to the same extent as HP-2s. Second, HP-1s dampens fluctuations at all frequencies - even those it is meant to extract. As a remedy, we propose two small adjustments to HP-1s, aligning its properties closely with HP-2s: (1) a lower value for the smoothing parameter and (2) a multiplicative rescaling of the extracted cyclical component. For example, for HP-2s with = 1,600 (value of smoothing parameter), the adjusted one-sided HP filter uses = 650 and rescales the extracted cyclical component by a factor of 1:1513. Using simulated and empirical data, we illustrate the relevance of the adjustments. For instance, financial cycles may appear 1.7 times more volatile than business cycles, where in fact volatilities differ only marginally.

On the Credit-to-GDP Gap and Spurious Medium-Term Cycles
Schüler, Yves Stephan
The Basel III framework advises considering a reference indicator at the country level to guide the setting of the countercyclical capital buffer: the credit-to-GDP gap. In this paper, I provide empirical evidence suggesting that the credit-to-GDP gap is subject to spurious medium-term cycles, i.e. artificial boom-bust cycles with a maximum duration of around 40 years. This may impair its use as a reference indicator.

On the dynamics emerging from pandemics and infodemics
Stephan Leitner

This position paper discusses emerging behavioral, social, and economic dynamics related to the COVID-19 pandemic and puts particular emphasis on two emerging issues: First, delayed effects (or second strikes) of pandemics caused by dread risk effects are discussed whereby two factors which might influence the existence of such effects are identified, namely the accessibility of (mis-)information and the effects of policy decisions on adaptive behavior. Second, the issue of individual preparedness to hazardous events is discussed. As events such as the COVID-19 pandemic unfolds complex behavioral patterns which are hard to predict, sophisticated models which account for behavioral, social, and economic dynamics are required to assess the effectivity and efficiency of decision-making.

PCAOB International Inspections and Merger and Acquisition Outcomes
Kim, Yongtae,Su, Lixin (Nancy),Zhou, Gaoguang,Zhu, Xindong (Kevin)
This study examines how PCAOB international inspections of non-U.S. auditors affect international Merger and Acquisition (M&A) outcomes. We find that clients of inspected auditors are more likely to become acquisition targets after the public disclosure of auditor’s inspection report. We also find that deal completion is more likely and deal announcement returns are higher if deals involve targets with auditors for which inspection reports are available. Engagement deficiencies and unremediated quality control deficiencies identified in inspection reports weaken the positive effect of PCAOB oversight on M&A outcomes. Collectively, our results suggest that PCAOB oversight reduces information uncertainty in M&A deals.

Pandemics and Systemic Financial Risk
Jackson, Howell E.,Schwarcz, Steven L.
The coronavirus has produced a public health debacle of the first-order. But the virus is also propagating the kind of exogenous shock that can precipitate â€" and to a considerable degree is already precipitating â€" a systemic event for our financial system. This currently unfolding systemic shock comes a little more than a decade after the last financial crisis. In the intervening years, much as been written about the global financial crisis of 2008 and its systemic dimensions. Additional scholarly attention has focused on first devising and then critiquing the macroprudential reforms that ensued, both in the Dodd-Frank Act and the many regulations and policy guidelines that implemented its provisions. In this essay, we consider the coronavirus pandemic and its implications for the financial system through the lens of the frameworks we had developed for the analysis of systemic financial risks in the aftermath of the last financial crisis. We compare and contrast the two crises in terms of systemic financial risks and then explore two dimensions on which financial regulatory authorities might profitably engage with public health officials. As we are writing this essay, the pandemic’s ultimate scope and consequences, financial and otherwise, are unknown and unknowable; our analysis, therefore, is necessarily provisional and tentative. We hope, however, it may be of interest and potential use to the academic community and policymakers.

Passengers' Travel Behavior in Response to Unplanned Transit Disruptions
Nima Golshani,Ehsan Rahimi,Ramin Shabanpour,Kouros Mohammadian,Joshua Auld,Hubert Ley

Public transit disruption is becoming more common across different transit services, which can have a destructive influence on the resiliency and reliability of the transportation system. Utilizing a recently collected data of transit users in the Chicago Metropolitan Area, the current study aims to analyze how transit users respond to unplanned service disruption and disclose the factors that affect their behavior.

