# Research articles for the 2021-06-02

Amnesty Policy and Elite Persistence in the Postbellum South: Evidence from a Regression Discontinuity Design
Jason Poulos
arXiv

This paper investigates the impact of Reconstruction-era amnesty policy on the officeholding and wealth of elites in the postbellum South. Amnesty policy restricted the political and economic rights of Southern elites for nearly three years during Reconstruction. I estimate the effect of being excluded from amnesty on elites' future wealth and political power using a regression discontinuity design that compares individuals just above and below a wealth threshold that determined exclusion from amnesty. Results on a sample of Reconstruction convention delegates show that exclusion from amnesty significantly decreased the likelihood of ex-post officeholding. I find no evidence that exclusion impacted later census wealth for Reconstruction delegates or for a larger sample of known slaveholders who lived in the South in 1860. These findings are in line with previous studies evidencing both changes to the identity of the political elite, and the continuity of economic mobility among the planter elite across the Civil War and Reconstruction.

Balancing the Protection of Business and Employment in Insolvency: An Anglo French Perspective
Gant, Jennifer L. L.
SSRN

Black Holes Are the Connectors of the Creation Theory
Challoumis ÎšÏ‰Î½ÏƒÏ„Î±Î½Ï„Î¯Î½Î¿Ï‚ Î§Î±Î»Î»Î¿Ï…Î¼Î®Ï‚, Constantinos
SSRN
This paper shows that black holes are inside them light holes and not black holes. Moreover, are the connectors between universes. This is a result of the paper of the â€œCreation Theoryâ€ and of â€œConnection Theory- Gravity and Light have the same dimensional patternâ€. Moreover, should be considered the work â€œTwo Spaces, the Light Space and the Dimensional Spaceâ€. Therefore, the black holes are the transmitters or the tube connectors of the multiple big bangs, based on the concept of the creation theory. Black holes at their starting point are mass tubes and their ending point tends to be light. Then, black holes are space-time connectors with a mixed form of light and gravitonian effect. Inside to a black hole is light and outside is space-time effect by the mass.

Corporate Debt Maturity and Monetary Policy
BrÃ¤uning, Falk,Fillat, Jose L.,Wang, J. Christina
SSRN
Do firms lengthen the maturity of their borrowing following a flattening of the Treasury yield curve that results from monetary policy operations? We explore this question separately for the years before and during the zero lower bound (ZLB) period, recognizing that the same change in the yield curve slope signifies different states of the economy and monetary policy over the two regimes. We find that the answer is robustly yes for the pre-ZLB period: Firms extended the maturity of their bond issuance by nearly three years in response to a policy-induced reduction of 1 percentage point in the maturity-matched Treasury term spread between the current and previous bond issuance. By comparison, the answer is more nuanced for the ZLB period: The magnitude and significance of the maturity response were even more pronounced during the peak quarter of the financial crisis (the fourth quarter of 2008), but they were much more muted afterward. In addition, we find that the corporate bond credit spread declined consistently following a policy-induced flattening of the yield curve, albeit not significantly after 2008:Q4. Most of these effects are due to the lower term premium, not due to the expected short-term rate. Taken together, these findings indicate that firms tend to adjust the maturity and composition of their debt issuance in order to benefit from changes in the term spread induced by monetary policy. Our analysis illustrates one channel through which unconventional policy operations can affect economic activity, especially when markets are under distress. This can help us understand the transmission of unconventional monetary policy, which has become a vital issue in the low-interest, low-inflation environment that has prevailed since the financial crisis.

Does a Financial Crisis Change a Bank's Exposure to Risk? A Difference-in-Differences Approach
MÃ¤kinen, Mikko
SSRN
Can a major financial crisis trigger changes in a bankâ€™s risk-taking behavior? Using the 2008 Global Financial Crisis as a quasi-natural experiment and a difference-in-differences approach, I examine whether the worst crisis-hit Russian banks â€" the banks that have strong incentives to behavior-altering changes â€" can decrease their post-crisis exposure to risk. A shift in risk-taking behavior by these banks indicates the learning hypothesis. The findings are mixed. The evidence concerning credit risk is inconsistent with the learning hypothesis. On the other hand, the evidence concerning solvency risk is consistent with the learning hypothesis and corroborates evidence from the Nordic countries (Berglund and MÃ¤kinen, 2019). As such, bank learning from a financial crisis may not depend on the institutional context and the level of development of national financial market. Several robustness checks with alternative regression specifications are provided.

