Research articles for the 2020-06-24

A Symmetric Super Bowl Stock Market Predictor Model
acherqui, yousra
Krueger and Kennedy (J Fin 45:691â€"697, 1990 ) were the first to empirically document the remarkable stock market predictive power of the winner of the Super Bowl. The original model had investors go “long” in the market when the Super Bowl was won by a team from the old NFL, but park their money in T-Bills when the Super Bowl was won by a team from the old AFL â€" a non-symmetric trading rule. We create a symmetric rule (go “long” in the market when the old NFL wins; go “short” when they lose) and compare its efficacy to the original formulation. The symmetric rule outperforms the original KK specification in the period covered by their study (1967â€"1988), but performs worse than the original specification (and the naïve buy-and-hold strategy) since 1988.

A note on Almgren-Chriss optimal execution problem with geometric Brownian motion
Bastien Baldacci,Jerome Benveniste

We solve explicitly the Almgren-Chriss optimal liquidation problem where the stock price process follows a geometric Brownian motion. Our technique is to work in terms of cash and to use functional analysis tools. We show that this framework extends readily to the case of a stochastic drift for the price process and the liquidation of a portfolio.

An Examination of Ex Ante Risk and Return in the Cross-Section Using Option-Implied Information
Kim, Dongcheol,Chen, Ren-Raw,Roh, Tai-Yong,Panda, Durga
This paper examines cross-sectional relations between ex ante expected returns and betas. As a proxy for ex ante expected returns, we use implied returns obtained from the risk-adjusted option pricing model suggested in this paper. We find that implied returns have a positive and significant cross-sectional relation with implied betas in all maturity groups considered. This significant relation is maintained regardless of the inclusion of the well-known CAPM-anomaly variables such as firm size, book-to-market, past returns, earnings-to-price ratio, and liquidity. Ex ante market risk premium estimates have a statistical significance as well as an economic significance in that they contain significant forward-looking information on future macroeconomic conditions. Thus, market betas are priced on an ex ante basis.

Asset Managers as Buyers of Last Resort
Golez, Benjamin,Rizzo, Antonino Emanuele,Zambrana, Rafael
Most brokerage firms are owned by publicly traded financial conglomerates. As much as 5% of the stock of these conglomerates is held by mutual funds that are broker's clients. Moreover, in times of distress when investors tend to decrease exposure to financial companies, client funds increase ownership in their brokers' parent companies. Consistent with the theory on liquidity provision, we find that these trades are not driven by superior information, and they contribute to the financial stability of brokers' parent companies. This suggests that business networks between financial institutions and their clients have broader implications for the systemic risk of the financial sector. We also show that funds' investments in their brokers' stocks are positively associated with funds' performance on non-connected stocks. The documented effects are strongest for funds that have long-lasting business ties with their brokers. Our findings suggest that asset managers act as buyers of last resort for brokers that provide more valuable investment services. Exogenous changes in business ties due to brokerage firm acquisitions by large financial groups confirm the causal nature of this relation.

Asymptotics for Small Nonlinear Price Impact: a PDE Approach to the Multidimensional Case
Erhan Bayraktar,Thomas Caye,Ibrahim Ekren

We provide an asymptotic expansion of the value function of a multidimensional utility maximization problem from consumption with small non-linear price impact. In our model cross-impacts between assets are allowed. In the limit for small price impact, we determine the asymptotic expansion of the value function around its frictionless version. The leading order correction is characterized by a nonlinear second order PDE related to an ergodic control problem and a linear parabolic PDE. We illustrate our result on a multivariate geometric Brownian motion price model.

Basis Risk and Pensions Schemes: A Relative Modelling Approach.
Hunt, Andrew,Blake, David P.
For many pension schemes, a shortage of data limits their ability to use sophisticated stochastic mortality models to assess and manage their longevity risk. In this study, we develop a relative model for mortality, which compares the evolution of mortality rates in a sub-population with that observed in a larger reference population. We apply this relative approach to data from the CMI Self-Administered Pension Scheme study, using UK population data as a reference. We then use the relative approach to investigate the potential differences in the evolution of mortality rates between these two populations and find that, in many practical situations, basis risk is much less of a problem than is commonly believed.

COVID-19, Government Policy Responses, and Stock Market Liquidity around the World
Zaremba, Adam,Aharon, David Y.,Demir, Ender,Kizys, Renatas,Zawadka, Dariusz
Unprecedented non-pharmaceutical interventions targeted to curb the spread of COVID-19 exerted a dramatic impact on the global economy and financial markets. This study is the first attempt to investigate the influence of these government policy responses on global stock market liquidity. To this end, we examine daily data from 49 countries for the period January-April 2020. We demonstrate that workplace and school closures deteriorate liquidity in emerging markets, while information campaigns on the novel coronavirus facilitate trading activity.

