Research articles for the 2021-06-16

Carter, Nic,Jeng, Linda
Decentralized Finance (or “DeFi”) is growing in volume and in importance. DeFi promises cheaper and more open access to financial services by reducing the costs and risks of using centralized intermediaries. DeFi also holds the promise of interoperability across blockchains that could help tear down financial sector silos, greatly promoting innovation and building vibrant financial ecosystems. However, DeFi is not without its challenges, which are understudied. This article does not seek to provide a comprehensive list of DeFi but to help readers conceptually understand the drivers behind the risks inherent in DeFi. Many of the risks described above stem from the decentralized nature of blockchains. The goal of automating the delivery of financial services and reducing human dependencies also has the congruent effect of reducing oversight and control. Disintermediating traditional intermediaries reduces high fees and entry friction, but also creates new opportunities for new types of intermediaries. This article discusses some of the new types of risks introduced by DeFi that are inherent to blockchain systems along with traditional types of financial risks in DeFi that manifest in new ways: (i) interconnections with the traditional financial system, (ii) operational risks stemming from underlying blockchains, (iii) smart contract-based vulnerabilities, (iv) other governance and regulatory risks, and (v) scalability challenges. In an effort to remove humans and automate as much as possible through smart contracts, DeFi has introduced or amplified these risks. The growth of DeFi will depend on its ability to navigate and build compatibility with traditional finance and on how laws and regulations respond. Perhaps the biggest challenge of all is that the DeFi ecosystem continues to grow while its underlying base layer (public infrastructure such as Bitcoin or Ethereum) faces growing pains. As DeFi grows in importance and becomes more mainstream, policymakers and industry representatives need to better understand the economic and policy consequences of these new types of risks in order to build regulatory approaches and risk management practices that can support and facilitate a healthy and robust DeFi ecosystem and, ultimately, the financial stability of the greater financial system and real economy.

A Unified Theory of Option Pricing Assuming Stochastic Volatility
Shanahan, Brett
Stochastic volatility models have grown in popularity in the past decade or two. However, for many stochastic volatility models, the functional form of volatility along with the description of the diffusion process for volatility have been posed with analytic convenience in mind. Here, we consider that analytic tractability may degenerate as realistic modelling improves and that a more general specification for the stock price and volatility processes may be necessary. This leads to an approximating polynomial for European option prices which is benchmarked to two popular stochastic volatility models, the Stein and Stein, and Heston models, before examining a more general specification which is compared to the corresponding Black Scholes price. Stochastic volatility and European option approximation and Heston and Stein and Stein and Black Scholes

Agglomerative Likelihood Clustering
Lionel Yelibi,Tim Gebbie

We consider the problem of fast time-series data clustering. Building on previous work modeling the correlation-based Hamiltonian of spin variables we present an updated fast non-expensive Agglomerative Likelihood Clustering algorithm (ALC). The method replaces the optimized genetic algorithm based approach (f-SPC) with an agglomerative recursive merging framework inspired by previous work in Econophysics and Community Detection. The method is tested on noisy synthetic correlated time-series data-sets with built-in cluster structure to demonstrate that the algorithm produces meaningful non-trivial results. We apply it to time-series data-sets as large as 20,000 assets and we argue that ALC can reduce compute time costs and resource usage cost for large scale clustering for time-series applications while being serialized, and hence has no obvious parallelization requirement. The algorithm can be an effective choice for state-detection for online learning in a fast non-linear data environment because the algorithm requires no prior information about the number of clusters.

An Information Filtering approach to stress testing: an application to FTSE markets
Isobel Seabrook,Fabio Caccioli,Tomaso Aste

We present a novel methodology to quantify the "impact" of and "response" to market shocks. We apply shocks to a group of stocks in a part of the market, and we quantify the effects in terms of average losses on another part of the market using a sparse probabilistic elliptical model for the multivariate return distribution of the whole market. Sparsity is introduced with an $L_0$-norm regularization, which forces to zero some elements of the inverse covariance according to a dependency structure inferred from an information filtering network. Our study concerns the FTSE 100 and 250 markets and analyzes impact and response to shocks both applied to and received from individual stocks and group of stocks. We observe that the shock pattern is related to the structure of the network associated with the sparse structure of the inverse covariance of stock returns. Central sectors appear more likely to be affected by shocks, and stocks with a large level of underlying diversification have a larger impact on the rest of the market when experiencing shocks. By analyzing the system during times of crisis and comparative market calmness, we observe changes in the shock patterns with a convergent behavior in times of crisis.

Banks' Liquidity Management During the COVID-19 Pandemic
Gounopoulos, Dimitrios,Luo, Kaisheng,Nicolae, Anamaria,Paltalidis, Nikos
How banks managed the COVID-19 pandemic shock? The eruption of the financial crisis in 2007 evolved to a crisis of banks as liquidity providers (Acharya and Mora, 2015). The COVID-19 pandemic shock was associated with a surge in households’ deposits and a subsequent liquidity injection by the Federal Reserve. We show how the pandemic affected banks’ liquidity management and therefore by extension, the creation of new loans. We empirically evaluate the creation and management of banks’ liquidity through three well established mechanisms: market discipline (supply-side), internal capital markets (demand-side), and the balance-sheet mechanism which captures banks’ exposure to liquidity demand risk. We provide novel empirical evidence showing that households increased savings as a precaution against future declines in their income. Also, depositors did not discipline riskier banks, and the internal capital market mechanism was not in work during the pandemic. Hence, weakly-capitalized banks were not forced to offer higher deposit rates to stem deposit outflows. Furthermore, weakly-capitalized banks increased lending in the first phase of the pandemic, while in the midst of the pandemic, they cut back new lending origination and increased their exposure to Fed’s liquidity facilities. Well-capitalized banks on the other hand, increased lending in line with the increase in their deposits. Banks with higher exposure to liquidity risk were vulnerable to deposit outflows and increased their exposure in Fed’s liquidity facilities significantly more than low-commitments exposed banks.

