# Research articles for the 2020-10-13

A Cost-Benefit Analysis of Capital Requirements Adjusted for Model Risk
SSRN
Capital adequacy is the key microprudential and macroprudential tool of banking regulation. Financial models of capital adequacy are subject to errors, which may prevent from estimating a sufficient capital base to absorb bank losses during economic downturns. In this paper, we propose a general method to account for model risk in capital requirements calculus related to market risk. We then evaluate and compare our capital requirements values with those obtained under Basel 2.5 and the new Basel 4 regulation. Capital requirements adjusted for model risk perform well in containing losses generates in normal and stressed times. In addition, they are as conservative as Basel 4 capital requirements, but they exhibit less fluctuations over time.

A Market-Based Analysis of Italyâ€™s Redenomination Risk: between EMU Limits and Eurosceptic Sentiments
Minenna, Marcello
SSRN
Since the global financial crisis Eurozoneâ€™s architectural flaws and risk segregating policies have raised an issue of euro sustainability for several member countries. This has often resulted in anti-Europeanist sentiments and rising consensus to populist parties. Italy, in particular, in recent years has experienced periodic upsurges in redenomination risk associated with a possible withdrawal from the monetary union. The present work analyzes this risk and its contribution to the countryâ€™s sovereign risk since September 2014. The main findings are that redenomination risk can become an important component of government yields and is sensitive to the internal political climate and to the European solidarity towards distressed countries. In particular, in front of the 2020â€™s pandemic shock, the orderly and decisive conduct of the Italian political leadership and the support interventions by the European institutions have kept redenomination risk under control, indicating the way forward to successfully pursue the European project.

A bi-directional approach to comparing the modular structure of networks
Daniel Straulino,Mattie Landman,Neave O'Clery
arXiv

Here we propose a new method to compare the modular structure of a pair of node-aligned networks. The majority of current methods, such as normalized mutual information, compare two node partitions derived from a community detection algorithm yet ignore the respective underlying network topologies. Addressing this gap, our method deploys a community detection quality function to assess the fit of each node partition with respect to the other network's connectivity structure. Specifically, for two networks A and B, we project the node partition of B onto the connectivity structure of A. By evaluating the fit of B's partition relative to A's own partition on network A (using a standard quality function), we quantify how well network A describes the modular structure of B. Repeating this in the other direction, we obtain a two-dimensional distance measure, the bi-directional (BiDir) distance. The advantages of our methodology are three-fold. First, it is adaptable to a wide class of community detection algorithms that seek to optimize an objective function. Second, it takes into account the network structure, specifically the strength of the connections within and between communities, and can thus capture differences between networks with similar partitions but where one of them might have a more defined or robust community structure. Third, it can also identify cases in which dissimilar optimal partitions hide the fact that the underlying community structure of both networks is relatively similar. We illustrate our method for a variety of community detection algorithms, including multi-resolution approaches, and a range of both simulated and real world networks.

Alpha Discovery Neural Network based on Prior Knowledge
Jie Fang,Shutao Xia,Jianwu Lin,Zhikang Xia,Xiang Liu,Yong Jiang
arXiv

Genetic programming (GP) is the state-of-the-art in financial automated feature construction task. It employs reverse polish expression to represent features and then conducts the evolution process. However, with the development of deep learning, more powerful feature extraction tools are available. This paper proposes Alpha Discovery Neural Network (ADNN), a tailored neural network structure which can automatically construct diversified financial technical indicators based on prior knowledge. We mainly made three contributions. First, we use domain knowledge in quantitative trading to design the sampling rules and object function. Second, pre-training and model pruning has been used to replace genetic programming, because it can conduct more efficient evolution process. Third, the feature extractors in ADNN can be replaced by different feature extractors and produce different functions. The experiment results show that ADNN can construct more informative and diversified features than GP, which can effectively enriches the current factor pool. The fully-connected network and recurrent network are better at extracting information from the financial time series than the convolution neural network. In real practice, features constructed by ADNN can always improve multi-factor strategies' revenue, sharpe ratio, and max draw-down, compared with the investment strategies without these factors.

Asset Price Forecasting using Recurrent Neural Networks
Hamed Vaheb
arXiv

This thesis serves three primary purposes, first of which is to forecast two stocks, i.e. Goldman Sachs (GS) and General Electric (GE). In order to forecast stock prices, we used a long short-term memory (LSTM) model in which we inputted the prices of two other stocks that lie in rather close correlation with GS. Other models such as ARIMA were used as benchmark. Empirical results manifest the practical challenges when using LSTM for forecasting stocks. One of the main upheavals was a recurring lag which we called "forecasting lag".

The second purpose is to develop a more general and objective perspective on the task of time series forecasting so that it could be applied to assist in an arbitrary that of forecasting by ANNs. Thus, attempts are made for distinguishing previous works by certain criteria so as to summarise those including effective information. The summarised information is then unified and expressed through a common terminology that can be applied to different steps of a time series forecasting task.

The last but not least purpose of this thesis is to elaborate on a mathematical framework on which ANNs are based. We are going to use the framework introduced in the book "Neural Networks in Mathematical Framework" by Anthony L. Caterini in which the structure of a generic neural network is introduced and the gradient descent algorithm (which incorporates backpropagation) is introduced in terms of their described framework. In the end, we use this framework for a specific architecture, which is recurrent neural networks on which we concentrated and our implementations are based. The book proves its theorems mostly for classification case. Instead, we proved theorems for regression case, which is the case of our problem.

