Research articles for the 2020-12-07

A Competitive Search Theory of Asset Pricing
Kargar, Mahyar,Passadore, Juan,Silva, Dejanir
We develop an asset-pricing model with heterogeneous investors and search frictions. The model nests standard asset pricing and competitive search models as special cases. Trade is intermediated by risk-neutral dealers subject to capacity constraints. Risk-averse investors can direct their search towards dealers based on price and execution speed. Order flows affect the risk premium, volatility, and equilibrium interest rate. Large negative shocks lead to portfolio reallocations and increased trading volume, bid-ask spreads, and trading delays. Simultaneously, the model generates increased risk premium and volatility and a reduction in interest rates, consistent with asset-pricing and trading behavior during the COVID-19 crisis.

A Note Concerning Government Bond Yields
Akram, Tanweer
This paper relates Keynes’s discussions of money, the state theory of money, financial markets, investors’ expectations, uncertainty, and liquidity preference to the dynamics of government bond yields for countries with monetary sovereignty. Keynes argued that the central bank can influence the long-term interest rate on government bonds and the shape of the yield curve mainly through the short-term interest rate. Investors’ psychology, herding behavior in financial markets, and uncertainty about the future reinforce the effects of the short-term interest rate and the central bank’s monetary policy actions on the long-term interest rate. Several recent empirical studies that examine the dynamics of government bond yields substantiate the Keynesian perspective that the long-term interest rate responds markedly to the short-term interest rate. These empirical studies not only vindicate the Keynesian perspective but also have relevance for macroeconomic theory and policy.

A Threshold for Quantum Advantage in Derivative Pricing
Shouvanik Chakrabarti,Rajiv Krishnakumar,Guglielmo Mazzola,Nikitas Stamatopoulos,Stefan Woerner,William J. Zeng

We give an upper bound on the resources required for valuable quantum advantage in pricing derivatives. To do so, we give the first complete resource estimates for useful quantum derivative pricing, using autocallable and Target Accrual Redemption Forward (TARF) derivatives as benchmark use cases. We uncover blocking challenges in known approaches and introduce a new method for quantum derivative pricing - the \emph{re-parameterization method} - that avoids them. This method combines pre-trained variational circuits with fault-tolerant quantum computing to dramatically reduce resource requirements. We find that the benchmark use cases we examine require 7.5k logical qubits and a T-depth of 46 million and thus estimate that quantum advantage would require a logical clock speed of 10Mhz. While the resource requirements given here are out of reach of current systems, we hope they will provide a roadmap for further improvements in algorithms, implementations, and planned hardware architectures.

Applying the Nash Bargaining Solution for a Reasonable Royalty
David M. Kryskowski,David Kryskowski

There has been limited success applying the Nash Bargaining Solution (NBS) in assigning intellectual property damages due to the difficulty of relating it to the specific facts of the case. Because of this, parties are not taking advantage of Georgia-Pacific factor fifteen. This paper intends to bring clarity to the NBS so it can be applied to the facts of a case. This paper normalizes the NBS and provides a methodology for determining the bargaining weight in Nash's solution. Several examples demonstrate this normalized form, and a nomograph is added for computational ease.

Are Cryptos Safe-Haven Assets during COVID-19? Evidence from Wavelet Coherence Analysis
Rubbaniy, Ghulame,Cheema, Ali Awais,Samitas, Aristeidis
We use wavelet coherence analysis on global COVID-19 fear index, cryptocurrency market specific implied volatility index (VCRIX) and cryptocurrency returns to investigate safe-haven properties of cryptocurrencies during COVID-19 pandemic. The findings of our paper show that a non-financial market-based proxy of market stress that represents fear of households and retail investors reveals cryptocurrencies as safe-haven assets; however, a financial market-based proxy of the market turbulence exposes that cryptocurrencies behave like traditional assets during the times of COVID-19 pandemic. Our findings support that long-term investors can invest in the cryptocurrency market to hedge the risks during the COVID-19 pandemic.

Assessing the effects of seasonal tariff-rate quotas on vegetable prices in Switzerland
Daria Loginova,Marco Portmann,Martin Huber

Causal estimation of the short-term effects of tariff-rate quotas (TRQs) on vegetable producer prices is hampered by the large variety and different growing seasons of vegetables and is therefore rarely performed. We quantify the effects of Swiss seasonal TRQs on domestic producer prices of a variety of vegetables based on a difference-in-differences estimation using a novel dataset of weekly producer prices for Switzerland and neighbouring countries. We find that TRQs increase prices of most vegetables by more than 20% above the prices in neighbouring countries during the main harvest time for most vegetables and even more than 50% for some vegetables. The effects are stronger for more perishable vegetables and for conventionally produced ones compared with organic vegetables. However, we do not find clear-cut effects of TRQs on the week-to-week price volatility of vegetables although the overall lower price volatility in Switzerland compared with neighbouring countries might be a result of the TRQ system in place.

Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects
Gao, Jiti,Liu, Fei,Peng, Bin
In this paper, we investigate binary response models for heterogeneous panel data with interactive fixed effects by allowing both the cross sectional dimension and the temporal dimension to diverge. From a practical point of view, the proposed framework can be applied to predict the probability of corporate failure, conduct credit rating analysis, etc. Theoretically and methodologically, we establish a link between a maximum likelihood estimation and a least squares approach, provide a simple information criterion to detect the number of factors, and achieve the asymptotic distributions accordingly. In addition, we conduct intensive simulations to examine the theoretical findings. In the empirical study, we focus on the sign prediction of stock returns, and then use the results of sign forecast to conduct portfolio analysis. By implementing rolling-window based outâ€"ofâ€" sample forecasts, we show the finiteâ€"sample performance and demonstrate the practical relevance of the proposed model and estimation method.

Building arbitrage-free implied volatility: Sinkhorn's algorithm and variants
Hadrien De March,Pierre Henry-Labordere

We consider the classical problem of building an arbitrage-free implied volatility surface from bid-ask quotes. We design a fast numerical procedure, for which we prove the convergence, based on the Sinkhorn algorithm that has been recently used to solve efficiently (martingale) optimal transport problems.

COVID-19, Lockdowns and Herding Towards Cryptocurrency Market Specific Implied Volatility Index
Rubbaniy, Ghulame
This study investigates herds effect in more than 100 cryptocurrencies during the period from January 2015 to June 2020. The results document a significant evidence of herding behavior in the cryptocurrency market. My findings show that herding asymmetry is present during bullish and bearish regimes of cryptocurrency market, where the herds investing is dominantly visible during extremely bullish percentile regimes of cryptocurrency market. Although the study finds no evidence of correlated trading when cryptocurrency specific fear prevails in the market, yet crypto investors mimic trading decisions of others during the times of COVID-19 except for the period of lockdowns.