Behavioral Aspects of Merger Decisions: The Effect of Average Purchase Price and Other Reference Prices
Lauterbach, Beni,Mugerman, Yevgeny,Shemesh, Joshua
We develop a novel measure of target shareholders’ average purchase price (TAPP). In a sample of all U.S. public firm merger offers from 1990 to 2019, we find that: (1) the offer premium is positively correlated with the ratio of TAPP to the target’s pre-offer stock price; (2) TAPP dominates several other purchase price estimators as an explanatory variable; (3) the TAPP effect is additive and about equal in its magnitude to that of the pre-offer 52-week-high price; and (4) reference prices affect merger offers primarily through adjusting the offer premium. Our results portray TAPP as a promising shareholder purchase-price indicator.

Behavioral Biases in the NFL Gambling Market: Overreaction to News and the Recency Bias
Durand, Robert B. B.,Patterson, Fernando,Shank, Corey A.
This paper examines the recency bias and overreaction in the NFL betting market from 2003 to 2017. Consistent with the recency bias, bettors are more likely to bet on teams who have won previous outcomes. We add to the literature and find that the magnitude of prior wins and losses in the previous weeks' plays a greater importance than the sole outcome of wins and losses in betting behavior. Additionally, our results show that bettors wager 2.1% less on the home team when their first-string quarterback does not play, and 3.1% more on the home team when the visitor's first-string quarterback does not play, which is consistent with overreaction. Finally, our results show that bookmakers earn "over the odds" thanks to bettors' quasi-rational behavior as they commit the recency bias.

CeFi vs. DeFi -- Comparing Centralized to Decentralized Finance
Kaihua Qin,Liyi Zhou,Yaroslav Afonin,Ludovico Lazzaretti,Arthur Gervais

To non-experts, the traditional Centralized Finance (CeFi) ecosystem may seem obscure, because users are typically not aware of the underlying rules or agreements of financial assets and products. Decentralized Finance (DeFi), however, is making its debut as an ecosystem claiming to offer transparency and control, which are partially attributable to the underlying integrity-protected blockchain, as well as currently higher financial asset yields than CeFi. Yet, the boundaries between CeFi and DeFi may not be always so clear cut.

In this work, we systematically analyze the differences between CeFi and DeFi, covering legal, economic, security, privacy and market manipulation. We provide a structured methodology to differentiate between a CeFi and a DeFi service. Our findings show that certain DeFi assets (such as USDC or USDT stablecoins) do not necessarily classify as DeFi assets, and may endanger the economic security of intertwined DeFi protocols. We conclude this work with the exploration of possible synergies between CeFi and DeFi.

Characterization of equilibrium existence and purification in general Bayesian games
Wei He,Xiang Sun,Yeneng Sun,Yishu Zeng

This paper studies Bayesian games with general action spaces, correlated types and interdependent payoffs. We introduce the condition of ``decomposable coarser payoff-relevant information'', and show that this condition is both sufficient and necessary for the existence of pure-strategy equilibria and purification from behavioral strategies. As a consequence of our purification method, a new existence result on pure-strategy equilibria is also obtained for discontinuous Bayesian games. Illustrative applications of our results to oligopolistic competitions and all-pay auctions are provided.

Contextualization of Numbers in Earnings Conference Calls
Allee, Kristian D.,Do, Chuong,Do, Huy
Prior research generally finds that using numbers in financial disclosure is beneficial. However, numbers can also be strategically proffered due to their perceived precision and informativeness. We hypothesize that firms use undercontextualized numbers to create a false impression of certainty and transparency. We find that the use of undercontextualized numbers in earnings conference calls is associated with short-term benefits but negative longer-term consequences. Specifically, undercontextualization is associated with a short-term positive market return, positive analysts’ forecast revisions, and more positive analysts’ tone on the call. Furthermore, undercontextualization is associated with a longer-term negative market return, a decrease in year-over-year earnings, and a higher incidence of restatements. In additional analyses, we find that undercontextualization appears to be intentional as it is associated with conference call casting and insider trading. Given the uniqueness of our undercontextualization measure, we also document its association with analyst scrutiny and other proxies for strategic call behavior.

Deep reinforcement learning on a multi-asset environment for trading
Ali Hirsa,Joerg Osterrieder,Branka Hadji-Misheva,Jan-Alexander Posth

Financial trading has been widely analyzed for decades with market participants and academics always looking for advanced methods to improve trading performance. Deep reinforcement learning (DRL), a recently reinvigorated method with significant success in multiple domains, still has to show its benefit in the financial markets. We use a deep Q-network (DQN) to design long-short trading strategies for futures contracts. The state space consists of volatility-normalized daily returns, with buying or selling being the reinforcement learning action and the total reward defined as the cumulative profits from our actions. Our trading strategy is trained and tested both on real and simulated price series and we compare the results with an index benchmark. We analyze how training based on a combination of artificial data and actual price series can be successfully deployed in real markets. The trained reinforcement learning agent is applied to trading the E-mini S&P 500 continuous futures contract. Our results in this study are preliminary and need further improvement.

