Research articles for the 2021-02-03

'U.S. Worldwide Taxation and Domestic Mergers and Acquisitions' A Discussion
Chen, Novia (Xi),Shevlin, Terry J.
Harris and O’Brien (2018) investigate whether U.S. tax policy distorts U.S. multinationals’ (MNCs) investment. They find that MNCs facing higher repatriation tax costs engage in fewer domestic acquisitions. The study re-examines the results in two prior studies that found no effect (Hanlon et al. 2015) and a positive effect (Martin et al. 2015) by introducing a new proxy for repatriation tax costs: A binary variable for whether the MNC uses the Double Irish structure. We critique the theory underlying the prediction as well as the proxy. We conclude that caution should be exercised in taking the results at face value.

A Study on Preference for Various Modes of Resources Mobilization in Indian Primary Market
Bantwa, Ashok,Bhatt, Kaushal
Companies in India have broad spectrum of choices available for mobilization of financial resources that incudes public issue, right issue, bonus issue and private placement. The present study contains a detailed analysis of amount of funds mobilized through these modes and change in their preference over the study period. We observed a significant surge in preference for debt capital over the equity capital among the Indian corporates. Private placement of debt security has remained the most preferred mode of resource mobilization followed by public issue of bonds and NCDs, IPO and right issue whereas the FPO has remained the least preferred mode of raising fund. Except offer for sale, all three modes of raising equity capital (i.e. IPO, Right issue and QIP) witnessed downward trend in terms of amount raised during study period. Highest amount of resources were mobilized by banking and financial Institution industry followed by power and finance industry.

A Time-Frequency Analysis of the Systemic Risk in China
WANG, BO,Li, Haoran
Although there have been many empirical studies about systemic risk since the financial crisis of 2008, few have analyzed the systemic risk in the time-frequency domain. In this paper, based on wavelet analysis, the main time-frequency characteristics of China’s systemic risk, the time-frequency similarity between financial institutions, the comovement and the systemic risk contagion channels are analyzed. We find that the systemic risk rose in the short-run but fell in the long-run. In addition, city commercial banks are becoming the main source and contributor of systemic risk.

A deep learning model for gas storage optimization
Nicolas Curin,Michael Kettler,Xi Kleisinger-Yu,Vlatka Komaric,Thomas Krabichler,Josef Teichmann,Hanna Wutte

To the best of our knowledge, the application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon. In this article, we utilize techniques inspired by reinforcement learning in order to optimize the operation plans of underground natural gas storage facilities. We provide a theoretical framework and assess the performance of the proposed method numerically in comparison to a state-of-the-art least-squares Monte-Carlo approach. Due to the inherent intricacy originating from the high-dimensional forward market as well as the numerous constraints and frictions, the optimization exercise can hardly be tackled by means of traditional techniques.

A survey of some recent applications of optimal transport methods to econometrics
Alfred Galichon

This paper surveys recent applications of methods from the theory of optimal transport to econometric problems.

Aggressive Boards and CEO Turnover
Aghamolla, Cyrus,Hashimoto, Tadashi
This study investigates a communication game between a CEO and a board of directors where the CEO's career concerns can potentially impede value-increasing informative communication. By adopting a policy of aggressive boards (excessive replacement), shareholders can facilitate communication between the CEO and the board. The results are in contrast to the multitude of models which generally find that management-friendly boards improve communication, and help to explain empirical results concerning CEO turnover. The results also provide the following novel predictions concerning variation in CEO turnover: (i) there is greater CEO turnover in firms or industries where CEO performance is relatively more difficult to assess; (ii) the board is more aggressive in their replacement of the CEO in industries or firms where the board's advisory role is more salient; and (iii) there is comparatively less CEO turnover in firms or industries where the variance of CEO talent is high.

Artificial intelligence applied to bailout decisions in financial systemic risk management
Daniele Petrone,Neofytos Rodosthenous,Vito Latora

We describe the bailout of banks by governments as a Markov Decision Process (MDP) where the actions are equity investments. The underlying dynamics is derived from the network of financial institutions linked by mutual exposures, and the negative rewards are associated to the banks' default. Each node represents a bank and is associated to a probability of default per unit time (PD) that depends on its capital and is increased by the default of neighbouring nodes. Governments can control the systemic risk of the network by providing additional capital to the banks, lowering their PD at the expense of an increased exposure in case of their failure. Considering the network of European global systemically important institutions, we find the optimal investment policy that solves the MDP, providing direct indications to governments and regulators on the best way of action to limit the effects of financial crises.