Change of drift in one-dimensional diffusions
Sascha Desmettre,Gunther Leobacher,L.C.G. Rogers

It is generally understood that a given one-dimensional diffusion may be transformed by Cameron-Martin-Girsanov measure change into another one-dimensional diffusion with the same volatility but a different drift. But to achieve this we have to know that the change-of-measure local martingale that we write down is a true martingale; we provide a complete characterization of when this happens. This is then used to discuss absence of arbitrage in a generalized Heston model including the case where the Feller condition for the volatility process is violated.

Collective Moral Hazard and the Interbank Market
Altinoglu, Levent,Stiglitz, Joseph E.
The concentration of risk within financial system is considered to be a source of systemic instability. We propose a theory to explain the structure of the financial system and show how it alters the risk taking incentives of financial institutions. We build a model of portfolio choice and endogenous contracts in which the government optimally intervenes during crises. By issuing financial claims to other institutions, relatively risky institutions endogenously become large and interconnected. This structure enables institutions to share the risk of systemic crisis in a privately optimal way, but channels funds to relatively risky investments and creates incentives even for smaller institutions to take excessive risks. Constrained efficiency can be implemented with macroprudential regulation designed to limit the interconnectedness of risky institutions.

Competition in Fund Management and Forward Relative Performance Criteria
Anthropelos, Michail,Geng, Tianran,Zariphopoulou, Thaleia
In an Ito-diffusion market, two fund managers trade under relative performance concerns. For both the asset specialization and diversi?cation settings, we analyze the passive and competitive cases. We measure the performance of the managers' strategies via forward relative performance criteria, leading to the respective notions of forward best-response criterion and forward Nash equilibrium. The motivation to develop such criteria comes from the need to relax various crucial, but quite stringent, existing assumptions - such as, the a priori choices of both the market model and the investment horizon, the commonality of the latter for both managers as well as the full a priori knowledge of the competitor's policies for the best-response case. We focus on locally riskless criteria and deduce the random forward equations. We solve the CRRA cases, thus also extending the related results in the classical setting. An important by-product of the work herein is the development of forward performance criteria for investment problems in Ito-diffusion markets under the presence of correlated random endowment process for both the perfectly and the incomplete market cases.

Competition, Politics, & Social Media
Benson Tsz Kin Leung,Pinar Yildirim

An increasing number of politicians are relying on cheaper, easier to access technologies such as online social media platforms to communicate with their constituency. These platforms present a cheap and low-barrier channel of communication to politicians, potentially intensifying political competition by allowing many to enter political races. In this study, we demonstrate that lowering costs of communication, which allows many entrants to come into a competitive market, can strengthen an incumbent's position when the newcomers compete by providing more information to the voters. We show an asymmetric bad-news-good-news effect where early negative news hurts the challengers more than the positive news benefit them, such that in aggregate, an incumbent politician's chances of winning is higher with more entrants in the market. Our findings indicate that communication through social media and other platforms can intensify competition, how-ever incumbency advantage may be strengthened rather than weakened as an outcome of higher number of entrants into a political market.

Complementarity or Substitution: Time-Variant Implications of Dividends and Stock Repurchases â€" An Explorative Study
Homburg, Carsten,Schick, Roman
Prior studies associate dividends with permanent earnings signals and stock repurchases with transitory earnings signals. In contrast, we provide empirical evidence that in recent decades, dividends and stock repurchases have converged to each other in terms of their signaling for future earnings. According to our explorative empirical analysis using a modified version of the Dechow et al. (2008) earnings persistence model, the relation between dividend and stock repurchase signals has changed from complementary to substitutive. However, cumulative abnormal returns following payout announcements indicate that the stock market is not fully aware of this convergence as cumulative abnormal returns in response to stock repurchases do not increase in conjunction with their changing signals. We also explore potential reasons for the documented convergence, and find that the declining signaling of dividends for future earnings may be driven by increased institutional ownership, reducing agency-problems between management and shareholders. Furthermore, the signaling of stock repurchases converges to that of dividends because the former are paid more persistently and are less related to transitory non-operating income. Our findings help investors and managers in allocating their assets more efficiently by improving their understanding of payout policy implications.

Constructing trading strategy ensembles by classifying market states
Michal Balcerak,Thomas Schmelzer

Rather than directly predicting future prices or returns, we follow a more recent trend in asset management and classify the state of a market based on labels. We use numerous standard labels and even construct our own ones. The labels rely on future data to be calculated, and can be used a target for training a market state classifier using an appropriate set of market features, e.g. moving averages. The construction of those features relies on their label separation power. Only a set of reasonable distinct features can approximate the labels. For each label we use a specific neural network to classify the state using the market features from our feature space. Each classifier gives a probability to buy or to sell and combining all their recommendations (here only done in a linear way) results in what we call a trading strategy. There are many such strategies and some of them are somewhat dubious and misleading. We construct our own metric based on past returns but penalising for a low number of transactions or small capital involvement. Only top score-performance-wise trading strategies end up in final ensembles. Using the Bitcoin market we show that the strategy ensembles outperform both in returns and risk-adjusted returns in the out-of-sample period. Even more so we demonstrate that there is a clear correlation between the success achieved in the past (if measured in our custom metric) and the future.

Corporate Capital Structure and Firm Value: International Evidence on the Special Roles of Bank Debt
Berger, Allen N.,El Ghoul, Sadok,Guedhami, Omrane,Guo, Jiarui
In this paper, we present novel findings that contribute to both the corporate capital structure and bank specialness literatures. We study the effects of bank debt on the value of nonfinancial corporations, applying novel methodology to over 40,000 firms in 110 countries over 18 years, over 300,000 observations in total. We find that bank term loans and credit lines are both strongly positively associated with firm value, but only when employed very intensively â€" on the order of 90% or more of total corporate debt. These effects are more than double those of other debt sources, consistent with bank specialness at high-intensity levels that apply to over one-third of our global sample of corporations. Our results hold broadly, but are more important for credit-constrained firms â€" small firms and those in low-income countries. Channel analysis suggests that term loans boost short-term firm performance more, while credit lines better promote long-run growth.