Determinants of Self-Help Groups lending to Enterprises in India: A Predictive Assessment using Supervised Machine Learning Algorithms
Dasgupta, Madhura,Gupta, Samarth
Despite several advantages of borrowing from Self Help Groups (SHGs), why does enterprise borrowing remain low from this source compared to informal channels and self-financing, in India (MoSPI, 2020)? To answer this question, we develop a novel village-enterprise matched dataset and apply a wide array of supervised learning methods which predict an enterprise's choice between SHG vis-a-vis informal credit as the major source of finance. With an AUC of 94%, Xtreme Gradient Boosting (XGBoost) model (Chen and Guestrin, 2016) performs the best. We conduct perturbation and counterfactual analyses to interpret the predictions of our model (Molnar, 2021). Distance from urban areas and socio-demographic factors such as literacy rates and sex ratio in a village are amongst the most important contributors in determining SHG loan pick-up. In contrast, village-level infrastructure such as roads and financial access points like commercial banks play a much smaller role in explaining demand for credit from SHG. We also extend the model to estimate the potential demand for SHG loans among the universe of firms outside any financial net, that is, the self-financed firms. The potential for SHG inclusion among these firms remains low and skewed toward the southern districts. The paper contributes to the existing literature by highlighting a need for reorientation of financial inclusion strategies.

Does CEOâ€"Audit Committee/Board Interlocking Matter for Corporate Social Responsibility?
Bose, Sudipta,Ali, Muhammad Jahangir,Hossain, Sarowar,Shamsuddin, Abul
This study examines the impact of the Chief Executive Officer (CEO)’s interlocking, created through serving on other companies’ audit committees and/or boards, on corporate social responsibility (CSR) performance of the focal company (interlocked CEO’s company) and that of its linked companies. We find that CEO interlocking positively affects CSR performance of both the focal company and its linked companies. Further analysis shows that interlocks created by the CEO enhance CSR performance and in turn the financial performance of both the focal company and its linked companies. Our findings are robust to a battery of analyses, including Heckman’s (1979) selection bias correction, propensity score matching (PSM), alternative measures of CSR performance, and CEO interlocks. These findings are important to regulators, company management teams, and other stakeholders with an interest in how the social ties of CEOs influence companies’ CSR performance and in the CSRâ€"financial performance nexus.

Dynamic Transparency and Rollover Risk
Wei, Xu,Zhou, Zhen
Regulatory disclosures, such as supervisory bank stress tests, are pre-scheduled and conducted on a regular basis to improve transparency and enhance market discipline. We build a dynamic model with asymmetric information to investigate the effectiveness of such regulatory disclosures. We find that the market strategically responds to the anticipated information from pre-scheduled disclosure by shortening debt maturity and requesting higher interest rates, thereby intensifying rollover risk. This negative consequence can outweigh the positive effect from screening out bad investments and make the regulatory disclosure ineffective in promoting stability and efficiency, especially when the improvement of transparency is only moderate and when the market possesses little information about individual banks' financial soundness. Our study highlights the limited effectiveness of pre-scheduled disclosure during a crisis time, provides a rationale for the BOS's and EBA's suspension of regular supervisory stress tests during the COVID-19 pandemic outbreak, and sheds light on a better dynamic design of regulatory disclosure.

Everywhere Differentiable Continuums, Mathematical Spaces, and Axioms: Modeling of Financial Phenomena
Obrimah, Oghenovo A.
Formal theoretical proofs show the statement, `modeling of stock returns on everywhere differentiable continuums, or using twice continuously differentiable functions, is inappropriate to modeling of rational expectations' always is true, as such, has characterization as an Axiom. Using same mathematical property, the statement, `modeling of stock returns in discrete time is robust to modeling of each of rational expectations, or feasibility of deviations from rational expectations' also is shown always to be true. Formal proofs show bane of modeling in continuous time is non-dichotomization of connectedness property of stock prices from evolution of stock prices, a structure at variance with the mathematical norm that connectedness properties for topologies be independent of specific elements that are located in topological spaces. Consistent with its robustness, changes to information sets (concept, not realizations) are connectedness properties of discrete time modeling and there is arrival at dichotomous sufficiency conditions for conformance with, or deviation of priors from rational expectations equilibriums. In aggregate, while assumptions of `everywhere differentiability' or `twice continuously differentiable functions' induce positive relations between risk (volatility) and returns, contrary to rational expectations, it is relatively low realizations for volatility that, rather unavoidably, are associated with either of positive or negative returns.

