Research articles for the 2020-06-16
SSRN
In this paper, we propose a continuous-time stochastic intensity model, namely, two-phase dynamic contagion process(2P-DCP),for modelling the epidemic contagion of COVID-19 and investigating the lockdown effect based on the dynamic contagion model introduced by Dassios and Zhao (2011). It allows randomness to the infectivity of individuals rather than a constant reproduction number as assumed by standard models. Key epidemiological quantities, such as the distribution of final epidemic size and expected epidemic duration, are derived and estimated based on real data for various regions and countries. The associated time lag of the effect of intervention in each country or region is estimated. Our results are consistent with the incubation time of COVID-19 found by recent medical study. We demonstrate that our model could potentially be a valuable tool in the modeling of COVID-19. More importantly, the proposed model of 2P-DCP could also be used as an important tool in epidemiological modelling as this type of contagion models with very simple structures is adequate to describe the evolution of regional epidemic and worldwide pandemic.
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Currency market is the most volatile & liquid in all financial markets of the world. The present study was conducted to know a good theoretical approach to Indian Currency Market & Rupee position in the global market. It analyzed the volatility of rupee and the normality in the daily changes in the value of rupee with respect to four currency pairs i.e. JPY/INR, GBP/INR, EUR/INR, and USD/INR during one last year. Kolmogorovsmirnov Test and Shapiro Wilks W Tests were used for the testing normality of data. The data was analyzed through Descriptive Statistics of the daily reference rates given by RBI. The study concluded that the rupee was highest volatile in the month of August during last year 2013 in respect of all the four currencies. The daily changes in the value of the rupee were not normally distributed. The study also found that rupee was more volatile to GBP currency in comparison of other three currencies.
SSRN
Amid the COVID-19 outbreak and related expected economic downturn, many developed and emerging market central banks around the world engaged in new long-term asset purchase programs, or so-called quantitative easing (QE) interventions. This paper conducts an event-study analysis of 20 COVID-19 QE announcements made by 17 global central banks on their local 10-year government bond yields. We find that the average developed market QE announcement had a statistically significant -0.14% 1-day impact, which is slightly smaller than past interventions during the Great Recession era. In contrast, the average impact of emerging market QE announcements was significantly larger, averaging -0.37% and -0.63% over 1-day and 3-day windows, respectively. Across developed and emerging bond markets, we estimate an overall average 1-day impact of -0.27%. We also show that all 10-year government bond yields in our sample rose sharply in mid-March 2020, but fell substantially after the period of QE announcements that we study in the paper.
arXiv
This paper presents a Python object-oriented software and code to compute the annual production resilience indicator. The annual production resilience indicator can be applied to different anthropic and natural systems such as agricultural production, natural vegetation and water resources. Here, we show an example of resilience analysis of the economic values of the agricultural production in Europe. The analysis is conducted for individual time-series in order to estimate the resilience of a single commodity and to groups of time-series in order to estimate the overall resilience of diversified production systems composed of different crops and/or different countries. The proposed software is powerful and easy to use with publicly available datasets such as the one used in this study.
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Asset pricing and climate policy are analyzed in a global economy where consumption goods are produced by both a green and a carbon-intensive sector. We allow for endogenous growth and three types of damages from global warming. It is shown that, initially, the desire to diversify assets complements the attempt to mitigate economic damages from climate change. In the longer run, however, a trade-off between diversification and climate action emerges. We derive the optimal carbon price, the equilibrium risk-free rate, and risk premia. Climate disasters, which are more likely to occur sooner as temperature rises, significantly affect asset prices.
SSRN
The regulation of financial products is generally increasing in response to misconduct or crises that have led to losses for (retail) investors. Under the guise of various directives and laws, international and national regulators have introduced rules and standards to restore investor confidence. We tested the success of these efforts by analyzing the distribution of attention in current product information sheets in an eye-tracking experiment. Our results suggest that regulation has been successful as attention profiles of the different sections of product information are highly similar for different products. This suggests first success of regulation with respect to the standardization of product information sheets, potentially leading to better investment decisions among retail investors.
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We combine transaction-level data from the largest retail bank in Denmark and individual-level data from government registers to study informal insurance within social networks. Accounting for transfers in cash (money transfers) and in kind (cohabitation), we estimate that family and friends jointly replace around 7 cents of the marginal dollar lost within the bottom income decile, but much less at higher income levels. We document that informal insurance covers other adverse events than income losses: expenditure shocks, family ruptures and financial distress. Parents appear to be the key providers of informal insurance with a small amount of insurance coming from siblings and virtually none from grandparents and friends. Replacement rates vary monotonically with parent economic resources.