COVID-19 and FinTech
Hill, Julie Andersen
The 2020 COVID-19 pandemic and the social distancing measures implemented to stop its spread will leave its mark on people, industries, and government policies long after the disease's health risk recede. One of the industries that has been transformed is financial services. As the pandemic spread, customers flocked to online and mobile platforms for financial services. Banks turned to fintech companies for the technology and expertise to be able to safely provide these products. Thus the pandemic hastened the adoption of technology by traditional banks and opened new partnership opportunities for non-bank fintech companies. The pandemic also reoriented financial regulators toward technology. By highlighting the risks that arise when technology does not live up to its promise, the pandemic encouraged regulators to scrutinize banks' use of technology and bank-fintech partnerships. At the same time, by highlighting the promise of technology, the pandemic encouraged regulators to use more technology in their supervision of banks. Finally, the pandemic will accelerate the transformation of some fintech companies from agile disruptors operating largely outside significant regulatory framework, to mainstream financial services companies that are regulated more like traditional banks. Policymakers will have difficult decisions about the best way to bring fintech companies within the regulatory fold. Nevertheless, the pandemic emphasized fintech is now of critical element of a modern financial system.

Central Counterparties’ Insolvency and Resolution â€" The New EU Regulation on CCP Recovery and Resolution
Binder, Jens-Hinrich
With its new Regulation on the Recovery and Resolution of Central Counterparties, European Capital Markets Law has filled an important void in the regulatory framework for the operation of central counterparties, which has been established with the EU Markets Infrastructures Regulation (EMIR) as early as 2012. With a comprehensive set of preventive and reactive provisions for the restructuring of central counterparties, the new instrument clearly takes a bold step. The provisions build on existing international standards, but are far more granular than these. The present paper assesses the underlying policy and the technical content of the Regulation in the light of the post-financial crisis literature on the systemic relevance of financial market infrastructures.

Clearing prices under margin calls and the short squeeze
Zachary Feinstein

In this paper, we propose a clearing model for prices in a financial markets due to margin calls on short sold assets. In doing so, we construct an explicit formulation for the prices that would result immediately following asset purchases and a margin call. The key result of this work is the determination of a threshold short interest ratio which, if exceeded, results in the discontinuity of the clearing prices due to a feedback loop. This model and threshold short interest ratio is then compared with data from early 2021 to consider the observed price movements of GameStop, AMC, and Naked Brand which have been targeted for a short squeeze by retail investors and, prominently, by the online community r/WallStreetBets.

Corporate Debt Restructuring: A Road to Survival
Bantwa, Ashok
Corporate Debt Restructuring (CDR) has been used by the companies while facing ugly finances and the bankers willing to consider a flexible mechanism such as CDR, as the banks /financial institutions have to reduce their Non Performing Assets (NPA). CDR is an effective financial tool to provide a flexible mechanism to the corporate management to get back to the top line growth oriented performance, cutting overheads and other unnecessary expenses consolidating their operations and streamlining their balance sheets, cash flows and finances. The main objective of corporate debt restructuring is to ensure timely and transparent mechanism for restructuring of corporate debts of viable entities facing problem, for the benefit of all concerned. To aim at preserving viable corporate that are affected by certain internal and external factors. To minimize the losses to the creditors and other stakeholders through and orderly and co-ordinate restructuring system. As the global economic resurges after several months of an economic slowdown, analysts fastidiously evaluate the impact of debt restructuring processes on the overall well being of the economy. It may be argued that these prevailing conditions are perhaps the appropriate litmus test to assess the success of the CDR system in emerging economies such as India. The paper shall study the practice and theory associated with corporate debt restructuring. Particular emphasis shall be laid on the RBI instituted CDR mechanism in India. The author shall present a present a critique on the CDR system apart from discussing current trends along with the scope of CDR in India and its future prospects.

Corporate Liquidity during the COVID-19 Crisis: The Trade Credit Channel
Bureau, Benjamin,Duquerroy, Anne,Vinas, Frédéric
Using unique daily data of payment defaults on suppliers in France, we show how the trade credit channel amplified the demand shock that firms met during the COVID-19 crisis. That channel dramatically increased short-term liquidity needs during the first months of the pandemic. A one standard deviation higher ratio of net debt to suppliers over sales increases the probability of payment default by roughly a third in sectors that were forced to shut down. This effect is extremely heterogeneous across sectors as well as across firms, depending on financing constraints. Understanding the cyclical trade credit dynamics, which drains corporate liquidity out when activity stops and rebuild it in the medium-term when activity recovers, is central for policy makers seeking to enable illiquid but solvent companies to remain afloat until revenues recover.

