Research articles for the 2021-02-01

A Class of Explicit optimal contracts in the face of shutdown
Jessica Martin,Stéphane Villeneuve

What type of delegation contract should be offered when facing a risk of the magnitude of the pandemic we are currently experiencing and how does the likelihood of an exogenous early termination of the relationship modify the terms of a full-commitment contract? We study these questions by considering a dynamic principal-agent model that naturally extends the classical Holmstr{\"o}m-Milgrom setting to include a risk of default whose origin is independent of the inherent agency problem. We obtain an explicit characterization of the optimal wage along with the optimal action provided by the agent. The optimal contract is linear by offering both a fixed share of the output which is similar to the standard shutdown-free Holmstr{\"o}m-Milgrom model and a linear prevention mechanism that is proportional to the random lifetime of the contract. We then tweak the model to add a possibility for risk mitigation through investment and study its optimality.

A new efficient approximation scheme for solving high-dimensional semilinear PDEs: control variate method for Deep BSDE solver
Akihiko Takahashi,Yoshifumi Tsuchida,Toshihiro Yamada

This paper introduces a new approximation scheme for solving high-dimensional semilinear partial differential equations (PDEs) and backward stochastic differential equations (BSDEs). First, we decompose a target semilinear PDE (BSDE) into two parts, namely "dominant" linear and "small" nonlinear PDEs. Then, we employ a Deep BSDE solver with a new control variate method to solve those PDEs, where approximations based on an asymptotic expansion technique are effectively applied to the linear part and also used as control variates for the nonlinear part. Moreover, our theoretical result indicates that errors of the proposed method become much smaller than those of the original Deep BSDE solver. Finally, we show numerical experiments to demonstrate the validity of our method, which is consistent with the theoretical result in this paper.

A penalized two-pass regression to predict stock returns with time-varying risk premia
Bakalli, Gaetan,Guerrier, Stéphane,Scaillet , O.
We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no arbitrage restrictions by regularizing appropriate groups of coefficients. The second pass delivers risk premia estimates to predict equity excess returns. Our Monte Carlo results and our empirical results on a large cross-sectional data set of US individual stocks show that penalization without grouping can yield to nearly all estimated time-varying models violating the no arbitrage restrictions. Moreover, our results demonstrate that the proposed method reduces the prediction errors compared to a penalized approach without appropriate grouping or a time-invariant factor model.

An evolutionary view on the emergence of Artificial Intelligence
Matheus E. Leusin,Bjoern Jindra,Daniel S. Hain

This paper draws upon the evolutionary concepts of technological relatedness and knowledge complexity to enhance our understanding of the long-term evolution of Artificial Intelligence (AI). We reveal corresponding patterns in the emergence of AI - globally and in the context of specific geographies of the US, Japan, South Korea, and China. We argue that AI emergence is associated with increasing related variety due to knowledge commonalities as well as increasing complexity. We use patent-based indicators for the period between 1974-2018 to analyse the evolution of AI's global technological space, to identify its technological core as well as changes to its overall relatedness and knowledge complexity. At the national level, we also measure countries' overall specialisations against AI-specific ones. At the global level, we find increasing overall relatedness and complexity of AI. However, for the technological core of AI, which has been stable over time, we find decreasing related variety and increasing complexity. This evidence points out that AI innovations related to core technologies are becoming increasingly distinct from each other. At the country level, we find that the US and Japan have been increasing the overall relatedness of their innovations. The opposite is the case for China and South Korea, which we associate with the fact that these countries are overall less technologically developed than the US and Japan. Finally, we observe a stable increasing overall complexity for all countries apart from China, which we explain by the focus of this country in technologies not strongly linked to AI.

Analytic Calibration in Andreasen-Huge SABR Model
K.E. Feldman

We derive analytic formulae which link $\alpha$, $\nu$ and $\rho$ parameters in Andreasen-Huge style SABR model to the ATM price and option prices at four strikes close to ATM. Based on these formulae we give a characterisation for the SABR parameters in terms of derivatives of the swap rate forward probability density function. We test the analytic result in the application to the interest rate futures option market.

Analytical Path-Integral Pricing of Deterministic Moving-Barrier Options Under Non-Gaussian Distributions
Catalão, André,Rosenfeld, Rogério
In this work, we present an analytical model, based on the path-integral formalism of statistical mechanics, for pricing options using first-passage time problems involving both fixed and deterministically moving absorbing barriers under possibly non-Gaussian distributions of the underlying object. We adapt to our problem a model originally pro- posed by De Simone et al. (2011) to describe the formation of galaxies in the universe, which uses cumulant expansions in terms of the Gaussian distribution, and we generalize it to take into account drift and cumulants of orders higher than three. From the probability density function, we obtain an analytical pricing model, not only for vanilla options (thus removing the need of volatility smile inherent to the Black & Scholes (1973) model), but also for fixed or deterministically moving barrier options. Market prices of vanilla options are used to calibrate the model, and barrier option pricing arising from the model is compared to the price resulted from the relative entropy model.

Auctioning Annuities
Gaurab Aryal,Eduardo Fajnzylber,Maria F. Gabrielli,Manuel Willington

The performance of a market for annuity contracts depends on the interaction between retirees and strategic life insurance companies with private information about their costs. We model this interaction using multi-stage and multi-attribute auctions where a retiree--auctioneer--maximizes her \emph{expected present discounted utility}. We estimate the model parameters using rich administrative data from Chile and find that retirees with low savings value firms' risk-ratings the most. The estimates also suggest that almost half the retirees who choose an annuity do not value bequest, and firms are more likely to have low annuitization cost for retirees in the top two savings deciles. Counterfactuals show that under the current mechanism, private information about costs harms only these high savers. Implementing English auctions \emph{and} prohibiting the use of risk-ratings lead to higher pensions, but only for these high savers.

COVID-19 Cases, Media Attention and Social Mood
Buigut, Steven,Kapar, Burcu
The study uses rolling regressions, both at global and country level, to analyze the impact of daily COVID-19 case numbers on four (Panic, Sentiment, Media coverage, and Fake news) indices. The indices are obtained from the Ravenpack Finance, while the daily COVID-19 cases and the policy response stringency index data is extracted from the Oxford COVID-19 Government Response Tracker. The results indicate that the impact of the number of daily COVID-19 cases on the indices is quite variable over time. Higher impact on the indices is reflected in periods where there is a significant surge in cases, in particular the initial surge in Spring, Summer and Fall. There is some evidence of diminished (increased) sensitivity of panic and media indices (fake news) to number of cases but this is not consistent across all countries. These results indicate that the public are concerned and respond to changes in the trends of the spread of the virus and highlight the importance of managing trends if halting its spread is not immediately feasible.