Asymmetric Information in Corporate Lending: Evidence from SME Bond Markets
Nobili, Stefano,Scalia, Antonio,Zaccaria, Luana,Iannamorelli, Alessandra
SSRN
Using a comprehensive dataset of Italian SMEs, we find that differences between private and public information on creditworthiness affect firmsâ€™ decisions to issue debt securities. Surprisingly, our evidence supports positive (rather than adverse) selection. Holding public information constant, firms with better private fundamentals are more likely to access bond markets. Additionally, credit conditions improve for issuers following the bond placement, compared with a matched sample of non-issuers. These results are consistent with a model where banks offer more flexibility than markets during financial distress and firms may use market lending to signal credit quality to outside stakeholders.

Banks, Non Banks, and Lending Standards
Darst, Matthew,Refayet, Ehraz,Vardoulakis, Alexandros
SSRN
We study how competition between banks and non-banks affects lending standards. Banks have private information about some borrowers and are subject to capital requirements to mitigate risk-taking incentives from deposit insurance. Non-banks are uninformed and market forces determine their capital structure. We show that lending standards monotonically increase in bank capital requirements. Intuitively, higher capital requirements raise banksâ€™ skin in the game and screening out bad projects assures positive expected lending returns. Non-banks enter the market when capital requirements are sufficiently high, but do not cause a deterioration in lending standards. Optimal capital requirements trade-off inefficient lending to bad projects under loose standards with inefficient collateral liquidation under tight standards.

Book-to-Market, Mispricing, and the Cross-Section of Corporate Bond Returns
Bartram, SÃ¶hnke M.,Grinblatt, Mark,Nozawa, Yoshio
SSRN
We study the role played by â€œbond book-to-marketâ€ ratios in U.S. corporate bond pricing. Controlling for numerous risk factors tied to default and priced asset risk, including yield-to-maturity, we find that the ratio of a corporate bondâ€™s book value to its market price strongly predicts the bondâ€™s future return. The quintile of bonds with the highest book-to-market ratios outperforms the quintile with the lowest ratios by more than 3% per year, other things equal. Additional evidence on signal delay, scope of signal efficacy, and factor risk rejects the thesis that the corporate bond market is perfectly informationally efficient, although significant positive alpha spreads are erased by transaction costs.

Break-even Inflation Rates: The Italian Case
Fanari, Marco,Di Iorio, Alberto
SSRN
This paper focuses on break-even inflation rates (BEIRs), a widely used market-based measure of expected inflation, computed from government bonds. In the first part of the paper, we regress the Italian BEIR on several financial variables to assess the contribution of inflation, credit and liquidity components. In the second, in order to disentangle market participantsâ€™ inflation expectations from risk premia, we estimate a term structure model for the joint pricing of the Italian nominal and real yield curves, considering also credit and liquidity factors. The results show that BEIRs could be a misleading measure of the expected inflation due to the importance of inflation risk premium and credit risk effect. According to our estimates, the decrease in market-based measures of inflation observed in the last part of the sample period seems to reflect a lowering of both inflation expectations and risk premia. Inflation premia co-move with a measure of tail risk of the long-term inflation distribution signalling that investors become more concerned with downside risks.

Company Headquarters, Local Conservatism and Earnings Management
Arioglu, Emrah
SSRN
Previous literature demonstrates that local cultural values influence the corporate decisions and outcomes of companies headquartered in those locations. Based on this premise, the findings of the current study demonstrate that the conservatism levels of provinces of company headquarters of non-financial public companies quoted at the Borsa Istanbul between the years 2009 and 2017 are positively related to companiesâ€™ discretionary current accruals levels. Similar findings are derived for the provinces of corporate incorporation. The findings also demonstrate that there is a statistically significantly positive relationship between discretionary current accruals and subsequent operating performances of companies, on average. Based on the argument in previous literature that more conservative individuals are likely to make more ethical decisions, these findings can be considered suggesting that earnings are managed with an informative intend in the Turkish setting where potential motives for company executives to manage earnings opportunistically are not likely to be strong. The findings are robust to various proxies and model specifications, and they contradict the findings of the majority of previous studies.

Competition versus Cooperation: A class of solvable mean field impulse control problems
Sören Christensen,Berenice Anne Neumann,Tobias Sohr
arXiv

We discuss a class of explicitly solvable mean field control problems/games with a clear economic interpretation. More precisely, we consider long term average impulse control problems with underlying general one-dimensional diffusion processes motivated by optimal harvesting problems in natural resource management. We extend the classical stochastic Faustmann models by allowing the prices to depend on the wood supply on the market using a mean field structure. In a competitive market model, we prove that, under natural conditions, there exists an equilibrium strategy of threshold-type and furthermore characterize the threshold explicitly. If the agents cooperate with each other, we are faced with the mean field control problem. Using a Lagrange-type argument, we prove that the optimizer of this non-standard impulse control problem is of threshold-type as well and characterize the optimal threshold. Furthermore, we compare the solutions and illustrate the findings in an example.

