Research articles for the 2020-11-24
A Note on the Comparison of NPV Profiles of Mutually Exclusive Projects
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One of the basic tenets of modern finance is that an asset’s required return depends on that asset’s risk. Hence, a project’s cost of capital should be matched with the riskiness of that project. From this standpoint, one may conclude that the standard textbook treatment of NPV profiles is restricted to the case where the projects being compared have the same risk and, therefore, have the same discount rate. When the same discount rate is inappropriately applied to different projects, the usual comparison of NPV profiles can be misleading. It is when possible differences in project risk and associated cost of capital are explicitly considered, that NPV profiles can become more useful in the comparative evaluation of mutually exclusive projects.
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One of the basic tenets of modern finance is that an asset’s required return depends on that asset’s risk. Hence, a project’s cost of capital should be matched with the riskiness of that project. From this standpoint, one may conclude that the standard textbook treatment of NPV profiles is restricted to the case where the projects being compared have the same risk and, therefore, have the same discount rate. When the same discount rate is inappropriately applied to different projects, the usual comparison of NPV profiles can be misleading. It is when possible differences in project risk and associated cost of capital are explicitly considered, that NPV profiles can become more useful in the comparative evaluation of mutually exclusive projects.
All that Glitters Is not Gold: The Corporate Debt Maturity Structure in Asia Pacific
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We show that the debt maturity structure of non-financial corporates in Asia Pacific has been generally decreasing over the last two decades. The percentage of total outstanding debt that have a maturity of over 3 years to total outstanding of debt decreased from 66% at the end of 2000 to 46% at the end of 2019, while the median percentage decreased from 40% to 26% over the same period. The decreasing trend could amplify adverse effects caused by credit and liquidity shock. Our result shows that both firms-specific and macroeconomic factors can explain the trend in both advanced economies and emerging market economies, this in turn providing relevant policy implications for policy-makers to contain rollover risk triggered by shortening debt maturity structure in the Asia Pacific.
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We show that the debt maturity structure of non-financial corporates in Asia Pacific has been generally decreasing over the last two decades. The percentage of total outstanding debt that have a maturity of over 3 years to total outstanding of debt decreased from 66% at the end of 2000 to 46% at the end of 2019, while the median percentage decreased from 40% to 26% over the same period. The decreasing trend could amplify adverse effects caused by credit and liquidity shock. Our result shows that both firms-specific and macroeconomic factors can explain the trend in both advanced economies and emerging market economies, this in turn providing relevant policy implications for policy-makers to contain rollover risk triggered by shortening debt maturity structure in the Asia Pacific.
Are In-Person Shareholder Meetings Outdated? The Value of Implicit Communication
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Following the COVID-19 pandemic, many Asian countries have allowed virtual shareholder meetings. These meetings can not only prevent the spread of infection but also lower companies’ costs and facilitate shareholders’ participation. However, in virtual meetings, shareholders may lose a significant portion of implicit communicationâ€"communication through nonverbal elementsâ€"with management and other shareholders, a factor which has not been fully recognized as a benefit of in-person meetings. If this benefit is not sizable, then making a shareholder meeting non-mandatory is reasonable, whether in-person or virtual, because the other benefits of these meetings are not significant. However, some evidence shows that the benefit of implicit communication may be important for shareholders in many cases. If virtual meetings cannot sufficiently realize this benefit through virtual reality, it would generally be desirable to use them as a complimentary tool for in-person meetings, not as a substitute.
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Following the COVID-19 pandemic, many Asian countries have allowed virtual shareholder meetings. These meetings can not only prevent the spread of infection but also lower companies’ costs and facilitate shareholders’ participation. However, in virtual meetings, shareholders may lose a significant portion of implicit communicationâ€"communication through nonverbal elementsâ€"with management and other shareholders, a factor which has not been fully recognized as a benefit of in-person meetings. If this benefit is not sizable, then making a shareholder meeting non-mandatory is reasonable, whether in-person or virtual, because the other benefits of these meetings are not significant. However, some evidence shows that the benefit of implicit communication may be important for shareholders in many cases. If virtual meetings cannot sufficiently realize this benefit through virtual reality, it would generally be desirable to use them as a complimentary tool for in-person meetings, not as a substitute.
Banks, Low Interest Rates, and Monetary Policy Transmission
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This paper studies how low interest rates weaken the short-run transmission of monetary policy and contract the long-run supply of bank credit. As U.S. bond rates have fallen, the pass-through of monetary shocks to loan and deposit rates has weakened while the spread on U.S. bank loans has risen. I build a model in which banks earn deposit and loan spreads, deposits compete with money, and banks’ lending capacity depends on their equity. The short-run transmission of monetary policy is dampened at low rates, because deposit spreads act as a better hedge for bank equity against unexpected monetary shocks. In the long run, persistent low rates decrease banks’ “seigniorage†revenue from deposit spreads, hence bank equity and loan supply contract, and loan spreads increase.
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This paper studies how low interest rates weaken the short-run transmission of monetary policy and contract the long-run supply of bank credit. As U.S. bond rates have fallen, the pass-through of monetary shocks to loan and deposit rates has weakened while the spread on U.S. bank loans has risen. I build a model in which banks earn deposit and loan spreads, deposits compete with money, and banks’ lending capacity depends on their equity. The short-run transmission of monetary policy is dampened at low rates, because deposit spreads act as a better hedge for bank equity against unexpected monetary shocks. In the long run, persistent low rates decrease banks’ “seigniorage†revenue from deposit spreads, hence bank equity and loan supply contract, and loan spreads increase.
Bayes Risk, Elicitability, and the Expected Shortfall
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Motivated by recent advances on elicitability of risk measures and practical considerations of risk optimization, we introduce the notions of Bayes pairs and Bayes risk measures. Bayes risk measures are the counterpart of elicitable risk measures, extensively studied in the recent literature. The Expected Shortfall (ES) is the most important coherent risk measure in both industry practice and academic research in finance, insurance, risk management, and engineering. One of our central results is that under a continuity condition, ES is the only class of coherent Bayes risk measures. We further show that entropic risk measures are the only risk measures which are both elicitable and Bayes. Several other theoretical properties and open questions on Bayes risk measures are discussed.
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Motivated by recent advances on elicitability of risk measures and practical considerations of risk optimization, we introduce the notions of Bayes pairs and Bayes risk measures. Bayes risk measures are the counterpart of elicitable risk measures, extensively studied in the recent literature. The Expected Shortfall (ES) is the most important coherent risk measure in both industry practice and academic research in finance, insurance, risk management, and engineering. One of our central results is that under a continuity condition, ES is the only class of coherent Bayes risk measures. We further show that entropic risk measures are the only risk measures which are both elicitable and Bayes. Several other theoretical properties and open questions on Bayes risk measures are discussed.
Boardroom Networks, Corporate Investment, and Uncertainty
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This paper investigates whether firms herd and whether they strategically herd by chasing valuable information if any. The paper also studies how uncertainty changes a firm's imitation behavior. We propose network firms' information quality is an important factor to determine a firm's herding behavior. By utilizing board-interlock as networks where two firms share at least one common board member, we estimate network effects on firms' investment decisions. Our identification strategy to resolve the endogeneity issue is to adopt peers' peers' characteristics (such as Tobin's Q and Cash Flow) as legitimate instrumental variables. Empirical findings confirm significant network effects on firms' investment. Results further show that firms follow more when the information quality of network firms is relatively high, which supports strategic herding behavior. The placebo test of non-existing network effects before and after the actual connection reinforces the argument of herding within the network. Moreover, empirical evidence suggests that uncertainty depresses network effects on investment. The results are robust to various uncertainty measures.