CRPS Learning
Jonathan Berrisch,Florian Ziel

Combination and aggregation techniques can improve forecast accuracy substantially. This also holds for probabilistic forecasting methods where full predictive distributions are combined. There are several time-varying and adaptive weighting schemes like Bayesian model averaging (BMA). However, the performance of different forecasters may vary not only over time but also in parts of the distribution. So one may be more accurate in the center of the distributions, and other ones perform better in predicting the distribution's tails. Consequently, we introduce a new weighting procedure that considers both varying performance across time and the distribution. We discuss pointwise online aggregation algorithms that optimize with respect to the continuous ranked probability score (CRPS). After analyzing the theoretical properties of a fully adaptive Bernstein online aggregation (BOA) method, we introduce smoothing procedures for pointwise CRPS learning. The properties are confirmed and discussed using simulation studies. Additionally, we illustrate the performance in a forecasting study for carbon markets. In detail, we predict the distribution of European emission allowance prices.

Capital Allocation for Set-Valued Risk Measures
Centrone, Francesca,Rosazza Gianin, Emanuela
We introduce the definition of set-valued capital allocation rule, in the context of set- valued risk measures. In analogy to some well known methods for the scalar case based on the idea of marginal contribution and hence on the notion of gradient and sub-gradient of a risk measure, and under some reasonable assumptions, we define some set-valued capital allocation rules relying on the representation theorems for coherent and convex set-valued risk measures and investigate their link with the notion of sub-differential for set-valued functions. We also introduce and study the set-valued analogous of some properties of classical capital allocation rules, such as the one of no undercut. Furthermore, we compare these rules with some of those mostly used for univariate (single-valued) risk measures. Examples and comparisons with the scalar case are provided at the end.

Controlling volatility of wind-solar power
Hans Lustfeld

The main advantage of wind and solar power plants is the power production free of CO2. Their main disadvantage is the volatility of the generated power. According to the estimates of H.-W. Sinn[1], suppressing this volatility requires pumped-storage plants with a huge capacity, several orders of magnitude larger than the present available capacity in Germany[2]. Sinn concluded that wind-solar power can be used only together with conventional power plants as backups. However, based on German power data[3] of 2019 we show that the required storage capacity can significantly be reduced, provided i) a surplus of wind-solar power plants is supplied, ii) smart meters are installed, iii) partly a different kind of wind turbines and solar panels are used in Germany. Our calculations suggest that all the electric energy, presently produced in Germany, can be obtained from wind-solar power alone. And our results let us predict that wind-solar power can be used to produce in addition the energy for transportation, warm water, space heating and in part for process heating, meaning an increase of the present electric energy production by a factor of about 5[1]. Of course, to put such a prediction on firm ground the present calculations have to be confirmed for a period of many years. And it should be kept in mind, that in any case a huge number of wind turbines and solar panels is required.

Coordinated Transaction Scheduling in Multi-Area Electricity Markets: Equilibrium and Learning
Mariola Ndrio,Subhonmesh Bose,Lang Tong,Ye Guo

Tie-line scheduling in multi-area power systems in the US largely proceeds through a market-based mechanism called Coordinated Transaction Scheduling (CTS). We analyze this market mechanism through a game-theoretic lens. Our analysis characterizes the effect of market liquidity, market participants' forecasts about inter-area price spreads, transactions fees and coupling of CTS markets with up-to-congestion virtual transactions. Using real data, we empirically verify that CTS bidders can employ simple learning algorithms to discover Nash equilibria that support the conclusions drawn from equilibrium analysis.

David vs Goliath (You against the Markets), A Dynamic Programming Approach to Separate the Impact and Timing of Trading Costs
Ravi Kashyap

We develop a fundamentally different stochastic dynamic programming model of trading costs. Built on a strong theoretical foundation, our model provides insights to market participants by splitting the overall move of the security price during the duration of an order into the Market Impact (price move caused by their actions) and Market Timing (price move caused by everyone else) components. We derive formulations of this model under different laws of motion of the security prices, starting with a simple benchmark scenario and extending this to include multiple sources of uncertainty, liquidity constraints due to volume curve shifts and relating trading costs to the spread. We develop a numerical framework that can be used to obtain optimal executions under any law of motion of prices and demonstrate the tremendous practical applicability of our theoretical methodology including the powerful numerical techniques to implement them. Our decomposition of trading costs into Market Impact and Market Timing allows us to deduce the zero sum game nature of trading costs. It holds numerous lessons for dealing with complex systems, wherein reducing the complexity by splitting the many sources of uncertainty can lead to better insights in the decision process.

Discrimination in Lending? Evidence from the Paycheck Protection Program
Atkins, Rachel M. B.,Cook, Lisa D.,Seamans, Robert
We assess the role of race in loans made through the Paycheck Protection Program (PPP). The PPP program, created by the US government as a response to the COVID-19 pandemic, provides loans to small businesses so they can keep employees on their payroll. We argue that the historical record and the PPP program design choices made it likely that many Black-owned businesses received smaller PPP loans than they should have. We find that Black-owned businesses received loans that were approximately 50% lower than observationally similar white-owned businesses. The effect is marginally smaller in areas with more bank competition and disappeared over time as changes to the PPP program were implemented.

Do Financial Market Experts Know Their Theory? New Evidence From Survey Data
Brueckbauer, Frank
Using a unique survey dataset, I study how financial market experts form their stock market expectations. I document a strong disagreement among experts about how important macroeconomic and financial variables are related to stock returns. The results of an analysis of the relationships between my main survey measure of expected returns and measures of economic conditions are largely consistent with the view that expected returns are counter-cyclical. In particular, I find a positive relationship between expected returns and the dividendâ€"price ratio, which is at odds with the findings of previous papers studying survey measures of expected returns. Finally, I find that an aggregated measure of the financial market experts’ stock return forecasts has weak predictive power for actual returns, but is a less precise forecast than a simple average of historical stock returns.

DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis
Chuheng Zhang,Yuanqi Li,Xi Chen,Yifei Jin,Pingzhong Tang,Jian Li

Modern machine learning models (such as deep neural networks and boosting decision tree models) have become increasingly popular in financial market prediction, due to their superior capacity to extract complex non-linear patterns. However, since financial datasets have very low signal-to-noise ratio and are non-stationary, complex models are often very prone to overfitting and suffer from instability issues. Moreover, as various machine learning and data mining tools become more widely used in quantitative trading, many trading firms have been producing an increasing number of features (aka factors). Therefore, how to automatically select effective features becomes an imminent problem. To address these issues, we propose DoubleEnsemble, an ensemble framework leveraging learning trajectory based sample reweighting and shuffling based feature selection. Specifically, we identify the key samples based on the training dynamics on each sample and elicit key features based on the ablation impact of each feature via shuffling. Our model is applicable to a wide range of base models, capable of extracting complex patterns, while mitigating the overfitting and instability issues for financial market prediction. We conduct extensive experiments, including price prediction for cryptocurrencies and stock trading, using both DNN and gradient boosting decision tree as base models. Our experiment results demonstrate that DoubleEnsemble achieves a superior performance compared with several baseline methods.

Economic Policy Uncertainty and Bank Stability: Does Bank Regulation and Supervision Matter in Major Europe Economies?
Nguyen, Thanh Cong
Motivated by the unprecedented high levels of recent economic policy uncertainty in Europe and the globe, this paper examines the relationship between economic policy uncertainty and bank stability, as well as the conditioning effects of bank regulation and supervision on this relationship. Using a sample of around 900 commercial banks in eight major European countries over the period 2005-2014, economic policy uncertainty has been consistently linked to a sharp decrease in bank stability, once other traditional drivers are controlled for. More importantly, this paper offers insight into how strengthening bank regulation and supervision, particularly regulations on activity restrictions, capital stringency, official supervisory power, and private monitoring, cushions the adverse effects of policy uncertainty on bank stability.

Efficient Modeling and Forecasting of the Electricity Spot Price
Florian Ziel,Rick Steinert,Sven Husmann

The increasing importance of renewable energy, especially solar and wind power, has led to new forces in the formation of electricity prices. Hence, this paper introduces an econometric model for the hourly time series of electricity prices of the European Power Exchange (EPEX) which incorporates specific features like renewable energy. The model consists of several sophisticated and established approaches and can be regarded as a periodic VAR-TARCH with wind power, solar power, and load as influences on the time series. It is able to map the distinct and well-known features of electricity prices in Germany. An efficient iteratively reweighted lasso approach is used for the estimation. Moreover, it is shown that several existing models are outperformed by the procedure developed in this paper.

Ethics, Earnings, ERISA and the Biden Administration
Feuer, Albert
Ethical-factor investing shall be defined as using ethics, such as an enterprise’s policies regarding social/economic/health/environmental justice, sustainability, climate change, or corporate governance, as a factor to determine whether to acquire, dispose of, or how to exercise ownership rights in an equity or debt interest in a business enterprise. Ethical-factor investing includes, but is not limited to the ESG, sustainable, socially responsible, impact, and faith-based investing. Ethical-factor investing may. but need not, be intended to enhance the investor’s financial performance. Ethical-factor investing also may, but need not, be intended to enhance an enterprise’s ethical behavior, i.e., to be socially beneficial.The Trump administration discouraged ethical-factor investing. Nevertheless, such investing is becoming increasingly popular among Americans, American mutual funds, and American retirement plans.The article introduces the current types of ethical investing, their history, their financial and ethical performance, and their pre-Biblical progenitors. All those issues are discussed more extensively in a longer referenced article. This article suggests how the Biden Administration may encourage ethical-factor investing by ERISA retirement plan fiduciaries. This may be done with revised ERISA regulations and other interpretative documents. No ERISA amendments would be needed. ERISA permits such investing if it does not adversely affect the expected financial performance of such plans’ investment portfolios or investment choices. Finally, such plans investors, including plan participants and beneficiaries, may thereby generate their preferred benefits for society. Such benefits are, like desired financial benefits, most likely to be achieved if such investors are explicit about their preferred benefits and they regularly monitor the performance of their investments.

Evidence on Internal Audit Effectiveness from Transitioning to Remote Audits Because of COVID-19
Eulerich, Marc,Wagener, Martin,Wood, David A.
As in-person audits were banned by governments and company policies due to COVID-19, internal auditors had to transition to remote audits to perform their work. Based on survey responses of 271 German internal auditors who have conducted both remote and traditional audits, we find that internal auditors perceive no difference in the efficiency and effectiveness of and stakeholders’ reliance on results from remote and traditional audits when considering all responses. However, we also find that perceptions of efficiency and effectiveness increase the more experience internal auditors have with remote audits. Additional analyses show that support from the auditee, but not management or the audit committee, is an important determinant of perceived remote audit success. This later finding is important for internal auditors to consider when designing remote audits as it indicates the importance of building support with the auditee to have a successful remote auditing experience.

Explicit Representations for Utility Indifference Prices
Hess, Markus
In this paper, we apply stochastic maximum principles to derive representations for exponential utility indifference prices. We also obtain the related optimal portfolio processes and utility indifference hedging strategies. To illustrate our theoretical results, we present several concrete examples and study the limit behavior of utility indifference prices for vanishing and infinite risk aversion. We further investigate how the optimal trading strategies and utility indifference prices alter, if one assumes that an investor has some additional information on the future behavior of the underlying stock price process available. In this regard, we propose a customized enlarged filtration approach and deduce a formula for the utility indifference price in this extended setup. We finally provide a representation for the information premium in our utility indifference pricing framework.

Flashot: A Snapshot of Flash Loan Attack on DeFi Ecosystem
Yixin Cao,Chuanwei Zou,Xianfeng Cheng

Flash Loan attack can grab millions of dollars from decentralized vaults in one single transaction, drawing increasing attention from the Decentralized Finance (DeFi) players. It has also demonstrated an exciting opportunity that a huge wealth could be created by composing DeFi's building blocks and exploring the arbitrage change. However, a fundamental framework to study the field of DeFi has not yet reached a consensus and there's a lack of standard tools or languages to help better describe, design and improve the running processes of the infant DeFi systems, which naturally makes it harder to understand the basic principles behind the complexity of Flash Loan attacks.