SSRN
Turkish Abstract: Global kriz bazı ülkeleri daha fazla etkilediÄi gibi, bazı bankaları da diÄerlerine göre daha fazla etkilemiÅtir. Bunun sonucu bazı ülkelerde kriz daha etkili oldu, birçok banka iflas etti, bazı bankalar devlet desteÄi ile faaliyetlerini sürdürmeye çalıÅtı. ABDâde ortaya çıkan ve Avrupaâda devam eden kriz, AB ülkelerini etkilemesi yanında bu ülkelerle ekonomik ve finansal iliÅki halinde olan ülkeleri ve bankalarını da etkilemiÅtir. Bankaların bilançolarında taÅıdıkları risk nedeniyle krize daha duyarlı olmaktadır. Krizin etkilerini oynaklık ve duyarlılık arttırmakta, oynaklık bankaların kontrolü altında deÄildir ve dıÅsal olarak belirlenir. Ancak, bankaların krizden etkilenmesi daha çok onların riske duyarlılıklarına baÄlı olmakta ve bunun bankaların kontrolü altında olduÄu kabul edilmektedir. Bankaların risk iÅtahına baÄlı olarak, bankalar bilanço yapılarını deÄiÅtirerek finansal ve finansal olmayan risklere karÅı duyarlılıklarını deÄiÅtirebilirler. Bu çalıÅmada da, 12 Balkan ülkesinde faaliyet gösteren 213 ticari, tasarruf ve kooperatif bankalarının 2006- 2012 dönemine iliÅkin bilanço yapısı incelenmiÅ ve karÅılaÅtırılmıÅtır. Bu kapsamda, kredi riski, likidite riski, faiz riski, operasyonel riskin krizin yaÅandıÄı bu dönemde nasıl bir seyir izlediÄinin analizi amaçlanmıÅtır. Bankalar, Avrupa BirliÄi (AB) üyesi ülkeler ile üye olmayan ülkeler, aktif büyüklükler, faaliyette bulunduÄu bölgeler ve ülkeler bazında ele alınmıÅtır.English Abstract: As it is the case the global financial crisis has affected some countries more than others and it also affected some banks more than others as well. Accordingly global crisis was more destructive in some countries where many banks went bankcrupt or survived wth the support from their states. The global financial crisis that is started in the USA and later spreaded in Europe, alongside damaging the EU countries, hurted other countries those were economically and/or financially integrated with EU countries. Banks are becoming sensitive to crisis due to the risk they carry on their balance sheets. The effects of crisis are strengthened by two dimensions of risk namely volatility and sensitivity. Volatility is exegenous for banks. Thus banks get affected by crisis due to their sensitivity to risk which is under their control. In line with their risk apetite banks change the structures of their balance sheets that eventually determine their sensitivity to risk. In this work, the balance sheet structures of 213 banks (commercial, savings and cooperative) in 12 Balkan countries were examined and compared for the period of 2006-2012. Within this framework it has been aimed to analyse the trends in credit risk, liquidity risk, interest rate risk and operational risk. Banks were handled from different perspectives such as being EU mamber or not, their sizes, and their geographical regions.
SSRN
Motivated by the rapid spread of novel coronavirus COVID-19 outbreak in the world. This study explores the stock marketsâ response to the global COVID-19 pandemic in developing countries. We make use of a panel dataset including 685 observations from 13 countries in the Middle East and North Africa area (MENA) for the period January 29, 2020, to June 1, 2020, which was analysed using ordinary least squares regressions (OLS regression). The regression results indicate that stock markets have responded negatively to the growth in COVID-19 deaths. Meanwhile, stock markets show no reaction to the daily growth in confirmed COVID-19 cases.
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This paper explores the extent to which commodity prices can predict GDP growth rates of various countries using indices of 27 commonly traded commodity futures. Commodity returns can strongly predict the next quarterâs GDP growth, while the basis shows a reasonable level of predictive power. Overall, commodity prices can be considered a leading indicator of economic growth; increasing commodity prices and basis values indicate a stronger future economy.
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The 2007-09 financial crisis did not prompt the far-reaching changes to financial sector regulation called for by many politicians, regulators and policy analysts. Instead of transformative reforms that would have restructured the financial sector, the regulatory changes adopted by national governments and international standard-setting bodies produced only incremental change that strengthened existing regulations and added new regulations but did not fundamentally alter industry practices. The lack of transformative change has been linked to the policy preferences of policymakers, who continued to see large, complex financial institutions as central to the economy. This limited the range of regulatory reforms policymakers were willing to consider and provided opportunities for the financial sector to influence policy proposals. This explanation does not, however, tell us why policymakers continued to see the prevailing industry structure as beneficial in the face of strong evidence to the contrary and intense public anger at large banks. Looking at the U.S. experience between 2008 and 2010, I argue that policymakers held on to their preference to largely maintain the status quo because it was necessary to stabilize the financial sector. Banks rely heavily on institutional investors to fund their activities. Transformative policy proposals that would have threatened the dominant position of largest banks would create uncertainty about the future value of bank securities, which would discourage institutional investors from holding them at a time when their support is needed. This feedback link between policies and marketsâ"where proposed regulations affect current market conditionsâ"led President Obama to appoint a policy centrist to Treasury secretary, who was well regarded by Wall Street for his experience dealing with financial crises and his pragmatic approach to regulatory reform. A desire to restore investor confidence in the banking sector also tempered the types of regulatory reforms the Obama administration and Congressional committee leaders were willing to put forward while the health of the banking sector was in doubt. Proposals to regulate banks that were less friendly to institutional investors were considered only after the financial sector had stabilized.
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Whilst pension assets (liabilities) are often neglected by external investors when making investment decisions, pension assets (liabilities) have a stealthy effect on the evaluation of corporatesâ performance. This paper studied the question of how pension assets (liabilities) are correlated with a holistic set of performance measurements including financial health, profitability, productivity, transformation, and social responsibility. Additional assessments are made to discover the driver of the differences in performance within the characteristics of pension plans.