Corporate Social Responsibility in Emerging Market Economies: Determinants, Consequences, and Future Research Directions
Boubakri, Narjess,El Ghoul, Sadok,Guedhami, Omrane,Wang, He (Helen)
The last two decades have witnessed a growing interest in corporate social responsibility (CSR) worldwide by corporations, investors, policy makers, and researchers across different disciplines. This paper is part of a Special Issue devoted to CSR practices of firms in emerging market economies (EMEs). It complements prior research focusing mainly on developed countries. We begin with an assessment of CSR practices in EMEs, and examine their determinants and performance implications. We then review key findings in the empirical CSR literature, including studies published in the Emerging Markets Review Special Issue. We conclude by describing pertinent avenues for future research.

Credit Risk and the Transmission of Interest Rate Shocks
Palazzo, Berardino,Yamarthy, Ram
Using daily credit default swap (CDS) data going back to the early 2000s, we find a positive and significant relation between corporate credit risk and unexpected interest rate shocks around FOMC announcement days. Positive interest rate movements increase the expected loss component of CDS spreads as well as a risk premium component that captures compensation for default risk. Not all firms respond in the same manner. Consistent with recent evidence, we find that firm-level credit risk (as proxied by the CDS spread) is an important driver of the response to monetary policy shocks â€" both in credit and equity markets â€" and plays a more prominent role in determining monetary policy sensitivity than other common proxies of firm-level risk such as leverage and market size. A stylized corporate model of monetary policy, firm investment, and financing decisions rationalizes our findings.

Data-Driven Option Pricing using Single and Multi-Asset Supervised Learning
Anindya Goswami,Sharan Rajani,Atharva Tanksale

We propose three different data-driven approaches for pricing European-style call options using supervised machine-learning algorithms. These approaches yield models that give a range of fair prices instead of a single price point. The performance of the models are tested on two stock market indices: NIFTY$50$ and BANKNIFTY from the Indian equity market. Although neither historical nor implied volatility is used as an input, the results show that the trained models have been able to capture the option pricing mechanism better than or similar to the Black-Scholes formula for all the experiments. Our choice of scale free I/O allows us to train models using combined data of multiple different assets from a financial market. This not only allows the models to achieve far better generalization and predictive capability, but also solves the problem of paucity of data, the primary limitation of using machine learning techniques. We also illustrate the performance of the trained models in the period leading up to the 2020 Stock Market Crash (Jan 2019 to April 2020).

Decision making in Economics -- a behavioral approach
Amitesh Saha

We review economic research regarding the decision making processes of individuals in economics, with a particular focus on papers which tried analyzing factors that affect decision making with the evolution of the history of economic thought. The factors that are discussed here are psychological, emotional, cognitive systems, and social norms. Apart from analyzing these factors, it deals with the reasons behind the limitations of rational decision-making theory in individual decision making and the need for a behavioral theory of decision making. In this regard, it has also reviewed the role of situated learning in the decision-making process.

Did Mortgage Forbearance Reach the Right Homeowners? Income and Liquid Assets Trends for Homeowners during the COVID-19 Pandemic
Farrell, Diana,Greig, Fiona,Zhao, Chen
COVID-19 devastated the US labor market threatening homeowners’ ability to stay current on their mortgage. During the Great Recession, payment relief was more difficult to come by whereas the Coronavirus Aid, Relief, and Economic Security (CARES) Act provided most impacted homeowners with up to 12 months of payment relief if they attested to COVID-related hardship. However, the CARES Act did not cover everyoneâ€"it was silent on non-federally backed mortgage holders and those experiencing non-COVID related hardship. Furthermore, many borrowers were either not aware of mortgage relief options and/or were worried about potential balloon payments after forbearance ends. How well did this widespread intervention work? Did it reach all those who might have benefitted? Is there evidence of widespread moral hazard? Using checking account data linked to loan-level mortgage servicing data, we explore these questions. We find that while a third of homeowners in forbearance made all payments to date, a small fraction of homeowners not in forbearance did miss payments. Also, we find little evidence of widespread moral hazard. Families using forbearance to miss mortgage payments showed larger drops in total income than other homeowners and experienced income changes similar to those who have gone delinquent without the protection of forbearance. Also, families in forbearance were more likely to have lost labor income and received UI than families not in forbearance. Finally, we find that forbearance helped families with low levels of liquid assets to maintain their cash buffers. Together these results suggest that CARES Act mortgage forbearance policies helped homeowners experiencing financial hardship in a material way by allowing them to miss payments without adversely affecting their credit scores and maintain their small cash buffers in a world with a lot more economic uncertainty. In addition, these benefits came with little evidence of material moral hazard. However, there is room for improvement in future legislation as a small fraction of homeowners facing hardship did not benefit from forbearance and one main impediment was confusion around balloon payments. All in all the CARES Act forbearance policies appear so far to have been a large step in the right direction relative to policies during the Great Recession.

Different Extrapolators and Stock Market Momentum and Reversal
Atmaz, Adem,Cassella, Stefano,Gulen, Huseyin,Ruan, Fangcheng
Using survey data, we document significant differences in investors' extrapolative expectations about future stock market returns. Namely, there are both negative and positive extrapolators. Compared to positive extrapolators, negative extrapolators are fewer in numbers but put relatively more weight on the recent stock returns when forming their expectations. Accordingly, we develop a dynamic equilibrium model that accounts for these differences in investors’ extrapolative expectations. In the model, the equilibrium stock price exhibits short-term momentum and long-term reversal as in the data. We also test the key predictions of the model and find supportive evidence for the model mechanism.

Discount Rate Uncertainty and Capital Investment
Bessembinder, Hendrik,H. Décaire, Paul
Firms obtain noisy estimates of investors’ required rates of return (discount rates) using market-based information. Discounted-cash-flow (DCF) methods as commonly taught in MBA courses lead to upward-biased estimates of project values in the presence of such noise, even when cash flow and discount rate estimates are unbiased, due to Jensen’s inequality. We show that this bias affects corporate investment decisions and firm financial performance, and we test additional predictions derived from the DCF model in the presence of noisy discount rates. Our evidence implies that a one standard-deviation increase in discount rate uncertainty is associated with increased firm investment of 8%, while profitability decreases by 4%.

Diversification, Efficiency and Risk of Banks: New Consolidating Evidence From Emerging Economies
Jeon, Bang Nam,Wu, Ji,Chen, Limei,Chen, Minghua
This paper examines the impact of business diversification of banks on their risk, with efficiency taken into consideration as a conduit. Using bank-level data from more than 1400 commercial banks in 39 emerging economies during 2000-2016, we find that increased business diversification exerts two competing effects on bank risk, and overall reduces bank risk. The direct effect of increased diversification bolsters the stability of banks, but this is offset partially by the indirect effect of lowered efficiency, which increases the riskiness of banks. This provides a consolidating evidence on the competing arguments on the diversification-efficiency nexus in banking â€" the “diversification-premium” argument vs. the “diversification-discount” argument â€" with its extended implications on bank risk. In addition, we also present evidence that the diversification-bank risk nexus is heterogeneous on the bank size, market power and the ownership of banks, which provides useful policy implications for diversification strategies by bank managers as well as for the effective surveillance by bank regulators.

