# Research articles for the 2021-03-22

A Deep Deterministic Policy Gradient-based Strategy for Stocks Portfolio Management
Huanming Zhang,Zhengyong Jiang,Jionglong Su
arXiv

With the improvement of computer performance and the development of GPU-accelerated technology, trading with machine learning algorithms has attracted the attention of many researchers and practitioners. In this research, we propose a novel portfolio management strategy based on the framework of Deep Deterministic Policy Gradient, a policy-based reinforcement learning framework, and compare its performance to that of other trading strategies. In our framework, two Long Short-Term Memory neural networks and two fully connected neural networks are constructed. We also investigate the performance of our strategy with and without transaction costs. Experimentally, we choose eight US stocks consisting of four low-volatility stocks and four high-volatility stocks. We compare the compound annual return rate of our strategy against seven other strategies, e.g., Uniform Buy and Hold, Exponential Gradient and Universal Portfolios. In our case, the compound annual return rate is 14.12%, outperforming all other strategies. Furthermore, in terms of Sharpe Ratio (0.5988), our strategy is nearly 33% higher than that of the second-best performing strategy.

A deep learning approach to data-driven model-free pricing and to martingale optimal transport
Ariel Neufeld,Julian Sester
arXiv

We introduce a novel and highly tractable supervised learning approach based on neural networks that can be applied for the computation of model-free price bounds of, potentially high-dimensional, financial derivatives and for the determination of optimal hedging strategies attaining these bounds. In particular, our methodology allows to train a single neural network offline and then to use it online for the fast determination of model-free price bounds of a whole class of financial derivatives with current market data. We show the applicability of this approach and highlight its accuracy in several examples involving real market data. Further, we show how a neural network can be trained to solve martingale optimal transport problems involving fixed marginal distributions instead of financial market data.

AI Specialization for Pathways of Economic Diversification
Saurabh Mishra,Robert Koopman,Giuditta De-Prato,Anand Rao,Israel Osorio-Rodarte,Julie Kim,Nikola Spatafora,Keith Strier,Andrea Zaccaria
arXiv

The growth in AI is rapidly transforming the structure of economic production. However, very little is known about how within-AI specialization may relate to broad-based economic diversification. This paper provides a data-driven framework to integrate the interconnection between AI-based specialization with goods and services export specialization to help design future comparative advantage based on the inherent capabilities of nations. Using detailed data on private investment in AI and export specialization for more than 80 countries, we propose a systematic framework to help identify the connection from AI to goods and service sector specialization. The results are instructive for nations that aim to harness AI specialization to help guide sources of future competitive advantage. The operational framework could help inform the public and private sector to uncover connections with nearby areas of specialization.

American options in the Volterra Heston model
Etienne Chevalier,Sergio Pulido,Elizabeth Zúñiga
arXiv

We price American options using kernel-based approximations of the Volterra Heston model. We choose these approximations because they allow simulation-based techniques for pricing. We prove the convergence of American option prices in the approximating sequence of models towards the prices in the Volterra Heston model. A crucial step in the proof is to exploit the affine structure of the model in order to establish explicit formulas and convergence results for the conditional Fourier-Laplace transform of the log price and an adjusted version of the forward variance. We illustrate with numerical examples our convergence result and the behavior of American option prices with respect to certain parameters of the model.

Are Credit Rating Disagreements Priced in the M&A Market?
Hasan, Iftekhar,Huang, He,To, Thomas Y.
SSRN
This paper examines the effect of credit rating disagreements on merger and acquisition (M&A) decisions. We show that acquirers with split ratings prefer to use stock to finance their acquisitions. More importantly, we find that acquirers with split ratings experience lower announcement returns. Further analysis shows that overpayment by acquirers with split ratings is concentrated in acquirers with entrenched managers. Our results are robust to alternative identification strategies. Overall, our evidence indicates that credit rating disagreements are heavily priced in the M&A market.

Auctioning Annuities
Gaurab Aryal,Eduardo Fajnzylber,Maria F. Gabrielli,Manuel Willington
arXiv

In this paper, we use rich administrative data to study a market system for annuity contracts in Chile. To this end, we develop a model of demand and supply of annuities using discrete choice and multi-attribute auctions where life insurance companies with different risk ratings and private information about their annuitization costs compete for savings of risk-averse retirees by offering them personalized pensions. We find that (1) retirees' preferences for firms' risk ratings decrease with their savings, (2) almost half of the retirees who choose annuities do not value bequests, (3) information processing costs are larger for low savers and for those who receive advise from sales-agent, and (4) firms are more likely to have lower annuitization costs for the top 40% savers. Counterfactuals suggest that information asymmetry hurts these 40% savers the most, and replacing the current mechanism with standard English auctions and "shutting-down" risk ratings increase pensions for everyone. But the increase is negligible for most, except for these top 40% savers, with little effect on welfare.

Big Data in Finance
Goldstein, Itay,Spatt, Chester S.,Ye, Mao
SSRN
Big data is revolutionizing the finance industry and has the potential to significantly shape future research in finance. This special issue contains articles following the 2019 NBER/ RFS conference on big data. In this Introduction to the special issue, we define the â€œBig Dataâ€ phenomenon as a combination of three features: large size, high dimension, and complex structure. Using the articles in the special issue, we discuss how new research builds on these features to push the frontier on fundamental questions across areas in finance â€" including corporate finance, market microstructure, and asset pricing. Finally, we offer some thoughts for future research directions.

CEO Compensation and Firm Performance in Emerging Market: Evidence from Indonesia Selected Listed Banks
Submitter, GATR Journals,Azazi, Anwar
SSRN
Objective - The objective of this study was to investigate empirically the relationship between the compensation of chief executive officers (CEO) and a firm's performance in the banking industry and to examine if CEO compensation affects bank performance differently between banks with and without prospect.Methodology/Technique - The author uses two measures of performance, total return on assets and Tobin, s Q, and concentrate on total CEO compensation. All data are collected from annual reports of banks listed in Indonesia Stock Exchange for a sample of 23 commercial banks or 167 firm-year observation over the 2009-2018 period utilizing the purposive random sampling technique. CEO compensation and bank performance are then analysed employing pooled regression method.Finding - This study finds supporting evidence for the agency-related problem in the banking industry in Indonesia. It then proves that high CEO compensation does have an inverse effect on bank performance, mainly on firm value. It also provides evidence that the pay-performance also demonstrates different patterns in firms with and without prospect.Novelty - This study uses novel and hand-collected data on CEO compensation in the banking industry and developing econometric evidence regarding CEO pay-performance relating to banks with and without prospect.Type of Paper - Empirical.

Capital Structure and Monitoring by Local Owners
Mavruk, Taylan,SjÃ¶gren, Stefan
SSRN
We examine how ownersâ€™ portfolio diversification influences their firmsâ€™ financial decision-making and performance. We find that firms with high local ownership use less leverage, but firms with local ownership by locally biased owners use higher debt levels relative to firms with diversified local owners. Firms with high local ownership in urban regions use higher debt levels. In rural regions, firms with high locally biased ownership use higher debt levels relative to firms with diversified local ownership. Finally, although we find weak evidence that firms with high local ownership underperform the market, the underperformance is smaller in firms with high locally biased ownership. Thus, locally biased owners, not local owners with diversified portfolios, have an informed monitoring role in firms, and this effect seems to mitigate negative liquidity consequences. The separation of local owners into those with locally biased and those with diversified portfolios determines when and how local ownership can be used as a good proxy for informed investors.

Carbon Risk and CEO Compensation
Amin, Abu S.,Hossain, Ashrafee T
SSRN
We explore the association between CEO compensation and carbon risk using firm level emission data for US firms between 2010 and 2018. Our results show that firmâ€™s carbon risk is positively associated with CEOâ€™s total compensation, implying that CEO total pay (equity pay) increases by 0.17% (0.52%) if the carbon emission increases by 1%. Additional analyses show that the relationship is not driven by sample composition or omitted variables bias. To explore the causal direction of the relationship, we employ a difference-in-differences analysis using the 2016 Presidential election as an exogenous shock and find that the CEO pay-pollution sensitivity decreases after the election. In cross-sectional tests, we find evidence that the result is driven by CEOs with stronger influence or power.

Cash sub-additive risk statistics for portfolios
Fei Sun,Weitao Liu,Xiaozhi Fan
arXiv

The portfolios are a critical factor not only in risk analysis, but also in insurance and financial applications. In this paper, we consider a special class of risk statistics from the perspective of time value of the money. This new risk statistic can be uesd for the quantification of portfolio risk. By further developing the properties related to cash sub-additive risk statistics, we are able to derive representation results for such risk.