Early COVID-19 Policy Response on Healthcare Equity Prices
Gurrib, Ikhlaas
SSRN
Purpose: This study investigates the implementation of the short selling ban policy imposed by the Italian stock exchange on healthcare stock prices, as a tool to mitigate COVID-19 price effects. Important contributions are in terms of assessing (i) the effect of the temporary short selling ban on restricted healthcare stocks, (ii) the effect of COVID-19 cases and crude oil price volatility onto healthcare stocks, and (iii) whether COVID-19 resulted in a change in the risk and average stock price of healthcare stocks.Design/methodology/approach: The methodology involves impulse responses to capture the shock of the short selling ban onto healthcare stocks, and Markov switching regimes to capture the effect of COVID-19 onto the risk and prices in the healthcare industry. Daily data from 9th November 2018 till 23rd December 2020 is used.Findings: Findings suggest there were significant changes in average prices in healthcare technology and healthcare services stocks before, during and after the short selling ban. Shocks to the number of COVID-19 cases and crude oil price volatility impacted healthcare stocks but lasted only for a few days. While daily changes in the number of COVID-19 cases impacted some healthcare stocks in the presence of a two-state Markov regime, insignificant coefficients and relatively low duration suggest that the short selling policy did not significantly change the average price and risk in healthcare stocks to explain a two-state regime in the healthcare industry.Research limitations/implications: Insignificant coefficients in a two-state Markov regime reinforce that short-selling policies have a short-lasting effect onto healthcare equity prices. The findings are limited by the duration of the short selling policy, the pandemic event and the healthcare industry. Originality/value (mandatory): This is the first study to look at the impact of early COVID-19 and short selling ban policy on healthcare stocks.

How Magic a Bullet is Machine Learning for Credit Analysis? An Exploration with Fintech Lending Data
Wang, J. Christina
SSRN
FinTech online lending to consumers has grown rapidly in the post-crisis era. As argued by its advocates, one key advantage of FinTech lending is that lenders can predict loan outcomes more accurately by employing complex analytical tools, such as machine learning (ML) methods. This study applies ML methods, in particular random forests and stochastic gradient boosting, to loan-level data from the largest FinTech lender of personal loans to assess the extent to which those methods can produce more accurate out-of-sample predictions of default on future loans relative to standard regression models. To explain loan outcomes, this analysis accounts for the economic conditions faced by a borrower after origination, which are typically absent from other ML studies of default. For the given data, the ML methods indeed improve prediction accuracy, but more so over the near horizon than beyond a year. This study then shows that having more data up to, but not beyond, a certain quantity enhances the predictive accuracy of the ML methods relative to that of parametric models. The likely explanation is that there has been data or model drift over time, so that methods that fit more complex models with more data can in fact suffer greater out-of-sample misses. Prediction accuracy rises, but only marginally, with additional standard credit variables beyond the core set, suggesting that unconventional data need to be sufficiently informative as a whole to help consumers with little or no credit history. This study further explores whether the greater functional flexibility of ML methods yields unequal benefit to consumers with different attributes or who reside in locales with varying economic conditions. It finds that the ML methods produce more favorable ratings for different groups of consumers, although those already deemed less risky seem to benefit more on balance.