Ethics, Earnings, and ERISA: Ethical-Factor Investing of Savings and Retirement Benefits
Feuer, Albert
Ethical-factor investing shall be defined as using ethics, such as an enterprise’s policies regarding social/economic/health 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. Such investing is becoming increasingly popular among Americans, and American savings and retirement plans. Ethical-factor investing includes, but is not limited to the ESG, sustainable, and faith-based investing. The article discusses a variety of the current types of such investments, their history, and their progenitors. 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. If intended to do so, it is an expansion of the broadly held pre-biblical and current beliefs that loans must be extended on benevolent terms to the beliefs that investments, including, but not limited to loans, must be made to encourage others, not merely the investor, to behave benevolently. A significant part of the 19th century campaign that led to the government action that abolished American slavery were such investments, with the most effective being the engagement efforts made in concert with other enterprise stakeholders. As with the abolition campaign, ethical-factor investing is most often socially beneficial when part of a broad engagement campaigns, which may, but need not, include (1) large institutional investors, who may make direct overtures to enterprises to behave more benevolently, (2) other enterprise stakeholders, who may encourage more benevolent behavior by affecting enterprise operations, and (3) governments, who may mandate more benevolent enterprise behavior. Two fiduciary and tax-qualification questions arise in the retirement/savings plan realm with respect to ethical factor investing. Fiduciaries of non-ERISA plans, such as many government plans, often must satisfy tax-qualification rules similar to fiduciary rules governing ERISA plans. To what extent may fiduciaries make available ethical-factor investment options to participants and beneficiaries, who self-direct their investments, such as those for 401(k) plans or 403(b) plans? To what extent may plan fiduciaries, make ethical-factor investments on behalf of participants and beneficiaries, such as those for defined benefit plans? Retirement/savings plan fiduciaries may encourage what they perceive as better ethical behavior with their investment choices, as long as no expected financial cost is imposed on participants or beneficiaries. This is called the Tie-Breaker approach. It is often the case that two or more investment alternatives have the same expected economic performance, and it is necessary to use non-economic factors, such as ethical factors, to decide between or among such alternatives. Individuals, unlike retirement/savings plan fiduciaries, may encourage what they perceive as better ethical behavior with their investment choices, as long as the expected financial cost is relatively small. The costs must be limited so that investments remain profit-seeking investments. This is called the Concessionary approach. IRA participants and beneficiaries may pursue this investment approach.Finally, retirement/savings plan fiduciaries may also use an ethical-factor, like any non-ethical factor, to enhance their investment’s expected financial performance in a cost-effective fashion. This is called the Incorporation approach. As with other financial approaches, if it is readily available to a fiduciary, the fiduciary has a duty to use the Incorporation approach.

Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks
Bojing Feng,Wenfang Xue,Bindang Xue,Zeyu Liu

Credit rating is an analysis of the credit risks associated with a corporation, which reflect the level of the riskiness and reliability in investing. There have emerged many studies that implement machine learning techniques to deal with corporate credit rating. However, the ability of these models is limited by enormous amounts of data from financial statement reports. In this work, we analyze the performance of traditional machine learning models in predicting corporate credit rating. For utilizing the powerful convolutional neural networks and enormous financial data, we propose a novel end-to-end method, Corporate Credit Ratings via Convolutional Neural Networks, CCR-CNN for brevity. In the proposed model, each corporation is transformed into an image. Based on this image, CNN can capture complex feature interactions of data, which are difficult to be revealed by previous machine learning models. Extensive experiments conducted on the Chinese public-listed corporate rating dataset which we build, prove that CCR-CNN outperforms the state-of-the-art methods consistently.

Explainable AI for Interpretable Credit Scoring
Lara Marie Demajo,Vince Vella,Alexiei Dingli

With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusiasm in Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. Credit scoring helps financial experts make better decisions regarding whether or not to accept a loan application, such that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. An interesting concept that has been recently introduced is eXplainable AI (XAI), which focuses on making black-box models more interpretable. In this work, we present a credit scoring model that is both accurate and interpretable. For classification, state-of-the-art performance on the Home Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework, which provides different explanations (i.e. global, local feature-based and local instance-based) that are required by different people in different situations. Evaluation through the use of functionallygrounded, application-grounded and human-grounded analysis show that the explanations provided are simple, consistent as well as satisfy the six predetermined hypotheses testing for correctness, effectiveness, easy understanding, detail sufficiency and trustworthiness.

Exploring the Predictability of Cryptocurrencies via Bayesian Hidden Markov Models
Constandina Koki,Stefanos Leonardos,Georgios Piliouras

In this paper, we consider a variety of multi-state Hidden Markov models for predicting and explaining the Bitcoin, Ether and Ripple returns in the presence of state (regime) dynamics. In addition, we examine the effects of several financial, economic and cryptocurrency specific predictors on the cryptocurrency return series. Our results indicate that the Non-Homogeneous Hidden Markov (NHHM) model with four states has the best one-step-ahead forecasting performance among all competing models for all three series. The dominance of the predictive densities over the single regime random walk model relies on the fact that the states capture alternating periods with distinct return characteristics. In particular, the four state NHHM model distinguishes bull, bear and calm regimes for the Bitcoin series, and periods with different profit and risk magnitudes for the Ether and Ripple series. Also, conditionally on the hidden states, it identifies predictors with different linear and non-linear effects on the cryptocurrency returns. These empirical findings provide important insight for portfolio management and policy implementation.

Flying Under the Radar: The Real Effects of Anonymous Trading
Attig, Najah,El Ghoul, Sadok
Using unique data on TSX Attributed Trading and a new proxy of Tobin’s Q that accounts for intangible capital (Peters and Taylor, 2017), we investigate the impact of anonymous trading (AT) on managers’ ability to obtain informative feedback from the stock market to improve investment efficiency. We show that AT reduces investment efficiency and that both anonymous buyer-initiated and seller-initiated trades have comparable effects. The negative effect of AT on managerial learning from stock prices is significant for both buyer- and seller-initiated trades but is significant only for tangible investments. Taken together, our new evidence indicates that AT distorts investment sensitivity to Tobin’s Q, plausibly because anonymity attracts additional (uninformed) liquidity trading, which negatively impacts the effectiveness of asset price in aggregating private information and in revealing fundamentals.

