Research articles for the 2019-12-06
(Debt) Overhang: Evidence from Resource Extraction
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I study the empirical importance of debt overhang using a unique dataset on resource extraction firms, which provides ex ante measures of investment opportunities and important variation in the terms of a firm's obligations. In particular, unsecured reclamation liabilities create overhang that is costly to resolve and induces firms to forgo and postpone positive NPV investments. Traditional debt, in contrast, imposes few overhang-related investment distortions. These results show that: (i) the overhang problem is potentially large and applies more broadly to a firm's non-debt liabilities; and (ii) overhang problems associated with traditional debt can be avoided through contracting and debt composition.
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I study the empirical importance of debt overhang using a unique dataset on resource extraction firms, which provides ex ante measures of investment opportunities and important variation in the terms of a firm's obligations. In particular, unsecured reclamation liabilities create overhang that is costly to resolve and induces firms to forgo and postpone positive NPV investments. Traditional debt, in contrast, imposes few overhang-related investment distortions. These results show that: (i) the overhang problem is potentially large and applies more broadly to a firm's non-debt liabilities; and (ii) overhang problems associated with traditional debt can be avoided through contracting and debt composition.
A General Approach for Parisian Stopping Times with Applications in Finance and Insurance
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This paper proposed a method based on CTMC approximation to compute the distribution of Parisian stopping times and prices of Parisian options under general jump-diffusion models. Convergence of the proposed approach is proved under general setting and sharp convergence rates are obtained for general diffusion models. We show that first order convergence holds in general, while if the Parisian barrier is exactly on the grid and all the discontinuities in payoff functions are in the midway between two adjacent grid points, second order convergence can be guaranteed. These theoretical results are confirmed with numerical experiments, which further show that our approach is efficient and accurate for both diffusion and jump-diffusion models. Moreover, our approach can be easily modified to incorporate early-exercise features, other types of Parisian options and more general model setting including regime-switching and stochastic volatility models, etc.
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This paper proposed a method based on CTMC approximation to compute the distribution of Parisian stopping times and prices of Parisian options under general jump-diffusion models. Convergence of the proposed approach is proved under general setting and sharp convergence rates are obtained for general diffusion models. We show that first order convergence holds in general, while if the Parisian barrier is exactly on the grid and all the discontinuities in payoff functions are in the midway between two adjacent grid points, second order convergence can be guaranteed. These theoretical results are confirmed with numerical experiments, which further show that our approach is efficient and accurate for both diffusion and jump-diffusion models. Moreover, our approach can be easily modified to incorporate early-exercise features, other types of Parisian options and more general model setting including regime-switching and stochastic volatility models, etc.
An Empirical Economic Assessment of the Costs and Benefits of Bank Capital in the United States
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We evaluate the economic costs and benefits of bank capital in the United States. The analysis is similar to that found in previous studies, though we tailor it to the specific features and experience of the U.S. financial system. We also make adjustments to account for the impact of liquidity- and resolution-related regulations on the probability of a financial crisis. We find that the level of capital that maximizes the difference between total benefits and total costs ranges from just over 13 percent to 26 percent. This range reflects a high degree of uncertainty and latitude in specifying important study parameters that have a significant influence on the resulting optimal capital level, such as the output costs of a financial crisis or the effect of increased bank capital on economic output. Finally, the article discusses a range of considerations and factors that are not included in the cost-benefit framework that could have a substantial impact on estimated optimal capital levels.
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We evaluate the economic costs and benefits of bank capital in the United States. The analysis is similar to that found in previous studies, though we tailor it to the specific features and experience of the U.S. financial system. We also make adjustments to account for the impact of liquidity- and resolution-related regulations on the probability of a financial crisis. We find that the level of capital that maximizes the difference between total benefits and total costs ranges from just over 13 percent to 26 percent. This range reflects a high degree of uncertainty and latitude in specifying important study parameters that have a significant influence on the resulting optimal capital level, such as the output costs of a financial crisis or the effect of increased bank capital on economic output. Finally, the article discusses a range of considerations and factors that are not included in the cost-benefit framework that could have a substantial impact on estimated optimal capital levels.