How Resilient is Mortgage Credit Supply? Evidence from the Covid-19 Pandemic
Fuster, Andreas,Hizmo, Aurel,Lambie-Hanson, Lauren,Vickery, James I.,Willen, Paul
SSRN
We study the evolution of US mortgage credit supply during the COVID-19 pandemic. Although the mortgage market experienced a historic boom in 2020, we show there was also a large and sustained increase in intermediation markups that limited the pass-through of low rates to borrowers. Markups typically rise during periods of peak demand, but this historical relationship explains only part of the large increase during the pandemic. We present evidence that pandemic-related labor market frictions and operational bottlenecks contributed to unusually inelastic credit supply, and that technology-based lenders, likely less constrained by these frictions, gained market share. Rising forbearance and default risk did not significantly affect rates on â€œplainvanillaâ€ conforming mortgages, but it did lead to higher spreads on mortgages without government guarantees and loans to the riskiest borrowers. Mortgage backed securities purchases by the Federal Reserve also supported the flow of credit in the conforming segment.

Identifying Taste-Based Discrimination: Effect of Black Electoral Victories on Racial Prejudice and Economic Gaps
-,
SSRN
I test for the causal impact of Black electoral victories in local elections on White Americansâ€™ attitude toward Black Americans. Using Race Implicit Attitude Test scores as a measure of racial prejudice and close-election regression-discontinuity design for causal inference, I find Black electoral victories cause measures of racial bias to rise, by 4% of the average Black-White difference in IAT scores. Simultaneously, they widen racial gaps in unemployment and mortgage denials. Interpreting these close electoral victories as instrumental variables, I find a large causal effect of prejudice-based racial discrimination on Black-White economic gaps.

Law-invariant functionals that collapse to the mean: Beyond convexity
Felix-Benedikt Liebrich,Cosimo Munari
arXiv

We establish general "collapse to the mean" principles that provide conditions under which a law-invariant functional reduces to an expectation. In the convex setting, we retrieve and sharpen known results from the literature. However, our results also apply beyond the convex setting. We illustrate this by providing a complete account of the "collapse to the mean" for quasiconvex functionals. In the special cases of consistent risk measures and Choquet integrals, we can even dispense with quasiconvexity. In addition, we relate the "collapse to the mean" to the study of solutions of a broad class of optimisation problems with law-invariant objectives that appear in mathematical finance, insurance, and economics. We show that the corresponding quantile formulations studied in the literature are sometimes illegitimate and require further analysis.

McKean-Vlasov equations involving hitting times: blow-ups and global solvability
Erhan Bayraktar,Gaoyue Guo,Wenpin Tang,Yuming Zhang
arXiv

This paper is concerned with the analysis of blow-ups for two McKean-Vlasov equations involving hitting times. Let $(B(t); \, t \ge 0)$ be standard Brownian motion, and $\tau:= \inf\{t \ge 0: X(t) \le 0\}$ be the hitting time to zero of a given process $X$. The first equation is $X(t) = X(0) + B(t) - \alpha \mathbb{P}(\tau \le t)$. We provide a simple condition on $\alpha$ and the distribution of $X(0)$ such that the corresponding Fokker-Planck equation has no blow-up, and thus the McKean-Vlasov dynamics is well-defined for all time $t \ge 0$. Our approach relies on a connection between the McKean-Vlasov equation and the supercooled Stefan problem, as well as several comparison principles. The second equation is $X(t) = X(0) + \beta t + B(t) + \alpha \log \mathbb{P}(\tau > t)$, whose Fokker-Planck equation is non-local. We prove that for $\beta > 0$ sufficiently large and $\alpha$ no greater than a sufficiently small positive constant, there is no blow-up and the McKean-Vlasov dynamics is well-defined for all time $t \ge 0$. The argument is based on a new transform, which removes the non-local term, followed by a relative entropy analysis.

Noncompliance in randomized control trials without exclusion restrictions
arXiv

This study proposes a method to identify treatment effects without exclusion restrictions in randomized experiments with noncompliance. Exploiting a baseline survey commonly available in randomized experiments, I decompose the intention-to-treat effects conditional on the endogenous treatment status. I then identify these parameters to understand the effects of the assignment and treatment. The key assumption is that a baseline variable maintains rank orders similar to the control outcome. I also reveal that the change-in-changes strategy may work without repeated outcomes. Finally, I propose a new estimator that flexibly incorporates covariates and demonstrate its properties using two experimental studies.

