# Research articles for the 2020-05-11

A Case Study on Tata Group's Plans to Acquire Stake in GMR Airports Limited (GAL) - in Pursuit of Synergy Gain
Ghosh, Kaushik
SSRN
This paper delves into the case study of Tata Groupâ€™s plan to acquire stake in GMR Airports Limited (GAL) citing the chronology of events, plausible arguments for the acquisition, statutory response for the acquisition proposal culminating into the clearance of the case by The Competition Commission of India (CCI). The case study also examines the controversies happened during the process. It has been identified that post-merger synergy gain is the motivator for the friendly acquisition. All parties engaged in this acquisition are likely to get a magnified benefit well over the sum of individual gains.

Bank Stress Tests and Accounting Discretion
Gounopoulos, Dimitrios,Papanikolaou, Nikolaos I.,Zopounidis, C.
SSRN
We examine stress tests through the lenses of an implicit, underlying incentives mechanism for bank managers to exercise accounting discretion. Our research aim and the relevant components are examined in defined steps by reference to Propensity Score Matching analysis, which is paired with a Difference-in-Difference approach, and a fixed effects regression model. Our findings show that larger banks with an asset portfolio of relatively low quality, reduced profitability, and higher exposure to market-based activities are more likely to be stress tested. Stress tests exacerbate the effort of tested banks to recapitalize their balance sheet and improve their risk profiles. However, they do not significantly affect the key tools that bank managers use to apply accounting discretion. In this vein, tested banks are highly engaged in accounting discretion over loan loss provisions to manage both income and capital figures. Banks with low capital adequacy are found to apply discretionary practices to a greater extent. Moreover, the banks that participated in early stress tests appear to engage in accounting discretion to a higher degree compared to those participated in some later exercise, which reveals an upward movement on the regulatory learning curve. Our results also show that stricter regulatory rules, more robust supervisory regimes, and more transparent economies mitigate the impact that stress tests have on the incentives of the managers of tested banks to exercise accounting discretion even though the relevant incentives cannot be eliminated.

Behavioral and Game-Theoretic Security Investments in Interdependent Systems Modeled by Attack Graphs
Mustafa Abdallah,Parinaz Naghizadeh,Ashish R. Hota,Timothy Cason,Saurabh Bagchi,Shreyas Sundaram
arXiv

We consider a system consisting of multiple interdependent assets, and a set of defenders, each responsible for securing a subset of the assets against an attacker. The interdependencies between the assets are captured by an attack graph, where an edge from one asset to another indicates that if the former asset is compromised, an attack can be launched on the latter asset. Each edge has an associated probability of successful attack, which can be reduced via security investments by the defenders. In such scenarios, we investigate the security investments that arise under certain features of human decision-making that have been identified in behavioral economics. In particular, humans have been shown to perceive probabilities in a nonlinear manner, typically overweighting low probabilities and underweighting high probabilities. We show that suboptimal investments can arise under such weighting in certain network topologies. We also show that pure strategy Nash equilibria exist in settings with multiple (behavioral) defenders, and study the inefficiency of the equilibrium investments by behavioral defenders compared to a centralized socially optimal solution.

Bond and Equity Issuance Activity during COVID-19
Halling, Michael,Yu, Jin,Zechner, Josef
SSRN
We find that bond issues increased substantially since the onset of the Covid-19 crisis in week 12 (March 16-20). This is the case for bonds rated A or higher, but also for bonds rated BBB or lower, although to a lesser extent. We find that the earliest issuers, issuing in weeks 12 and 13, have mostly ratings A or better, have substantial experience from previous bond issues, and are larger. Over the subsequent crisis weeks, the average issue rating deteriorates, issuers are less experienced and they are smaller. This may be partly due to Fed programs which were announced in week 13, possibly enabling a broader segment of firms to access the bond market. Compared to their previous issues, firms choose longer maturities during the crisis. Determinants of corporate bond spreads change substantially during Covid-19. Before, asset tangibility has a highly significant negative effect on spreads, but this is not the case in the crisis. Also, in contrast to normal periods, being a dividend payer has a significant spread-increasing effect during the crisis. Finally, experience from past bond issues significantly reduces credit spreads during the crisis, but not in normal periods. We also provide descriptive statistics for equity issuance activities during the Covid-19 crisis. Here a very different picture emerges. Issuance activity slowed considerably during the crisis, both in terms of numbers and capital raised.

COVID-19: Is the Great Outbreak a Sign of What the Future Has Stowed for the Human Race?
SSRN
COVID-19, the novel coronavirus pandemic, placed the U.S. economy (and capitalism) on a ventilator. A new recently published study has revealed that close to 90% of patients who needed ventilators to breathe did not make it. Of course this is a metaphoric inference, but valuable lessons provided by coronavirus crisis should not be ignored as the previous signs were in the past. The Fed must realize that â€œcreating money out of thin airâ€ (i.e. credit expansion) is nothing but â€œ"legalized counterfeiting" which will only foster even greater pandemics and financial crises in the near future. Since the Fed was created in 1913, financial and economic crises have become more damaging, longer lasting, and costlier. Every time a high-magnitude crisis strikes (financial, economic, or pandemic), to calm people and restore confidence, governments of advance nations and their high profile central banks (Federal Reserve, European Central Bank, Bank of Japan, and Bank of England) rush to enact unprecedented economic relief/stimulus packages which got larger and larger over the years but sources of systemic crises have remained unresolved since the stock market crash of 1929 and the subsequent Great Depression. In todayâ€™s economy, $5 trillion or$10 trillion virus relief package is mindboggling, but will it be enough to prevent a looming recession? A better question to ask is, will the Fedâ€™s infinite money creation out of thin air send American capitalism on a ventilator to the burial ground? In the near future (by 2050), global warming induced climate changes and the resultant catastrophes will make the coronavirus pandemic trivial. Unfortunately, one thing that never changes, in the long-run great financial crises and pandemics kill deprived people in developing and poorest countries.

