Research articles for the 2021-07-22

A Liquidity Risk Early Warning Indicator for Italian Banks: A Machine Learning Approach
Nobili, Stefano,Drudi, Maria Ludovica
The paper develops an early warning system to identify banks that could face liquidity crises. To obtain a robust system for measuring banks’ liquidity vulnerabilities, we compare the predictive performance of three models â€" logistic LASSO, random forest and Extreme Gradient Boosting â€" and of their combination. Using a comprehensive dataset of liquidity crisis events between December 2014 and January 2020, our early warning models’ signals are calibrated according to the policymaker's preferences between type I and II errors. Unlike most of the literature, which focuses on default risk and typically proposes a forecast horizon ranging from 4 to 6 quarters, we analyse liquidity risk and we consider a 3-month forecast horizon. The key finding is that combining different estimation procedures improves model performance and yields accurate out-of-sample predictions. The results show that the combined models achieve an extremely low percentage of false negatives, lower than the values usually reported in the literature, while at the same time limiting the number of false positives.

A Sparsity Algorithm with Applications to Corporate Credit Rating
Dan Wang,Zhi Chen,Ionut Florescu

In Artificial Intelligence, interpreting the results of a Machine Learning technique often termed as a black box is a difficult task. A counterfactual explanation of a particular "black box" attempts to find the smallest change to the input values that modifies the prediction to a particular output, other than the original one. In this work we formulate the problem of finding a counterfactual explanation as an optimization problem. We propose a new "sparsity algorithm" which solves the optimization problem, while also maximizing the sparsity of the counterfactual explanation. We apply the sparsity algorithm to provide a simple suggestion to publicly traded companies in order to improve their credit ratings. We validate the sparsity algorithm with a synthetically generated dataset and we further apply it to quarterly financial statements from companies in financial, healthcare and IT sectors of the US market. We provide evidence that the counterfactual explanation can capture the nature of the real statement features that changed between the current quarter and the following quarter when ratings improved. The empirical results show that the higher the rating of a company the greater the "effort" required to further improve credit rating.

A Stochastic Control Approach to Public Debt Management
Matteo Brachetta,Claudia Ceci

We discuss a class of debt management problems in a stochastic environment model. We propose a model for the debt-to-GDP (Gross Domestic Product) ratio where the government interventions via fiscal policies affect the public debt and the GDP growth rate at the same time. We allow for stochastic interest rate and possible correlation with the GDP growth rate through the dependence of both the processes (interest rate and GDP growth rate) on a stochastic factor which may represent any relevant macroeconomic variable, such as the state of economy. We tackle the problem of a government whose goal is to determine the fiscal policy in order to minimize a general functional cost. We prove that the value function is a viscosity solution to the Hamilton-Jacobi-Bellman equation and provide a Verification Theorem based on classical solutions. We investigate the form of the candidate optimal fiscal policy in many cases of interest, providing interesting policy insights. Finally, we discuss two applications to the debt reduction problem and debt smoothing, providing explicit expressions of the value function and the optimal policy in some special cases.

A Time-Varying Network for Cryptocurrencies
Li Guo,Wolfgang Karl Härdle,Yubo Tao

Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a dynamic covariate-assisted spectral clustering method to consistently estimate the latent community structure of cryptocurrencies network that accounts for both sets of information. We demonstrate that investors can achieve better risk diversification by investing in cryptocurrencies from different communities. A cross-sectional portfolio that implements an inter-crypto momentum trading strategy earns a 1.08% daily return. By dissecting the portfolio returns on behavioral factors, we confirm that our results are not driven by behavioral mechanisms.

A characterisation of cross-impact kernels
Rosenbaum, Mathieu,Tomas, Mehdi
Trading a financial asset pushes its price as well as the prices of other assets, a phenomenon known as cross-impact. We consider a general class of kernel-based cross-impact models and investigate suitable parameterisations for trading purposes. We focus on kernels that guarantee that prices are martingales and anticipate future order flow (martingale-admissible kernels) and those that ensure there is no possible price manipulation (no-statistical-arbitrage-admissible kernels). We determine the overlap between these two classes and provide formulas for calibration of cross-impact kernels on data. We illustrate our results using SP500 futures data.

A tail of three occasionally-binding constraints: a modelling approach to GDP-at-Risk
Aikman, David,Bluwstein, Kristina,Karmakar, Sudipto
We build a semi-structural New Keynesian model with financial frictions to study the drivers of macroeconomic tail risk (‘GDP-at-Risk’). We analyse the empirically observed fat left tail of the GDP distribution by modelling three key non-linearities emphasised in the literature: 1) an effective lower bound on nominal interest rates, 2) a credit crunch in bank credit supply when bank capital depletes, and 3) deleveraging by borrowers when debt service burdens become excessive. We obtain three key results. First, our model generates a significantly fat-tailed distribution of GDP â€" a finding that is absent in most linear New Keynesian and RBC models. Second, we show how these constraints interact with each other. We find that an economy prone to debt deleveraging will experience significantly more credit crunch and effective lower bound episodes than otherwise. Moreover, as the effective lower bound becomes more proximate, the frequency of credit crunch episodes increases significantly. As a rule of thumb, we find that each 50 basis point decline in monetary policy headroom requires additional capital buffers of 1% of assets or 2%â€"2.5% points lower debt service burdens to hold the risk level constant. Third, we use the model to generate a historical decomposition of GDP-at-Risk for the United Kingdom. The implied risk outlook deteriorates significantly in the run-up to the Global Financial Crisis, driven by depleted capital buffers and increasing debt burdens. Since then, GDP-at-Risk has remained elevated, with greater bank resilience and lower debt offset by the limited capacity of monetary policy to cushion adverse shocks.

An Information Quality-Based Explanation for Loan Loss Allowance Inadequacy during the 2008 Financial Crisis
Yang, Ling
I study whether commercial banks’ loan loss allowances were inadequate during the 2008 financial crisis because bank managers relied on low-quality information to estimate loan losses. To measure the quality of information collected on bank-held mortgages prior to the crisis, I create a bank exposure-to-mortgage fraud risk index (EFI) that captures overstatement of borrower income in mortgage applications. I find banks that originated more loans in high-risk neighborhoods had less adequate loan loss allowances during the crisis. My study is consistent with the hypothesis that fraudulent borrower information adversely affected banks’ loan loss provisioning.