Impact of weather factors on migration intention using machine learning algorithms
John Aoga,Juhee Bae,Stefanija Veljanoska,Siegfried Nijssen,Pierre Schaus

A growing attention in the empirical literature has been paid to the incidence of climate shocks and change in migration decisions. Previous literature leads to different results and uses a multitude of traditional empirical approaches.

This paper proposes a tree-based Machine Learning (ML) approach to analyze the role of the weather shocks towards an individual's intention to migrate in the six agriculture-dependent-economy countries such as Burkina Faso, Ivory Coast, Mali, Mauritania, Niger, and Senegal. We perform several tree-based algorithms (e.g., XGB, Random Forest) using the train-validation-test workflow to build robust and noise-resistant approaches. Then we determine the important features showing in which direction they are influencing the migration intention. This ML-based estimation accounts for features such as weather shocks captured by the Standardized Precipitation-Evapotranspiration Index (SPEI) for different timescales and various socioeconomic features/covariates.

We find that (i) weather features improve the prediction performance although socioeconomic characteristics have more influence on migration intentions, (ii) country-specific model is necessary, and (iii) international move is influenced more by the longer timescales of SPEIs while general move (which includes internal move) by that of shorter timescales.

Infinite-horizon risk-aware optimal switching under general filtration and related systems of reflected backward stochastic difference equations
Randall Martyr,John Moriarty,Magnus Perninge

We solve non-Markovian optimal switching problems in discrete time on an infinite horizon, when the decision maker is risk aware and the filtration is general, and establish existence and uniqueness of solutions for the associated reflected backward stochastic difference equations. An example illustrates the interaction between delayed or missing observations and risk sensitivity.

Insider Ownership and Investment Efficiency
Bhatta, Bibek
Agency problems in firms are known to influence suboptimal capital investment decisions. Using panel data of publicly listed firms in India, we find evidence that increased insider ownership is associated with lower investment efficiency, i.e. as insider ownership increases, firms show tendency to make capital investments beyond the optimal level. However, we do not find evidence of increased insider ownership leading to underinvestment (below the optimal level of capital investment). A plausible explanation, consistent with theory, is that such insiders are making capital investments for private gain and empire-building rather than in the best interest of the firm. Additional analyses show that the presence of independent directors on the board of firms mitigates such value-destroying investments stemming from increased insider ownership.

Management as the sine qua non for M&A success
Delis, Manthos D.,Iosifidi, Maria,Kazakis, Pantelis,Ongena, Steven,Tsionas, Mike G.
This paper studies whether management quality in acquiring firms determines merger and acquisition (M&A) success. We model management practices as an unobserved (latent) variable in a standard microeconomic model of the firm and derive firm-year management estimates. We show that our measure is among the most important determinants of value creation in M&A deals. Our results are robust to the inclusion of acquirer fixed effects, to a large set of control variables, and to several other sensitivity tests. We also show that management explains, albeit to a lesser extent, acquirers’ return on equity and Tobin’s q.

Mispricing and Anomalies: An Exogenous Shock to Short Selling from the Dividend Tax Law Change
Han, Yufeng ,Lu, Yueliang (Jacques),Xu, Weike,Zhou, Guofu
We study the causal effect of short selling on asset pricing anomalies by exploiting a novel exogenous shock to short selling. After the Job and Growth Tax Relief Reconciliation Act (JGTRRA) of 2003, equity lenders are reluctant to lend shares around the dividend record dates because substitute dividends that they would receive are taxed at ordinary income rates while qualified dividends are taxed at 15 percent, thus creating a negative shock to short selling. Using arguably the most comprehensive set of anomalies to date and the difference-in-differences (DID) regression framework, we find that anomalies become stronger after the dividend record months in the post-JGTRRA periods, driven by stronger mispricing in the dividend record months. We further show that the effect mainly comes from the overpriced stocks. Overall, our results provide strong evidence that most anomalies are likely due to mispricing, with valuation anomalies as an exception.

New Perspectives to Reduce Stress through Digital Humor
Misnal Munir,Amaliyah,Moses Glorino Rumambo Pandin

This study aimed to find new perspectives on the use of humor through digital media. A qualitative approach was used to conduct this study, where data were collected through a literature review. Stress is caused by the inability of a person to adapt between desires and reality. All forms of stress are basically caused by a lack of understanding of human's own limitations. Inability to fight limitations that will cause frustration, conflict, anxiety, and guilt. Too much stress can threaten a person's ability to deal with the environment. As a result, employees develop various kinds of stress symptoms that can interfere with their work performance. Thus, the management of work stress is important to do, one of which uses humor. However, in the digital age, the spread of humor can be easily facilitated. The results of this review article find new perspectives to reduce stress through digital humor, namely interactive humor, funny photos, manipulations, phanimation, celebrity soundboards, and PowerPoint humor. The research shows that the use of humor as a coping strategy is able to predict positive affect and well-being work-related. Moreover, digital humor which has various forms as well as easy, fast, and wide spread, then the effect is felt increasingly significant

On Lower Partial Moments for an Investment Portfolio With Variance-Gamma Distributed Returns
Ivanov, Roman
The paper discusses the lower partial moments for the return of the investment portfolio which consists of the assets whose random incomes are modeled by variance-gamma, gamma distributions and constants.The formulas depend on values of generalized hypergeometric functions. As a corollary, the target semideviation is computed.It is shown also how the obtained results generate analytical expressions for the value at risk and the expected shortfall monetary risk measures.

Optimal Insurance to Minimize the Probability of Ruin: Inverse Survival Function Formulation
Bahman Angoshtari,Virginia R. Young

We find the optimal indemnity to minimize the probability of ruin when premium is calculated according to the distortion premium principle with a proportional risk load, and admissible indemnities are such that both the indemnity and retention are non-decreasing functions of the underlying loss. We reformulate the problem with the inverse survival function as the control variable and show that deductible insurance with maximum limit is optimal. Our main contribution is in solving this problem via the inverse survival function.

Optimal portfolios for different anticipating integrals under insider information
Carlos Escudero,Sandra Ranilla-Cortina

We consider the non-adapted version of a simple problem of portfolio optimization in a financial market that results from the presence of insider information. We analyze it via anticipating stochastic calculus and compare the results obtained by means of the Russo-Vallois forward, the Ayed-Kuo, and the Hitsuda-Skorokhod integrals. We compute the optimal portfolio for each of these cases. Our results give a partial indication that, while the forward integral yields a portfolio that is financially meaningful, the Ayed-Kuo and the Hitsuda-Skorokhod integrals do not provide an appropriate investment strategy for this problem.