Cyclical phenomena in technological change
Mario Coccia
arXiv

The process of technological change can be regarded as a non-deterministic system governed by factors of a cumulative nature that generate cyclical phenomena. In this context, the process of growth and decline of technology can be systematically analyzed to design best practices for technology management of firms and innovation policy of nations. In this perspective, this study focuses on the evolution of technologies in the U.S. recorded music industry. Empirical findings reveal that technological change in the sector under study here has recurring fluctuations of technological innovations. In particular, cycle of technology has up wave phase longer than down wave phase in the process of evolution in markets before it is substituted by a new technology. Results suggest that radical innovation is one of the main sources of cyclical phenomena for industrial and corporate change, and as a consequence, economic and social change.

Deep Learning-Based Least Square Forward-Backward Stochastic Differential Equation Solver for High-Dimensional Derivative Pricing
Jian Liang,Zhe Xu,Peter Li
arXiv

We propose a new forward-backward stochastic differential equation solver for high-dimensional derivatives pricing problems by combining deep learning solver with least square regression technique widely used in the least square Monte Carlo method for the valuation of American options. Our numerical experiments demonstrate the efficiency and accuracy of our least square backward deep neural network solver and its capability to provide accurate prices for complex early exercise derivatives such as callable yield notes. Our method can serve as a generic numerical solver for pricing derivatives across various asset groups, in particular, as an efficient means for pricing high-dimensional derivatives with early exercises features.

Determinants of Banksâ€™ Liquidity: A French Perspective on Interactions between Market and Regulatory Requirements
de Bandt, Olivier,lecarpentier, celine,Pouvelle, Cyril
SSRN
The paper investigates the impact of solvency and liquidity regulation on banks' balance sheet structure. The Covid-19 pandemics shows that periods of sharp increase in risk aversion often result in liquidity strains for banks due to the volatility of long-term funding markets.According to a simple portfolio allocation model banksâ€™ liquidity increases when the regulatory constraint is binding. We provide evidence, using the â€œliquidity coefficientâ€ implemented in France ahead of Basel III's Liquidity Coverage Ratio, of a positive effect of the solvency ratio on the liquidity coefficient. We also show that in times of crisis, measured by financial variables, French banks actually decreased the liquidity coefficient, with the transmission channel materialising mainly on the liability side.

Do Investors Care about Tail Risk? Evidence from Mutual Fund Flows
Chen, Yong,Dai, Wenting
SSRN
This paper examines investor attitude toward tail risk in investment decision-making. Based on a large sample of mutual funds, we show that investor flows are significantly sensitive to tail risk in the cross-section, even after controlling for fund performance and characteristics. Using terrorist attacks and COVID-19 as exogenous shocks to the investor fear level, we find that fund flows become increasingly sensitive to tail risk following the shocks, suggesting that fear is a driving force of the tail risk aversion. In particular, the flow-tail risk sensitivity during the onset of COVID-19 is about 4.5-10 times as large as that in other periods. In addition, tail risk is associated with the activeness of mutual fund investment strategies. The results are robust to alternative measures of tail risk. Overall, our findings suggest that investors care about tail risk beyond traditional risks.

Does the Impact of the COVID-19 Pandemic Influence the FX? A Note
Vasileiou, Evangelos
SSRN
This note shows that the effective response of a country in its battle against COVID-19 influences the exchange rate of its currency. Particularly, we examine the GBPUSD, AUDUSD and AUDGBP pairs of currency during the COVID-19 outbreak and the results show that the domestic currency of the country which documents more COVID-19 cases in each pair is depreciated against the foreign one. Therefore, a country which cannot effectively mitigate the impact of COVID-19 and whose currency is depreciated may present further economic consequences in the future. Such consequences extend beyond economic recession and may include sovereign and interest rate risk. These findings may be useful for policy makers in order to estimate the cost of the pandemic.

Does the Wealth Tax Kill Jobs?
BjÃ¸rneby, Marie,Markussen, Simen,RÃ¸ed, Knut
SSRN
Fueled by increasing inequality and rising fiscal deficits, the interest in wealth taxation has increased over the last years, both in the public debate and in academia. Yet, knowledge about the behavioral effects of a wealth tax is limited. We utilize rich Norwegian register data and a series of tax reforms implemented between 2007 and 2017 to study how a net wealth tax imposed on owners of small and medium sized businesses affects their firms' investment and employment decisions. Identification of causal effects is based on a generalized difference-in-differences strategy.We find no empirical support for the claim that a moderate wealth tax adversely affects investments and employment in firms controlled by the taxpayers. To the contrary, our results indicate a positive causal relationship between the level of a household's wealth tax and subsequent employment growth in the firm it controls. The rationale behind this result appears to be that the tax value of a given wealth can be reduced by being invested in a non-traded firm, and that this incentive becomes stronger the higher is the wealth tax.

Double the Insurance, Double the Funds?
Stone, Anna-Leigh
SSRN
The Depositors Insurance Fund and Share Insurance Fund, private organizations in Massachusetts, have provided a full guarantee on deposits held at Massachusetts state-chartered savings and cooperative institutions since 1934. I instrument for the choice to participate in these funds and find that member institutions held on average between 31.7% to 35.5% more deposits over the Federal Deposit Insurance Corporation limit when compared to similar state institutions. In addition, I find that member institutions experienced on average an additional 12% larger flight to deposits than nonmember institutions during the financial crisis. These results show that private deposit insurance might lessen concerns of deposit loss and reduce the burden on federal guarantee programs.