Corporate Performance Evaluation through Economic Value Added (EVA) and Other Conventional Measures: An Empirical Study
Bantwa, Ashok,Bhuva, DR. Krunal
A successful performance measure evaluates how well an organisation performs in relation to its objectives. Since the primary objective of commercial organizations is normally assumed to be the maximisation of the wealth of its shareholders, it follows that performance measures should evaluate this. Besides the traditional measures like Return on Net worth (RONW), Return on Capital Employed (ROCE) and Earnings per Share (EPS), EVA is a new measure available to the corporate managers. The EVA framework developed by Stern Stewart & Company is gradually replacing the traditional measures of financial performance on account of its robustness and its immunity from creative accounting. The present paper measures corporate performance of selected Indian companies by using EVA and other conventional measures. An attempt has been made to analyze the effectiveness of Economic Value Added over conventional measures of corporate performance. The relationship between different measures of performance is studied by using correlation and regression analysis. After detailed analysis it was found that HUL is the best performing company as per three measures of performance namely, EVACE, ROCE and RONW. It was also found that RONW and EVACE are highly correlated and RONW and ROCE significantly affect EVACE.

Corporate Social Responsibility (CSR) and Environmental Social Governance (ESG) â€" Disclosure of European Banks
Loew, Edgar,Erichsen, Giulia,Liang, Benjamin,Postulka, Margret Louise
In the European Union the goal of transition towards a carbon-neutral economy by 2050 is identified as a major aim of the European Commission. The integral role of the financial services sector in funding such investments and enhancing the (re-) direction and allocation of capital flows towards sustainable projects and investments is increasingly acknowledged. Besides the integration of ESG and CSR into the banks’ operations, it can serve and be utilized as risk management tools. This paper investigates the effectiveness of the EU Non-Financial Reporting Directive (2014/95/EU) with respect to the CSR disclosure quality in European banks from 2017-2019. After an overview of the EU’s path towards a sustainable financial sector from 2013 till present, the most important regulations will be assessed in detail, namely the Non-Financial Reporting Directive (2014/95/EU) and the Taxonomy Regulation on Sustainable Economic Activity (2020/852/EU). By constructing a multi-category CSR disclosure index to “translate” and quantify disclosed CSR information in the banks’ annual filings, a positive comparative development over the years is identified â€" indicating the general effectiveness of the directive. The construction of the disclosure index and the content analysis of the banks’ CSR disclosures is based on textual analysis. In order to not only analyse and evaluate the level of CSR disclosure in European banks from 2017-2019, but also to identify potential factors influencing its quality, the collection of multiple data inputs is necessary. The paper examines the association between three factors of the balance sheet and the P&L on their significance on the disclosure score. These factors are asset size in million euro (proxy for size), common equity tier 1 ratio (proxy for market discipline) as defined in the Basel III regulatory framework (proxy for market discipline) and pre-tax return on assets (proxy for profitability). Furthermore, the association of other non-balance sheet factors and their correlation on the disclosure score is examined which are listing status (listed or non-listed), country of the head-quarter, (external) auditor and bank´s CSR report type. Therefore, this adds detail to research, as existing majorly focus on how CSR-related information is disclosed and not to what extent and quality.This paper targets accountants, financial institutions, regulatory authorities, shareholders, investors and stakeholders in general who are affected by and interested in the overall CSR disclosure quality of European banks.

Corruption-Related Disclosure in the Banking Industry: Evidence From GIPSI Countries
Andrés, Pablo de,Polizzi, Salvatore,Scannella, Enzo,Suárez, Nuria
This paper empirically investigates corruption-related disclosure in the banking industry, aiming to identify the most relevant theories that explain the reasons why financial institutions disclose corruption-related information to the public in their annual financial reports. Using a total sample of 83 banks from the GIPSI countries during the period 2011-2019, our results reveal that, on average, banks that have been involved in corruption issues disclose less on corruption-related information than banks that have not been involved in any corruption scandal. Moreover, banks not involved in corruption cases disclose even more information after other banks’ corruption events become public. These basic relationships, however, are shaped by the characteristics of each particular country in terms of control of corruption and the specific regulation on non-traditional banking activities. Our results are robust to different specifications of the econometric models, and to alternative empirical methods accounting for potential reverse causality and sample selection concerns.

Crypto Carry
Franz, Friedrich-Carl,Schmeling, Maik
We study carry trades in the cryptocurrency market and document that the lack of sufficient arbitrage capital in combination with highly levered speculators creates ample carry opportunities. We find that carry in bitcoin, as measured by the funding rate of BTC/USD perpetual swaps, resembles components of a fear-and-greed index and past returns. Carry in perpetual swaps positively predicts returns in the time-series of BTC/USD and in a cross-section of 51 cryptocurrencies.

Deep Hedging under Rough Volatility
Blanka Horvath,Josef Teichmann,Zan Zuric

We investigate the performance of the Deep Hedging framework under training paths beyond the (finite dimensional) Markovian setup. In particular we analyse the hedging performance of the original architecture under rough volatility models with view to existing theoretical results for those. Furthermore, we suggest parsimonious but suitable network architectures capable of capturing the non-Markoviantity of time-series. Secondly, we analyse the hedging behaviour in these models in terms of P\&L distributions and draw comparisons to jump diffusion models if the the rebalancing frequency is realistically small.