Asymmetries in Risk Premia, Macroeconomic Uncertainty and Business Cycles
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A large literature suggests that the expected equity risk premium is countercyclical. Using a variety of different measures for this risk premium, we document that it also exhibits growth asymmetry, i.e. the risk premium rises sharply in recessions and declines much more gradually during the following recoveries. We show that a model with recursive preferences, in which agents cannot perfectly observe the state of current productivity, can generate the observed asymmetry in the risk premium. Key for this result are endogenous fluctuations in uncertainty which induce procyclical variations in agent’s nowcast accuracy. In addition to matching moments of the risk premium, the model is also successful in generating the growth asymmetry in macroeconomic aggregates observed in the data, and in matching the cyclical relation between quantities and the risk premium.
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A large literature suggests that the expected equity risk premium is countercyclical. Using a variety of different measures for this risk premium, we document that it also exhibits growth asymmetry, i.e. the risk premium rises sharply in recessions and declines much more gradually during the following recoveries. We show that a model with recursive preferences, in which agents cannot perfectly observe the state of current productivity, can generate the observed asymmetry in the risk premium. Key for this result are endogenous fluctuations in uncertainty which induce procyclical variations in agent’s nowcast accuracy. In addition to matching moments of the risk premium, the model is also successful in generating the growth asymmetry in macroeconomic aggregates observed in the data, and in matching the cyclical relation between quantities and the risk premium.
Dynamic Unravelling and Low Volume Crashes
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Rapid declines in the prices of financial securities on low trading volume -- low volume crashes -- are ubiquitous. This paper proposes a dynamic model with informational asymmetries and costly short-selling to explain this phenomenon. Owing to short-selling constraints, no-trade events are bad news. No-trade therefore lowers prices, worsens adverse selection, increases bid-ask spreads and causes liquidity traders to leave the market, making no-trade more likely. This generates endogenous auto-correlation in no-trade events -- dynamic unravelling -- and causes low volume crashes. Short-selling prohibitions harm price discovery and make crashes more likely. Liquidity interventions aid price discovery and avert crashes.
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Rapid declines in the prices of financial securities on low trading volume -- low volume crashes -- are ubiquitous. This paper proposes a dynamic model with informational asymmetries and costly short-selling to explain this phenomenon. Owing to short-selling constraints, no-trade events are bad news. No-trade therefore lowers prices, worsens adverse selection, increases bid-ask spreads and causes liquidity traders to leave the market, making no-trade more likely. This generates endogenous auto-correlation in no-trade events -- dynamic unravelling -- and causes low volume crashes. Short-selling prohibitions harm price discovery and make crashes more likely. Liquidity interventions aid price discovery and avert crashes.
How Have Banks Been Managing the Composition of High-Quality Liquid Assets?
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Banks’ liquidity management practices are fundamental to understanding the implementation and transmission of monetary policy. Since the Global Financial Crisis of 2007-09, these practices have been shaped importantly by the liquidity coverage ratio requirement. Given the lack of public data on how banks have been meeting this requirement, we construct estimates of U.S. banks’ high-quality liquid assets (HQLA) and examine how banks have managed these assets since the crisis. We find that banks have adopted a wide range of HQLA compositions and show that this empirical finding is consistent with a risk-return framework that hinges on banks’ aversion to liquidity and interest rate risks. We discuss how various regulations and business model choices can drive HQLA compositions in general, and connect many of the specific compositions we see to banks’ own public statements regarding their liquidity strategies. Finally, we highlight how banks’ preferences for the share of HQLA met with reserves affect the Fed’s monetary policy implementation framework.
