Research articles for the 2021-05-14
A Model of Anchoring and Adjustment for Decision-Making under Risk
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We introduce a general model of anchoring and adjustment for decision-making under risk. To evaluate a lottery, agents first anchor on the simple mean of the lottery's outcomes, ignoring the outcomes' probabilities. Then, they adjust the anchor (insufficiently) in the direction of the lottery's expected utility. The resulting model implies behavior which is observationally similar to established prospect theory models. However, it is not rank-dependent and naturally applies to lotteries with any given number of outcomes, including continuous lotteries. We consider a number of applications to demonstrate the model's range. For the discrete case, we show how the model can explain four commonly-observed choice anomalies - both Allais paradoxes, the St. Petersburg paradox, event-splitting effects, and the effect of probability in the fourfold pattern of risk attitudes. For the continuous case, we show how the anchoring model can be applied to the equity premium puzzle. We also use historical data on asset returns to show that equity premiums can be explained by reasonable combinations of preference parameters. The anchoring model is an attractive and flexible tool that assumes people want to simplify complex decision processes.
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We introduce a general model of anchoring and adjustment for decision-making under risk. To evaluate a lottery, agents first anchor on the simple mean of the lottery's outcomes, ignoring the outcomes' probabilities. Then, they adjust the anchor (insufficiently) in the direction of the lottery's expected utility. The resulting model implies behavior which is observationally similar to established prospect theory models. However, it is not rank-dependent and naturally applies to lotteries with any given number of outcomes, including continuous lotteries. We consider a number of applications to demonstrate the model's range. For the discrete case, we show how the model can explain four commonly-observed choice anomalies - both Allais paradoxes, the St. Petersburg paradox, event-splitting effects, and the effect of probability in the fourfold pattern of risk attitudes. For the continuous case, we show how the anchoring model can be applied to the equity premium puzzle. We also use historical data on asset returns to show that equity premiums can be explained by reasonable combinations of preference parameters. The anchoring model is an attractive and flexible tool that assumes people want to simplify complex decision processes.
A Note on GameStop, Short Squeezes, and Autodidactic Herding: An Evolution in Financial Literacy?
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This note explores a secondary effect of the GameStop short squeeze event and links the exalted focus of retail investors on meme stocks to financial literacy and autodidacticism. From an overview of stylized facts about the short squeeze of GameStop based on high frequency data, short interest, and key figures of related derivatives, it is shown that these financial concepts are reflected in keyword searches across multiple platforms. This self-education with regard to financial terms, keywords, and products and the understanding of basic market speculation mechanisms such as short sales plays a significant role for the influx of retail investors.
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This note explores a secondary effect of the GameStop short squeeze event and links the exalted focus of retail investors on meme stocks to financial literacy and autodidacticism. From an overview of stylized facts about the short squeeze of GameStop based on high frequency data, short interest, and key figures of related derivatives, it is shown that these financial concepts are reflected in keyword searches across multiple platforms. This self-education with regard to financial terms, keywords, and products and the understanding of basic market speculation mechanisms such as short sales plays a significant role for the influx of retail investors.
A Note on Least-Squares Method For Pricing American Options
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The ultimate goal of this note is to provide a background for, and describe, the Longstaff-Schwartz LSM (Least-Squares Monte Carlo) algorithm for evaluating derivatives with early (American/Bermudan) exercise.The reason for writing yet another paper on the matter is that this method is often presented in a complicated context, which makes the reading difficult, and obfuscates the rather simple idea of the algorithm. For example, the presentation in (Longstaff & Schwartz, 2001) comprises of a little example, and applications of the algorithm to more complex derivatives, without actually spelling out the algorithm. Indeed, Section 2.2, promisingly entitled “LSM algorithmâ€, elaborates more on which regression method should be used for approximating the expected value from continuation rather than the actual algorithm. An informal description of the method is arguably given in a short paragraph, which nevertheless fails to lay down the steps used in the backward traversal algorithm. Perhaps the authors thought that the actual steps of the algorithm are too simple, but wouldn’t that be a compelling reason to write them down? In addition to introducing unnecessary regression details, the presentation in (Brigo & Mercurio, 2006) brings in another complication: the algorithm is formalized in the context of a path-dependent payoff function, leading to a formalism arguably hard to follow.This paper aims at revealing that neither the regression method used for approximating the value from continuation, nor the path dependence (or not) of the derivative payoff are relevant to the method, from the algorithmic and conceptual prospective. They are implementation details that should be left at the latitude of the quant developer.Besides the presentation of the LSM algorithm, this note has a hidden agenda: to provide an introduction to the basic notions and concepts that can be heard in any discussion about derivatives with an early exercise provision. In Section 1 we introduced stopping times and stopped processes, optimal exercise strategies and exercise boundaries. Many statements are left without proof, or even justification at times, to allow the attention stay focused on the intuition behind these notions rather than be distracted by an exhaustive presentation of the framework in all its complexity. In the course of this section, we hope to have simplified even further the rather clear presentation in (Shreve, 2004), source which we recommend to be kept at hand. Incidentally, we hope to have clarified a rather ambiguous construct, which can be found in Shreve’s first book, Chapter 4 (page 98), in the definition of stopped process: the lattice operator ∧ (min) is rather ambiguously used over time steps and stopping times, despite of the fact that the stopping times are random variables over a tree’s paths (or equivalently, leaves) and not on the internal tree nodes. This remark will be mentioned in Section 2.2 in more detail. In addition, an equivalent definition of stopping times, in filtration terms and with a rather non-trivial justification, is presented in the second part of Section 2.1.Section 3 fulfills the mandate of this note: we revisit the example in (Longstaff & Schwartz, 2001) with additional details and hopefully in a more organized manner, and we spell out the mere one-page-short LSM algorithm.
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The ultimate goal of this note is to provide a background for, and describe, the Longstaff-Schwartz LSM (Least-Squares Monte Carlo) algorithm for evaluating derivatives with early (American/Bermudan) exercise.The reason for writing yet another paper on the matter is that this method is often presented in a complicated context, which makes the reading difficult, and obfuscates the rather simple idea of the algorithm. For example, the presentation in (Longstaff & Schwartz, 2001) comprises of a little example, and applications of the algorithm to more complex derivatives, without actually spelling out the algorithm. Indeed, Section 2.2, promisingly entitled “LSM algorithmâ€, elaborates more on which regression method should be used for approximating the expected value from continuation rather than the actual algorithm. An informal description of the method is arguably given in a short paragraph, which nevertheless fails to lay down the steps used in the backward traversal algorithm. Perhaps the authors thought that the actual steps of the algorithm are too simple, but wouldn’t that be a compelling reason to write them down? In addition to introducing unnecessary regression details, the presentation in (Brigo & Mercurio, 2006) brings in another complication: the algorithm is formalized in the context of a path-dependent payoff function, leading to a formalism arguably hard to follow.This paper aims at revealing that neither the regression method used for approximating the value from continuation, nor the path dependence (or not) of the derivative payoff are relevant to the method, from the algorithmic and conceptual prospective. They are implementation details that should be left at the latitude of the quant developer.Besides the presentation of the LSM algorithm, this note has a hidden agenda: to provide an introduction to the basic notions and concepts that can be heard in any discussion about derivatives with an early exercise provision. In Section 1 we introduced stopping times and stopped processes, optimal exercise strategies and exercise boundaries. Many statements are left without proof, or even justification at times, to allow the attention stay focused on the intuition behind these notions rather than be distracted by an exhaustive presentation of the framework in all its complexity. In the course of this section, we hope to have simplified even further the rather clear presentation in (Shreve, 2004), source which we recommend to be kept at hand. Incidentally, we hope to have clarified a rather ambiguous construct, which can be found in Shreve’s first book, Chapter 4 (page 98), in the definition of stopped process: the lattice operator ∧ (min) is rather ambiguously used over time steps and stopping times, despite of the fact that the stopping times are random variables over a tree’s paths (or equivalently, leaves) and not on the internal tree nodes. This remark will be mentioned in Section 2.2 in more detail. In addition, an equivalent definition of stopping times, in filtration terms and with a rather non-trivial justification, is presented in the second part of Section 2.1.Section 3 fulfills the mandate of this note: we revisit the example in (Longstaff & Schwartz, 2001) with additional details and hopefully in a more organized manner, and we spell out the mere one-page-short LSM algorithm.
A Short Introduction to the World of Cryptocurrencies
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In this article, we give a short introduction to cryptocurrencies and blockchain technology. The focus of the introduction is on Bitcoin, but many elements are shared by other blockchain implementations and alternative cryptoassets. The article covers the original idea and motivation, the mode of operation and possible applications of cryptocurrencies, and blockchain technology. We conclude that Bitcoin has a wide range of interesting applications and that cryptoassets are well suited to become an important asset class.
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In this article, we give a short introduction to cryptocurrencies and blockchain technology. The focus of the introduction is on Bitcoin, but many elements are shared by other blockchain implementations and alternative cryptoassets. The article covers the original idea and motivation, the mode of operation and possible applications of cryptocurrencies, and blockchain technology. We conclude that Bitcoin has a wide range of interesting applications and that cryptoassets are well suited to become an important asset class.