Ownership Concentration in Listed Firms in the Gulf Cooperation Council: Implications for Corporate Governance
Basco, Rodrigo,Ghaleb, Fatima,Gómez-Ansón, Silvia,Hamdan, Rana,malik, sadia,Martínez-García, Irma
In this study, we analyze 692 listed firms in the GCC for a period of seven years (2009â€"2015). This study provides valuable insights into corporate ownership in the emerging economies of the GCC. First, the findings provide major and minor investors with a comprehensive understanding of the corporate ownership structure in the GCC region, including the ownership concentration and ownership identity of firms in financial and non-financial industries. Second, the findings shed light on the change of ownership concentration in listed firms in the GCC, over seven years (2009â€"2015), during which, various policies were enacted to encourage investments and promote minority investor protection. Finally, this study prioritizes the public awareness of policy formulation to enhance corporate governance practices and mechanisms in the GCC region.

Pandemic risk management: resources contingency planning and allocation
Xiaowei Chen,Wing Fung Chong,Runhuan Feng,Linfeng Zhang

Repeated history of pandemics, such as SARS, H1N1, Ebola, Zika, and COVID-19, has shown that pandemic risk is inevitable. Extraordinary shortages of medical resources have been observed in many parts of the world. Some attributing factors include the lack of sufficient stockpiles and the lack of coordinated efforts to deploy existing resources to the location of greatest needs. The paper investigates contingency planning and resources allocation from a risk management perspective, as opposed to the prevailing supply chain perspective. The key idea is that the competition of limited critical resources is not only present in different geographical locations but also at different stages of a pandemic. This paper draws on an analogy between risk aggregation and capital allocation in finance and pandemic resources planning and allocation for healthcare systems. The main contribution is to introduce new strategies for optimal stockpiling and allocation balancing spatio-temporal competitions of medical supply and demand.

Pension Benefits, Retirement and Human Capital Depreciation in Late Adulthood
Plamen Nikolov,Alan Adelman

Economists have mainly focused on human capital accumulation and considerably less on the causes and consequences of human capital depreciation in late adulthood. Studying human capital depreciation over the life cycle has powerful economic consequences for decision-making in old age. Using data from China, we examine how a new retirement program affects cognitive performance. We find large negative effects of pension benefits on cognitive functioning among the elderly. We detect the most substantial impact of the program on delayed recall, a significant predictor of the onset of dementia. We show suggestive evidence that the program leads to larger negative impacts among women. We demonstrate that retirement and access to a retirement pension plan plays a significant role in explaining cognitive decline at older ages.

Persuasion Produces the (Diamond) Paradox
Mark Whitmeyer

This paper extends the sequential search model of Wolinsky (1986) by allowing firms to choose how much match value information to disclose to visiting consumers. This restores the Diamond paradox (Diamond 1971): there exist no symmetric equilibria in which consumers engage in active search, so consumers obtain zero surplus and firms obtain monopoly profits. Modifying the scenario to one in which prices are advertised, we discover that the no-active-search result persists, although the resulting symmetric equilibria are ones in which firms price at marginal cost.

Policy Maker's Credibility with Predetermined Instruments for Forward-Looking Targets
Jean-Bernard Chatelain,Kirsten Ralf

The aim of the present paper is to provide criteria for a central bank of how to choose among different monetary-policy rules when caring about a number of policy targets such as the output gap and expected inflation. Special attention is given to the question if policy instruments are predetermined or only forward looking. Using the new-Keynesian Phillips curve with a cost-push-shock policy-transmission mechanism, the forward-looking case implies an extreme lack of robustness and of credibility of stabilization policy. The backward-looking case is such that the simple-rule parameters can be the solution of Ramsey optimal policy under limited commitment. As a consequence, we suggest to model explicitly the rational behavior of the policy maker with Ramsey optimal policy, rather than to use simple rules with an ambiguous assumption leading to policy advice that is neither robust nor credible.

Properties of the Variance-Gamma Process With Drift Switching Component With Financial Applications
Ivanov, Roman
We consider an extension of the variance-gamma process implying that the linear drift rate of the process can switch suddenly by a jump. The value of jump is modeled by the multidimensional distribution, the jump time is simulated by the exponential distribution. Together with the simplest properties of the new process, its distribution function is derived explicitly. Applications to the credit risk measurement are supplied. Obtained results exploit values of some special mathematical functions including the generalized hypergeometric ones.

Real Effects of Share Repurchases Legalization on Corporate Behaviors
Wang, Zigan,Yin, Qie Ellie,Yu, Luping
We use staggered share repurchases legalization from 1985 to 2010 across the world to examine its impact on corporate behaviors. We find that share-repurchasing firms do not cut dividends as a substitution. The cash for repurchasing shares comes more from internal cash than external debt issuance, leading to reductions in capital expenditures and R&D expenses. While this strategy boosts stock prices, it results in lower long-run Tobin’s Q, profitability, growth, and innovation, accompanied by lower insider ownership. Tax benefits and paying out temporary earnings are two primary reasons that firms repurchase.

Reverse Engineering in Accounting-Based Equity Valuation Part I: A Fundamentalist Perspective and a New Model Approach
Huefner, Bernd,Rueenaufer, Marcel
This paper serves as the first in a two-part discussion on existing applications and empirical study of reverse engineering (RE) in accounting-based equity valuation from the perspective of a fundamental investor. Despite a history of over 80 years, the literature on RE shows a considerable degree of heterogeneity, without a clear consensus on the modeling, conceptualization and operationalization of the technique. In the first part, we derive a new assessment framework for applied RE in accounting-based equity valuation from the perspective of a fundamental investor. Based on the framework, we find that there is no approach to reverse engineering that fulfils the needs of fundamental investors in the literature and clarifies what RE can be used for. In search for a remedy, we derive a model based on the Ohlson (1995) model that is tailored to reverse engineering and complements a more extensive fundamental analysis. The second part applies the theoretical framework to stock screening based on fundamentals and market expectations, focusing on characteristics of quality and cheapness. That said, the two parts of the series belong together and we recommend them to be regarded accordingly.