R Minus G Negative: Can We Sleep More Soundly?
Mauro, Paolo ,Zhou, Jing
Contrary to the traditional assumption of interest rates on government debt exceeding economicgrowth, negative interest-growth differentials have become prevalent since the global financialcrisis. As these differentials are a key determinant of public debt dynamics, can we sleep moresoundly, despite high government debts? Our paper undertakes an empirical analysis of interestgrowthdifferentials, using the largest historical database on average effective governmentborrowing costs for 55 countries over up to 200 years. We document that negative differentialshave occurred more often than not, in both advanced and emerging economies, and have oftenpersisted for long historical stretches. Moreover, differentials are no higher prior to sovereigndefaults than in normal times. Marginal (rather than average) government borrowing costs oftenrise abruptly and sharply, but just prior to default. Based on these results, our answer is: notreally.

Rating a Robo-Rater
Nanigian, David
Since 2011, Morningstar has issued Morningstar Analyst Ratings on many of the largest mutual funds in the USA. In June 2017, Morningstar launched the Morningstar Quantitative Rating™ to provide a forward-looking rating on all mutual funds. Morningstar uses a “robo-rater” machine-learning model to assign Morningstar Quantitative Ratings. However, the “robo-rater” cannot utilize the complete set of information available to Morningstar’s analyst as it cannot process “soft information”. The purpose of this study is to evaluate if and how this “robo-rater” is conducive to mutual fund selection. I find that the only value of the “robo-rater” is in its assessment of mutual fund expenses and that its inability to process “soft information” makes the Morningstar Quantitative Rating™ much less useful than the Morningstar Analyst Rating™.

Real implications of Quantitative Easing in the euro area: a complex-network perspective
Chiara Perillo,Stefano Battiston

The long-lasting socio-economic impact of the global financial crisis has questioned the adequacy of traditional tools in explaining periods of financial distress, as well as the adequacy of the existing policy response. In particular, the effect of complex interconnections among financial institutions on financial stability has been widely recognized. A recent debate focused on the effects of unconventional policies aimed at achieving both price and financial stability. In particular, Quantitative Easing (QE, i.e., the large-scale asset purchase programme conducted by a central bank upon the creation of new money) has been recently implemented by the European Central Bank (ECB). In this context, two questions deserve more attention in the literature. First, to what extent, by injecting liquidity, the QE may alter the bank-firm lending level and stimulate the real economy. Second, to what extent the QE may also alter the pattern of intra-financial exposures among financial actors (including banks, investment funds, insurance corporations, and pension funds) and what are the implications in terms of financial stability. Here, we address these two questions by developing a methodology to map the macro-network of financial exposures among institutional sectors across financial instruments (e.g., equity, bonds, and loans) and we illustrate our approach on recently available data (i.e., data on loans and private and public securities purchased within the QE). We then test the effect of the implementation of ECB's QE on the time evolution of the financial linkages in the macro-network of the euro area, as well as the effect on macroeconomic variables, such as output and prices.

Riding the Yield Curve: Risk Taking Behavior in a Low Interest Rate Environment
Chami, Ralph ,Cosimano, Thomas,Rochon, Celine,Yung, Julieta
Investors seek to hedge against interest rate risk by taking long or short positions on bonds ofdifferent maturities. We study changes in risk taking behavior in a low interest rateenvironment by estimating a market stochastic discount factor that is non-linear and thereforeconsistent with the empirical properties of cashflow valuations identified in the literature. Weprovide evidence that non-linearities arise from hedging strategies of investors exposed tointerest rate risk. Capital losses are amplified when interest rates increase and risk averseinvestors have taken positions on instruments with longer maturity, expecting instead interestrates to revert back to their historical average.

Risk Characteristics of Covered Bonds: Monitoring Beyond Ratings
Grothe, Magdalena,Zeyer, Jana
This paper proposes a set of indicators relevant for the risk characteristics of covered bonds, as based on granular publicly available transparency data. The indicators capture various aspects of cash flow risks related to the issuer, the cover pool and the payment structure. They offer unified risk metrics for the European covered bond universe, which ensures comparability across covered bonds issued by different issuers and rated by different credit rating agencies. The availability of granular risk indicators adds to the overall transparency of the market in the context of risk monitoring.