Analyse comparative des modèles financiers appliqués à la BRVM : cas de la famille du MEDAF et de Fama-French (Comparative Analysis of Financial Models Applied to the BRVM: The Case of the CAPM Family and Fama-French)
Abdoulaziz, Alhassane
French abstract: Dans le présent papier, nous essayons d’étudier les actions de la bourse régionale de valeur mobilière (BRVM) tout en mettant en relief le modèle de Fama-French et les dérivés du modèle d’évaluation des actifs financiers (MEDAF). Les résultats montrent que le modèle à trois facteurs de Fama et French semble être le meilleur modèle optimal pour le cas des actions de la BRVM, dans lequel le facteur bêta de marché est considéré comme un facteur de rentabilité, de même que SMB pour les portefeuilles SL, SM et SH. Toutefois, on constate que selon les résultats du modèle de Fama-French (93 et 2015), les portefeuilles à petite capitalisation boursière (Smal caps) sont plus rentables que les grandes capitalisations.English abstract: In this paper, we try to study the stocks of the regional stock exchange (BRVM) while highlighting the Fama-French model and derivatives of the capital assets pricing model (CAPM). We are also used both the OLS and QR to estimate equations. The results show that the three-factor model of Fama and French seems to be the best optimal model for the case of BRVM stocks, in which the market beta factor is considered to be a factor of profitability, as well as SMB for the portfolios of SL, SM and SH. However, we note that according to the results of the Fama-French model (93 and 2015), portfolios with small market capitalization (Smal caps) are more profitable than large capitalizations (Big caps).

Bank interest rate margins in a negative interest rate environment
Freriks, Jorien,Kakes, Jan
This paper studies the impact of the negative interest rate policy (NIRP) on euro area banks’ interest rate margins, using bank-individual data for the 2007-2019 period. An important extension to other studies is our breakdown of banks’ interest rate margin into a funding and lending component. Because of banks’ reluctance to reduce the interest rate on household deposits below zero, the funding margin of banks more reliant on deposit funding has declined compared to that of other banks. Our evidence shows that these banks have been unwilling or unable to compensate this by boosting their lending margins. Therefore, negative rates have significantly reduced the overall net interest margin of deposit-dependent banks compared to other banks.

Capital Requirements and Claims Recovery: A New Perspective on Solvency Regulation
Cosimo Munari,Lutz Wilhelmy,Stefan Weber

Protection of creditors is a key objective of financial regulation. Where the protection needs are high, i.e., in banking and insurance, regulatory solvency requirements are an instrument to prevent that creditors incur losses on their claims. The current regulatory requirements based on Value at Risk and Average Value at Risk limit the probability of default of financial institutions, but they fail to control the size of recovery on creditors' claims in the case of default. We resolve this failure by developing a novel risk measure, Recovery Value at Risk. Our conceptual approach can flexibly be extended and allows the construction of general recovery risk measures for various risk management purposes. By design, these risk measures control recovery on creditors' claims and integrate the protection needs of creditors into the incentive structure of the management. We provide detailed case studies and applications: We analyze how recovery risk measures react to the joint distributions of assets and liabilities on firms' balance sheets and compare the corresponding capital requirements with the current regulatory benchmarks based on Value at Risk and Average Value at Risk. We discuss how to calibrate recovery risk measures to historic regulatory standards. Finally, we show that recovery risk measures can be applied to performance-based management of business divisions of firms and that they allow for a tractable characterization of optimal tradeoffs between risk and return in the context of investment management.

Characteristic-based factors and market return
XIE, Jinming
We provide the first comprehensive study of examining the predictive power of characteristic-based factors on market excess return. We find that characteristic-based factors negatively predict market excess return regardless of whether they are originated from investment, mispricing or behavioral models. The negative predictability exists both in-sample and out-of-sample and emonstrates strong economic value from the perspective of asset allocation. Both the long legs and short legs of the characteristic-based factors positively predict the market return, with short legs showing stronger predictability. Exploring different market states, the predictability of characteristics-based factors is stronger in recessions. Finally, the predictability of characteristic-based factors on market return also holds internationally.

Coexistence of Money and Interest-Bearing Bonds
van Buggenum, Hugo
This paper revisits how coexistence of money and bonds can make a society better off. For this purpose, a model is constructed in which payment instruments matter for settling real transactions and savings instruments matter because agents differ in how they discount future utility. Because bonds and money differ in their characteristics as payment and savings instruments, the model is able to explain the coexistence puzzle for an optimally chosen monetary policy. Such a policy trades-offs efficiency in financial markets, in which money is traded for bonds, with efficiency in goods markets, in which money is traded for a real good. Financial markets can achieve a better distribution of savings when agents are constrained by their money holdings, but this is bad for efficiency in goods markets. The former effect can dominate the latter so that optimal policy deviates from the Friedman rule.

Critical Illness in India
Kumar, Sonjai
This paper gives the first cut-rate of Indian incidence of critical illness of cardiovascular,Strokes and Cancer diseases. This paper also compares the critical illness rates currentlyused in India, CIBT 93 with the rates calculated in this paper. The paper concludes that CIBT93 should be modified before using in the Indian conditions.

Do Analysts' Cash Flow Forecasts Mitigate the Accrual Anomaly? International Evidence
Gordon, Elizabeth A.,Petruska, Karin A.,Yu, Minna
We investigate the role of analysts’ cash flow forecasts in mitigating the accrual anomaly in an international setting. Based on a sample from 20 world market economies, we find less market overestimation of the accrual component of earnings for firms where analysts issue both cash flow forecasts and earnings forecasts, compared with firms where analysts only issue earnings forecasts. Further tests show that analysts’ provisions of cash flow forecasts are more likely to be a mechanism that attenuates investors’ fixations on earnings in common law countries as opposed to code law countries. This finding is consistent with cash flow predictions by analysts being useful in countries where public disclosures are the primary communication channels in the capital markets. We also find the accrual anomaly to be less severe when analysts provide more accurate cash flow forecasts in common law countries. Our results are robust to additional sensitivity tests, including controlling for potential sample selection bias and an endogeneity bias.