Central bank gold reserves and sovereign credit risk
Rathi, Sawan,Mohapatra, Sanket,Sahay, Arvind
SSRN
Gold holdings with central banks are often considered to play a stabilizing role in times of crisis. This study performs a cross-country panel data analysis of developed and developing countries to determine whether gold holdings of central banks contribute to sovereign creditworthiness. Our analysis confirms that an increase in central bank gold reserves reduces the credit default swap (CDS) spreads of a country. We also observe that during global crisis and country-specific crisis episodes, the role of central bank gold becomes even more important. In robustness tests, we account for potential endogeneity of central bank gold reserves using a Generalized Method of Moments (GMM) approach. The findings highlight the importance of gold in central bank reserves and indicate a positive role of gold in mitigating a nation's external vulnerabilities in an uncertain global economic environment.

Common Mutual Fund Ownership and Systemic Risk
Iselin, Michael,Liao, Scott,Zhang, Haiwen
SSRN
We examine whether bank connections via common mutual fund ownership serve as a contagion channel affecting the systemic risk of the banking system. We first document that the extent of a bankâ€™s connection with other banks via common ownership increases its contribution to systemic risk. We further find that this association is primarily driven by passive mutual funds. We provide evidence that common passive ownership results in higher systemic risk through two mechanisms: non-discretionary sell-offs of bank stocks and a common pattern of voting. Our findings are also robust to two alternate instrumental variable analyses. This study contributes to the literature by documenting an unintended, macro-level consequence of common mutual fund ownership.

Confident Risk Premia: Economics and Econometrics of Machine Learning Uncertainties
Allena, Rohit
SSRN
This paper derives ex-ante standard errors of risk premium predictions from neural networks (NNs). Considering standard errors, I provide improved investment strategies and ex-post out-of-sample (OOS) statistical inferences relative to existing literature. The equal-weighted (value-weighted) confident high-low strategy that takes long-short positions exclusively on stocks that have precise risk premia earns an OOS average monthly return of 3.61% (2.21%). In contrast, the conventional high-low portfolio yields 2.52% (1.48%). Existing OOS inferences do not account for ex-ante estimation uncertainty and thus are not adequate to statistically compare the OOS returns, Sharpe ratios and mean squared errors of competing trading strategies and return prediction models (e.g., linear, NN, and random forest). I develop a bootstrap procedure that delivers robust OOS inferences. The bootstrap tests reveal that large OOS return and Sharpe ratio differences between NN and benchmark linear models' traditional high-low portfolios are statistically insignificant. However, the NN-based confident high-low portfolios significantly outperform all competing strategies. Economically, standard errors reflect time-varying market uncertainty and spike after financial shocks. In the cross-section, the level and precision of risk premia are correlated, thus NN-based investments deliver more gains in the long positions.

Convergence of Deep Fictitious Play for Stochastic Differential Games
Jiequn Han,Ruimeng Hu,Jihao Long
arXiv

Stochastic differential games have been used extensively to model agents' competitions in Finance, for instance, in P2P lending platforms from the Fintech industry, the banking system for systemic risk, and insurance markets. The recently proposed machine learning algorithm, deep fictitious play, provides a novel efficient tool for finding Markovian Nash equilibrium of large $N$-player asymmetric stochastic differential games [J. Han and R. Hu, Mathematical and Scientific Machine Learning Conference, pages 221-245, PMLR, 2020]. By incorporating the idea of fictitious play, the algorithm decouples the game into $N$ sub-optimization problems, and identifies each player's optimal strategy with the deep backward stochastic differential equation (BSDE) method parallelly and repeatedly. In this paper, we prove the convergence of deep fictitious play (DFP) to the true Nash equilibrium. We can also show that the strategy based on DFP forms an $\eps$-Nash equilibrium. We generalize the algorithm by proposing a new approach to decouple the games, and present numerical results of large population games showing the empirical convergence of the algorithm beyond the technical assumptions in the theorems.

Convolution Bounds on Quantile Aggregation
Jose Blanchet,Henry Lam,Yang Liu,Ruodu Wang
arXiv

Quantile aggregation with dependence uncertainty has a long history in probability theory with wide applications in finance, risk management, statistics, and operations research. Using a recent result on inf-convolution of quantile-based risk measures, we establish new analytical bounds for quantile aggregation which we call convolution bounds. In fact, convolution bounds unify every analytical result and contribute more to the theory of quantile aggregation, and thus these bounds are genuinely the best one available. Moreover, convolution bounds are easy to compute, and we show that they are sharp in many relevant cases. Convolution bounds enjoy several other advantages, including interpretability on the extremal dependence structure, tractability, and theoretical properties. The results directly lead to bounds on the distribution of the sum of random variables with arbitrary dependence, and we illustrate a few applications in operations research.

Corporate Tax Avoidance and Annual Report Tone Ambiguity
Lei, Jin,Tang, Tanya Y. H.,Wu, Wei
SSRN
We examine how corporate tax avoidance relates to the tone of narrative disclosure in annualreports. Using a difference-in-differences matching approach, we find that firms engaging in moretax avoidance issue more ambiguous annual reports. The positive relation is more pronounced forfirms with higher audit probability, greater analyst coverage, and weaker internal monitoring. Wefurther find firms with greater tax avoidance are associated with earlier and more frequentvoluntary disclosures to investors. Our findings indicate that tax-avoiding firmsâ€™ disclosuredecisions are determined by the tradeoff between the benefits of reducing information asymmetryand the costs of tax-based proprietary information.

Creating Ethics Guidelines for Artificial Intelligence (AI) and Big Data Analytics: The Case of the European Consumer Insurance Market
Holland, Christopher P.,Mullins, Martin,Cunneen, Martin
SSRN
The European Union (EU) has a strong reputation and track record for being proactive in the development of guidelines and regulations for the ethical use of AI generally. In this paper, we discuss the development of an AI and ethical framework by the European Insurance and Occupational Pensions Authority (EIOPA), for the European insurance market. EIOPAâ€™s earlier report on big data analytics (EIOPA 2019) provided an important foundation to analyse and evaluate the complex range of issues and ethical problems associated with the wave of new AI technologies being deployed in insurance such as behavioural insurance, parametric products, novel pricing and risk assessment algorithms, e-service and claims management, which exploit big data and machine learning. In this paper we present an overview of the use of AI in insurance based on the full range of insurance applications throughout the insurance value chain. A general discussion of ethics and AI is illustrated with the specific use-case of insurance, and a new hierarchical model is presented that describes insurance as a complex system that can be analysed by taking a layered, multi-level approach, on which ethical issues can be mapped directly to specific level(s).

Deep Hedging: Learning Risk-Neutral Implied Volatility Dynamics
Hans Buehler,Phillip Murray,Mikko S. Pakkanen,Ben Wood
arXiv

We present a numerically efficient approach for learning a risk-neutral measure for paths of simulated spot and option prices up to a finite horizon under convex transaction costs and convex trading constraints. This approach can then be used to implement a stochastic implied volatility model in the following two steps: 1. Train a market simulator for option prices, as discussed for example in our recent; 2. Find a risk-neutral density, specifically the minimal entropy martingale measure. The resulting model can be used for risk-neutral pricing, or for Deep Hedging in the case of transaction costs or trading constraints. To motivate the proposed approach, we also show that market dynamics are free from "statistical arbitrage" in the absence of transaction costs if and only if they follow a risk-neutral measure. We additionally provide a more general characterization in the presence of convex transaction costs and trading constraints. These results can be seen as an analogue of the fundamental theorem of asset pricing for statistical arbitrage under trading frictions and are of independent interest.

Detecting Financial Misreporting with Real Production Activity: Evidence from an Electricity Consumption Analysis
Allee, Kristian D.,Baik, Bok,Roh, Yongoh
SSRN
This study examines whether a real production activity measure, firm-level electricity consumption growth, is useful in detecting firm financial misreporting. Identifying proxies for a firmâ€™s underlying financial performance that are not a function of the firmâ€™s accounting system is essential for misreporting detection. We propose that the difference between revenue growth and electricity consumption growth (i.e., growth wedge) is a useful signal of financial misreporting. Using electricity consumption data for Korean firms from 2006 to 2014, we find that the growth wedge is positively associated with discretionary revenues and accruals and the likelihood of financial misreporting as proxied by accounting restatements, qualified audit opinions, and regulatory enforcement actions. The growth wedge provides incremental information over firm characteristics and earnings management signals examined by prior research. Our findings are robust to a battery of additional tests, including within-firm and industry comparisons that do not require access to cross-sectional firm-level electricity data. Overall, our study documents new evidence on the role of a real production activity measure from an independent reporting entity in detecting financial misreporting. Our evidence speaks to the potential usefulness of real activity metrics in forensic economics.

Differentiation in a Two-Dimensional Market with Endogenous Sequential Entry
Jeffrey D. Michler,Benjamin M. Gramig
arXiv

Previous research on two-dimensional extensions of Hotelling's location game has argued that spatial competition leads to maximum differentiation in one dimensions and minimum differentiation in the other dimension. We expand on existing models to allow for endogenous entry into the market. We find that competition may lead to the min/max finding of previous work but also may lead to maximum differentiation in both dimensions. The critical issue in determining the degree of differentiation is if existing firms are seeking to deter entry of a new firm or to maximizing profits within an existing, stable market.

