Research articles for the 2020-08-16

A Spatial Stochastic SIR Model for Transmission Networks with Application to COVID-19 Epidemic in China
Tatsushi Oka,Wei Wei,Dan Zhu

Governments around the world have implemented preventive measures against the spread of the coronavirus disease (COVID-19). In this study, we consider a multivariate discrete-time Markov model to analyze the propagation of COVID-19 across 33 provincial regions in China. This approach enables us to evaluate the effect of mobility restriction policies on the spread of the disease. We use data on daily human mobility across regions and apply the Bayesian framework to estimate the proposed model. The results show that the spread of the disease in China was predominately driven by community transmission within regions and the lockdown policy introduced by local governments curbed the spread of the pandemic. Further, we document that Hubei was only the epicenter of the early epidemic stage. Secondary epicenters, such as Beijing and Guangdong, had already become established by late January 2020, and the disease spread out to connected regions. The transmission from these epicenters substantially declined following the introduction of human mobility restrictions across regions.

A theory for combinations of risk measures
Marcelo Brutti Righi

We study combinations of risk measures under no restrictive assumption on the set of alternatives. We develop and discuss results regarding the preservation of properties and acceptance sets for the combinations of risk measures. One of the main results is the representation for resulting risk measures from the properties of both alternative functionals and combination functions. To that, we build on the development of a representation for arbitrary mixture of convex risk measures. In this case, we obtain a penalty that recalls the notion of inf-convolution under theoretical measure integration. As an application, we address the context of probability-based risk measurements for functionals on the set of distribution functions. We develop results related to this specific context. We also explore features of individual interest generated by our framework, such as the preservation of continuity properties, the representation of worst-case risk measures, stochastic dominance and elicitability. We also address model uncertainty measurement under our framework and propose a new class of measures for this task.

Algorithmic Discrimination and Input Accountability under the Civil Rights Acts
Bartlett, Robert P.,Morse, Adair,Wallace, Nancy,Stanton, Richard
The disproportionate burden of COVID-19 among communities of color, together with a necessary renewed attention to racial inequalities, have lent new urgency to concerns that algorithmic decision-making can lead to unintentional discrimination against members of historically marginalized groups. These concerns are being expressed through Congressional subpoenas, regulatory investigations, and an increasing number of algorithmic accountability bills pending in both state legislatures and Congress. To date, however, prominent efforts to define algorithmic accountability have tended to focus on output-oriented policies that may facilitate illegitimate discrimination or involve fairness corrections unlikely to be legally valid. Worse still, other approaches focus merely on a model’s predictive accuracyâ€"an approach at odds with long-standing U.S. antidiscrimination law.

An estimator for predictive regression: reliable inference for financial economics
Neil Shephard

Estimating linear regression using least squares and reporting robust standard errors is very common in financial economics, and indeed, much of the social sciences and elsewhere. For thick tailed predictors under heteroskedasticity this recipe for inference performs poorly, sometimes dramatically so. Here, we develop an alternative approach which delivers an unbiased, consistent and asymptotically normal estimator so long as the means of the outcome and predictors are finite. The new method has standard errors under heteroskedasticity which are easy to reliably estimate and tests which are close to their nominal size. The procedure works well in simulations and in an empirical exercise. An extension is given to quantile regression.

Capital Markets, COVID-19 and Policy Measures
ElFayoumi, Khalid,Hengge, Martina
The COVID-19 pandemic and associated policy responses triggered a historically large wave of capital reallocation between markets, asset classes, and industries. Using high-frequency country-level data, we examine if and how the number of infections, the stringency of the lockdown, and the fiscal and monetary policy response determined the dynamics of portfolio flows, market-implied sovereign risk, and stock prices. We find that these factors played an important role, particularly for emerging markets. Our results indicate that domestic infections had an initial negative impact on flows. Cumulatively, however, the effect was positive and reflected increased demand for financing by affected economies. We also find that both lockdown and fiscal measures supported portfolio flows, driven by an increased supply of funds. Bonds, not equities, were the primary driver of portfolio flows, highlighting a pattern of reallocation to safety. Finally, we show that monetary policy loosening in developed markets led to a cumulative decline in flows, as investors searched for higher yield.