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This paper investigates whether firms herd and whether they strategically herd by chasing valuable information if any. The paper also studies how uncertainty changes a firm's imitation behavior. We propose network firms' information quality is an important factor to determine a firm's herding behavior. By utilizing board-interlock as networks where two firms share at least one common board member, we estimate network effects on firms' investment decisions. Our identification strategy to resolve the endogeneity issue is to adopt peers' peers' characteristics (such as Tobin's Q and Cash Flow) as legitimate instrumental variables. Empirical findings confirm significant network effects on firms' investment. Results further show that firms follow more when the information quality of network firms is relatively high, which supports strategic herding behavior. The placebo test of non-existing network effects before and after the actual connection reinforces the argument of herding within the network. Moreover, empirical evidence suggests that uncertainty depresses network effects on investment. The results are robust to various uncertainty measures.
Bubbles on Altcoins: Rush versus Manipulation
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The aim of this research is to explore the bubble effects on different crypto-currencies. Bubbles are generated by investors’ urge to step-in a promising market and by price pumping trades. The main goal of the paper is to assess the presence of bubble effects in this market with customized tests able to detect the timing of various bubbles. We analyze the evolution of a representative sample crypto-currencies over time encompassing both high and low liquidity coins. The results show that several crypto-currencies prices had episodes of rapid inflation in 2017 related to the Bitcoin bubble and a few emerging coins saw their prices pumped by speculative actions.
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The aim of this research is to explore the bubble effects on different crypto-currencies. Bubbles are generated by investors’ urge to step-in a promising market and by price pumping trades. The main goal of the paper is to assess the presence of bubble effects in this market with customized tests able to detect the timing of various bubbles. We analyze the evolution of a representative sample crypto-currencies over time encompassing both high and low liquidity coins. The results show that several crypto-currencies prices had episodes of rapid inflation in 2017 related to the Bitcoin bubble and a few emerging coins saw their prices pumped by speculative actions.
Commonality in Disagreement and Stock Returns
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We examine the relationship between firms’ individual disagreement and the aggregate disagreement. We find that a commonality in firms’ individual disagreements exists at the market level, industry level, and geographic level. This commonality increases with a firm’s asymmetric information, uncertainty, and the degree of coverage, but decreases with a firm’s accounting information quality. We find a positive relationship between the commonality in disagreement and stock returns. A higher disagreement commonality may indicate lower usefulness of firm-specific information that strengthens the synchronicity between a firm’s stock return and the market return.
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We examine the relationship between firms’ individual disagreement and the aggregate disagreement. We find that a commonality in firms’ individual disagreements exists at the market level, industry level, and geographic level. This commonality increases with a firm’s asymmetric information, uncertainty, and the degree of coverage, but decreases with a firm’s accounting information quality. We find a positive relationship between the commonality in disagreement and stock returns. A higher disagreement commonality may indicate lower usefulness of firm-specific information that strengthens the synchronicity between a firm’s stock return and the market return.
Copula-Based Black-Litterman Portfolio Optimization
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We extend the Black-Litterman (BL) approach to incorporate tail dependency in portfolio optimization and estimate the posterior joint distribution of returns using vine copulas. Our novel copula-based BL (CBL) model leads to flexibility in modeling returns symmetric and asymmetric multivariate distribution from a range of copula families. Based on a sample of 30 stocks, we evaluate the performance of the suggested CBL approach and portfolio optimization technique using out-of-sample back-testing. Our empirical analysis and robustness check indicate better performance for the CBL portfolios in terms of lower tail risk and higher risk-adjusted returns, compared to the benchmark strategies.
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We extend the Black-Litterman (BL) approach to incorporate tail dependency in portfolio optimization and estimate the posterior joint distribution of returns using vine copulas. Our novel copula-based BL (CBL) model leads to flexibility in modeling returns symmetric and asymmetric multivariate distribution from a range of copula families. Based on a sample of 30 stocks, we evaluate the performance of the suggested CBL approach and portfolio optimization technique using out-of-sample back-testing. Our empirical analysis and robustness check indicate better performance for the CBL portfolios in terms of lower tail risk and higher risk-adjusted returns, compared to the benchmark strategies.
Critical Audit Matters, Cross Listings and Trading Volume: Evidence from Emerging Markets
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In this paper, we investigate the market reaction to the disclosure of critical audit matters (CAM) in terms of trading volumes and the effect of cross-listings on such a reaction in the emerging markets of China. We find that the first time CAM disclosures increase auditor report’s value as there are significant and negative abnormal trading volumes for firms disclosing CAMs. Such results are significant for companies listed only within the domestic exchanges. The alleviation of information asymmetry and the improvement of audit quality both play significant roles in investors’ reaction to the CAM disclosure. Overall, this study supplements the empirical studies on CAMs, and have implications for accounting firms, investors and other stakeholders in China and other emerging markets.
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In this paper, we investigate the market reaction to the disclosure of critical audit matters (CAM) in terms of trading volumes and the effect of cross-listings on such a reaction in the emerging markets of China. We find that the first time CAM disclosures increase auditor report’s value as there are significant and negative abnormal trading volumes for firms disclosing CAMs. Such results are significant for companies listed only within the domestic exchanges. The alleviation of information asymmetry and the improvement of audit quality both play significant roles in investors’ reaction to the CAM disclosure. Overall, this study supplements the empirical studies on CAMs, and have implications for accounting firms, investors and other stakeholders in China and other emerging markets.
Deep Learning for Equity Time Series Prediction
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We examine the performance of Deep Learning methods applied to equity financial time series. Predicting equity time series is a crucial topic in Finance. To form equity portfolios and do asset allocation, we need to predict returns, compute their risk, and optimize market impact. One of the modeling benefits of Deep Learning architectures is the ability to model non-linear highly dimensional problems. The lack of transparency and a rigorous mathematical theory could be considered less positive sides. The fact that most progress in Deep Learning has been made by trial and error is also cumbersome. Equity financial time series is a challenging domain with some stylized facts: weak stationarity, fat tails in return distributions, small data sets compared to other areas of Artificial Intelligence (AI), slow decay of autocorrelation in returns, and volatility clustering, to name the most important ones. We perform a comparative study between Long ShortTerm Memory Networks (LSTM), Recurrent Neural Networks (RNN), Deep Feed-Forward neural networks (DNN), and Gated Recurrent Unit Networks (GRU). We perform two types of studies. The first focused on a univariate test, and the second a multivariate test. Our tests show that the LSTM performs the best compared to other Deep Learning and classical machine learning models. In terms of performance metrics, the LSTM is better than the baseline model. We also show that the predictions are better than chance. There is enough evidence thatRNN and LSTM can deal with stationary time series and learn the data generating process. Nevertheless, predicting equity non-stationary time series, with market developments like the one caused by the COVID-19 pandemic in 2020, is challenging.
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We examine the performance of Deep Learning methods applied to equity financial time series. Predicting equity time series is a crucial topic in Finance. To form equity portfolios and do asset allocation, we need to predict returns, compute their risk, and optimize market impact. One of the modeling benefits of Deep Learning architectures is the ability to model non-linear highly dimensional problems. The lack of transparency and a rigorous mathematical theory could be considered less positive sides. The fact that most progress in Deep Learning has been made by trial and error is also cumbersome. Equity financial time series is a challenging domain with some stylized facts: weak stationarity, fat tails in return distributions, small data sets compared to other areas of Artificial Intelligence (AI), slow decay of autocorrelation in returns, and volatility clustering, to name the most important ones. We perform a comparative study between Long ShortTerm Memory Networks (LSTM), Recurrent Neural Networks (RNN), Deep Feed-Forward neural networks (DNN), and Gated Recurrent Unit Networks (GRU). We perform two types of studies. The first focused on a univariate test, and the second a multivariate test. Our tests show that the LSTM performs the best compared to other Deep Learning and classical machine learning models. In terms of performance metrics, the LSTM is better than the baseline model. We also show that the predictions are better than chance. There is enough evidence thatRNN and LSTM can deal with stationary time series and learn the data generating process. Nevertheless, predicting equity non-stationary time series, with market developments like the one caused by the COVID-19 pandemic in 2020, is challenging.