Corporate Board Committees and Corporate Outcomes: An International Systematic Literature Review and Agenda for Future Research
Alhossini, Mohammed A.,Ntim, Collins G.,Zalata, Alaa
This paper comprehensively reviews the current body of international accounting literature regarding advisory/monitoring committees and corporate outcomes. Specifically, it synthesizes, appraises and extends current knowledge on the (i) theoretical (i.e., economic, accounting/corporate governance, sociological and socio-psychological) perspectives and (ii) empirical evidence of the observable and less visible attributes at both the individual and committee levels and their link with a wide range (financial/non-financial) of corporate outcomes. Using the systematic literature review method, 304 articles from 59 journals in the fields of accounting and finance that were published between January 1992 and December 2018 are reviewed. The main findings are as follows. First and theoretically, agency theory is the most dominant applied theory/studies with no application of theory at all (descriptive), whilst the application of integrated theoretical frameworks is lacking in the reviewed articles. Second, the existing empirical evidence focuses excessively on:(i) monitoring instead of advisory committees and (ii) observable rather than less visible committee attributes. Third, scarcity of cross- country studies along with methodological limitations relating to measurement inconsistencies, insufficiency of variables, and dominance of quantitative studies, among others, are identified. Finally, promising future research avenues are outlined.

Crises in Prosperous Societies: Are they Inevitable?
Kirsh, Yoram
Crises are integral part of economic history. Analysis of economic crises, from the tulip mania in the 17th century to the 2008 crisis, reveals that in prosperous societies most of the crises are associated not with a shortage of resources but rather with abundance. These crises are instigated by processes inherent to the free market, which allows phenomena such as hazardous investments, reckless bank lending policies and the formation of speculative bubbles. The 2008 crisis, for example, involved irresponsible behavior of major financial institutions as well as failure of the regulators to prevent this behavior by confronting the institutions they were entrusted to oversee. The debate between fiscalists and monetarists is less relevant today, while the cooperation between the government, the central bank, and the financial sector becomes a key factor. In countries where this cooperation was successful, the 2008 crisis was less harmful. E.g., in Australia, where the banks had accumulated fewer bad debts than in other countries and the government and the Reserve Bank were quick to act, the crisis was hardly noticed. Another class of economic crises are those triggered by an external cause, such as a natural disaster or an epidemic. In the COVID-19 event, where handling the crisis demanded a combination of health and economic measures, the importance of the private sectors was proven as well. For example, in South Korea, where the government acted quickly and private companies were summoned to participate in developing tests for the virus, the epidemic was blocked earlier and with fewer deaths than in other countries.

DeFi Protocols for Loanable Funds: Interest Rates, Liquidity and Market Efficiency
Lewis Gudgeon,Sam M. Werner,Daniel Perez,William J. Knottenbelt

We coin the term *Protocols for Loanable Funds (PLFs)* to refer to protocols which establish distributed ledger-based markets for loanable funds. PLFs are emerging as one of the main applications within Decentralized Finance (DeFi), and use smart contract code to facilitate the intermediation of loanable funds. In doing so, these protocols allow agents to borrow and save programmatically. Within these protocols, interest rate mechanisms seek to equilibrate the supply and demand for funds. In this paper, we review the methodologies used to set interest rates on three prominent DeFi PLFs, namely Compound, Aave and dYdX. We provide an empirical examination of how these interest rate rules have behaved since their inception in response to differing degrees of liquidity. We then investigate the market efficiency and inter-connectedness between multiple protocols, examining first whether Uncovered Interest Parity holds within a particular protocol and second whether the interest rates for a particular token market show dependence across protocols, developing a Vector Error Correction Model for the dynamics.

Deep Investing in Kyle's Single Period Model
Paul Friedrich,Josef Teichmann

The Kyle model describes how an equilibrium of order sizes and security prices naturally arises between a trader with insider information and the price providing market maker as they interact through a series of auctions. Ever since being introduced by Albert S. Kyle in 1985, the model has become important in the study of market microstructure models with asymmetric information. As it is well understood, it serves as an excellent opportunity to study how modern deep learning technology can be used to replicate and better understand equilibria that occur in certain market learning problems.

We model the agents in Kyle's single period setting using deep neural networks. The networks are trained by interacting following the rules and objectives as defined by Kyle. We show how the right network architectures and training methods lead to the agents' behaviour converging to the theoretical equilibrium that is predicted by Kyle's model.

Determining Secondary Attributes for Credit Evaluation in P2P Lending
Revathi Bhuvaneswari,Antonio Segalini

There has been an increased need for secondary means of credit evaluation by both traditional banking organizations as well as peer-to-peer lending entities. This is especially important in the present technological era where sticking with strict primary credit histories doesn't help distinguish between a 'good' and a 'bad' borrower, and ends up hurting both the individual borrower as well as the investor as a whole. We utilized machine learning classification and clustering algorithms to accurately predict a borrower's creditworthiness while identifying specific secondary attributes that contribute to this score. While extensive research has been done in predicting when a loan would be fully paid, the area of feature selection for lending is relatively new. We achieved 65% F1 and 73% AUC on the LendingClub data while identifying key secondary attributes.