Can Cryptocurrencies Be a Future Safe Haven for Investors? A Case Study of Bitcoin
SSRN
Purpose: This study attempts to determine whether or not Bitcoin prices are affected by real economic activity, financial markets, and foreign exchange markets; and whether Bitcoin has the potential to be a safe haven for investors.Background: Cryptocurrencies in general, and Bitcoin in particular, have generated a huge interest from both practitioners and academicians as an alternative mechanism for electronic payment systems. While some argue that â€œBitcoin works in practice, but not in theoryâ€, others argue that Bitcoinâ€™s stability depends on unknown variables in unknown combination, and thus it is difficult if not impossible to model precisely, thereby raising doubts about the soundness of the system. The research community should assume the task of precisely identifying the factors driving Bitcoin prices, and develop some theoretical foundation to help understand how Bitcoin will be affected when the legal environment and practices change.Significance of the Study: The extensive discussion of Bitcoin on blogs and in the mainstream financial media has primarily focused on the cryptocurrencyâ€™s technical, legal and safety issues. Even the limited academic research on the subject has focused on the technical and safety issues, leaving the question of financial and economic aspects largely untouched. The present study thus seeks to determine the extent to which Bitcoin is affected, if at all, by changes in economic growth, financial markets, and forex markets. The paper also seeks to determine whether Bitcoin behaves differently from forex markets and financial markets and if it has a hedging or diversification potential?Design/methodology/approach: A Markov regime-switching regression model is employed in this study to determine the relationship between real economic activity (proxied by the Baltic Dry Index), financial markets (proxied by the Dow Jones Industrial Average Index), and foreign exchange markets (proxied by the USD-Euro & USD-Yen exchange rates).Findings: The results indicate that, unlike USD-Euro and USD-Yen exchange rates, Bitcoin exhibits significantly different behavior in terms of its association with other financial and economic variables. While equity markets or forex markets are significantly related to each other in varying levels in both the bullish and the bearish regimes, real economic activity appears to be unrelated to either of them. Bitcoin meanwhile, is affected neither by real economic activity, nor equity markets, nor forex markets in either bullish and the bearish regimes. Indeed, it differs significantly in its behavior from forex markets. Furthermore, the results indicate that Bitcoin tends largely to exhibit a bullish trend and the probability of transition to a bearish trend is very small. Thus, it can be concluded that Bitcoin may offer potential for both hedging and diversification to varying degrees.Originality/value: Although the technical, legal and safety issues in cryptocurrencies have been widely discussed, financial and economic aspects remain largely unexplored. The current study thus makes a modest contribution to the sparse literature on the subject.

Co-Movement of COVID-19 and Bitcoin: Evidence from Wavelet Coherence Analysis
Goodell, John W.,Goutte, Stephane
SSRN
We apply wavelet method to daily data of COVID-19 world deaths and daily Bitcoin prices from 31th December 2019 to 29th April 2020. We find especially for the period post April 5 that levels of COVID-19 caused a rise in Bitcoin prices. We contribute both to the fast-growing body of work on the financial impacts of COVID-19, as well as to ongoing consideration of whether Bitcoin is a safe haven investment. Our results should be of great interest to both scholars and policy makers, as well as investment professionals interested in the financial implications of both COVID-19 and cryptocurrencies.

Comparing Investor Networks in Different Market Conditions
Le, Viet Hung
SSRN
We investigate differences in the structures of investor stock trading networks prior to and during the global financial crisis of 2007-2008. We find that the population of investor networks for 45 securities have different structures between pre-crisis and crisis periods with statistical significance. Moreover, we observe the herding tendency and high synchronization in trade timing during the crisis, which is supported by the literature. These findings can be used to develop early-warning signals for crises in stock markets.

Compliance Management Systems: Do They Make a Difference?
Coglianese, Cary,Nash, Jennifer
SSRN
Regulatory compliance is vital for promoting the public values served by regulation. Yet many businesses remain out of compliance with some of the regulations that apply to them â€" presenting not only possible dangers to the public but also exposing themselves to potentially significant liability risk. Compliance management systems (CMSs) may help reduce the likelihood of noncompliance. In recent years, managers have begun using CMSs in an effort to address compliance issues in a variety of domains: environment, workplace health and safety, finance, health care, and aviation, among others. CMSs establish systematic, checklist-like processes by which managers seek to improve their organizationsâ€™ compliance with government regulation. They can help managers identify compliance obligations, assign responsibility for meeting them, track progress, and take corrective action as needed. In effect, CMSs constitute firmsâ€™ own internal inspection and enforcement responsibilities. At least in theory, CMSs reduce noncompliance by increasing information available to employees and managers, facilitating internal incentives to correct instances of noncompliance once identified, and helping to foster a culture of compliance. Recognizing these potential benefits, some government policymakers and regulators have even started to require certain firms to adopt CMSs. But do CMSs actually achieve their theoretical benefits? We review the available empirical research related to CMSs in an effort to discern how they work, paying particular attention to whether CMSs help firms fulfill both the letter as well as the spirit of the law. We also consider lessons that can be drawn from research on the effectiveness of still broader systems for risk management and corporate codes of ethics, as these systems either include regulatory compliance as one component or they present comparable challenges in terms of internal monitoring and shaping of organizational behavior. Overall, we find evidence that firms with certain types of CMSs in place experience fewer compliance violations and show improvements in risk management. But these effects also appear to be rather modest. Compliance in large organizations generally requires more than just a CMS; it also demands appropriate managerial attitudes, organizational cultures, and information technologies that extend beyond the systematic, checklist processes characteristic of CMSs. We address implications of what we find for policy and future research, especially about the conditions under which CMSs appear to work best, the types or features of CMSs that appear to work better than others, and the possible value of regulatory mandates that firms implement CMSs.

Corporate Hiring under COVID-19: Labor Market Concentration, Downskilling, and Income Inequality
SSRN
Big data on job-vacancy postings reveal several dimensions of the impact of COVID-19 on the U.S. job market. Firms have cut back on postings for high-skill jobs more than for low-skill jobs, with small firms nearly halting their new hiring altogether. New-hiring cuts and downskilling are most pronounced in local labor markets lacking depth (where employment is concentrated within a few firms), in low-income areas, and in areas with greater income inequality. Cuts are deeper in industries where workers are more unionized and in the non-tradable sector. Access to finance modulates corporate hiring, with credit-constrained firms curtailing their job postings the most. Our study shows how the early-2020 global pandemic is shaping the dynamics of hiring, identifying the firms, jobs, places, industries, and labor markets most affected by it. Our results point to important challenges to the scale and speed of a recovery.