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Banks’ liquidity management practices are fundamental to understanding the implementation and transmission of monetary policy. Since the Global Financial Crisis of 2007-09, these practices have been shaped importantly by the liquidity coverage ratio requirement. Given the lack of public data on how banks have been meeting this requirement, we construct estimates of U.S. banks’ high-quality liquid assets (HQLA) and examine how banks have managed these assets since the crisis. We find that banks have adopted a wide range of HQLA compositions and show that this empirical finding is consistent with a risk-return framework that hinges on banks’ aversion to liquidity and interest rate risks. We discuss how various regulations and business model choices can drive HQLA compositions in general, and connect many of the specific compositions we see to banks’ own public statements regarding their liquidity strategies. Finally, we highlight how banks’ preferences for the share of HQLA met with reserves affect the Fed’s monetary policy implementation framework.
Initial Coin Offerings, Information Disclosure, and Fraud
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We study the extent of fraud in initial coin offerings (ICOs), and whether information disclosure prior to the issuance predicts fraud. We document different types of fraud, and that fraudulent ICOs are on average much larger than the sample average. Issuers that disclose their code on GitHub are more likely to be targeted by phishing and hacker activities, which suggests that there are risks related to disclosing the code. Generally, we find it extremely difficult to predict fraud with the information available at the time of issuance. This calls for the need to install a third-party that certifies the quality of the issuers, such as specialized platforms, or the engagement of institutional investors and venture capital funds that can perform a due diligence and thus verify the quality of the project.
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We study the extent of fraud in initial coin offerings (ICOs), and whether information disclosure prior to the issuance predicts fraud. We document different types of fraud, and that fraudulent ICOs are on average much larger than the sample average. Issuers that disclose their code on GitHub are more likely to be targeted by phishing and hacker activities, which suggests that there are risks related to disclosing the code. Generally, we find it extremely difficult to predict fraud with the information available at the time of issuance. This calls for the need to install a third-party that certifies the quality of the issuers, such as specialized platforms, or the engagement of institutional investors and venture capital funds that can perform a due diligence and thus verify the quality of the project.
Is the Sky Falling? New Evidence on Accruals Quality Over Time and Around the World
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Prior research finds a long-term trend of declining earnings quality in the U.S. based primarily on analyses through the end of the 20th century. Using the Dechow and Dichev (2002) accruals quality measure, we provide new evidence that this decline began to reverse around 2000, with accruals quality generally improving through 2016. We find that this pattern is primarily attributable to trends in the volatility of underlying firm performance over time, suggesting that “low†accruals quality is not necessarily a product of a poorly functioning accounting system or management discretion, but rather it reflects the economic (cash flow) uncertainty of the firm’s operating environment. Extending our analyses to an international sample, we corroborate the negative association between cash flow volatility and accruals quality in the U.S., although the inter-temporal patterns differ across regions of the world and regulatory and political environments. The results are also robust to changes in the composition of U.S. public firms over time. Our evidence suggests that concerns about a decline in the quality of earnings in the U.S. may be overstated.
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Prior research finds a long-term trend of declining earnings quality in the U.S. based primarily on analyses through the end of the 20th century. Using the Dechow and Dichev (2002) accruals quality measure, we provide new evidence that this decline began to reverse around 2000, with accruals quality generally improving through 2016. We find that this pattern is primarily attributable to trends in the volatility of underlying firm performance over time, suggesting that “low†accruals quality is not necessarily a product of a poorly functioning accounting system or management discretion, but rather it reflects the economic (cash flow) uncertainty of the firm’s operating environment. Extending our analyses to an international sample, we corroborate the negative association between cash flow volatility and accruals quality in the U.S., although the inter-temporal patterns differ across regions of the world and regulatory and political environments. The results are also robust to changes in the composition of U.S. public firms over time. Our evidence suggests that concerns about a decline in the quality of earnings in the U.S. may be overstated.
Machine + Man: A Field Experiment on the Role of Discretion in Augmenting AI-Based Lending Models
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Does human discretion improve or diminish lending outcomes? We assess this question in the context of a randomized, controlled experiment using a large group of lenders that rely on machine-generated credit scoring models provided by a third party to make monthly credit decisions. Working with the credit scoring company, we design a new feature for their platform â€" the slider feature â€" which allows lenders to incorporate their discretion into the credit score. We randomly assign half of the lenders to the treatment group that gets the slider; the control group does not get the slider feature and thus makes credit decisions based primarily on the machine-generated model. Consistent with discretion aiding in loan decisions, we find that the treatment group’s credit model adjustments are predictive of forward looking portfolio performance. However, we find that discretion is not useful in all cases. In fact, the control group does just as well as the treatment group in predicting credit risk for borrowers that have been traditionally classified as opaque. Our study highlights the growing prominence of AI-based lending models in crowding out some of the human’s role.