Development Banks and the Syndicate Structure: Evidence From a World Sample
Degl'Innocenti, Marta,Frigerio, Marco,Zhou, Si
Do development banks affect the structure of loan syndicates? We argue that development banks’ participation in syndicates can reduce the lead banks’ monitoring efforts and generate higher risk diversification across lenders. Using a global dataset of syndicated loans from 2001 to 2016 for 105 countries and 44,899 deals, we show that the lead banks decrease their loan shares and form less concentrated syndicates when development banks are participant lenders. These syndicates are also composed of a higher number of foreign lenders that retain greater loan shares. We also find that development banks are not associated with higher covenant violations. Our results are robust when accounting for relationship lending, asymmetric information within the syndicate, lenders’ lending expertise, borrower opacity, types of loan, and ranking hierarchy in the syndicate, among others.

Does Board Gender Diversity Matter?
Poshakwale, Sunil S.,Agarwal, Vineet
We analyze the relationship between the proportion of women directors on the boards of UK listed firms and their financial performance and risk. We find that firms with greater gender diversity are more profitable and enjoy higher market valuations. However, consistent with prior research, we do not find any reliable evidence to suggest that gender diversity affects stock returns or risk. Our findings make two important contributions to the literature. First, we show that greater representation of women on boards is not merely an issue about social inclusion but it is economically beneficial. Second, the UK’s approach of using voluntary targets to increase the board gender diversity seems to be effective.

Does Gold or Bitcoin Hedge Economic Policy Uncertainty?
Derbali, Abdelkader
Calculating the hedge and safe-haven properties of gold and Bitcoin via GARCH model and quantile regression with dummy variables. We find that: (1) Neither gold nor Bitcoin can serve as a strong hedge or safe-haven for economic policy uncertainty (EPU) at the average condition. (2) Bitcoin is more responsive to EPU shocks, while gold maintains stability with smaller hedge and safe-haven coefficients. (3) In most cases, both gold and Bitcoin can act as the weak hedge and weak safe-haven against EPU during the extreme bearish and bullish markets, which two can be considered for portfolio diversification during the normal market.

Effects of Macroprudential Policy: Evidence from Over 6,000 Estimates
Araujo, Juliana,Patnam, Manasa,Popescu, Adina,Valencia, Fabián,Yao, Weija
This paper builds a novel database on the effects of macro-prudential policy drawing from 58 empirical studies, comprising over 6,000 results on a wide range of instruments and outcome variables. It encompasses information on statistical significance, standardized magnitudes, and other characteristics of the estimates. Using meta-analysis techniques, the paper estimates average effects to find:i) statistically significant effects on credit, but with considerable heterogeneity across instruments; ii) weaker and more imprecise effects on house prices; iii) quantitatively stronger effects in emerging markets and among studies using micro-level data; and iv) statistically significant evidence of leakages and spillovers. Other findings include relatively stronger impacts for tightening than loosening actions and negative effects on economic activity in the near term.

Entrepreneur Debt Aversion and the Effectiveness of SME Support Programs: Evidence from the COVID-19 Pandemic
Paaso, Mikael,Pursiainen, Vesa,Torstila, Sami
This project studies the effect of entrepreneur debt aversion on the use of debt within the business that they run. We use the COVID-19 pandemic as a natural experiment providing a clean and exogenous liquidity shock that is unrelated to the firm's long-term viability. To measure debt aversion and firm actions, we conduct a large-scale survey of about 1,000 Finnish SMEs, asking entrepreneurs about their attitude towards debt, personality traits, expectations for their business, as well as the measures taken in response to COVID-19. We match the survey responses to financial statement data, allowing us to observe both detailed financial data of the firms, as well as their long-term behavior. It will also allow us to follow the firm outcomes in future extensions of this study. We also conduct an experiment testing whether the framing of support instruments as debt affects SMEs' willingness to use them.

Examining the Liquidity and Productivity Relationship: Evidence from Post-reform China
Wang, Tianyu,Li, Kui-Wai
A loose financial policy through the provision of loans and fiscal subsidies to state-owned enterprises and households has long been practiced in China, though financial liberalization since the 1980s has revitalized banks and other institutions. By using provincial data, this paper attempts to show the relationship between liquidity and productivity in post-reform China. China’s total factor productivity growth is estimated by the Malmquist index. A total of four regression models have been employed and the findings support the inverse relationship between liquidity and productivity, especially since 2008. China’s loose financial policy that promoted “cash-richness” must be reexamined as excessive liquidity coexisted with decline in total factor productivity. An increase of 1% in liquidity would result in about 0.6% loss in total factor productivity due to market distortion.

Fast calibration of the LIBOR Market Model with Stochastic Volatility based on analytical gradient
Hervé Andres,Pierre-Edouard Arrouy,Paul Bonnefoy,Alexandre Boumezoued,Sophian Mehalla

We propose to take advantage of the common knowledge of the characteristic function of the swap rate process as modelled in the LIBOR Market Model with Stochastic Volatility and Displaced Diffusion (DDSVLMM) to derive analytical expressions of the gradient of swaptions prices with respect to the model parameters. We use this result to derive an efficient calibration method for the DDSVLMM using gradient-based optimization algorithms. Our study relies on and extends the work by (Cui et al., 2017) that developed the analytical gradient for fast calibration of the Heston model, based on an alternative formulation of the Heston moment generating function proposed by (del Ba{\~n}o et al., 2010). Our main conclusion is that the analytical gradient-based calibration is highly competitive for the DDSVLMM, as it significantly limits the number of steps in the optimization algorithm while improving its accuracy. The efficiency of this novel approach is compared to classical standard optimization procedures.

