Research articles for the 2021-07-28
A new expression for the true one-year prediction uncertainty in the chain-ladder model of Mack
SSRN
In this paper we provide a new expression for the true one-year prediction uncertainty within the chain-ladder model of Mack, which can be useful for quantification and sensitivity analysis.We also show that, in case sufficiently large sized claims trapezoids are considered (which might not be easily available in practice though), classical estimators for the true one-year prediction uncertainty (notably the Merz-Wüthrich and the Gisler formulas) mostly succeed to respectably estimate the true uncertainty. This seems to be mainly due to the fact that in this case the traditional estimator for the sigma squared parameters results to have a sufficiently low variance. Otherwise, for instance when considering small sized claims triangles, the classical estimators might be particularly prone to materially fail in estimating the true value.
SSRN
In this paper we provide a new expression for the true one-year prediction uncertainty within the chain-ladder model of Mack, which can be useful for quantification and sensitivity analysis.We also show that, in case sufficiently large sized claims trapezoids are considered (which might not be easily available in practice though), classical estimators for the true one-year prediction uncertainty (notably the Merz-Wüthrich and the Gisler formulas) mostly succeed to respectably estimate the true uncertainty. This seems to be mainly due to the fact that in this case the traditional estimator for the sigma squared parameters results to have a sufficiently low variance. Otherwise, for instance when considering small sized claims triangles, the classical estimators might be particularly prone to materially fail in estimating the true value.
Analysis and Critical Investigation of the Financial Statements of Food Sector Companies in Thessaly Region (Greece) that Were Included in the Investment Development Programs the Period 2013-2016
SSRN
Purpose: The aim of this research study is to assess the progress of the food sector companies that received financial aid for investment through the Developmental Laws in the last years (2013-16) of the economic recession in Thessaly Region (Greece), by choosing a random and representative sample of companies. Design/methodology/approach: In order to achieve this goal, financial data was collected which would be able to provide us with information in order to study the evolution of the food industry in Thessaly Region (Greece). The statistical analysis of the data was done with the statistical analysis package IBM SPSS Statistics 23. The descriptive statistics on the distributions and frequency diagrams (Histograms) and normal frequencies curves for the percentages of changes were also calculated. To test the significance of the differences between the mean values of the financial statements items, the method of One-Way Analysis Of Variance (One-Way ANOVA) was used. The same method was used to test the mean values of both the changes in amounts and in their percentages. Findings: In short, we can say that the percentage changes in Total Assets and Equity the time period 2013-16 are small and concentrated around zero. The same period, we have a significant increase in L/M Debt and Sales, except for a few extreme cases, while the percentage changes in Gross Profit and Profit before Tax are limited. The year 2016 there were significant changes in Taxes and Net Profit from AM2005-16. Research limitations/implications: The collection, processing, and analysis of the financial data of the undertakings were limited to the undertakings which have the obligation to publish their financial data. Originality/value: In recent years, very few studies have been carried out on the effectiveness of investment by private companies subsidized by Greek or European Investment Programs.
SSRN
Purpose: The aim of this research study is to assess the progress of the food sector companies that received financial aid for investment through the Developmental Laws in the last years (2013-16) of the economic recession in Thessaly Region (Greece), by choosing a random and representative sample of companies. Design/methodology/approach: In order to achieve this goal, financial data was collected which would be able to provide us with information in order to study the evolution of the food industry in Thessaly Region (Greece). The statistical analysis of the data was done with the statistical analysis package IBM SPSS Statistics 23. The descriptive statistics on the distributions and frequency diagrams (Histograms) and normal frequencies curves for the percentages of changes were also calculated. To test the significance of the differences between the mean values of the financial statements items, the method of One-Way Analysis Of Variance (One-Way ANOVA) was used. The same method was used to test the mean values of both the changes in amounts and in their percentages. Findings: In short, we can say that the percentage changes in Total Assets and Equity the time period 2013-16 are small and concentrated around zero. The same period, we have a significant increase in L/M Debt and Sales, except for a few extreme cases, while the percentage changes in Gross Profit and Profit before Tax are limited. The year 2016 there were significant changes in Taxes and Net Profit from AM2005-16. Research limitations/implications: The collection, processing, and analysis of the financial data of the undertakings were limited to the undertakings which have the obligation to publish their financial data. Originality/value: In recent years, very few studies have been carried out on the effectiveness of investment by private companies subsidized by Greek or European Investment Programs.
Analyst Forecasts, Managerial Learning, and Corporate Investments
SSRN
Prior research shows that managers learn from the capital market; however, it remains unclear what specific information that managers seek to learn. Building on prior results that financial analysts have information advantage relative to managers at the macroeconomic level, we show that such information advantage is an important source for what managers learn from analysts in making investment decisions. Specifically, the sensitivity of corporate capital investment to analyst forecasts of firm earnings or long-term growth significantly increases with the exposures of a firm’s operations to macroeconomic factors, especially business cycles. These results are stronger when firms have higher capital intensity and hence stronger incentives to learn, and are robust to direct controls for macroeconomic factors. Overall, our results suggest that managers learn from analysts regarding the implications of macroeconomic factors for firm-specific prospects and incorporate them into their capital investment decisions.