In this paper, we are the first to propose Flashot, a prototype that is able to transparently illustrate the precise asset flows intertwined with smart contracts in a standardized diagram for each Flash Loan event. Some use cases are shown and specifically, based on Flashot, we study a typical Pump and Arbitrage case and present in-depth economic explanations to the attacker's behaviors. Finally, we conclude the development trends of Flash Loan attacks and discuss the great impact on DeFi ecosystem brought by Flash Loan. We envision a brand new quantitative financial industry powered by highly efficient automatic risk and profit detection systems based on the blockchain.

Gender Match and the Gender Gap in Venture Capital Financing: Evidence from Shark Tank
Jetter, Michael,Stockley, Kieran
Although the gender gap in entrepreneurs' success rates to secure funding is staggering, we know little about its causes. This is because observing both sides of investor-entrepreneur interactions (especially for unsuccessful pitches) is difficult in reality, and the associated extraordinary stakes complicate appropriate simulations in the laboratory. Using comprehensive data of 4,893 interactions from the popular US television show Shark Tank, we test whether gender match with entrepreneurs can explain investors' likelihood to extend funding offers. We find female investors are 30% more likely to engage with female (rather than male) entrepreneurs, while no systematic gender preferences emerge for male investors. This result is exclusive to entrepreneurs in non-male-dominated product categories but disappears in male-dominated products. Estimates are robust to the inclusion of a comprehensive set of control variables (such as asking valuation, investor-, and season-fixed effects) and a range of alternative specifications. These findings from a field setting with large, real-life stakes provide empirical support for the industry representation hypothesis regarding the gender gap in venture capital funding. While results should be interpreted with caution, our findings suggest increased numbers of women in key venture capital positions could facilitate access to funds for female entrepreneurs. Nevertheless, our setting is not suited to fully explore associated efficiency considerations.

Global health science leverages established collaboration network to fight COVID-19
Stefano Bianchini,Moritz Müller,Pierre Pelletier,Kevin Wirtz

How has the science system reacted to the early stages of the COVID-19 pandemic? Here we compare the (growing) international network for coronavirus research with the broader international health science network. Our findings show that, before the outbreak, coronavirus research realized a relatively small and rather peculiar niche within the global health sciences. As a response to the pandemic, the international network for coronavirus research expanded rapidly along the hierarchical structure laid out by the global health science network. Thus, in face of the crisis, the global health science system proved to be structurally stable yet versatile in research. The observed versatility supports optimistic views on the role of science in meeting future challenges. However, the stability of the global core-periphery structure may be worrying, because it reduces learning opportunities and social capital of scientifically peripheral countries -- not only during this pandemic but also in its "normal" mode of operation.

Insights on Trends in U.S. Cross-Border M&A Transactions After the Tax Cuts and Jobs Act
Lyon, Andrew B.
Cross-border mergers and acquisitions data show that for the first time since 2013, the dollar share of outbound transactions â€" that is, acquisitions by U.S. companies of foreign assets and companies â€" exceeded inbound transactions â€" that is, acquisitions by foreign companies of U.S. assets and companies â€" in 2018 and in 2019. The average annual dollar value of outbound transactions in 2018 and 2019 was 50 percent greater than the average in the two preceding years, before enactment of the Tax Cuts and Jobs Act. Over the same period, the dollar value of inbound transactions declined by 25 percent.Although a range of factors likely affects the volume of outbound and inbound M&A in any year, the data are consistent with a view that the 2017 tax reform legislation improved the attractiveness of the United States as the tax domicile for multinational companies.

Measuring Human Adaptation to AI in Decision Making: Application to Evaluate Changes after AlphaGo
Minkyu Shin,Jin Kim,Minkyung Kim

Across a growing number of domains, human experts are expected to learn from and adapt to AI with superior decision making abilities. But how can we quantify such human adaptation to AI? We develop a simple measure of human adaptation to AI and test its usefulness in two case studies. In Study 1, we analyze 1.3 million move decisions made by professional Go players and find that a positive form of adaptation to AI (learning) occurred after the players could observe the reasoning processes of AI, rather than mere actions of AI. These findings based on our measure highlight the importance of explainability for human learning from AI. In Study 2, we test whether our measure is sufficiently sensitive to capture a negative form of adaptation to AI (cheating aided by AI), which occurred in a match between professional Go players. We discuss our measure's applications in domains other than Go, especially in domains in which AI's decision making ability will likely surpass that of human experts.

Network geometry and market instability
Areejit Samal,Hirdesh K. Pharasi,Sarath Jyotsna Ramaia,Harish Kannan,Emil Saucan,Jürgen Jost,Anirban Chakraborti

The complexity of financial markets arise from the strategic interactions among agents trading stocks, which manifest in the form of vibrant correlation patterns among stock prices. Over the past few decades, complex financial markets have often been represented as networks whose interacting pairs of nodes are stocks, connected by edges that signify the correlation strengths. However, we often have interactions that occur in groups of three or more nodes, and these cannot be described simply by pairwise interactions but we also need to take the relations between these interactions into account. Only recently, researchers have started devoting attention to the higher-order architecture of complex financial systems, that can significantly enhance our ability to estimate systemic risk as well as measure the robustness of financial systems in terms of market efficiency. Geometry-inspired network measures, such as the Ollivier-Ricci curvature and Forman-Ricci curvature, can be used to capture the network fragility and continuously monitor financial dynamics. Here, we explore the utility of such discrete Ricci curvatures in characterizing the structure of financial systems, and further, evaluate them as generic indicators of the market instability. For this purpose, we examine the daily returns from a set of stocks comprising the USA S&P-500 and the Japanese Nikkei-225 over a 32-year period, and monitor the changes in the edge-centric network curvatures. We find that the different geometric measures capture well the system-level features of the market and hence we can distinguish between the normal or `business-as-usual' periods and all the major market crashes. This can be very useful in strategic designing of financial systems and regulating the markets in order to tackle financial instabilities.

Network-centric indicators for fragility in global financial indices
Areejit Samal,Sunil Kumar,Yasharth Yadav,Anirban Chakraborti

Over the last two decades, financial systems have been studied and analysed from the perspective of complex networks, where the nodes and edges in the network represent the various financial components and the strengths of correlations between them. Here, we adopt a similar network-based approach to analyse the daily closing prices of 69 global financial market indices across 65 countries over a period of 2000-2014. We study the correlations among the indices by constructing threshold networks superimposed over minimum spanning trees at different time frames. We investigate the effect of critical events in financial markets (crashes and bubbles) on the interactions among the indices by performing both static and dynamic analyses of the correlations. We compare and contrast the structures of these networks during periods of crashes and bubbles, with respect to the normal periods in the market. In addition, we study the temporal evolution of traditional market indicators, various global network measures and the recently developed edge-based curvature measures. We show that network-centric measures can be extremely useful in monitoring the fragility in the global financial market indices.