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Does human discretion improve or diminish lending outcomes? We assess this question in the context of a randomized, controlled experiment using a large group of lenders that rely on machine-generated credit scoring models provided by a third party to make monthly credit decisions. Working with the credit scoring company, we design a new feature for their platform â€" the slider feature â€" which allows lenders to incorporate their discretion into the credit score. We randomly assign half of the lenders to the treatment group that gets the slider; the control group does not get the slider feature and thus makes credit decisions based primarily on the machine-generated model. Consistent with discretion aiding in loan decisions, we find that the treatment group’s credit model adjustments are predictive of forward looking portfolio performance. However, we find that discretion is not useful in all cases. In fact, the control group does just as well as the treatment group in predicting credit risk for borrowers that have been traditionally classified as opaque. Our study highlights the growing prominence of AI-based lending models in crowding out some of the human’s role.
Monk: Mortgages in a New-Keynesian Model
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We propose a tractable framework for monetary policy analysis in which both short- and long-term debt affect equilibrium outcomes. This objective is motivated by observations from two literatures suggesting that monetary policy contains a dimension affecting expected future interest rates and thus the costs of long-term financing. In New-Keynesian models, however, long-term loans are redundant assets. We use the model to address three questions: what are the effects of statement vs. action policy shocks; how important are standard New- Keynesian vs. cash flow effects in their transmission; and what is the interaction between these two effects?
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We propose a tractable framework for monetary policy analysis in which both short- and long-term debt affect equilibrium outcomes. This objective is motivated by observations from two literatures suggesting that monetary policy contains a dimension affecting expected future interest rates and thus the costs of long-term financing. In New-Keynesian models, however, long-term loans are redundant assets. We use the model to address three questions: what are the effects of statement vs. action policy shocks; how important are standard New- Keynesian vs. cash flow effects in their transmission; and what is the interaction between these two effects?
Mortgage Lending, Monetary Policy, and Prudential Measures in Small Euro-Area Economies: Evidence from Ireland and the Netherlands
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This paper examines whether the increased use of macroprudential policies since the global financial crisis has affected the impact of (euro area and foreign) monetary policy on mortgage lending in Ireland and the Netherlands, which are both small open economies in the euro area. Using bank-level data on domestic lending in both countries during the period 2003-2018, we find that restrictive euro area monetary policy shocks reduce the growth of mortgage lending. We find evidence that stricter domestic prudential regulation mitigates this effect in Ireland, but not so in the Netherlands. There is weak evidence for an international bank lending channel.
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This paper examines whether the increased use of macroprudential policies since the global financial crisis has affected the impact of (euro area and foreign) monetary policy on mortgage lending in Ireland and the Netherlands, which are both small open economies in the euro area. Using bank-level data on domestic lending in both countries during the period 2003-2018, we find that restrictive euro area monetary policy shocks reduce the growth of mortgage lending. We find evidence that stricter domestic prudential regulation mitigates this effect in Ireland, but not so in the Netherlands. There is weak evidence for an international bank lending channel.
Perceived Precautionary Savings Motives: Evidence from FinTech
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We study the consumption response to the provision of credit lines to individuals that previously did not have access to credit combined with the possibility to elicit directly a large set of preferences, beliefs, and motives. As expected, users react to the availability of credit by increasing their spending permanently and reallocating consumption from non-discretionary to discretionary goods and services. Surprisingly, though, liquid users react more than others and this pattern is a robust feature of the data. Moreover, liquid users lower their savings rate, but do not tap into negative deposits. The credit line seems to act as a form of insurance against future negative shocks and its mere presence makes users spend their existing liquidity without accumulating any debt. By eliciting preferences, beliefs, and motives directly, we show these results are not fully consistent with models of financial constraints, buffer stock models with and without durables, present-bias preferences, uncertainty about future income, bequest motives, or the canonical life-cycle permanent income model. We label this channel the perceived precautionary savings channel, because liquid households behave as if they faced strong precautionary savings motives even though no observables suggest they should based on standard theoretical models.