Delta Hedging and Volatility-Price Elasticity: A Two-Step Approach
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Traditional Black-Scholes delta do not minimize variance of hedging risk since there exists a long run negative relationship between implied volatility and underlying price. This paper presents a two-step empirical approach of option delta hedging in which the hedging ratio is determined by volatility-price relationship. Specifically, we find that the dependency of minimum variance (MV) hedging ratio on volatility-price elasticity is quite stable and that the volatility-price elasticity exhibits characteristic of mean-reverting. Therefore we first estimate a model which can capture the dependency of hedging ratio on volatility-price elasticity, and then substitute predictions of future volatility-price elasticity into the pre-fixed model to obtain the MV hedging ratio. We test the new approach using the S&P 500 daily option data and show that our approach results in higher hedging gain than related methods appeared in recent works.
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Traditional Black-Scholes delta do not minimize variance of hedging risk since there exists a long run negative relationship between implied volatility and underlying price. This paper presents a two-step empirical approach of option delta hedging in which the hedging ratio is determined by volatility-price relationship. Specifically, we find that the dependency of minimum variance (MV) hedging ratio on volatility-price elasticity is quite stable and that the volatility-price elasticity exhibits characteristic of mean-reverting. Therefore we first estimate a model which can capture the dependency of hedging ratio on volatility-price elasticity, and then substitute predictions of future volatility-price elasticity into the pre-fixed model to obtain the MV hedging ratio. We test the new approach using the S&P 500 daily option data and show that our approach results in higher hedging gain than related methods appeared in recent works.
Earnings Momentum and Bank Loan Quality
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In the aftermath of the financial crisis, bankers, regulators, lawmakers, and others claimed that bank CEOs’ incentives to sustain high past earnings growth (earnings momentum) led banks to originate lower quality loans, which subsequently defaulted at high rates in the financial crisis. We examine this claim. Using a sample of 267 U.S. bank holding companies from 2001 to 2009, we find banks with high earnings momentum have more future non-performing loans (NPLs). In contrast, neither CEO compensation incentives nor benchmark beating metrics are associated with future NPLs. Consistent with career concerns, the effect of earnings momentum on future NPLs is concentrated in banks with younger CEOs. We also find earnings momentum is positively associated with bank failures. Further analyses show results are independent of competition pressures and earnings guidance. Our study contributes to the literature on factors associated with the dramatic collapse of the banking system during the financial crisis.
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In the aftermath of the financial crisis, bankers, regulators, lawmakers, and others claimed that bank CEOs’ incentives to sustain high past earnings growth (earnings momentum) led banks to originate lower quality loans, which subsequently defaulted at high rates in the financial crisis. We examine this claim. Using a sample of 267 U.S. bank holding companies from 2001 to 2009, we find banks with high earnings momentum have more future non-performing loans (NPLs). In contrast, neither CEO compensation incentives nor benchmark beating metrics are associated with future NPLs. Consistent with career concerns, the effect of earnings momentum on future NPLs is concentrated in banks with younger CEOs. We also find earnings momentum is positively associated with bank failures. Further analyses show results are independent of competition pressures and earnings guidance. Our study contributes to the literature on factors associated with the dramatic collapse of the banking system during the financial crisis.
Explaining the Nonlinear Response of Stock Markets to Oil Price Shocks
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This paper is set to reconcile the existent conflicting empirical evidence on the effect of oil prices on stock prices. We estimate various nonlinear models where the response changes according to a first-order Markov switching process. More importantly, we model the transition probabilities between the high- and low-response regimes to depend on state variables to allow us to explain the forces behind the asymmetry in the response. The results show statistically significant asymmetries that can be explained by economic recessions and to a lower extent depend on the magnitude of the oil price shift and on whether the shift is positive or negative. In the high response regime, the effect is positive and lasts longer. We also find evidence of asymmetries in the response of stock prices to crude oil supply shocks, global aggregate demand shocks, and oil-specific demand shocks.
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This paper is set to reconcile the existent conflicting empirical evidence on the effect of oil prices on stock prices. We estimate various nonlinear models where the response changes according to a first-order Markov switching process. More importantly, we model the transition probabilities between the high- and low-response regimes to depend on state variables to allow us to explain the forces behind the asymmetry in the response. The results show statistically significant asymmetries that can be explained by economic recessions and to a lower extent depend on the magnitude of the oil price shift and on whether the shift is positive or negative. In the high response regime, the effect is positive and lasts longer. We also find evidence of asymmetries in the response of stock prices to crude oil supply shocks, global aggregate demand shocks, and oil-specific demand shocks.
Financing Costs and the Efficiency of Public-Private Partnerships
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The paper compares provision of public infrastructure via public-private partnerships (PPPs) with provision under government management. Due to soft budget constraints of government management, PPPs exert more effort and therefore have a cost advantage in building infrastructure. At the same time, hard budget constraints for PPPs introduce a bankruptcy risk and bankruptcy costs. Consequently, if bankruptcy costs are high, PPPs may be less efficient than public management, although this does not result from PPPs’ higher interest costs.
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The paper compares provision of public infrastructure via public-private partnerships (PPPs) with provision under government management. Due to soft budget constraints of government management, PPPs exert more effort and therefore have a cost advantage in building infrastructure. At the same time, hard budget constraints for PPPs introduce a bankruptcy risk and bankruptcy costs. Consequently, if bankruptcy costs are high, PPPs may be less efficient than public management, although this does not result from PPPs’ higher interest costs.
Forecasting Uncertainty of the Oil Future Prices Via Machine Learning
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This paper compares and analyzes the predictability of realized volatility of the crude oil future prices with time-series and machine learning models. The autoregressive integrated moving average with exogenous variables shows a high predictive power of 33.8%, and the random forest regression robustly exceeds it with 40.8% in out-of-sample R2 measure. We find that nonlinear machine learning models could benefit from using uncertainty indices.
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This paper compares and analyzes the predictability of realized volatility of the crude oil future prices with time-series and machine learning models. The autoregressive integrated moving average with exogenous variables shows a high predictive power of 33.8%, and the random forest regression robustly exceeds it with 40.8% in out-of-sample R2 measure. We find that nonlinear machine learning models could benefit from using uncertainty indices.
Government Subsidy Dependence and Stock Price Crash Risk
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We evaluate the association between government subsidy dependence and stock price crash risk for a large sample of Chinese non-financial listed firms from 2003 to 2018. The results show that firms with high dependence on government subsidies are associated with great stock price crash risk. The decomposition of government subsidies shows that fiscal subsidies are the driving force of stock price crashes. The decomposition of government subsidies shows that fiscal subsidies are the driving force of stock price crashes. The positive association is more pronounced in state-owned enterprises and firms with strong political connection and weak monitoring. Moreover, we identify financial restatement and valuation uncertainty as the two possible mechanisms through which government subsidy dependence increase crash risk. Our findings highlight the negative consequences of government subsidy on financial risk of firms and provide implications for regulatory agencies and investors.