Diffusers of Entrepreneurship
Cao, Sean,He, Jie,Lin, Zhilu,Ren, Xiao
SSRN
We examine an emerging phenomenon that talented employees leave successful entrepreneurial firms to join less mature ones. Using a unique person-level dataset and a comprehensive sample of private firms from the U.S. Census Bureau, we find that these â€œentrepreneurial diffusersâ€, by potentially passing on entrepreneurial knowledge and institutional wisdom, can enhance their new colleaguesâ€™ innovation productivity and help their new employers successfully exit. We further find that these diffusers are motivated by an entrepreneurial culture that prizes risk-taking rather than by the prospect of monetary gain. Finally, the departure of entrepreneurial diffusers contributes to the well-documented long-run IPO underperformance in accounting and stock returns. Our paper offers new insights into a labor market channel of the cross-firm diffusion of entrepreneurship, which is critical to the sustainability of a vibrant entrepreneurial ecosystem.

Discontinued Positive Feedback Trading and the Decline in Asset Pricing Factor Profitability
Ben-David, Itzhak,Li, Jiacui,Rossi, Andrea,Song, Yang
SSRN
We show that a June 2002 reform in Morningstarâ€™s mutual fund rating methodology led to substantial drop in the profitability of momentum-related asset pricing factors. Before the reform, funds pursuing the same investment style had correlated ratings heavily influenced by recent style performance. Therefore, ratings-chasing flows generated large style-level positive feedback trading. The reform decoupled ratings from style-level performance; consequently, factors that benefited from positive feedback trading experienced a precipitous return decline. The performance decline was limited to the U.S. market where the reform happened. We estimate that the reform explains 25%â€"50% of the long-term profitability drop in momentum-related factors.

Does the Executive Labor Market Discipline? Labor Market Incentives and Earnings Management
Peng, Qiyuan,Yin, David
SSRN
This paper investigates the role of outside options in the executive labor market on earnings management decisions. To proxy for executivesâ€™ outside options, we use the number of times other firms cite the executiveâ€™s firm as a compensation peer. We find that executives with more citations conduct less earnings management. Exploiting the 2006 SEC requirement for compensation peer disclosure as a quasi-natural shock to executivesâ€™ awareness of outside options, we show that the executives who should be more responsive to outside options significantly reduce earnings management. Cross-sectional tests support a labor market discipline channel of outside options. Finally, we exploit state-level recognition of Inevitable Disclosure Doctrine and enforcement of non-compete agreements as cross-sectional restrictions on labor mobility and show that the impact of peer citations on reducing earnings management is stronger when there are fewer restrictions on mobility.

Dynamic Term Structure Models for SOFR Futures
Jacob Bjerre Skov,David Skovmand
arXiv