Numerical valuation of American basket options via partial differential complementarity problems
Karel in 't Hout,Jacob Snoeijer
arXiv

We study the principal component analysis based approach introduced by Reisinger & Wittum (2007) and the comonotonic approach considered by Hanbali & Linders (2019) for the approximation of American basket option values via multidimensional partial differential complementarity problems (PDCPs). Both approximation approaches require the solution of just a limited number of low-dimensional PDCPs. It is demonstrated by ample numerical experiments that they define approximations that lie close to each other. Next, an efficient discretisation of the pertinent PDCPs is presented that leads to a favourable convergence behaviour.

Optimization of the Double Crossover Strategy for the S&P500 Market Index
Gurrib, Ikhlaas
SSRN
The contribution of behavioral finance in the investorâ€™s world cannot be fully justified without the existence and use of technical indicators which are all clear indications of investorsâ€™ representativeness and availability bias. Existing literature on technical analysis based trading systems are abundant where some studies have concluded the use of technical analysis and fundamental analysis techniques in stock trading strategies, with a preference over the former in predicting turning points. This study aims to answer whether an optimized moving average crossover strategy based on daily data outperforms a buy and hold strategy. The paper investigates the use of an optimized moving average crossover strategy for the S&P500, by using the SPDR S&P500 Exchange Traded Fund as a proxy for the US market index. The optimized strategy is evaluated against a buy and hold strategy over the five distinct waves which were witnessed during the 1993-2014 period. The annualized returns, annualized risk and the Sharpe performance measure are used as indicators to compare between the two strategies. Findings tend to support higher absolute returns and risk for the buy-and-hold strategy, particularly during correction waves. When compared to the buy-and-hold strategy over the post financial crisis period, the optimized double cross over strategy resulted in a relatively lower risk and returns. The market timing strategy still outperformed the naÃ¯ve buy-and-hold strategy, with a relatively higher Sharpe performance measure. Alternatively stated, the rather simple moving average strategy which fits easily in the investorâ€™s world due to his or her availability and representativeness bias, is preferred over the buy and hold strategy where the latter requires more effort when bearing the two correction waves witnessed during the 1993-2014 period.

Piercing Through Opacity: Relationships and Credit Card Lending to Consumers and Small Businesses During Normal Times and the Covid-19 Crisis
Berger, Allen N.,Bouwman, Christa,Norden, Lars,Roman, Raluca A.,Udell, Gregory F.,Wang, Teng
SSRN
We investigate bank relationships in a rarely considered context â€" consumer and small business credit cards. Using over one million accounts, we find during normal times, consumer relationship customers enjoy relatively favorable credit terms, consistent with the bright side of relationships, while the dark side dominates for small businesses. During the COVID-19 crisis, both groups benefit, reflecting intertemporal smoothing, with more benefits flowing to safer relationship customers. Conventional banking relationships benefit consumers more than credit card relationships, with mixed findings for small businesses. Important identification issues are addressed. The Coronavirus Aid, Relief, and Economic Security (CARES) Act consumer-delinquency reporting impediments reduce the informational value of consumer credit scores, penalizing safer borrowers.

Pricing Algorithmic Insurance
Dimitris Bertsimas,Agni Orfanoudaki
arXiv

As machine learning algorithms start to get integrated into the decision-making process of companies and organizations, insurance products will be developed to protect their owners from risk. We introduce the concept of algorithmic insurance and present a quantitative framework to enable the pricing of the derived insurance contracts. We propose an optimization formulation to estimate the risk exposure and price for a binary classification model. Our approach outlines how properties of the model, such as accuracy, interpretability and generalizability, can influence the insurance contract evaluation. To showcase a practical implementation of the proposed framework, we present a case study of medical malpractice in the context of breast cancer detection. Our analysis focuses on measuring the effect of the model parameters on the expected financial loss and identifying the aspects of algorithmic performance that predominantly affect the price of the contract.