Early warnings of COVID-19 outbreaks across Europe from social media?
Milena Lopreite,Pietro Panzarasa,Michelangelo Puliga,Massimo Riccaboni
arXiv

We analyze social network data from Twitter to uncover early-warning signals of COVID-19 outbreaks in Europe in the winter season 2020, before the first public announcements of local sources of infection were made. We show evidence that unexpected levels of concerns about cases of pneumonia were raised across a number of European countries. Whistleblowing came primarily from the geographical regions that eventually turned out to be the key breeding grounds for infections. These findings point to the urgency of setting up an integrated digital surveillance system in which social media can help geo-localize chains of contagion that would otherwise proliferate almost completely undetected.

Effects of the 2008 Financial Crisis on the Linkages among the Oil, Gold, and Platinum Markets
Aruga, Kentaka,Kannan, Sudha
SSRN
To find out if gold remains to be unlinked with the crude oil market after the 2008 financial crisis, we investigated how long-run price linkages and price causalities among crude oil and gold markets changed before and after the crisis. To have a good reference, we also tested the same issue for the oil-platinum relationship. Using the cointegration methods, we found little evidence that gold began to have a price linkage with the crude oil market after the 2008 financial crisis. Conversely, we identified a long-run relationship between the crude oil and platinum markets after the crisis. Hence, we found that compared to the platinum market, the gold market remained unlinked with the oil market after the 2008 financial crisis indicating that it continued to be independent of the crude oil market.

Financial Literacy and Financial Decision-Making at Older Ages
Fong, Joelle H.,Koh, Benedict S.,Mitchell, Olivia S.,Rohwedder, Susann
SSRN
How well older households manage their wealth holdings is an important determinant of their financial security during retirement, yet little is known about their financial decision-making and how this relates to their financial literacy. Our paper fills this gap by measuring financial literacy among older persons in the Singapore Life Panel and examining its association with timely credit card debt repayment, stock market participation, and age-based investment risk diversification. Most older respondents understand interest compounding and inflation, but fewer than half know about risk diversification. Almost all older credit card holders pay off their balances in a timely manner, but only 40% hold stocks; fewer than 18% with $1,000+ in assets hold portfolios consistent with age-appropriate investment glide paths. We further show that a one-unit higher financial literacy score is associated with a greater propensity to timely pay off credit card balances (1.5 ppts), to hold stock (8.3 ppts), and to follow an age-appropriate investment glide path (1.7 ppts). Generalized Autoregressive Score asymmetric Laplace Distribution and Extreme Downward Risk Prediction Hong Shaopeng arXiv Due to the skessed distribution, high peak and thick tail and asymmetry of financial return data, it is difficult to describe the traditional distribution. In recent years, generalized autoregressive score (GAS) has been used in many fields and achieved good results. In this paper, under the framework of generalized autoregressive score (GAS), the asymmetric Laplace distribution (ALD) is improved, and the GAS-ALD model is proposed, which has the characteristics of time-varying parameters, can describe the peak thick tail, biased and asymmetric distribution. The model is used to study the Shanghai index, Shenzhen index and SME board index. It is found that: 1) the distribution parameters and moments of the three indexes have obvious time-varying characteristics and aggregation characteristics. 2) Compared with the commonly used models for calculating VaR and ES, the GAS-ALD model has a high prediction effect. Government Subsidy in the U.S. Mortgage Market: A Structural Analysis with Bunching Shi, Bowen,Zhao, Yunhui SSRN It is of vital importance to better understand the US housing market, a market where the global financial crisis was originated from. In this paper, we build an infinite-horizon continuous-time structural model to study the effects of the long-standing and widespread Government-Sponsored Enterprisesâ€™ (GSEs) mortgage default insurance subsidy on banksâ€™ equilibrium lending behavior in the US. Despite the richness of the model, we obtain analytical solutions for the equilibrium loan size and interest rate. We then use truncated loan-level data to obtain the maximum likelihood estimate of the magnitude of the default insurance subsidy, despite the fact that the pre-subsidy data are unavailable. We do so by using a salient feature in the data that a large number of borrowers â€œbunchâ€ at a loan size exactly equal to the subsidy eligibility cutoff. We find that the subsidy is about 25 basis points per dollar, reduces the equilibrium mortgage interest rate by the same amount (3.6% of the sample average), and increases the loan size by$15,026 (10.4% of the sample average). To the best of our knowledge, this is the first paper to estimate the size of the GSE subsidy using a structural approach with loan-level data. The estimation of this crucial parameter, along with our modeling framework, would allow future work to conduct welfare assessment and evaluation of the housing finance system in the US.