Does Prudential Regulation Contribute to Effective Measurement and Management of Interest Rate Risk? Evidence From Italian Banks
Cerrone, Rosaria,Cocozza, Rosa,Curcio, Domenico,Gianfrancesco, Igor
This paper contributes to prior literature and to the current debate concerning recent revisions of the regulatory approach to measuring bank exposure to interest rate risk in the banking book by focusing on assessment of the appropriate amount of capital banks should set aside against this specific risk. We first discuss how banks might develop internal measurement systems to model changes in interest rates and measure their exposure to interest rate risk that are more refined and effective than are regulatory methodologies. We then develop a backtesting framework to test the consistency of methodology results with actual bank risk exposure. Using a representative sample of Italian banks between 2006 and 2013, our empirical analysis supports the need to improve the standardized shock currently enforced by the Basel Committee on Banking Supervision. It also provides useful insights for properly measuring the amount of capital to cover interest rate risk that is sufficient to ensure both financial system functioning and banking stability.

Does Social Media Sentiment Matter in the Pricing of U.S. Stocks?
Koeppel, Christian
This paper applies a recently developed social media-based sentiment proxy for the construction of a new risk factor for sentiment-augmented asset pricing models on U.S. equities. Accounting for endogeneity, autocorrelation and heteroskedasticity in a GMM framework, we find that the inclusion of sentiment significantly improves the performance of the five-factor model from Fama and French (2015, 2017) for di ff erent industry and style portfolios like size, value, profitability, investment. The sentiment risk premium provides the missing component in the behavioral asset pricing theory of Shefrin and Belotti (2008) and (partially) resolves the pricing puzzles of small extreme growth, small extreme investment stocks and small stocks that invest heavily despite low profitability.

Done Deal! Advisor impact on Pricing, Premia, Returns, and Deal Completion in M&A
Ecer, C. Fuat. J.,Trautmann, Stefan T.
We study the role of financial advisors in M&A for different advisor engagement constellations. We observe positive effects of both target and acquirer advisors on deal completion and prices. The unexpected positive price effect of acquirer advisors is further supported by evidence for higher premia and lower announcement bidder returns. We establish causality of pricing effects using matching and instrumental-variable approaches, making use of the impact of Lehman’s collapse on former Lehman clients. We explain our findings in terms of governance: advisors’ and executives’ incentives form a potential source of value destruction.

Equity Duration
Mullins, Gary
The concept of bond duration was originally introduced by Macaulay (1938) and nowadays is well- established in the fixed-income literature. In this paper, I lift the same concepts from the fixed-income asset class and apply them to equities. I derive three candidate models for estimating the duration of a stock. The models are vastly different in their theoretical underpinnings, yet there is strong empirical evidence of positive co-movements between all three models in my sample. Furthermore, I investigate the relationship between the equity duration factor and various common equity factors. Empirical evidence suggests that low-duration stocks are also high-value, high-profitability, low-investment and low-risk stocks. In particular, there is a strong link between duration and the classical value factor â€" both theoretically and empirically. Importantly, however, the correspondence between the two factors is not one-to-one in my sample. I perform numerous empirical tests suggesting that a duration strategy out-performed a value-strategy in the period following the Great Financial Crisis (2007â€"08).

Factor Investing with Black-Litterman-Bayes: Incorporating Factor Views and Priors in Portfolio Construction
Kolm, Petter N.,Ritter, Gordon
The authors propose a general framework referred to as Black-Litterman-Bayes (BLB) for constructing optimal portfolios for factor-based investing. In the spirit of the classical Black-Litterman model, the framework allows for the incorporation of investor views and different priors on factor risk premia, including data-driven and benchmark priors. Computationally efficient closed-form formulas are provided for the (posterior) expected returns and return covariance matrix that result from integrating factor views into an APT multi-factor model. In a step-by-step procedure, the authors show how to build the prior and incorporate the factor views, demonstrating in a realistic empirical example, using a number of well-known cross-sectional U.S. equity factors, that the BLB approach can add value to mean-variance optimal multi-factor risk premia portfolios.

Fundamental Analysis of XBRL Data: A Machine Learning Approach
Chen, Xi,Cho, Yang Ha,Dou, Yiwei,Lev, Baruch
Since 2012, all U.S. public companies must tag quantitative amounts in financial statements and footnotes of their 10-K reports using the eXtensible Business Reporting Language (XBRL). We conduct a fundamental analysis of this large set of detailed financial information to predict earnings. Using machine learning methods, we combine the XBRL data into a summary measure for the direction of one-year-ahead earnings changes. Hedge portfolios are formed based on this measure during the period 2015-2018. The annual size-adjusted returns to the hedge portfolios range from 5.02 to 9.7 percent. These returns persist after accounting for transaction costs and risk. Our strategies outperform those of Ou and Penman (1989), who extract the summary measure from 65 accounting variables using logistic regressions. Additional analyses suggest that the outperformance stems from both nonlinear predictor interactions missed by regressions and more detailed financial data in XBRL documents.