Nonstationary Portfolios: Diversification in the Spectral Domain
Bruno Scalzo,Alvaro Arroyo,Ljubisa Stankovic,Danilo P. Mandic

Classical portfolio optimization methods typically determine an optimal capital allocation through the implicit, yet critical, assumption of statistical time-invariance. Such models are inadequate for real-world markets as they employ standard time-averaging based estimators which suffer significant information loss if the market observables are non-stationary. To this end, we reformulate the portfolio optimization problem in the spectral domain to cater for the nonstationarity inherent to asset price movements and, in this way, allow for optimal capital allocations to be time-varying. Unlike existing spectral portfolio techniques, the proposed framework employs augmented complex statistics in order to exploit the interactions between the real and imaginary parts of the complex spectral variables, which in turn allows for the modelling of both harmonics and cyclostationarity in the time domain. The advantages of the proposed framework over traditional methods are demonstrated through numerical simulations using real-world price data.

Parametric Rules for State Contingent Claims
Chatterjee, Siddhartha,Ertemel, Sinan,Kumar, Rajnish
We study bankruptcy rules in a setting where individuals have state contingent claims. A rule must distribute shares before uncertainty resolves. Within a wide class of parametric rules, we first characterize rules of ex-ante form in terms of the way that the rule processes inherent uncertainty in the individual claims. The key property is: No Penalty for Risk. It says that the rule does not penalize an individual in a situation that differs from another only in terms of the this individual's claim in that the former situation has a risky version of the riskless claim in the latter situation. With regard to the ex-post characterization, our key property is: Indifference to Independent Combinations. It says that if an individual is risk neutral with expected utility preferences then any rule that makes her indifferent between any bankruptcy problem and a corresponding independent combination of gamble between a degenerate gamble and a zero game (any bankruptcy game with zero endowment) forces the rule to be in the ex{post form. Finally, a partial comparative static result is provided which formalizes the claim that individuals generally and ex-ante rules more appealing when the level of the resource is suffciently low.

Political Economy of Financial Crisis Duration
Nguyen, Thanh Cong,Castro, Vítor,Wood, Justine A
Over the last four decades, banking crises around the globe have become longer. This, along with the unprecedented government responses to the Great Recession of 2007/08, has led to a critical question of whether political decisions were somehow to blame for these more prolonged crises. Despite growing concerns, little attention has been given to the political and institutional determinants of financial crisis duration. Using an extensive database with 125 countries observed over the period 1976-2017 and employing a discrete-time duration model, we find that the electoral cycle, political ideology, majority governments, and institutional quality matter for the understanding of the length of financial crises. This study also shows that, to better understand the dynamics of the duration of financial crises, it is essential to look at the duration dynamics in each type of financial crisis. Finally, allowing for more flexible duration dependence patterns, we observe that the duration of banking and twin/triple crises presents a non-monotonic cubic behavior, while the probability of debt crisis ending decreases monotonically over time.

Quantum crypto-economics: Blockchain prediction markets for the evolution of quantum technology
Peter P. Rohde,Vijay Mohan,Sinclair Davidson,Chris Berg,Darcy Allen,Gavin K. Brennen,Jason Potts

Two of the most important technological advancements currently underway are the advent of quantum technologies, and the transitioning of global financial systems towards cryptographic assets, notably blockchain-based cryptocurrencies and smart contracts. There is, however, an important interplay between the two, given that, in due course, quantum technology will have the ability to directly compromise the cryptographic foundations of blockchain. We explore this complex interplay by building financial models for quantum failure in various scenarios, including pricing quantum risk premiums. We call this quantum crypto-economics.

Ramadan and Infants Health Outcomes
Hossein Abbaszadeh Shahri

Previous studies show that prenatal shocks to embryos could have adverse impacts on health endowment at birth. Using the universe of birth data and a difference-in-difference-in-difference strategy, I find that exposure to Ramadan during prenatal development has negative birth outcomes. Exposure to a full month of fasting is associated with 96 grams lower birth-weight. These results are robust across specifications and do not appear to be driven by mothers selective fertility.

Rapid detection of fast innovation under the pressure of COVID-19
Nicola Melluso,Andrea Bonaccorsi,Filippo Chiarello,Gualtiero Fantoni

Covid-19 has rapidly redefined the agenda of technological research and development both for academics and practitioners. If the medical scientific publication system has promptly reacted to this new situation, other domains, particularly in new technologies, struggle to map what is happening in their contexts. The pandemic has created the need for a rapid detection of technological convergence phenomena, but at the same time it has made clear that this task is impossible on the basis of traditional patent and publication indicators. This paper presents a novel methodology to perform a rapid detection of the fast technological convergence phenomenon that is occurring under the pressure of the Covid-19 pandemic. The fast detection has been performed thanks to the use of a novel source: the online blogging platform Medium. We demonstrate that the hybrid structure of this social journalism platform allows a rapid detection of innovation phenomena, unlike other traditional sources. The technological convergence phenomenon has been modelled through a network-based approach, analysing the differences of networks computed during two time periods (pre and post COVID-19). The results led us to discuss the repurposing of technologies regarding "Remote Control", "Remote Working", "Health" and "Remote Learning".

Recruitment, effort, and retention effects of performance contracts for civil servants: Experimental evidence from Rwandan primary schools
Clare Leaver,Owen Ozier,Pieter Serneels,Andrew Zeitlin

This paper reports on a two-tiered experiment designed to separately identify the selection and effort margins of pay-for-performance (P4P). At the recruitment stage, teacher labor markets were randomly assigned to a 'pay-for-percentile' or fixed-wage contract. Once recruits were placed, an unexpected, incentive-compatible, school-level re-randomization was performed, so that some teachers who applied for a fixed-wage contract ended up being paid by P4P, and vice versa. By the second year of the study, the within-year effort effect of P4P was 0.16 standard deviations of pupil learning, with the total effect rising to 0.20 standard deviations after allowing for selection.