Financial Reporting Frequency and Managerial Learning from Stock Price
Hillegeist, Stephen A.,Kausar, Asad,Kraft, Arthur G.,Park, You-il (Chris)
We provide evidence that increased reporting frequency enhances the extent to which stock prices guide managers’ investment decisions. Using a generalized difference-in-differences research design, we find the sensitivity of investment to stock price increased for Mandatory Adopters following an increase in reporting frequency over the period 1951-1974, relative to Voluntary Adopters. The results are concentrated among firms traded by more privately informed investors. Consistent with managers making better investment decisions when stock prices provide more investment-relevant information, we find managers at Mandatory Adopters with high levels of privately informed trading made relatively better investment decisions during the post-adoption period.

Firm-Level Media Coverage, Bank Loans, and the Role of Institutional Environments
Jia, Zhehao,Li, Donghui,Shi, Yukun,Xing, Lu
Employing an international sample of 12,422 bank loan facilities across 37 countries spanning the period from 2000-2016, we find that both media coverage and positive media sentiment reduce the bank loan interest rate spread, which can be achieved through the media’s roles in mitigating information frictions, reducing information risks, and enhancing the competition among lenders. Moreover, we show that favourable aggregate media sentiment increases the participation probability of non-leader bank lenders and reduces the percentage share of the leading banks involving syndicated loan issuances to borrowers, which implies the information feedback effects of media sentiment in the syndicate loan structure. We further document that this negative relationship is more pronounced in countries with better accounting information environments, better share trading regulation environments, higher representation of privately owned media newspapers, and lower government control of banks. Our main conclusions remain valid after carefully considering endogeneity issues and conducting various robustness tests.

Global Sensitivity and Domain-Selective Testing for Functional-Valued Responses: An Application to Climate Economy Models
Matteo Fontana,Massimo Tavoni,Simone Vantini

Complex computational models are increasingly used by business and governments for making decisions, such as how and where to invest to transition to a low carbon world. Complexity arises with great evidence in the outputs generated by large scale models, and calls for the use of advanced Sensitivity Analysis techniques.

To our knowledge, there are no methods able to perform sensitivity analysis for outputs that are more complex than scalar ones and to deal with model uncertainty using a sound statistical framework.

The aim of this work is to address these two shortcomings by combining sensitivity and functional data analysis. We express output variables as smooth functions, employing a Functional Data Analysis (FDA) framework. We extend global sensitivity techniques to function-valued responses and perform significance testing over sensitivity indices.

We apply the proposed methods to computer models used in climate economics. While confirming the qualitative intuitions of previous works, we are able to test the significance of input assumptions and of their interactions. Moreover, the proposed method allows to identify the time dynamics of sensitivity indices.

How to manage the post pandemic opening? A Pontryagin Maximum Principle approach
R. Mansilla

The COVID-19 pandemic has completely disrupted the operation of our societies. Its elusive transmission process, characterized by an unusually long incubation period, as well as a high contagion capacity, has forced many countries to take quarantine and social isolation measures that conspire against the performance of national economies. This situation confronts decision makers in different countries with the alternative of reopening the economies, thus facing the unpredictable cost of a rebound of the infection. This work tries to offer an initial theoretical framework to handle this alternative.

Impact of COVID-19 Behavioral Inertia on Reopening Strategies for New York City Transit
Ding Wang,Brian Yueshuai He,Jingqin Gao,Joseph Y. J. Chow,Kaan Ozbay,Shri Iyer

The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing. A baseline model was previously developed and calibrated for pre-COVID conditions as MATSim-NYC. A new COVID model is calibrated that represents travel behavior during the COVID-19 pandemic by recalibrating the population agendas to include work-from-home and re-estimating the mode choice model for MATSim-NYC to fit observed traffic and transit ridership data. Assuming the change in behavior exhibits inertia during reopening, we analyze the increase in car traffic due to the phased reopen plan guided by the state government of New York. Four reopening phases and two reopening scenarios (with and without transit capacity restrictions) are analyzed. A Phase 4 reopening with 100% transit capacity may only see as much as 73% of pre-COVID ridership and an increase in the number of car trips by as much as 142% of pre-pandemic levels. Limiting transit capacity to 50% would decrease transit ridership further from 73% to 64% while increasing car trips to as much as 143% of pre-pandemic levels. While the increase appears small, the impact on consumer surplus is disproportionately large due to already increased traffic congestion. Many of the trips also get shifted to other modes like micromobility. The findings imply that a transit capacity restriction policy during reopening needs to be accompanied by (1) support for micromobility modes, particularly in non-Manhattan boroughs, and (2) congestion alleviation policies that focus on reducing traffic in Manhattan, such as cordon-based pricing.