Factor Analysis of SPACs: Impact on SPACs Performance by Management Factors
Hung, Haoyun,Liu, Jiaming,Yao, Xinyu,Zhang, Haoyuan,Zhumabayev, Mukhamejan,Zhang, Tony
Special Purpose Acquisition Companies (“SPACs”) are shell companies formed for the purpose of acquiring an existing operating company. A typical characteristic of a SPAC is that it has no specific business plan at the time of establishment or that it is still unclear of its target company. A SPAC raises capital through an initial public offering ("IPO") process, and the investment of SPACs is related to the credibility, reputation, previous achievement, and other factors of the management team. For that reason, we want to determine whether the performances of SPACs are correlated with various factors of their founders.In our study, we apply factor analysis to analyze and seek a correlation between the management factors and the performance of the SPACs. We have collected by far the largest SPAC data set including information about the management team of each SPAC and quantify that information into factors. The factors include education level, previous financial experience, experience heterogeneity, the average age of the team, size of the management team, and ownership of any patent. To obtain a deeper perspective of the factors' impact on SPACs' performance, we divided the SPACs into 8 sectors based on their target industries and run factor analysis over the management factors for each sector.

Foreign Direct Investment and Domestic Private Investment in Sub-Saharan African Countries: Crowding-In or Out ?
RPS Submitter, Banque de France,Askandarou, Diallo,Jacolin, Luc,Rabaud, Isabelle
This paper investigates the relationship between FDI and private investment in Sub-Saharan Africa (SSA), using a sample of 40 countries over 1980-2017. To disentangle short term from long-term dynamics, our empirical analysis is based on Pooled Mean Group (PMG), Mean Group (MG) and Dynamic Full Effects (DFE) models. We find that FDI has little effect on private investment in the short run but significant crowding-in effects in the long-run: a one percentage point increase of the share of FDI in GDP leads to a 0.29% rise in private investment, in the long run. Our results also show that FDI interacts with public domestic investment to boost these positive effects. Finally, we show that the impact of FDI on domestic private investment is stronger in non-natural resource exporting diversified countries as opposed to non-diversified commodity exporters.

Governmental Risk Taking Under Market Imperfections: Working Paper 2021-07
Falkenheim, Michael
An extensive literature debates whether market prices should be used to measure the benefits and costs of risk in government activities or whether the government should be treated as risk neutral. This paper explores the benefits and costs of governmental risk taking in formal models of market imperfections, in which the government serves as an intermediary between different stakeholders in its finances. Some stakeholders cannot participate in markets, either because they belong to future generations or because they have no funds to invest and face borrowing constraints.

Grit, Loss Aversion, and Investor Behavior
Bazley, William J.,Jannati, Sima,Korniotis, George M.
We examine whether grit affects individuals' preferences and trading decisions. Grit is the sustained effort toward a goal despite setbacks. It is malleable and distinct from the Big Five personality traits. Using experiments formalized in prospect theory, we find that grit reduces loss aversion. By diminishing loss aversion, gritty investors exhibit a lower disposition effect since they are more willing to exit losing investments. Consequently, they accumulate about 7% more wealth relative to control participants. Overall, grit affects the quality of investment decisions. Ultimately, our results suggest that interventions cultivating grit could improve households' financial outcomes.

High-Frequency Technical Analysis and Systemic Risk Indicators
Yamamoto, Ryuichi
This study conducts a high-frequency technical analysis of individual stocks listed on the Tokyo Stock Exchange. We propose novel technical rules that derive the timing of trades according to traditional systemic risksâ€"such as shock-propagation, quote-stuffing, and tail risksâ€"measured by auto- and cross-correlations in order flows, quote-to-trade ratios, and CoVaRs. We demonstrate that both price-based technical strategiesâ€"commonly used in technical analysis such as moving average rulesâ€"and the newly proposed rules can exploit the significantly superior performance to the buy-and-hold rule when we trade volatile momentum or trend-reversal stocks of small-sized firms. Accordingly, this study improves stock price forecasts in high-frequency trading. Our results suggest that historic prices and systemic risk indicators assist in the risk management and portfolio choices of stock investors. To the best of our knowledge, this is the first study to demonstrate the superior trading performance of individual stocks using a high-frequency technical analysisâ€"even after considering data-snooping bias and transaction costs.

Is Financial Reporting Quality Affected by Economic Policy Uncertainty? Evidence from Conditional Conservatism around the World
Chui, Andy C.W.,Wei, K.C. John
This paper explores the relationship between economic policy uncertainty and financial reporting quality. Quality is operationalized using Ball and Shivakumar’s (2005) accruals-based measure of incremental bad news’ sensitivity. We hypothesize that increased uncertainty raises the demand for conditional conservatism that, in turns, leads to a larger incremental bad news’ sensitivity. Using firm-level data from 22 countries over 1995 to 2019, we find robust evidence that supports a positive relationship between economic policy uncertainty and incremental bad news’ sensitivity. This relationship is shaped by the prevalence of bank debt usage, judicial independence, and debt enforcement efficiency that is a measure of creditor protection.

Less Pollution, Fewer Crashes: The Impacts of Green Credit Policy on Stock Price Crash Risk
Ding, Mingfa,Han , Yikai ,Shen, Mi,Zhong, Rui
We use the implementation of “Green Credit Guidelines” as a quasi-natural experiment to examine its impact on high-pollution firms’ stock crash risk. By applying a Difference-in-Difference (DiD) model to a sample of Chinese listed firms from 2009 to 2016, we document that high-pollution firms’ stock crash risk increase significantly after the passage of the policy. Channels to explain the association are investigated. Since high-pollution firms are less likely to receive the “green credits”, their financial constraints increase. It motivates these firms to hoard more firm-specific negative information and as a result, high-pollution firms are covered with fewer analysts, attract less media attention, and are more likely to be shorted after the policy. The increased information asymmetry between the corporations and the investors lead to a higher level of crash risk. In addition, we show that the impact of the policy is more pronounced for high-pollution firms which are state-owned enterprises, located in areas with better legal and market environment, or in the eastern provinces. Further, our study suggests that high-pollution firms should follow the government’s initiative and transfer themselves into more environmental-friendly enterprises. By doing that, these firms could stabilize their stock prices and thus decrease the stock crash risk.