Green Shoe Option And Its Role In Post Issue Price Stabilization In Indian Capital Market
Bantwa, Ashok
A green shoe option (GSO) provides the option of allotting equity shares in excess of the equity shares offered in the public issue as a post-listing price stabilizing mechanism. This research paper covers the process overview of green shoe option in India and compares it with the process prevailing in other countries. The paper also consists of rationale for including green shoe option in IPO programs. The paper further examines whether companies need to include GSOs in their initial public offerings (IPOs), and explores the reasons for the indifference on the part of issuer companies and merchant banks in India towards GSOs. The aftermarket price performance of companies that included GSOs in their IPOs is analysed; however, the results of this analysis do not lead to any generalization due to the small number of companies that opted for GSO.

Hedging and machine learning driven crude oil data analysis using a refined Barndorff-Nielsen and Shephard model
Humayra Shoshi,Indranil SenGupta

In this paper, a refined Barndorff-Nielsen and Shephard (BN-S) model is implemented to find an optimal hedging strategy for commodity markets. The refinement of the BN-S model is obtained with various machine and deep learning algorithms. The refinement leads to the extraction of a deterministic parameter from the empirical data set. The problem is transformed to an appropriate classification problem with a couple of different approaches: the volatility approach and the duration approach. The analysis is implemented to the Bakken crude oil data and the aforementioned deterministic parameter is obtained for a wide range of data sets. With the implementation of this parameter in the refined model, the resulting model performs much better than the classical BN-S model.

Inf-convolution and optimal risk sharing with vountable sets of risk measures
Marcelo Brutti Righi,Marlon Ruoso Moresco

The inf-convolution of risk measures is directly related to risk sharing and general equilibrium, and it has attracted considerable attention in mathematical finance and insurance problems. However, the theory is restricted to finite sets of risk measures. In this study, we extend the inf-convolution of risk measures in its convex-combination form to a countable (not necessarily finite) set of alternatives. The intuitive principle of this approach a generalization of convex weights in the finite case. Subsequently, we extensively generalize known properties and results to this framework. Specifically, we investigate the preservation of properties, dual representations, optimal allocations, and self-convolution.

Investors' Climate Sentiment and Financial Markets
Santi, Caterina
We propose a measure of investors’ climate sentiment by performing sentiment analysis on StockTwits posts on climate change and global warming. We find that investors’ climate sentiment generates a mispricing in the Emission-minus-Clean (EMC) portfolio (Choi et al.,2020), the portfolio that invests in emission stocks and goes short on clean stocks. Specifically, when investors share a positive attitude towards climate change, they tend to overvalue the negative externalities produced by emission stocks. Moreover, we show that carbon prices area successful incentive to reduce CO2 emissions. Finally, a portfolio strategy that uses information on investors’ climate sentiment and carbon prices generates a return of 9.77% annually.

Macroprudential Policy Interactions in a Sectoral DSGE Model with Staggered Interest Rates
Hinterschweiger, Marc,Khairnar, Kunal,Ozden, Tolga,Stratton, Thomas
We develop a two-sector DSGE model with a detailed banking sector along the lines of Clerc et al (2015) to assess the impact of macroprudential tools (minimum, countercyclical and sectoral capital requirements, as well as a loan-to-value limit) on key macroeconomic and financial variables. The banking sector features residential mortgages and corporate lending subject to staggered interest rates à la Calvo (1983), which is motivated by the sluggish movement of lending rates due to fixed interest rate loan contracts. Other distortions in the model include limited liability, bankruptcy costs and penalty costs for deviations from regulatory capital. We estimate the model using Bayesian methods based on quarterly UK data over 1998 Q1â€"2016 Q2. Our contributions are threefold. We show that: (i) co-ordination of macroprudential tools may have a welfare-improving effect, (ii) macroprudential tools would have improved some macroeconomic indicators but, within our model, not have prevented the Global Financial Crisis, (iii) staggered interest rates may alter the transmission of macroprudential tools that work through interest rates.

Matching Function Equilibria with Partial Assignment: Existence, Uniqueness and Estimation
Liang Chen,Eugene Choo,Alfred Galichon,Simon Weber

In this paper, we argue that models coming from a variety of fields share a common structure that we call matching function equilibria with partial assignment. This structure revolves around an aggregate matching function and a system of nonlinear equations. This encompasses search and matching models, matching models with transferable, non-transferable and imperfectly transferable utility, and matching with peer effects. We provide a proof of existence and uniqueness of an equilibrium as well as an efficient algorithm to compute it. We show how to estimate parametric versions of these models by maximum likelihood. We also propose an approach to construct counterfactuals without estimating the matching functions for a subclass of models. We illustrate our estimation approach by analyzing the impact of the elimination of the Social Security Student Benefit Program in 1982 on the marriage market in the United States.