Markowitz portfolio selection for multivariate affine and quadratic Volterra models
Eduardo Abi Jaber,Enzo Miller,Huyên Pham

This paper concerns portfolio selection with multiple assets under rough covariance matrix. We investigate the continuous-time Markowitz mean-variance problem for a multivariate class of affine and quadratic Volterra models. In this incomplete non-Markovian and non-semimartingale market framework with unbounded random coefficients, the optimal portfolio strategy is expressed by means of a Riccati backward stochastic differential equation (BSDE). In the case of affine Volterra models, we derive explicit solutions to this BSDE in terms of multi-dimensional Riccati-Volterra equations. This framework includes multivariate rough Heston models and extends the results of \cite{han2019mean}. In the quadratic case, we obtain new analytic formulae for the the Riccati BSDE and we establish their link with infinite dimensional Riccati equations. This covers rough Stein-Stein and Wishart type covariance models. Numerical results on a two dimensional rough Stein-Stein model illustrate the impact of rough volatilities and stochastic correlations on the optimal Markowitz strategy. In particular for positively correlated assets, we find that the optimal strategy in our model is a `buy rough sell smooth' one.

Model-independent pricing with insider information: a Skorokhod embedding approach
Beatrice Acciaio,Alexander M.G. Cox,Martin Huesmann

In this paper, we consider the pricing and hedging of a financial derivative for an insider trader, in a model-independent setting. In particular, we suppose that the insider wants to act in a way which is independent of any modelling assumptions, but that she observes market information in the form of the prices of vanilla call options on the asset. We also assume that both the insider's information, which takes the form of a set of impossible paths, and the payoff of the derivative are time-invariant. This setup allows us to adapt recent work of Beiglboeck, Cox and Huesmann (2016) to prove duality results and a monotonicity principle, which enables us to determine geometric properties of the optimal models. Moreover, we show that this setup is powerful, in that we are able to find analytic and numerical solutions to certain pricing and hedging problems.

Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction
Jiexia Ye,Juanjuan Zhao,Kejiang Ye,Chengzhong Xu

Stock price movement prediction is commonly accepted as a very challenging task due to the volatile nature of financial markets. Previous works typically predict the stock price mainly based on its own information, neglecting the cross effect among involved stocks. However, it is well known that an individual stock price is correlated with prices of other stocks in complex ways. To take the cross effect into consideration, we propose a deep learning framework, called Multi-GCGRU, which comprises graph convolutional network (GCN) and gated recurrent units (GRU) to predict stock movement. Specifically, we first encode multiple relationships among stocks into graphs based on financial domain knowledge and utilize GCN to extract the cross effect based on these pre-defined graphs. To further get rid of prior knowledge, we explore an adaptive relationship learned by data automatically. The cross-correlation features produced by GCN is concatenated with historical records and fed into GRU to model the temporal dependency of stock prices. Experiments on two stock indexes in China market show that our model outperforms other baselines. Note that our model is rather feasible to incorporate more effective stock relationships containing expert knowledge as well as learn relationship on the basis of data dynamically.

OPEC News and Predictability of Energy Futures Returns and Volatility: Evidence From a Conditional Quantile Regression
Derbali, Abdelkader
This paper aims to provide an important perspective to the predictive capacity of Organization of the Petroleum Exporting Countries (OPEC) meeting dates and production announcements for energy futures (crude oil West Texas Intermediate (WTI), gasoline reformulated gasoline blendstock for oxygen blending (RBOB), Brent oil, London gas oil, natural gas and heating oil) market returns and volatilities.To examine the impact of OPEC news on energy futures market returns and volatilities, the authors use a conditional quantile regression methodology during the period from April 01, 2013 to June 30, 2017.From the empirical findings, the authors show a conditional dependence between energy futures returns and OPEC-based predictors; hence, the authors can find clear the significance of relationship in the process of financialization of the OPEC announcements and energy futures in the case of this paper. From the quantile-causality test, the authors find that the effect of OPEC news is important to energy futures. Specifically, OPEC announcements dates predict the quantiles of the conditional distribution of energy futures market returns.The authors confirm the presence of unidirectional nexus between OPEC news and energy commodities futures in the long term.

Optimal Tracking Portfolio with A Ratcheting Capital Benchmark
Lijun Bo,Huafu Liao,Xiang Yu

This paper studies the finite horizon portfolio management by optimally tracking a ratcheting capital benchmark process. To formulate such an optimal tracking problem, we envision that the fund manager can dynamically inject capital into the portfolio account such that the total capital dominates the nondecreasing benchmark floor process at each intermediate time. The control problem is to minimize the cost of the accumulative capital injection. We first transform the original problem with floor constraints into an unconstrained control problem, however, under a running maximum cost. By identifying a controlled state process with reflection, we next transform the problem further into an equivalent auxiliary problem, which leads to a nonlinear Hamilton-Jacobi-Bellman (HJB) with a Neumann boundary condition. By employing the dual transform, the probabilistic representation approach and some stochastic flow arguments, the existence of the unique classical solution to the dual HJB is established. The verification theorem is carefully proved, which gives the complete characterization of the primal value function and the feedback optimal portfolio.