Measuring the Impact of a Bank Failure on the Real Economy: An EU-Wide Analytical Framework
Vacca, Valerio Paolo,Bichlmeier, Fabian,Biraschi, Paolo,Boschi, Natalie,Bravo, Antonio,Primio, Luciano Di,Ebner, André,Hoeretzeder, Silvia,Ballesteros, Elisa Llorente,Miani, Claudia,Ricci, Giacomo,Santioni, Raffaele,Schellerer, Stefan,Westman, Hanna
The crisis management framework for banks in the European Union (EU) requires the resolution authorities to identify the existence of a public interest to resolve an ailing bank, rather than to open normal insolvency proceedings (NIPs). The Public Interest Assessment (PIA) determines whether resolution objectives, including the safeguard of financial stability, can be better preserved using resolution tools than NIPs .This paper provides a contribution to the ongoing discussion on the implementation of the PIA, by presenting an analytical framework to quantify the potential impact on the real economy stemming from a bank’s failure under NIPs through the interruption of the lending activity (“credit channel”). The framework is harmonized across the jurisdictions belonging to the Banking Union and aims to improve the quantitative leg of the PIA, to be coupled with qualitative elements. In a first step, we quantify the potential credit shortfall faced by firms and households due to the abrupt closure of a bank. In a second step, the impact of the credit shortfall on real outcomes is estimated via a FAVAR model and via a micro-econometric model. Reference values are provided to assess the relevance of the estimated outcomes. The illustrative results show that such a harmonized approach can be applied across the Banking Union and to banks of heterogeneous size. In case of mid-sized banks, this common analytical framework could reduce the uncertainty regarding the extent to which the failure of the institution could have a negative impact to the real economy if the lending activity is interrupted as possibly the case under NIPs.

Net asset value discounts and premiums in maritime shipping industry
Andrikopoulos, Andreas,Merika, Anna,Sigalas, Christos
This paper examines net asset value (NAV) discounts and premiums in the setting of the maritime shipping industry. We employ a qualitative study with equity analysts as well as a quantitative study with a unique panel data, to explore and empirically investigate, respectively, the reasons underpinning NAV discounts and premiums. Our findings suggest that deviations of market capitalisation from NAV are associated with firm-specific factors, such as public maritime shipping companies’ capital structure, stock liquidity, fleet acquisition cost, operating performance, institutional ownership, cost of capital, corporate governance, dividend policy, and related party transactions.

Predicting VIX with Adaptive Machine Learning
Bai, Yunfei,Cai, Charlie X.
Using 278 economic and financial variables we study the power of machine learning (ML) in predicting the daily CBOE implied volatility index (VIX). Designing and applying an automated three-step ML framework with a large number of algorithms we identify Adaptive Boosting as the best classification model chosen at the validation stage. It produces an average rate of 57% during the 11-year out-of-sample period. Potential significant economic gains are demonstrated in various applications with tradable instruments. Besides the modelling techniques, the weekly US jobless report is the most important contributor to the predictability along with some S&P 500 members’ technical indicators.

Pricing and Risk Analysis in Hyperbolic Local Volatility Model with Quasi Monte Carlo
Julien Hok,Sergei Kucherenko

Local volatility models usually capture the surface of implied volatilities more accurately than other approaches, such as stochastic volatility models. We present the results of application of Monte Carlo (MC) and Quasi Monte Carlo (QMC) methods for derivative pricing and risk analysis based on Hyperbolic Local Volatility Model. In high-dimensional integration QMC shows a superior performance over MC if the effective dimension of an integrand is not too large. In application to derivative pricing and computation of Greeks effective dimensions depend on path discretization algorithms. The results presented for the Asian option show the superior performance of the Quasi Monte Carlo methods especially for the Brownian Bridge discretization scheme.

Public Environmental Enforcement and Private Lender Monitoring: Evidence from Environmental Covenants
Choy, Stacey,Jiang, Shushu,Liao, Scott,Wang, Emma
In this study, we examine the interplay between public environmental enforcement and private lenders’ monitoring and its effects on borrowers’ environmental activities. To capture lender environmental monitoring, we use environmental covenants in loan agreements that require borrowers to take environmental remedial actions, disclose adverse environmental events, or conduct environmental audits. We predict and find that, in the presence of higher regulatory enforcement intensity, loan agreements are more likely to include environmental covenants when loans are secured by real property versus non-real property and when borrowers belong to industries subject to higher environmental risks. We further find that after loan initiations, borrowers with environmental covenants in loan contracts, especially those located in states with higher regulatory enforcement intensity, have lower toxic chemical releases. Taken together, our study suggests that public environmental enforcement reinforces lenders’ private environmental monitoring that has positive externalities in shaping borrower environmental activities.