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We evaluate the association between government subsidy dependence and stock price crash risk for a large sample of Chinese non-financial listed firms from 2003 to 2018. The results show that firms with high dependence on government subsidies are associated with great stock price crash risk. The decomposition of government subsidies shows that fiscal subsidies are the driving force of stock price crashes. The decomposition of government subsidies shows that fiscal subsidies are the driving force of stock price crashes. The positive association is more pronounced in state-owned enterprises and firms with strong political connection and weak monitoring. Moreover, we identify financial restatement and valuation uncertainty as the two possible mechanisms through which government subsidy dependence increase crash risk. Our findings highlight the negative consequences of government subsidy on financial risk of firms and provide implications for regulatory agencies and investors.
How News Affects Sectoral Stock Prices Through Earnings Expectations and Risk Premia
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A growing body of literature analyses the impact of news on companies’ equity prices. We add to this literature by showing that the transmission channel of news to prices differs across sectors. First, we disentangle sectoral equity prices into components of expected future earnings and equity risk premia. Then, we evaluate how these react to general and sector specific sentiment shocks constructed from Reuters news articles. We find that price changes for especially the financial sector are mainly driven by changes in equity risk premia, while changes in earnings expectations play a comparatively larger role for other sectors.
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A growing body of literature analyses the impact of news on companies’ equity prices. We add to this literature by showing that the transmission channel of news to prices differs across sectors. First, we disentangle sectoral equity prices into components of expected future earnings and equity risk premia. Then, we evaluate how these react to general and sector specific sentiment shocks constructed from Reuters news articles. We find that price changes for especially the financial sector are mainly driven by changes in equity risk premia, while changes in earnings expectations play a comparatively larger role for other sectors.
Impact Investing and the Fostering of Entrepreneurship in Disadvantaged Urban Areas: Evidence from Microdata in French Banlieues
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We examine whether impact investing is more effective in fostering business venture success and social impact when directed toward ventures located in vs. outside disadvantaged urban areas (i.e., areas with high crime, unemployment, and poverty). We explore this question in the context of loans made to business ventures located in French “banlieues†vs. “non-banlieuesâ€. We find that loans issued to banlieue ventures, compared to non-banlieue ventures, yield greater improvements in financial performance, as well as greater social impact in terms of the creation of local employment opportunities, quality jobs, and jobs for minorities â€" all of which contribute to the social inclusion of marginalized communities and the development of sustainable cities.
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We examine whether impact investing is more effective in fostering business venture success and social impact when directed toward ventures located in vs. outside disadvantaged urban areas (i.e., areas with high crime, unemployment, and poverty). We explore this question in the context of loans made to business ventures located in French “banlieues†vs. “non-banlieuesâ€. We find that loans issued to banlieue ventures, compared to non-banlieue ventures, yield greater improvements in financial performance, as well as greater social impact in terms of the creation of local employment opportunities, quality jobs, and jobs for minorities â€" all of which contribute to the social inclusion of marginalized communities and the development of sustainable cities.
Institutional Trading in Firms Rumored to be Takeover Targets
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In this paper we examine institutional trading in proximity to takeover rumors by combining the ANcerno dataset of transaction-level institutional trades with a unique sample of takeover rumor ‘scoops’. We find that institutions are net buyers in firms which subsequently become subject to takeover speculation and that institutional trading predicts which rumored firms will eventually receive takeover bids. Segregating funds according to their propensity to trade, we show that those less likely to purchase rumored targets by chance over the pre-rumor period are more likely to identify firms which will receive bid proposals and that they trade more profitably over both the pre- and post-rumor periods. We test for the presence of informed trading in a variety of ways and conclude that institutional investors appear to trade on material private information which identifies the firms soon to be the target of takeover speculation.
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In this paper we examine institutional trading in proximity to takeover rumors by combining the ANcerno dataset of transaction-level institutional trades with a unique sample of takeover rumor ‘scoops’. We find that institutions are net buyers in firms which subsequently become subject to takeover speculation and that institutional trading predicts which rumored firms will eventually receive takeover bids. Segregating funds according to their propensity to trade, we show that those less likely to purchase rumored targets by chance over the pre-rumor period are more likely to identify firms which will receive bid proposals and that they trade more profitably over both the pre- and post-rumor periods. We test for the presence of informed trading in a variety of ways and conclude that institutional investors appear to trade on material private information which identifies the firms soon to be the target of takeover speculation.
Is Carbon Risk Priced in the Cross Section of Corporate Bond Returns?
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This paper examines the pricing of a firm's carbon risk, measured by its carbon emissions intensity, in the cross-section of corporate bond returns. Contrary to the "carbon risk premium" hypothesis, we find bonds of firms with higher carbon emissions intensity earn significantly lower returns. This effect cannot be explained by a comprehensive list of bond characteristics and exposure to known risk factors. Investigating sources of the low carbon premium, we find the underperformance of bonds issued by carbon-intensive firms cannot be fully explained by divestment from institutional investors. Instead, our evidence is most consistent with investor underreaction to carbon risk, as carbon emissions intensity is predictive of lower future cash flow news, deteriorating firm creditworthiness, and more frequent environmental incidents.
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This paper examines the pricing of a firm's carbon risk, measured by its carbon emissions intensity, in the cross-section of corporate bond returns. Contrary to the "carbon risk premium" hypothesis, we find bonds of firms with higher carbon emissions intensity earn significantly lower returns. This effect cannot be explained by a comprehensive list of bond characteristics and exposure to known risk factors. Investigating sources of the low carbon premium, we find the underperformance of bonds issued by carbon-intensive firms cannot be fully explained by divestment from institutional investors. Instead, our evidence is most consistent with investor underreaction to carbon risk, as carbon emissions intensity is predictive of lower future cash flow news, deteriorating firm creditworthiness, and more frequent environmental incidents.
Job Transitions in a Time of Automation and Labor Market Crises
arXiv
arXiv
Job security can never be taken for granted, especially in times of rapid, widespread and unexpected social and economic change. These changes can force workers to transition to new jobs. This may be because technologies emerge or production is moved abroad. Perhaps it is a global crisis, such as COVID-19, which shutters industries and displaces labor en masse. Regardless of the impetus, people are faced with the challenge of moving between jobs to find new work. Successful transitions typically occur when workers leverage their existing skills in the new occupation. Here, we propose a novel method to measure the similarity between occupations using their underlying skills. We then build a recommender system for identifying optimal transition pathways between occupations using job advertisements (ads) data and a longitudinal household survey. Our results show that not only we can accurately predict occupational transitions (Accuracy = 76%), but we account for the asymmetric difficulties of moving between jobs (it is easier to move in one direction than the other). We also build an early warning indicator for new technology adoption (showcasing Artificial Intelligence), a major driver of rising job transitions. By using real-time data, our systems can respond to labor demand shifts as they occur (such as those caused by COVID-19), and can be leveraged by policy-makers, educators, and jobseekers who are forced to confront the often distressing challenges of having to find new jobs.
Modelling volatile time series with v-transforms and copulas
arXiv
arXiv
An approach to the modelling of volatile time series using a class of uniformity-preserving transforms for uniform random variables is proposed. V-transforms describe the relationship between quantiles of the stationary distribution of the time series and quantiles of the distribution of a predictable volatility proxy variable. They can be represented as copulas and permit the formulation and estimation of models that combine arbitrary marginal distributions with copula processes for the dynamics of the volatility proxy. The idea is illustrated using a Gaussian ARMA copula process and the resulting model is shown to replicate many of the stylized facts of financial return series and to facilitate the calculation of marginal and conditional characteristics of the model including quantile measures of risk. Estimation is carried out by adapting the exact maximum likelihood approach to the estimation of ARMA processes and the model is shown to be competitive with standard GARCH in an empirical application to Bitcoin return data.