D'Acunto, Francesco,Rossi, Alberto G.
In this chapter, we first discuss the limitations of traditional financial advice, which led to the emergence of robo-advising. We then describe the main features of robo-advising and propose a taxonomy of robo-advisors based on four defining dimensions---personalization, discretion, involvement, and human interaction. Building on these premises, we delve into the theoretical and empirical evidence on the design and effects of robo-advisors on two major sets of financial decisions, that is, investment choices (for both short- or long-term horizons) and the allocation of financial resources between spending and saving. We conclude by elaborating on five broadly open issues in robo-advising, which beget theoretical and empirical research by scholars in economics, finance, psychology, law, philosophy, as well as regulators and industry practitioners.

Robust Arbitrage Conditions for Financial Markets
Derek Singh,Shuzhong Zhang

This paper investigates arbitrage properties of financial markets under distributional uncertainty using Wasserstein distance as the ambiguity measure. The weak and strong forms of the classical arbitrage conditions are considered. A relaxation is introduced for which we coin the term statistical arbitrage. The simpler dual formulations of the robust arbitrage conditions are derived. A number of interesting questions arise in this context. One question is: can we compute a critical Wasserstein radius beyond which an arbitrage opportunity exists? What is the shape of the curve mapping the degree of ambiguity to statistical arbitrage levels? Other questions arise regarding the structure of best (worst) case distributions and optimal portfolios. Towards answering these questions, some theory is developed and computational experiments are conducted for specific problem instances. Finally some open questions and suggestions for future research are discussed.

Sequential hypothesis testing in machine learning driven crude oil jump detection
Michael Roberts,Indranil SenGupta

In this paper we present a sequential hypothesis test for the detection of general jump size distrubution. Infinitesimal generators for the corresponding log-likelihood ratios are presented and analyzed. Bounds for infinitesimal generators in terms of super-solutions and sub-solutions are computed. This is shown to be implementable in relation to various classification problems for a crude oil price data set. Machine and deep learning algorithms are implemented to extract a specific deterministic component from the crude oil data set, and the deterministic component is implemented to improve the Barndorff-Nielsen and Shephard model, a commonly used stochastic model for derivative and commodity market analysis.

Smart technologies for fighting Pandemics: The techno- and human- driven approaches in controlling the virus transmission
How do governments in China and Western democracies differ in their technological response to control the transmission of the pandemic? Based on an analysis of academic papers, World Health Organization reports and newspapers, this research compares two opposing approaches, whereas the Chinese cities and government have adopted a techno-driven approach, Western governments have adopted a human-driven approach to control the transmission of Covid-19. The findings highlight that although the techno driven approach may be more productive to identify, isolate and quarantine infected individuals, it also results in the suppression and censoring the citizen views. It is further emphasized that human interaction with the technology is mediated by the political and institutional context in which the technologies are implemented. This paper contributes to literature by understanding the human-technology relationship, and offers five practical observations for controlling virus transmissions during pandemics.

Soft International Law and the Promotion of Financial Regulation and Responsibility
Bradlow, Daniel
This paper is a contribution to a book on exploring how hard and soft international law are used in advocating for social change. It focuses on the two sets of international standards that are applicable to international finance. The first are the international financial regulatory standards (hereinafter referred to as “international financial regulatory standards”) developed by the standard setting bodies, such as the Basel Committee on Banking Supervision. The second are the standards dealing with enhancing the social and environmental responsibility of the financial sector (hereinafter referred to as “financial responsibility standards”). The paper is divided into four sections. The first section will discuss the international international financial regulatory standards. The second section will discuss some examples of soft law initiatives dealing with enhancing the social and environmental responsibility of the financial sector (hereinafter referred to as “financial responsibility standards”). The third section draws some lessons relating to the use of soft law in promoting change. The final section is a conclusion.