Do Financial Advisors Exploit Responsible Investment Preferences?
Weitzel, Utz,Laudi, Marten,Smeets, Paul
An unprecedented number of investors are giving their financial advisors a mandate for socially responsible investing (SRI). Yet, the impact of SRI mandates on consumers is unclear. In a pre-registered lab-in-the-field experiment with 345 professional advisors, we find that advisors charge a premium to SRI clients that cannot be justified by higher effort, skill, or costs. This suggests that advisors exploit the SRI preferences of their clients (who accept these higher fees). In an independent survey, financial regulators predict higher SRI fees but do not predict exploitation. Regulators confirm that our findings are externally valid and require attention from policymakers.

Earnings Forecasts of Female CEOs: Quality and Consequences
Francoeur, Claude,Li, Yuntian,Singer, Zvi,Zhang, Jing
This study examines the voluntary disclosure of earnings forecasts by female CEOs. We find that in the backdrop of increased pressure to perform from investors and other stakeholders, female CEOs tend to issue more earnings forecasts than male CEOs, and those forecasts are more accurate. We also find that while financial analysts generally prefer to follow companies headed by male CEOs, female CEOs’ efforts to issue accurate earnings forecasts pay off as these efforts help them close the analyst coverage gap. We provide complementary evidence on the disclosure efforts of female CEOs with regard to updates to the forecast and the 10-K report. Lastly, we show that financial analysts rely more on the earnings forecasts of female CEOs, possibly because they realize female CEOs’ superior forecasting quality. Our results are robust to the use of alternative research designs, including difference-in-difference, propensity score matching, and entropy balancing. Overall, our study documents gender differences in voluntary disclosure by senior management.

Everything You Always Wanted to Know About XVA Model Risk but Were Afraid to Ask
Lorenzo Silotto,Marco Scaringi,Marco Bianchetti

Valuation adjustments, collectively named XVA, play an important role in modern derivatives pricing. XVA are an exotic pricing component since they require the forward simulation of multiple risk factors in order to compute the portfolio exposure including collateral, leading to a significant model risk and computational effort, even in case of plain vanilla trades. This work analyses the most critical model risk factors, meant as those to which XVA are most sensitive, finding an acceptable compromise between accuracy and performance. This task has been conducted in a complete context including a market standard multi-curve G2++ model calibrated on real market data, both Variation Margin and ISDA-SIMM dynamic Initial Margin, different collateralization schemes, and the most common linear and non-linear interest rates derivatives. Moreover, we considered an alternative analytical approach for XVA in case of uncollateralized Swaps. We show that a crucial element is the construction of a parsimonious time grid capable of capturing all periodical spikes arising in collateralized exposure during the Margin Period of Risk. To this end, we propose a workaround to efficiently capture all spikes. Moreover, we show that there exists a parameterization which allows to obtain accurate results in a reasonable time, which is a very important feature for practical applications. In order to address the valuation uncertainty linked to the existence of a range of different parameterizations, we calculate the Model Risk AVA (Additional Valuation Adjustment) for XVA according to the provisions of the EU Prudent Valuation regulation. Finally, this work can serve as an handbook containing step-by-step instructions for the implementation of a complete, realistic and robust modelling framework of collateralized exposure and XVA.

Exploring the Critical Factors Affecting Lender Trust to Invest in Online Peer-to-Peer Lending in Indonesia
Ardelia, Amanda,Dalimunthe, Zuliani ,Triono, Rachmadi Agus
This study aims to evaluate critical factors that drive lender trust in the platform to provide lend through a Peer-to-Peer (P2P) Lending Platform. P2P lending is a financial technology that facilitates lending mechanisms between lenders and borrowers through the Internet without collateral and financial institutions' involvement. Thus, lenders should trust the platform to willingly making a transaction using the online lending systems. We hypothesize that perceived regulatory protection, service quality, and security protection build the lender's trust in the P2P lending platform and investor willingness to invest. We test the model using empirical data from 180 participants experienced in Indonesia P2P lending using the structural equation (SEM) method. The results show that Willingness to Lend is significantly affected by Trust in Platform, while Perceived Regulatory Protection, Service Quality and Security Protection are significant factors influencing lenders' Trust in the Platform.

Factors determining maximum energy consumption of Bitcoin miners
Jesus M. Gonzalez-Barahona

Background: During the last years, there has been a lot of discussion and estimations on the energy consumption of Bitcoin miners. However, most of the studies are focused on estimating energy consumption, not in exploring the factors that determine it.

Goal: To explore the factors that determine maximum energy consumption of Bitcoin miners. In particular, analyze the limits of energy consumption, and to which extent variations of the factors could produce its reduction.

Method: Estimate the overall profit of all Bitcoin miners during a certain period of time, and the costs (including energy) that they face during that time, because of the mining activity. The underlying assumptions is that miners will only consume energy to mine Bitcoin if they have the expectation of profit, and at the same time they are competitive with respect of each other. Therefore, they will operate as a group in the point where profits balance expenditures.

Results: We show a basic equation that determines energy consumption based on some specific factors: minting, transaction fees, exchange rate, energy price, and amortization cost. We also define the Amortization Factor, which can be computed for mining devices based on their cost and energy consumption, helps to understand how the cost of equipment influences total energy consumption.

Conclusions: The factors driving energy consumption are identified, and from them, some ways in which Bitcoin energy consumption could be reduced are discussed. Some of these ways do not reduce the most important properties of Bitcoin, such as the chances of control of the aggregated hashpower, or the fundamentals of the proof of work mechanism. In general, the methods presented can help to predict energy consumption in different scenarios, based on factors that can be calculated from available data, or assumed in scenarios.

Financial Network Games
Panagiotis Kanellopoulos,Maria Kyropoulou,Hao Zhou

We study financial systems from a game-theoretic standpoint. A financial system is represented by a network, where nodes correspond to firms, and directed labeled edges correspond to debt contracts between them. The existence of cycles in the network indicates that a payment of a firm to one of its lenders might result to some incoming payment. So, if a firm cannot fully repay its debt, then the exact (partial) payments it makes to each of its creditors can affect the cash inflow back to itself. We naturally assume that the firms are interested in their financial well-being (utility) which is aligned with the amount of incoming payments they receive from the network. This defines a game among the firms, that can be seen as utility-maximizing agents who can strategize over their payments.