A Value-at-Risk Modeling Techniques to Computing Equity Trading Risk Exposure in Emerging Stock Markets
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The attempt of this article is to fill a gap in the equity trading risk management literature and particularly from the perspective of emerging and illiquid financial markets, such as in the context of the Moroccan stock market. This paper provides real-world risk management techniques and strategies that can be applied to equity trading/investment portfolios in emerging markets. In this work, we divulge a proactive approach for the measurement/management of risk exposure for financial trading portfolios that contain illiquid equity securities.
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The attempt of this article is to fill a gap in the equity trading risk management literature and particularly from the perspective of emerging and illiquid financial markets, such as in the context of the Moroccan stock market. This paper provides real-world risk management techniques and strategies that can be applied to equity trading/investment portfolios in emerging markets. In this work, we divulge a proactive approach for the measurement/management of risk exposure for financial trading portfolios that contain illiquid equity securities.
Analysis of Capital Structure and Performance of Banking Sector in Middle East Countries
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The research aims to empirically study the capital structure and the performance of the banking sector in Middle East countries during a period of 6 years (between 2011 and 2016). By using 143 banks and 723 observations, the study shows that the capital structure of the banking sector was very volatile during the studied period due to the economic conditions of the region. The results also reveal the existence of positive and significant impacts of total debt and short-term debt on the return on equity of the banking sector in Middle East region. However, the results show negative and significant impacts of total debt and short-term debt on the return on assets (ROA). Additional analysis reveals a positive impact of long-term debt on the ROA ratio. Finally, this study refuses the endogeneity hypothesis of the capital structure and the performance measured by the profitability of the banking sector, and considers that the capital structure design is highly influenced by the decision taken by the international and national regulatory boards.
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The research aims to empirically study the capital structure and the performance of the banking sector in Middle East countries during a period of 6 years (between 2011 and 2016). By using 143 banks and 723 observations, the study shows that the capital structure of the banking sector was very volatile during the studied period due to the economic conditions of the region. The results also reveal the existence of positive and significant impacts of total debt and short-term debt on the return on equity of the banking sector in Middle East region. However, the results show negative and significant impacts of total debt and short-term debt on the return on assets (ROA). Additional analysis reveals a positive impact of long-term debt on the ROA ratio. Finally, this study refuses the endogeneity hypothesis of the capital structure and the performance measured by the profitability of the banking sector, and considers that the capital structure design is highly influenced by the decision taken by the international and national regulatory boards.
Are Firms as Liquid as they Appear in Annual Reports?
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We find that firms report significantly higher cash holdings in the 4th fiscal quarter, followed by subsequent reversal. Such a phenomenon cannot be explained by traditional determinants of cash holdings, calendar year-end effect, and the choice of fiscal year-end quarter. We identify real, financial, and timing apparatuses that firms employ to maneuver such cash hike within a fiscal year. Furthermore, the 4th-quarter cash hike appears to be more pronounced for informationally opaque firms requiring frequent access to external capital markets and for firms with reduced external monitoring and lower financial constraints. Our results suggest that within-year cash holding dynamics is important in fully assessing the liquidity and credit-risk situations of firms.
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We find that firms report significantly higher cash holdings in the 4th fiscal quarter, followed by subsequent reversal. Such a phenomenon cannot be explained by traditional determinants of cash holdings, calendar year-end effect, and the choice of fiscal year-end quarter. We identify real, financial, and timing apparatuses that firms employ to maneuver such cash hike within a fiscal year. Furthermore, the 4th-quarter cash hike appears to be more pronounced for informationally opaque firms requiring frequent access to external capital markets and for firms with reduced external monitoring and lower financial constraints. Our results suggest that within-year cash holding dynamics is important in fully assessing the liquidity and credit-risk situations of firms.
Business Formation: A Tale of Two Recessions
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The trajectory of new business applications and transitions to employer businesses differ markedly during the Great Recession and the COVID-19 recession. Both applications and transitions to employer startups decreased slowly but persistently in the post-Lehman crisis period of the Great Recession. In contrast, during the COVID-19 recession new applications initially declined but have since sharply rebounded, resulting in a surge in applications during 2020. Projected transitions to employer businesses also rise, but this projection is dampened by a change in the composition of applications in 2020 toward applications that are more likely to be nonemployers.
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The trajectory of new business applications and transitions to employer businesses differ markedly during the Great Recession and the COVID-19 recession. Both applications and transitions to employer startups decreased slowly but persistently in the post-Lehman crisis period of the Great Recession. In contrast, during the COVID-19 recession new applications initially declined but have since sharply rebounded, resulting in a surge in applications during 2020. Projected transitions to employer businesses also rise, but this projection is dampened by a change in the composition of applications in 2020 toward applications that are more likely to be nonemployers.
Debt Covenant Violations and Employee Safety
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We study the impact of creditor control rights increases on rank-and-file employees. Using a regression discontinuity design, we provide evidence that workplace safety deteriorates when creditors gain bargaining power in the event of a debt covenant violation. The frequency of workplace injuries and illnesses increase more for firms with severe financing constraints and a less active labor union. These results are robust to entropy balancing and a host of alternative specifications. In sum, our results provide compelling evidence on how increased interference and cost-cutting pressures by creditors can impair the working conditions of employees.
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We study the impact of creditor control rights increases on rank-and-file employees. Using a regression discontinuity design, we provide evidence that workplace safety deteriorates when creditors gain bargaining power in the event of a debt covenant violation. The frequency of workplace injuries and illnesses increase more for firms with severe financing constraints and a less active labor union. These results are robust to entropy balancing and a host of alternative specifications. In sum, our results provide compelling evidence on how increased interference and cost-cutting pressures by creditors can impair the working conditions of employees.
Decentralized Finance: On Blockchain- and Smart Contract-Based Financial Markets
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The term decentralized finance (DeFi) refers to an alternative financial infrastructure built on top of the Ethereum blockchain. DeFi uses smart contracts to create protocols that replicate existing financial services in a more open, interoperable, and transparent way. This article highlights opportunities and potential risks of the DeFi ecosystem. I propose a multi-layered framework to analyze the implicit architecture and the various DeFi building blocks, including token standards, decentralized exchanges, decentralized debt markets, blockchain derivatives, and on-chain asset management protocols. I conclude that DeFi still is a niche market with certain risks but that it also has interesting properties in terms of efficiency, transparency, accessibility, and composability. As such, DeFi may potentially contribute to a more robust and transparent financial infrastructure.
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The term decentralized finance (DeFi) refers to an alternative financial infrastructure built on top of the Ethereum blockchain. DeFi uses smart contracts to create protocols that replicate existing financial services in a more open, interoperable, and transparent way. This article highlights opportunities and potential risks of the DeFi ecosystem. I propose a multi-layered framework to analyze the implicit architecture and the various DeFi building blocks, including token standards, decentralized exchanges, decentralized debt markets, blockchain derivatives, and on-chain asset management protocols. I conclude that DeFi still is a niche market with certain risks but that it also has interesting properties in terms of efficiency, transparency, accessibility, and composability. As such, DeFi may potentially contribute to a more robust and transparent financial infrastructure.
Determinants of Bank Liquidity in the Middle East Region
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The objective of this study is to examine the determinants of bank liquidity in the Middle East region. It also aims to compare the liquidity levels of banking sectors between Middle Eastern countries. Two different liquidity measures, four bank specific factors and three macroeconomic factors have been manipulated by using the WLS regression on 183 banks from eight different countries during a period of 3 years (2014, 2015 and 2016).The research employed “loans-to-assets†and “loans-to-deposits†as proxies to measure the bank’s liquidity level. The bank specific factors include assets quality, performance level, capitalization ratio and bank size. The macro economic factors used in this study are economic growth, unemployment and inflation rates. The results indicate that Lebanese banks have the highest level of liquidity whereas Omani banks have the lowest level of liquidity. In addition, the study shows a decreasing of bank liquidity during 2016 in Middle Eastern countries. The additional analysis reveals the significant impacts of economic growth, assets quality, capital level and bank size on liquidity in the banking sector. Finally, the results reveal that larger banks have to monitor their liquidity risks by controlling the level of provided loans and, they recommend central banks keep an eye on equity ratio and non-performing percentage of loans especially during economic growth.
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The objective of this study is to examine the determinants of bank liquidity in the Middle East region. It also aims to compare the liquidity levels of banking sectors between Middle Eastern countries. Two different liquidity measures, four bank specific factors and three macroeconomic factors have been manipulated by using the WLS regression on 183 banks from eight different countries during a period of 3 years (2014, 2015 and 2016).The research employed “loans-to-assets†and “loans-to-deposits†as proxies to measure the bank’s liquidity level. The bank specific factors include assets quality, performance level, capitalization ratio and bank size. The macro economic factors used in this study are economic growth, unemployment and inflation rates. The results indicate that Lebanese banks have the highest level of liquidity whereas Omani banks have the lowest level of liquidity. In addition, the study shows a decreasing of bank liquidity during 2016 in Middle Eastern countries. The additional analysis reveals the significant impacts of economic growth, assets quality, capital level and bank size on liquidity in the banking sector. Finally, the results reveal that larger banks have to monitor their liquidity risks by controlling the level of provided loans and, they recommend central banks keep an eye on equity ratio and non-performing percentage of loans especially during economic growth.