Optimal Trading with Differing Trade Signals
Ryan Donnelly,Matthew Lorig

We consider the problem of maximizing portfolio value when an agent has a subjective view on asset value which differs from the traded market price. The agent's trades will have a price impact which affect the price at which the asset is traded. In addition to the agent's trades affecting the market price, the agent may change his view on the asset's value if its difference from the market price persists. We also consider a situation of several agents interacting and trading simultaneously when they have a subjective view on the asset value. Two cases of the subjective views of agents are considered, one in which they all share the same information, and one in which they all have an individual signal correlated with price innovations. To study the large agent problem we take a mean-field game approach which remains tractable. After classifying the mean-field equilibrium we compute the cross-sectional distribution of agents' inventories and the dependence of price distribution on the amount of shared information among the agents.

Predicting Bond Returns: 70 Years of International Evidence
Baltussen, Guido,Martens, Martin,Penninga, Olaf
We examine the predictability of government bond returns using a deep sample spanning 70 years of international data across the major bond markets. Using an economic, trading-based testing framework we find strong economic and statistical evidence of bond return predictability with a Sharpe ratio of 0.87 since 1950. This finding is robust over markets and time periods, including 30 years of out-of-sample data on international bond markets and a set of nine additional countries. Furthermore, the results are consistent over economic environments, including prolonged periods of rising or falling rates, and is exploitable after transaction costs. The predictability relates to predictability in inflation and economic growth. Overall, government bond premia display predictable dynamics and the timing of international bond market returns offers exploitable opportunities to investors.

Proving prediction prudence
Dirk Tasche

We study how to perform tests on samples of pairs of observations and predictions in order to assess whether or not the predictions are prudent. Prudence requires that that the mean of the difference of the observation-prediction pairs can be shown to be significantly negative. For safe conclusions, we suggest testing both unweighted (or equally weighted) and weighted means and explicitly taking into account the randomness of individual pairs. The test methods presented are mainly specified as bootstrap and normal approximation algorithms. The tests are general but can be applied in particular in the area of credit risk, both for regulatory and accounting purposes.

R&D Investment and Pre-Committed Payouts: An Empirical Analysis Using the R&D Investment-Incentivizing Tax Reform as a Natural Experiment
Nagasawa, Kenichi,Ito, Akitoshi
This paper examines the pre-committed payout hypothesis that firms which increase R&D investment also increase payouts in order to mitigate the information asymmetry and agency problems. We use the R&D tax reform which came into effect in 2007 in Japan as a natural experiment. We find that increases in R&D investments lead to increased payouts, as predicted by the pre-committed payout hypothesis. Furthermore, we find that the positive relationship between R&D investment and payouts is affected by the degree of corporate governance and agency problems measured by managerial ownership, the status of net debt and the adoption of anti-takeover provisions.

Risk Forecasting in the Light of Big Data
Kernchen, Roman
Life in modern society is increasingly connected by networks that link the world around us and create many new opportunities, services and benefits for humanity. But at the same time, the underlying networks have created pathways through which potentially hazardous and damaging incidents can spread quickly and worldwide. This complexity poses a considerable challenge for risk analysis and forecasting. Conventional methods of risk analysis tend to underestimate the probability and impact of risks (e.g. pandemics, financial collapses, terrorist attacks), as sometimes the existence of independent observations is wrongly assumed and cascading errors that can occur in complex systems are not considered. The purpose of this article is to assess critically the potential of big data to profoundly change the current capability for risk forecasting in diverse areas and the assertion that big data leads to better risk predictions. In particular, this article focuses on big data implications for risk forecasting in the areas of economic and financial risks, environmental and sustainable development risks, and public and national security risks.

Searching for Safe Havens during the COVID-19 Pandemic: Determinants of Spillovers between Islamic and Conventional Financial Markets
Yarovaya, Larisa,Elsayed, Ahmed H.,Hammoudeh, Shawkat M.
We analyse the impact of the COVID-19 pandemic on spillover between conventional and Islamic stock and bond markets. We further analyse comparatively whether gold, oil, and Bitcoin prices, VIX and EPU index affect the relationships between these markets during the COVID-19 pandemic. The results show that the Islamic bonds (Sukuk) demonstrate safe haven properties during this pandemic, while the spillovers between conventional and Islamic stock markets become stronger during the pandemic. COVID-19, Oil and gold are strong predictors of conventional-Islamic markets spillovers, while Bitcoin is not a significant determinant of these relationships.

Stress Testing During Times of War
Judge, Kathryn
The Covid crisis raises important questions about the role of stress testing during periods of systemic distress. Should stress testing of banks be abandoned? Modified? Proceed as scheduled? Different jurisdictions have taken different tacks, reflecting contestation over these fundamental issues. This essay argues that stress tests become more important, not less, in the midst of systemic distress, but only if the stress scenarios are modified to reflect the distinct challenges an economy is facing. Well-designed stress tests can provide critical information to policy makers and others, promoting more timely efforts to address underlying weaknesses. Given that regulators will rationally be hesitant to produce, much less disclose, information that could exacerbate the very crisis regulators are seeking to contain, crisis-time stress testing is only viable if regulators also have the tools needed to address any bad news the testing may reveal.