Optimally Imprecise Memory and Biased Forecasts
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We propose a model of optimal decision making subject to a memory constraint. The constraint is a limit on the complexity of memory measured using Shannon’s mutual information, as in models of rational inattention; but our theory differs from that of Sims (2003) in not assuming costless memory of past cognitive states. We show that the model implies that both forecasts and actions will exhibit idiosyncratic random variation; that average beliefs will also differ from rational-expectations beliefs, with a bias that fluctuates forever with a variance that does not fall to zero even in the long run; and that more recent news will be given disproportionate weight in forecasts. We solve the model under a variety of assumptions about the degree of persistence of the variable to be forecasted and the horizon over which it must be forecasted, and examine how the nature of forecast biases depends on these parameters. The model provides a simple explanation for a number of features of reported expectations in laboratory and field settings, notably the evidence of over-reaction in elicited forecasts documented by Afrouzi et al. (2020) and Bordalo et al. (2020a).
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We propose a model of optimal decision making subject to a memory constraint. The constraint is a limit on the complexity of memory measured using Shannon’s mutual information, as in models of rational inattention; but our theory differs from that of Sims (2003) in not assuming costless memory of past cognitive states. We show that the model implies that both forecasts and actions will exhibit idiosyncratic random variation; that average beliefs will also differ from rational-expectations beliefs, with a bias that fluctuates forever with a variance that does not fall to zero even in the long run; and that more recent news will be given disproportionate weight in forecasts. We solve the model under a variety of assumptions about the degree of persistence of the variable to be forecasted and the horizon over which it must be forecasted, and examine how the nature of forecast biases depends on these parameters. The model provides a simple explanation for a number of features of reported expectations in laboratory and field settings, notably the evidence of over-reaction in elicited forecasts documented by Afrouzi et al. (2020) and Bordalo et al. (2020a).
Origin of Outperformance for Stock Recommendations by Sell-Side Analysts
SSRN
We examine the sources of added value in analysts’ recommendations by investigating the detailed content of sell-side analysts’ recommendation-based portfolios. We clarify whether the well-documented positive excess returns earned by recommendation portfolios span all market sectors and size deciles during the period 2002-2017. Our study documents that the excess returns are explained primarily by the analysts’ within-sector stock-picking ability and partially by the effect of overweighting in industries, such as Materials, Consumer Discretionary and Information Technology, and in small-cap stocks. The outperformance of Star analysts stems from superior information content in their recommendations, which contain both within- and across-industry information.
SSRN
We examine the sources of added value in analysts’ recommendations by investigating the detailed content of sell-side analysts’ recommendation-based portfolios. We clarify whether the well-documented positive excess returns earned by recommendation portfolios span all market sectors and size deciles during the period 2002-2017. Our study documents that the excess returns are explained primarily by the analysts’ within-sector stock-picking ability and partially by the effect of overweighting in industries, such as Materials, Consumer Discretionary and Information Technology, and in small-cap stocks. The outperformance of Star analysts stems from superior information content in their recommendations, which contain both within- and across-industry information.
Pay for Performance, Partnership Success, and the Internal Organization of Venture Capital Firms
SSRN
We show how the structure of partner incentives and the decision processes within a venture capital firm contribute to fund performance, partner retention and partnership success. Optimal capital allocation during staged financing requires that partner incentives encourage cooperation by linking a partner’s compensation to the return on the entire fund rather than return on the investment sponsored by an individual partner. Incentives for individual performance of senior partners are optimally provisioned by a higher profit share in a subsequent fund. For junior partners with lower profit shares, partner pay may be linked to performance of current individually sponsored deals. Our paper provides an economic underpinning to empirical observations about the internal organization of venture capital firms.
SSRN
We show how the structure of partner incentives and the decision processes within a venture capital firm contribute to fund performance, partner retention and partnership success. Optimal capital allocation during staged financing requires that partner incentives encourage cooperation by linking a partner’s compensation to the return on the entire fund rather than return on the investment sponsored by an individual partner. Incentives for individual performance of senior partners are optimally provisioned by a higher profit share in a subsequent fund. For junior partners with lower profit shares, partner pay may be linked to performance of current individually sponsored deals. Our paper provides an economic underpinning to empirical observations about the internal organization of venture capital firms.
Personalized Robo-Advising: Enhancing Investment through Client Interaction
arXiv
arXiv
Automated investment managers, or robo-advisors, have emerged as an alternative to traditional financial advisors. The viability of robo-advisors crucially depends on their ability to offer personalized financial advice. We introduce a novel framework, in which a robo-advisor interacts with a client to solve an adaptive mean-variance portfolio optimization problem. The risk-return tradeoff adapts to the client's risk profile, which depends on idiosyncratic characteristics, market returns, and economic conditions. We show that the optimal investment strategy includes both myopic and intertemporal hedging terms which are impacted by the dynamics of the client's risk profile. We characterize the optimal portfolio personalization via a tradeoff faced by the robo-advisor between receiving client information in a timely manner and mitigating behavioral biases in the risk profile communicated by the client. We argue that the optimal portfolio's Sharpe ratio and return distribution improve if the robo-advisor counters the client's tendency to reduce market exposure during economic contractions when the market risk-return tradeoff is more favorable.
Private Overborrowing under Sovereign Risk
SSRN
This paper argues that excessive international private debt increases the frequency and severity of sovereign debt crises. I develop a quantitative theory of private and public debt that allows me to measure the level of private overborrowing and its effect on the interest rate spread paid on public debt. In an environment where private credit is constrained by the market value of income, individually optimal private borrowing decisions are inefficient at the aggregate level. High private debt increases the probability of a financial crisis. During such crises, drops in consumption cause a decline in the market value of collateral that in turn further reduces consumption. To mitigate this financial amplification mechanism, the government responds with large fiscal bailouts financed with risky external public debt. I show that this response then causes a sovereign debt crisis, characterized by high interest rate spreads and in some cases default. I find that the model is quantitatively consistent with the evolution of international private debt, international public debt, and sovereign spreads in Spain from 1999 to 2015. I estimate that private debt was 5% of GDP above the socially optimal level in the lead-up to the crisis. Private overborrowing increased the annual probability of a financial crisis by 2.4 percentage points. Finally, excessive private debt raised the interest rate spread on public bonds by at least 3.8 percentage points at its peak in 2012.
SSRN
This paper argues that excessive international private debt increases the frequency and severity of sovereign debt crises. I develop a quantitative theory of private and public debt that allows me to measure the level of private overborrowing and its effect on the interest rate spread paid on public debt. In an environment where private credit is constrained by the market value of income, individually optimal private borrowing decisions are inefficient at the aggregate level. High private debt increases the probability of a financial crisis. During such crises, drops in consumption cause a decline in the market value of collateral that in turn further reduces consumption. To mitigate this financial amplification mechanism, the government responds with large fiscal bailouts financed with risky external public debt. I show that this response then causes a sovereign debt crisis, characterized by high interest rate spreads and in some cases default. I find that the model is quantitatively consistent with the evolution of international private debt, international public debt, and sovereign spreads in Spain from 1999 to 2015. I estimate that private debt was 5% of GDP above the socially optimal level in the lead-up to the crisis. Private overborrowing increased the annual probability of a financial crisis by 2.4 percentage points. Finally, excessive private debt raised the interest rate spread on public bonds by at least 3.8 percentage points at its peak in 2012.