National Culture of Secrecy and Stock Price Synchronicity: Cross-Country Evidence
Gaganis, Chrysovalantis,Leledakis, George N.,Pasiouras, Fotios,Pyrgiotakis, Emmanouil G.
Stock price synchronicity has been associated with various market outcomes like the return-sentiment relations, stock liquidity, and asset pricing models. Therefore, researchers have devoted a lot of time in revealing the underlying factors that drive stock price synchronicity. Using a sample of 49 countries over the period 1990 to 2019 we find a robust association between higher cultural secretiveness and stock price synchronicity. Our results suggest that a deep-rooted country characteristic like the culture of secrecy can diminish the information environment of stock markets. The results are robust to the use of various control variables suggested in earlier studies and alternative regression techniques, including ones that address endogeneity concerns.

On (r, g) and the Identity ROE = EP*MTB
Ohlson, James A.,Zhai, Sophia Weihuan
The paper concerns concepts of equity valuation. Three primary financial ratios -- (forward) return on equity (ROE), (forward) earnings to price ratio (EP), and the (current) market-to-book ratio (MTB) â€" are connected to the standard valuation parameters, r = cost of equity (discount factor), and g = growth. The framework relies on a Gordon-Williams type of PVD model and combines it with an add-on steady-state growth requirement: Subject to clean surplus accounting, (the expected) earnings, dividends, and book values all grow at the same rate. This condition is adapted from “The Second Fundamental Law of Capitalism”, articulated by Piketty (2014). Applying these ideas result in a benchmark model: (i) a weighted average representation, EP = BTM*r + (1- BTM) *Div.Yield , and (ii) the inequalities 0 < BTM < 1 and Div.Yield < EP < r < ROE. Additional analysis relaxes the steady-state growth condition: the g-parameter is now replaced by forecasts of near future earnings growth which get updated as time passes. An empirical part of the paper evaluates whether the steady-state growth concept holds as an average for S&P 500 firms. Results are generally supportive.

On the Origin of Systemic Risk
Montagna, Mattia,Torri, Gabriele,Covi, Giovanni
Systemic risk in the banking sector is usually associated with long periods of economic downturn and very large social costs. On one hand, shocks coming from correlated exposures towards the real economy may induce correlation in banks’ default probabilities thereby increasing the likelihood for systemic tail events like the 2008 Great Financial Crisis. On the other hand, financial contagion also plays an important role in generating large-scale market failures, amplifying the initial shocks coming from the real economy. To study the sources of these rare phenomena, we propose a new definition of systemic risk (ie the probability of a large number of banks going into distress simultaneously) and thus we develop a multilayer microstructural model to study empirically the determinants of systemic risk. The model is then calibrated on the most comprehensive granular dataset for the euro-area banking sector, capturing roughly 96% or €23.2 trillion of euro-area banks’ total assets over the period 2014â€"2018. The outputs of the model decompose and quantify the sources of systemic risk showing that correlated economic shocks, financial contagion mechanisms, and their interaction are the main sources of systemic events. The results obtained with the simulation engine resemble common market-based systemic risk indicators and empirically corroborate findings from existing literature. This framework gives regulators and central bankers a tool to study systemic risk and its developments, pointing out that systemic events and banks’ idiosyncratic defaults have different drivers, hence implying different policy responses.

Optimal Intervention in Economic Networks using Influence Maximization Methods
Ariah Klages-Mundt,Andreea Minca

We consider optimal intervention in the Elliott-Golub-Jackson network model and show that it can be transformed into an influence maximization problem, interpreted as the reverse of a default cascade. Our analysis of the optimal intervention problem extends well-established targeting results to the economic network setting, which requires additional theoretical steps. We prove several results about optimal intervention: it is NP-hard and additionally hard to approximate to a constant factor in polynomial time. In turn, we show that randomizing failure thresholds leads to a version of the problem which is monotone submodular, for which existing powerful approximations in polynomial time can be applied. In addition to optimal intervention, we also show practical consequences of our analysis to other economic network problems: (1) it is computationally hard to calculate expected values in the economic network, and (2) influence maximization algorithms can enable efficient importance sampling and stress testing of large failure scenarios. We illustrate our results on a network of firms connected through input-output linkages inferred from the World Input Output Database.

Pyramid scheme in stock market: a kind of financial market simulation
Yong Shi,Bo Li,Guangle Du

Artificial stock market simulation based on agent is an important means to study financial market. Based on the assumption that the investors are composed of a main fund, small trend and contrarian investors characterized by four parameters, we simulate and research a kind of financial phenomenon with the characteristics of pyramid schemes. Our simulation results and theoretical analysis reveal the relationships between the rate of return of the main fund and the proportion of the trend investors in all small investors, the small investors' parameters of taking profit and stopping loss, the order size of the main fund and the strategies adopted by the main fund. Our work are helpful to explain the financial phenomenon with the characteristics of pyramid schemes in financial markets, design trading rules for regulators and develop trading strategies for investors.