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We study the consumption response to the provision of credit lines to individuals that previously did not have access to credit combined with the possibility to elicit directly a large set of preferences, beliefs, and motives. As expected, users react to the availability of credit by increasing their spending permanently and reallocating consumption from non-discretionary to discretionary goods and services. Surprisingly, though, liquid users react more than others and this pattern is a robust feature of the data. Moreover, liquid users lower their savings rate, but do not tap into negative deposits. The credit line seems to act as a form of insurance against future negative shocks and its mere presence makes users spend their existing liquidity without accumulating any debt. By eliciting preferences, beliefs, and motives directly, we show these results are not fully consistent with models of financial constraints, buffer stock models with and without durables, present-bias preferences, uncertainty about future income, bequest motives, or the canonical life-cycle permanent income model. We label this channel the perceived precautionary savings channel, because liquid households behave as if they faced strong precautionary savings motives even though no observables suggest they should based on standard theoretical models.
Quantitative Easing and Exuberance in Stock Markets: Evidence from the Euro Area
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In response to a prolonged period of low inflation, the European Central Bank (ECB) introduced Quantitative Easing (QE) in an attempt to steer inflation to its target of below, but close to, 2% in the medium term. This paper examines whether QE contributes to exuberance in euro area stock markets by using recent advances in bubble detection techniques (the GSADF-test). We do so by linking price developments in 10 euro area stock markets to a series of country specific macro fundamentals and QE. The results indicate that periods of QE coincide with exuberant investor behaviour, even after controlling for improving macro fundamentals.
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In response to a prolonged period of low inflation, the European Central Bank (ECB) introduced Quantitative Easing (QE) in an attempt to steer inflation to its target of below, but close to, 2% in the medium term. This paper examines whether QE contributes to exuberance in euro area stock markets by using recent advances in bubble detection techniques (the GSADF-test). We do so by linking price developments in 10 euro area stock markets to a series of country specific macro fundamentals and QE. The results indicate that periods of QE coincide with exuberant investor behaviour, even after controlling for improving macro fundamentals.
Ring-Fencing Digital Corporations: Investor Reaction to the European Commission’s Digital Tax Proposals
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We study the effect of digital tax measures on firm value. By employing an event study methodology, we analyze investor reaction to the European Commission’s proposals on the taxation of digital corporations. Examining the stock returns of potentially affected corporations surrounding the draft directives’ release, we find a significant abnormal capital market reaction of -0.692 percentage points. The investor reaction is more pronounced for firms that engage more actively in tax avoidance, have a higher profit shifting potential, and for those with higher exposure to the EU. The market value of digital and innovative corporations decreased by at least 52 billion euro in excess of the regular market movement during the event window. Overall, our study reveals that expectations about ringfencing digital tax measures impact firm values.
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We study the effect of digital tax measures on firm value. By employing an event study methodology, we analyze investor reaction to the European Commission’s proposals on the taxation of digital corporations. Examining the stock returns of potentially affected corporations surrounding the draft directives’ release, we find a significant abnormal capital market reaction of -0.692 percentage points. The investor reaction is more pronounced for firms that engage more actively in tax avoidance, have a higher profit shifting potential, and for those with higher exposure to the EU. The market value of digital and innovative corporations decreased by at least 52 billion euro in excess of the regular market movement during the event window. Overall, our study reveals that expectations about ringfencing digital tax measures impact firm values.
Supplemental Appendix - Factors that Fit the Time Series and Cross-Section of Stock Returns
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The Supplemental Appendix to "Factors that Fit the Time Series and Cross-Section of Stock Returns" provides additional tables and figures supporting the main text. Among others it includes robustness results for the large cross-section of all decile portfolios and the extended cross-section with 49 anomalies. We also collect the results for a large number of double-sorted portfolios.