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This paper empirically shows that stock-level margin trading commoves significantly with market-aggregate margin trading even after controlling for market return, market-wide liquidity, and individual determinants of margin trading. A closer examination suggests that the common influences and positive feedback mechanisms exhibit considerable variations depending heavily on market conditions, firm characteristics, and the direction of margin change. Specifically, the commonality during the deleveraging process is more pronounced than when leverage increases, and such deleveraging commonality contributes to the worsening of stock liquidity and heightened surges in liquidity commonality. Relying on empirical measures constructed through the limit order book, we further find that market-aggregate margin trading generates much larger impacts on selling pressures and investorsâ order submission strategies than its stock-level counterpart during market crashes. Overall, recognizing the existence of commonality offers a simple and alternative way to think about the systematic risk arising from leverage trading.
arXiv
Quantitative risk management, particularly volatility forecasting, is critically important to traders, portfolio managers as well as policy makers. In this paper, we applied quantum reservoir computing for forecasting VIX (the CBOE volatility index), a highly non-linear and memory intensive `real-life' signal that is driven by market dynamics and trader psychology and cannot be expressed by a deterministic equation. As a first step, we lay out the systematic design considerations for using a NISQ reservoir as a computing engine (which should be useful for practitioners). We then show how to experimentally evaluate the memory capacity of various reservoir topologies (using IBM-Q's Rochester device) to identify the configuration with maximum memory capacity. Once the optimal design is selected, the forecast is produced by a linear combination of the average spin of a 6-qubit quantum register trained using VIX and SPX data from year 1990 onwards. We test the forecast performance over the sub-prime mortgage crisis period (Dec 2007 - Jun 2009). Our results show a remarkable ability to predict the volatility during the Great Recession using today's NISQs.
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We examine the effect of the COVID-19 pandemic on firmsâ decisions to suspend dividends and estimate a model that quantifies the effect of suspensions on growth in aggregate dividends. Our estimates show that dividend suspensions had a large impact on expected future dividend growth and also helped predict the sharp declines observed in broader measures of economic activity. Firms with high leverage and low profitability were more likely to have suspended their dividends during the pandemic as were firms with the largest negative stock returns prior to the dividend announcement date. While firms that suspended their dividends experienced large negative abnormal returns, firms that substantially reduced but did not entirely eliminate dividends saw large positive abnormal returns around the announcement date.
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Do burdensome regulatory environments inhibit firm growth? In this paper, we analyzed the effects of regulatory compliance in terms of time and money costs on the probability that a Small and Medium Enterprise (SME) can grow. We also tested whether the relationship between regulatory compliance and firm growth is affected by firm age. Using a sample of 590 SMEs in Metro Manila, Metro Cebu, and Metro Davao in the Philippines, we found that while additional time spent on regulatory compliance decreased the probability of growth among SMEs in general, additional monetary costs in proportion to total business costs spent on regulatory compliance decreased the probability of growth among younger firms with less than five years of operations. Additionally, SMEs that were more familiar with new developments in the regulatory environment and were able to take advantage of recent technological improvements in regulatory procedures were more likely to have grown than those that were not able to take advantage of such. Our findings suggest that the government must continue to work towards easing the burden of regulation on firms, especially younger, smaller businesses whose potential as engines of economic growth and job creation is undermined by time and money costs of compliance.
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Using a proprietary database that tracks secrecy with respect to a hedge fund's own investors, we find few benefits to own-investor secrecy. These findings contrast with research on secrecy regarding public disclosure. Secretive funds do not outperform transparent funds, and significantly underperform their strategy-matched peers through the financial crisis, consistent with secretive funds loading on unmeasured risks, but inconsistent with own-investor secrecy signalling skill. Though no different in terms of portfolio concentration and leverage, secretive funds are larger, less liquid, more complex, and more likely to file 13F disclosures and request confidential treatment from those disclosures. Secretive funds have lower flow-to-performance sensitivity, even controlling for illiquidity, suggesting that investors do view secretive and transparent funds differently.
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We study the legal provisions of 92 European systemic banks from 18 countries over the years 2008-2017. Since legal provisions may be viewed as a mechanism for disclosing information to capital markets, the creation of legal provisions is determined by the risk taken by the bank and the managerial incentives to disclose information. Our results show an initial negative relationship between managersâ discretionary investments and legal provisions, even when we control for risk taking. We also find that board of director independence has a moderating effect in order to guard against future lawsuits. Similarly, a better institutional framework amplifies the positive influence of the board of directors.
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We generalize Stein's lemma. That is, we do not assume a specific probability distribution. We also provide new additional results for the covariance and the variance.
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The primary goal of our paper is to quantify the importance of imperfect competition in the U.S. construction industry by estimating the size of rents earned by American ï¬rms and workers. To obtain a comprehensive measure of the total rents and to understand its sources, we take into account that rents may arise both due to markdown of wages and markup of prices. Our analyses combine the universe of U.S. business and worker tax records with newly collected records from U.S. procurement auctions. We first examine how ï¬rms respond to a plausibly exogenous shift in product demand through a difference-in-differences design that compares first-time procurement auction winners to the ï¬rms that lose, both before and after the auction. Motivated and guided by these estimates, we next develop, identify, and estimate a model where construction ï¬rms compete with one another for projects in the product market and for workers in the labor market. The ï¬rms may participate both in the private market and in government projects, the latter of which are procured through first-price sealed-bid auctions. We find that American construction ï¬rms have significant wage- and price-setting power. This imperfect competition generates a considerable amount of rents, two-thirds of which is captured by the ï¬rms. Lastly, we use the estimated model to perform counterfactual analyses which reveal how increases in the market power of ï¬rms, in the product market or the labor market, would affect the outcomes and behavior of workers and ï¬rms in the construction industry.
arXiv
Several well-established benchmark predictors exist for Value-at-Risk (VaR), a major instrument for financial risk management. Hybrid methods combining AR-GARCH filtering with skewed-$t$ residuals and the extreme value theory-based approach are particularly recommended. This study introduces yet another VaR predictor, G-VaR, which follows a novel methodology. Inspired by the recent mathematical theory of sublinear expectation, G-VaR is built upon the concept of model uncertainty, which in the present case signifies that the inherent volatility of financial returns cannot be characterized by a single distribution but rather by infinitely many statistical distributions. By considering the worst scenario among these potential distributions, the G-VaR predictor is precisely identified. Extensive experiments on both the NASDAQ Composite Index and S\&P500 Index demonstrate the excellent performance of the G-VaR predictor, which is superior to most existing benchmark VaR predictors.