Do Cryptocurrencies Have Fundamental Values?
Liu, Yukun,Sheng, Jinfei,Wang, Wanyi
SSRN
This paper studies the role of technological fundamentals in Initial Coin Offering (ICO) successes and valuations. Using various machine learning methods, we construct four technology indexes for all cryptocurrencies from their ICO whitepapers. We find that the cryptocurrencies with high technology indexes are more likely to succeed and less likely to be delisted subsequently. Moreover, the technology indexes strongly and positively predict the long-run performances of the ICOs. Overall, the results suggest that technological fundamentals are an important determinant of cryptocurrency valuations.

Earnings Management During the Oil Price Crisis
Bugshan, Abdullah salem,Lafferty, George ,Bakry, Walid,Li, Yongqing
SSRN
Starting in mid-2014, oil prices began to fall drastically, hereafter this is referred to as the oil price crisis. This paper investigates the impacts of this crisis on earnings management behaviour in Gulf Cooperation Council (GCC) countries. Earnings management is measured in terms of accrual based earning management (AEM) and real activity based earnings management (REM). The modified Jones model is adopted to estimate AEM, and three models from Roychowdhury (2006) are used to estimate REM. The results reveal that companies have tended to use downward REM during the oil price crisis, engaging less with AEM. Control variables covering firm characteristics, including ROA, leverage, growth and OCF exhibit significant relationships with EM. The present study examines EM during the oil price crisis, considering both accrual and real activity earnings management. In contrast to most previous research in this domain which has only considered upward REM, a non-directional approach is used herein whereby the absolute (unsigned) term is applied to capture this metric.

Energy Limits to the Gross Domestic Product on Earth
Andreas M. Hein,Jean-Baptiste Rudelle
arXiv

Once carbon emission neutrality and other sustainability goals have been achieved, a widespread assumption is that economic growth at current rates can be sustained beyond the 21st century. However, even if we achieve these goals, this article shows that the overall size of Earth's global economy is facing an upper limit purely due to energy and thermodynamic factors. For that, we break down global warming into two components: the greenhouse gas effect and heat dissipation from energy consumption related to economic activities. For the temperature increase due to greenhouse gas emissions, we take 2 {\deg}C and 5 {\deg}C as our lower and upper bounds. For the warming effect of heat dissipation related to energy consumption, we use a simplified model for global warming and an extrapolation of the historical correlation between global gross domestic product (GDP) and primary energy production. Combining the two effects, we set the acceptable global warming temperature limit to 7 {\deg}C above pre-industrial levels. We develop four scenarios, based on the viability of large-scale deployment of carbon-neutral energy sources. Our results indicate that for a 2% annual GDP growth, the upper limit will be reached at best within a few centuries, even in favorable scenarios where new energy sources such as fusion power are deployed on a massive scale. We conclude that unless GDP can be largely decoupled from energy consumption, thermodynamics will put a hard cap on the size of Earth's economy. Further economic growth would necessarily require expanding economic activities into space.

Evolution of the Chinese Guarantee Network under Financial Crisis and Stimulus Program
Yingli Wang,Qingpeng Zhang,Xiaoguang Yang
arXiv

Our knowledge about the evolution of guarantee network in downturn period is limited due to the lack of comprehensive data of the whole credit system. Here we analyze the dynamic Chinese guarantee network constructed from a comprehensive bank loan dataset that accounts for nearly 80% total loans in China, during 01/2007-03/2012. The results show that, first, during the 2007-2008 global financial crisis, the guarantee network became smaller, less connected and more stable because of many bankruptcies; second, the stimulus program encouraged mutual guarantee behaviors, resulting in highly reciprocal and fragile network structure; third, the following monetary policy adjustment enhanced the resilience of the guarantee network by reducing mutual guarantees. Interestingly, our work reveals that the financial crisis made the network more resilient, and conversely, the government bailout degenerated network resilience. These counterintuitive findings can provide new insight into the resilience of real-world credit system under external shocks or rescues.

Examining Lead-Lag Relationships In-Depth, With Focus On FX Market As Covid-19 Crises Unfolds
arXiv

The lead-lag relationship plays a vital role in financial markets. It is the phenomenon where a certain price-series lags behind and partially replicates the movement of leading time-series. The present research proposes a new technique which helps better identify the lead-lag relationship empirically. Apart from better identifying the lead-lag path, the technique also gives a measure for adjudging closeness between financial time-series. Also, the proposed measure is closely related to correlation, and it uses Dynamic Programming technique for finding the optimal lead-lag path. Further, it retains most of the properties of a metric, so much so, it is termed as loose metric. Tests are performed on Synthetic Time Series (STS) with known lead-lag relationship and comparisons are done with other state-of-the-art models on the basis of significance and forecastability. The proposed technique gives the best results in both the tests. It finds paths which are all statistically significant, and its forecasts are closest to the target values. Then, we use the measure to study the topology evolution of the Foreign Exchange market, as the COVID-19 pandemic unfolds. Here, we study the FX currency prices of 29 prominent countries of the world. It is observed that as the crises unfold, all the currencies become strongly interlinked to each other. Also, USA Dollar starts playing even more central role in the FX market. Finally, we mention several other application areas of the proposed technique for designing intelligent systems.

Financing Vaccines for Global Health Security
Vu, Jonathan,Kaplan, Benjamin,Chaudhuri, Shomesh,Mansoura, Monique,Lo, Andrew W.
SSRN
Recent outbreaks of infectious pathogens such as Zika, Ebola, and COVID-19 have underscored the need for the dependable availability of vaccines against emerging infectious diseases (EIDs). The cost and risk of R&D programs and uniquely unpredictable demand for EID vaccines have discouraged vaccine developers, and government and nonprofit agencies have been unable to provide timely or sufficient incentives for their development and sustained supply. We analyze the economic returns of a portfolio of EID vaccine assets, and find that under realistic financing assumptions, the expected returns are significantly negative, implying that the private sector is unlikely to address this need without public-sector intervention. We have sized the financing deficit for this portfolio and propose several potential solutions, including price increases, enhanced public-private partnerships, and subscription models through which individuals would pay annual fees to obtain access to a portfolio of vaccines in the event of an outbreak.