Responses of International Central Banks to the Covid-19 Crisis
SSRN
This article reviews and explains the recent policy reactions of the Federal Reserve, the European Central Bank, the Bank of England, and the Bank of Japan to the financial and macroeconomic turmoil caused by the COVID-19 pandemic. The financial and monetary policy actions of major central banks in the most recent crisis have, by some metrics, surpassed their responses to the Global Financial Crisis of 2007-09 in both swiftness and scope.
SSRN
This article reviews and explains the recent policy reactions of the Federal Reserve, the European Central Bank, the Bank of England, and the Bank of Japan to the financial and macroeconomic turmoil caused by the COVID-19 pandemic. The financial and monetary policy actions of major central banks in the most recent crisis have, by some metrics, surpassed their responses to the Global Financial Crisis of 2007-09 in both swiftness and scope.
Round Number Reference Points and Irregular Patterns in Reported Gross Margins
SSRN
We observe irregular patterns in the distribution of firms’ reported quarterly gross margin percentages. Specifically, we find that there are significantly more firm-quarter observations just above whole percentage integers (e.g., between 55.0% and 55.1%) than there are just below whole percentage integers (e.g., between 54.9% and 55.0%), as compared to what would be expected by mere chance. These patterns are especially pronounced at percentage integers that are particularly “round†(e.g., multiples of 10, such as 30%, 40%, etc.) or are neatly “divisible†(e.g., 25%, 50%, 75%). Further investigation reveals that some investors reward firms with gross margins just above these psychological benchmarks at the time of the earnings announcement, but the presence of more sophisticated and informed investors mitigates such tendencies. In addition, firms reporting gross margins just above round numbers show deteriorated future operating performance. We find no evidence that the irregular patterns of gross margins are related to the presence of management or analyst forecasts of gross margins, nor are they related to these forecasts themselves being round. Our findings are consistent with round numbers being used as implicit psychological reference points for gross margins, as suggested by the theory of reference-dependent preferences, and with gross margins likely being managed to meet or beat these psychological benchmarks.
SSRN
We observe irregular patterns in the distribution of firms’ reported quarterly gross margin percentages. Specifically, we find that there are significantly more firm-quarter observations just above whole percentage integers (e.g., between 55.0% and 55.1%) than there are just below whole percentage integers (e.g., between 54.9% and 55.0%), as compared to what would be expected by mere chance. These patterns are especially pronounced at percentage integers that are particularly “round†(e.g., multiples of 10, such as 30%, 40%, etc.) or are neatly “divisible†(e.g., 25%, 50%, 75%). Further investigation reveals that some investors reward firms with gross margins just above these psychological benchmarks at the time of the earnings announcement, but the presence of more sophisticated and informed investors mitigates such tendencies. In addition, firms reporting gross margins just above round numbers show deteriorated future operating performance. We find no evidence that the irregular patterns of gross margins are related to the presence of management or analyst forecasts of gross margins, nor are they related to these forecasts themselves being round. Our findings are consistent with round numbers being used as implicit psychological reference points for gross margins, as suggested by the theory of reference-dependent preferences, and with gross margins likely being managed to meet or beat these psychological benchmarks.
Shaming Microloan Delinquents: Evidence from a Field Experiment in China
SSRN
We study the effects of village credit information sharing on individual microloan repayment, using a randomized experiment with loan applicants from 40 villages in rural China. In our main treatment, customers received a message on the loan application form that “overdue payment (40 days after each installment due date) will be considered for public disclosure among the village by showing debtors’ names on a blackboard outside the village office of the microlending institution.†On average, this social appeal reduces the share of delinquents and the individual delinquency rate by 18.6% and 5.6% from baseline rates of 79.5% and 15.2%, respectively. The effects appear more pronounced among male and older borrowers. Additional treatments help to benchmark the effect against lender credit information sharing and separate the effects on adverse selection and moral hazard. Mechanism analysis shows that the publicly disclosed “blacklist†of delinquents affects borrowers’ repayment behaviors, partially through borrowers’ fear of losing informal risk insurance from the village society, and predominately through public shaming penalties. Overall, these results support that, in traditional societies, social appeals can provide not only pecuniary, but also psychological incentives to improve loan repayment. Psychological incentives, to some extent, have stronger effects.
SSRN
We study the effects of village credit information sharing on individual microloan repayment, using a randomized experiment with loan applicants from 40 villages in rural China. In our main treatment, customers received a message on the loan application form that “overdue payment (40 days after each installment due date) will be considered for public disclosure among the village by showing debtors’ names on a blackboard outside the village office of the microlending institution.†On average, this social appeal reduces the share of delinquents and the individual delinquency rate by 18.6% and 5.6% from baseline rates of 79.5% and 15.2%, respectively. The effects appear more pronounced among male and older borrowers. Additional treatments help to benchmark the effect against lender credit information sharing and separate the effects on adverse selection and moral hazard. Mechanism analysis shows that the publicly disclosed “blacklist†of delinquents affects borrowers’ repayment behaviors, partially through borrowers’ fear of losing informal risk insurance from the village society, and predominately through public shaming penalties. Overall, these results support that, in traditional societies, social appeals can provide not only pecuniary, but also psychological incentives to improve loan repayment. Psychological incentives, to some extent, have stronger effects.
Soft Information in the Financial Press and Analyst Revisions
SSRN
Both sell-side analysts and the media are information intermediaries in capital markets. This study investigates the association between sell-side analyst research and information in firm-specific news coverage. More frequent recent news coverage is associated with stronger market reactions to analysts’ research revisions, and primarily explained by soft information in news coverage. The primary result is robust to using both an instrumental variable and a quasi-natural experimental setting to generate exogenous variation in media coverage, alleviating concerns about endogeneity. In addition, using textual analysis, we document that explicit media references in analyst research reports are significantly associated with more frequent analyst revisions and stronger market reactions to revisions. Our study provides empirical evidence of analysts’ assimilation of information from the financial press and their role in the efficiency of capital markets.
SSRN
Both sell-side analysts and the media are information intermediaries in capital markets. This study investigates the association between sell-side analyst research and information in firm-specific news coverage. More frequent recent news coverage is associated with stronger market reactions to analysts’ research revisions, and primarily explained by soft information in news coverage. The primary result is robust to using both an instrumental variable and a quasi-natural experimental setting to generate exogenous variation in media coverage, alleviating concerns about endogeneity. In addition, using textual analysis, we document that explicit media references in analyst research reports are significantly associated with more frequent analyst revisions and stronger market reactions to revisions. Our study provides empirical evidence of analysts’ assimilation of information from the financial press and their role in the efficiency of capital markets.
Stock Returns and Cash Flows: A New Asset Pricing Approach
SSRN
On this purpose, this work is focused on a non-conventional profitability measure, at least in terms of assets pricing models, where dividends or profits are widely used. The attention is focused on a proxy measure of Operating Cash Flows: the “Ebitda after Capexâ€. The relationship returns â€" cash flows’ volatility has been examined through an empirical analysis conducted on the stocks of the S&P500 Index combining the main quantitative and statistical approach with a qualitative overview respect the macroeconomic background. Starting from a correlation rolling window approach, three different regressions techniques have been implemented; the simple Ordinary Least Squares regressions (OLS), the linear Quantile (LQR) regression and the Multiple regression model (MLR), all performed at different levels in terms of stocks (QoQ and YoY) and sectors (MoM, QoQ, YoY).The cross-sectional and time-series results support the effects of cash flow’ volatility on the stocks’ performance and highlighted its sensitivity respect not only the different short-term and long-term horizons, but also in terms of sector’ exposure.