Determinants of Insider Trading Windows
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Most publicly-traded firms adopt insider trading policies that establish pre-specified quarterly windows when insiders are allowed to trade. However, relatively little is known about how boards determine the length and timing of these windows, in part, because disclosure is voluntary and sparse. We use observed insider trading data to estimate the start and end points of quarterly trading windows, and the corresponding “blackout†periods when trading is restricted. We find that restrictions on trading reflect a heightened concern about expected information asymmetry, both with respect to how long insiders must wait after an earnings announcement before trading can begin, and how quickly the trading window closes as information builds up over the quarter. In addition, we find that trading is more restrictive when the firm has stronger external monitoring, and is more relaxed when insiders have greater liquidity needs. We also present evidence on event-specific “ad hoc blackout windows,†where insiders appear to be largely prohibited from trading during a given quarter. These ad hoc blackout periods tend to be followed by disclosure of future material corporate events, such as M&A activity or changes in the board or top management, are associated with contemporaneously higher information asymmetry, and are followed by increased trading volume and higher stock returns, suggesting that investors may not immediately incorporate the information conveyed by these unscheduled restrictions.
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Most publicly-traded firms adopt insider trading policies that establish pre-specified quarterly windows when insiders are allowed to trade. However, relatively little is known about how boards determine the length and timing of these windows, in part, because disclosure is voluntary and sparse. We use observed insider trading data to estimate the start and end points of quarterly trading windows, and the corresponding “blackout†periods when trading is restricted. We find that restrictions on trading reflect a heightened concern about expected information asymmetry, both with respect to how long insiders must wait after an earnings announcement before trading can begin, and how quickly the trading window closes as information builds up over the quarter. In addition, we find that trading is more restrictive when the firm has stronger external monitoring, and is more relaxed when insiders have greater liquidity needs. We also present evidence on event-specific “ad hoc blackout windows,†where insiders appear to be largely prohibited from trading during a given quarter. These ad hoc blackout periods tend to be followed by disclosure of future material corporate events, such as M&A activity or changes in the board or top management, are associated with contemporaneously higher information asymmetry, and are followed by increased trading volume and higher stock returns, suggesting that investors may not immediately incorporate the information conveyed by these unscheduled restrictions.
Environmental, Social, and Governance Theory: Defusing a Major Threat to Shareholder Rights
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In recent years environmental, social, and governance (ESG) theory has become increasingly influential in the world of corporate management and investing. Despite significant problems with inconsistent definitions and controversial policies, many proponents, including members of Congress and the Biden administration, are suggesting that ESG goals be mandated via government enforcement. Such mandates would constitute a major threat to the property, due process, and association rights of investors, but can be avoided if policy makers embrace a voluntary system of “benefit corporation†charters, augmented by private certification standards.
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In recent years environmental, social, and governance (ESG) theory has become increasingly influential in the world of corporate management and investing. Despite significant problems with inconsistent definitions and controversial policies, many proponents, including members of Congress and the Biden administration, are suggesting that ESG goals be mandated via government enforcement. Such mandates would constitute a major threat to the property, due process, and association rights of investors, but can be avoided if policy makers embrace a voluntary system of “benefit corporation†charters, augmented by private certification standards.
Eviction Risk of Rental Housing: Does it Matter How Your Landlord Finances the Property?
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We show, using a stylized model, how the ï¬nancing choice of landlords can impact eviction decisions in rental markets. Since multifamily loans rely on timely cash flows from tenants, strict underwriting factors can increase the chances that landlords are able to weather income shocks. Lender provided relief may create further leeway for landlords to work out a deal with tenants who default on rental payments. Using comprehensive data on nationwide evictions in the U.S. and performance records on multifamily mortgages, we conï¬rm predictions from our model by documenting a negative relation between evictions and the ï¬nancing activity by government-sponsored enterprises (GSE) that impose strict underwriting criteria but generally offer borrowers relief during unprecedented income shocks. We also quantify the eviction risks induced by the COVID-19 pandemic for 12 U.S. cities using our empirical model.
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We show, using a stylized model, how the ï¬nancing choice of landlords can impact eviction decisions in rental markets. Since multifamily loans rely on timely cash flows from tenants, strict underwriting factors can increase the chances that landlords are able to weather income shocks. Lender provided relief may create further leeway for landlords to work out a deal with tenants who default on rental payments. Using comprehensive data on nationwide evictions in the U.S. and performance records on multifamily mortgages, we conï¬rm predictions from our model by documenting a negative relation between evictions and the ï¬nancing activity by government-sponsored enterprises (GSE) that impose strict underwriting criteria but generally offer borrowers relief during unprecedented income shocks. We also quantify the eviction risks induced by the COVID-19 pandemic for 12 U.S. cities using our empirical model.
Export Complexity and the Product Space: Any Role for Finance?
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In this paper we compute economic complexity following Tacchella et al. (2012) for Italian provinces (NUTS 3) in the period 1998-2017 and test the impact of provincial financial structure on the former. We implement several dynamic panel models and tackle endogeneity issues and the persistence of dependent variable by estimating system GMM specifications. We find that non-bank financial sources have a positive role on complexity. Moreover, a greater importance of finance firms relative to banks increases complexity. Finally, a greater orientation towards small local banks is detrimental for production complexity. We show that such effects are heterogeneous from a temporal perspective and jointly interpret them as pointing to a positive role of financial development as a driver of complexity.
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In this paper we compute economic complexity following Tacchella et al. (2012) for Italian provinces (NUTS 3) in the period 1998-2017 and test the impact of provincial financial structure on the former. We implement several dynamic panel models and tackle endogeneity issues and the persistence of dependent variable by estimating system GMM specifications. We find that non-bank financial sources have a positive role on complexity. Moreover, a greater importance of finance firms relative to banks increases complexity. Finally, a greater orientation towards small local banks is detrimental for production complexity. We show that such effects are heterogeneous from a temporal perspective and jointly interpret them as pointing to a positive role of financial development as a driver of complexity.
Financial Consequences of Identity Theft
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We examine how a negative shock from identity theft affects consumer credit market behavior. We show that the immediate effects of fraud on credit files are typically negative, small, and transitory. After those immediate effects fade, identity theft victims experience persistent increases in credit scores and declines in reported delinquencies, with a significant proportion of affected consumers transitioning from subprime-to-prime credit scores. Those consumers take advantage of their improved creditworthiness to obtain additional credit, including auto loans and mortgages. Despite having larger balances, these individuals default on their loans less than prior to identity theft.
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We examine how a negative shock from identity theft affects consumer credit market behavior. We show that the immediate effects of fraud on credit files are typically negative, small, and transitory. After those immediate effects fade, identity theft victims experience persistent increases in credit scores and declines in reported delinquencies, with a significant proportion of affected consumers transitioning from subprime-to-prime credit scores. Those consumers take advantage of their improved creditworthiness to obtain additional credit, including auto loans and mortgages. Despite having larger balances, these individuals default on their loans less than prior to identity theft.
Financial Resistance of Islamic Banks in Middle East Region: A Comparative Study with Conventional Banks During the Arab Crises
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The research aims to empirically test the impacts of political crisis and economic recession during 2010-2015 on the performance and financial behavior of Islamic and conventional banks in the Middle East region. The period of the study (2010-2015) is divided to three phases (stability, economic crisis and political crisis) to reveal the implication of political and economic crises on the performance and financial behavior of Islamic and conventional banks, first, by tracking the sample of Islamic banks during different phases, then, by comparing the sample of Islamic banks with a paired and non paired samples of conventional banks.The results of this study reveal negative impacts from politic crises and economic recession on the performance of Islamic banks. The results also reveal that the Islamic banks increase their capital adequacy and focus on the reduction of costs to increase the efficiency level during politic crises while they focus on increasing liquidity and assets quality during economic crisis. Additional analyses show the absence of any significant difference between the performance of Islamic and conventional banks during the periods of stability and crises. Finally, this research reveals that the conventional banks have more ability to manage their assets quality and their expenses, whereas the Islamic banks havemore capacity to manage their liquidity level. This research reveals the new challenges facing Islamic and conventional banks in Middle East countries. The last Arab spring and oil prices drops highlight a new issue that has not received the needed attention and provide a natural experiment to evaluate the financial resistance and capacity of both Islamic and conventional banks in the Middle East region.