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The Supplemental Appendix to "Factors that Fit the Time Series and Cross-Section of Stock Returns" provides additional tables and figures supporting the main text. Among others it includes robustness results for the large cross-section of all decile portfolios and the extended cross-section with 49 anomalies. We also collect the results for a large number of double-sorted portfolios.
Technological Innovation in Mortgage Underwriting and the Growth in Credit, 1985–2015
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The application of information technology to finance, or â€Å"fintech,� is expected to revolutionize many aspects of borrowing and lending in the future, but technology has been reshaping consumer and mortgage lending for many years. During the 1990s, computerization allowed mortgage lenders to reduce loan-processing times and largely replace human-based assessments of credit risk with default predictions generated by sophisticated empirical models. Debt-to-income ratios at origination add little to the predictive power of these models, so the new automated underwriting systems allowed higher debt-to-income ratios than previous underwriting guidelines would have allowed. In this way, technology brought about an exogenous change in lending standards that was especially relevant for borrowers with low current incomes relative to their expected future incomesâ€â€in particular, young college graduates. By contrast, the data suggest that the credit expansion during the 2000s housing boom was an endogenous response to widespread expectations of higher future house prices, as average mortgage sizes rose for borrowers across the entire income distribution.
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The application of information technology to finance, or â€Å"fintech,� is expected to revolutionize many aspects of borrowing and lending in the future, but technology has been reshaping consumer and mortgage lending for many years. During the 1990s, computerization allowed mortgage lenders to reduce loan-processing times and largely replace human-based assessments of credit risk with default predictions generated by sophisticated empirical models. Debt-to-income ratios at origination add little to the predictive power of these models, so the new automated underwriting systems allowed higher debt-to-income ratios than previous underwriting guidelines would have allowed. In this way, technology brought about an exogenous change in lending standards that was especially relevant for borrowers with low current incomes relative to their expected future incomesâ€â€in particular, young college graduates. By contrast, the data suggest that the credit expansion during the 2000s housing boom was an endogenous response to widespread expectations of higher future house prices, as average mortgage sizes rose for borrowers across the entire income distribution.
The Real Term Premium in a Stationary Economy with Segmented Asset Markets
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This article proposes a general equilibrium model to explain the positive and sizable term premia implied by the data. The authors introduce a slow mean-reverting process of consumption growth and a segmented asset-market mechanism with heterogeneous trading technologies into an otherwise standard heterogeneous agent general equilibrium model. First, the slow mean-reverting consumption growth process implies that the expected consumption growth rate is only slightly countercyclical and the process can exhibit near-zero first-order autocorrelation, as observed in the data. This slight countercyclicality suggests that long-term bonds are risky, and hence the term premia should be positive. Second, the segmented asset-market mechanism amplifies the magnitude of the term premia because aggregate risk is highly concentrated in a small fraction of marginal traders who demand high compensation for taking risk. For sensitivity analysis, the role of each assumption is further investigated by removing each factor one at a time.
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This article proposes a general equilibrium model to explain the positive and sizable term premia implied by the data. The authors introduce a slow mean-reverting process of consumption growth and a segmented asset-market mechanism with heterogeneous trading technologies into an otherwise standard heterogeneous agent general equilibrium model. First, the slow mean-reverting consumption growth process implies that the expected consumption growth rate is only slightly countercyclical and the process can exhibit near-zero first-order autocorrelation, as observed in the data. This slight countercyclicality suggests that long-term bonds are risky, and hence the term premia should be positive. Second, the segmented asset-market mechanism amplifies the magnitude of the term premia because aggregate risk is highly concentrated in a small fraction of marginal traders who demand high compensation for taking risk. For sensitivity analysis, the role of each assumption is further investigated by removing each factor one at a time.