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We collect survey data from a representative population sample to examine whether informative social interactions significantly influence perceptions of past returns, expectations, participation and exposure to a widely known financial instrument in a developed economy with multiple information sources. Respondents report perceptions about peers with whom they discuss financial matters, their social circle, and the population. We provide evidence for the presence of an information channel through which social interactions influence perceptions and expectations about stock returns, stock market participation and portfolio share. We find only mixed evidence of mindless imitation of peers, permeating fewer layers of financial behavior.
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This paper examines the impact of bank financial characteristics, environmental, social and governance (ESG) scores and variation in the stringency of government policy responses to bank stock returns as a response to COVID-19 pandemic. We use a sample of 1927 listed banks from 110 countries. Our findings indicate that stock prices of banks with higher capital, more diversification, higher deposit share, less non-performing loans and with bigger size are more resilient to the COVID-19 pandemic. While the environment and governance scores of banks do not have a significant impact, higher social and corporate social responsibility (CSR) strategy scores of banks intensify the negative stock price reaction to the COVID-19. We further observe that the pandemic-induced reduction in bank stock prices is mitigated as the strictness of government policy responses increases, mainly through economic responses such as the income support, debt &contract relief and fiscal measures from governments.
SSRN
Assumptions about how credit constraints change over time are important for modeling amplification effects from credit markets to house prices. We show that the distribution of combined loan-to-value ratios (CLTVs) for mortgage purchases has been remarkably stable in the US over the last two and a half decades. While the private provision of high-CLTV loans soared during the housing boom of the early 2000s, the share taken up by these loans primarily replaced high-CLTV loans directly guaranteed by government entities such as FHA and VA. FHA/VA loans then increased back to 30% of all purchases after the financial crisis. This stability of the CLTV distribution also holds across ZIP codes, properties and even types of borrowers over our whole sample period. We also show that the increase in private high-CLTV lending follows sharp house price increases at the local level rather than preceding them. These findings suggest that the housing boom was not accompanied by a shift towards more high-CLTV loans, and instead favor models that rely on changes in collateral values or broad changes in house price expectations.
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We use machine-learning to determine the information content of the Item 1A Risk Factors section of S&P 1500 10-Ks. We identify and quantify 30 risk-factors and show a strong positive relation between levels of and contemporaneous changes in risk-factors and proxies for the associated risks. Typically, 28% of cross-firm variation in a risk-proxy is explained by cross-firm variation in the associated risk-factor. Risk disclosure is not found to be forward-looking. Item 1Aâs informativeness has not declined through time despite previously documented increases in boilerplate content, stickiness and redundancy. Indices of operating and financing risk help explain asset and equity volatility.
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The aim of this paper is to analyze the liquidity levels of various banks in the UAE for the period 2005-2009. To understand the behavior of liquidity indicators especially during the financial crisis, the researcher will analyze the four liquidity indicators over the years 2005 to 2009. The findings highlight how the banks in question have been impacted by the 2007-2008 crisis. This can most obviously be seen in the notable decline of each of the banks liquidity level in 2009. The effect of loans to total assets, loans to customersâ deposit, and investment to total assets ratios for the five banks was most notable in 2009. Two liquidity ratios were analyzed in order to determine the banksâ ability to honor its debt obligations, these being loans to total assets and loans to customers respectively. The third ratio was the total equity to total assets to assess the liquidity level in the capital structure, while the fourth ratio was the investment to total assets to measure the managing of liquidity. While Bank liquidity was affected by the crisis, bank performance remained relatively stable, as measured by coefficient of variation, since these banks were able to yield more control over cash flows in comparison to revenues and costs.
arXiv
Geometric mean market makers (G3Ms), such as Uniswap and Balancer, comprise a popular class of automated market makers (AMMs) defined by the following rule: the reserves of the AMM before and after each trade must have the same (weighted) geometric mean. This paper extends several results known for constant-weight G3Ms to the general case of G3Ms with time-varying and potentially stochastic weights. These results include the returns and no-arbitrage prices of liquidity pool (LP) shares that investors receive for supplying liquidity to G3Ms. Using these expressions, we show how to create G3Ms whose LP shares replicate the payoffs of financial derivatives. The resulting hedges are model-independent and exact for derivative contracts whose payoff functions satisfy an elasticity constraint. These strategies allow LP shares to replicate various trading strategies and financial contracts, including standard options. G3Ms are thus shown to be capable of recreating a variety of active trading strategies through passive positions in LP shares.
SSRN
We explore whether and how liquidity factors influence risk transfers between commodity and stock markets using a composite liquidity index and five different types of liquidity measures. We find that liquidity shocks, including both funding liquidity and market liquidity, are positively associated with comovements between commodity and stock markets after 2000, though the relationship is insignificant prior to 2000. The structural change indicates that financialization creates a role for adverse liquidity shocks to increase cross-market correlations. Further evidence shows that the effect of liquidity on cross-market correlations is state-dependent and intensifies when liquidity conditions deteriorate and asset returns sustain substantial declines. Our findings are not explained by business cycles.
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The paper investigates whether and how a stateâs local corruption environment affects firmsâ financing costs. We find that firms in high-corruption states are associated with significantly higher loan spreads and tighter loan covenants. We use an instrumental variable approach and a quasi-experiment of firmsâ headquarters re-locations to establish causality. Moreover, the passage of whistle-blowing laws, in the name of anti-corruption, increases firmsâ bank loan costs and amplifies the impact of local corruption. Overall, we document the externality of local corruption environment on resident firmsâ financing costs and the unintended outcomes of whistle-blowing laws.