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The research aims to empirically test the impacts of political crisis and economic recession during 2010-2015 on the performance and financial behavior of Islamic and conventional banks in the Middle East region. The period of the study (2010-2015) is divided to three phases (stability, economic crisis and political crisis) to reveal the implication of political and economic crises on the performance and financial behavior of Islamic and conventional banks, first, by tracking the sample of Islamic banks during different phases, then, by comparing the sample of Islamic banks with a paired and non paired samples of conventional banks.The results of this study reveal negative impacts from politic crises and economic recession on the performance of Islamic banks. The results also reveal that the Islamic banks increase their capital adequacy and focus on the reduction of costs to increase the efficiency level during politic crises while they focus on increasing liquidity and assets quality during economic crisis. Additional analyses show the absence of any significant difference between the performance of Islamic and conventional banks during the periods of stability and crises. Finally, this research reveals that the conventional banks have more ability to manage their assets quality and their expenses, whereas the Islamic banks havemore capacity to manage their liquidity level. This research reveals the new challenges facing Islamic and conventional banks in Middle East countries. The last Arab spring and oil prices drops highlight a new issue that has not received the needed attention and provide a natural experiment to evaluate the financial resistance and capacity of both Islamic and conventional banks in the Middle East region.
Financial Shocks or Productivity Slowdown: Contrasting the Great Recession and Recovery in the United States and United Kingdom
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This article contrasts the experiences of the United States and United Kingdom during and after the Great Recession to understand the role of financial shocks in the magnitude of the crises and length of the recoveries. It starts from the common consensus that the Great Recession first and foremost was a financial crisis. It shows that relative to the United States, the Great Recession in the United Kingdom was more closely associated with a decline in productivity. Motivated by the similar behavior of financial variables at a business cycle frequency, it contrasts the behavior of the U.S. and U.K. economies through the lens of a simple real business cycle model augmented with financial shocks. A credit channel that operates on firm hiring decisions captures the magnitude of the output decline in both the United States and United Kingdom but exaggerates the response of the hours margin for the United Kindgom. The conclusion is that the financial channel supported in the U.S. data seems less appropriate for understanding the U.K. experience.
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This article contrasts the experiences of the United States and United Kingdom during and after the Great Recession to understand the role of financial shocks in the magnitude of the crises and length of the recoveries. It starts from the common consensus that the Great Recession first and foremost was a financial crisis. It shows that relative to the United States, the Great Recession in the United Kingdom was more closely associated with a decline in productivity. Motivated by the similar behavior of financial variables at a business cycle frequency, it contrasts the behavior of the U.S. and U.K. economies through the lens of a simple real business cycle model augmented with financial shocks. A credit channel that operates on firm hiring decisions captures the magnitude of the output decline in both the United States and United Kingdom but exaggerates the response of the hours margin for the United Kindgom. The conclusion is that the financial channel supported in the U.S. data seems less appropriate for understanding the U.K. experience.
Financing Innovation with Future Equity
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This article studies future equity financing in a continuous-time principal-agent setup whereby career concerns generate moral hazard tension. Our framework admits precise closed-form expressions. The higher firm value leading up to conversion, the fewer equity investors attain and the less risk the entrepreneur takes. We implement the contract using a convertible note with a valuation cap, that if set too high, developing innovation becomes suboptimal. Lastly, we introduce the implied probability of success: a novel measure allowing for risk comparison across different innovative technologies. We demonstrate its use to empirically estimate investors' skill and correct selection bias in realized returns.
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This article studies future equity financing in a continuous-time principal-agent setup whereby career concerns generate moral hazard tension. Our framework admits precise closed-form expressions. The higher firm value leading up to conversion, the fewer equity investors attain and the less risk the entrepreneur takes. We implement the contract using a convertible note with a valuation cap, that if set too high, developing innovation becomes suboptimal. Lastly, we introduce the implied probability of success: a novel measure allowing for risk comparison across different innovative technologies. We demonstrate its use to empirically estimate investors' skill and correct selection bias in realized returns.
How can Innovation Screening be Improved? A Machine Learning Analysis with Economic Consequences for Firm Performance
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Using USPTO patent application data, I apply a machine-learning algorithm to analyze how the current patent examination process in the U.S. can be improved in terms of granting higher quality patents. I make use of the quasi-random assignment of patent applications to examiners to show that screening decisions aided by a machine learning algorithm lead to a 15.5% gain in patent generality and a 35.6% gain in patent citations. To analyze the economic consequences of current patent screening on both public and private firms, I construct an ex-ante measure of past false acceptance rate for each examiner by exploiting the disagreement in patent screening decisions between the algorithm and current patent examiner. I first show that patents granted by examiners with higher false acceptance rates have lower announcement returns around patent grant news. Moreover, these patents are more likely to expire early. Next, I find that public firms whose patents are granted by such examiners are more likely to get sued in patent litigation cases. Consequently, these firms cut R&D investments and have worse operating performance. Lastly, I find that private firms whose patents are granted by such examiners are less likely to exit successfully by an IPO or an M&A. Overall, this study suggests that the social and economic cost of an inefficient patent screening system is large and can be mitigated with the help of a machine learning algorithm.
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Using USPTO patent application data, I apply a machine-learning algorithm to analyze how the current patent examination process in the U.S. can be improved in terms of granting higher quality patents. I make use of the quasi-random assignment of patent applications to examiners to show that screening decisions aided by a machine learning algorithm lead to a 15.5% gain in patent generality and a 35.6% gain in patent citations. To analyze the economic consequences of current patent screening on both public and private firms, I construct an ex-ante measure of past false acceptance rate for each examiner by exploiting the disagreement in patent screening decisions between the algorithm and current patent examiner. I first show that patents granted by examiners with higher false acceptance rates have lower announcement returns around patent grant news. Moreover, these patents are more likely to expire early. Next, I find that public firms whose patents are granted by such examiners are more likely to get sued in patent litigation cases. Consequently, these firms cut R&D investments and have worse operating performance. Lastly, I find that private firms whose patents are granted by such examiners are less likely to exit successfully by an IPO or an M&A. Overall, this study suggests that the social and economic cost of an inefficient patent screening system is large and can be mitigated with the help of a machine learning algorithm.
How the New Fed Municipal Bond Facility Capped Muni-Treasury Yield Spreads in the Covid-19 Recession
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For over two centuries, the municipal bond market has been a source of systemic risk, which returned early in the COVID-19 downturn when borrowing from securities markets became costly for many private and public entities, and some found it difficult to borrow at all. Indeed, just before the Fed announced its unprecedented intervention into the municipal (muni) bond market, spreads of muni over Treasury yields rose in line with the unemployment rate and appeared headed to levels not seen since the Great Depression, when real municipal gross investment plunged 35 percent below 1929 levels. To prevent a repeat, the Fed created the Municipal Liquidity Facility (MLF) to purchase newly issued, (near) investment grade state and local government bonds at normal ratings-based interest rate spreads over Treasury bonds plus a fee of 100 basis points, later reduced to 50 basis points. Despite a modest take-up, the MLF has effectively capped muni spreads at near normal levels plus the Fed fee and limited the extent to which interest rate spreads could have amplified the impact of the COVID pandemic. To establish the MLF the Fed needed Treasury indemnification against default losses. There are concerns about whether the creation of the MLF could undermine the efficiency of the bond market if the facility lasts too long and could induce moral hazard among borrowers. How the MLF will be unwound will affect these downside aspects and help answer the question whether the program’s benefits exceed its costs.
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For over two centuries, the municipal bond market has been a source of systemic risk, which returned early in the COVID-19 downturn when borrowing from securities markets became costly for many private and public entities, and some found it difficult to borrow at all. Indeed, just before the Fed announced its unprecedented intervention into the municipal (muni) bond market, spreads of muni over Treasury yields rose in line with the unemployment rate and appeared headed to levels not seen since the Great Depression, when real municipal gross investment plunged 35 percent below 1929 levels. To prevent a repeat, the Fed created the Municipal Liquidity Facility (MLF) to purchase newly issued, (near) investment grade state and local government bonds at normal ratings-based interest rate spreads over Treasury bonds plus a fee of 100 basis points, later reduced to 50 basis points. Despite a modest take-up, the MLF has effectively capped muni spreads at near normal levels plus the Fed fee and limited the extent to which interest rate spreads could have amplified the impact of the COVID pandemic. To establish the MLF the Fed needed Treasury indemnification against default losses. There are concerns about whether the creation of the MLF could undermine the efficiency of the bond market if the facility lasts too long and could induce moral hazard among borrowers. How the MLF will be unwound will affect these downside aspects and help answer the question whether the program’s benefits exceed its costs.
Impact of Changes in Oil Price on Indian Stock Market
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The purpose of this present paper is to contribute to the literature on stock markets and energy prices by studying the impact of oil price changes on Indian stock market returns. The study employed various statistical tools like trend analysis, correlation analysis and regression based modelling in order to try and establish a relationship between Crude Oil Prices and Indian Stock Market based on available past data. The span of this study includes data of Crude Oil Price (Brent Crude) and Indian Stock Market Index (BSE Sensex) for last 10 years (2003-12) in monthly Time Series format. As the above mentioned period (i.e. 2003-12) has witnessed various turmoils and changes in both Indian and World economy, namely the Global Recession (2008), Iraq War (2003), Arab Spring (2011), Iran Nuclear Crisis (2007), etc. Among the four last three had a significant impact on Oil Prices as they have caused political instability to major oil producing nations of the Middle East like Iraq, Iran, Libya, Bahrain, etc. Consequently, the period experienced marked fluctuations in global crude oil prices and thus, would prove to be significant for our study. The findings of the study indicate that oil prices generally follow economic principles of supply and demand in the long run. Also there exists a weak but significant relationship between oil price changes and returns on Indian stock market (BSE Sensex).