The Role of Institutional Investors in Voting: Evidence from the Securities Lending Market
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This paper investigates the voting preferences of institutional investors using the unique setting of the securities lending market. Institutional investors restrict lendable supply and/or call back loaned shares prior to the proxy record date to exercise voting rights. Recall is higher for investors with greater incentives to monitor and exert governance, for firms with poor performance and weak governance, and for proposals where the returns to governance are likely to be higher such as those relating to corporate control. Loan demand and the borrowing fee also increase around the record date. In the subsequent vote outcome we find higher share recall to be associated with less support for management and more support for shareholder proposals. Our results indicate that institutions value their vote and use the proxy process as an important channel for affecting corporate governance.
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This paper investigates the voting preferences of institutional investors using the unique setting of the securities lending market. Institutional investors restrict lendable supply and/or call back loaned shares prior to the proxy record date to exercise voting rights. Recall is higher for investors with greater incentives to monitor and exert governance, for firms with poor performance and weak governance, and for proposals where the returns to governance are likely to be higher such as those relating to corporate control. Loan demand and the borrowing fee also increase around the record date. In the subsequent vote outcome we find higher share recall to be associated with less support for management and more support for shareholder proposals. Our results indicate that institutions value their vote and use the proxy process as an important channel for affecting corporate governance.
The Use of Artificial Neural Networks for Extracting Actions and Actors from Requirements Document
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Context: The automatic extraction of actors and actions (i.e., use cases) of a system from natural language-based requirement descriptions, is considered a common problem in requirements analysis. Numerous techniques have been used to resolve this problem. Examples include rule-based (e.g., inference), keywords, query (e.g., bi-grams), library maintenance, semantic business vocabularies, and rules. The question remains: can combination of natural language processing (NLP) and artificial neural networks (ANNs) perform this job successfully and effectively?Objective: This paper proposes a new approach to automatically identify actors and actions in a natural language-based requirements’ description of a system. Included are descriptions of how NLP plays an important role in extracting actors and actions, and how ANNs can be used to provide definitive identification.Method: We used an NLP parser with a general architecture for text engineering, producing lexicons, syntaxes, and semantic analyses. An ANN was developed using five different use cases, producing different results due to their complexity and linguistic formation.Results: Binomial classification accuracy techniques were used to evaluate the effectiveness of this approach. Based on the five use cases, the results were 17â€"63% for precision, 5â€"6100% for recall, and 29â€"71% for F-measure.Conclusion: We successfully used a combination of NLP and ANN artificial intelligence techniques to reveal specific domain semantics found in a software requirements specification. An Intelligent Technique for Requirements Engineering (IT4RE) was developed to provide a semi-automated approach, classified as Intelligent Computer Aided Software Engineering (I-CASE).
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Context: The automatic extraction of actors and actions (i.e., use cases) of a system from natural language-based requirement descriptions, is considered a common problem in requirements analysis. Numerous techniques have been used to resolve this problem. Examples include rule-based (e.g., inference), keywords, query (e.g., bi-grams), library maintenance, semantic business vocabularies, and rules. The question remains: can combination of natural language processing (NLP) and artificial neural networks (ANNs) perform this job successfully and effectively?Objective: This paper proposes a new approach to automatically identify actors and actions in a natural language-based requirements’ description of a system. Included are descriptions of how NLP plays an important role in extracting actors and actions, and how ANNs can be used to provide definitive identification.Method: We used an NLP parser with a general architecture for text engineering, producing lexicons, syntaxes, and semantic analyses. An ANN was developed using five different use cases, producing different results due to their complexity and linguistic formation.Results: Binomial classification accuracy techniques were used to evaluate the effectiveness of this approach. Based on the five use cases, the results were 17â€"63% for precision, 5â€"6100% for recall, and 29â€"71% for F-measure.Conclusion: We successfully used a combination of NLP and ANN artificial intelligence techniques to reveal specific domain semantics found in a software requirements specification. An Intelligent Technique for Requirements Engineering (IT4RE) was developed to provide a semi-automated approach, classified as Intelligent Computer Aided Software Engineering (I-CASE).