The LIBOR rate is currently scheduled for discontinuation, and the replacement advocated by regulators in the US is the Secured Overnight Financing Rate (SOFR). The change has the potential to disrupt the 200 trillion market of derivatives and debt tied to the LIBOR. The only SOFR linked derivative with any significant liquidity and trading history is the SOFR futures contract, traded at the CME since 2018. We use the historical record of futures prices to construct dynamic arbitrage-free models for the SOFR term structure. The models allow you to construct forward-looking SOFR term rates, imply a SOFR discounting curve and price and risk and risk manage SOFR derivatives, not yet liquidly traded in the market. We find that a standard three-factor Gaussian arbitrage-free Nelson-Siegel model describes term rates very well but a shadow-rate extension is needed to describe the behaviour near the zero-boundary. We also find that the jumps and seasonal effects observed in SOFR, do not need to be specifically accounted for in a model for the futures prices. Finally we study the so-called convexity correction and find that it becomes significant beyond the 2 year maturity. For validation purposes we demonstrate that our model aligns closely with the methodology used by the Federal Reserve to publish indicative SOFR term rates. ESG rating events and stock market reactions GlÃ¼ck, Maximilian,Hübel, Benjamin,Scholz, Hendrik SSRN This paper examines the effect of MSCI Environmental-, Social- and Governance- (ESG) rating events on returns and risk measures of stocks based on a large sample of US firms. Using event study methodology, we find that markets react with significant negative abnormal returns to downgrades in environmental and in social scores. Thus, ESG rating changes seem to provide new value-relevant information to market partici-pants. Further, applying a difference-in-differences approach, we assess whether and how changes in ESG rating impact the risk of stocks by examining downside, system-atic and total risk. Our findings suggest that especially upgrades in environmental scores materialize already shortly after the rating event and unfold a significant risk mitigation effect with regard to downside risk whereas upgrades in governance scores seem to mitigate systematic risk. Therefore, by improving the ESG profile managers can almost immediately mitigate value-relevant risks of their firms. Evaluation Criteria of High-Flyer Tech Startup Investments by Venture Capital Firms in Indonesia Pramana, Anestya,Dalimunthe, Zuliani ,Gandakusuma, Imo,Rizkianto, Eko SSRN This study aims to analyze what criteria are used by Venture Capital (VC) firms to value Indonesian tech startups as high-flyer investments. We define high-flyer investment here as start-ups projected to generate a return five times or more than its initial investment. We evaluated what criteria are considered more important than the others by VCs. Research carried out on variables considered as main aspects evaluated by VC firms are a company, product, and market factors. We used mixed qualitative and quantitative primary data collected through questionnaires for quantitative data and interviews for qualitative data. Logistic regression results revealed that companies with product performance passed the minimum viable product (MVP) phase have a significant chance to value as high-flyer investment compared to those who offer products or services that are not market-proven yet. Sequential qualitative interview findings indicate VC preferences for companies that are fast followers over the pioneers and also suggest that the founder, or human-related aspect, is a critical factor in investment decisions made by VCs Explainable AI Models of Stock Crashes: A Machine-Learning Explanation of the Covid March 2020 Equity Meltdown Ohana, Jean-Jacques,Ohana, Steve,Benhamou, Eric,Saltiel, David,Guez, Beatrice SSRN We consider a gradient boosting decision trees (GBDT) approach to predict large S&P 500 price drops from a set of 150 technical, fundamental and macroeconomic features. We report an improved accuracy of GBDT over other machine learning (ML) methods on the S&P 500 futures prices. We show that retaining fewer and carefully selected features provides improvements across all ML approaches. Shapley values have recently been introduced from game theory to the field of ML. They allow for a robust identification of the most important variables predicting stock market crises, and of a local explanation of the crisis probability at each date, through a consistent features attribution. We apply this methodology to analyze in detail the March 2020 financial meltdown, for which the model offered a timely out of sample prediction. This analysis unveils in particular the contrarian predictive role of the tech equity sector before and after the crash. External Credit Ratings Benavente, Sergio SSRN It is well known that credit ratings agencies (CRAs) are a key component of financial markets. But it is less well understood that its decisions reach far beyond the financial system boundaries, and have a direct impact in our daily lives. But, how is that so? If CRAs are important in the functioning of financial markets, why are they criticized?The aim of this study guide is to answer these questions as well as some other relevant issues, such as the main uses of a credit rating and the measurement of its performance, by giving a broad overview of the working of CRAs. As such, it can be used by practitioners as well as students with a background in finance or business.The study guide is organized as follow: Section 1 explains the basics of CRAs, defining what a credit rating is as well as what it is not intended to signal, its principal uses, the role it plays in financial markets, the different scales to assess the creditworthiness of an issuer, and how default is defined. Section 2 focuses on the concept of country ceiling. It begins by explaining sovereign ratings, then describes country ceiling in global scale and local scale, and explains the relationship between country ceiling and sovereign rating. Section 3 examines the default tables produced by CRAs, and defines the following: cumulative default probability, conditional and unconditional probability of default, and probability of survival. Then it explains the main uses and abuses of default tables. It ends with the important distinction between Rating Point in Time and Rating Through the Cycle. Section 4 describes the methodology used by CRAs to assess the creditworthiness of a corporate issuer. Section 5 presents the following criticisms against CRAs: conflicts of interest, inflation ratings, and opacity in their methodologies. Section 6 finishes by explaining how to measure the performance of credit ratings, and the tension between stability and accuracy. Financial Forecasting with Machine Learning: Price Vs Return Kamalov, Firuz,Gurrib, Ikhlaas,Rajab, Khairan SSRN Forecasting directional movement of stock price using machine learning tools has attracted a considerable amount of research. Two of the most common input features in a directional forecasting model are stock price and return. The choice between the former and the latter variables is often subjective. In this study, we compare the effectiveness of stock price and return as input features in directional forecasting models. We perform an extensive comparison of the two input features using 10-year historical data of ten large cap US companies. We employ four popular classification algorithms as the basis of the forecasting models used in our study. The results show that stock price is a more effective standalone input feature than return. The effectiveness of stock price and return equalize when we add technical indicators to the input feature set. We conclude that price is generally a more potent input feature than return value in predicting the direction of price movement. Our results should aid researchers and practitioners interested in applying machine learning models to stock price forecasting. Fitness and Propriety Assessment to Ensure Financial Firms Have Good People Doing Right Things during Perilous Times Thennakoon, Nishadi SSRN In the context of rapidly evolving challenges, imposing regulations applicable to every transaction, every market innovation and every business scenario will have practical difficulties. It will increase the regulatory burden and promote a culture of compliance with the letter of law in a mechanical way by financial institutions. Eventually, taking business decisions during crisis periods in compliance with applicable rules and regulations will be in the hands of Board of Directors (BODs) and Key Management Personnel (KMP) of such institutions. Their fitness and propriety to hold such positions should, therefore, be a foremost requirement which needs to be emphasized in any given circumstance. No amount of regulations will ensure that public deposits are safe unless the decisions pertaining to management of such money are taken by individuals who are honest, well-qualified and experienced as well as acting with integrity. Not only the robust systems and procedures in financial institutions but also the quality and competence of individuals make a huge impact on effective governance in financial firms. This article sheds light on the significance of efficient enforcement of regulations applicable to fitness and propriety of BODs and KMP of financial institutions, to have robust risk governance structures in a crisis time like COVID-19 outbreak. Grand challenges and emergent modes of convergence science Alexander M. Petersen,Mohammed E. Ahmed,Ioannis Pavlidis arXiv To address complex problems, scholars are increasingly faced with challenges of integrating diverse knowledge domains. We analyzed the evolution of this convergence paradigm in the broad ecosystem of brain science, which provides a real-time testbed for evaluating two modes of cross-domain integration - subject area exploration via expansive learning and cross-disciplinary collaboration among domain experts. We show that research involving both modes features a 16% citation premium relative to a mono-disciplinary baseline. Further comparison of research integrating neighboring versus distant research domains shows that the cross-disciplinary mode is essential for integrating across relatively large disciplinary distances. Yet we find research utilizing cross-domain subject area exploration alone - a convergence shortcut - to be growing in prevalence at roughly 3% per year, significantly faster than the alternative cross-disciplinary mode, despite being less effective at integrating domains and markedly less impactful. By measuring shifts in the prevalence and impact of different convergence modes in the 5-year intervals before and after 2013, our results indicate that these counterproductive patterns may relate to competitive pressures associated with global Human Brain flagship funding initiatives. Without additional policy guidance, such Grand Challenge flagships may unintentionally incentivize such convergence shortcuts, thereby undercutting the advantages of cross-disciplinary teams in tackling challenges calling on convergence. Greek-based Bond Factors Balduzzi, Pierluigi,Connolly, Michael,Marcus, Alan J. SSRN We analyze characteristics that explain the cross-section of risk premiums in the market for U.S. Treasury bonds using the methodology more commonly applied to the equity market. We demonstrate that the relevant characteristics for bond pricing are the set of scaled bond Greeks, i.e., the absolute values of the price-scaled derivatives of bond prices with respect to yield. The first two factors---the exposures to which are Duration and Convexity---explain almost the entirety of the overall variation in bond returns: 99.5%. In turn, the exposures to the two factors explain almost all of the cross-sectional differences in bond risk premia: 99.6%. The risk premium is positive for exposure to the Duration factor and negative for exposure to the Convexity factor, and both risk premium estimates are highly significant, especially when imposing the hypothesis of stationarity of yields. The corresponding Sharpe ratios are substantial and of absolute magnitude comparable to or exceeding that of the overall equity market. The negative convexity risk premium can be interpreted as compensation for protection against an inversion in the term structure of interest rates and the corresponding increased probability of recession and is responsible for the observed negatively-sloped term structure of bond Sharpe ratios. Our two Treasury bond factors are significant in explaining returns on other asset classes, such as equities, corporate bonds, and mortgage-backed securities, consistent with the notion that the high compensation for the two sources of risk reflects their pervasiveness. High policy uncertainty and low implied market volatility â€" an academic puzzle? Bialkowski, Jedrzej Pawel,Dang, Huong D.,Wei, Xiaopeng SSRN Motivated by the extremely low level of the CBOE VIX accompanied by the high level of US economic policy uncertainty in the period of late 2016 to the end of 2017, we examine the factors affecting the relationship between market volatility and economic policy uncertainty in the United States and the United Kingdom. Our analysis shows that low-quality political signals, higher opinion divergence among investors, and exceptional equity market performance consistently weaken the positive relationship between implied market volatility and policy uncertainty. Our findings help to explain the divergence between the market volatility index and economic policy uncertainty post the 2016 US presidential election and the UK Brexit referendum. Higher-Order Orthogonal Causal Learning for Treatment Effect Yiyan Huang,Cheuk Hang Leung,Xing Yan,Qi Wu arXiv Most existing studies on the double/debiased machine learning method concentrate on the causal parameter estimation recovering from the first-order orthogonal score function. In this paper, we will construct thek^{\mathrm{th}}$-order orthogonal score function for estimating the average treatment effect (ATE) and present an algorithm that enables us to obtain the debiased estimator recovered from the score function. Such a higher-order orthogonal estimator is more robust to the misspecification of the propensity score than the first-order one does. Besides, it has the merit of being applicable with many machine learning methodologies such as Lasso, Random Forests, Neural Nets, etc. We also undergo comprehensive experiments to test the power of the estimator we construct from the score function using both the simulated datasets and the real datasets. Income and Balance Sheet Diversification Effects on Banksâ€™ Cost and Profit Efficiency: Evidence from the US Abbas, Faisal ,Rubbaniy, Ghulame,Ali, Shoaib SSRN Using two-stage instrumental variable technique and two-step system GMM approach, we provide empirical evidence on impact of income, asset, and funding diversifications on the cost and profit efficiency of the US commercial banks over the period from 2002 to 2019. Our results show that funding and income (assets) diversification has a positive (detrimental) effect on the cost efficiency of the banks, while funding (income) diversification has a significantly negative (positive) effect on profit efficiency. Our findings reveal that during the global financial crisis asset diversification is not beneficial for the banks, whereas funding â€Ždiversification has a positive effect on cost and profit efficiency.â€Ž Our results confirm bi-directional causality between cost and profit efficiency in commercial banks of the US. Out mixed results of the influence of income, asset, and funding diversifications on the cost and profit efficiency of the banks with varying characteristics have useful implications for policymakers and regulatorsâ€Ž. Inference on the New Keynesian Phillips Curve with Very Many Instrumental Variables Max-Sebastian Dovì arXiv Limited-information inference on New Keynesian Phillips Curves (NKPCs) and other single-equation macroeconomic relations is characterised by weak and high-dimensional instrumental variables (IVs). Beyond the efficiency concerns previously raised in the literature, I show by simulation that ad-hoc selection procedures can lead to substantial biases in post-selection inference. I propose a Sup Score test that remains valid under dependent data, arbitrarily weak identification, and a number of IVs that increases exponentially with the sample size. Conducting inference on a standard NKPC with 359 IVs and 179 observations, I find substantially wider confidence sets than those commonly found. Inflation Expectations and Risk Premia in Emerging Bond Markets: Evidence from Mexico Beauregard, Remy,Christensen, Jens Henrik Eggert,Fischer, Eric,Zhu, Simon SSRN To study inflation expectations and associated risk premia in emerging bond markets, this paper provides estimates for Mexico based on an arbitrage-free dynamic term structure model of nominal and real bond prices that accounts for their liquidity risk. In addition to documenting the existence of large and time-varying liquidity premia in nominal and real bond prices that are only weakly correlated, the results indicate that long-term inflation expectations in Mexico are well anchored close to the inflation target of the Bank of Mexico. Furthermore, Mexican inflation risk premia are larger and more volatile than those in Canada and the United States. Inner Workings of Collateral-based Stablecoins and its Implications Choi, Gongpil SSRN While early cryptoassets such as Bitcoin have reputation for high price fluctuation and limited scalability, stablecoin, a new class of cryptographic token, emerged with the purpose of mitigating price volatility. What distinguishes stablecoin from pre-existing cryptocurrency is its use of collateral and use of specific pegging mechanism to mitigate volatility risks. The paper sheds light on the inner workings of stablecoins by analyzing stabilizing property with various aspects of collateral mix as well as pegging mechanism. Specifically, it evaluates initiatives to effectively utilize gold and government bonds as combinatorial collateral. Insolvency Risk of European SMEs during Pandemic Kaya, Orcun SSRN Covid-19 pandemic poses an existential threat to European SMEs' financial resilience with significant consequences for the European economy. Using a unique firmâ€level survey data on SME financing conditions and a new measurement approach, this paper focuses on the insolvency risk of European SMEs and their access to finance around pandemic. We show that SME insolvency risk increased, on average, by around 21% during the pandemic. Finding customers and the cost of production and labor have contributed notably to SME insolvency risk around this period. Heightened insolvency risk results in deterioration in expected access to finance in general. During the pandemic, though, no particular worsening is observed in access to bank lending. Overall, our results have important policy implications for designing suitable policy measures to mitigate SME liquidity shortages and avoid unnecessary insolvencies during and in the aftermath of the pandemic. Interest and credit risk management in German banks Drager, Vanessa,Heckmann-Draisbach, Lotta,Memmel, Christoph SSRN Using unique data of a survey among small and medium-sized German banks, we analyze various aspects of risk management. We especially analyze the effect of a 200-bp increase in the interest level. We find that banks seem to reduce the volatility of their net interest margin by exposing themselves to interest rate risk, that they act as if they have a risk budget which they allocate either to interest rate risk or credit risk and that banksâ€™ exposures to interest rate risk and to credit risk are remunerated. In addition, we find that, in the first year, the impairments of banksâ€™ bond portfolios are much larger than the reductions in their net interest income, that banks attenuate the resulting write-downs by liquidating hidden reserves and that banks which use interest derivatives have lower impairments in their bond portfolios. Internal Capital and Investment: Evidence from 2012 Pension Relief Kubick, Thomas R.,Lockhart, G. Brandon,Robinson, John R. SSRN We use changes in mandatory pension funding to investigate the relation between internal financing constraints and incremental investment. Pension funding relief enacted in 2012 significantly reduced mandatory employer contributions to defined benefit pension plans. Prior to learning of the pension relief, firms disclosed their expected pension contributions under FAS 132R, which allows us to infer changes in investment plans in response to this unanticipated pension relief. Although our setting is pensions, our inferences contribute to the broader literature on how access to finance impacts the firm. We predict and find that pension relief resulted in increased non-pension investment in the year after enactment for financially constrained firms, and is stronger for constrained firms with greater pension underfunding. Our results are consistent with pension relief providing an important means of funding investment for financially constrained firms. Our identification strategy and results provide an important innovation to the literature examining the effect of financing constraints on investment. Interplay between Accounting Conservatism, Auditing Conservatism and Quality of Earnings in Oman Al Ani, Mawih Kareem,Chong, H Gin SSRN This paper examines the impact of accounting conservatism and auditing conservatism on earnings quality. Four proxies were used to measure earnings quality; persistence, accrual quality, value relevance and smoothness. We use the published annual reports of all the listed firms in the Muscat Securities Market (MSM) for the 6-year period from 2012-2017 to assess the interplay between accounting conservatism, auditing conservatism and earnings quality. The result reveals a positive and significant effect of auditing conservatism on earnings quality but no significant effect of accounting conservatism on earnings quality in terms of smoothness of earnings. This implies users tend to rely on auditorsâ€™ reports when assessing earnings quality. Our results are robust to the inclusion of four control variables; size of the firm, risk, audit firm size and industry