Random feature neural networks learn Black-Scholes type PDEs without curse of dimensionality
Lukas Gonon

This article investigates the use of random feature neural networks for learning Kolmogorov partial (integro-)differential equations associated to Black-Scholes and more general exponential L\'evy models. Random feature neural networks are single-hidden-layer feedforward neural networks in which only the output weights are trainable. This makes training particularly simple, but (a priori) reduces expressivity. Interestingly, this is not the case for Black-Scholes type PDEs, as we show here. We derive bounds for the prediction error of random neural networks for learning sufficiently non-degenerate Black-Scholes type models. A full error analysis is provided and it is shown that the derived bounds do not suffer from the curse of dimensionality. We also investigate an application of these results to basket options and validate the bounds numerically.

These results prove that neural networks are able to \textit{learn} solutions to Black-Scholes type PDEs without the curse of dimensionality. In addition, this provides an example of a relevant learning problem in which random feature neural networks are provably efficient.

Refining the General Equilibrium Relation That Subsists Between Stock Returns, and Each of Investors Risk Preferences and Information Sets
Obrimah, Oghenovo A.
For arrival at stock prices, Financial Economics theory predicts investors condition newly arriving information on heterogeneous realizations of risk preferences. In reality, typically, formal theoretical models of stock returns condition price formation on either of investors' information sets (changes to information) or risk preferences, but not both. It appears then, on face of things, that formal theoretical models of stock returns are lacking in conformity with canonical theory. The formal theory in this study provides clarification, shows modeling of IPO offer prices, which of necessity transpires in context of primary equity markets, necessarily is in relation to risk preference parameters of investors that already hold portfolios of stocks in secondary equity markets. On the other hand, absent alterations to market completeness properties of individual stocks, pricing of stocks in secondary equity markets necessarily only is in relation to changes to investors' information sets. In light of study findings, convention of modeling of stock returns with reference to investors' risk aversion parameters is inconsistent with canonical theory. Modeling of market completeness (asymptotically, diversification) properties and/or quality of new issues explicitly is shown to be equivalent to pricing of stocks in relation to risk aversion parameters of investors who already own portfolios of stocks in secondary equity markets.

STO vs. ICO: A Theory of Token Issues under Moral Hazard and Demand Uncertainty
Miglo, Anton
This paper considers a financing problem for an innovative firm that is launching a web-based platform. The entrepreneur, on one hand, faces a large degree of demand uncertainty onhis product and on the other hand has to deal with incentive problems of professional blockchainparticipants who contribute to the development and sales of the product. We argue that hybrid tokens can be a better option for the firm compared to straight utility tokens or security tokens because they help the firm better deal with both the moral hazard problems (via profit sharing incentives) and demand uncertainty (they help the firm learn the market demand for the product). This finding is consistent with some recent evidence. The paper also generates new predictions regarding the effect of different variables on the choice of financing method that have not yet been tested

State-Promoted Investment for Industrial Reforms: An Information Design Approach
Rhee, Keeyoung,Shim, Myungkyu,Zhang, Ji
We analyze the optimal strategy for a government to promote large-scale investment projects under information frictions. Specifically, we propose a model where the government collects information on profitability of each investment and discloses it to private investors a la Kamenica and Gentzkow(2011). We derive the government's optimal information policy, which is characterized as threshold values for the unknown profitability of the projects released to the private investors, and study how the underlying features of the economy affect the optimal policies. We find that when multiple projects are available, the government promotes the project with a bigger spillover effect by fully revealing the true state of the economy only when its profitability is substantially high. Moreover, the development of the financial/information market also affects the optimal rule.

Stock Market Reactions to Legislated Tax Changes: Evidence from the United States, Germany, and the United Kingdom
Hayo, Bernd,Mierzwa, Sascha
We study the effect of tax policy on stock market returns in the United States, Germany, and the United Kingdom using GARCH models and a unique daily dataset of legislative tax changes during the period 1978 to 2018. We find that days of discretionary tax legislation during all stages of the process often matter for returns, both in terms of statistical significance as well as economic relevance. Further disaggregating the tax shocks shows that news about personal income tax cuts affects stock market returns positively, whereas business tax legislation is rarely influential. We find evidence of stock market spillovers, mainly from US tax changes to European stock markets. In several cases, we measure significant effects of changes in tax legislation on the days the changes are implemented. The US House Committee Report appears to be the most influential legisla-tive stage. During the financial crisis, stock markets were more responsive to tax legislation. Finally, S&P500 returns tend to react at earlier legislative stages than do DAX returns, whereas FT30 returns barely react on days of domestic legislative action.

Survey: Market Risk Premium and Risk-Free Rate used for 88 countries in 2021
Fernandez, Pablo,Bañuls, Sofia,Fernandez Acin, Pablo
This paper contains the statistics of a survey about the Risk-Free Rate (RF) and the Market Risk Premium (MRP) used in 2021 for 88 countries. We got answers for 92 countries, but we only report the results for 88 countries with more than 6 answers. Many respondents use for European countries a RF higher than the yield of the 10-year Government bonds. The coefficient of variation (standard deviation / average) of RF is higher than the coefficient of variation of MRP for the Euro countries. The paper also contains the links to previous years surveys, from 2008 to 2020.