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The purpose of this present paper is to contribute to the literature on stock markets and energy prices by studying the impact of oil price changes on Indian stock market returns. The study employed various statistical tools like trend analysis, correlation analysis and regression based modelling in order to try and establish a relationship between Crude Oil Prices and Indian Stock Market based on available past data. The span of this study includes data of Crude Oil Price (Brent Crude) and Indian Stock Market Index (BSE Sensex) for last 10 years (2003-12) in monthly Time Series format. As the above mentioned period (i.e. 2003-12) has witnessed various turmoils and changes in both Indian and World economy, namely the Global Recession (2008), Iraq War (2003), Arab Spring (2011), Iran Nuclear Crisis (2007), etc. Among the four last three had a significant impact on Oil Prices as they have caused political instability to major oil producing nations of the Middle East like Iraq, Iran, Libya, Bahrain, etc. Consequently, the period experienced marked fluctuations in global crude oil prices and thus, would prove to be significant for our study. The findings of the study indicate that oil prices generally follow economic principles of supply and demand in the long run. Also there exists a weak but significant relationship between oil price changes and returns on Indian stock market (BSE Sensex).
Incorporating Asset Liquidity Effects in Economic-Capital Modeling Algorithms
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Recent turmoil in financial markets endorses the need for rigorous handling and integration of asset liquidity risk into Value-at-Risk (VaR) models. In this work we develop and test measures of certain kinds of asset liquidity risk that is useful for completing the definition of market risk and for predicting liquidity-adjusted VaR (LVaR) under adverse market conditions.
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Recent turmoil in financial markets endorses the need for rigorous handling and integration of asset liquidity risk into Value-at-Risk (VaR) models. In this work we develop and test measures of certain kinds of asset liquidity risk that is useful for completing the definition of market risk and for predicting liquidity-adjusted VaR (LVaR) under adverse market conditions.
Market Instability, Investor Sentiment, and Probability Judgment Error in Index Option Prices
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In a natural experiment with index option prices, we study how probability judgment error, and probabilistic risk attitudes, characterize investors’ sentiment about the ranking of index option attractiveness, the weight they place on each rank, and their ability to discriminate between prices. We introduce a novel behavioral process that (1) characterizes investor sentiment about tail events in index option prices over time and probability ranks, (2) provides early warning signals of market instability, and (3) crash probability estimates from a closed form expression for the time varying transition probability that a seemingly stable market state will become unstable and crash.
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In a natural experiment with index option prices, we study how probability judgment error, and probabilistic risk attitudes, characterize investors’ sentiment about the ranking of index option attractiveness, the weight they place on each rank, and their ability to discriminate between prices. We introduce a novel behavioral process that (1) characterizes investor sentiment about tail events in index option prices over time and probability ranks, (2) provides early warning signals of market instability, and (3) crash probability estimates from a closed form expression for the time varying transition probability that a seemingly stable market state will become unstable and crash.
More Stories of Unconventional Monetary Policy
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This article extends the work of Fawley and Neely (2013) to describe how major central banks have evolved unconventional monetary policies to encourage real activity and maintain stable inflation rates from 2013 through 2019. By 2013, central banks were moving from lump-sum asset purchase programs to open-ended asset purchase programs, which are conditioned on economic conditions, careful communication strategies, bank lending programs with incentives, and negative interest rates. This article reviews how central banks tailored their unconventional monetary methods to their various challenges and the structures of their respective economies.
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This article extends the work of Fawley and Neely (2013) to describe how major central banks have evolved unconventional monetary policies to encourage real activity and maintain stable inflation rates from 2013 through 2019. By 2013, central banks were moving from lump-sum asset purchase programs to open-ended asset purchase programs, which are conditioned on economic conditions, careful communication strategies, bank lending programs with incentives, and negative interest rates. This article reviews how central banks tailored their unconventional monetary methods to their various challenges and the structures of their respective economies.
Optimal Bailouts in Banking and Sovereign Crises
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We study optimal bailout policies in the presence of banking and sovereign crises. First, we use European data to document that asset guarantees are the most prevalent way in which sovereigns intervene during banking crises. Then, we build a model of sovereign borrowing with limited commitment, where domestic banks hold government debt and also provide credit to the private sector. Shocks to bank capital can trigger banking crises, with government sometimes finding it optimal to extend guarantees over bank assets. This leads to a trade-off: Larger bailouts relax domestic financial frictions and increase output, but also imply increasing government fiscal needs and possible heightened default risk (i.e., they create a ‘diabolic loop’). We find that the optimal bailouts exhibit clear properties. Other things equal, the fraction of banking losses that the bailouts would cover is: (i) decreasing in the level of government debt; (ii) increasing in aggregate productivity; and (iii) increasing in the severity of the banking crisis. Even though bailouts mitigate the adverse effects of banking crises, we find that the economy is ex ante better off without bailouts: the ‘diabolic loop’ they create is too costly.
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We study optimal bailout policies in the presence of banking and sovereign crises. First, we use European data to document that asset guarantees are the most prevalent way in which sovereigns intervene during banking crises. Then, we build a model of sovereign borrowing with limited commitment, where domestic banks hold government debt and also provide credit to the private sector. Shocks to bank capital can trigger banking crises, with government sometimes finding it optimal to extend guarantees over bank assets. This leads to a trade-off: Larger bailouts relax domestic financial frictions and increase output, but also imply increasing government fiscal needs and possible heightened default risk (i.e., they create a ‘diabolic loop’). We find that the optimal bailouts exhibit clear properties. Other things equal, the fraction of banking losses that the bailouts would cover is: (i) decreasing in the level of government debt; (ii) increasing in aggregate productivity; and (iii) increasing in the severity of the banking crisis. Even though bailouts mitigate the adverse effects of banking crises, we find that the economy is ex ante better off without bailouts: the ‘diabolic loop’ they create is too costly.
PEAD.Txt: Post-Earnings-Announcement Drift Using Text
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We construct a new numerical measure of earnings announcement surprises, standardized unexpected earnings call text (SUE.txt), that does not explicitly incorporate the reported earnings value. SUE.txt generates a text-based post-earnings announcement drift (PEAD.txt) larger than the classic PEAD and can be used to create a profitable trading strategy. Leveraging the prediction model underlying SUE.txt, we propose new tools to study the news content of text: paragraph-level SUE.txt and paragraph classification scheme based on the business curriculum. With these tools, we document many asymmetries in the distribution of news across content types, demonstrating that earnings calls contain a wide range of news about firms and their environment
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We construct a new numerical measure of earnings announcement surprises, standardized unexpected earnings call text (SUE.txt), that does not explicitly incorporate the reported earnings value. SUE.txt generates a text-based post-earnings announcement drift (PEAD.txt) larger than the classic PEAD and can be used to create a profitable trading strategy. Leveraging the prediction model underlying SUE.txt, we propose new tools to study the news content of text: paragraph-level SUE.txt and paragraph classification scheme based on the business curriculum. With these tools, we document many asymmetries in the distribution of news across content types, demonstrating that earnings calls contain a wide range of news about firms and their environment
Paycheck Protection Program: County-Level Determinants and Effect on Unemployment
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This paper uses U.S. county-level data to study the determinants and effects of the Paycheck Protection Program (PPP). The paper first overviews the timeline and institutional aspects of the PPP, implemented in the second quarter of 2020 and worth about $669 billion in forgivable small business loans guaranteed by the Small Business Administration (SBA). It then studies the determinants of the county-level ratios of PPP loans per job lost during the original unemployment surge associated with the onset of the COVID-19 pandemic in late March 2020 and finds that it does not appear to be a major driver of the PPP loan concentration; instead, it was primarily driven by the local banking conditions and demographic factors. The second part of this paper uses the method of local projections to determine whether the participation in the PPP program improved economic conditions following its implementation. Impulse responses in the standard linear framework are positive and statistically significant, albeit economically negligible, suggesting that the PPP was entirely ineffective in stabilizing labor market conditions. Extending the framework to state-dependent local projections reverses this result: PPP lending had a significant effect on reducing unemployment on average and especially in counties with strong banking liquidity and an educated labor force.
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This paper uses U.S. county-level data to study the determinants and effects of the Paycheck Protection Program (PPP). The paper first overviews the timeline and institutional aspects of the PPP, implemented in the second quarter of 2020 and worth about $669 billion in forgivable small business loans guaranteed by the Small Business Administration (SBA). It then studies the determinants of the county-level ratios of PPP loans per job lost during the original unemployment surge associated with the onset of the COVID-19 pandemic in late March 2020 and finds that it does not appear to be a major driver of the PPP loan concentration; instead, it was primarily driven by the local banking conditions and demographic factors. The second part of this paper uses the method of local projections to determine whether the participation in the PPP program improved economic conditions following its implementation. Impulse responses in the standard linear framework are positive and statistically significant, albeit economically negligible, suggesting that the PPP was entirely ineffective in stabilizing labor market conditions. Extending the framework to state-dependent local projections reverses this result: PPP lending had a significant effect on reducing unemployment on average and especially in counties with strong banking liquidity and an educated labor force.