SSRN
On this purpose, this work is focused on a non-conventional profitability measure, at least in terms of assets pricing models, where dividends or profits are widely used. The attention is focused on a proxy measure of Operating Cash Flows: the “Ebitda after Capexâ€. The relationship returns â€" cash flows’ volatility has been examined through an empirical analysis conducted on the stocks of the S&P500 Index combining the main quantitative and statistical approach with a qualitative overview respect the macroeconomic background. Starting from a correlation rolling window approach, three different regressions techniques have been implemented; the simple Ordinary Least Squares regressions (OLS), the linear Quantile (LQR) regression and the Multiple regression model (MLR), all performed at different levels in terms of stocks (QoQ and YoY) and sectors (MoM, QoQ, YoY).The cross-sectional and time-series results support the effects of cash flow’ volatility on the stocks’ performance and highlighted its sensitivity respect not only the different short-term and long-term horizons, but also in terms of sector’ exposure.
Stocks and Cryptocurrencies: Anti-fragile or Robust?
arXiv
arXiv
Antifragility was recently defined as a property of complex systems that benefit from disorder. However, its original formal definition is difficult to apply. Our approach has been to define and test a much simpler measure of antifragility for complex systems. In this work we use our antifragility measure to analyze real data from the stock market and cryptocurrency prices. Results vary between different antifragility interpretations and for each system. Our results suggest that the stock market favors robustness rather than antifragility, as in most cases the highest and lowest antifragility values are reached either by young agents or constant ones. There are no clear correlations between antifragility and different good-performance measures, while the best performers seem to fall within a robust threshold. In the case of cryptocurrencies, there is an apparent correlation between high price and high antifragility.
Surprise in Short Interest
SSRN
We extract the news component of short-selling activity by accounting for important cross-sectional, distributional differences in short interest. The resulting measure of surprise in short interest negatively predicts the cross section of both U.S. and international equity returns. Our results also indicate that this predictability originates from short sellers' informed trading on mispricing and the market's underreaction to the news component of short-sale reports. Consistent with the notion of costly arbitrage, the return predictability is stronger among illiquid, volatile stocks and stocks with high information uncertainty, but importantly, unrelated short-selling frictions.
SSRN
We extract the news component of short-selling activity by accounting for important cross-sectional, distributional differences in short interest. The resulting measure of surprise in short interest negatively predicts the cross section of both U.S. and international equity returns. Our results also indicate that this predictability originates from short sellers' informed trading on mispricing and the market's underreaction to the news component of short-sale reports. Consistent with the notion of costly arbitrage, the return predictability is stronger among illiquid, volatile stocks and stocks with high information uncertainty, but importantly, unrelated short-selling frictions.
The Externalities of Fire Sales: Evidence from Collateralized Loan Obligations
SSRN
I investigate how covenants, intrinsic to Collateralized Loan Obligation (CLO) indentures, provide a mechanism through which idiosyncratic shocks may be amplified, imposing negative externalities on other firms in CLO portfolios. To this aim, I exploit cross-sectional variation in CLO exposure to the Oil & Gas (O&G) industry, as well as the timing of the O&G bust in 2014 to study how non-O&G firms in CLO portfolios are affected. I find that when CLOs are subject to idiosyncratic shocks that push them closer to their covenant constraints, they fire-sell unrelated loans in the secondary loan market to alleviate these constraints. The ex-post, secondary market spread becomes the effective cost of capital for these innocent bystanders, as the expected rate of return across debt instruments is equalized in market equilibrium. In response, firms make financial and real adjustments. These adjustments are most pronounced for riskier firms held in CLO portfolios, whose loans are marked at market prices, as selling mark-to-market loans can generate greater slack in the covenant constraints. As the sample period for this study is 2012-2017, a relatively benign macroeconomic period, the effects may be significantly larger during times of stress such as Spring of 2020, at the outset of the COVID-19 pandemic.
SSRN
I investigate how covenants, intrinsic to Collateralized Loan Obligation (CLO) indentures, provide a mechanism through which idiosyncratic shocks may be amplified, imposing negative externalities on other firms in CLO portfolios. To this aim, I exploit cross-sectional variation in CLO exposure to the Oil & Gas (O&G) industry, as well as the timing of the O&G bust in 2014 to study how non-O&G firms in CLO portfolios are affected. I find that when CLOs are subject to idiosyncratic shocks that push them closer to their covenant constraints, they fire-sell unrelated loans in the secondary loan market to alleviate these constraints. The ex-post, secondary market spread becomes the effective cost of capital for these innocent bystanders, as the expected rate of return across debt instruments is equalized in market equilibrium. In response, firms make financial and real adjustments. These adjustments are most pronounced for riskier firms held in CLO portfolios, whose loans are marked at market prices, as selling mark-to-market loans can generate greater slack in the covenant constraints. As the sample period for this study is 2012-2017, a relatively benign macroeconomic period, the effects may be significantly larger during times of stress such as Spring of 2020, at the outset of the COVID-19 pandemic.
The Interaction of Borrower-Targeted Macroprudential Tools in the Irish Mortgage Market: A Baseline Multi-Agent Approach
SSRN
Lax credit conditions and speculative behaviors can combine to bring about leveraged real estate bubbles that pose a threat to financial stability. This risk can be pushed away by the adoption of proper macroprudential polices. Borrower-based macroprudential tools, namely loan-to-value and debt-to-income ratios, are designed to dampen the procyclicality of credit and to enhance the resilience of financial institutions. By putting a ceiling to borrowing the financial sustainability of mortgages can be improved for borrowers and lenders. This paper studies the interaction of the two instruments employing an agent-based model calibrated on the Irish mortgage market. I construct several policy scenarios grounded on residential loan data to run counterfactual experiments and explore alternative settings of macroprudential policy. This approach provides granular artificial data about the distribution of loan-to-value and debt-to-income ratios at origination, credit, and house prices.
SSRN
Lax credit conditions and speculative behaviors can combine to bring about leveraged real estate bubbles that pose a threat to financial stability. This risk can be pushed away by the adoption of proper macroprudential polices. Borrower-based macroprudential tools, namely loan-to-value and debt-to-income ratios, are designed to dampen the procyclicality of credit and to enhance the resilience of financial institutions. By putting a ceiling to borrowing the financial sustainability of mortgages can be improved for borrowers and lenders. This paper studies the interaction of the two instruments employing an agent-based model calibrated on the Irish mortgage market. I construct several policy scenarios grounded on residential loan data to run counterfactual experiments and explore alternative settings of macroprudential policy. This approach provides granular artificial data about the distribution of loan-to-value and debt-to-income ratios at origination, credit, and house prices.
The Market Value of Public Interventions in the Corporate Sector: Evidence from COVID-19
SSRN
We link government interventions in the corporate sector to the disconnect between stock market valuations and real economic indicators observed in the COVID-19 pandemic. We collect firm-level data on COVID-19-related news in several European countries, which differ in public spending for immediate liquidity injections and debt guarantees to corporations. We find that interventions help firms negatively affected by the pandemic raise debt and boost market valuations, in spite of the deterioration of their revenues. Remarkably, the financial sector internalizes part of the benefits of interventions specifically targeting non-financial firms. To interpret these results, we lay out a model of corporate borrowing and public interventions. The model suggests that interventions in the corporate sector are effective to address credit market failures and simulate recovery in the long run. Lenders benefit from guarantees on corporate debt as a compensation to finance firms with severe debt overhang problems.