Speculation and Lottery-Like Demand in Cryptocurrency Markets
Grobys, Klaus,Junttila, Juha-Pekka
This is the first paper that explores lottery-like demand in cryptocurrency markets. Since recent research provides evidence that cryptocurrency returns are rather short-memory processes in their nature, we modify Bali et al.’s (2011, 2017) MAX measure and employ a weekly forecast horizon and last week’s daily log-returns for calculating the metric for our portfolio sorts. From an econometric point of view, this study proposes statistical tests that are robust to unknown dynamic dependency structures in the cryptocurrency data. Our results show that average raw and risk adjusted return differences between stocks in the lowest and highest MAX deciles exceed 1.50% per week. These results are robust to controls for Bitcoin risk, or potential microstructure effects. Our findings are important also from a theoretical point of view because they suggest that parallel to the stock markets, similar behavioral mechanisms of underlying investor behavior are present also in new virtual currency markets.

Start-Up Law in Israel
Kamar, Ehud,Shenhav, Ayal,Yanovsky, Shay
This is a chapter on Israel in a forthcoming book on start-up law in several jurisdictions. Israel is a world-renowned leader in innovation and entrepreneurship. The country’s legal environment is an important basis for this success. It allows foreign investors to invest freely and repatriate funds without currency controls, and offers tax exemptions, a legal system based on common law, and an independent judiciary.The law permits a variety of investment structures, including equity investments, debt financing, and combinations of the two. Entrepreneurs can also apply for public funding. In addition, the law provides strong protections for trade secrets, patents, copyright, trademarks, trade dresses, and designs. Commercial entities in Israel commonly enter into licensing agreements, including in-licensing agreements, cross-licensing agreements, and technology dispositions.The law charges a company’s board of directors with supervision of management. Companies reduce the exposure of their directors and officers to liability using exculpation, indemnification, and insurance.Finally, the law contemplates various scenarios for distressed companies, including voluntary liquidation, court-ordered liquidation, court-supervised voluntary liquidation and reorganization.

Systemic Risk Modeling: How Theory Can Meet Statistics
Espinoza, Raphael,Segoviano, Miguel,Yan, Ji
We propose a framework to link empirical models of systemic risk to theoretical network/general equilibrium models used to understand the channels of transmission of systemicrisk. The theoretical model allows for systemic risk due to interbank counterpartyrisk, common asset exposures/fire sales, and a 'Minsky" cycle of optimism. The empiricalmodel uses stock market and CDS spreads data to estimate a multivariate density of equityreturns and to compute the expected equity return for each bank, conditional on a badmacro-outcome. Theses 'cross-sectional" moments are used to re-calibrate the theoreticalmodel and estimate the importance of the Minsky cycle of optimism in driving systemicrisk.

The Bigger, the Better?
Kronies, Alexander
I empirically investigate wind energy production dynamics and correlations to electricity prices on a turbine-individual level. I find that large turbine production outputs are less negatively correlated to electricity prices than those of small turbines. Apart from the fact that large turbines produce more and are less volatile in their production outputs over time, they sell electricity at a higher average price, outperforming their smaller peers. Additional tests on high-frequency data confirm these results and indicate that the financial impacts are large when considering short-term dynamics. These findings are important for investors to consider when allocating capital to alternative venues as renewable energy.

The Dynamics of Non-Performing Loans During Banking Crises: A New Database
Ari, Anil,Chen, Sophia,Ratnovski, Lev
This paper presents a new dataset on the dynamics of non-performing loans (NPLs) during 88 banking crises since 1990. The data show similarities across crises during NPL build-ups but less so during NPL resolutions. We find a close relationship between NPL problemsâ€"elevated and unresolved NPLsâ€"and the severity of post-crisis recessions. A machine learning approach identifies a set of pre-crisis predictors of NPL problems related to weak macroeconomic, institutional, corporate, and banking sector conditions. Our findings suggest that reducing pre-crisis vulnerabilities and promptly addressing NPL problems during a crisis are important for post-crisis output recovery.

The Global RegTech Industry Benchmark Report
Schizas, Emmanuel,McKain, Grigory,Zhang, Bryan Zheng,Garvey, Kieran,Ganbold, Altantsetseg,Hussain, Hatim,Kumar, Pankajesh,Huang, Eva,Wang, Shaoxin,Yerolemou, Nikos
The first global benchmark study of the RegTech sector by the Cambridge Centre for Alternative Finance, conducted with the support of EY Japan, is based on a survey of 111 firms as well as regulators and industry experts. We estimate that the global RegTech industry generated $5 billion in revenue in 2018, and had raised $9.7 billion in external funding as of early 2019. The financial services sector dominates demand for RegTech services but most vendors now target non-financial sectors, and this share is set to grow.The RegTech market can be broken down into five distinct segments: 1. Profiling and due diligence 2. Dynamic compliance 3. Reporting and dashboards 4. Risk analytics 5. Market monitoring.The sector underwent a period of rapid growth between 2014 and 2018, but is now growing mostly through incumbent expansion rather than market entry. Long sales cycles, complex IT planning within client institutions, difficulties in establishing trust and high levels of competition have left some vendors struggling to gain traction. A handful of larger vendors thus dominate most funding and commercial activity.