type, the use of an additional analysis of one special case of auditing conservatism and earnings quality, and tests of potential relationship between auditing conservatism. The findings have implications to regulators when formulating standards and guidelines, auditors in the course of an audit, investors in reviewing the financial statements, and preparers when preparing their financial reports. The study recommends that the Omani stockholders as well as international stockholders rely heavily on auditing conservatism which means that stockholders are prepared to receive modified audit reports. Interpreting Deep Learning Models with Marginal Attribution by Conditioning on Quantiles M. Merz,R. Richman,T. Tsanakas,M.V. Wüthrich arXiv A vastly growing literature on explaining deep learning models has emerged. This paper contributes to that literature by introducing a global gradient-based model-agnostic method, which we call Marginal Attribution by Conditioning on Quantiles (MACQ). Our approach is based on analyzing the marginal attribution of predictions (outputs) to individual features (inputs). Specificalllly, we consider variable importance by mixing (global) output levels and, thus, explain how features marginally contribute across different regions of the prediction space. Hence, MACQ can be seen as a marginal attribution counterpart to approaches such as accumulated local effects (ALE), which study the sensitivities of outputs by perturbing inputs. Furthermore, MACQ allows us to separate marginal attribution of individual features from interaction effect, and visually illustrate the 3-way relationship between marginal attribution, output level, and feature value. Investors Activity and Contagion Park, A-young,Oh, Gabjin SSRN The systemic risk induced by a connection among financial objects is generally measured by returns, volatility, interbank loans, etc. Nevertheless, these measures do not capture the microscale component of the interconnections induced by heterogeneous investor activity. In this paper, we exploit the information in the connectedness among heterogeneous investors to develop an alternative systemic risk measure. The interconnections embed the systemic risk induced by heterogeneous investors' trading activity because investor strategies incorporate information into stock prices in terms of market stability and information spillover in the network through the information-sharing process among different investor groups. We test whether systemic risk stems from heterogeneous investors' activity. We find that the retail investors with positive net information flows, which are defined as the source in an information network, engage in destabilization regardless of their market status, suggesting that retail investors could significantly contribute to market instability. The connectedness among investors disentangles the financial crisis period from the normal market states, quantifies the systemic risk associated with "too interconnected to fail", and plays a detrimental role in financial stability. We find that the portfolio set constructed based on the systemic risk using the heterogeneous investor activity has a negative relationship with the expected returns. Our findings suggest that the connectedness among investor groups has a significant impact on systematic risk and would be crucial for regulations and policymaking. Labor Unions and Product Quality Failures Kini, Omesh,Shen, Mo ,Shenoy, Jaideep,Subramaniam, Venkat SSRN In this paper, we study the impact of labor unions on product quality failures. We use a product recall as our measure of quality failure because it is an objective metric that is applicable to a broad cross-section of industries. Our analysis employs a union panel setting and close union elections in a regression discontinuity design framework to overcome identification issues. In the panel regressions, we find that firms that are unionized and those that have higher unionization rates experience a greater frequency of quality failures. The results obtain even at a more granular establishment level in a subsample where we can identify the manufacturing establishment associated with the recalled product. When comparing firms in close elections, we find that firms with close union wins are followed by significantly worse product quality outcomes than those with close union losses. These results are amplified in non-right-to-work states, where unions have a relatively greater influence on the workforce. We find that unionization increases firmsâ€™ costs and operating leverage and, consequently, crowds out investments that potentially impact quality. We also find some suggestive evidence that unions may compromise quality by hurting employee morale and by resisting technological upgrades in the firm. Overall, our results suggest that unions have an adverse impact on product recalls and, thus, product quality is an important dimension along which unions impact businesses. Leading from behind: Sovereign credit ratings during COVID-19 pandemic , Yen,, Huong,Klusak, Patrycja,, Moritz,, Tri SSRN Using 311 sovereign rating actions by the three leading global rating agencies between January and August 2020, we show that severity of sovereign ratings actions is not affected by the intensity of the COVID-19 health crisis (proxied by case and mortality rates). We find that economic repercussions of the pandemic such as economic outlook of a country and governmentsâ€™ response to the health crisis, and not the severity of the pandemic itself, determine the intensity of negative rating actions. Contrary to expectations, credit rating agencies pursued mostly a business-as-usual approach and reviewed sovereign ratings when they were due for regulatory purposes rather than in response to the rapid developments of the pandemic. Despite the disappointing reaction to the ongoing pandemic, sovereign rating news from S&P and Fitch still conveyed price-relevant information to the bond markets. Multicriteria asset allocation in practice Kerstin Dächert,Ria Grindel,Elisabeth Leoff,Jonas Mahnkopp,Florian Schirra,Jörg Wenzel arXiv In this paper we consider the strategic asset allocation of an insurance company. This task can be seen as a special case of portfolio optimization. In the 1950s, Markowitz proposed to formulate portfolio optimization as a bicriteria optimization problem considering risk and return as objectives. However, recent developments in the field of insurance require four and more objectives to be considered, among them the so-called solvency ratio that stems from the Solvency II directive of the European Union issued in 2009. Moreover, the distance to the current portfolio plays an important role. While literature on portfolio optimization with three objectives is already scarce, applications with four and more objectives have not yet been solved so far by multi-objective approaches based on scalarizations. However, recent algorithmic improvements in the field of exact multi-objective methods allow the incorporation of many objectives and the generation of well-spread representations within few iterations. We describe the implementation of such an algorithm for a strategic asset allocation with four objective functions and demonstrate its usefulness for the practitioner. Our approach is in operative use in a German insurance company. Our partners report a significant improvement in their decision making process since, due to the proper integration of the new objectives, the software proposes portfolios of much better quality than before within short running time. Municipal Markets and the Municipal Liquidity Facility Fritsch, Nicholas,Bagley, John,Nee, Shawn SSRN Municipal bond markets experienced a significant amount of strain in response to the COVID-19 crisis, creating liquidity and credit concerns among market participants. During the economic shutdown resulting from the pandemic, income tax revenues were deferred and sales tax revenues decreased beginning in spring 2020, while the cost of borrowing significantly increased for municipal issuers. To aid municipal borrowing needs, the Federal Reserve implemented the Municipal Liquidity Facility (MLF) on April 9, 2020. In this analysis we describe the municipal market conditions as they evolved during 2020, we document the response by the Federal Reserve to municipal market distress with a focus on the MLF, and we conduct an event study to examine MLF-related impacts on market index yield spreads. We detail two case studies that compare yield spreads for two issuers that had sold debt to the MLF and find that yield spreads in secondary market transactions for these two issuers were notably reduced after a public announcement of intent to sell debt to the MLF. Our results present additional evidence that the MLF had a positive impact on municipal market functioning during the pandemic period. On the asymmetric relationship between stock market development, energy efficiency and environmental quality: A nonlinear analysis Goutte, Stephane,Guesmi, Khaled,Mhadhbi, Mayssa,Gallali, Mohamed SSRN It has been widely documented in the literature that financial development drives up the impact of CO2 emissions through increases in real economic activities and the consumption of polluting fossil fuel energy. However, when dealing with stock market development, such upward effects on economic growth, energy efficiency, and carbon emissions seems to give away to a positive impact especially in emerging markets. This paper contributes to this debate by exploring both the symmetric and asymmetric responses of CO2 emission to changes in stock market development indicators. In particular, using both the panel linear and nonlinear ARDL, our results demonstrate the asymmetric effects of stock market development indicators on carbon emissions in the context of emerging markets. In particular, the long-run elasticities results suggest that positive and negative shocks on stock market indicator decreases environ- mental quality by increasing carbon emissions. Based on these empirical findings, this study offers some crucial policy implications Optimal exit decision of venture capital under time-inconsistent preferences Yanzhao Li,Ju'e Guo,Yongwu Li,Xu Zhang arXiv This paper proposes two kinds of time-inconsistent preferences (i.e. time flow inconsistency and critical time point inconsistency) to further advance the research on the exit decision of venture capital. Time-inconsistent preference, different from time-consistent preference, assumes that decision makers prefer recent returns rather than future returns. Based on venture capitalists' understanding of future preferences, we consider four types of venture capitalists, namely time-consistent venture capitalists, venture capitalists who only realize critical time point inconsistency, naive venture capitalists and sophisticated venture capitalists, of which the latter three are time-inconsistent. All types of time-inconsistent venture capitalists are aware of critical time point inconsistency. Naive venture capitalists misunderstand time flow inconsistency while sophisticated ones understand it correctly. We propose an optimal exit timing of venture capital model. Then we derive and compare the above four types of venture capitalists' exit thresholds. The main results are as follows: (1) all types of time-inconsistent venture capitalists tend to exit earlier than time-consistent venture capitalists. (2) The longer the expire date are, the more likely venture capitalists are to delay the exit, but the delay degree decreases successively (venture capitalists who only realize critical time point inconsistency > naive venture capitalists > sophisticated venture capitalists). Platform Reputation Effects in Lendersâ€™ Decisions to Invest in Peer-to-Peer Lending in Indonesia Zaehira, Inesya,Dalimunthe, Zuliani ,Triono, Rachmadi Agus SSRN The advancement of financial technology has given rise to the online peer-to-peer lending (P2P lending) platform to provide loans. P2P lending provides loans without the role of financial intermediaries such as banks. Further, P2P lending is expected to increase access to financing for businesses, especially for micro and small businesses, which typically find it difficult to obtain funding from formal financial institutions. However, P2P lending platforms include several concerns to lenders, as they canâ€™t always find the same guaranteed security in P2P lending as conventional banks provide. Thus, we predict that platform reputation is critical in this sector. Specifically, this study aims to determine what factors form investorsâ€™ perceptions on P2P lending platform reputation, evaluate the relationship between platform reputation and lendersâ€™ willingness to provide loans, and evaluate whether the trust will strengthen the relationship. Using 160 lenders as respondents, this study was conducted using the regression analysis method with the help of SPSS 23 software to test the proposed models. The results show that security and protection have the greatest influence on platform reputation. Reputation itself was found to have a positive impact on lendersâ€™ willingness to lend; however, trust is found to have no moderating effect and, instead, has a positive influence on the lendersâ€™ investment decisions as an independent variable. Considering that P2P lending can increase SMEâ€™s access to finance, the collaboration of conventional financial institutions and technology will create a relationship that benefits both parties. With collaboration, P2P lending platforms can generate a good reputation quickly and economically, while conventional financial institutions can increase customer bases to a wider range of borrowers from the SME sector. Political Insider Trading: A narrow versus comprehensive approach Hanousek Jr., Jan,Park, Jung Chul,Pantzalis, Christos SSRN We examine senatorsâ€™ electronically filed stock transactions between 2012 and 2019 to assess the extent of politicianâ€™s insider trading. Our results suggest that senators use inside political information when investing and earn significant market-adjusted returns. To extend traditional return-based methods, we propose a new comprehensive approach based on abnormal idiosyncratic volatility (AIV), which captures the degree of information asymmetry around their trading dates. We document that senator trades are associated with substantially high levels of AIV, suggesting that they represent only a tip of the iceberg, since the mass of unfiled transactions using the same inside information remains undetected. Purchase history and product personalization Laura Doval,Vasiliki Skreta arXiv Product personalization opens the door to price discrimination. A rich product line allows for higher consumer satisfaction, but the mere choice of a product carries valuable information about the consumer that the firm can leverage for price discrimination. Controlling the degree of product personalization provides the firm with an additional tool to curb ratcheting forces arising from consumers' awareness of being price discriminated. Indeed, a firm's inability to not engage in price discrimination introduces a novel distortion: The firm offers a subset of the products that it would offer if, instead, the firm could commit to not price discriminate. Doing so gives commitment power to the firm: By "pooling" consumers with different tastes to the same variety the firm commits not to learn their tastes. Real GDP per capita: global redistribution of economic power Ivan Kitov arXiv Growth rate of real GDP per capita, GDPpc, is represented as a sum of two components, a monotonically decreasing economic trend and fluctuations related to population change. The economic trend is modelled by an inverse function of GDPpc with a constant numerator which varies for the largest developed economies. In 2006, a statistical analysis conducted for 19 selected OECD countries for the period between 1950 and 2003 showed a very weak linear trend in the annual GDPpc increment for the largest economies: the USA, Japan, France, Italy, and Spain. The UK, Australia, and Canada showed a slightly steeper positive linear trend. The 2012 revision showed that the positive trends became much lower and some of them fell below zero due to the Great Recession. The fluctuations around the trend values are characterized by a quasi-normal distribution with heavy and asymmetric tails. This research revises the previous estimates and extends the set of studied countries by economies in East Europe, Latin America, BRICS, Africa, and Asia including several positive outliers with extremely fast growth. The change in GDP definitions and measuring procedures with time and economic source is discussed in relation to the statistical significance of the trend estimates and data quality requirements for a consistent economic model. The relative performance of all counties since 1960 is compared according to the predicted total GDPpc growth as a function of the initial value. The performance in the 21st century is analyzed separately as revealing potential and actual shifts in the global economic powers. Rethinking Capital Structure Decision and Corporate Social Responsibility in Response to COVID-19 Huang, He,Ye, April SSRN COVID-19 has severely constricted the global economic activities. This paper examines the joint effect of capital structure and corporate social responsibility (CSR) activities on firm risk during COVID-19. We find that firms having excessive debt beyond the optimal level experienced high firm risk during the pandemic and the effect is more prevalent among firms with poor CSR performance. In contrast, firms with a debt level below the optimum are self-protected regardless of their CSR practices. Our study provides businesses with insights of post-pandemic directions on capital structure and CSR policies to build up sustainability and resilience in a volatile market. Risk aggregation and capital allocation using a new generalized Archimedean copula Fouad Marri,Khouzeima Moutanabbir arXiv In this paper, we address risk aggregation and capital allocation problems in the presence of dependence between risks. The dependence structure is defined by a mixed Bernstein copula which represents a generalization of the well-known Archimedean copulas. Using this new copula, the probability density function and the cumulative distribution function of the aggregate risk are obtained. Then, closed-form expressions for basic risk measures, such as tail value-at-risk(TVaR) and TVaR-based allocations, are derived. Risk, Agricultural Production, and Weather Index Insurance in Village India Jeffrey D. Michler,Frederi G. Viens,Gerald E. Shively arXiv We investigate the sources of variability in agricultural production and their relative importance in the context of weather index insurance for smallholder farmers in India. Using parcel-level panel data, multilevel modeling, and Bayesian methods we measure how large a role seasonal variation in weather plays in explaining yield variance. Seasonal variation in weather accounts for 19-20 percent of total variance in crop yields. Motivated by this result, we derive pricing and payout schedules for actuarially fair index insurance. These calculations shed light on the low uptake rates of index insurance and provide direction for designing more suitable index insurance. Robust Portfolio Rebalancing with Cardinality and Diversification Constraints Zhao, Zhi-Hua SSRN In this paper, we develop a robust conditional value at risk (CVaR) optimal portfolio rebalancing model under various financial constraints to construct sparse and diversified rebalancing portfolios. Our model includes transaction costs and double cardinality constraints in order to capture the trade-off between the limit of investment scale and the diversified industry coverage requirement. We first derive a closed-form solution for the robust CVaR portfolio rebalancing model with only transaction costs. It allows us to conduct industry risk analysis for sparse portfolio rebalancing in the absence of diversification constraints. Then, we attempt to remedy the hidden industry risk by establishing a new robust portfolio rebalancing model with both sparse and diversified constraints. This is followed by the development of a distributed-version of the Alternating Direction Method of Multipliers (ADMM) algorithm, where each subproblem admits a closed-form solution. Finally, we conduct empirical tests to compare our proposed strategy with the standard sparse rebalancing and no-rebalancing strategies. The computational results demonstrate that our rebalancing approach produces sparse and diversified portfolios with higher industry coverage. Additionally, to measure out-of-sample performance, two superiority indices are created based on the worst-case CVaR and annualized return, respectively. Our ADMM strategy also outperforms the sparse rebalancing and no-rebalancing strategies in terms of the two indices. Runs to Bank: The Role of Sweep Banking Deposits During Market Downturns Barth, James R.,Mitchell, Mark L.,Sun, Yanfei SSRN Sweep deposits from brokerage firms to banks vary inversely with the stock market. When the stock market declines, retail investors reduce risk and sell stocks, with the proceeds typically swept out of brokerage firms and into banks. The relation is asymmetric as sweep deposits do not appear to respond to positive movements in the stock market. Overall, sweep deposits are a primary driver backing the same asymmetric relation between domestic bank deposits and the stock market, and are not destabilizing, but instead stabilizing for banks as households reduce risk by converting stock to cash during periods of high stress. Simple Bounds for Utility Maximization with Small Transaction Costs Bruno Bouchard,Johannes Muhle-Karbe arXiv Using elementary arguments, we show how to derive$\mathbf{L}_p\$-error bounds for the approximation of frictionless wealth process in markets with proportional transaction costs. For utilities with bounded risk aversion, these estimates yield lower bounds for the frictional value function, which pave the way for its asymptotic analysis using stability results for viscosity solutions. Using tools from Malliavin calculus, we also derive simple sufficient conditions for the regularity of frictionless optimal trading strategies, the second main ingredient for the asymptotic analysis of small transaction costs.