Retrospective causal inference via matrix completion, with an evaluation of the effect of European integration on cross-border employment
Jason Poulos,Andrea Albanese,Andrea Mercatanti,Fan Li
arXiv

We propose a method of retrospective counterfactual imputation in panel data settings with later-treated and always-treated units, but no never-treated units. We use the observed outcomes to impute the counterfactual outcomes of the later-treated using a matrix completion estimator. We propose a novel propensity-score and elapsed-time weighting of the estimator's objective function to correct for differences in the observed covariate and unobserved fixed effects distributions, and elapsed time since treatment between groups. Our methodology is motivated by studying the effect of two milestones of European integration -- the Free Movement of persons and the Schengen Agreement -- on the share of cross-border workers in sending border regions. We apply the proposed method to the European Labour Force Survey (ELFS) data and provide evidence that opening the border almost doubled the probability of working beyond the border in Eastern European regions.

SoK: Yield Aggregators in DeFi
Simon Cousaert,Jiahua Xu,Toshiko Matsui
arXiv

Yield farming has been an immensely popular activity for cryptocurrency holders since the explosion of Decentralized Finance (DeFi) in the summer of 2020. In this Systematization of Knowledge (SoK), we study a general framework for yield farming strategies with empirical analysis. First, we summarize the fundamentals of yield farming by focusing on the protocols and tokens used by aggregators. We then examine the sources of yield and translate those into three example yield farming strategies, followed by the simulations of yield farming performance, based on these strategies. We further compare four major yield aggregrators -- Idle, Pickle, Harvest and Yearn -- in the ecosystem, along with brief introductions of others. We systematize their strategies and revenue models, and conduct an empirical analysis with on-chain data from example vaults, to find a plausible connection between data anomalies and historical events. Finally, we discuss the benefits and risks of yield aggregators.

The Black Medea: An Introduction
Klose-Ullmann, Barbara
SSRN
Abstract: The paper discusses three adaptations of Euripides` Medea which stipulate the heroine as Black woman: a novella by Paul Heyse and theater plays by Hans Henny Jahnn and Guy Butler. Various dimensions of cultural, racist, and aesthetic discrimination are distilled from this material and analyzed with respect to the impact they have on the course of the action which, in all three adaptations, implies Medea killing her children â€" but for different reasons.

The Global Financial Crisis and Employee Mental Health
Karpati, Daniel,Renneboog, Luc
SSRN
We find that the Global Financial Crisis (2007-2009) had an adverse effect on employee mental health. To identify the causal effects of the credit shock, we exploit the plausibly exogenous variation in firmsâ€™ need to refinance their long-term debt in 2008, a period when refinancing became more difficult due to tightening bank lending standards. Using administrative data from the Netherlands, we demonstrate that antidepressant use grew significantly more in the 2008-2012 period among employees of firms in need of high debt refinancing. A higher probability of job separation in these firms suggests lower job security as a transmission channel.

The Relationship between the Nasdaq Composite Index and Energy Futures Markets
Gurrib, Ikhlaas
SSRN
This paper sheds light on the relationship between the Nasdaq Composite Index and a newly proposed Energy Futures Conditions Index (EFCI). While various financial conditions indices provide information about the financial stability of a country, the existence of an energy condition index, using futures markets, is scarce. Using weekly data over the period 1992â€"2017, this paper introduces an energy futures index using principal component analysis and test its predictability over the Nasdaq Composite Index. The EFCI captures 95% of the variability inherent in crude oil, heating oil and natural gas futuresâ€™ total reportable positions. Stability in forecast errors over different lags suggests a one week lag is sufficient to forecast weekly Nasdaq Composite Index. 95% prediction levels support that the estimated model captures actual equity market index values, except for the 2000 technology bubble. Distributions of level data were non-normal, not serially correlated and homoscedastic under the whole sample period, with diagnostics on pre and post technology bubble crisis showing mixed results. While differencing ensured homoscedastic errors in the forecasting model, Granger causality supported non-causality from both energy futures and equity markets, suggesting no evidence of cross market information flows.