The Impact of Birth Order on Behavior in Contact Team Sports: the Evidence of Rugby Teams in Argentina
Fernando Delbianco,Federico Fioravanti,Fernando Tohmé

Several studies have shown that birth order and the sex of siblings may have an influence on individual behavioral traits. In particular, it has been found that second brothers (of older male siblings) tend to have more disciplinary problems. If this is the case, this should also be shown in contact sports. To assess this hypothesis we use a data set from the South Rugby Union (URS) from Bah\'ia Blanca, Argentina, and information obtained by surveying more than four hundred players of that league. We find a statistically significant positive relation between being a second-born male rugby player with an older male brother and the number of yellow cards received.

\textbf{Keywords:} Birth Order; Behavior; Contact Sports; Rugby.

The Impact of Uncertainty and Certainty Shocks
Schüler, Yves S.
I propose a Bayesian quantile VAR to identify and assess the impact of uncertainty and certainty shocks, unifying Bloom's (2009) two identification steps into one. I find that an uncertainty shock widens the conditional distribution of future real economic activity growth, in line with a risk shock. Conversely, a certainty shock (a shock strongly decreasing uncertainty) narrows the conditional distribution of future real activity growth. In addition to the difference in signs, I show that the two shocks are different shocks. Each shock impacts the real economy uniquely. I support this with the underlying events: For instance, uncertainty shocks relate to events such as Black Monday and 9/11, but also to fears about future negative economic outcomes. In contrast, certainty shocks often link to phases of irrational exuberance. Commonly, no distinction is made between uncertainty and certainty shocks. I show that uncertainty shocks become more important if distinguished from certainty shocks.

The Market Impact of Systemic Risk Capital Surcharges
Gündüz, Yalin
This paper tests whether an increase or decrease of the capital surcharge for being a global systemically important bank (G-SIB) envisaged by regulators has an impact on the CDS prices of these banks. We find evidence that the CDS spreads of a G-SIB bank increase (decrease) after the announcement of a higher (lower) capital surcharge. However, this effect is temporary, as the mean CDS spreads revert to pre-announcement level, dropping sharply after the initial rise. Our analysis contributes to the debate on whether being designated as a G-SIB bank necessarily leads to implicit "too-big-to-fail" subsidies. The findings imply that the investors immediately update their beliefs on the systemic risk of the bank after the bucket reallocation announcement and temporarily demand more hedging against systemic risk.

The Prediction for the Outbreak of COVID-19 for 15 States in USA by Using Turning Phase Concepts As of April 10, 2020
Yuan, George Xianzhi,Di, Lan,Gu, Yudi,Qian, Guoqi,Qian, Xiaosong
Based on a new concept called “Turning Period”, the goal of this report is to show how we can conduct the prediction for the outlook in the different stages for the battle with outbreak of COVID-19 currently in US, in particular, to identify when each of top 15 states in USA (basically on their populations) is going to enter into the stage that the outbreak of COVID-19 is under the control by the criteria such as daily change of new patients is less than 10% smoothly. Indeed, based on the data of April 10, 2020 with the numerical analysis, we are able to classify 15 states of US into the following four different categories for the Prevention and Control of Infectious Diseases Today and the main conclusion are: First, staring around April 14, 20202, three states which are Washington State, Louisiana and Indiana are entering the stage that the outbreak of COVID-19 is under the control, which means daily change of new patients is less than 10% and the gamma is less than zero in general. Second, staring around April 15, 20202, two states which are New Jersey, and New York are entering the stage that the outbreak of COVID-19 is under the control, which means daily change of new patients is less than 10% and the gamma is less than zero in general. Third, staring around April 16, 20202, seven states which are California, Florida, Georgia (GA), Illinois, Maryland, Indiana, Michigan, and Pennsylvania are entering the stage that the outbreak of COVID-19 is under the control, which means daily change of new patients is less than 10% and the gamma is less than zero in general. Fourth, staring around April 17, 20202, three states which are Texas, Massachusetts, and Connecticut are entering the stage that the outbreak of COVID-19 is under the control, which means daily change of new patients is less than 10% and the gamma is less than zero in general. Finally, we want to reinforce that emergency risk management is always associated with the implementation of an emergency plan. The identification of the Turning Time Period is key to emergency planning as it provides a timeline for effective actions and solutions to combat a pandemic by reducing as much unexpected risk as soon as possible.