Solving the Equity Premium Puzzle by unifying Economics and Finance
Vanoverberghe, Didier
SSRN
The Equity risk-premium and volatility puzzle - is it possible to have a high equity premium and a low risk-free rate with a plausible risk aversion- have received a great deal of attention but beyond this question, the fundamental issues of that puzzle are the followings: what are the economic representations that can provide such results? What are the relevant links between finance and economics? And what should be the consequences for economic decisions makers?The classic ways to model the financial economy with a representative agent placed in a Lucas tree model, i.e. maximizing consumption-based utility, where fruit is equivalent to dividend and consumption, failed to explain a high equity premium and a low risk free rate. Even more, simple changes in reasoning failed to provide a consistent macroeconomic and finance representation that sticks to reality. This paper presents a new eco-financial approach based on three major changes: the definition of Wealth and wealth increment and their utility for any agent instead of consumption, a permanent change of equilibrium theory, a more realistic model in which the agents fear much more crises than ordinary fluctuations. This model borrows two key principles from the model developed in Modigliani and Millerâ€™s seminal papers: firstly an economy with investment opportunities in the market of goods and services and secondly an economy where rational agents always prefer more wealth to less and are indifferent as to whether a given increment to their wealth takes the form of dividend or growth in value .Main changes come from, we generalized this wealth approach to any agent, in a changing of equilibrium world, where crises are much more dreaded than ordinary negative events.We will show that it is a way to solve the equity premium and to make consistent: macro, micro, finance and reality.