We are the first to study financial network games that arise under a natural set of payment strategies called priority-proportional payments. We investigate the existence and (in)efficiency of equilibrium strategies, under different assumptions on how the firms' utility is defined, on the types of debt contracts allowed between the firms, and on the presence of other financial features that commonly arise in practice. Surprisingly, even if all firms' strategies are fixed, the existence of a unique payment profile is not guaranteed. So, we also investigate the existence and computation of valid payment profiles for fixed payment strategies.

Financial regulatory structure in China: Challenges and transitioning to Twin Peaks
Huang, (Robin) Hui
This chapter examines the legal and institutional regulatory framework for China’s financial markets and evaluates how China may need to restructure its regulatory regime in order to keep up with market developments. This chapter first provides a detailed discussion of the current Chinese financial regulatory framework, and then identifies its major structural problems. In search of an appropriate agenda for reform of China’s financial regulatory structure, it conducts a comparative analysis of financial regulatory structures in overseas jurisdictions, as well as a contextual consideration of China’s local conditions. Finally, it discusses the recent developments and their implications for the future prospects of China’s transition to a twin-peaks model of financial regulation.

Gender, age and nationality diversity in UK banks
Suss, Joel,Angeli, Marilena,Eckley, Peter
Using a novel regulatory dataset, we study board and senior manager diversity of gender, age and nationality in UK banks. Gender diversity increased steadily over the last two decades, albeit from a very low base and to only 20% by the end of 2020. Moreover, we find evidence of a ‘glass ceiling’, with the proportion of females increasing more slowly in the most influential roles. Age and nationality diversity changed less over time. Empirical results suggest that gender and nationality diversity are related to positive risk and performance outcomes, whereas the reverse is true for age diversity. However, these findings are derived from analysing differences between banks, which exhibit substantially more variation than changes in diversity within banks over time. When we only exploit variation in diversity within banks, we do not find any relationship between diversity and outcomes.

Hodge theoretic reward allocation for generalized cooperative games on graphs
Tongseok Lim

We define cooperative games on general graphs and generalize Lloyd S. Shapley's celebrated allocation formula for those games in terms of stochastic path integral driven by the associated Markov chain on each graph. We then show that the value allocation operator, one for each player defined by the stochastic path integral, coincides with the player's component game which is the solution to the least squares (or Poisson's) equation, in light of the combinatorial Hodge decomposition on general weighted graphs. Several motivational examples and applications are also presented.

How Digitalization Can Help Manage Risks in Insurance Sector
Kumar, Sonjai
Digitalization such as big data, artificial intelligence, the internet of things, cloud computing, and Blockchain is an enabler in the insurance sector. The key areas from where some of the risks in the insurance sector arise are customers, claims, cross-selling, churning, mis-selling, fraud, etc. Such areas often lead to the cropping up of operational risks. Digitalization helps in managing such risks.

Impact of Investors’ Sentiments on Selected Sectoral Indices Return Volatility: Special Reference to NSE
Sharma, Dr. Pooja Chaturvedi
The study attempts to assess the influence of investor sentiment onselected sectoral indices returns volatility in the Indian stock market over theperiod from 2015-2019. GARCH, EGARCH, and Bivariate VAR models wereapplied for data analysis after checking unit root issue of the data. Nine Sectoralindices namely NIFTY Oil and Gases, NIFTY Metal, NIFTY Media, NIFTYFMCG, NIFTY Financial Service, NIFTY Consumer Durables, NIFTY Auto,NIFTY Bank, and NIFTY IT are considered. A significant influence of investorsentiment on sectoral indices return volatility is traced. The stronger theinfluence of investor sentiment and higher will be the current market volatility.The results of this study may assist individuals, institutional investors, and thegovernment for an improved understanding of the Indian stock market and toupsurge their returns by integrating investor sentiment into their decision-making.

Indonesian Mutual Fund Performance
Vidal, Marta,Vidal-García, Javier
In this paper, we examine the Indonesian mutual fund industry and its performance evaluation. Indonesia is the fourth most populous country in the world with 273 million inhabitants in 2021 and it is expected that in 10 years it will enter the top 4 of the largest economies by GDP (by then it will be 300 million Indonesians). Indonesia is the largest economy in Southeast Asia (ASEAN region), a member of the G-20 and has been growing at rates of 6% since 2010. Despite the covid, the country's growth forecasts are 5.1% for the period 2023-2027. Buying stocks in Indonesia is still quite challenging, unless these stocks are listed in addition to the Asian country in Europe or the US. Considering that individual investors do not have much to choose from and that their stock market is small, perhaps the most attractive option is to do it through mutual funds. The economic growth expected for the next few years will possibly boost the country's stock market, as happened in China in the previous decade (2010-2020).

Information or Persuasion in the Mortgage Market: The Role of Brand Names
Michelangeli, Valentina,Carella, Agnese
The role, informative or persuasive, of brand names in driving purchasing decisions is very much under debate. We exploit the rebranding of a mortgage lender to analyse households’ choice behaviour in response to brand popularity. Loan-level data on new mortgages suggest that (1) brand awareness reduces the equilibrium price of residential mortgage contracts and (2) the reduction mainly reflects consumers’ selection of cheaper products due to better information. Our calibrated model implies an overall gain equal to 6 per cent of the initial loan amount and a roughly 10 percentage point increase in the share of households that shift to cheaper lenders.

Innovation, Industry Equilibrium, and Discount Rates
Bustamante, Maria Cecilia,Zucchi, Francesca
We develop a model to examine how discount rates affect the nature and composition of innovation within an industry. Challenging conventional wisdom, we show that higher discount rates do not discourage firm innovation when accounting for the industry equilibrium. Higher discount rates deter fresh entry---effectively acting as entry barriers---but encourage innovation through the intensive margin, which can lead to a higher industry innovation rate on net. Simultaneously, high discount rates foster explorative over exploitative innovation. Considering fluctuations in discount rates, the model further rationalizes observed patterns in innovation cyclicality, and shows that innovation by rivals inflates incumbents' risk premia.

Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions.
Merl, Robert,Stockl, Thomas,Palan, Stefan
Modern capital markets are subject to many interventions and regulations, some of which curtail the implementation of specific trading strategies in a market. While we understand much of these regulations’ individual effects, the picture is less clear about their joint effects. This paper considers the interaction of two regulations, namely rules limiting shorting of assets and cash, and rules limiting insider trading. For these regulations, prior research shows spikes in short-selling activity around the revelation of insider information, which different studies trace to different causes. Among other results, we find that both allowing short positions and allowing informed trading causes informed traders to increase their market activity and causes mispricing and spreads to diminish. Nevertheless, we find no evidence for significant interaction effects between the two regulations.

International High-Frequency Arbitrage for Cross-Listed Stocks
Poutré, Cédric,Dionne, Georges,Yergeau, Gabriel
We explore latency arbitrage activities with a new arbitrage strategy that we test with high-frequency data during the first six months of 2019. We study the profitability of mean-reverting arbitrage activities of 74 cross-listed stocks involving three exchanges in Canada and the United States. Our arbitrage strategy is a hybrid between triangular arbitrage and pairs trading. We synchronize the high-frequency data feeds from the three exchange venues considering explicitly the latency that comes from the transportation of information between the exchanges and its treatment time. Other trading costs and arbitrage risks are also considered. The annual net profit of an HFT firm that uses limit orders is around CAD $8 million (USD $6 million), a result that we consider reasonable when compared with the previous literature. International latency arbitrage with market orders is never profitable.

Learning from History-A Risk Management Perspective
Kumar, Sonjai
Though risk management and its application in the corporate world are new, its essence is very old. One must remember that risk is always about the future and the future is always unknown. There is no idea what will unfold in the future, however, some ideas about the future can be assessed based on past data, present information, and judicious judgment about the future. In the life insurance business which is about long-term assessment of risk is based on similar methodologies. Life insurance businesses have been successful throughout the world based on actuarial principles. To address any risk, it is very important to first identify the risk, assess its likely impact, and plan for preventative action now, to minimize the impact of the risk, should risk materializes. Risk assessment requires a mindset with a vision to anticipate the future based on certain key risk indicators of the present which always emanate important information about the expected future.In the very recent past we witnessed economic slowdown resulting from reduced economic activities, the key question that is addressed in this article is, could this have been anticipated to address the economic risks to take mitigating actions earlier than later

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,Primio, Luciano Di,Ebner, André,Hoeretzeder, Silvia,Ballesteros, Elisa Llorente,Miani, Claudia,Ricci, Giacomo,Santioni, Raffaele,Schellerer, Stefan,Westman, Hanna,Bravo Alvarez, Antonio Josè
We present an analytical framework for quantifying the potential impact on the real economy stemming from a bank’s sudden liquidation, focusing on the consequences that arise when a credit institution interrupts its lending activities. In a first step, we quantify the potential credit shortfall faced by firms and households due to the sudden liquidation of a bank. In a second step, we estimate the impact of a firm’s credit shortfall on real outcomes via both a Factor-Augmented Vector Autoregression (FAVAR) model and a micro-econometric model. Appropriate reference values (benchmarks) are provided to assess the estimated outcomes. The illustrative results show that this harmonized approach is feasible across the Banking Union and it is applicable to banks of heterogeneous size and significance. Particularly in the case of the medium-sized banks, the implementation of this common analytical framework could provide useful insights to reduce the uncertainty about whether resolution is in the public interest, i.e. to what extent the failure of an institution would endanger financial stability.

Of Access and Inclusivity Digital Divide in Online Education
Bheemeshwar Reddy A,Sunny Jose,Vaidehi R

Can online education enable all students to participate in and benefit from it equally? Massive online education without addressing the huge access gap and disparities in digital infrastructure would not only exclude a vast majority of students from learning opportunities but also exacerbate the existing socio-economic disparities in educational opportunities.

Optimal Bank Contributions and Household Risk-Aversion
Papageorgiou, Stylianos
We study bank contributions that ex ante fund government guarantees supported by a fiscal backstop in a general equilibrium setting where banks intermediate between risk-averse households and state-contingent investments. We offer an analytical characterization of optimal bank contributions as a function of household risk-aversion and guarantees. Showing that higher risk-aversion expedites the way bank contributions internalize guarantees' boost of household risk-taking, we establish a non-trivial relationship between optimal bank contributions and household risk-aversion: Higher risk-aversion optimally induces higher contributions when guarantees exceed a threshold; otherwise, higher contributions shall be observed in economies with less risk-averse households.

Optimal Portfolio with Power Utility of Absolute and Relative Wealth
Andrey Sarantsev

Portfolio managers often evaluate performance relative to benchmark, usually taken to be the Standard & Poor 500 stock index fund. This relative portfolio wealth is defined as the absolute portfolio wealth divided by wealth from investing in the benchmark (including reinvested dividends). The classic Merton problem for portfolio optimization considers absolute portfolio wealth. We combine absolute and relative wealth in our new utility function. We also consider the case of multiple benchmarks. To both absolute and relative wealth, we apply power utility functions, possibly with different exponents. We obtain an explicit solution and compare it to the classic Merton solution. We apply our results to the Capital Asset Pricing Model setting.

Organizational Capital, Corporate Tax Avoidance, and Firm Value
Hasan, Mostafa Monzur,Lobo, Gerald J.,Qiu, Buhui
We show that firms with higher levels of organizational capital (OC) exhibit higher levels of tax avoidance and that shareholders view tax avoidance of high OC firms as value-enhancing. We also show that the OC-tax avoidance relation mainly manifests in firms with good internal governance and information environment and in firms that face tight financial constraints. In addition, we document that tax avoidance by high OC firms increases future cash flow and that high OC firms are more likely to invest in tax haven subsidiaries. Overall, our evidence suggests that OC enhances firm’s tax efficiency.

Pathwise Grid Valuation of Fixed-Income Portfolios with Applications to Risk Management
zamani, shiva,Chaghazardi, Ali,Arian, Hamidreza
Numerical calculation of Value-at-Risk (VaR) for large-scale portfolios poses great challenges to financial institutions. The problem is even more daunting for large fixed-income portfolios as their underlying instruments have exposure to higher dimensions of risk factors. This article provides an efficient algorithm for calculating VaR using a historical grid-based approach with volatility updating and shows its efficiency in computational cost and accuracy. Our VaR computation algorithm is flexible and simple, while one can easily extend it to cover other nonlinear portfolios such as derivative portfolios on equities and FX securities.