The Voice of Risk: Wall Street CEOs’ Vocal Masculinity and the 2008 Financial Crisis
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Did the masculinity-driven corporate culture of Wall Street change after the 2008 global financial crisis? According to the neuroendocrinology literature, the voice pitch of a male is an ‘honest signal’ of his testosterone level that affects risk taking for social dominance. We use digitally analyzed voice pitch of 167 Wall Street male CEO interviews on CNBC during the 2008 financial crisis and find a negative association between the voice pitch of the CEO and the risk of the firm. Additionally, deep-voiced male CEOs were more likely to be fired after the crisis.
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Did the masculinity-driven corporate culture of Wall Street change after the 2008 global financial crisis? According to the neuroendocrinology literature, the voice pitch of a male is an ‘honest signal’ of his testosterone level that affects risk taking for social dominance. We use digitally analyzed voice pitch of 167 Wall Street male CEO interviews on CNBC during the 2008 financial crisis and find a negative association between the voice pitch of the CEO and the risk of the firm. Additionally, deep-voiced male CEOs were more likely to be fired after the crisis.
What Determines Debt Maturity?
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What determines the maturity structure of debt? In this article, I develop a simple model to explore how the optimal maturity of debt issued by a firm (or a country) depends both on the firm’s cyclical state and other features of the economic environment in which it operates. I find that firms with better current earnings and better growth prospects issue debt with longer maturity, while firms operating in more-volatile environments issue debt with shorter maturity. Yield to maturity is a poor indicator of the risk of debt issued by a firm. The reason is simple: Yield to maturity captures both default risk and a component that is a pseudo term premium. In the model, the market does require a term premium and one appears only because of the risk of default. It is not possible to separate the impact of maturity and risk.
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What determines the maturity structure of debt? In this article, I develop a simple model to explore how the optimal maturity of debt issued by a firm (or a country) depends both on the firm’s cyclical state and other features of the economic environment in which it operates. I find that firms with better current earnings and better growth prospects issue debt with longer maturity, while firms operating in more-volatile environments issue debt with shorter maturity. Yield to maturity is a poor indicator of the risk of debt issued by a firm. The reason is simple: Yield to maturity captures both default risk and a component that is a pseudo term premium. In the model, the market does require a term premium and one appears only because of the risk of default. It is not possible to separate the impact of maturity and risk.
What Is the Conditional Autocorrelation on the Stock Market?
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We derive lower and upper bounds on the conditional market autocorrelation index at various investment horizons without using the precise form of the utility function. The bounds are derived in terms of option prices and can be computed at daily frequency for any given horizon. The bounds incorporate all the information contained in the entire distribution of returns. We use options on the S&P 500 index to quantify the bounds and document that asset prices imply a negative upper bound on the market conditional autocorrelation index. The upper bound on the market conditional autocorrelation index is highly volatile, skewed, and exhibits fat tails. It varies from -28% to -3% and takes extremely negative values during crisis or recession periods while being close to zero during normal times. On average, the upper bound on the market conditional autocorrelation index is -14%. We also document that periods of extremely negative market conditional autocorrelation index coincide with periods of a high Sharpe ratio, and we show that leading asset pricing models cannot reproduce both the negative market conditional autocorrelation index and the negative average market conditional autocorrelation index implied by asset prices.
SSRN
We derive lower and upper bounds on the conditional market autocorrelation index at various investment horizons without using the precise form of the utility function. The bounds are derived in terms of option prices and can be computed at daily frequency for any given horizon. The bounds incorporate all the information contained in the entire distribution of returns. We use options on the S&P 500 index to quantify the bounds and document that asset prices imply a negative upper bound on the market conditional autocorrelation index. The upper bound on the market conditional autocorrelation index is highly volatile, skewed, and exhibits fat tails. It varies from -28% to -3% and takes extremely negative values during crisis or recession periods while being close to zero during normal times. On average, the upper bound on the market conditional autocorrelation index is -14%. We also document that periods of extremely negative market conditional autocorrelation index coincide with periods of a high Sharpe ratio, and we show that leading asset pricing models cannot reproduce both the negative market conditional autocorrelation index and the negative average market conditional autocorrelation index implied by asset prices.