The Prediction for the Outbreak of COVID-19 in European Countries by Using Turning Phase Concepts as of April 9, 2020
Yuan, George Xianzhi,Di, Lan,Gu, Yudi,Qian, Guoqi,Qian, Xiaosong
Based on a new concept called “ Turning Period”, the goal of this report is to show how we can conduct the prediction for the outlook in the different stages for the battle with outbreak of COVID-19 currently worldwide. Indeed, based on the data of April 9, 2020 with the numerical analysis, we are able to classify 13 countries in Europe fighting with COVID-19 into three different categories stages for the Prevention and Control of Infectious Diseases Worldwide Today.We want to reinforce that emergency risk management is always associated with the implementation of an emergency plan. The identification of the Turning Time Period is key to emergency planning as it provides a timeline for effective actions and solutions to combat a pandemic by reducing as much unexpected risk as soon as possible.

The effect of stay-at-home orders on COVID-19 infections in the United States
James H. Fowler,Seth J. Hill,Remy Levin,Nick Obradovich

In March and April 2020, public health authorities in the United States acted to mitigate transmission of COVID-19. These actions were not coordinated at the national level, which creates an opportunity to use spatial and temporal variation to measure their effect with greater accuracy. We combine publicly available data sources on the timing of stay-at-home orders and daily confirmed COVID-19 cases at the county level in the United States (N = 124,027). We then derive from the classic SIR model a two-way fixed-effects model and apply it to the data with controls for unmeasured differences between counties and over time. Mean county-level daily growth in COVID-19 infections peaked at 17.2% just before stay-at-home orders were issued. Two way fixed-effects regression estimates suggest that orders were associated with a 3.9 percentage point (95% CI 1.2 to 6.6) reduction in the growth rate after one week and an 6.9 percentage point (2.4 to 11.5) reduction after two weeks. By day 27 the reduction (22.6 percentage points, 14.8 to 30.5) had surpassed the growth at the peak, indicating that growth had turned negative and the number of new daily infections was beginning to decline. A hypothetical national stay-at-home order issued on March 13, 2020 when a national emergency was declared might have reduced cumulative infections by 63.3%, and might have helped to reverse exponential growth in the disease by April 10. The results here suggest that a coordinated nationwide stay-at-home order may have reduced by hundreds of thousands the current number of infections and by tens of thousands the total number of deaths from COVID-19. Future efforts in the United States and elsewhere to control pandemics should coordinate stay-at-home orders at the national level, especially for diseases for which local spread has already occurred and testing availability is delayed.

Transfer Learning and Textual Analysis of Accounting Disclosures: Applying Big Data Methods to Small(er) Data Sets
Siano, Federico,Wysocki, Peter D.
We introduce and apply machine transfer learning methods to analyze accounting disclosures. We use the examples of the new BERT language model and sentiment analysis of quarterly earnings disclosures to demonstrate the key transfer learning concepts of: (i) pre-training on generic “Big Data”, (ii) fine-tuning on small accounting data-sets, and (iii) using a language model that captures context rather than stand-alone words. Overall, we show that this new approach is easy to implement, uses widely-available and low-cost computing resources, and has superior performance relative to existing textual analysis tools in accounting. We conclude with suggestions for opportunities to apply transfer learning to address important accounting research questions.