Recent Developments in Financing and Bank Lending to the Non-financial Private Sector
Alves, Pana,Arrizabalaga, Fabian,Delgado, Javier,Galán, Jorge,Perez Asenjo, Eduardo,Pérez Montes, Carlos,Trucharte, Carlos
Financing conditions for households and businesses have remained accommodating in the second half of 2020, assisted by the support measures introduced by the economic and monetary authorities to contend with the fallout of the COVID-19 pandemic. However, there are signs of some tightening in credit standards, linked to financial institutions’ increased risk concerns. The recovery in economic activity since the summer has favoured a more dynamic flow of credit to individuals, while, after the large volume of financing granted to productive activities over the spring, new lending to this sector declined significantly. The strong negative impact of the COVID-19 pandemic on economic activity did not filter through in any significant way into the quality of deposit institutions’ private sector credit portfolio until 2020 Q3. Although the decline in non-performing loans observed in previous years generally slowed across portfolios in 2020, and despite the pick-up in specific portfolios, such as those relating to consumer credit, total non-performing loans have continued to decline on a year-on-year basis. Growth in the volume of public guarantees for lending to business eased in the second half of the year and the volume of credit subject to non-expired moratoria, which is concentrated mostly in banking association schemes, stabilised.

Sequential Reporting Bias
Aghamolla, Cyrus,Guttman, Ilan,Petrov, Evgeny
Firms with correlated fundamentals often issue reports sequentially, leading to information spillovers. The theoretical literature has investigated multi-firm reporting, but only when firms report simultaneously. We examine the implications of sequential reporting, where firms aim to maximize their market price and can manipulate their reports. Our model demonstrates that the introduction of sequentiality in the presence of information spillovers significantly alters the biasing behavior of firms and the resulting informational environment relative to simultaneous reporting. In particular, a lead firm always manipulates more when reports are issued sequentially than under simultaneous reporting. Interestingly, this occurs because follower firms, who benefit from information spillovers, place less weight on their own private information when issuing a report. This information loss leads the market to place greater weight on the leader's report, which increases the incentive of the lead manager to manipulate her report. Moreover, the information loss from sequentiality leads to less efficient and less volatile prices. Additionally, we find that stronger correlation in firm fundamentals can amplify the lead firm's incentive for manipulation under sequentiality, in contrast to simultaneous reporting. We offer additional results regarding, for example, market response coefficients, and provide a number of empirical implications.

SoK: Decentralized Finance (DeFi)
Sam M. Werner,Daniel Perez,Lewis Gudgeon,Ariah Klages-Mundt,Dominik Harz,William J. Knottenbelt

Decentralized Finance (DeFi), a blockchain powered peer-to-peer financial system, is mushrooming. One year ago the total value locked in DeFi systems was approximately 600m USD, now, as of January 2021, it stands at around 25bn USD. The frenetic evolution of the ecosystem makes it challenging for newcomers to gain an understanding of its basic features. In this Systematization of Knowledge (SoK), we delineate the DeFi ecosystem along its principal axes. First, we provide an overview of the DeFi primitives. Second, we classify DeFi protocols according to the type of operation they provide. We then go on to consider in detail the technical and economic security of DeFi protocols, drawing particular attention to the issues that emerge specifically in the DeFi setting. Finally, we outline the open research challenges in the ecosystem.

Ten Days Late and Billions of Dollars Short: The Employment Effects of Delays in Paycheck Protection Program Financing
Doniger, Cynthia,Kay, Benjamin
Delay in the provision of Paycheck Protection Program (PPP) loans due to insufficient initial funding under the CARES Act substantially and persistently reduced employment. Delayed loans increased job losses in May and persistently reduced recalls throughout the summer. The magnitude and heterogeneity of effects suggest significant barriers to obtaining external financing, particularly among small firms. Effects are inequitably distributed: larger among the self-employed, less well paid, less well educated andâ€"importantly for the design of future programsâ€"in very small firms. Our estimates imply the PPP saved millions of jobs but larger initial funding could have saved millions more, particularly if it had been directed toward the smallest firms. About half of the jobs lost to insufficient PPP funding are lost in firms with fewer than 10 employees, despite such firms accounting for less than 20 percent of employment.