Reverse Engineering in Accounting-Based Equity Valuation Part II: Empirical Evidence on Errors in Market Expectations and Subsequent Stock Returns
Huefner, Bernd,Rueenaufer, Marcel
This paper is the second in a two-part discussion and application of reverse engineering in accounting-based equity valuation from the perspective of a fundamental investor. Building upon the “errors-in-expectations” hypothesis, we develop a theoretically funded yet practical tool for stock screening in this paper. We use the Ohlson (1995) model to apply and extend the framework from the first paper by connecting total stock returns to accounting-based fundamentals and (changes in) expected residual income levels, both in the short-term and long-term future. In a similar manner to Piotroski & So (2012), we construct a scoring index â€" VScore. VScore includes both fundamental data and short-term market expectations that stem from the theoretical framework we provide beforehand, where short-term expectations perform a verifying function for historical fundamentals for the determination of quality. We document that the book-to-price (B/P) effect is concentrated among firms for which long-term speculation is simultaneously incongruent to the underlying fundamentals and short-term expectation (stocks that combine cheapness with quality), indicating that those stocks are interesting subjects for a more extensive fundamental analysis.

Review of Financial Markets (August 15-30, 2020)
Abramov, Alexander E.,Kosyrev, Andrey,Radygin, Alexander,Chernova, Maria
During the two weeks from August 15 to August 30, 2020, the global and national stock and bond markets generally remained stable, while the stock indexes continued their post-crisis recovery. The business media switched over their focus of attention to the discussion of the recovery rates being demonstrated by the world’s top economies, the changes in the US monetary policies (MP), and government debt growth. The prevailing point of view has been that the USA and the other developed economies have so far failed to display a V-shaped recovery trajectory. Although, in August, the price of oil and the RTS Index continued to gradually recover, the month-end rates for August 2020 of the ruble weakening and the Index’s decline relative to their year-beginning values placed these indices among the topmost losers in the group of economies included in our review.

Risk management of guaranteed minimum maturity benefits under stochastic mortality and regime-switching by Fourier space time-stepping framework
Wenlong Hu

In this paper, we adopted a net liability model which assesses both market risk on the liability side and revenue risk on the asset side for a Guaranteed Minimum Maturity Benefit (GMMB) embedded in variable annuity (VA) contracts. Numeric solutions for net liabilities, fair rate of fees and Greeks of GMMB are obtained by a more accurate and fast Fourier Space time-stepping (FST) algorithm. Monte Carlo results are provided for comparative purpose. The unhedged and three statically hedged portfolios are introduced, and their performances are assessed by comparing the short term and long term portfolio's volatility. Recently, we noticed FST algorithm can only be used to hedge the gross liability of GMMB and it leads to an incorrect result when applying FST algorithm to the net liability model. We have modified the method we used in the hedging part and obtained the reliable result now. They will be reported in near future.

Screening and Loan Origination Time: Lending Standards, Loan Defaults and Bank Failures
Bedayo, Mikel,Jiménez, Gabriel,Peydró, José-Luis,Sánchez, Raquel Vegas
We show that loan origination time is key for bank lending standards, cycles, defaults and failures. We exploit the credit register from Spain, with the time of a loan application and its granting. When VIX is lower (booms), banks shorten loan origination time, especially to riskier firms. Bank incentives (capital and competition), capacity constraints, and borrower-lender information asymmetries are key mechanisms driving results. Moreover, shorter (loan-level) origination time is associated with higher ex-post defaults, also using variation from holidays. Finally, shorter precrisis origination time â€"more than other lending conditionsâ€" is associated with more bank-level failures in crises, consistent with lower screening.

Shareholders Right Directive II: The Italian Implementation
Martino, Edoardo,Pacces, Alessio M.
The revision of the Shareholders Rights Directive (“SRDII”) entered into force in 2017. The SRDII represents the landing point of a long process started in 2012 with the “Action Plan: European company law and corporate governance” drafted by the EU Commission. Already in the Action plan, the two key objectives pursued by the European Legislator were clearly displayed: enhancing companies’ transparency and enhancing shareholders’ engagement. These objectives are, in turn, instrumental in attracting stock market funding, alleviating the dependence of European companies on bank funding. This article discusses the implementation of the SRDII into the Italian legal system. The analysis discusses the main choices of the Italian legislation in implementing the SRDII rules and the coordination between these new rules and the Italian legal system, with specific reference to the general corporate law and the existing provisions on listed companies. We analyse four main features of the SRDII1) Rules on shareholder identification. These may create more problems than they solve. In particular, these rules may slightly curb the incentives of activists to engage target companies, fearing the free-ride of other investors. The coordination with the existing Italian law may worsen the situation.2) Rules on shareholder engagement. The Italian legislator provides Consob with interesting enforcement powers, close to a duty to demonstrate engagement. The Italian approach to the supervision of proxy advisors is also interesting, but likely ineffective because of the limited extraterritorial effect of the provisions.3) Say-on-pay legislation. The biggest novelty is the binding vote on the Remuneration Policy. Moreover, the coordination may prove problematic between the implementation of SRD II and the existing Italian law on variable remuneration awarded through financial instruments.4) Related-Party Transaction. The Italian legal system was ahead of its time in this regard. This can be explained by the peculiarities of Italian capitalism. The SRD II brought very few amendments to the existing regime. Notably, the definition of Related-Party has been widened and now directly refers to the international accounting principles (IAS 24).

Skip: A Small-sample Correction for Return Prediction with Valuation Ratios
Gakidis, Harry
It has been known since Stambaugh (1986) that persistent valuation-ratio regressors lead to biased coefficients in small-sample return regressions. We propose a “Skip” estimator that corrects this bias by averaging estimates from sub-samples of non-consecutive observations. Each sub-sample simply skips adjacent observations and so weakens the bias-inducing link between the innovation in returns and the innovation in subsequent valuation ratios. The estimator does not require estimates of the unknown persistence and covariance parameters, and it facilitates inference since the t-statistic is now distributed approximately standard normal. Finally, it compares favorably to OLS in terms of mean square error and is easily implementable with standard software.

Social Capital Contributions to Food Security: A Comprehensive Literature Review
Saeed Nosratabadi,Nesrine Khazami,Marwa Ben Abdallah,Zoltan Lackner,Shahab S. Band,Amir Mosavi,Csaba Mako

Social capital creates a synergy that benefits all members of a community. This review examines how social capital contributes to the food security of communities. A systematic literature review, based on Prisma, is designed to provide a state-of-the-art review on capacity social capital in this realm. The output of this method led to finding 39 related articles. Studying these articles illustrates that social capital improves food security through two mechanisms of knowledge sharing and product sharing (i.e., sharing food products). It reveals that social capital through improving the food security pillars (i.e., food availability, food accessibility, food utilization, and food system stability) affects food security. In other words, the interaction among the community members results in sharing food products and information among community members, which facilitates food availability and access to food. There are many shreds of evidence in the literature that sharing food and food products among the community member decreases household food security and provides healthy nutrition to vulnerable families and improves the food utilization pillar of food security. It is also disclosed that belonging to the social networks increases the community members' resilience and decreases the community's vulnerability that subsequently strengthens the stability of a food system. This study contributes to the common literature on food security and social capital by providing a conceptual model based on the literature. In addition to researchers, policymakers can use this study's findings to provide solutions to address food insecurity problems.