Transitioning out of the Coronavirus Lockdown: A Framework for Zone-Based Social Distancing
Eric Friedman,John Friedman,Simon Johnson,Adam Landsberg

In the face of elevated pandemic risk, is it necessary to completely lock down the population, imposing extreme social distancing? Canonical epidemiological models suggest this may be unavoidable for months at a time, despite the heavy social and human cost of physically isolating people. Alternatively, people could retreat into socially or economically defined defensive zones, with more interactions inside their zone than across zones. Starting from a complete lockdown, zones could facilitate responsible reopening of education, government, and firms, as a well-implemented structure can dramatically slow the diffusion of the disease. This paper provides a framework for understanding and evaluating the effectiveness of zones for social distancing.

Transitory and Permanent Shocks in the Global Market for Crude Oil
Rebei, Nooman,Sbia, Rashid
This paper documents the determinants of real oil price in the global market based onSVAR model embedding transitory and permanent shocks on oil demand and supply aswell as speculative disturbances. We find evidence of significant differences in thepropagation mechanisms of transitory versus permanent shocks, pointing to theimportance of disentangling their distinct effects. Permanent supply disruptions turn out tobe a bigger factor in historical oil price movements during the most recent decades, whilespeculative shocks became less influential.

Unconventional Monetary Policy Shocks in the Euro Area and the Sovereign-Bank Nexus
Hristov, Nikolay,Hülsewig, Oliver,Scharler, Johann
We explore the effects of the ECB’s unconventional monetary policy on the banks’ sovereign debt portfolios. In particular, using panel vector autoregressive (VAR) models we analyze whether banks increased their domestic government bond holdings in response to non-standard monetary policy shocks, thereby possibly promoting the sovereign-bank nexus, i.e. the exposure of banks to the debt issued by the national government. Our results suggest that euro area crisis countries’ banks enlarged their exposure to domestic sovereign debt after innovations related to unconventional monetary policy. Moreover, the restructuring of sovereign debt portfolios was characterized by a home bias.

Unlocking Access to Finance for Smes: A Cross-Country Analysis
Fouejieu, Armand,Ndoye, Anta,Sydorenko, Tetyana
Countries in the MENAP and CCA regions have the lowest levels of financial inclusion of small and medium enterprises (SMEs) in the world. The paper provides empirical evidence on the drivers of SME access to finance for a large sample of countries, and identifies key policy priorities for these two regions: economic and institutional stability, competition, public sector size and government effectiveness, credit information infrastructure (e.g., credit registries), the business environment (e.g., legal frameworks for contract enforcement), and financial supervisory and regulatory capacity. The analysis also shows that improving credit information, economic competition, the business environment along with economic development and better governance would help close the SME financial inclusion gap between MENAP and CCA regions and the best performers. The paper concludes on the need to adopt holistic policy strategies that take into account the full range of macro and institutional requirements and reforms, and prioritize these reforms in accordance with each country's specific characteristics.

Voluntary Disclosure with Evolving News
Aghamolla, Cyrus,An, Byeong-Je
We study a dynamic voluntary disclosure setting where the manager's information and the firm's value evolve over time. The manager is not limited in her disclosure opportunities but disclosure is costly. The results show that the manager discloses even if this leads to a price decrease in the current period. The manager absorbs this price drop in order to increase her option value of withholding disclosure in the future. That is, by disclosing today the manager can improve her continuation value. The results provide a number of novel empirical predictions regarding asset prices and disclosure patterns over time. These include, among others, that disclosures are negatively correlated in time, and stock return skewness is negatively correlated with lagged returns for firms with low uncertainty over their future profitability, in more competitive industries, and in industries with less informative public news.

What Happens When You Assume
Park, Kevin A.
Mortgage assumption allows a borrower to transfer both the property and the outstanding balance of their mortgage to a new homebuyer. Assumption of a loan has value when the note rate is below prevailing market rates. However, assumptions also create risk for lenders and insurers. This paper uses survival analysis to estimate the likelihood of assumption and the effect of assumption on the likelihood of refinance and default. We find that every additional $1,000 in assumption value is associated with a 2 percent increase in the assumption hazard. Assumption is not associated with a statistically significant change in refinance or default hazard when loan performance is dated from the original closing date, but assumptors are less likely to refinance or default compared to other borrowers when loan performance is based on time since assumption. The growth of FHA-insured lending since the Great Recession has increased the stock of mortgages that are assumable. However, the economic significance of the assumption option depends on the future level of mortgage rates.