Payments on Digital Platforms: Resiliency, Interoperability and Welfare
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Digital platforms, such as Alibaba and Amazon, operate an online marketplace to facilitate transactions. This paper studies a platform’s business model choice between accepting cash and issuing tokens, as well as the implications for welfare, resiliency, and interoperability. A cash platform free rides on the existing payment infrastructure and proï¬ts from collecting transaction fees. A token platform earns seigniorage, albeit bearing the costs of setting up the system and holding reserves to mitigate the cyber risk. Tokens earn consumers a return, insulating transactions from the liquidity costs of using cash, but also expose them to the remaining cyber risk. The platform issues tokens if the interest rate is high, the platform scope is large, and the cyber risk is small. Unbacked floating tokens with zero transaction fees or interest-bearing stablecoins can implement the equilibrium business model, which is not necessarily socially optimal because the platform does not internalize its impacts on off-platform activities. The model explains why Amazon does not issue tokens, but Alipay issues tokens circulatable outside its Alibaba platforms. Regulations such as a minimum reserve requirement can reduce welfare
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Digital platforms, such as Alibaba and Amazon, operate an online marketplace to facilitate transactions. This paper studies a platform’s business model choice between accepting cash and issuing tokens, as well as the implications for welfare, resiliency, and interoperability. A cash platform free rides on the existing payment infrastructure and proï¬ts from collecting transaction fees. A token platform earns seigniorage, albeit bearing the costs of setting up the system and holding reserves to mitigate the cyber risk. Tokens earn consumers a return, insulating transactions from the liquidity costs of using cash, but also expose them to the remaining cyber risk. The platform issues tokens if the interest rate is high, the platform scope is large, and the cyber risk is small. Unbacked floating tokens with zero transaction fees or interest-bearing stablecoins can implement the equilibrium business model, which is not necessarily socially optimal because the platform does not internalize its impacts on off-platform activities. The model explains why Amazon does not issue tokens, but Alipay issues tokens circulatable outside its Alibaba platforms. Regulations such as a minimum reserve requirement can reduce welfare
Returns to Seeking Political Influence: Early Evidence from the COVID-19 Stimulus
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Measuring various ways of seeking political influence and potential monetary return from such an endeavor is difficult. To address that challenge, we collect data on four mechanisms of acquiring political influence: lobbying spending, PAC contributions, lobbying through trade associations, and an invitation to testify in Congress for 10 years spanning 2010-2020. We investigate associations between these four mechanisms benefits from the largest stimulus package passed by Congress to address COVID-19. The odds of receiving governmental assistance for publicly listed firms that seek political influence are 94.2% for those who lobby directly, 170.7% higher for those who give PAC contributions, and 77.3% higher for those that lobby through a trade association. Beneficiary organizations of all stripes received from $3.26 (for lobbying) to $51.28 (for PAC contributions) of additional COVID-19 stimulus for each dollar they spent on political influence. Government organizations, which are usually not studied in the literature, earned larger returns to their political influence spending relative to public companies. Generally, a dollar spent on political influence by 2,758 unique firms on COMPUSTAT is associated with $20.67 of higher annual earnings in the future. This return is orders of magnitude larger than the payoff to R&D or advertising. Our work highlights how lucrative political influence can be for firm value.
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Measuring various ways of seeking political influence and potential monetary return from such an endeavor is difficult. To address that challenge, we collect data on four mechanisms of acquiring political influence: lobbying spending, PAC contributions, lobbying through trade associations, and an invitation to testify in Congress for 10 years spanning 2010-2020. We investigate associations between these four mechanisms benefits from the largest stimulus package passed by Congress to address COVID-19. The odds of receiving governmental assistance for publicly listed firms that seek political influence are 94.2% for those who lobby directly, 170.7% higher for those who give PAC contributions, and 77.3% higher for those that lobby through a trade association. Beneficiary organizations of all stripes received from $3.26 (for lobbying) to $51.28 (for PAC contributions) of additional COVID-19 stimulus for each dollar they spent on political influence. Government organizations, which are usually not studied in the literature, earned larger returns to their political influence spending relative to public companies. Generally, a dollar spent on political influence by 2,758 unique firms on COMPUSTAT is associated with $20.67 of higher annual earnings in the future. This return is orders of magnitude larger than the payoff to R&D or advertising. Our work highlights how lucrative political influence can be for firm value.
Revisiting the Implied Remaining Variance framework of Carr and Sun (2014): Locally consistent dynamics and sandwiched martingales
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Implied volatility is at the very core of modern ï¬nance, notwithstanding standard option pricing models continue to derive option prices starting from the joint dynamics of the underlying asset price and the spot volatility. These models often cause difficulties: no closed formulas for prices, demanding calibration techniques, unclear maps between spot and implied volatility. Inspired by the practice of using implied volatility as quoting system for option prices, models for the joint dynamics of the underlying asset price and the implied volatility have been proposed to replace standard option pricing models. Starting from Carr and Sun (2014), we develop a framework based on the Implied Remaining Variance where minimal conditions for absence of arbitrage are identiï¬ed, and smile bubbles are dealt with. The key concepts arising from the new IRV framework are those of locally consistent dynamics and sandwiched martingale. Within the new IRV framework, the results of Schweizer and Wissel (2008b) are reformulated, while those of El Amrani, Jacquier, and Martini (2021) are independently derived.
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Implied volatility is at the very core of modern ï¬nance, notwithstanding standard option pricing models continue to derive option prices starting from the joint dynamics of the underlying asset price and the spot volatility. These models often cause difficulties: no closed formulas for prices, demanding calibration techniques, unclear maps between spot and implied volatility. Inspired by the practice of using implied volatility as quoting system for option prices, models for the joint dynamics of the underlying asset price and the implied volatility have been proposed to replace standard option pricing models. Starting from Carr and Sun (2014), we develop a framework based on the Implied Remaining Variance where minimal conditions for absence of arbitrage are identiï¬ed, and smile bubbles are dealt with. The key concepts arising from the new IRV framework are those of locally consistent dynamics and sandwiched martingale. Within the new IRV framework, the results of Schweizer and Wissel (2008b) are reformulated, while those of El Amrani, Jacquier, and Martini (2021) are independently derived.
Risk and The Market’s Reaction to M&A Announcements
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We estimate how an acquiring firm’s risk changes depending on whether the market initially judges the acquisition to be neutral, strongly negative, or strongly positive for the shareholders of the acquiring firm. We find that for an average neutral acquisition, the annualized standard deviation of an acquiring firm’s total return declines by 5%. In contrast, acquisitions judged negatively by the market result in a 5% increase in total risk, while acquisitions judged positively by the market feature a 30 basis point increase in total risk. We find the median acquisition to be value creating, not value destructive. Value destruction tends to be concentrated among large firms, and to be associated with extreme negative outliers. Acquiring firms with longholder CEOs are more prone to undertake acquisitions, and more prone to take on risk, but are less prone to engage in value destructive acquisitions than acquiring firms with non-longholder CEOs. In this respect, acquiring firms with non-longholder CEOs are more apt to undertake risky bad acquisitions, especially when their prior returns lie above the industry average. In addition, acquiring firms with non-longholder CEOs are less prone to take on good acquisitions that are high in risk. As a general matter, firms with longholder CEOs are less risk-sensitive to changes in prior returns than firms with non-longholder CEOs.
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We estimate how an acquiring firm’s risk changes depending on whether the market initially judges the acquisition to be neutral, strongly negative, or strongly positive for the shareholders of the acquiring firm. We find that for an average neutral acquisition, the annualized standard deviation of an acquiring firm’s total return declines by 5%. In contrast, acquisitions judged negatively by the market result in a 5% increase in total risk, while acquisitions judged positively by the market feature a 30 basis point increase in total risk. We find the median acquisition to be value creating, not value destructive. Value destruction tends to be concentrated among large firms, and to be associated with extreme negative outliers. Acquiring firms with longholder CEOs are more prone to undertake acquisitions, and more prone to take on risk, but are less prone to engage in value destructive acquisitions than acquiring firms with non-longholder CEOs. In this respect, acquiring firms with non-longholder CEOs are more apt to undertake risky bad acquisitions, especially when their prior returns lie above the industry average. In addition, acquiring firms with non-longholder CEOs are less prone to take on good acquisitions that are high in risk. As a general matter, firms with longholder CEOs are less risk-sensitive to changes in prior returns than firms with non-longholder CEOs.