System-Wide and Banks' Internal Stress Tests: Regulatory Requirements and Literature Review
Pliszka, Kamil
This paper deals with both system-wide and banks' internal stress tests. For system-wide stress tests it describes the evolution over time, compares the stress test design in major jurisdictions, and discusses academic research. System-wide stress tests have gained in importance and nowadays serve as a key regulatory tool. For instance, they feed into the calculation of capital requirements in the EU. The literature shows that the disclosure of stress test results reveals new information to the market. Furthermore, banks that participate in system-wide stress tests increase their capital ratios and shift lending to less risky borrowers. For banks' internal stress tests, this paper gives an overview of the regulatory requirements under Pillars 1 to 3 of Basel III and reviews the academic literature. Stress testing is deeply embedded in the Basel III framework. Banks that choose to apply internal models for calculating capital requirements are subject to more stringent stress testing requirements and, for example, have to ensure capital adequacy if the internal risk parameters are being stressed. The academic research on banks' internal stress tests shows that stress scenarios derived from expert judgment should be complemented by scenarios which are selected on the basis of algorithms that consider historical characteristics of the risk factors. Furthermore, banks' conventional credit risk models can be modified and used for stress testing. As stress testing is exposed to considerable model and estimation risk, banks should carry out extensive robustness checks. In sum, both system-wide and banks' internal stress tests play a complementary role in ensuring the resilience of individual banks and the financial system to adverse shocks.

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

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

The CSR Supply Chain Risk Management Hypothesis Evidence from the Suez Canal Ever Given Obstruction
de Bodt, Eric,Cousin, Jean-Gabriel,Dupire-Declerck, Marion
We investigate whether Corporate Social Responsibility (CSR) activities help reduce supply chain disruption risks thanks to supply sources diversification strategy building. The Suez Canal obstruction in March 2021 by the Ever Given container ship provides us the empirical setup to identify firms exposed to supply management shocks. Firms’ intensity of CSR activities is collected in the Refinitiv Environmental, Social and Governance (ESG) database for a sample of 299 European listed firms. During the first three days of the Ever Given stranding, the portfolio of firms in the lowest quantile of ESG score distribution experiments negative and statistically significant Cumulative Abnormal Returns (CARs), in contrast with a portfolio composed by firms in the corresponding highest quintile. This result is consistent with CSR-active firms being less exposed to supply chain disruption risks. Robustness checks (day-by-day abnormal returns, disaggregated ESG scores, sector-specific analyses) and cross-sectional regressions controlling for confounding effects (firm size, valuation, and stock volatility) brings additional support to this interpretation.

The Economic Impact of Critical National Infrastructure Failure Due to Space Weather
Edward J. Oughton

Space weather is a collective term for different solar or space phenomena that can detrimentally affect technology. However, current understanding of space weather hazards is still relatively embryonic in comparison to terrestrial natural hazards such as hurricanes or earthquakes. Indeed, certain types of space weather such as large Coronal Mass Ejections (CMEs) are an archetypal example of a low probability, high severity hazard. Few major events, short time-series data and a lack of consensus regarding the potential impacts on critical infrastructure have hampered the economic impact assessment of space weather. Yet, space weather has the potential to disrupt a wide range of Critical National Infrastructure (CNI) systems including electricity transmission, satellite communications and positioning, aviation and rail transportation. Recently there has been growing interest in these potential economic and societal impacts. Estimates range from millions of dollars of equipment damage from the Quebec 1989 event, to some analysts reporting billions of lost dollars in the wider economy from potential future disaster scenarios. Hence, this provides motivation for this article which tracks the origin and development of the socio-economic evaluation of space weather, from 1989 to 2017, and articulates future research directions for the field.

The Impact of Corona Populism: Empirical Evidence from Austria and Theory
Patrick Mellacher

I study the impact of opposition politics aimed at downplaying the threat of Covid-19. Exploiting a policy U-turn of a major Austrian right-wing party (FPOE), I show that Covid-19 deaths per capita are significantly positively correlated with support for the FPOE only after the turn using panel regressions. Paradoxically, there is no statistically significant correlation with the reported number of infections. I hypothesize that this can be traced back to a self-selection bias in testing, which causes a higher dark figure in FPOE strongholds. I find empirical support for this hypothesis in individual-level data from a Covid-19 prevalence study showing a much higher share of (undetected) cases among "corona skeptics". I finally extend the classical SIRD model to incorporate conditional quarantine and heterogeneous mixing of two groups of agents with behavioral differences and explore its partly non-trivial properties using thousands of numerical simulations. This model can explain the puzzling empirics: if the behavioral differences between the two groups are sufficiently different, an increase in the share of corona skeptics can cause an increase in the number of deaths without increasing the number of reported infections.

The Rise of Bankruptcy Directors
Ellias, Jared A.,Kamar, Ehud,Kastiel, Kobi
In this Article, we use hand-collected data to shed light on a troubling innovation in bankruptcy practice. We show that distressed companies, especially those controlled by private-equity sponsors, often now prepare for a Chapter 11 filing by appointing bankruptcy experts to their boards of directors and giving them the board’s power to make key bankruptcy decisions. These directors often seek to wrest control of self-dealing claims against shareholders from creditors. We refer to these directors as “bankruptcy directors” and conduct the first empirical study of their rise as key players in the world of corporate bankruptcy. While these directors claim to be neutral experts that act to maximize value for the benefit of creditors, we argue that they suffer from a structural bias because they are part of a small community of repeat private-equity sponsors and law firms. Securing future directorships may require pleasing this clientele at the expense of creditors. Consistent with this prediction, we find that unsecured creditors recover on average 21% less when the company appoints a bankruptcy director. While other explanations are possible, this finding at least shifts the burden of proof to those claiming that bankruptcy directors improve the governance of distressed companies. Our policy recommendation, however, does not require a resolution of this controversy. We propose that the court regard bankruptcy directors as independent only if all creditors support their appointment, making them accountable to all sides of bankruptcy disputes.