Statistical properties of the aftershocks of stock market crashes: evidence based on the 1987 crash, 2008 financial crisis and COVID-19 pandemic
Anish Rai,Ajit Mahata,Md Nurujjaman,Om Prakash

Every unique crisis, a new and novel risk factor, leads to a rapid, synchronous and panic sell-off by the investors that lead to a massive stock market crash, termed as mainshock, which usually continues for more than one day. Though most of the stocks start recovering from the crash within a short period, the effect of the crash remains throughout the recovery phase. During the recovery, as the market remains in stress, any small perturbation leads to a relatively smaller aftershock, which may also occur for a few days. Statistical analysis of the mainshock and the aftershocks for the crash of 1987, the financial crisis of 2008 and the COVID-19 pandemic shows that they follow the Gutenberg-Richter (G-R) power law. The duration of the influence of the mainshock, within which aftershocks are considered, has been calculated using structural break analysis. The analysis shows that high magnitude aftershocks comparable to the mainshock are rare but low magnitude aftershocks can be seen frequently till the full recovery of the market. It is also consistent with the psychology of the investors that when the unique crisis becomes known, the market does not react too irrationally as it did initially, and hence subsequent crashes become relatively smaller. The results indicate that there is a possibility of the occurrence of future low magnitude aftershocks due to the ongoing COVID-19 pandemic. The analysis may help investors make rational investment decisions during the stressed period after a major market crash.

The Downside and Upside Beta Valuation in the Variance-Gamma Model
Ivanov, Roman
Throughout the paper, investment portfolios which consist of assets with variance-gamma, gamma and deterministically distributed returns are considered. We derive formulas which characterize the impact of a particular asset on the risks and gains of the portfolio. Namely, we obtain analytical expressions for the downside and upside betas. The returns on the assets are assumed to be dependent. The established formulas depend on the values of special mathematical functions including the values of the generalized hypergeometric ones.

The Risk Measurement in the Skewed Student’s t Model
Ivanov, Roman
Throughout this paper, we discuss the problem of calculation of the value at risk and the expected shortfall risk measures in the skewed Student's t model. The investment portfolio which consists of assets with deterministic, inverse gamma and skewed Student's t returns is considered. Analytical results for the mentioned above monetary risk measures are derived basing on the formulas for the distribution functions of losses. The obtained formulas depend on the values of special mathematical functions including the generalized hypergeometric ones. Numerical examples of the computations are given.

The Testing Multiplier: Fear vs Containment
Francesco Furno

I study the economic effects of testing during the outbreak of a novel disease. I propose a model where testing permits isolation of the infected and provides agents with information about the prevalence and lethality of the disease. Additional testing reduces the perceived lethality of the disease, but might increase the perceived risk of infection. As a result, more testing could increase the perceived risk of dying from the disease - i.e. "stoke fear" - and cause a fall in economic activity, despite improving health outcomes. Two main insights emerge. First, increased testing is beneficial to the economy and pays for itself if performed at a sufficiently large scale, but not necessarily otherwise. Second, heterogeneous risk perceptions across age-groups can have important aggregate consequences. For a SARS-CoV-2 calibration of the model, heterogeneous risk perceptions across young and old individuals mitigate GDP losses by 50% and reduce the death toll by 30% relative to a scenario in which all individuals have the same perceptions of risk.

Wealth concentration in systems with unbiased binary exchanges
Ben-Hur Francisco Cardoso,Sebastián Gonçalves,José Roberto Iglesias

Aiming to describe the wealth distribution evolution, several models consider an ensemble of interacting economic agents that exchange wealth in binary fashion. Intriguingly, models that consider an unbiased market, that gives to each agent the same chances to win in the game, are always out of equilibrium until the perfect inequality of the final state is attained. Here we present a rigorous analytical demonstration that any system driven by unbiased binary exchanges are doomed to drive the system to perfect inequality and zero mobility.

Why Working From Home Will Stick
Barrero, Jose Maria,Bloom, Nicholas,Davis, Steven J.
We survey 15,000 Americans over several waves to investigate whether, how, and why working from home will stick after COVID-19. The pandemic drove a mass social experiment in which half of all paid hours were provided from home between May and October 2020. Our survey evidence says that 22 percent of all full work days will be supplied from home after the pandemic ends, compared with just 5 percent before. We provide evidence on five mechanisms behind this persistent shift to working from home: diminished stigma, better-than-expected experiences working from home, investments in physical and human capital enabling working from home, reluctance to return to pre-pandemic activities, and innovation supporting working from home. We also examine some implications of a persistent shift in working arrangements: First, high-income workers, especially, will enjoy the perks of working from home. Second, we forecast that the postpandemic shift to working from home will lower worker spending in major city centers by 5 to 10 percent. Third, many workers report being more productive at home than on business premises, so post-pandemic work from home plans offer the potential to raise productivity as much as 2.4 percent.

Within-firm Labor Heterogeneity and Firm Performance: Evidence from Employee Political Ideology Conflicts
Ren, Xiao
This paper explores the implication of within-firm labor heterogeneity for firm performance through the lens of employee political ideology. Using individual campaign donation information to capture political ideology, I find that political ideology conflicts, both those within employees and those between CEOs and employees, are negatively associated with firms’ future operating performance. This effect is stronger for firms whose employees are more geographically concentrated and more sophisticated. The reduced labor productivity and abnormal employee turnover are two plausible mechanisms through which employee political ideology conflicts hurt firm performance. To establish causality, I use an instrumental variable approach which relies on the exogenous variation in political ideology caused by local television station ownership changes.

Zombie Firms: Prevalence, Determinants, and Corporate Policies
El Ghoul, Sadok,Fu, Zhengwei,Guedhami, Omrane
Using a comprehensive dataset of firms from seventy-nine countries, we document the incidence, determinants, and corporate policies of zombie firms from 2005 through 2016. Zombie firms account for roughly 10% of our observations. Using logit regressions, we find strong and robust evidence that countries with more efficient debt enforcement environments tend to have fewer zombie firms. In contrast, we find no evidence that the prevalence of zombie firms is related to national culture. We further find that zombie firms have conservative dividend and investment policies, aggressive leverage policies, and higher idiosyncratic risk. We conclude that zombie firms may impose a cost on the economy by impeding efficient resource allocation.