L'Indice D'Inclusion FinanciÃ¨re Ã€ L'Heure Du Mobile Money (The Index of Financial Inclusion at Time of Mobile Money)
Ngono, Jean Francky Landry
SSRN
French Abstract: Depuis quelques annÃ©es, le mobile money a pris une Ã©norme place dans les habitudes financiÃ¨res des populations surtout les populations des pays en dÃ©veloppement. Dans ce contexte, lâ€™indice dâ€™inclusion financiÃ¨re qui informe sur la situation dâ€™inclusion financiÃ¨re des diffÃ©rentes rÃ©gions du monde, se doit dâ€™incorporer aussi cet aspect. Lâ€™objectif de cette Ã©tude est dâ€™introduire le mobile money dans le calcul de lâ€™indice dâ€™inclusion financiÃ¨re. En appliquant le calcul de cet indice multidimensionnel de lâ€™inclusion financiÃ¨re compris entre 0 et 1 sur quelques pays dâ€™Afrique, il ressort bien que le mobile money a un effet significatif sur lâ€™indice dâ€™inclusion financiÃ¨re. Toutefois, le manque de donnÃ©es sur certains des indicateurs de lâ€™inclusion financiÃ¨re est une limite dans le calcul de cet indice.English Abstract: For some years mobile money has been a favorite place for financial populations, especially people in developing countries. In this context, the financial inclusion index, which provides information on the situation of financial inclusion of the different regions of the world, must also be included in this aspect. The objective of this study is to introduce mobile money in the calculation of the financial inclusion index. Applied the calculation of this multidimensional index of financial inclusion between 0 and 1 in some African countries, it does have a significant effect on the indication of financial inclusion. However, data on some indicators of financial inclusion are missing, which limits the calculation of this index.

Learning in a Small/Big World
Benson Tsz Kin Leung
arXiv

Savage (1972) lays down the foundation of Bayesian decision theory, but asserts that it is not applicable in big worlds where the environment is complex. Using the theory of finite automaton to model belief formation, this paper studies the characteristics of optimal learning behavior in small and big worlds, where the complexity of the environment is low and high, respectively, relative to the cognitive ability of the decision maker. Confirming Savage's claim, optimal learning behavior is closed to Bayesian in small worlds but significantly different in big worlds. In addition, I show that in big worlds, the optimal learning behavior could exhibit a wide range of well-documented non-Bayesian learning behavior, including the use of heuristic, correlation neglect, persistent over-confidence, inattentive learning, and other behaviors of model simplification or misspecification. These results establish a clear and testable relationship between the prominence of non-Bayesian learning behavior, complexity and cognitive ability.

Local and Non-local Fractional Porous Media Equations
Fatemeh Gharari,Karina Arias-Calluari,Fernando Alonso-Marroquin,Morteza. N. Najafi
arXiv

Recently it was observed that the probability distribution of the price return in S\&P500 can be modeled by $q$-Gaussian distributions, where various phases (weak, strong super diffusion and normal diffusion) are separated by different fitting parameters (Phys Rev. E 99, 062313, 2019). Here we analyze the fractional extensions of the porous media equation and show that all of them admit solutions in terms of generalized $q$-Gaussian functions. Three kinds of "fractionalization" are considered: \textit{local}, referring to the situation where the fractional derivatives for both space and time are local; \textit{non-local}, where both space and time fractional derivatives are non-local; and \textit{mixed}, where one derivative is local, and another is non-local. Although, for the \textit{local} and \textit{non-local} cases we find $q$-Gaussian solutions , they differ in the number of free parameters. This makes differences to the quality of fitting to the real data. We test the results for the S\&P 500 price return and found that the local and non-local schemes fit the data better than the classic porous media equation.

Modeling and Probababilistic Forecasting of Natural Gas Prices
Jonathan Berrisch,Florian Ziel
arXiv

In this paper, we examine the problem of modeling and forecasting European Day-Ahead and Month-Ahead natural gas prices. For this, we propose two distinct probabilistic models that can be utilized in risk- and portfolio management. We use daily pricing data ranging from 2011 to 2020. Extensive descriptive data analysis shows that both time series feature heavy tails, conditional heteroscedasticity, and show asymmetric behavior in their differences. We propose state-space time series models under skewed, heavy-tailed distribution to capture all stylized facts in the data. They include the impact of autocorrelation, seasonality, risk premia, temperature, storage levels, the price of European Emission Allowances, and related fuel prices of oil, coal, and electricity. We provide a rigorous model diagnostic and interpret all model components in detail. Additionally, we conduct a probabilistic forecasting study with significance test and compare the predictive performance against literature benchmarks. The proposed Day-Ahead (Month-Ahead) model leads to a $13\%$ ($9$\%) reduction in out of sample CRPS compared to the best performing benchmark model, mainly due to adequate modeling of the volatility and heavy tails.

Monitoring War Destruction from Space: A Machine Learning Approach
Hannes Mueller,Andre Groger,Jonathan Hersh,Andrea Matranga,Joan Serrat
arXiv

Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep learning techniques combined with data augmentation to expand training samples. We apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. The approach allows generating destruction data with unprecedented scope, resolution, and frequency - only limited by the available satellite imagery - which can alleviate data limitations decisively.

Neural Network-based Automatic Factor Construction
Jie Fang,Jianwu Lin,Shutao Xia,Yong Jiang,Zhikang Xia,Xiang Liu
arXiv

Instead of conducting manual factor construction based on traditional and behavioural finance analysis, academic researchers and quantitative investment managers have leveraged Genetic Programming (GP) as an automatic feature construction tool in recent years, which builds reverse polish mathematical expressions from trading data into new factors. However, with the development of deep learning, more powerful feature extraction tools are available. This paper proposes Neural Network-based Automatic Factor Construction (NNAFC), a tailored neural network framework that can automatically construct diversified financial factors based on financial domain knowledge and a variety of neural network structures. The experiment results show that NNAFC can construct more informative and diversified factors than GP, to effectively enrich the current factor pool. For the current market, both fully connected and recurrent neural network structures are better at extracting information from financial time series than convolution neural network structures. Moreover, new factors constructed by NNAFC can always improve the return, Sharpe ratio, and the max draw-down of a multi-factor quantitative investment strategy due to their introducing more information and diversification to the existing factor pool.