Reexamining the Influence of Conditional Cash Transfers on Migration from a Gendered Lens: Comment
Stark, Oded
In a recent article, "Reexamining the influence of conditional cash transfers on migration from a gendered lens," Hughes (2019) claimed that conditional cash transfers, CCT, limit the likelihood of migration by women, compensating them for giving up an attractive migration option. I question the analysis that lies behind this claim. I argue that in seeking to understand the likelihood of women migrating if they participate in a CCT program, issues of selectivity, endogeneity, and optimization cannot be set aside. In particular, it is not that receiving CCT curtails a migration option; it is that not contemplating migration encourages women to accept CCT. And if a household perspective is brought to bear, then a household's free choices weaken the appeal of migration to women. This reduction in appeal does not arise from an exogenously imposed curb but rather from endogenously determined preferences.

Reporting Transparency and Labor Market Outcomes
Golshan, Nargess,Khurana, Inder K.,Silva, Felipe Bastos G.
Financial reporting transparency can affect labor markets directly by mitigating information asymmetries and optimizing the matching of heterogeneous firms and employees (matching efficiency channel) and indirectly through the effect of transparency on firms’ capital inputs (capital utilization channel). Using the mandatory IFRS adoption by European Union countries as a setting to proxy for enhanced reporting transparency, we document subsequent increases in labor productivity and wages. The main effect is mostly driven by increases in output rather than decreases in labor demand and is amplified by changes in earnings quality after the IFRS adoption. Collectively, our results underscore that the effects of reporting transparency go beyond the market for capital inputs, with implications for labor markets equilibria.

Risk-Taking Adaptation to Macroeconomic Experiences: Theory and Evidence from Developing Countries
Levin, Remy,Vidart, Daniela
How do lifetime experiences of macroeconomic risk shape attitudes towards risk? We study this question theoretically and empirically for individuals in developing countries. We build a Bayesian model of choice in which agents' risk attitude adapts to their evolving beliefs about background risk. Our model predicts that risk aversion will increase monotonically in the variance of the background risk, and will decrease convexly in the mean. We test the model by linking longitudinal surveys from Indonesia and Mexico, containing elicited measures of risk aversion for the same subjects years apart, with state-level real GDP growth time series capturing their lifetime macroeconomic experiences. In both countries measured risk aversion significantly increases in experienced growth volatility and significantly decreases in experienced mean growth. The effect of volatility is 0.9-4.3 times the effect of the mean, indicating that experiences of volatility are first-order drivers of risk attitudes.

Social Mobility in India
A. Singh,A. Forcina,K. Muniyoor

Rapid rise in income inequality in India is a serious concern. While the emphasis is on inclusive growth, it seems difficult to tackle the problem without looking at the intricacies of the problem. Social mobility is one such important tool which helps in reaching the cause of the problem and focuses on bringing long term equality in the country. The purpose of this study is to examine the role of social background and education attainment in generating occupation mobility in the country. By applying an extended version of the RC association model to 68th round (2011-12) of the Employment and Unemployment Survey by the National Sample Survey Office of India, we found that the role of education is not important in generating occupation mobility in India, while social background plays a critical role in determining one's occupation. This study successfully highlights the strong intergenerational occupation immobility in the country and also the need to focus on education. In this regard, further studies are needed to uncover other crucial factors limiting the growth of individuals in the country.

Social Responsibility in the Time of Uncertainty: A Natural Experiment
Mahmoud, Ola,Meyer, Julia
This paper studies social responsibility in the financial market under uncertainty. Using the COVID-19 induced stock market crash as a natural experiment, we present causal evidence for a significant market-wide increase in sentiment for and attention to socially responsible investments. An artefactual field experiment suggests three behavioral channels for this shift in preferences. First, investors view socially responsible assets as less risky and uncertain. Second, the crisis triggered an increase in prosocial preferences in general. Third, the affect heuristic, in which the emotional response acts as a mental shortcut in relation to a stimulus, triggers favorable expectations of socially responsible investment performance. Our insights provide evidence for the time varying nature of morality in the market, and may explain the recently documented resilience of socially responsible stocks in times of market turmoil.

Spoofing in Equilibrium
Williams, Basil,Skrzypacz, Andrzej
We present a model of dynamic trading with exogenous and strategic cancellation of orders. We define spoofing as the strategic placing and canceling of orders in order to move prices and trade later in the opposite direction. We show that spoofing can occur in equilibrium. Consistent with regulator concerns, we show that spoofing slows price discovery, raises bid-ask spreads, and raises return volatility. A novel prediction is that the prevalence of equilibrium spoofing is single-peaked in the measure of informed traders, suggesting that spoofing should be more prevalent in markets of intermediate liquidity. We consider within-market and cross-market spoofing and discuss how regulators should allocate resources towards cross-market surveillance.

Strict Local Martingales Via Filtration Enlargement
Dandapani, Aditi,Protter, Philip
A strict local martingale is a local martingale that is not a martingale. We investigate how such a process might arise from a true martingale as a result of an enlargement of the filtration and a change of measure. We study and implement a particular type of enlargement, initial expansion of filtration, for stochastic volatility models with and without jumps and provide sufficient conditions in each of these cases such that initial expansion can create a strict local martingale. We provide examples of initial enlargement that effect this change.

T\^atonnement, Approach to Equilibrium and Excess Volatility in Firm Networks
Théo Dessertaine,José Moran,Michael Benzaquen,Jean-Philippe Bouchaud

We study the conditions under which input-output networks can dynamically attain competitive equilibrium, where markets clear and profits are zero. We endow a classical firm network model with simple dynamical rules that reduce supply/demand imbalances and excess profits. We show that the time needed to reach equilibrium diverges as the system approaches an instability point beyond which the Hawkins-Simons condition is violated and competitive equilibrium is no longer realisable. We argue that such slow dynamics is a source of excess volatility, through accumulation and amplification of exogenous shocks. Factoring in essential physical constraints, such as causality or inventory management, we propose a dynamically consistent model that displays a rich variety of phenomena. Competitive equilibrium can only be reached after some time and within some region of parameter space, outside of which one observes periodic and chaotic phases, reminiscent of real business cycles. This suggests an alternative explanation of the excess volatility that is of purely endogenous nature. Other regimes include deflationary equilibria and intermittent crises characterised by bursts of inflation. Our model can be calibrated using highly disaggregated data on individual firms and prices, and may provide a powerful tool to describe out-of-equilibrium economies.