Forward BSDEs and backward SPDEs for utility maximization under endogenous pricing
arXiv

We study the expected utility maximization problem of a large investor who is allowed to make transactions on a tradable asset in a financial market with endogenous permanent market impacts as suggested in [24] building on [6, 7]. The asset price is assumed to follow a nonlinear price curve quoted in the market as the utility indifference curve of a representative liquidity supplier. We show that optimality can be fully characterized via a system of coupled forward-backward stochastic differential equations (FBSDEs) which is equivalent to a highly non-linear backward stochastic partial differential equation (BSPDE). Existence results can be achieved in the case where the driver function of the representative market maker is quadratic or the utility function of the large investor is exponential. Explicit examples are provided when the market is complete or the driver function is positively homogeneous.

From COVID-19 herd immunity to investor herding in international stock markets: The role of government and regulatory restrictions
SSRN
We study if government response to the novel coronavirus COVID-19 pandemic can mitigate investor herding behaviour in international stock markets. Our empirical analysis is informed by daily stock market data from 72 countries from both developed and emerging economies in the first quarter of 2020. The government response to the COVID-19 outbreak is measured by means of the Oxford COVID-19 Government Response Tracker, where higher scores are associated with greater stringency. Three main findings are in order. First, results show evidence of investor herding in international stock markets. Second, we document that the Oxford Government Response Stringency Index mitigates investor herding behaviour, by way of reducing multidimensional uncertainty. Third, short-selling restrictions, temporarily imposed by the national and supranational regulatory authorities of the European Union, appear to exert a mitigating effect on herding. Finally, our results are robust to a range of model specifications.

Generalised Liouville Processes and their Properties
Edward Hoyle,Levent Ali Mengütürk
arXiv

We define a new family of multivariate stochastic processes over a finite time horizon that we call Generalised Liouville Processes (GLPs). GLPs are Markov processes constructed by splitting L\'evy random bridges into non-overlapping subprocesses via time changes. We show that the terminal values and the increments of GLPs have generalised multivariate Liouville distributions, justifying their name. We provide various other properties of GLPs and some examples.

Influence Of Climate Change On The Corn Yield In Ontario And Its Impact On Corn Farms Income At The 2068 Horizon
Antoine Kornprobst,Matt Davison
arXiv

Our study aims at quantifying the impact of climate change on corn farming in Ontario under several warming scenarios at the 2068 horizon. It is articulated around a discrete-time dynamic model of corn farm income with an annual time-step, corresponding to one agricultural cycle from planting to harvest. At each period, we compute the income given the corn yield, which is highly dependent on weather variables. We also provide a reproducible forecast of the yearly distribution of corn yield for 10 cities in Ontario. The price of corn futures at harvest time is taken into account and we fit our model by using 49 years of historical data. We then conduct out-of-sample Monte-Carlo simulations to obtain the farm income forecasts under a given climate change scenario.

Liquidity Measures of Bond Market
SSRN
The purpose of this document is to review a variety of liquidity measures actively used by academics and practitioners, and to stress their advantages and limits. We focus on bond market that is an Over-The-Counter (OTC) dealer market. In a typical OTC transaction, a buyer or seller contacts an inter-mediating agent (market maker), who proposes bid and ask prices and quantities.

Liquidity Networks
SSRN
Network analysis has become a key framework in financial economics in understanding how interconnectedness among market participants results in spillovers, amplifies or absorbs shocks, and creates other nonlinear effects that ultimately impact market health. In this paper, we propose a new directed network constructâ€"the liquidity networkâ€"to capture the urgency to trade by connecting the initiating party in a trade to the passive party. Alongside the conventional trading network connecting sellers to buyers, we show both network types capture different information and complement each other, leading to a more comprehensive characterization of interconnectivity in the overnight-lending market and improved forecasts of macroeconomic variables.

Managing COVID-19 Pandemic without Destructing the Economy
David Gershon,Alexander Lipton,Hagai Levine
arXiv

We analyze an approach to managing the COVID-19 pandemic without shutting down the economy while staying within the capacity of the healthcare system. We base our analysis on a detailed heterogeneous epidemiological model, which takes into account different population groups and phases of the disease, including incubation, infection period, hospitalization, and treatment in the intensive care unit (ICU). We model the healthcare capacity as the total number of hospital and ICU beds for the whole country. We calibrate the model parameters to data reported in several recent research papers. For high- and low-risk population groups, we calculate the number of total and intensive care hospitalizations, and deaths as functions of time. The main conclusion is that countries, which enforce reasonable hygienic measures on time can avoid lockdowns throughout the pandemic provided that the number of spare ICU beds per million is above the threshold of about 100. In countries where the total number of ICU beds is below this threshold, a limited period quarantine to specific high-risk groups of the population suffices. Furthermore, in the case of an inadequate capacity of the healthcare system, we incorporate a feedback loop and demonstrate that quantitative impact of the lack of ICU units on the death curve. In the case of inadequate ICU beds, full- and partial-quarantine scenarios outcomes are almost identical, making it unnecessary to shut down the whole economy. We conclude that only a limited-time quarantine of the high-risk group might be necessary, while the rest of the economy can remain operational.

Market-Wide Analysis of Investor Networks in Finland
Le, Viet Hung
SSRN
We estimate investor stock trading networks based on Granger Causality under vector autoregressive model. The obtained network is directed, and this can capture the flow of information and trading patterns across the portfolio. For most securities, the investor stock trading network forms a bow-tie structure with a giant robust strongly connected component and tiny disconnected components. Trading networks are inferred separately for financial-insurance companies and household investors. In addition, an aggregated network is inferred, whose nodes contain the trading information of different investors based on similarities in their socioeconomic attributes. We observe that only the networks inferred for financial-insurance companies feature scale-free property. This indicates that there are institutional investors that serve as information hubs, sending and receiving information about their entire portfolio, and in addition, there are active institutions who either share information among themselves or not share information and trade independently. By analyzing investor categories, rather than individual investors, households represent the most central investor nodes. We also find that the level of information transfer across all securities of households and financial institutions is different. Institutional investors have lagged synchronization in trade timing or possibly transfer information on a large number of securities while this for households is observed on a small number of securities.