Centralizing-Unitizing Standardized High-Dimensional Directional Statistics and Its Applications in Finance
Yijian Chuan,Lan Wu

Cross-sectional "Information Coefficient" (IC) is a widely and deeply accepted measure in portfolio management. The paper gives an insight into IC in view of high-dimensional directional statistics: IC is a linear operator on the components of a centralizing-unitizing standardized random vector of next-period cross-sectional returns. Our primary research first clearly defines IC with the high-dimensional directional statistics, discussing its first two moments. We derive the closed-form expressions of the directional statistics' covariance matrix and IC's variance in a homoscedastic condition. Also, we solve the optimization of IC's maximum expectation and minimum variance. Simulation intuitively characterizes the standardized directional statistics and IC's p.d.f.. The empirical analysis of the Chinese stock market uncovers interesting facts about the standardized vectors of cross-sectional returns and helps obtain the time series of the measure in the real market. The paper discovers a potential application of directional statistics in finance, proves explicit results of the projected normal distribution, and reveals IC's nature.

Financial Knowledge Overconfidence and Early Withdrawals from Retirement Accounts
Lee, Sunwoo T.,Hanna, Sherman D.
Early distributions from retirement accounts could endanger future retirement income security, and the U.S. has restrictions to discourage them, including possible tax penalties. On the other hand, tapping one’s retirement assets may be rational when an individual encounters financial hardship. With the 2020 Coronavirus Aid, Relief, and Economic Security Act (CARES Act), early distribution from retirement accounts became an even more attractive option to individuals. In this study, we examined factors related to individuals’ decision of taking hardship withdrawals and plan loans, focusing on financial knowledge and overconfidence in financial knowledge, using the 2018 National Financial Capability Study dataset. We found evidence that people may be making early withdrawals without understanding possible consequences. Objective financial knowledge was negatively related to hardship withdrawals and plan loans, but the subjective assessment of financial knowledge was positively related to hardship withdrawals. Respondents with financial knowledge overconfidence (high subjective and low objective knowledge) were more likely to take early withdrawals than those with other combinations of objective and subjective knowledge. We discuss implications for public policy and financial education and advice.

Household Asset Portfolios during the Covid-19 Pandemic
Agarwal, Sumit,Li, Keyang,Qin, Yu,Wu, Jing,Yan, Jubo
In this paper, we investigate the determinants of household asset portfolios during the COVID-19 outbreak in China. Using an online longitudinal survey and focusing on a broad spectrum of assets, including stocks, gold, wealth management products, and houses, we show that more confirmed COVID-19 cases in a city led to both more upward and downward adjustments for all four types of assets. The results indicate potential divergent beliefs among the respondents during the pandemic. In addition, respondents were more likely to invest more in gold if they felt more anxious, whereas higher inflation expectation led to less investment in gold.

How Shifting from In-Person to Virtual Shareholder Meetings Affects Shareholders’ Voice
Schwartz-Ziv, Miriam
Shareholder meetings are one of the only opportunities for most investors to interact directly with management. Due to Covid-19, however, shareholder meetings have moved to a virtual format. Analysis of transcripts and recordings of in-person and virtual shareholder meetings in 2019â€"2020 shows that, relative to in-person meetings, the overall time of virtual meetings is 18% shorter, and 29% less time is spent by firms on answering each question. These findings indicate that communication between companies and shareholders is more limited at virtual meetings. To examine if shareholders face challenges in their attempts to increase such communication in virtual meetings, I construct a dataset on shareholders’ attempts to submit questions to virtual shareholder meetings and document several tactics firms use to avoid addressing them. For example, firms explicitly state that no (additional) questions were submitted, whereas I document that multiple questions were submitted by shareholders, but were ignored. Finally, a mechanism that imposes severe restrictions on shareholders’ ability to submit questions at virtual shareholder meetings is uncovered: the use of a non-Broadridge platform to broadcast the meeting. Overall, the paper documents that with regard to 55% of the firms to which shareholders attempted to submit questions, shareholders faced obstacles. The paper concludes with policy recommendations on how virtual shareholder meetings can be designed in ways that foster communication between management and companies.