Ten Meditations on (Public) Venture Capital â€" Revisited
Murray, Gordon C.
This paper reflects on the policy formation process in the burgeoning area of government’s involvement venture capital finance (VC) over the two decades 2000-2020. It looks at both why and how government VC funds (GVC) have evolved. The increasingly common vehicle of ‘hybrid’ co-investment funds, which include both public and private VC investors managed by a jointly approved private fund manager, is analysed. The evolution and greater refinement of public intervention in VC markets over time is acknowledged while noting that significant operational challenges remain. There is some evidence that later iterations of GVC programmes have started to add net value which may imply a public-policy learning process. A fluctuating supply over time for venture capital finance, particularly at the earliest stages of firm formation and growth, suggests the benefits of well-designed and complementary government venture capital activity. The rubric of Ten Meditations is employed as a device to communicate both problem and prescription across the academic/policy maker divide. The paper is intended to be relevant to policy makers while grounded in robust academic research.

The Great Equalizer: Medicare and the Geography of Consumer Financial Strain
Paul Goldsmith-Pinkham,Maxim Pinkovskiy,Jacob Wallace

We use a five percent sample of Americans' credit bureau data, combined with a regression discontinuity approach, to estimate the effect of universal health insurance at age 65-when most Americans become eligible for Medicare-at the national, state, and local level. We find a 30 percent reduction in debt collections-and a two-thirds reduction in the geographic variation in collections-with limited effects on other financial outcomes. The areas that experienced larger reductions in collections debt at age 65 were concentrated in the Southern United States, and had higher shares of black residents, people with disabilities, and for-profit hospitals.

The Impact of Enhanced Information Flows Across Firms: Cross-Sectional Evidence From the EDGAR Roll-Out
Abhyankar, Abhay,wang, yaning
We exploit the quasi-natural experiment created by the roll-out of the EDGAR system to study the causal impact of the additional flow of stock-specific information on firms. We find that this information flow to investors resulted in statistically significant and economically essential changes in illiquidity and trading volume but not in idiosyncratic volatility. Across firms, illiquidity fell for smallest firms more than it did for the largest firms. Across industry groups, the mining and manufacturing sectors have the largest decreases illiquidity and increases in trading volume.

The More the Merrier? Evidence from the Global Financial Crisis on the Value of Multiple Requirements in Bank Regulation
Buckmann, Marcus,Gallego Marquez, Paula,Gimpelewicz, Mariana,Kapadia, Sujit,Rismanchi, Katie
This paper assesses the value of multiple requirements in bank regulation using a novel empirical ruleâ€'based methodology. Exploiting a dataset of capital and liquidity ratios for a sample of global banks in 2005 and 2006, we apply simple threshold-based rules to assess how different regulations individually and in combination might have identified banks that subsequently failed during the global financial crisis. Our results generally support the case for a small portfolio of different regulatory metrics. Under the objective of correctly identifying a high proportion of banks which subsequently failed, we find that a portfolio of a leverage ratio, a risk-weighted capital ratio, and a net stable funding ratio yields fewer false alarms than any of these metrics individually â€" and at less stringent calibrations of each individual regulatory metric. We also discuss how these results apply in different robustness exercises, including out-of-sample evaluations. Finally, we consider the potential role of market-based measures of bank capitalisation, showing that they provide complementary value to their accounting-based counterparts.

The Rise of Covenant-Lite Bond Contracting
Gietzmann, Miles B.,Isidro, Helena,Raonic, Ivana
We investigate the trading and yield effects of covenant-lite (cov-lite) high-yield bond contracts, which have a restricted (lite) set of covenants. The excluded covenants often are those that use accounting performance measures. Although much research has focused on the potential benefits of accounting as a basis for debt contracting, little is known about settings where it may be optimal to exclude accounting performance statistics from public debt contracts. We find that cov-lite high-yield bonds have a higher trading turnover and lower yield spreads. Our findings provide empirical support for theory, which predicts, for optimal bond covenant design, that a trade-off between improving trading ease versus enhanced investor protection needs to be managed. These results enhance our understanding of the limits of accounting’s role in (bond) contracting design.

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.

Uptake, Use, and Impact of Islamic Savings: Evidence from a Field Experiment in Pakistan
Ahmad, Syedah,Lensink, Robert,Mueller, Annika
This study examines the take-up, use, and impact of Islamic savings accounts for poor Muslim clients of an MFI in Pakistan, using a randomized controlled trial. We specifically focus on the impact of opening Islamic savings accounts on women’s empowerment. The main results strongly suggest that a successful intervention to increase uptake of savings among a sample of mainly poor, Islamic women needs to address a combination of economic external constraints (being illiterate, facing credit constraints) and internal constraints, shaped by religious and cultural barriers. We find strong evidence that women who have opened savings accounts obtain more bargaining power with respect to health and marriage-related issues. Moreover, they display a much higher degree of self-esteem, which may provide a basis for gaining more bargaining power relative to their spouses or parents. Thus, an active policy that motivates poor Islamic women to open savings accounts may be an effective strategy to kick-start a process of women’s empowerment.