SSRN
American consumers today access financial services in fragmented, product-specific marketplaces in which each provider optimizes its consumer relationships based on profitability. Providers regularly exploit information advantages, geographical proximity, behavioral biases, high âshopping costsâ and other asymmetries. Consumers, under pressure to make quick personal decisions, frequently make suboptimal or affirmatively damaging choices that benefit the provider and constrain the consumersâ options in follow-on decisions. The responsibility for managing outcomes in consumer financial services is â" absent the most egregious abuse â" left in the hands of the individual consumer. These practices arguably have led to suboptimal outcomes for all consumers and high levels of financial insecurity among the most vulnerable populations.In the face of these problems, state and federal governments have, over time, adopted a variety of statutory and regulatory regimes intended to protect consumers. The resulting system of consumer financial regulation has had an inconsistent record in advancing the interests of consumers, particularly more vulnerable lower-income consumers, despite the existence of large bodies of law and regulation, extensive consumer disclosure, the enforcement efforts of dozens of state and federal regulatory agencies and an enormous investment in regulatory compliance by financial services providers. The system has historically operated in a data vacuum where regulators had little insight into the results of product usage, relying instead on disclosure-based regimes intended to inform consumer choice regarding product pricing and terms, narrow proscriptions regarding provider practices that impede informed decision-making and limited interventions regarding pricing or fees. This situation has begun to change. Digitization and the ongoing âbig dataâ revolution, coupled with the emergence of new measures of âfinancial healthâ outcomes, now make it possible to analyze the impact on individuals of the use of financial services. This, in turn, may allow historic regulatory regimes to be reimagined using these new data capabilities. Over time, a regulatory framework based upon outcomes measurement has the potential to supplement and ultimately replace the current system. Something similar has begun to occur in the health care field, where insurers (and the state and federal governments that administer Medicare and Medicaid) represent powerful payor interests that are largely aligned with patient wellbeing. These health care market participants now use outcomes-based data to guide a wide range of medical practices and clinical decisions on the part of hospitals, physicians and medical service providers. Increasingly, a variety of standardized and publicly disclosed metrics enable payors to reward providers for lowering costs and improving patient outcomes. Drawing from the health care example, we advance a three-stage proposal to better align financial services provider interests with improved customer outcomes, through data analysis, public disclosure and market-based regulatory intervention. The proposal would introduce into the financial services marketplace a form of âoutcomes-based regulationâ that has been advanced elsewhere. Implementation of the new framework would not be an immediate substitute for existing consumer financial protection law; but by generating an empirical basis for identifying harms and benefits correlated with particular practices or product features, it would for the first time allow policymakers to measure the impact of statutory and regulatory interventions, determine product/practice âappropriatenessâ for particular consumer circumstances, and tailor policies to remedy harms incurred by users of particular products and/or providers. When fully tested and implemented, the three-stage process should shift provider incentives meaningfully towards improved customer outcomes, leading to a gradual shift away from prescriptive and disclosure-based regulation to a âlearningâ system that is principles-based, data-driven, transparent and leverages market mechanisms to deliver improved financial health for consumers.
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We develop a detailed epidemiological multi-factor model, the K-Susceptible-Exposed-Infected-Removed (K-SEIR) model, as well as several simpler sub-models, as its building blocks. The general model enables us to account for all the relevant COVID-19 features, its disparate impact on different population groups, and interactions within and between the groups. It also includes the availability (or lack thereof) of spare hospital beds and intensive care units (ICU) to accommodate the pent-up demand due to the pandemic. We use the most recent hospitalization and mortality data to calibrate the model. Since our model is multi-factor, it can be used to simulate and analyze the consequences of the sheltering-in-place for each specific group, as well as compare lives saved and lost due to this measure. We show that in countries with well-developed healthcare systems and a population willing to abide by sensible containment and mitigation procedures, the sheltering-in-place of the entire community is excessive and harmful when considered holistically. At the same time, sealing nursing homes as best as possible to avoid high infection and mortality rates is an absolute must.
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We identify a novel economic mechanism through which passive ownership positively affects informational efficiency in the cross-section of firms. Passive ownership lowers the cost of capital, encouraging firms to invest more aggressively in risky growth opportunities. The resultant higher cash flow volatility induces active investors to acquire more information, implying higher price informativeness for firms with high passive ownership. These firms also have higher stock prices and higher stock-return variances. In aggregate, a rise in passive ownership can also improve informational efficiency if uninformed investors are crowded out. We document that our mechanism applies more generally to benchmarked institutional investors.
arXiv
When common factors strongly influence two cross-correlated time series recorded in complex natural and social systems, the results will be biased if we use multifractal detrended cross-correlation analysis (MF-DXA) without considering these common factors. Based on multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA) proposed by our group and multifractal partial cross-correlation analysis (MF-DPXA) proposed by Qian et al., we propose a new method---multifractal temporally weighted detrended partial cross-correlation analysis (MF-TWDPCCA) to quantify intrinsic power-law cross-correlation of two non-stationary time series affected by common external factors in this paper. We use MF-TWDPCCA to characterize the intrinsic cross-correlations between the two simultaneously recorded time series by removing the effects of other potential time series. To test the performance of MF-TWDPCCA, we apply it, MF-TWXDFA and MF-DPXA on simulated series. Numerical tests on artificially simulated series demonstrate that MF-TWDPCCA can accurately detect the intrinsic cross-correlations for two simultaneously recorded series. To further show the utility of MF-TWDPCCA, we apply it on time series from stock markets and find that there exists significantly multifractal power-law cross-correlation between stock returns. A new partial cross-correlation coefficient is defined to quantify the level of intrinsic cross-correlation between two time series.