Multi-View Graph Convolutional Networks for Relationship-Driven Stock Prediction
Jiexia Ye,Juanjuan Zhao,Kejiang Ye,Chengzhong Xu
arXiv

Stock price movement prediction is commonly accepted as a very challenging task due to the extremely volatile nature of financial markets. Previous works typically focus on understanding the temporal dependency of stock price movement based on the history of individual stock movement, but they do not take the complex relationships among involved stocks into consideration. However it is well known that an individual stock price is correlated with prices of other stocks. To address that, we propose a deep learning-based framework, which utilizes recurrent neural network (RNN) and graph convolutional network (GCN) to predict stock movement. Specifically, we first use RNN to model the temporal dependency of each related stock' price movement based on their own information of the past time slices, then we employ GCN to model the influence from involved stock based on three novel graphs which represent the shareholder relationship, industry relationship and concept relationship among stocks based on investment decisions. Experiments on two stock indexes in China market show that our model outperforms other baselines. To our best knowledge, it is the first time to incorporate multi-relationships among involved stocks into a GCN based deep learning framework for predicting stock price movement.

Neural networks for option pricing and hedging: a literature review
Johannes Ruf,Weiguan Wang
arXiv

Neural networks have been used as a nonparametric method for option pricing and hedging since the early 1990s. Far over a hundred papers have been published on this topic. This note intends to provide a comprehensive review. Papers are compared in terms of input features, output variables, benchmark models, performance measures, data partition methods, and underlying assets. Furthermore, related work and regularisation techniques are discussed.

No-arbitrage concepts in topological vector lattices
Eckhard Platen,Stefan Tappe
arXiv

We provide a general framework for no-arbitrage concepts in topological vector lattices, which covers many of the well-known no-arbitrage concepts as particular cases. The main structural condition which we impose is that the outcomes of trading strategies with initial wealth zero and those with positive initial wealth have the structure of a convex cone. As one consequence of our approach, the concepts NUPBR, NAA$_1$ and NA$_1$ may fail to be equivalent in our general setting. Furthermore, we derive abstract versions of the fundamental theorem of asset pricing. We also consider a financial market with semimartingales which does not need to have a num\'{e}raire, and derive results which show the links between the no-arbitrage concepts by only using the theory of topological vector lattices and well-known results from stochastic analysis in a sequence of short proofs.

Nonparametric Expected Shortfall Forecasting Incorporating Weighted Quantiles
Giuseppe Storti,Chao Wang
arXiv

A new semi-parametric Expected Shortfall (ES) estimation and forecasting framework is proposed. The proposed approach is based on a two step estimation procedure. The first step involves the estimation of Value-at-Risk (VaR) at different levels through a set of quantile time series regressions. Then, the ES is computed as a weighted average of the estimated quantiles. The quantiles weighting structure is parsimoniously parameterized by means of a Beta function whose coefficients are optimized by minimizing a joint VaR and ES loss function of the Fissler-Ziegel class. The properties of the proposed approach are first evaluated with an extensive simulation study using various data generating processes. Two forecasting studies with different out-of-sample sizes are conducted, one of which focuses on the 2008 Global Financial Crisis (GFC) period. The proposed models are applied to 7 stock market indices and their forecasting performances are compared to those of a range of parametric, non-parametric and semi-parametric models, including GARCH, Conditional AutoRegressive Expectile (CARE, Taylor 2008), joint VaR and ES quantile regression models (Taylor, 2019) and simple average of quantiles. The results of the forecasting experiments provide clear evidence in support of the proposed models.

Out of Reach: Regressive Trends in Credit Card Access
Lux, Marshall,Greene, Robert
SSRN
Despite the ubiquity of credit cards, it was not until the mid-1990s that a large share of lower-income Americans gained access to these useful financial products, which enable cost-saving consumer purchases, small business financing, and economic inclusion. High credit card debt, of course, can cause individual harm, and on the aggregate, booming household debt levels are a serious policy concern. Yet credit card balances account for just 6 percent of U.S. household debt levels, and as a share of disposable personal income fell from nearly 8 percent in the mid-2000s to 5.3 percent in 2015. We identify regressive trends driving decreased card usage, including that between 2007 and 2015, origination to lower-score accounts (generally lower-income consumers) fell 50 percent, and average credit card lines for these accounts shrunk 31 percent, likely forcing down card utilization. Lower-income Americans increasingly lack credit cards.Consumer credit demand, however, remains high, particularly among lower-income Americans. Supply-side factors â€" including: (1) a 250 percent rise in credit card regulatory restrictions by financial regulators; (2) bans on risk-based pricing; (3) a rising share of unbanked Americans; and (4) unpredictable Consumer Financial Protection Bureau (CFPB) actions â€" are likely constraining lower-score Americansâ€™ access to credit cards, revealing a tension between consumer financial protection and financial product access. Yet recent regulatory activity has at best only modestly improved customer experiences with credit cards, likely because Americans have historically used cards quite reasonably and expressed satisfaction with these products. We examine how regressive trends in credit card access will likely force consumers into more expensive credit products, hurt small business financing, and impede economic mobility, while also cautioning against high consumer debt levels. We conclude by recommending that policymakers act to curtail unintended regulatory impacts on credit card access by:(1) repealing some unnecessary restrictions on risk-based credit card pricing brought about by the CARD Act; (2) reforming the CFPB to better balance consumer protection with consumer financial product access; and (3) streamlining banking regulations to decrease the number of unbanked Americans.

Pandemic, Shutdown and Consumer Spending: Lessons from Scandinavian Policy Responses to COVID-19
Asger Lau Andersen,Emil Toft Hansen,Niels Johannesen,Adam Sheridan
arXiv

This paper uses transaction data from a large bank in Scandinavia to estimate the effect of social distancing laws on consumer spending in the COVID-19 pandemic. The analysis exploits a natural experiment to disentangle the effects of the virus and the laws aiming to contain it: Denmark and Sweden were similarly exposed to the pandemic but only Denmark imposed significant restrictions on social and economic activities. We estimate that aggregate spending dropped by around 25 percent in Sweden and, as a result of the shutdown, by 4 additional percentage points in Denmark. This implies that most of the economic contraction is caused by the virus itself and occurs regardless of social distancing laws. The age gradient in the estimates suggest that social distancing reinforces the virus-induced drop in spending for low health-risk individuals but attenuates it for high-risk individuals by lowering the overall prevalence of the virus in the society.