Technology Adoption and Leapfrogging: Racing for Mobile Payments
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Paying with a mobile phone is a cutting-edge innovation transforming the global payments industry. However, some advanced economies like the U.S. are lagging behind in mobile payment adoption. We construct a dynamic model with sequential payment innovations to explain this puzzle, which uncovers how advanced economies' past success in adopting card-payment technology holds them back in the mobile-payment race. Our calibrated model matches the cross-country adoption patterns of card and mobile payments and also explains why advanced and developing countries favor different mobile payment solutions. Based on the model, we conduct several quantitative exercises for welfare and policy analyses.
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Paying with a mobile phone is a cutting-edge innovation transforming the global payments industry. However, some advanced economies like the U.S. are lagging behind in mobile payment adoption. We construct a dynamic model with sequential payment innovations to explain this puzzle, which uncovers how advanced economies' past success in adopting card-payment technology holds them back in the mobile-payment race. Our calibrated model matches the cross-country adoption patterns of card and mobile payments and also explains why advanced and developing countries favor different mobile payment solutions. Based on the model, we conduct several quantitative exercises for welfare and policy analyses.
The
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This paper develops a dynamic general equilibrium model with heterogeneous firms that face search complementarities in the formation of vendor contracts. Search complementarities amplify small differences in productivity among firms. Market concentration fosters monopsony power in the labor market, magnifying profits and further enhancing the output share of high-productivity firms. The combination of search complementarities and monopsony power induce a strong "Matthew effect" that endogenously generates superstar firms out of uniform idiosyncratic productivity distributions. Reductions in search costs increase market concentration, lower the labor income share, and increase wage inequality. The model also transforms short-lived negative aggregate shocks into persistent recessions that heighten market concentration.
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This paper develops a dynamic general equilibrium model with heterogeneous firms that face search complementarities in the formation of vendor contracts. Search complementarities amplify small differences in productivity among firms. Market concentration fosters monopsony power in the labor market, magnifying profits and further enhancing the output share of high-productivity firms. The combination of search complementarities and monopsony power induce a strong "Matthew effect" that endogenously generates superstar firms out of uniform idiosyncratic productivity distributions. Reductions in search costs increase market concentration, lower the labor income share, and increase wage inequality. The model also transforms short-lived negative aggregate shocks into persistent recessions that heighten market concentration.
The Case for Central Bank Electronic Money and the Non-Case for Central Bank Cryptocurrencies
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We characterize various currencies according to their control structure, focusing on cryptocurrencies such as Bitcoin and government-issued fiat money. We then argue that there is a large unmet demand for a liquid asset that allows households and firms to save outside of the private financial sector.
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We characterize various currencies according to their control structure, focusing on cryptocurrencies such as Bitcoin and government-issued fiat money. We then argue that there is a large unmet demand for a liquid asset that allows households and firms to save outside of the private financial sector.
The Closing of a Major Airport: Immediate and Longer-Term Housing Market Effects
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The closing of a busy airport has large effects on noise and economic activity. Using a unique dataset, we examine the effects of closing Denver’s Stapleton Airport on nearby housing markets. We find evidence of immediate anticipatory price effects upon announcement, but no price changes at closing and little evidence of upward trending prices between announcement and closing. However, after airport closure, more higher income and fewer black households moved into these locations, and developers built higher quality houses. Finally, post-closing, these demographic and housing stock changes had substantial effects on housing prices, even after restricting the sample to sales of pre-existing housing.
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The closing of a busy airport has large effects on noise and economic activity. Using a unique dataset, we examine the effects of closing Denver’s Stapleton Airport on nearby housing markets. We find evidence of immediate anticipatory price effects upon announcement, but no price changes at closing and little evidence of upward trending prices between announcement and closing. However, after airport closure, more higher income and fewer black households moved into these locations, and developers built higher quality houses. Finally, post-closing, these demographic and housing stock changes had substantial effects on housing prices, even after restricting the sample to sales of pre-existing housing.
The Effect of the China Connect
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We document the effect on Chinese firms of the Shanghai (Shenzhen)-Hong Kong Stock Connect. The Connect was an important capital account liberalization introduced in the mid-2010s. It created a channel for cross-border equity investments into a selected set of Chinese stocks while China's overall capital controls policy remained in place. Using a difference-in-difference approach, and with careful attention to sample selection issues, we find that mainland Chinese firm-level investment is negatively affected by contractionary U.S. monetary policy shocks and that firms in the Connect are more adversely affected than those outside of it. These effects are economically large, robust, and stronger for firms whose stock return has a higher covariance with the world market return. We also find that firms in the Connect enjoy lower financing costs, invest more, and have higher profitability than unconnected firms. We discuss the implications of our results for the debate on capital controls and independence of Chinese monetary policy.
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We document the effect on Chinese firms of the Shanghai (Shenzhen)-Hong Kong Stock Connect. The Connect was an important capital account liberalization introduced in the mid-2010s. It created a channel for cross-border equity investments into a selected set of Chinese stocks while China's overall capital controls policy remained in place. Using a difference-in-difference approach, and with careful attention to sample selection issues, we find that mainland Chinese firm-level investment is negatively affected by contractionary U.S. monetary policy shocks and that firms in the Connect are more adversely affected than those outside of it. These effects are economically large, robust, and stronger for firms whose stock return has a higher covariance with the world market return. We also find that firms in the Connect enjoy lower financing costs, invest more, and have higher profitability than unconnected firms. We discuss the implications of our results for the debate on capital controls and independence of Chinese monetary policy.
The Firm Size and Leverage Relationship and its Implications for Entry and Business Concentration
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Larger firms (by sales or employment) have higher leverage. This pattern is explained using a model in which firms produce multiple varieties and borrow with the option to default against their future cash flow. A variety can die with a constant probability, implying that bigger firms (those with more varieties) have a lower coefficient of variation of sales and higher leverage. A lower risk-free rate benefits bigger firms more as they are able to lever more and existing firms buy more of the new varieties arriving into the economy. This leads to lower startup rates and greater concentration of sales.
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Larger firms (by sales or employment) have higher leverage. This pattern is explained using a model in which firms produce multiple varieties and borrow with the option to default against their future cash flow. A variety can die with a constant probability, implying that bigger firms (those with more varieties) have a lower coefficient of variation of sales and higher leverage. A lower risk-free rate benefits bigger firms more as they are able to lever more and existing firms buy more of the new varieties arriving into the economy. This leads to lower startup rates and greater concentration of sales.
The Impact of Financial Structure on the Performance of European Listed Firms
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By considering different systems of legal protection this study examines the impact of capital structure on the performance of listed firms in European region. Based on 5050 listed firms in eight European countries, the results of the study reveal that the owners in low level of legal protection are more likely to use the capital structure of the firms in order to serve their proper interests. In high level of legal protection, the market based system and the debts are enrolled to constraint the expropriation of private benefits.
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By considering different systems of legal protection this study examines the impact of capital structure on the performance of listed firms in European region. Based on 5050 listed firms in eight European countries, the results of the study reveal that the owners in low level of legal protection are more likely to use the capital structure of the firms in order to serve their proper interests. In high level of legal protection, the market based system and the debts are enrolled to constraint the expropriation of private benefits.
The Impact of Oil Prices on Stocks Markets: New Evidence During and After the Arab Spring in Gulf Cooperation Council Economies
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This study investigates the impact of stock price fluctuations on stock markets in six countries in Gulf Cooperation Council (GCC) (Saudi Arabia, Kuwait, Oman, Bahrain, United Arab Emirates (UAE) and Qatar) during and after the recent geopolitics conflicts, known as Arab Spring, from January 2011 to December 2017. Two statistical models were implemented to measure the relationship between oil price fluctuations and stock markets returns. The logistic smooth transition model was implemented to measure the relationship between oil price direction (positive/negative) and stock markets returns.The exponential smooth transition model (ESTR) was applied to capture the relationship between the magnitude of oil price fluctuations (small/large) and stock markets returns. The results reveal several asymmetrical results of oil price directions (positive/negative) on stock markets returns in some GCC countries. In Saudi Arabia, Kuwait and Bahrain, the negative oil price fluctuations have larger impact on the returns of stocks markets than positive oil price fluctuations. The results reveal also that the existence of political instability increases the sensitivity of stock markets returns on negative oil price shocks. In addition, the results of ESTR model do not reveal any asymmetrical relationship between the magnitude of oil price changes and stock markets returns in GCC region except Oman. A high level of oil price shocks has larger impact on Omani stock market returns than small oil price shocks.
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This study investigates the impact of stock price fluctuations on stock markets in six countries in Gulf Cooperation Council (GCC) (Saudi Arabia, Kuwait, Oman, Bahrain, United Arab Emirates (UAE) and Qatar) during and after the recent geopolitics conflicts, known as Arab Spring, from January 2011 to December 2017. Two statistical models were implemented to measure the relationship between oil price fluctuations and stock markets returns. The logistic smooth transition model was implemented to measure the relationship between oil price direction (positive/negative) and stock markets returns.The exponential smooth transition model (ESTR) was applied to capture the relationship between the magnitude of oil price fluctuations (small/large) and stock markets returns. The results reveal several asymmetrical results of oil price directions (positive/negative) on stock markets returns in some GCC countries. In Saudi Arabia, Kuwait and Bahrain, the negative oil price fluctuations have larger impact on the returns of stocks markets than positive oil price fluctuations. The results reveal also that the existence of political instability increases the sensitivity of stock markets returns on negative oil price shocks. In addition, the results of ESTR model do not reveal any asymmetrical relationship between the magnitude of oil price changes and stock markets returns in GCC region except Oman. A high level of oil price shocks has larger impact on Omani stock market returns than small oil price shocks.