Non-Performing Loans, Governance Indicators and Systemic Liquidity Risk: Evidence from Greece
Anastasiou, Dimitrios,Bragoudakis, Zacharias ,Malandrakis, Ioannis
SSRN
In this study we propose a new determinant of non-performing loans for the case of the Greek banking sector. We employ aggregate yearly data for the period 1996-2016 and we conduct a Principal Component Analysis for all the Worldwide Governance Indicators (WGI) for Greece, aiming to isolate the common component and thus to create the GOVERNANCE indicator. We find that the GOVERNANCE indicator is a significant determinant of Greek banksâ€™ non-performing loans indicating that both political and governance factors impact on the level of the Greek non-performing loans. An additional variable that also has a statistically significant impact on the level of Greek non-performing loans, when combined with WGI in the dynamic specification of our model, is systemic liquidity risk. Our results could be of interest to policy makers and regulators as a macro prudential policy tool.

Nonparametric Efficiency Estimation in Stochastic Environments (Ii)
Cherchye, L.,Post, Thierry
SSRN
We consider the issues of noise-to-signal estimation, finite sample performance and hypothesis testing for the nonparametric efficiency estimation technique proposed in Cherchye, L., T. Kuosmanen and G. T. Post (2001) 'Nonparametric efficiency estimation in stochastic environments', forthcoming in Operations Research. In addition, we apply the technique for analyzing European banks.

On Option Greeks and Corporate Finance
Chang, Kuo-Ping
SSRN
This paper has proposed new option Greeks and new upper and lower bounds for European and American options. It also shows that because of the put-call parity, the Greeks of put and call options are interconnected and should be shown simultaneously. In terms of the theory of the firm, it is found that both the Black-Scholes-Merton and the binomial option pricing models implicitly assume that maximizing the market value of the firm is not equivalent to maximizing the equity-holdersâ€™ wealth. The binomial option pricing model implicitly assumes that further increasing (decreasing) the promised payment to debt-holders affects neither the speed of decreasing (increasing) in the equity nor the speed of increasing (decreasing) in the insurance for the promised payment. The Black-Scholes-Merton option pricing model, on the other hand, implicitly assumes that further increasing (decreasing) in the promised payment to debt-holders will: (1) decrease (increase) the speed of decreasing (increasing) in the equity though bounded by upper and lower bounds, and (2) increase (decrease) the speed of increasing (decreasing) in the insurance though bounded by upper and lower bounds. The paper also extends the put-call parity to include senior debt and convertible bond. It is found that when the promised payment to debt-holders is approaching the market value of the firm and the risk-free interest rate is small, both the owner of the equity and the owner of the insurance will be more reluctant to liquidate the firm. The lower bound for the risky debt is: the promised payment to debt-holders is greater or equal to the market value of the firm times one plus the risk-free interest rate.

Portfolio Effects of Cryptocurrencies during the COVID-19 Crisis
Gonzalez, Maria,JareÃ±o, Francisco,Skinner, Frank S.
SSRN
We investigate the performance of optimised three asset portfolios comprised of stocks, bonds and a cryptocurrency or gold for the period immediately before and during the COVID-19 financial crisis. We compare the performance of these portfolios with a two-asset cash portfolio comprised of stocks and bonds. Cryptocurrencies have the potential to control risk as most portfolios that include cryptocurrencies consistently experienced risk no greater than 50 basis points above the risk experienced by cash portfolios. However, there is no free lunch. While three asset portfolios can control risk, they also have a lower return per unit of risk.

Price Dividend Ratio and Long-Run Stock Returns: A Score Driven State Space Model
Delle Monache, Davide,Petrella, Ivan,Venditti, Fabrizio
SSRN
In this paper we develop a general framework to analyse state space models with time-varying system matrices, where time variation is driven by the score of the conditional likelihood. We derive a new filter that allows for the simultaneous estimation of the state vector and of the time-varying matrices. We use this method to study the time-varying relationship between the price dividend ratio, expected stock returns and expected dividend growth in the US since 1880. We find a significant increase in the long-run equilibrium value of the price dividend ratio over time, associated with a fall in the long-run expected rate of return on stocks. The latter can be attributed mainly to a decrease in the natural rate of interest, as the long-run risk premium has only slightly fallen.

Pricing Red Scare: Investor Behavior and Regime Uncertainty During the Spanish Second Republic
Battilossi, Stefano,Houpt, Stefan O.,Verdickt, Gertjan
SSRN
The Spanish Second Republic was a unique experiment of democratization in interwar Europe, which led to extreme levels of political uncertainty. With a measure of â€œRed Scareâ€ news, we show that regime uncertainty negatively affected investor sentiment on the Madrid Stock Exchange. First, the negative effect of uncertainty was long-lived, even when we control for other risks. Second, although negative for the aggregate index, we document that this uncertainty is priced in the cross-section of expected returns. Third, as regime uncertainty increased a stockâ€™s crash risk, investors fled towards less-risky assets such as government debt. We document temporary flight-to-safety effects both in returns and traded volume. Overall, the results suggest that regime uncertainty was an important risk factor for Spanish investors.