SSRN
We link government interventions in the corporate sector to the disconnect between stock market valuations and real economic indicators observed in the COVID-19 pandemic. We collect firm-level data on COVID-19-related news in several European countries, which differ in public spending for immediate liquidity injections and debt guarantees to corporations. We find that interventions help firms negatively affected by the pandemic raise debt and boost market valuations, in spite of the deterioration of their revenues. Remarkably, the financial sector internalizes part of the benefits of interventions specifically targeting non-financial firms. To interpret these results, we lay out a model of corporate borrowing and public interventions. The model suggests that interventions in the corporate sector are effective to address credit market failures and simulate recovery in the long run. Lenders benefit from guarantees on corporate debt as a compensation to finance firms with severe debt overhang problems.
The Materiality of Quantitative Disclosure in Annual Reports
SSRN
Firms are not required to disclose immaterial information (i.e., information that fails to influence a current or prospective stakeholder). Nevertheless, regulators have recently called attention to high levels of immaterial disclosure in firms’ annual reports, and express concern that such disclosure makes it difficult for investors to identify and respond to information that is relevant for their decision-making. I examine the determinants of disclosure materiality for quantitative disclosures in annual reports (i.e., the magnitude of quantitative disclosure relative to firm assets). I find that the disclosure of lower materiality information is positively associated with macroeconomic uncertainty, firm-level litigation risk, and manager-level risk-aversion, but not with a manager’s incentive to obfuscate. Finally, I find some evidence that lower materiality disclosure is associated with negative capital market consequences. Overall, these results imply that regulators might be able to improve the materiality of quantitative disclosure by (1) reducing ‘one-size-fits-all’ disclosure regulations, and (2) providing more legal (i.e., safe harbor) protection for firms and managers as they make decisions about disclosure materiality.
SSRN
Firms are not required to disclose immaterial information (i.e., information that fails to influence a current or prospective stakeholder). Nevertheless, regulators have recently called attention to high levels of immaterial disclosure in firms’ annual reports, and express concern that such disclosure makes it difficult for investors to identify and respond to information that is relevant for their decision-making. I examine the determinants of disclosure materiality for quantitative disclosures in annual reports (i.e., the magnitude of quantitative disclosure relative to firm assets). I find that the disclosure of lower materiality information is positively associated with macroeconomic uncertainty, firm-level litigation risk, and manager-level risk-aversion, but not with a manager’s incentive to obfuscate. Finally, I find some evidence that lower materiality disclosure is associated with negative capital market consequences. Overall, these results imply that regulators might be able to improve the materiality of quantitative disclosure by (1) reducing ‘one-size-fits-all’ disclosure regulations, and (2) providing more legal (i.e., safe harbor) protection for firms and managers as they make decisions about disclosure materiality.
The Role of P2P Platforms in Enhancing Financial Inclusion in US â€" An Analysis of Peer-to-Peer Lending Across the Rural-Urban Divide
SSRN
In this paper, we examine the role of P2P platforms in enhancing financial inclusion from the borrowers’ point of view across the rural-urban dimension. We show that when number of bank branches decrease in a rural community, the P2P loan requests increase if there is at least one bank branch in the community allowing people to participate in the P2P market. We also find that the number of P2P loan requests from urban areas are higher when such areas have fewer pawnshops per capita. Our results suggest that P2P enhances financial inclusion of those lacking traditional institutions in rural communities and offers an alternative to those with fewer fringe banks in urban communities.
SSRN
In this paper, we examine the role of P2P platforms in enhancing financial inclusion from the borrowers’ point of view across the rural-urban dimension. We show that when number of bank branches decrease in a rural community, the P2P loan requests increase if there is at least one bank branch in the community allowing people to participate in the P2P market. We also find that the number of P2P loan requests from urban areas are higher when such areas have fewer pawnshops per capita. Our results suggest that P2P enhances financial inclusion of those lacking traditional institutions in rural communities and offers an alternative to those with fewer fringe banks in urban communities.
The risk of death in newborn businesses during the first years in market
arXiv
arXiv
In this paper, we analyzed how business age and mortality are related during the first years of life, and tested the different hypotheses proposed in the literature. For that, we used data on U.S. business establishments, with 1-year resolution in the range of age of 0-5 years, in the period 1977-2016, published by the United States Census Bureau. First, we explored the adaptation of classical techniques of survival analysis (the Life Table and Peto-Turnbull methods) to the business survival analysis. Then, we considered nine parametric probabilistic models, most of them well-known in reliability analysis and in the actuarial literature, with different shapes of the hazard function, that we fitted by maximum likelihood method and compared with the Akaike information criterion. Our findings show that newborn firms seem to have a decreasing failure rate with the age during the first five years in market, with the exception of the first months of some years in which the risk can rise.
This Time Is Different: On Similarity and Risk Taking After Experienced Gains and Losses
SSRN
How do experienced prior loss or gains affect risk-taking? A large literature reports significant but seemingly inconsistent effects of prior outcomes on risk-taking. We resolve these inconsistencies by proposing a similarity based mechanism determining which outcomes are jointly evaluated and state conditions under which we expect no behavioral changes. In line with our theory, we find in a pre-registered experiment, that the less similar a prior decision situation is in task-relevant dimensions, the weaker is the effect of the prior outcomes on the current decision; variation in non-task relevant dimensions will not change the impact of prior outcomes.
SSRN
How do experienced prior loss or gains affect risk-taking? A large literature reports significant but seemingly inconsistent effects of prior outcomes on risk-taking. We resolve these inconsistencies by proposing a similarity based mechanism determining which outcomes are jointly evaluated and state conditions under which we expect no behavioral changes. In line with our theory, we find in a pre-registered experiment, that the less similar a prior decision situation is in task-relevant dimensions, the weaker is the effect of the prior outcomes on the current decision; variation in non-task relevant dimensions will not change the impact of prior outcomes.
Using Machine Learning to Create an Early Warning System for Welfare Recipients
arXiv
arXiv
Using novel nation-wide social security data combined with machine learning tools, we develop predictive models of income support receipt intensities for any payment enrolee in the Australian social security system between 2014 and 2018. We show that off-the-shelf machine learning algorithms can significantly improve predictive accuracy compared to simpler heuristic models or early warning systems currently in use. Specifically, the former predicts the proportion of time individuals will be on income support in the next four years with greater accuracy, by a magnitude of at least 22% (14 percentage points increase in the R2), compared to the latter. This gain can be achieved at little extra cost to practitioners since it uses data currently available to them. Consequently, our machine learning algorithms can improve the detection of long-term income support recipients accruing a welfare cost nearly AUD 1 billion higher than individuals identified in the current system.
Who Wants to Be a Volatility Timer?
SSRN
The exact conditions under which volatility timing strategies yield value are documented. These conditions include the skill of the user in terms of their ability to correctly forecast the next period volatility level, and the risk preference of the investor. Within the context of Merton's ICAPM, only investors with particular risk preferences will enjoy benefits. Counter to the intuition that only highly risk-averse investors employ volatility timing strategies, the variation in performance over the risk preference space is subtle and non-monotonic. Empirical evidence is provided that calculates the risk preferences of volatility timers who enjoy significant benefits. The results suggest that different fees could be applied to different investors over this space.
SSRN
The exact conditions under which volatility timing strategies yield value are documented. These conditions include the skill of the user in terms of their ability to correctly forecast the next period volatility level, and the risk preference of the investor. Within the context of Merton's ICAPM, only investors with particular risk preferences will enjoy benefits. Counter to the intuition that only highly risk-averse investors employ volatility timing strategies, the variation in performance over the risk preference space is subtle and non-monotonic. Empirical evidence is provided that calculates the risk preferences of volatility timers who enjoy significant benefits. The results suggest that different fees could be applied to different investors over this space.