Pricing Path-Dependent Derivatives under Multiscale Stochastic Volatility Models: a Malliavin Representation
Yuri F. Saporito
arXiv

In this paper we derive a efficient Monte Carlo approximation for the price of path-dependent derivatives under the multiscale stochastic volatility models of Fouque \textit{et al}. Using the formulation of this pricing problem under the functional It\^o calculus framework and making use of Greek formulas from Malliavin calculus, we derive a representation for the first-order approximation of the price of path-dependent derivatives in the form $\mathbb{E}[\mbox{payoff} \times \mbox{weight}]$. The weight is known in closed form and depends only on the group market parameters arising from the calibration of the multiscale stochastic volatility to the market's implied volatility. Moreover, only simulations of the Black-Scholes model is required. We exemplify the method for a couple path-dependent derivatives.

Projected Dynamic Conditional Correlations
Brownlees, Christian T.,Llorens-Terrazas, Jordi
SSRN
We propose a novel specification of the Dynamic Conditional Correlation (DCC) model based on an alternative normalization of the pseudo-correlation matrix called Projected DCC (Pro-DCC). Our modification consists in projecting, rather than re-scaling, the pseudo-correlation matrix onto the set of correlation matrices in order to obtain a well defined conditional correlation matrix. A simulation study shows that projecting performs better than re-scaling when the dimensionality of the correlation matrix is large. An empirical application to the constituents of the S&P 100 shows that the proposed methodology performs favorably to the standard DCC in an out-of-sample asset allocation exercise.

Punishing the Victim: IRC Â§162(m) and the Limitation on Deducting Executive Compensation
Elkins, David
SSRN
Section 162(m) of the Internal Revenue Code provides that a publicly held corporation may not deduct compensation in excess of $1,000,000 paid to certain of its principal officers. Until 2017, IRC Â§162(m) was fairly easy to avoid as it did not apply to performance-based compensation: bonuses, stock options, and so forth. However, the 2017 Tax Cuts and Jobs Act eliminated this escape route. Today, regardless of how the compensation package is structured, the corporation can deduct a maximum of$1,000,000 for each of its covered employees.Underlying IRC Â§162(m) is the concern that the entrenched power of corporate management and the lack of effective oversight foster excessive executive compensation. The idea is that structural problems inherent in corporate governance result in publicly held corporations paying their top executives more than the fair market value of the services that those executives provide.This article argues that the IRC Â§162(m) is misguided. The victims of the alleged malfeasance are the corporationâ€™s shareholders. Salaries paid to executives reduce earnings available for distribution to shareholders. The overpayment of executives is effectively a misappropriation of money beneficially owned by shareholders by those who have the power to manage that money.In response to this phenomenon, Congress chose to deny a deduction for that portion of the compensation presumed to be excessive. However, denying the deduction reduces the corporationâ€™s after-tax earnings, and those who are beneficially entitled to the corporationâ€™s after-tax earnings are none other than the shareholders. Thus, by denying a deduction for excessive executive compensation, U.S. tax policy is effectively punishing the victim.

RM-CVaR: Regularized Multiple $\beta$-CVaR Portfolio
Kei Nakagawa,Shuhei Noma,Masaya Abe
arXiv

The problem of finding the optimal portfolio for investors is called the portfolio optimization problem. Such problem mainly concerns the expectation and variability of return (i.e., mean and variance). Although the variance would be the most fundamental risk measure to be minimized, it has several drawbacks. Conditional Value-at-Risk (CVaR) is a relatively new risk measure that addresses some of the shortcomings of well-known variance-related risk measures, and because of its computational efficiencies, it has gained popularity. CVaR is defined as the expected value of the loss that occurs beyond a certain probability level ($\beta$). However, portfolio optimization problems that use CVaR as a risk measure are formulated with a single $\beta$ and may output significantly different portfolios depending on how the $\beta$ is selected. We confirm even small changes in $\beta$ can result in huge changes in the whole portfolio structure. In order to improve this problem, we propose RM-CVaR: Regularized Multiple $\beta$-CVaR Portfolio. We perform experiments on well-known benchmarks to evaluate the proposed portfolio. Compared with various portfolios, RM-CVaR demonstrates a superior performance of having both higher risk-adjusted returns and lower maximum drawdown.

Review of Stock Markets' Reaction to COVID-19 News: Fresh Evidence from Quantile-On-Quantile Regression Approach
Ã‡Ä±tak, Ferhat,Bagci, Bugra,Åžahin, EyyÃ¼p Ensari,HoÅŸ, Safa,Sakinc, Ä°lker
SSRN
This study investigates how the stock markets react to the COVID-19 outbreak by applying Quantile-on-Quantile regression (QQR) approach proposed by Sim and Zhou (2015). The main findings of this study can be documented as follows: (1) there exists a weakly short-term market reactions effect on the stock market (2) all stock markets exhibit similar trends during the pandemic (3) a pronounced negative link was found across for all countries.

SARS-CoV-2, COVID-19, Infection Fatality Rate (IFR) Implied by the Serology, Antibody, Testing in New York City
Wilson, Linus
SSRN
The SARS-CoV-2, COVID-19, infection fatality rate (IFR) has been hard to accurately estimate. It is a key parameter for disease modeling and policy decisions. Asymptomatic spread and limited testing have understated infections in hard to predict ways across jurisdictions. We survey serology, antibody, studies of the COVID-19 infection to find official cases are understated by an average of 25-to-1. Further, we analyze the deaths and infections in New York City to estimate an overall IFR for the United States of 0.863 percent.

Shariah Compliance and Corporate Cash Holdings
Bugshan, Abdullah salem,Alnori, Faisal,Bakry, Walid
SSRN
This study investigates the influence of shariah compliance status on firmsâ€™ cash holding levels and speed of adjustment in non-financial firms listed in six Gulf Cooperation Council (GCC countries) from 2005 to 2016. It shows that shariah compliance status has a significant effect on firmsâ€™ cash holding decisions. Shariah-compliant firms have significantly higher cash holding levels than non-shariah-compliant firms. Further, shariah-compliant firms adjust more quickly to their target cash holdings than do their conventional counterparts. In our view, shariah-compliant firms are subject to multiple restrictions that limit their external financing channels; therefore, holding larger cash reserves is important as such firms are financially constrained and gain from the transaction cost motive of holding cash. The findings of the study have important implications for regulators, investors and managers. To the best of our knowledge, this study is the first to compare the effect of shariah compliance on firmsâ€™ cash holdings and speed of adjustment towards the trade-off theoryâ€™s optimal cash holding target.