Precautionary motives with multiple instruments
Heinzel, Christoph,Peter, Richard
Using a unified approach, we show how precautionary saving, self-protection and self-insurance are jointly determined by risk preferences and the preference over the timing of uncertainty resolution. We cover higher-order risk effects and examine both risk averters and risk lovers. When decision-makers use several instruments simultaneously to respond to income risk, substitutive interaction effects arise. We quantify precautionary and substitution effects numerically and discuss the role of instrument interaction for the inference of preference parameters from precautionary motives. Instruments can differ substantially in the size of the precautionary motive and in the susceptibility to substitution effects. This affects their suitability for the identification of precautionary preferences.

Pricing Art and the Art of Pricing: On Returns and Risk in Art Auction Markets
Li, Yuexin,Ma, Marshall Xiaoyin,Renneboog, Luc
We study price determinants and investment performance of art based on a vast sample of transactions around the world over the past 60 years. Art has appreciated at a real (nominal) annual return of 2.49% (6.24%). Higher art returns are reached for paintings at high-end of the price distribution, for oil paintings, for more recent art movements, for transactions by reputable auction houses. The risk-return tradeoff of paintings underperforms that of other passion investments. Paintings’ Sharpe ratios are below those of stocks, bonds, and gold but outperform commodities and real estate. Investments in paintings also enter the optimal investment portfolio.

Shareholder Litigation and Corporate Social Responsibility
Freund, Steven,Nguyen, Nam,Phan, Hieu V.
This research examines the relation between shareholder litigation and corporate social responsibility (CSR). Exploiting exogenous changes in shareholder litigation rights following the staggered adoption of universal demand laws by U.S. states and the Ninth Circuit Court of Appeals' ruling on securities class action lawsuits, we show that weaker shareholder litigation rights lead to lower CSR scores. Moreover, the relation is stronger for firms facing higher litigation risk, and a decreased CSR score enhances firm value. Our evidence suggests that firms engage in CSR activities partly to reduce shareholder litigation risk ex ante and mitigate its consequences ex post.

The Bank of Korea Watch
Kim, Hyerim,Kang, Kyu H.
Traders closely watch the Bank of Korea (BOK) base rate decisions since theshort rate is the primary factor in bond and currency valuations. The surveyof professional forecasters (SPF) has been widely used as the most reliableBOK base rate decision forecaster. In this paper, we investigate whether theSPF's prediction ability can be improved further. To this end, we use adynamic multinomial ordered probit prediction model of the BOK base ratewith a large number of predictors, and apply a Bayesian variable selectionalgorithm. Through an empirical exercise, we show that our approachsubstantially outperforms the SPF in terms of out-of-sample prediction. Thekey predictors are found to be the SPF, short-term bond yields, lagged baserate, federal funds rate, and inflation expectation survey data. Further,allowing for the prediction abilities to change over time is essential forimproving predictive accuracy.

The Determinants of ESG Ratings: Rater Ownership Matters
Tang, Dragon Yongjun,Yan, Jiali,Yao, Chelsea Yaqiong
Ratings on environmental, social, and governance (ESG) are largely obscure but have become widely used by investors. We show that firms held by the same owners as the rater (“sister firms”) receive higher ESG ratings. Exogenously created sister firms through acquisitions reveals causality for the common ownership effect. Sister firms receive higher ratings when the common owners have larger stakes in the ESG rater. Notwithstanding their higher initial ratings, sister firms have worse future ESG outcomes. These findings suggest that the quality of ESG ratings can be undermined by conflicts of interest and have important implication for practitioners and regulators.

The Economics of Non-bank Financial Intermediation: Why Do We Need to Fill the Regulation Gap?
Trapanese, Maurizio
This paper presents an overall analysis of the economics of non-bank financial intermediation, and argues that the financial stability concerns stemming from this sector support the need to fill the regulation gap that exists with respect to other segments. It examines the structure of markets, the economic incentives of the agents involved, and the institutional aspects characterizing this form of intermediation as compared with that performed by banks. The policy framework developed so far has been based mainly on micro-prudential tools, looking at individual institutions and activities. The focus of the regulatory actions should not be (or should not only be) the stability of individual entities. Financial regulators should pay more attention to the effects that the collective actions and activities of non-bank financial entities may have on the financial system as a whole and on the real economy. I find that the effectiveness of micro-prudential tools is strengthened if they are accompanied by a framework containing policy measures to address systemic risk.

The Effect of Financing Constraints on Corporate Investment: The SA-Index Approach
Anri, Novi,Dalimunthe, Zuliani ,Wasilah, Wasilah
This study aims to analyze how the company's internal funds and investment opportunities impact the company’s investment decisions conditional to the financing constraints experienced. We classify companies into three categories based on the SAIndex; low, neutral, and high-financing constraints. We analyze 278 non-financial companies listed on the Indonesia Stock. Exchange during 2014-2018 using multiple regression methods. There is an indication that companies experiencing a financing constraint have a higher investment-cash flow sensitivity than companies with a low financing constraint. We found that internal funds and investment opportunities positively and significantly affect companies' investment with high financing constraints and companies with the neutral category. Meanwhile, we did not find a statistically significant relationship between independent variables - the internal fund and investment opportunity - and investment for companies with low financing constraints.

The Evolution from Life Insurance to Financial Engineering
J. Koijen, Ralph,Yogo, Motohiro
Since the mid-1980s, the share of household net worth intermediated by US financial institutions has shifted from defined benefit plans to life insurers and defined contribution plans. Life insurers have primarily grown through variable annuities, which are mutual funds with longevity insurance, a potential tax advantage, and minimum return guarantees. The minimum return guarantees change the primary function of life insurers from traditional insurance to financial engineering. Variable annuity insurers are exposed to interest and equity risk mismatch and suffered especially low stock returns during the COVID-19 crisis. We consider regulatory changes, such as more detailed financial disclosure and standardized stress tests, to monitor potential risk mismatch and to ensure stability of the insurance sector.