Kyle-Back Models with risk aversion and non-Gaussian Beliefs
Shreya Bose,Ibrahim Ekren

We show that the problem of existence of equilibrium in Kyle's continuous time insider trading model ([31]) can be tackled by considering a system of quasilinear parabolic equation and a Fokker-Planck equation coupled via a transport type constraint. By obtaining a stochastic representation for the solution of such a system, we show the well-posedness of solutions and study the properties of the equilibrium obtained for small enough risk aversion parameter. In our model, the insider has exponential type utility and the belief of the market on the distribution of the price at final time can be non-Gaussian.

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

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.

No-Arbitrage Symmetries
I.L. Degano,S.E. Ferrando,A.L. Gonzalez

The no-arbitrage property is widely accepted to be a centerpiece of modern financial mathematics and could be considered to be a financial law applicable to a large class of (idealized) markets. The paper addresses the following basic question: can one characterize the class of transformations that leave the law of no-arbitrage invariant? We provide a geometric formalization of this question in a non probabilistic setting of discrete time, the so-called trajectorial models. The paper then characterizes, in a local sense, the no-arbitrage symmetries and illustrates their meaning in a detailed example. Our context makes the result available to the stochastic setting as a special case

Pandemic Risk Factors and the Role of Government Intervention: Evidence from COVID-19 and CMBS Mortgage Performance
Agarwal, Sumit,Ambrose, Brent W.,Lopez, Luis A.,Xiao, Xue
We examine the effects of pandemic risk factors and the Federal government's Payment Protection Program (PPP) on the performance of securitized commercial mortgages. Using administrative data that allow us to observe county-level variation in mortgage performance by property type, we find that the delinquency rate of commercial mortgages increases by approximately 3.4% for every new coronavirus case per 100 capita, on average. We do not find strong evidence of a decrease in delinquencies in counties with more jobs retained through PPP relative to the local labor force, suggesting that PPP loans to small businesses did not ease the financial distress of commercial borrowers. We also document within industry spillover effects at the loan level. A one percentage point increase in the delinquency rate of nearby loans with properties of the same type increases the likelihood of default by approximately 8.8% for lodging, 2.7% for office, and 2.8% for retail.

Short Term Stress of Covid-19 On World Major Stock Indices
Muhammad Rehan,Jahanzaib Alvi,Suleyman Serdar Karaca

The main objective of this study is to check short term stress of COVID-19 on the American, European, Asian, and Pacific stock market indices, furthermore, the correlation between all the stock markets during the pandemic. Secondary data of 41 stock exchange from 32 countries have been collected from website from 1st July 2019 to 14th May 2020 for the stock market and the COVID-19 data has been collected according to the first cases reported in the country, stocks market are classified either developed or emerging economy, further divided according to the subcontinent i.e. America, Europe, and Pacific/Asia, the main focus in the data is the report of first COVID-19 cases. The study reveals that there is volatility in the all the 41 stock market (American, Europe, Asia, and Pacific) after reporting of the first case and volatility increase with the increase of COVID-19 cases, moreover, there is a significant negative relationship between the number of COVID-19 cases and 41 major stock indices of American, Europe, Asia and Pacific, European subcontinent market found more effected from the COVID-19 than another subcontinent, there is Clustering effect of COVID-19 on all the stock market except American's stock market due to smart capital investing.

Understanding Gambling Behavior and Risk Attitudes Using Cryptocurrency-based Casino Blockchain Data
Jonathan Meng,Feng Fu

The statistical concept of Gambler's Ruin suggests that gambling has a large amount of risk. Nevertheless, gambling at casinos and gambling on the Internet are both hugely popular activities. In recent years, both prospect theory and lab-controlled experiments have been used to improve our understanding of risk attitudes associated with gambling. Despite theoretical progress, collecting real-life gambling data, which is essential to validate predictions and experimental findings, remains a challenge. To address this issue, we collect publicly available betting data from a \emph{DApp} (decentralized application) on the Ethereum Blockchain, which instantly publishes the outcome of every single bet (consisting of each bet's timestamp, wager, probability of winning, userID, and profit). This online casino is a simple dice game that allows gamblers to tune their own winning probabilities. Thus the dataset is well suited for studying gambling strategies and the complex dynamic of risk attitudes involved in betting decisions. We analyze the dataset through the lens of current probability-theoretic models and discover empirical examples of gambling systems. Our results shed light on understanding the role of risk preferences in human financial behavior and decision-makings beyond gambling.