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We analyze government interventions to support firms facing liquidity needs during a lockdown in a competitive model of financial intermediation. Banks and firms have legacy balance sheets at the lockdown date. Firms' liquidity needs can be financed by banks that are subject to risk-weighted capital requirements and funded with insured deposits. An increase in firms' overall claims to external investors aggravates moral hazard problems and reduces expected output. The government can support firms directly through transfers or indirectly through guarantees to new bank loans or reductions in the capital requirement. As a result of the diversification of idiosyncratic firm risks conducted by banks, a reduction in the capital requirement only creates costs for the government following negative aggregate shocks that lead to banks' failure. A pecking order on the government policies that maximize output as a function of the government's budget is derived. For low budget, a reduction in capital requirements is optimal and is fully transmitted to firms through increases in banks' leverage. For medium budget, the capital requirement reduction becomes slack and needs be combined with transfers to firms or loan guarantees. For high budget, transfers are strictly necessary.
arXiv
One of the major characteristics of financial time series is that they contain a large amount of non-stationary noise, which is challenging for deep neural networks. People normally use various features to address this problem. However, the performance of these features depends on the choice of hyper-parameters. In this paper, we propose to use neural networks to represent these indicators and train a large network constructed of smaller networks as feature layers to fine-tune the prior knowledge represented by the indicators. During back propagation, prior knowledge is transferred from human logic to machine logic via gradient descent. Prior knowledge is the deep belief of neural network and teaches the network to not be affected by non-stationary noise. Moreover, co-distillation is applied to distill the structure into a much smaller size to reduce redundant features and the risk of overfitting. In addition, the decisions of the smaller networks in terms of gradient descent are more robust and cautious than those of large networks. In numerical experiments, we find that our algorithm is faster and more accurate than traditional methods on real financial datasets. We also conduct experiments to verify and comprehend the method.
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This study examines the impact of uncertainty on mergers and acquisition (M&A) activity. We focus on product market uncertainty in the oil and gas sector. Analysing this industry enables us to construct a natural forward-looking measure of product market uncertainty, namely the implied crude oil volatility. Using a sample of U.S. firms in the oil and gas sector from 1994-2018 and 4,323 announced transactions, we document that product market uncertainty is negatively related to future M&A activity. Uncertainty is mainly a driver of horizontal and vertical M&A, while output price uncertainty of upstream firms is a more important driver of M&A activity than the input price uncertainty of downstream firms. Our results lend support to a real options explanation of investment under uncertainty where firms choose to defer investments as a response to increased uncertainty.
SSRN
Event study, panel regression, and difference-in-difference techniques are among the most prominent research methodologies in corporate finance. However, these techniques are inappropriate if corporate events are anticipated to some degree, as most events are. This paper proposes options as an additional model-free source of information to identify the likelihood and impact of corporate events. We show how to quantify event impact in a simple example and assert that few restrictions on the state space are required for the approach to work in more complex settings.
SSRN
We use social network data from Facebook to show that institutional investors are more likely to invest in firms from regions to which they have stronger social ties. This effect of social proximity on investment behavior is distinct from the effect of geographic proximity. Social connections have the largest influence on investments of small investors with concentrated holdings as well as on investments in firms with a low market capitalization and little analyst coverage. We also find that the response of investment decisions to social connectedness affects equilibrium capital market outcomes: firms in locations with stronger social ties to places with substantial institutional capital have higher institutional ownership, higher valuations, and higher liquidity. These effects of social proximity to capital on capital market outcomes are largest for small firms with little analyst coverage. We find no evidence that investors generate differential returns from investments in locations to which they are socially connected. Our results suggest that the social structure of regions affects firms' access to capital and contributes to geographic differences in economic outcomes.
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Using high-frequency intraday trading and quoting data, we study the temporal effects in index credit default swap (CDS) trading and liquidity. We find strong intraday variations in index CDS trading activities and liquidity. Unlike the U-shaped pattern in the equity market, index CDSs exhibit a hump-shaped pattern in intraday trading. Trading costs are substantially lower in active trading hours than in other periods within the trading day. We also observe strong day-of-the-week effects. Tuesdays display a significant decrease in trading costs, whereas Fridays accompany with an opposite pattern. Overall, these findings improve our understanding of the trading costs and liquidity in the over-the-counter derivatives market.
SSRN
Using detailed and highly-disaggregated data on spending, income, bank account balances, and consumer credit, we examine the tendency of individuals to âco-holdâ, i.e., to simultaneously hold low-interest liquid deposit balances and high-interest debt in the form of overdrafts. The disaggregated nature of the data allows us to calculate co-holding at daily frequency, while prior studies have relied on more aggregated measures. Daily measures reveal that co-holding is less common than these prior studies have documented, occurring on approximately 15% of individual x days in our baseline calculations. Most spells of co-holding are also short, lasting less than one calendar month. The detailed data allow us to examine the empirical relevance of the competing explanations for co-holding. When brought to the data, we find that co-holding appears to be driven by behavioral rather than rational forces. More specifically, we find evidence in support of explanations for co-holding based upon mental accounting while we find rational explanations for co-holding to be empirically much less relevant.
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We show that U.S. dollar movements affect syndicated loan terms for U.S. borrowers, even for those without trade exposure. We identify the effect of dollar movements using spread and loan amount adjustments during the syndication process. Using this high-frequency, within loan variation, we find that a one standard deviation increase in the dollar index increases spreads by up to 15 basis points and reduces loan amounts and underpricing by up to 2 percent and 7 basis points, respectively. These effects are concentrated in dollar appreciations. Our results suggest that global factors reflected in the dollar determine U.S. borrowing costs.