Statistical inference for the EU portfolio in high dimensions
Taras Bodnar,Solomiia Dmytriv,Yarema Okhrin,Nestor Parolya,Wolfgang Schmid
arXiv

In this paper, using the shrinkage-based approach for portfolio weights and modern results from random matrix theory we construct an effective procedure for testing the efficiency of the expected utility (EU) portfolio and discuss the asymptotic behavior of the proposed test statistic under the high-dimensional asymptotic regime, namely when the number of assets $p$ increases at the same rate as the sample size $n$ such that their ratio $p/n$ approaches a positive constant $c\in(0,1)$ as $n\to\infty$. We provide an extensive simulation study where the power function and receiver operating characteristic curves of the test are analyzed. In the empirical study, the methodology is applied to the returns of S\&P 500 constituents.

Sustainability and Private Wealth Investment Flows
SSRN
In this paper we examine the sustainability preferences of wealthy private investors and the effect of sustainability ratings on their asset allocation decisions. Using a large proprietary data set of a private bank with monthly investment holdings of European private wealth investors, we document significantly larger investment flows into assets with a high sustainability rating compared to those with a low sustainability rating. We further find that investors react to changes in sustainability ratings of their portfolio assets by re-balancing their portfolios towards assets with higher sustainability ratings. Exploiting a quasi-natural experiment and an event study design our study documents a plausibly causal relationship between private investors' investment flows and firms' sustainability ratings.

The Consumption Effects of the Disposition to Sell Winners and Hold Losers
Loos, Benjamin,Meyer, Steffen,Pagel, Michaela
SSRN
We study the effects of an exogenous change in the displayed purchase prices of all mutual funds in individualsâ€™ portfolios using data on all security trades, holdings, spending, and income from an online retail bank. We find that individuals are more likely to sell what we call fictitious winners, i.e., funds that are winners under the newly displayed purchase prices but are losers under the actual purchase prices. We then show that individual consumption increases in response to realizing fictitious winners, i.e., realizing fictitious capital gains even though the investors are subject to actual capital losses. The effects of fictitious capital gains on trading and consumption are more prevalent for less-informed investors. This marginal propensity to consume out of (confused) capital gains is thus informative about the literature on consumption out of stock market wealth.

The Effect of Mandatory Disclosure Dissemination on Information Asymmetry: Evidence from the Implementation of the EDGAR System
Gomez, Enrique
SSRN
I study the effect of the implementation of the SECâ€™s EDGAR system on two unique forms of information asymmetry: (1) asymmetry between managers and investors, and (2) asymmetry among different groups of investors. Information asymmetry theory suggests that firmsâ€™ adoption of the EDGAR system can have two effects â€" one that benefits investors and one that is detrimental to at least some investors. I find that the implementation of EDGAR lowered information asymmetry between managers and investors but had the unintended consequence of increasing information asymmetry (i.e., widening the information gap) between more- and less-sophisticated investors. I also validate Kim and Verrecchiaâ€™s (1997) measure of information asymmetry among investors. Taken together, my results suggest that while EDGAR was beneficial to investors, it also benefited some investors at the expense of others. Moreover, employing only traditional information asymmetry measures (e.g., bid-ask spreads) does not provide a complete picture of the consequences of disclosure.

The State and Fate of Community Banking
Lux, Marshall,Greene, Robert
SSRN
This working paper focuses on the plight of community banks in the United States. It begins by examining different definitions of what constitutes a community bank, and goes on to review what makes these institutions unique and distinguishes them from larger regional or national peers. Our assessment of Federal Deposit Insurance Corporation data finds that community banks service a disproportionately large amount of key segments of the U.S. commercial bank lending market â€" specifically, agricultural, residential mortgage, and small business loans. However, community banksâ€™ share of U.S. banking assets and lending markets has fallen from over 40 percent in 1994 to around 20 percent today. Interestingly, we find that community banks emerged from the financial crisis with a market share 6 percent lower, but since the second quarter of 2010 â€" around the time of the passage of the Dodd-Frank Act â€" their share of U.S. commercial banking assets has declined at a rate almost double that between the second quarters of 2006 and 2010. Particularly troubling is community banksâ€™ declining market share in several key lending markets, their decline in small business lending volume, and the disproportionate losses being realized by particularly small community banks. We review studies on the impact of regulation, consumer trends and other factors on community banks, and examine the consequences of consolidation on U.S. lending markets. We conclude with a discussion of policies that could promote a more competitive and robust banking sector.

The Underpricing of Spanish REITs When Going Public
CastaÃ±o, Leticia,FarinÃ³s ViÃ±as, JosÃ© Emilio,IbÃ¡Ã±ez, Ana M.
SSRN
This study analyses underpricing in a sample of 41 Real Estate Investment Trusts (REITs) from the Spanish market between November 2013 and January 2019. The results show a significant underpricing on the initial-day (either when we compute raw or market-adjusted initial returns) concentrated in the primary market. Besides, price adjustment continues until the third day as we find significant raw and market-adjusted buy-and-hold returns. This underpricing is not accounted for by the theories of information asymmetry but instead by some signalling theories related to capital structure, by the pre-listing stock market conditions and by the peculiarities of the market analysed.