Stock Market Participation and Portfolio Shares Over the Life-Cycle
Gomes, Francisco,Smirnova, Oksana
SSRN
We estimate the life-cycle profile of stock market participation and risky portfolio share. We address the classical identification problem by running the estimations in first differences, which allows us to estimate the age profiles without making any assumptions on time or cohort effects. We find that stock market participation is a hump-shaped function of age, increasing early in life and decreasing from age 60 onwards. The conditional risky share also decreases late in life, but it is a flat function of age before that. We also investigate the economic mechanisms driving this behavior. Our results provide empirical support for the importance of participation costs in explaining stock market participation, and for models where investors have decreasing relative risk aversion and where human capital is a close substitute for bonds, although not completely uncorrelated with stock returns. Finally, background risks are also likely to play a role, particularly late in life. We conclude by presenting a structural life-cycle model that closely replicates our empirical results.

Strategic Asset Allocation for Endowment Funds
SSRN
An endowment fund serves as a permanent source of capital to support a specific mission. It has, in the case of a university, dual goals of providing current financial support to the university and preserving long-term purchasing power to ensure that support continues in perpetuity. This article explores methodology for constructing strategic asset allocation for endowment funds in the context of these competing long-term investment goals.Key findings:â–ª In constructing a strategic asset allocation for an endowment, a traditional mean-variance optimization framework alone does not provide sufficient context to generate comprehensive forward-looking analysis. However, adding additional measures such as maximizing the probability of achieving investment goals or minimizing relevant downside distress measures over the long term can improve the analysis.â–ª A typical endowmentâ€™s asset allocation, with an equity orientation and tilt towards alternative asset classes, nearly maximizes the probability of achieving the dual long-term investment goals of meeting a spending rate target while preserving real capital, as well as minimizes endowment-specific downside risk measures.â–ª Optimal portfolio choices are dependent on underlying return expectations across asset classes. Although expected returns are typically the highest for private investments, there are limits to investing in private asset classes, including an endowmentâ€™s liquidity requirements and ability to access the best-performing managers.

Strategic Press Release When Employee Unemployment Risk is Better Insured
Ding, Yi,Li, Bing,Liu, Zhenbin,Wang, Xiaoqiao
SSRN
Using the staggered changes in unemployment insurance (UI) benefits across states in the US as an exogenous shock to employeesâ€™ costs of unemployment and the hiring firmâ€™s costs of disclosure, this study investigates whether and how a firmâ€™s relationship with its employees impacts its strategic disclosure through press releases. We find that firms increase (reduce) bad (good) press releases after their employeesâ€™ UI benefits become more generous. This finding is consistent with the notion that larger UI benefits reduce the costs of employee employment risk borne by hiring firms and thus reduce firmsâ€™ costs to release more bad news items. Interestingly, we find that the large increase in UI benefits is associated with more discretionary, but not nondiscretionary, bad press releases. The findings are robust when we exclude earnings-related press releases from our sample. Further evidence shows that the increase in negative press releases is mainly driven by news regarding earnings, products-services, marketing, credit-ratings, assets, and legal issues, which are more closely related to employee perceptions of unemployment risk.

Support Vector Regression Parameters Optimization using Golden Sine Algorithm and its application in stock market
arXiv

Support vector machine modeling is a new approach in machine learning for classification showing good performance on forecasting problems of small samples and high dimensions. Later, it promoted to Support Vector Regression (SVR) for regression problems. A big challenge for achieving reliable is the choice of appropriate parameters. Here, a novel Golden sine algorithm (GSA) based SVR is proposed for proper selection of the parameters. For comparison, the performance of the proposed algorithm is compared with eleven other meta-heuristic algorithms on some historical stock prices of technological companies from Yahoo Finance website based on Mean Squared Error and Mean Absolute Percent Error. The results demonstrate that the given algorithm is efficient for tuning the parameters and is indeed competitive in terms of accuracy and computing time.

Technical Note: Parameterised-Response Zero-Intelligence (PRZI) Traders
Dave Cliff
arXiv

Term Structure of Equity and Bond Yields over Business Cycles
Zeng, Ming,Zhao, Guihai
SSRN
Recent findings on the term structure of equity and bond yields pose serious challenges to existing equilibrium asset pricing models. This paper presents a new equilibrium model to explain the joint historical dynamics of equity and bond yields (and their yield spreads). Equity/bond yields movements are mainly driven by subjective dividend/GDP growth expectation. Yields on short-term dividend claims are more volatile because the short-term dividend growth expectation is mean-reverting to its less volatile long-run counterpart. The procyclical slopes of spot and forward equity yields are due to the counter-cyclical slope of dividend growth expectations. Returns on long-term dividend claims have higher volatilities and co-move more strongly with the market, because of stronger belief revisions over long-term dividend growth. The correlation between equity returns/yields and nominal bond returns/yields switched from positive to negative after the late 1990s, owing to (1) procyclical inflation and (2) higher correlation between expectations of real GDP and of real dividend growth post-2000. The model is also consistent with the data in generating persistent and volatile price-dividend ratios, excess return volatility, and return predictability.