The Mutual Impacts of Corporate Governance Dimensions and Legal Protection Systems on the Performance of European Banks: A Post-Crisis Study
SSRN
The paper provides new evidence on the relation between corporate governance practices, legal rights and European banks’ performance during the post-crisis period. Using a sample of 935 banks in 30 European countries, the results reveal that at a high level of legal protection European banks are more able to follow the international recommendations and codes of corporate governance practices and vice-versa. Additional analysis shows that all the corporate governance variables have the same impacts on the banks’ performance. At low, middle and high levels of legal protection, the results reveal positive impacts of committees’ number (such as remuneration, nomination and audit committee) and independent members of banks’ boards. The other dimensions of corporate governance (ownership concentration, executive pay and CEO duality) do not have any impact on bank performance. Only at the low level of legal protection the results show a negative impact on board size on European banks’ performance.
SSRN
The paper provides new evidence on the relation between corporate governance practices, legal rights and European banks’ performance during the post-crisis period. Using a sample of 935 banks in 30 European countries, the results reveal that at a high level of legal protection European banks are more able to follow the international recommendations and codes of corporate governance practices and vice-versa. Additional analysis shows that all the corporate governance variables have the same impacts on the banks’ performance. At low, middle and high levels of legal protection, the results reveal positive impacts of committees’ number (such as remuneration, nomination and audit committee) and independent members of banks’ boards. The other dimensions of corporate governance (ownership concentration, executive pay and CEO duality) do not have any impact on bank performance. Only at the low level of legal protection the results show a negative impact on board size on European banks’ performance.
The Success Keys for Family Firms: A Comparison Between Lebanese and French Systems
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This study investigates the success factors that can influence the performance and the continuity of the French and Lebanese family firms. Based on the literature review, the performance of the family firms was found to be linked to effective success keys like the succession planning, networking strategy, financial structure, management practices and finally the governance structure. Effectively, the results of this study indicate that French family firms are linked to four success keys (Planning for Succession, Using of Emotional intelligence, Professional HR management and Long Term Overview) while the Lebanese family firms are linked to five success keys (Financial structure with low leverage, Planning for Succession, Using of Emotional intelligence, professional HR management and Governmental Networking). Due to the non-significant impact of governance structure, an advanced investigation has been applied to detect the impact of this variable on the performance of family firm. The results of this advanced study indicate a negative correlation between the performance of family firms and the board of directors' size. Moreover, a positive correlation has been found between family firm performance and the presence of the outsiders in the board of directors.
SSRN
This study investigates the success factors that can influence the performance and the continuity of the French and Lebanese family firms. Based on the literature review, the performance of the family firms was found to be linked to effective success keys like the succession planning, networking strategy, financial structure, management practices and finally the governance structure. Effectively, the results of this study indicate that French family firms are linked to four success keys (Planning for Succession, Using of Emotional intelligence, Professional HR management and Long Term Overview) while the Lebanese family firms are linked to five success keys (Financial structure with low leverage, Planning for Succession, Using of Emotional intelligence, professional HR management and Governmental Networking). Due to the non-significant impact of governance structure, an advanced investigation has been applied to detect the impact of this variable on the performance of family firm. The results of this advanced study indicate a negative correlation between the performance of family firms and the board of directors' size. Moreover, a positive correlation has been found between family firm performance and the presence of the outsiders in the board of directors.
The U.S. Syndicated Loan Market: Matching Data
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We introduce a new software package for determining linkages between datasets without common identifiers. We apply these methods to three datasets commonly used in academic research on syndicated lending: Refinitiv LPC DealScan, the Shared National Credit Database, and S&P Global Market Intelligence Compustat. We benchmark the results of our match using results from the literature and previously matched files that are publicly available. We find that the company level matching is enhanced by careful cleaning of the data and considering hierarchical relationships. For loan level matching, a tailored approach based on a good understanding of the data can be better in certain dimensions than a more pure machine learning approach. The R package for the company level match can be found on Github.
SSRN
We introduce a new software package for determining linkages between datasets without common identifiers. We apply these methods to three datasets commonly used in academic research on syndicated lending: Refinitiv LPC DealScan, the Shared National Credit Database, and S&P Global Market Intelligence Compustat. We benchmark the results of our match using results from the literature and previously matched files that are publicly available. We find that the company level matching is enhanced by careful cleaning of the data and considering hierarchical relationships. For loan level matching, a tailored approach based on a good understanding of the data can be better in certain dimensions than a more pure machine learning approach. The R package for the company level match can be found on Github.
The Value of Bitcoin in the Year 2141 (and Beyond!)
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The emergence of bitcoin poses an important question for monetary theorists: can bitcoin compete with or even replace existing fiat monies? To answer this question, one must be able to determine what gives intrinsically useless monies their value, what determines the coexistence of alternative monies, and under what conditions economic agents would prefer to hold one money relative to another. We attempt to answer these questions in light of the emergence of bitcoin. In particular, we outline a theoretical model in which an intrinsically useless money is essential.
SSRN
The emergence of bitcoin poses an important question for monetary theorists: can bitcoin compete with or even replace existing fiat monies? To answer this question, one must be able to determine what gives intrinsically useless monies their value, what determines the coexistence of alternative monies, and under what conditions economic agents would prefer to hold one money relative to another. We attempt to answer these questions in light of the emergence of bitcoin. In particular, we outline a theoretical model in which an intrinsically useless money is essential.
Unintended Consequences of Unemployment Insurance Benefits: The Role of Banks
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We use disaggregated U.S. data and a border discontinuity design to show that more generous unemployment insurance (UI) policies lower bank deposits. We test several channels that could explain this decline and find evidence consistent with households lowering their precautionary savings. Since deposits are the largest and most stable source of funding for banks, the decrease in deposits affects bank lending. Banks that raise deposits in states with generous UI policies squeeze their small business lending. Furthermore, counties that are served by these banks experience a higher unemployment rate and lower wage growth.
SSRN
We use disaggregated U.S. data and a border discontinuity design to show that more generous unemployment insurance (UI) policies lower bank deposits. We test several channels that could explain this decline and find evidence consistent with households lowering their precautionary savings. Since deposits are the largest and most stable source of funding for banks, the decrease in deposits affects bank lending. Banks that raise deposits in states with generous UI policies squeeze their small business lending. Furthermore, counties that are served by these banks experience a higher unemployment rate and lower wage growth.
Which Lenders are More Likely to Reach Out to Underserved Consumers: Banks Versus Fintechs Versus Other Nonbanks?
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There has been a great deal of interest recently in understanding the potential role of fintech firms in expanding credit access to the underbanked and credit-constrained consumers. We explore the supply side of fintech credit, focusing on unsecured personal loans and mortgage loans. We investigate whether fintech firms are more likely than other lenders to reach out to “underserved consumers,†such as minorities; those with low income, low credit scores, or thin credit histories; or those who have a history of being denied for credit. Using a rich data set of credit offers from Mintel, in conjunction with credit information from TransUnion and other consumer credit data from the FRBNY/Equifax Consumer Credit Panel, we compare similar credit offers that were made by banks, fintech firms, and other nonbank lenders. Fintech firms are more likely than banks to offer mortgage credit to consumers with lower income, lower-credit scores, and those who have been denied credit in the recent past. Fintechs are also more likely than banks to offer personal loans to consumers who had filed for bankruptcy (thus also more likely to receive credit card offers overall) and those who had recently been denied credit. For both personal loans and mortgage loans, fintech firms are more likely than other lenders to reach out and offer credit to nonprime consumers.
SSRN
There has been a great deal of interest recently in understanding the potential role of fintech firms in expanding credit access to the underbanked and credit-constrained consumers. We explore the supply side of fintech credit, focusing on unsecured personal loans and mortgage loans. We investigate whether fintech firms are more likely than other lenders to reach out to “underserved consumers,†such as minorities; those with low income, low credit scores, or thin credit histories; or those who have a history of being denied for credit. Using a rich data set of credit offers from Mintel, in conjunction with credit information from TransUnion and other consumer credit data from the FRBNY/Equifax Consumer Credit Panel, we compare similar credit offers that were made by banks, fintech firms, and other nonbank lenders. Fintech firms are more likely than banks to offer mortgage credit to consumers with lower income, lower-credit scores, and those who have been denied credit in the recent past. Fintechs are also more likely than banks to offer personal loans to consumers who had filed for bankruptcy (thus also more likely to receive credit card offers overall) and those who had recently been denied credit. For both personal loans and mortgage loans, fintech firms are more likely than other lenders to reach out and offer credit to nonprime consumers.