The Corporate Governance Machine
Lund, Dorothy S.,Pollman, Elizabeth
The conventional view of corporate governance is that it is a neutral set of processes and practices that govern how a company is managed. We demonstrate that this view is profoundly mistaken: in the United States, corporate governance has become a “system” composed of an array of institutional players, with a powerful shareholderist orientation. Our original account of this “corporate governance machine” generates insights about the past, present, and future of corporate governance. As for the past, we show how the concept of corporate governance developed alongside the shareholder primacy movement. This relationship is reflected in the common refrain of “good governance” that pervades contemporary discourse and the maturation of corporate governance as an industry oriented toward serving shareholders and their interests. As for the present, our analysis explains why the corporate social responsibility movement transformed into shareholder value-oriented ESG, stakeholder capitalism became relegated to a new separate form of entity known as the benefit corporation, and public company boards of directors became homogenized across industries. As for the future, our analysis suggests that absent a major paradigm shift, advocacy pushing corporations to consider the interests of employees, communities, and the environment will likely fail if such effort is not framed as advancing shareholder interests.

The Economics of Enlightenment: Time Value of Knowledge and the Net Present Value (NPV) of Knowledge Machines, A Proposed Approach Adapted from Finance
Ravi Kashyap

We formulate one methodology to put a value or price on knowledge using well accepted techniques from finance. We provide justifications for these finance principles based on the limitations of the physical world we live in. We start with the intuition for our method to value knowledge and then formalize this idea with a series of axioms and models. To the best of our knowledge this is the first recorded attempt to put a numerical value on knowledge. The implications of this valuation exercise, which places a high premium on any piece of knowledge, are to ensure that participants in any knowledge system are better trained to notice the knowledge available from any source. Just because someone does not see a connection does not mean that there is no connection. We need to try harder and be more open to acknowledging the smallest piece of new knowledge that might have been brought to light by anyone from anywhere about anything.

The Economics of Social Data
Dirk Bergemann,Alessandro Bonatti,Tan Gan

We propose a model of data intermediation to analyze the incentives for sharing individual data in the presence of informational externalities. A data intermediary acquires signals from individual consumers regarding their preferences. The intermediary resells the information in a product market wherein firms and consumers can tailor their choices to the demand data. The social dimension of the individual data-whereby an individual's data are predictive of the behavior of others -- generates a data externality that can reduce the intermediary's cost of acquiring the information. We derive the intermediary's optimal data policy and establish that it preserves the privacy of consumer identities while providing precise information about market demand to the firms. This policy enables the intermediary to capture the total value of the information as the number of consumers becomes large.

The Impact of the COVID-19 Pandemic on Scientific Research in the Life Sciences
Massimo Riccaboni,Luca Verginer

The COVID-19 outbreak has posed an unprecedented challenge to humanity and science. On the one side, public and private incentives have been put in place to promptly allocate resources toward research areas strictly related to the COVID-19 emergency. But on the flip side, research in many fields not directly related to the pandemic has lagged behind. In this paper, we assess the impact of COVID-19 on world scientific production in the life sciences. We investigate how the usage of medical subject headings (MeSH) has changed following the outbreak. We estimate through a difference-in-differences approach the impact of COVID-19 on scientific production through PubMed. We find that COVID-related research topics have risen to prominence, displaced clinical publications, diverted funds away from research areas not directly related to COVID-19 and that the number of publications on clinical trials in unrelated fields has contracted. Our results call for urgent targeted policy interventions to reactivate biomedical research in areas that have been neglected by the COVID-19 emergency.

The Impacts of the Gender Imbalance on Marriage and Birth: Evidence from World War II in Japan
Kota Ogasawara,Erika Igarashi

This study uses the unprecedented changes in the sex ratio due to the losses of men during World War II to identify the impacts of the gender imbalance on marriage market and birth outcomes in Japan. Using newly digitized census-based historical statistics, we find evidence that men had a stronger bargaining position in the marriage market and intra-household fertility decisions than women. Under relative male scarcity, while people, especially younger people, were more likely to marry and divorce, widowed women were less likely to remarry than widowed men. We also find that women's bargaining position in the marriage market might not have improved throughout the 1950s. Given the institutional changes in the abortion law after the war, marital fertility and stillbirth rates increased in the areas that suffered relative male scarcity. Our result on out-of-wedlock births indicates that the theoretical prediction of intra-household bargaining is considered to be robust in an economy in which marital fertility is dominant.

The Politics of Personalized News Aggregation
Lin Hu,Anqi Li,Ilya Segal

We study how personalized news aggregation for rational inattentive voters (NARI) affects policy polarization and public opinion. In a two-candidate electoral competition game, an attention-maximizing infomediary aggregates information about candidates' valence into news. Voters decide whether to consume news, trading off the expected gain from improved expressive voting against the attention cost. NARI generates policy polarization even if candidates are office-motivated. Personalized news serves extreme voters with skewed signals and makes them the disciplining entities of policy polarization. Analysis of disciplining voters' identities and policy latitudes sheds light on the political effects of recent regulatory proposals to tame tech giants.

The Real Effects of Corruption on Corporate Takeover Activity: Evidence From China’s Anti-Corruption Campaign
Huang, Chenghao,Jin, Zhi,Tian, Siyang,Wu, Eliza
This paper examines how political corruption affects M&A activities. By exploiting the public enforcement of the anti-corruption campaign across different regions in China, we find in a difference-in-difference (DID) setting that the reduction in corruption increases cross-region takeover activities by 40% and deal volume more than doubles. Further analysis reveals that the reduction in market entry barriers and the decreased potential for political rent extraction are two plausible economic channels behind these real effects on corporate investments. Reduction in corruption also leads to higher bidder returns and improves post-acquisition performance. Furthermore, such a campaign significantly strengthens local economic development (higher GDP per capita growth, higher general government revenue per capita, and lower unemployment rate).