The Importance of Funding Space-Based Research
Devan Taylor
arXiv

When it comes to conversations about funding, the questions of whether the United States should be spending its resources on space-based research often rears its head. Opponents of the idea tend to share the opinion that the resources would be better spent helping citizens on the ground. With an estimated homeless population around 562,000 throughout the country, roughly 39.4 million Americans (12.3% of the population) living below the poverty level, and 63.1 million tons of food waste per year, it's hard to argue that the United States does not have its share of problems that need to be addressed. However, a history of space-based research has proven time and time again to bring forth advances in technology and scientific understanding that benefit humans across the globe and provide crucial protection for life on Earth.

Schwartz, David S.,Mikhail, John
SSRN

The black hole of logistics costs of digitizing commodity money
Boliang Lin,Ruixi Lin
arXiv

In this paper, we reveal the depreciation mechanism of representative money (banknotes) from the perspective of logistics warehousing costs. Although it has long been the dream of economists to stabilize the buying power of the monetary units, the goal we have honest money always broken since the central bank depreciate the currency without limit. From the point of view of modern logistics, the key functions of money are the store of value and low logistics (circulation and warehouse) cost. Although commodity money (such as gold and silver) has the advantages of a wealth store, its disadvantage is the high logistics cost. In comparison to commodity money, credit currency and digital currency cannot protect wealth from loss over a long period while their logistics costs are negligible. We proved that there is not such honest money from the perspective of logistics costs, which is both the store of value like precious metal and without logistics costs in circulation like digital currency. The reason hidden in the back of the depreciation of banknotes is the black hole of storage charge of the anchor overtime after digitizing commodity money. Accordingly, it is not difficult to infer the inevitable collapse of the Bretton woods system. Therefore, we introduce a brand-new currency named honest devalued stable-coin and built a attenuation model of intrinsic value of the honest money based on the change mechanism of storage cost of anchor assets, like gold, which will lay the theoretical foundation for a stable monetary system.

The impact of genetic diversity of executive board directors on corporate misconduct: Evidence from US banks
Thornton, John,Vasilakis, Chrysovalantis
SSRN
We examine the link between the genetic diversities of executive board members and bank financial misconduct. The premise is that genetic diversity results in different perspectives, skills, and abilities that impact on the effectiveness of executive board guidance and monitoring, including with respect to misconduct. Employing a panel of US banks over 1998-2019 we find that adding directors from countries with different levels of genetic diversity is negatively associated with financial misconduct as measured by enforcements and class action litigation against banks by the main regulatory agencies. In addition, the relation between genetic diversity and misconduct is hump-shaped, suggesting that there a trade-off between the beneficial and detrimental effects of genetic diversity on board monitoring and guidance. These results are robust to controlling for bank specific variables, including other board characteristics, and to the use of instrumental variables.

To vote, or not to vote: on the epidemiological impact of electoral campaigns at the time of COVID-19
Davide Cipullo,Marco Le Moglie
arXiv

Elections are crucial for legitimating modern democracies, and giving all candidates the possibility to run a proper electoral campaign is necessary for elections' success in providing such legitimization. Yet, during a pandemic, the risk that electoral campaigns would enhance the spread of the disease exists and is substantive. In this work, we estimate the causal impact of electoral campaigns on the spread of COVID-19. Exploiting plausibly exogenous variation in the schedule of local elections across Italy, we show that the electoral campaign preceding this latter led to a significant worsening of the epidemiological situation related to the disease. Our results strongly highlight the importance of undertaking stringent measures along the entire electoral process to minimize its epidemiological consequences.

Unbiased estimation and backtesting of risk in the context of heavy tails
Marcin Pitera,Thorsten Schmidt
arXiv

While the estimation of risk is an important question in the daily business of banks and insurances, many existing plug-in estimation procedures suffer from an unnecessary bias. This often leads to the underestimation of risk and negatively impacts backtesting results, especially in small sample cases. In this article we show that the link between estimation bias and backtesting can be traced back to the dual relationship between risk measures and the corresponding performance measures, and discuss this in reference to value-at-risk and expected shortfall frameworks. Motivated by this finding, we propose a new algorithm for bias correction and show how to apply it for generalized Pareto distributions. In particular, we consider value-at-risk and expected shortfall plug-in estimators, and show that the application of our algorithm leads to gain in efficiency when heavy tails exist in the data.

Understanding algorithmic collusion with experience replay
Bingyan Han
arXiv

In an infinitely repeated pricing game, pricing algorithms based on artificial intelligence (Q-learning) may consistently learn to charge supra-competitive prices even without communication. Although concerns on algorithmic collusion have arisen, little is known on underlying factors. In this work, we experimentally analyze the dynamics of algorithms with three variants of experience replay. Algorithmic collusion still has roots in human preferences. Randomizing experience yields prices close to the static Bertrand equilibrium and higher prices are easily restored by favoring the latest experience. Moreover, relative performance concerns also stabilize the collusion. Finally, we investigate the scenarios with heterogeneous agents and test robustness on various factors.

WOMEN AND FINANCE
Capelle-Blancard, Gunther,Reberioux, Antoine
SSRN
This literature review brings together the main results of gender studies in finance. We first examine possible gender differences in financial preferences and attitudes, as well as stereotypes against women. We then discuss the consequences in terms of saving and portfolio choices, careers in the financial industry and access to leadership positions in banks and central banks. Six main results emerge: (i) surveys suggest small differences, if any, across gender in risk preferences and financial attitudes; (ii) the differences should not be seen as a mere revelation of preferences, but rather as the result of strong impregnation of stereotypes; (iii) these stereotypes are detrimental to womenâ€™s investment choices and access to credit; (iv) observed differences are sensitive to socioeconomic context, past experience, and education; (v) women have a persistent limited access to top positions in the financial industry; (vi) there is no strong evidence for a specific female-style of leadership in the financial sector or for supervisory authorities. Overall, and as summarized in Figure 1, our review strongly suggests that even small differences (if any) lead to large gender inequalities, through socialization and self-fulfilling stereotypes.

What Factors are Relevant for Success in UK Equity Crowdfunding?
Du, Lisha,Bartholomae, Florian W.,Stumpfegger, Eva
SSRN
Equity crowdfunding has been gaining more and more relevance as an alternative way for entrepreneurs to raise capital. This paper examines the impact of retained equity, business angel backing, grants and intellectual property rights on the success of equity crowdfunding. Using data from Crowdcube, one of the leading equity crowdfunding platforms in the world, we obtain 473 pitches within the period from March 2017 to February 2020. We apply three empirical methods to analyse our data set: logistic regression, multiple linear regression and negative binomial regression. By running univariate test and several regression analyses, we find that retained equity has a significantly negative impact on funding success; also, the support of business angels helps entrepreneurs to achieve a funding success. While winning grants is more likely to attract investors, there is no direct evidence associated with funding success. Furthermore, it turns out that intellectual property rights are not relevant for funding success. In an extension, we re-examine our analysis in the light of the Corona crisis by analysing 95 pitches from March to November 2020. We find no structural changes in relevance of the success factors.

Why Do Wealthy Investors have a Higher Return on their Stocks?
Bonaparte, Yosef
SSRN
An analysis of the Survey of Consumer Finance shows that wealthy investors have a higher return on their stocks than their poorer counterparts. Three key empirical facts emerge: (i) wealthy investors employ more productive search efforts, (ii) financial risk bearing and search efforts are complementary, and (iii) wealthy investors have a higher risk adjusted return. These facts present a challenge to the â€œstandardâ€ asset pricing theory, which assumes that the return on stocks is uncorrelated with wealth and omits any relationship between search activity and portfolio returns. This study presents a search theoretic model of portfolio choice to understand the relationship between wealth, return, and search behavior.

â€œ503 Service Unavailableâ€: When Acquiring Information Goes Wrong
Heilig, Malte,MÃ¼ller, Maximilian A.,Peter, Caspar David
SSRN
Investors increasingly rely on digital infrastructure to acquire information and we study how a large, unexpected server outage of the SECâ€™s EDGAR system affects liquidity to characterize the resilience and capital market effect of information acquisition in the digital age. The SECâ€™s EDGAR system receives millions of requests per day for information on publicly listed firms and we find that liquidity worsens for firms more strongly affected by its outage. This effect is stronger when failed requests stem from funds and financial institutions, when they are algorithmic in nature, and when they target information not easily available elsewhere.