The Fake News Effect: Experimentally Identifying Motivated Reasoning Using Trust in News
Michael Thaler

Motivated reasoning posits that people distort how they process new information in the direction of beliefs they find more attractive. This paper introduces a novel experimental paradigm that is able to portably identify motivated reasoning from Bayesian updating across a variety of factual questions; the paradigm analyzes how subjects assess the veracity of information sources that tell them the median of their belief distribution is too high or too low. A Bayesian would infer nothing about the source veracity from this message, but motivated reasoners would infer that the source were more truthful if it reported the direction that they find more attractive. I find novel evidence for politically-motivated reasoning about immigration, income mobility, crime, racial discrimination, gender, climate change, gun laws, and the performance of other subjects. Motivated reasoning from messages on these topics leads people's beliefs to become more polarized, even though the messages are uninformative.

The Heterogeneous Relationship of Owner-Occupied and Investment Property with Household Portfolio Choice
Felici, Marco,Fuerst, Franz
The special, dual nature of property as both a consumption and an investment goodmakes it salient for portfolio choice. In fact, the theoretical literature predicts a con-straint imposed by property on investment and the empirical literature has broughtevidence that this constraint, in some form, exists, but neglecting to investigate its het-erogeneity and to differentiate between owner-occupied and investment property. Withreference to the predictions of a stochastic control model, we turn to the Wealth and As-sets Survey panel for the UK, which allows to break down in detail households’ port-folios, to show empirically how the relationship between property and stockholdingsdepends on the value of property relative to the size of the entire portfolio. While onaverage, an increase in the share of property in the total portfolio is estimated to cor-respond to a slight decrease or to no change in the share of stocks in liquid assets, thisnexus potentially goes from positive to negative depending on the weight of propertyin the portfolio. Consistent with the prediction that only consumption-relevant prop-erty places a constraint on portfolio choice, the relationship can be identified robustlyfor owner-occupied property only.

The Real Explanation of Nominal Bond-Stock Puzzles
Chernov, Mikhail,Lochstoer, Lars A.,Song, Dongho
We present evidence that the mix of transitory and permanent shocks to consumption is changing over time. We study implications of this finding for asset prices. The uncovered dynamics of consumption implies modestly upward sloping real bond and equity curves, upward sloping nominal yield curve, and sign-switching correlation between equities and bonds consistent with the stylized facts. This is achieved without relying on the nominal channel too much. That is, as in the data, the variation of inflation in the model is under 40\% as a fraction of variation in nominal yields.

The Role of the End Time in Experimental Asset Markets
Kopányi-Peuker, Anita,Weber, Matthias
By now there are hundreds of scientific articles on experimental asset markets. Almost all of these experiments use a short and definite horizon. This may be one of the starkest differences to financial asset markets outside the laboratory, which usually have indefinite and comparatively long horizons. We analyze the role of the end time in an asset market experiment in which we vary the length of the horizon and whether the end time is definite or indefinite. We find recurring bubbles and similar price dynamics in all treatments (with moderately lower prices in the treatments with a long horizon).

The determinants and consequences of reverse factoring: Early evidence from the UK
Chuk, Elizabeth,Lourie, Ben,Yoo, Il Sun
Our study examines the determinants and consequences of reverse factoring. Despite the increasing popularity of reverse factoring, neither US GAAP nor IFRS offers any guidance for the financial reporting for the obligations owed under reverse factoring. Using a sample of UK firms in 2018 to 2020, we find that the adoption of reverse factoring is more likely for buyers that use more trade credit, are larger in size, use less leverage, pay dividends, have lower return volatility, and are more prone to financial distress. In our tests of consequences, we find that compared to firms that do not use reverse factoring, firms that have adopted reverse factoring on average take 10.6 days longer to pay their invoices, pay 12.5% fewer of their invoices within 30 days, and pay 13.8% more of their invoices later than 60 days. We also document several favorable accounting outcomes for firms that have adopted reverse factoring, such as reporting higher ROA, higher profit margins, lower ROA volatility, and lower return volatility than firms that have not adopted reverse factoring. Our study contributes to the nascent literature on reverse factoring by empirically building a profile of the type of firm that adopts reverse factoring.

The environmental and financial performance of green energy investments: European evidence
Andrade, Nuno Miguel, Cortez , Maria C.,Silva, Florinda
This paper investigates the environmental and financial performance of investments in energy firms. For this purpose, we analyze portfolios of green energy European stocks compared to their non-green counterparts from January 2008 to November 2020. Within firms with environmental ratings, those that are green perform better in environmental terms than their non-green counterparts, although the difference has narrowed in recent years. Regarding financial performance, our results show that, in general, the green energy portfolio outperforms the market but compared to the non-green portfolio, the difference is only statistically significant when an energy stock index is used as the market factor. Furthermore, we find that the outperformance of the green portfolio is mainly due to a performance improvement in most recent years. Overall, our results show that over this period investments in green energy firms perform at least as well as their non-green energy counterparts.

The impact of machine learning and big data on credit markets
Eccles, Peter,Grout, Paul,Siciliani, Paolo,Zalewska, Anna (Ania)
There is evidence that machine learning (ML) can improve the screening of risky borrowers, but the empirical literature gives diverse answers as to the impact of ML on credit markets. We provide a model in which traditional banks compete with fintech (innovative) banks that screen borrowers using ML technology and show that the impact of the adoption of the ML technology on credit markets depends on the characteristics of the market (eg borrower mix, cost of innovation, the intensity of competition, precision of the innovative technology, etc.). We provide a series of scenarios. For example, we show that if implementing ML technology is relatively expensive and lower-risk borrowers are a significant proportion of all risky borrowers, then all risky borrowers will be worse off following the introduction of ML, even when the lower-risk borrowers can be separated perfectly from others. At the other extreme, we show that if costs of implementing ML are low and there are few lower-risk borrowers, then lower-risk borrowers gain from the introduction of ML, at the expense of higher-risk and safe borrowers. Implications for policy, including the potential for tension between micro and macroprudential policies, are explored.

The role of Environmental, Social, and Governance rating on corporate debt structure
Asimakopoulos, Stylianos,Asimakopoulos, Panagiotis,Li, Xinyu
This paper examines the impact of having an Environmental, Social, and Governance (ESG) rating on a firm’s debt structure, i.e. how firms change their leverage ratios and debt components when becoming ESG rated. Targeted market and book leverage ratios are reduced when firms become ESG rated. We show that the provision of ESG rating mitigates information asymmetry. Current leverage ratios are not altered significantly for ESG rated firms but these firms redistribute their financing sources from public debt (bonds debt) to private debt (bank loans). This substitution effect is mainly driven by environmental and social factors and is more pronounced for firms with high financial pressure, low growth opportunities and specialized assets. Debt restructuring remains valid under various robustness and endogeneity tests. These results are consistent with the trade-off and pecking order theories of capital structure.