The Dynamic Effects of Quantitative Easing on Stock Price: Evidence from Asian Emerging Markets, 2001-2016
Miyakoshi, Tatsuyoshi,Shimada, Junji,Li, Kui-Wai
This empirical paper examines the impact of monetary policy of the United States, European Union and Japan on the stock prices of eight Asian Emerging Markets (AEMs) during the different quantitative easing (QE) policies in 2001-2016. Five VAR models are constructed to incorporate different scenarios. The empirical results show that the QE policy has increased the stock prices of the AEMs, and their stock price inflation is consistent with “carry trade”. As the main driver of stock price inflation in the AEMs was Japan before 2008 and US after 2008, the paper concludes that financial integration and interest differentials played an important role in the transmission of monetary policy.

The Impacts of the 2008 and 2011 Crises on the Japan REIT Market
Miyakoshi, Tatsuyoshi,Shimada, Junji,Li, Kui-Wai
This empirical paper studies the fundamental value of the J-REIT price from impact of the 2008 financial crisis and the 2011 Japan earthquake between May 2003 and December 2014. The results show that the fundamental value of the J-REIT is determined only by the real estate price in the long-run. The short-run deviations from the fundamental value of the J-REIT price occur during the 2008 crisis and the 2011 earthquake because the trading volume by foreigners exceeded 50%. The deviations from the fundamental value were less persistent during 2008 and 2011 because the 2011 earthquake caused Japanese investors to focus on earthquake risk while foreigners departed from investing in the J-REIT market.

The Next 'Big Short': COVID-19, Student Loan Discharge in Bankruptcy, and the SLABS Market
Roy, Samantha,Ryan, CJ
Even before the spread of the COVID-19 pandemic, student loan debtâ€"totaling over $1.64 trillionâ€"was a cause for concern, as it is the second largest source of consumer debt in the United States, trailing only mortgage debt. Like mortgage-backed securities, student loan asset-backed securities, or “SLABS,” are the securitized form of student loan debt, repackaged as a marketable financial instrument. Also like mortgage-backed securities, SLABS are backed by income streams generated by loans to individuals. As with any investment vehicle, asset-backed securities like SLABS come with risk, particularly when borrowers default on their loans or have their debt discharged through bankruptcy proceedings. However, historically, SLABS have been a relatively sure betâ€"yielding consistent returns on investmentâ€"given that student loans are guaranteed by the government and that student loan debt obligations are difficult for borrowers to escape. This is because there has been a long-standing prohibition on student loan discharge via bankruptcy proceedings. A recent decision rendered by the Chief Judge in the United States Bankruptcy Court in the Southern District of New York could eliminate that prohibition. In turn, this decision could negatively impact the SLABS market, and in a broad sense, the United States economy.This Article addresses this possibility, especially in light of the fact that rising unemployment in the wake of the COVID-19 crisis is sure to increase the rate of default on student loans. Part I of this Article describes the present student loan crisis in terms of available statistics and common student loan repayment programs. Next, Part II chronicles the development of and operation of bankruptcy law doctrine in the context of student loans. Further, the second part of this Article explains the general prohibition against discharge of student loans in bankruptcy proceedings via the Brunner Test. Part III focuses on student loan asset-backed securities: what they are, how they operate, and how they generate profit. This final section will draw the connection between student loan discharge via bankruptcy and its potential impacts on the SLABS market and the economy at large. This Article concludes with observations about how the current crisis levels of student loan debt, when combined with rising unemployment and recent bankruptcy court decisions could impact the stability of the SLABS market and the broader economy.

The Role of Corporate Culture in Bad Times: Evidence from the COVID-19 Pandemic
Li, Kai,Liu, Xing,Mai, Feng,Zhang, Tengfei
After fitting a topic model to 79,597 COVID-19-related paragraphs in 11,183 conference calls over the period January to April 2020, we obtain measures of firm-level exposure and response to COVID-19 for 3,019 U.S. firms. We show that despite many different ways through which COVID-19 affects their operations, firms with a strong corporate culture do better in the midst of a pandemic than their peers without a strong culture. Moreover, firms with a strong culture are more likely to emphasize community engagement and adopt digital technology, and are no more likely to engage in cost cutting than their peers without a strong culture. To explore the channels through which culture makes firms resilient to the pandemic, we show that firms with a strong culture have higher sales per employee and lower cost of goods sold per employee during the first quarter of 2020. Our results provide support for the notion that corporate culture is an intangible asset designed to meet unforeseen contingencies as they arise (Kreps 1990).

The Vanke-Baoneng Case and Beyond: Hostile Takeover and Corporate Governance in China
Kang, Sang Yop
This draft explores the takeover war between Vanke (target) and Baoneng Group (bidder) and related issues on hostile takeovers in China. The Vanke-Baoneng case (hereinafter Vanke case) has raised many questions about corporate governance, a market for corporate control, market institutions, regulatory issues, and political economy implications. The draft consists of three Parts. Part I sketches the Vanke case. Part II examines some selective corporate governance topics relating to the Vanke case and the hostile takeover regime in China. Part III summarizes the draft and concludes.