SSRN
We study how overlapping ownership affects the timing of investments in duopoly. In a model of dynamic competition between symmetric firms we find that overlapping ownership typically hastens first-mover entry and delays follow-on investment. Provided it relaxes product market competition, a positive degree of overlapping ownership is optimal for shareholders. In markets with low growth and volatility, the dynamic effect of overlapping ownership on welfare can offset its static anticompetitive effect. We show our results hold for a broad range of duopoly product market interactions but can be reversed if products are differentiated or if firms make product market and R&D choices.
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Researchers have recently studied the interactions between corporate and government bond issuances within many countries. Some conclude that government bonds compete with private bond issuances, while others maintain that government bonds provide valuable reference entities that improve the private sectorâs ability to issue its own bonds. We study the special case of Chinaâs 2017 issuance of two sovereign bonds denominated in U.S. dollars (USD). We find that USD-denominated Chinese corporate bonds experienced a decline in yield spreads, bid-ask spreads, and price volatility around the time of this sovereign issuesâ announcement. The yield spread changes are particularly large for corporate bonds with maturities similar to those of the USD sovereigns. We conclude that these new bonds serve as useful reference instruments, helping investors to price and hedge the risks impounded in Chinese corporate bonds.
SSRN
This paper aims to assess whether there is a behavioral bias of Turkish FDI investors in Ethiopia. Besides, it addresses the influence of firm size, investment duration, target customers and amount of investment on the behavioral variables. In order to do so, a survey was conducted on a sample of Turkish FDI investors in Ethiopia which tries to examine their cognitive psychological factors towards their investment decisions. The survey result was analyzed using factor analysis. The statistical findings confirm that some psychological anomalies such as representativeness, herding, regret aversion and mental accounting have been observed on Turkish FDI investors. The regression analysis shows that amount of investment of the firms significantly and positively affects herding, representativeness, regret aversion and mental accounting behaviors. Furthermore, duration of investment in Ethiopia affects their representativeness and mental accounting behavioral biases of investors positively.
arXiv
There is a pervasive assumption that low latency access to an exchange is a key factor in the profitability of many high-frequency trading strategies. This belief is evidenced by the "arms race" undertaken by certain financial firms to co-locate with exchange servers. To the best of our knowledge, our study is the first to validate and quantify this assumption in a continuous double auction market with a single exchange similar to the New York Stock Exchange. It is not feasible to conduct this exploration with historical data in which trader identity and location are not reported. Accordingly, we investigate the relationship between latency of access to order book information and profitability of trading strategies exploiting that information with an agent-based interactive discrete event simulation in which thousands of agents pursue archetypal trading strategies. We introduce experimental traders pursuing a low-latency order book imbalance (OBI) strategy in a controlled manner across thousands of simulated trading days, and analyze OBI trader profit while varying distance (latency) from the exchange. Our experiments support that latency is inversely related to profit for the OBI traders, but more interestingly show that latency rank, rather than absolute magnitude, is the key factor in allocating returns among agents pursuing a similar strategy.
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This paper provides details on the nature of the equity holdings of the Swiss National Bank (SNB)and estimates its carbon footprint. By analyzing the holdings of the SNB in the 100 most polluting companies in the world, I find that the share of assets owned by the SNB are responsible for at least a quarter of the domestic CO2 emissions of Switzerland. This represents as much as the greenhouse gas emissions of all Swiss households combined or 0.05% of global greenhouse gas emissions. Using two different estimation methods, I find that the SNBâs portfolio generates between 12 and 21 million metric tons of CO2 per year. This could be reduced by 99.7% with an investment reallocation of just 2% of the equity portfolio of the SNB.
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The matrix algebra associated with finding minimum variance portfolio weights and tangency portfolio weights is greatly simplified by using an Excel presentation. A further simplification of the tangency portfolio weights process is also presented using excess returns for the risky securities. The lesson drawn from this presentation is readily performed online by sharing or recording an Excel screen with students.
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We examine the role of ESG performance during market-wide financial crisis, triggered in responseto physical and economic lockdowns arising from the COVID-19 global pandemic. These uniquecircumstances create an inimitable opportunity to question if investors interpret ESG performance as a signal of future stock performance and/or risk mitigation. Using a novel dataset covering Chinaâs CSI300 constituents, we illustrate that (i) high ESG portfolios tend to outperform low ESG portfolios (ii) that ESG performance mitigates financial risk during financial crisis and (iii) the role of ESG performance is attenuated in ânormalâ times, confirming their incremental importance during crisis. We phrase the results in the context of ESG investment practices.
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I review the recent literature that applies search-and-matching theory to the study of Over-the-Counter (OTC) financial markets. I formulate and solve a simple model in order to illustrate the typical assumptions and economic forces at play in existing work. I then offer thematic tours of the literature and, in the process, discuss avenues for future research.
arXiv
The recent emergence of a new coronavirus, COVID-19, has gained extensive coverage in public media and global news. As of 24 March 2020, the virus has caused viral pneumonia in tens of thousands of people in Wuhan, China, and thousands of cases in 184 other countries and territories. This study explores the potential use of Google Trends (GT) to monitor worldwide interest in this COVID-19 epidemic. GT was chosen as a source of reverse engineering data, given the interest in the topic. Current data on COVID-19 is retrieved from (GT) using one main search topic: Coronavirus. Geographical settings for GT are worldwide, China, South Korea, Italy and Iran. The reported period is 15 January 2020 to 24 March 2020. The results show that the highest worldwide peak in the first wave of demand for information was on 31 January 2020. After the first peak, the number of new cases reported daily rose for 6 days. A second wave started on 21 February 2020 after the outbreaks were reported in Italy, with the highest peak on 16 March 2020. The second wave is six times as big as the first wave. The number of new cases reported daily is rising day by day. This short communication gives a brief introduction to how the demand for information on coronavirus epidemic is reported through GT.