Prudential Policies, Credit Supply and House Prices: Evidence from Italy
SSRN
We estimate the causal effect of a mortgage supply expansion on house prices by using an exogenous change in prudential regulation: the abolition in 2006 of a banks' maturity transformation limit. After the repeal of the prudential rule, credit increased only for the banks that were previously constrained by the regulation, while it remained unchanged for the other banks. Such differential response rules out demand-based explanations and fully identify the rule abolition as an exogenous shock that we exploit as an instrument for mortgage supply expansion. We estimate the elasticity of house price growth to new mortgage credit to be close to 5 percent. Our results also show that the effect of a mortgage supply expansion on house prices significantly differs across municipalities' and borrowers' characteristics.

Regional Differences in Retail Payment Habits in Italy
Ardizzi, Guerino,Bonifacio, Elisa,Demma, Cristina,Painelli, Laura
SSRN
Economic operators have a number of different procedures and instruments for regulating their cashless monetary transactions safely and quickly. Nevertheless, divergences in the use of non-cash payment instruments persist among European countries and, in Italy, between the Centre and North and the southern regions. In this paper, we study which factors are associated with the backwardness of South and Islands in the use of non-cash payment instruments. We focus on the period 2013-18, when there was a widespread increase in non-cash transactions among the main advanced economies, spurred by technological innovation and the new legal framework supporting security, efficiency and transparency in digital payments. We find that the main factors associated with a lower demand for cash are technological innovation in payments and the populationâ€™s digital skills and education levels; criminality and tax evasion are also significantly and positively correlated to the use of cash, but their correlations with the observed heterogeneity among Italian provinces are not predominant.

Restructuring vs. Bankruptcy
Donaldson, Jason Roderick,Morrison, Edward R.,Piacentino, Giorgia,Yu, Xiaobo
SSRN
We develop a model of a firm in financial distress. Distress can be mitigated by filing for bankruptcy, which is costly, or preempted by restructuring, which is impeded by a collective action problem. We find that bankruptcy and restructuring are complements, not substitutes: Reducing bankruptcy costs facilitates restructuring, rather than crowding it out. And so does making bankruptcy more debtor-friendly, under a condition that seems likely to hold now in the United States. The model gives new perspectives on current relief policies (e.g., subsidized loans to firms in bankruptcy) and on long-standing legal debates (e.g., the efficiency of the absolute priority rule).

Retail Investorsâ€™ Trading Activity and the Predictability of Stock Return Correlations
Ballinari, Daniele
SSRN
Considerable theoretical and empirical evidence links price comovements with the behavior of retail investors. Nevertheless, when predicting stock return correlations, research has focused on the leverage effect. We propose a new model of realized covariances that allows exogenous predictors to influence the correlation dynamics while ensuring the predicted matrices' positive definiteness. Using this model, the predictive power of retail investors' sentiment and attention for the correlations of 35 Dow Jones stocks is analyzed. We find retail investors' attention to have predictive power for return correlations, especially for longer forecasting horizons and during the COVID-19 pandemic. Value-at-risk forecasts confirm these results.

Short-Termism, Shareholder Payouts, and Investment in the EU
Fried, Jesse M.,Wang, Charles C. Y.
SSRN
Investor-driven "short-termism'" is said to harm EU public firms' ability to invest for the long term, prompting calls for the EU to better insulate managers from shareholder pressure. But the evidence offered---in the form of rising levels of repurchases and dividends---is incomplete and misleading, as it ignores large offsetting equity issuances that move capital from investors to EU firms. We show that net shareholder payouts have been moderate, that both investment levels and investment intensity have been rising, and that cash balances have increased. In sum, the data provide little basis for the view that short-termism in the EU warrants corporate governance reforms.

Testing the Local Martingale Theory of Bubbles using Cryptocurrencies
Choi, Soon Hyeok,Jarrow, Robert
SSRN
Cryptocurrencies provide the ideal and natural experimental setting to test the local martingale theory of bubbles, because they have no cash flows. Using this theory, we test for the existence of price bubbles in eight cryptocurrencies from January 1, 2019 to July 17, 2019. The cryptocurrencies are Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Ripple (XRP), Bitcoin Cash (BCH), EOS (EOS), Monero (XMR), and Zcash (ZEC). A novel, simple, and robust testing methodology is created to facilitate this estimation. During this time frame, five of the eight currencies (BTC, BCH, EOS, XMR, ZEC) exhibit price bubbles, Litecoin does not, and the evidence for Ethereum and Ripple is inconclusive. The paper provides strong evidence for the prevalence of bubbles in cryptocurrencies and supports the feasibility of applying the local martingale theory of bubbles to various asset classes.

The Argentine Collective Action Clause Controversy (La Controversia Argentina sobre las ClÃ¡usulas de AcciÃ³n Colectiva)
Buchheit, Lee C.,Gulati, G. Mitu
SSRN
English Abstract: Argentina is once again seeking to restructure its external debt. To facilitate this process, the country is proposing to use the state-of-the-art collective action clause that was included in the bonds Argentina started issuing in the Spring of 2016. When it uncloaked its restructuring offer to creditors in April of this year, however, Argentina sought the consent of bondholders to amend those clauses in ways that have sparked an outcry from certain of those holders. At stake in this controversy is the question of which version of a collective action clause will be incorporated in future bonds issued by sovereign borrowers.Spanish Abstract: Las clÃ¡usulas de acciÃ³n colectiva (â€œCACsâ€) son disposiciones contractuales que permiten a una mayorÃ­a o a una mayorÃ­a calificada de tenedores de un instrumento de deuda con mÃºltiples acreedores, tales como un bono, tomar decisiones que obliguen a todos los tenedores de ese mismo instrumento. Aclamadas como una tÃ©cnica innovadora para facilitar la reestructuraciÃ³n de deuda soberana en este siglo, las CACs se han convertido en el objeto de una amarga disputa en la propuesta de reestructuraciÃ³n de la deuda en bonos de la RepÃºblica Argentina este aÃ±o 2020.