The Role of Lockups in Stock Mergers
Chen, Zhong,Liu, Yi,Rossi, Stefano
We document the frequent use of lockup agreements in mergers and acquisitions (M&As) and examine the corporate determinants and consequences of lockups’ use and duration. Lockup agreements prohibit target shareholders from selling shares issued by the acquirer as means of payment for a pre-specified period. We find support for the hypothesis that lockups in M&As represent a pre-commitment device to hold on to the acquirer’s stock by target shareholders who believe the long-term fundamentals of the merged firm are strong. Consistent with our hypothesis, we find lockups are associated with larger acquirer announcement returns; higher likelihood of deal completion; shorter duration of merger negotiations; and higher long-term operating performance. Ex ante, the likelihood of lockups adoption increases with the acquirer firms’ valuation. We conclude that the market interprets lockups as a signal of strong fundamentals, particularly when market valuations are high.

The VIX index under scrutiny of machine learning techniques and neural networks
Ali Hirsa,Joerg Osterrieder,Branka Hadji Misheva,Wenxin Cao,Yiwen Fu,Hanze Sun,Kin Wai Wong

The CBOE Volatility Index, known by its ticker symbol VIX, is a popular measure of the market's expected volatility on the SP 500 Index, calculated and published by the Chicago Board Options Exchange (CBOE). It is also often referred to as the fear index or the fear gauge. The current VIX index value quotes the expected annualized change in the SP 500 index over the following 30 days, based on options-based theory and current options-market data. Despite its theoretical foundation in option price theory, CBOE's Volatility Index is prone to inadvertent and deliberate errors because it is weighted average of out-of-the-money calls and puts which could be illiquid. Many claims of market manipulation have been brought up against VIX in recent years.

This paper discusses several approaches to replicate the VIX index as well as VIX futures by using a subset of relevant options as well as neural networks that are trained to automatically learn the underlying formula. Using subset selection approaches on top of the original CBOE methodology, as well as building machine learning and neural network models including Random Forests, Support Vector Machines, feed-forward neural networks, and long short-term memory (LSTM) models, we will show that a small number of options is sufficient to replicate the VIX index. Once we are able to actually replicate the VIX using a small number of SP options we will be able to exploit potential arbitrage opportunities between the VIX index and its underlying derivatives. The results are supposed to help investors to better understand the options market, and more importantly, to give guidance to the US regulators and CBOE that have been investigating those manipulation claims for several years.

Time Series Momentum Predictability via Dynamic Binary Classification
Bruno P. C. Levy,Hedibert F. Lopes

Time series momentum strategies are widely applied in the quantitative financial industry and its academic research has grown rapidly since the work of Moskowitz, Ooi and Pedersen (2012). However, trading signals are usually obtained via simple observation of past return measurements. In this article we study the benefits of incorporating dynamic econometric models to sequentially learn the time-varying importance of different look-back periods for individual assets. By the use of a dynamic binary classifier model, the investor is able to switch between time-varying or constant relations between past momentum and future returns, dynamically combining different look-back periods and improving trading signals accuracy and portfolio performance. Using data from 56 future contracts we show that a mean-variance investor will be willing to pay a considerable managment fee to switch from the traditional naive time series momentum strategy to the dynamic classifier approach.

Urban Co-operative Banks: Key Regulatory and Supervisory Issues and the Road Ahead
Srivastava, Dr Ashish
Soundness of the financial system participants is a key element of the financial stability framework. In India, the banking system plays a dominant role in the overall financial system. Therefore, one of the core objectives of the banking regulations is to ensure the safety of the banks from the financial distress and to protect the interests of depositors. However, the regulatory and supervisory structure has to remain alive to the existing weaknesses, unresolved issues and emerging challenges in order to stay on the curve. As a small though important part of the banking system, the urban co-operative banks (UCBs) perform important functions through their superior customer service and local reach. This paper discusses the key regulatory and supervisory challenges relating to the UCBs and suggests a way forward towards a healthy and vibrant UCB sector.

What does Network Analysis teach us about International Environmental Cooperation?
Stefano Carattini,Sam Fankhauser,Jianjian Gao,Caterina Gennaioli,Pietro Panzarasa

Over the past 70 years, the number of international environmental agreements (IEAs) has increased substantially, highlighting their prominent role in environmental governance. This paper applies the toolkit of network analysis to identify the network properties of international environmental cooperation based on 546 IEAs signed between 1948 and 2015. We identify four stylised facts that offer topological corroboration for some key themes in the IEA literature. First, we find that a statistically significant cooperation network did not emerge until early 1970, but since then the network has grown continuously in strength, resulting in higher connectivity and intensity of cooperation between signatory countries. Second, over time the network has become closer, denser and more cohesive, allowing more effective policy coordination and knowledge diffusion. Third, the network, while global, has a noticeable European imprint: initially the United Kingdom and more recently France and Germany have been the most strategic players to broker environmental cooperation. Fourth, international environmental coordination started with the management of fisheries and the sea, but is now most intense on waste and hazardous substances. The network of air and atmosphere treaties is weaker on a number of metrics and lacks the hierarchical structure found in other networks. It is the only network whose topological properties are shaped significantly by UN-sponsored treaties.