What Happens when Separate and Unequal School Districts Merge?
Robert Aue,Thilo Klein,Josue Ortega

We study the welfare effects of school district consolidation, i.e. the integration of disjoint school districts into a centralised clearinghouse. We show theoretically that, in the worst-case scenario, district consolidation may unambiguously reduce students' welfare, even if the student-optimal stable matching is consistently chosen. However, on average all students experience expected welfare gains from district consolidation, particularly those who belong to smaller and over-demanded districts. Using data from the Hungarian secondary school assignment mechanism, we compute the actual welfare gains from district consolidation in Budapest and compare these to our theoretical predictions. We empirically document substantial welfare gains from district consolidation for students, equivalent to attending a school five kilometres closer to the students' home addresses. As an important building block of our empirical strategy, we describe a method to consistently estimate students' preferences over schools and vice versa that does not fully assume that students report their preferences truthfully in the student-proposing deferred acceptance algorithm.

What’s in a (Green) Name? The Consequences of Greening Fund Names on Fund Flows, Turnover, and Performance
El Ghoul, Sadok,Karoui, Aymen
We examine a sample of U.S. mutual funds and find that, between 2003 and 2018, 28 funds have changed their name to a sustainability-related appellation. Following the name change, we observe three main outcomes: (i) an increase in fund flows, (ii) a significant rise in portfolio turnover, and (iii) no substantial change in fund betas and alpha.

When Does Forecasting GAAP Earnings Entail Unreasonable Effort?
Laurion, Henry,Sloan, Richard G.
SEC rules require managers to reconcile their non-GAAP forecasts with the most directly comparable GAAP measure unless doing so would entail ‘unreasonable effort’. A large and growing number of managers invoke the unreasonable efforts exception to justify the omission of comparable GAAP forecasts. We analyze firms where managers invoke the unreasonable efforts exception and find that these firms are characterized by non-GAAP measures with significantly greater recurring expense exclusions. Our findings suggest that these firms opportunistically exploit the unreasonable efforts exception to avoid reconciling elevated non-GAAP forecasts to predictably lower GAAP earnings.

Who are Impact Investors?
Cojoianu, Theodor,Hoepner, Andreas G. F.,Lin, Yanan
Impact investing aims to simultaneously deliver two objectives: (i) social and environmental benefits and (ii) financial returns for a desired investment risk level. This dual objective function differentiates impact investing from other forms of investing that integrate environmental, social or governance (ESG) aspects which consider financial returns as primary objective with ESG outcomes being secondary ambitions. Despite being one the fastest growing asset classes, academic studies of impact investing remain rare, and an extensive analysis of impact investor characteristics is yet to be undertaken. Using a large data set of over eight thousand private markets investment (PMI) firms around the world, we are able to differentiate between impact investors, ESG and conventional PMI firms. We unveil differences in their ownership structure, their asset class preferences, their sectoral focus and the types of partnerships they pursue to deliver impact in addition to financial returns. We find that impact investing firms to be younger than ESG investment firms and more likely to be owned by governments. They invest over-proportionally in agriculture, clean-tech and education sectors and under-proportionally in “sin” industries such as gambling or tobacco. Finally, in comparison to ESG investors, impact investors are more likely to forge partnerships towards delivering their investment model, particularly with academic institutions.

Why Did They Get in Trouble? The Influence of Firm Characteristics and Institutional Distance in the Case of Chinese Outward Foreign Direct Investment
Derbali, Abdelkader
Existing research on Chinese multinational companies has overlooked the fact that China’s outward foreign direct investment is facing a high failure rate even in their initial attempt to enter a foreign market. Grounded on institutional theory, this study provides a more holistic view of the globalization dynamic using a unique dataset that contains both successful and failed Chinese outward foreign direct investment attempts during the year 2017 to 2018. Firm characteristics have an important influence on the probability of successful foreign market entry attempt, but the effect is conditioned on institutional discrepancies between China and host countries.

Why Do Models that Predict Failure Fail?
Kiefer, Hua,Mayock, Tom
In the first portion of this paper, we utilize millions of loan-level servicing records for mortgages originated between 2004 and 2016 to study the performance of predictive models of mortgage default. We find that the logistic regression model -- the traditional workhorse for consumer credit modeling -- as well as machine learning methods can be very inaccurate when used to predict loan performance in out-of-time samples. Importantly, we find that this model failure was not unique to the early-2000s housing boom.We use the Panel Study of Income Dynamics in the second part of our paper to provide evidence that this model failure can be attributed to intertemporal heterogeneity in the relationship between variables that are frequently used to predict mortgage performance and the realized post-origination path of variables that have been shown to trigger mortgage default. Our findings imply that model instability is a significant source of risk for lenders, such as financial technology firms ("Fintechs"), that rely heavily on predictive statistical models and machine learning algorithms for underwriting and account management.