Time Varying Risk in U.S. Housing Sector and Real Estate Investment Trusts Equity Return
Masud Alam

This study examines how housing sector volatilities affect real estate investment trust (REIT) equity return in the United States. I argue that unexpected changes in housing variables can be a source of aggregate housing risk, and the first principal component extracted from the volatilities of U.S. housing variables can predict the expected REIT equity returns. I propose and construct a factor-based housing risk index as an additional factor in asset price models that uses the time-varying conditional volatility of housing variables within the U.S. housing sector. The findings show that the proposed housing risk index is economically and theoretically consistent with the risk-return relationship of the conditional Intertemporal Capital Asset Pricing Model (ICAPM) of Merton (1973), which predicts an average maximum of 5.6 percent of risk premium in REIT equity return. In subsample analyses, the positive relationship is not affected by sample periods' choice but shows higher housing risk beta values for the 2009-18 sample period. The relationship remains significant after controlling for VIX, Fama-French three factors, and a broad set of macroeconomic and financial variables. Moreover, the proposed housing beta also accurately forecasts U.S. macroeconomic and financial conditions.

Time-adaptive high-order compact finite difference schemes for option pricing in a family of stochastic volatility models
Düring, Bertram,Heuer, Christof
We propose a time-adaptive high-order compact finite difference scheme for option pricing in a family of stochastic volatility models. We employ a semi-discrete high-order compact finite difference method for the spatial discretisation, and combine this with an adaptive time discretisation, extending ideas from [LSRHF02] to fourth-order multistep methods in time.

Trading Behavior of Retail Investors in Derivatives Markets: Evidence from Mini Options
Li, Yubin,Zhao, Chen,Zhong, Zhaodong
Mini options are specially catered to retail investors with limited capital for trading options on extremely high-priced securities. The coexistence of both Mini and standard options for the same underlying security provides us a novel setting to investigate whether and how small retail investors use derivatives contracts differently compared to their counterparts. First, we find that the Mini option investors are more subject to constraints of limited attention. Specifically, Mini option investors trade more intensively near market opens, and their trading activities are more heavily influenced by attention-grabbing events and attention-distracting events. Second, we document that Mini option investors’ trading is more likely to be driven by market sentiment than standard option investors. Third, the trading performance of Mini option investors is also worse than that of standard option investors, with less positive intraday returns and more negative overnight returns.

Volatility Bursts: A Discrete-time Option Model with Multiple Volatility Components
Lilla, Francesca
I propose an affine discrete-time model, called Vector Autoregressive Gamma with volatility Bursts (VARG-B) in which volatility experiences, in addition to frequent and small changes, periods of sudden and extreme movements generated by a latent factor which evolves according to the Autoregressive Gamma Zero process. A key advantage of the discrete-time specification is that it makes it possible to estimate the model via the Extended Kalman Filter. Moreover, the VARG-B model leads to a fully analytic conditional Laplace transform, resulting in a closed-form option pricing formula. When estimated on S&P500 index options and returns the new model provides more accurate option pricing and modelling of the IV surface compared with some alternative models.

Voluntary Disclosure and Adverse Selection: Bayesian Game-Theoretical Inferences for Green Bond Labelling Regimes
Henide, Karim
The impact finance market has sought to 'internalise externalities and adjust risk perceptions' (G20 Green Finance Study Group, 2016), demonstrating the private sector's capability in resolving the climate free-rider problem through the 'greening' of economic activities, partially bypassing corrective government intervention. As the market continues to develop, however, the voluntary disclosure regime that the market operates under threatens to enforce an adverse selection problem and contribute to a fundamental erosion of confidence in the market segment, constraining the potential of impact finance instruments to affect positive social and environmental change. I relate the work of Crawford and Sobel (1982); Milgrom (1981); Verrecchia (1983); Jung and Kwon (1988); Myers and Majluf (1984); Frantz (1997); Frantz and Instefjord (2006) to the green bond market and draw inferences to inform recommendations for policy-led solutions.

Why are there Racial Disparities in the Small Business Loan Market
Rakshit, Atanu,Peterson, Jonathan
We investigate patterns of racial bias in small business loans denial rates in the U.S. across different credit risk scores. We motivate this inquiry with a simple and generalizable statistical discrimination model where banks observe noisy signals of creditworthiness and hold prior beliefs of repayment probability based on the applicant’s group. Our model predicts that differences in approval rating across groups are more pronounced at middle range values and disappear at very high and very low credit scores. Using data constructed from the 1998 Survey of Small Business Finances and the restricted access Kauffman Firm Survey we find disparities in loan approval ratings between Black and White entrepreneurs in intermediate risk categories but not for the best and worst categories.

Why is there so much side-by-side management in the ETF industry?
Luo, Mancy,Schumacher, David
We document the dramatic rise of side-by-side management (“SbS”) in the global ETF industry. As of 2018, around 60% of individual ETF fund managers manage mutual funds in a SbS arrangement, most of which are “active” mutual funds. We argue that mutual fund firms employ SbS arrangements to exploit institutional client relationships of their mutual fund managers to help channel mutual fund TNA at risk of withdrawal to the firms’ new ETF business. Mutual fund managers are most likely to become SbS ETF managers if they generate revenue from institutional TNA and face strong ETF competition. SbS initiations lead to discretionary institutional (but not retail) outflows from mutual funds and contemporaneous inflows in the ETFs overseen by thosesame SbS managers. Client level holdings tests link these flows to those institutional clients with likely stronger relationship to the SbS managers, suggesting that SbS arrangements are an important tool for traditional mutual fund firms to meet and manage the rise of ETFs.

cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptope
Gautier Marti,Victor Goubet,Frank Nielsen

We propose a methodology to approximate conditional distributions in the elliptope of correlation matrices based on conditional generative adversarial networks. We illustrate the methodology with an application from quantitative finance: Monte Carlo simulations of correlated returns to compare risk-based portfolio construction methods. Finally, we discuss about current limitations and advocate for further exploration of the elliptope geometry to improve results.