The Real Effects of Rating Inflation: Evidence from the Chinese Corporate Credit Ratings
Liu, Shida,Wang, Hao
SSRN
Holding firm fundamentals constant, credit ratings of the Chinese firms have increased by one notch on average during 2009-2017. The rating standard relaxation coincides with rating inflation, as the feedback effects of higher ratings that help reduce financing cost and improve investment and future credit quality can only explain a small portion of the rating hikes. Aided by partially reduced debt costs, the more inflated firms have higher leverage, hold less cash, and invest more in capital assets but not research and development. They exhibit moderately higher risk but no improvements to growth, profitability and efficiency. Regulatory arbitrage and conflict of interest rooted in the issuer-pays business model play prominent roles in explaining the rating inflation.

The Returns to Early-Stage Investment in Innovation
Kisseleva, Katja,MjÃ¸s, Aksel,Robinson, David T.
SSRN
This paper uses highly detailed administrative records from the Norwegian Tax Authority to provide direct measures of the returns from investing in newly established, innovative companies. We trace out the entire funding and pricing histories of each firm and study performance measures at the transaction and investment levels. Many investments result in total loss, but returns exhibit extreme right-skewness. Cross-sectional analysis shows that different investor types earn widely differing returns even in the same investment. This arises not just because they invest on different terms, but because they make different decisions about holding or selling shares. The opaqueness and uncertainty implied by this heterogeneity is indicative of the market frictions associated with early-stage investment in innovation.

Thematic Innovation Investing with Textual Data Analysis
Elsaesser, Georg,Gardin, FranÃ§ois,Kolrep, Martin,Rosentritt, Michael
SSRN
Thematic investing has always existed, whenever there were ideas and topics to focus investment objectives and decisions on, and assets to be invested in. However, the way to execute on this approach in the fund industry has changed rapidly in recent years, and is likely to change more than ever in the future. In this article, after having a look at the development of thematic investing over the past 40 years, we shed some light on the approach taken at Invesco Quantitative Strategies.

Tight Bounds for a Class of Data-Driven Distributionally Robust Risk Measures
Derek Singh,Shuzhong Zhang
arXiv

This paper expands the notion of robust moment problems to incorporate distributional ambiguity using Wasserstein distance as the ambiguity measure. The classical Chebyshev-Cantelli (zeroth partial moment) inequalities, Scarf and Lo (first partial moment) bounds, and semideviation (second partial moment) in one dimension are investigated. The infinite dimensional primal problems are formulated and the simpler finite dimensional dual problems are derived. A principal motivating question is how does data-driven distributional ambiguity affect the moment bounds. Towards answering this question, some theory is developed and computational experiments are conducted for specific problem instances in inventory control and portfolio management. Finally some open questions and suggestions for future research are discussed.

Transfer Payment Systems and Financial Distress: Insights from Health Insurance Premium Subsidies
Schmid, Christian P.R,Schreiner, Nicolas,Stutzer, Alois
SSRN
How should payment systems of means-tested benefits be designed to improve the financial situation of needy recipients most effectively? We study this question in the context of mandatory health insurance in Switzerland, where recipients initially received either a cash transfer or subsidized insurance premiums (a form of in-kind transfer). A federal reform in 2014 forced cantons (i.e. states) to universally switch to in-kind provision. We exploit this setting based on a difference-in-differences design, analyzing rich individual-level accounting data and applying a machine learning approach to identify cash recipients prior to the reform.We find that switching from cash to in-kind transfers reduces the likelihood of late premiums payments by about 20% and of government debt collection for long-term missed payments by approximately 16%. There is no evidence for a negative spillover effect on the timely payment of the non-subsidized coinsurance bills for health services after the regime change.

Using Equity, Index and Commodity Options to Obtain Forward-Looking Measures of Equity and Commodity Betas, and Idiosyncratic Variance
Ronn, Ehud I.
SSRN
This paper presents a parsimonious and theoretically-sound basis for extracting forward-looking measures of equity and commodity betas, and idiosyncratic variance.Defining forward-looking betas and idiosyncratic variance as perturbations of historical estimates, we use the market prices of equity and index options under a single-factor market model to compute forward-looking term structures of equity betas and idiosyncratic variance. When applying the model to the market prices of options on oil company stocks and a market index, we are able to discern the market's perceptions regarding these oil companies' prospective beta, and hence signaling their future sensitivity to market changes. In turn, the prospective fraction of idiosyncratic variance relative to total variance provides a forward-looking market measure for onset of crises, when idiosyncratic risk fades relative to systematic, and complementing the information conveyed by VIX and the CBOE's equity implied correlation.The model bears a natural extension to the joint use of options on equities and the futures price of oil. In so doing, we are able to discern a forward-looking oil beta. Such a beta, in conjunction with risk-neutral futures prices, gives rise to a CAPM-based forecast of oil prices.

What Matters More in Board Independence? Form or Substance?