SSRN
It is well-known that luck increases the compensation of CEOs at their current firm. In this paper, we explore how luck affects CEOs' outside options in the labor market, and the performance of firms that hire lucky CEOs. Our results show that luck at their current firm makes CEOs move to a new firm and be appointed as both CEO and chairman. Lucky CEOs tend to match with firms subject to low analyst coverage and operating in less competitive industries. Moreover, lucky CEOs are able to obtain a higher pay at the new firm (both in absolute terms and compared to new industry peers). Finally, difference-in-differences results show that hiring lucky CEOs hurts firm performance, mostly due to a surge in operating costs and a poorer usage of corporate assets.
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In this paper, we wish to examine how prospects of unemployment might change predictions of consumption based CAPM with regards to equity premium and term structure of interest. In particular, instead of agents having varied consumption growth, we study agents with probability of losing significant portion of their consumption. Thus, volatility of unemployment determines risk in the economy, rather than volatility of the consumption. Within framework of Epstein-Zin utility function, our model correctly predicts term structure premium and equity premium. In particular, the model predicts one-year risk-free rate of 1.71%, 30-year risk-free rate of 3.17% and equity rate of 7.33% given parameter of risk aversion of 1,2, EIS of 1,54, and time discount factor of 0.9578. Additionally, we study importance of third moments in our model and discover new pricing factor that is missing in current skewness models. Empirical tests under various conditions confirm statistical significance of our factor. Finally, we utilize our model to predict market changes based on shifts in term structure and then empirically verify model predictions. This paper demonstrates that standard C-based CAPM is more than enough to predict observed term structure and equity premium and no model modification is required if proper consumption variation statistics is used.
SSRN
Purpose â" Determinants of credit growth in Saudi Arabia are investigated. Design/methodology/approach â" A panel approach is applied to macroeconomic and bank-level data spanning 2000 -15. Findings â" Bank lending is supported by strong bank balance sheet conditions (high capital ratio, and growth of NPL provisioning and deposits), and higher growth of both oil prices and non-oil private sector GDP. Lower bank concentration also helps, likely through greater competition, so does stronger institution. Consistent with the literature, lending by Islamic banks may be more responsive to economic activity. Lending remained robust in 2015 despite oil prices having declined, helped by strong bank balance sheets and as banks reduced their holdings of âexcess liquidityâ.To support bank lending in the period ahead, bank balance sheets need to remain strong. Fiscal adjustment and a reduced reliance on banks to finance the budget deficit would support credit provision to the private sector. Originality/value â" The paper is first to analyze in detail determinants of bank lending in Saudi Arabia applying a panel approach to bank level data, and draws critical policy implications.
SSRN
Much work in finance is devoted to identifying characteristics of firms, such as measures of fundamentals and beliefs, that explain differences in asset prices and expected returns. We develop a framework to quantitatively trace the connection between valuations, expected returns, and characteristics back to institutional investors and households. We use it to analyze\ (i) what information is important to investors in forming their demand beyond prices and (ii) what is the relative importance of different investors -- differentiated by type, size, and active share -- in the price formation process. We first show that a small set of characteristics explains the majority of variation in a panel of firm-level valuation ratios across countries. We then estimate an asset demand system using investor-level holdings data, allowing for flexible substitution patterns within and across countries. We find that hedge funds and small, active investment advisors are most influential per dollar of assets under management, while long-term investors, such as pension funds and insurance companies are least influential. In terms of pricing characteristics, small, active investment advisors are most important for the pricing of payout policy, cash flows, and the fraction of sales sold abroad. Large, passive investment advisors are most influential in pricing the Lerner index, a measure of markups, and hedge funds for the CAPM beta.
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Disagreements about risk regimes, aka heterogeneous beliefs, need to be pandemic to account for the massive trading we observe. Yet finance theorists have struggled to find rational causes. Heterogeneity fades away when risks are stable, as observed history eventually reveals what they are. Heterogeneity can dissipate even when risks are unstable, provided the risk-generating process is stable and repeated often enough to identify its parameters.However, in real financial life, no past stability can ever be guaranteed going forward, and even current risks are too complex for any single trader to grasp. Once we allow for doubts, rational learning can easily fan disagreements, with some traders dismissing surprises as meaningless outliers and others seeing new trends. Indeed, the rapid learning in response to big shifts in evidence exhibits some mathematical parallels to the rapid flows that induce physical turbulence. I demonstrate this in three kinds of simulations: a roulette wheel that gets stuck on a single outcome, recurring Bernoulli shocks, and equities subject to disaster risks.COVID-19 provides a practical illustration. The risks of its onset, contagion and severity could not be predicted with much confidence. Neither could the odds of mandatory lockdown or voluntary social distancing, much less their associated costs and the likely impact of central bank intervention. No one can predict with much confidence the odds and intensity of recurrence Sharp disagreements are inevitable, with many bound to resonate longer-term.
SSRN
This paper introduces the R package "edgar" to download and analyze the Securities and Exchange Commission's (SEC) mandatory public disclosures in the United States. Corporations in the U.S. submit their periodic reports, registration statements, and financial reports electronically to the SEC. The SEC makes these reports publicly accessible to everyone through the Electronic Data Gathering, Analysis, and Retrieval System (EDGAR). As financial reporting is one of the most crucial aspects of the financial system, efficient retrieval of EDGAR filings becomes imperative for analysts and researchers. We summarize the implementation of the "edgar" package that facilitates downloading, parsing, searching, and sentiment analysis of corporate reports.