The Role of Asset Payouts in the Estimation of Default Barriers
Bougias, Alexandros,Episcopos, Athanasios,Leledakis, George N.
In the barrier option model of corporate security valuation, the firm's creditors impose a default-triggering barrier on the firm value to protect their claim. One of the disputed issues in the literature is whether the implied default barrier is positive, and whether it is above or below the firm's leverage. We extend the model of Brockman and Turtle by embedding asset payouts in the valuation of shareholder's equity. Using a sample of US stocks from the NYSE, AMEX, and NASDAQ, our paper exploits market and firm information to arrive at more accurate estimates of the parameters. The implied default barrier is computed for thirty 2-digit SIC groups, including industrials and banks. Our results show that the implied default barrier is lower than it is in the received literature, and it can be less than leverage, even zero for some firms. The implied physical probabilities of default are significantly lower in the presence of payouts, providing a closer fit to the historical corporate default rates, particularly for issuers of speculative-grade bonds.

The Role of the Precautionary Principle in Mitigating Financial and Banking Crisis
Komandoko, Kukuh
The theory of the precautionary principle has developed. In its implementation, the precautionary principle is used to deal with hazards in the environmental fields and has been widely used in health and food technology. In general, the precautionary principle is understood as making decisions in encountering threats or situations that are dangerous and uncertain. Likewise, the financial and banking crisis is a phenomenon that dangerous and often threatens the stability of a state. In extreme cases, crises create a panic that makes no sense or can even be made the economy of the state to collapse. This article will discuss the precautionary principle theory and its role in mitigating the threat of a financial and banking crisis.

The Transmission of Financial Shocks and Leverage of Banks: An Endogenous Regime Switching Framework
Hubrich, Kirstin,Waggoner, Daniel F.
We investigate the transmission of financial shocks through the macroeconomy. To that end we develop an endogenous regime-switching structural vector autoregressive model with time-varying transition probabilities. First, we allow for the transition probabilities to be dependent on the state of the economy, and thereby to be time-varying. Second, we facilitate rather general, non-recursive structural identification restrictions. Third, we allow the identification restrictions to differ across regimes. We employ a model with conventional and unconventional monetary policy, where the latter is modelled via the Fed balance sheet. Using bank-level data, we shed light on the role of leverage of banks for the transmission of financial shocks.

The leverage effect and other stylized facts displayed by Bitcoin returns
F.N.M. de Sousa Filho,J.N. Silva,M.A. Bertella,E. Brigatti

In this paper, we explore some stylized facts of the Bitcoin market using the BTC-USD exchange rate time series of historical intraday data from 2013 to 2020. Bitcoin presents some very peculiar idiosyncrasies, like the absence of macroeconomic fundamentals or connections with underlying assets or benchmarks, an asymmetry between demand and supply and the presence of inefficiency in the form of strong arbitrage opportunity. Nevertheless, all these elements seem to be marginal in the definition of the structural statistical properties of this virtual financial asset, which result to be analogous to general individual stocks or indices. In contrast, we find some clear differences, compared to fiat money exchange rates time series, in the values of the linear autocorrelation and, more surprisingly, in the presence of the leverage effect. We also explore the dynamics of correlations, monitoring the shifts in the evolution of the Bitcoin market. This analysis is able to distinguish between two different regimes: a stochastic process with weaker memory signatures and closer to Gaussianity between the Mt. Gox incident and the late 2015, and a dynamics with relevant correlations and strong deviations from Gaussianity before and after this interval.

To VaR, or Not to VaR, That is the Question
Olkhov, Victor
This paper discusses the value-at-risk (VaR) concept and assesses the financial adequacy of the price probability determined by frequency of trades at price p. We take the price definition as the ratio of executed trade value to volume and show that it leads to price statistical moments, which differ from those, generated by frequency price probability. We derive the price n-th statistical moments as ratio of n-th statistical moments of the value and the volume of executed transactions. We state that the price probability determined by frequency of trades at price p doesn’t describe probability of executed trade prices and VaR based on frequency price probability may be origin of unexpected and excessive losses. We explain the need to replace frequency price probability by frequency probabilities of the value and the volume of executed transactions and derive price characteristic function. After 50 years of the VaR usage main problems of the VaR concept are still open. We believe that VaR commitment to forecast the price probability for the time horizon T seems to be one of the most tough and expensive puzzle of modern finance.

Workforce Policies and Operational Risk: Evidence from U.S. Bank Holding Companies
Curti, Filippo,Fauver, Larry,Mihov, Atanas
Using supervisory data on operational losses from large U.S. bank holding companies (BHCs), we show that BHCs with socially responsible workforce policies suffer lower operational losses per dollar of total assets and incidence of tail risk events. The association is more pronounced for institutions that: (i) are larger and more complex, (ii) have better corporate governance, or (iii) have recently experienced larger operational losses. It also significantly varies by the type of workforce policies and the type of operational losses. Our findings have important implications for banking organization performance, risk and supervision.

Zero-Commission Individual Investors, High Frequency Traders, and Stock Market Quality
Eaton, Gregory W.,Green, T. Clifton,Roseman, Brian,Wu, Yanbin
Contrasting with recent evidence that retail traders are informed, we find that Robinhood ownership changes are unrelated with future returns, suggesting that zero-commission investors behave as noise traders. We exploit Robinhood platform outages to identify the causal effects of commission-free traders on financial markets. Exogenous negative shocks to Robinhood participation are associated with increased market liquidity and lower return volatility among stocks favored by Robinhood investors, as proxied by WallStreetBets mentions. Platform outages are also associated with reduced high frequency trader (HFT) activity, indicative of payments for order flow. However, outages have the strongest effect on stocks neglected by HFTs, suggesting that zero-commission traders have direct negative effects on market quality.

xVA: Definition, Evaluation and Risk Management
Wu, Lixin,Zhang, DaWei
xVA is a collection of valuation adjustments made to the classical risk-neutral valuation of a derivative or derivatives portfolio for pricing or for accounting purposes, and it has been a matter of debate and controversy. This paper is intended to clarify the notion of xVA as well as the usage of the xVA items in pricing, accounting or risk management. Based on bilateral replication pricing using shares and credit default swaps, we attribute the P&L of a derivatives trade into the compensation for counterparty default risks and the costs of funding. The expected present values of the compensation and the funding costs under the risk-neutral measure are defined to be the bilateral CVA and FVA, respectively. The latter further breaks down into FCA, MVA, ColVA and KVA. We show that the market funding liquidity risk, but not any idiosyncratic funding risks, can be bilaterally priced into a derivative trade, without causing price asymmetry between the counterparties. We call for the adoption of VaR or CVaR methodologies for managing funding risks. The pricing of xVA of an interest-rate swap is presented.