What's Behind the Non-Bank Mortgage Boom?
Lux, Marshall,Greene, Robert
SSRN
Mortgages constitute a large, complex, and controversial market in the United States, shaped largely by federal policy-making. Since 2010, the role of non-banks â€" a term commonly used to define firms un-associated with a depository institution â€" in the overall mortgage market has grown handedly. In 2014, non-banks accounted for over 40 percent of total origination in terms of dollar volume versus 12 percent in 2010. Of the 40 largest servicers, 16 were non-banks, accounting for 20.5 percent of the total market and 28 percent of outstanding top-40 servicing balances, versus just 8 percent in 2010. We find that both regulatory factors and market factors are helping drive the non-bank boom, and identify key distinctions between pre-crisis non-banks and non-banks now. Todayâ€™s non-banks are: 1) subject to much more regulation and supervision; 2) more active in mortgage servicing than ever before; and 3) using technology to transform the mortgage market. Without non-banks, todayâ€™s sluggish mortgage market would be much less vibrant, and our analysis reveals positive impacts of non-banks on customers. However, non-banksâ€™ growing involvement in riskier non-prime FHA-insured origination is concerning. And while reducing the counter-party risk non-banks pose to Fannie Mae and Freddie Mac is a worthwhile policy goal, implementing bank-like standards for non-banks is not the best strategy to substantially mitigate risks in the housing system, and could stunt innovation. Instead, reforming the GSEs and FHA insurance is critical to reducing both counter-party and borrower default risk. Policymakers should act to do so, embrace non-banks, and address unintended regulatory impacts driving depository institutions out of the market.

With Great Power Comes Great Flexibility: The Impact of Prestigious CEO Awards on Innovation
Atanassov, Julian,Park, Keun Jae
SSRN
Contrary to the previous literature, we document that winning a prestigious CEO award can be beneficial to firms by reducing managerial career concerns and encouraging long-term productivity. Using propensity score matching techniques, we find that award-winning CEOs innovate more than the control group, both in terms of the number of patents and the number of citations per patent. This finding is consistent with both managerial flexibility and overconfidence theories, and inconsistent with the private benefits view. After further analysis, we show that there is a clear increase in CEO power and job security after winning a prestigious award and no such increase in several measures of overconfidence. We also document that the positive effect of CEO awards on innovation is weaker for firms with high institutional ownership. These results provide overall support for the managerial flexibility theory.

å…¬å¸æ"¶è´­æ³•çš„ç§»æ¤ä¸Žå˜å¼‚ï¼šéƒ¨åˆ†è¦çº¦ä¸ŽæŠ•èµ„è€…ä¿æŠ¤ (The Transplantation and Mutation of Takeover Law in China: Partial Bids and Investor Protection)
Huang, (Robin) Hui,Wang, (Charles) Chao
SSRN
Chinese Abstract: 2006å¹´ä¹‹åŽæˆ'å›½æ"¶è´­äººè¢«å…è®¸ä½¿ç"¨éƒ¨åˆ†è¦çº¦æ¥å±¥è¡Œå¼ºåˆ¶è¦çº¦æ"¶è´­ä¹‰åŠ¡ã€‚å·²æœ‰ç "ç©¶å¯¹äºŽæ–°éƒ¨åˆ†è¦çº¦è§„åˆ™å°šæœªå…¨é¢æŽ¢è®¨ã€‚æœ¬æ–‡æ¯"è¾ƒè€ƒå¯Ÿäº†ä¸­å›½å†…åœ°å'Œè‹±å›½ã€æ—¥æœ¬ç­‰æ³•åŸŸçš„éƒ¨åˆ†è¦çº¦è§„åˆ™ï¼Œå®žè¯åˆ†æžäº†æˆ'å›½æ"¶è´­äººå'å‡ºçš„éƒ¨åˆ†è¦çº¦ã€‚ç "ç©¶å'çŽ°ï¼šæˆ'å›½éƒ¨åˆ†è¦çº¦åˆ¶åº¦ä¸Žæ—¥æœ¬çš„å¼ºåˆ¶éƒ¨åˆ†è¦çº¦æ¨¡å¼åœ¨åŠŸèƒ½ä¸Šæ®Šé€"åŒå½'ï¼›æˆ'å›½çš„éƒ¨åˆ†è¦çº¦éƒ½æ˜¯æ"¶è´­äººè‡ªæ„¿å'å‡ºï¼Œç›®çš„ä¸»è¦æ˜¯å·©å›ºå…¬å¸æŽ§åˆ¶æƒï¼Œè§„é¿çŽ°è±¡ç½•è§ï¼Œè¿™äº§ç"Ÿäº†ä¸Žæ—¥æœ¬æ¨¡å¼ç±»ä¼¼çš„æ•ˆæžœï¼›é™¤äº†â€œè‹æ³Šå°"æ¡ˆâ€å¤–ï¼Œæ²¡æœ‰æ"¶è´­äººå'å‡ºéƒ¨åˆ†è¦çº¦æ¥å±¥è¡Œåè®®æ"¶è´­è§¦å'çš„æ³•å®šè¦çº¦ä¹‰åŠ¡ã€‚æœ¬æ–‡å¯¹ç "ç©¶ç»"æžœè¿›è¡Œäº†è§£é‡Šï¼Œå¹¶å»ºè®®å°†éƒ¨åˆ†è¦çº¦é€‚ç"¨èŒƒå›´æ‰©å±•åˆ°åè®®æ"¶è´­ç­‰ä¸»æµæ"¶è´­æ–¹å¼ï¼Œé€šè¿‡æ³•å¾‹æ­£å¼å€Ÿé‰´æ—¥æœ¬æ¨¡å¼ã€‚English Abstract: After 2006, Chinese acquirers were allowed to use partial bids to fulfill the obligation under the mandatory bid rule. The new partial bid rule has not been fully discussed in the existing research. This paper makes a comparative study of the partial bid regimes in mainland China, the United Kingdom, Japan and other relevant jurisdictions, and makes an empirical analysis of partial bids launched by Chinese acquirers. It is found that the partial bid regimes in China and Japan have similar functions. In China, partial bids were launched on a voluntary basis by acquirers mainly to consolidate the control of the company. With the exception of the Supor case, no acquirer has made a partial bid to fulfill the statutory obligation triggered by negotiated takeover. This paper explains the results of the study and proposes to extend the applicable scope of partial bids to negotiated takeovers which are a mainstream takeover method. China should formally learn from the Japanese model through formal legislation.

èµ„æœ¬å¸‚åœºæ°'äº‹èµ"å¿é‡'ã€è¡Œæ"¿ç½šæ¬¾ä¸Žåˆ'æ³•ç½šé‡'åˆ¶åº¦ç "ç©¶ (Research on Civil Compensation, Administrative Fine and Criminal Fine in the Chinese Capital Markets)
Huang, (Robin) Hui,Huang, Jiangdong,Hailong, Li,Xiao, Yu
SSRN