SSRN
Prior research shows that managers learn from the capital market; however, it remains unclear what specific information that managers seek to learn. Building on prior results that financial analysts have information advantage relative to managers at the macroeconomic level, we show that such information advantage is an important source for what managers learn from analysts in making investment decisions. Specifically, the sensitivity of corporate capital investment to analyst forecasts of firm earnings or long-term growth significantly increases with the exposures of a firm’s operations to macroeconomic factors, especially business cycles. These results are stronger when firms have higher capital intensity and hence stronger incentives to learn, and are robust to direct controls for macroeconomic factors. Overall, our results suggest that managers learn from analysts regarding the implications of macroeconomic factors for firm-specific prospects and incorporate them into their capital investment decisions.
Arbitrage-Based Recovery
SSRN
We develop a novel recovery theorem based on no-arbitrage principles. Our Arbitrage-Based Recovery Theorem does not require assuming time homogeneity of either the physical probabilities, the Arrow-Debreu prices, or the stochastic discount factor; and it requires the observation of Arrow-Debreu prices only for one single maturity. We perform several different density tests and mean prediction tests using 25 years of S&P 500 options data, and we find evidence that our method can correctly recover the probability distribution of the S&P 500 index level on a monthly horizon.
SSRN
We develop a novel recovery theorem based on no-arbitrage principles. Our Arbitrage-Based Recovery Theorem does not require assuming time homogeneity of either the physical probabilities, the Arrow-Debreu prices, or the stochastic discount factor; and it requires the observation of Arrow-Debreu prices only for one single maturity. We perform several different density tests and mean prediction tests using 25 years of S&P 500 options data, and we find evidence that our method can correctly recover the probability distribution of the S&P 500 index level on a monthly horizon.
Bank Branching Applications and Window Dressing: Evidence on Banks’ Strategic Use of Loan Loss Provisions
SSRN
After the removal of geographic restrictions on branching in 2006, China’s city commercial banks (CCBs) can apply for permission to branch outside their province. This paper shows that CCBs report higher loan loss provisions before filing an application, thereby increasing the provision coverage ratio of nonperforming loans and making the bank look safer to regulators.Our finding is robust to controlling for possible endogeneity of the branching application decision by employing propensity score matching estimators, and it is confirmed when we consider a quasi-natural experiment of deregulation reversal.
SSRN
After the removal of geographic restrictions on branching in 2006, China’s city commercial banks (CCBs) can apply for permission to branch outside their province. This paper shows that CCBs report higher loan loss provisions before filing an application, thereby increasing the provision coverage ratio of nonperforming loans and making the bank look safer to regulators.Our finding is robust to controlling for possible endogeneity of the branching application decision by employing propensity score matching estimators, and it is confirmed when we consider a quasi-natural experiment of deregulation reversal.
Climate Policy and Corporate Green Bond Issuances
SSRN
We find that green bonds exhibit higher capacity to borrow foreign capital in local currency than regular bonds issued by the same firm, which reduces currency mismatch risk in corporates' balance sheets while increasing that in investors'. We further show that this is driven by climate policy, which attract sustainable, responsible, and impact (SRI) investments that are willing to tolerate higher currency-mismatch risk for holding green bonds. In particular, adopting climate policy triples the probability of local currency green bond issuances in foreign markets. The impact of climate policy strengthens as carbon price rises. In response to rising carbon price, firms with stronger ESG and financial fundamentals, richer international financing experience, and from countries with better environmental performance, issue more local currency green bonds in foreign markets. There is no evidence that green bonds differ from regular bonds in the absence of climate policy.
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We find that green bonds exhibit higher capacity to borrow foreign capital in local currency than regular bonds issued by the same firm, which reduces currency mismatch risk in corporates' balance sheets while increasing that in investors'. We further show that this is driven by climate policy, which attract sustainable, responsible, and impact (SRI) investments that are willing to tolerate higher currency-mismatch risk for holding green bonds. In particular, adopting climate policy triples the probability of local currency green bond issuances in foreign markets. The impact of climate policy strengthens as carbon price rises. In response to rising carbon price, firms with stronger ESG and financial fundamentals, richer international financing experience, and from countries with better environmental performance, issue more local currency green bonds in foreign markets. There is no evidence that green bonds differ from regular bonds in the absence of climate policy.
Clustering Structure of Microstructure Measures
SSRN
This paper builds the clustering model of measures of market microstructure features which are popular in predicting the stock returns. In a 10-second time frequency, we study the clustering structure of different measures to find out the best ones for predicting. In this way, we can predict more accurately with a limited number of predictors, which removes the noise and makes the model more interpretable.
SSRN
This paper builds the clustering model of measures of market microstructure features which are popular in predicting the stock returns. In a 10-second time frequency, we study the clustering structure of different measures to find out the best ones for predicting. In this way, we can predict more accurately with a limited number of predictors, which removes the noise and makes the model more interpretable.
Combining Machine Learning Classifiers for Stock Trading with Effective Feature Extraction
arXiv
arXiv
The unpredictability and volatility of the stock market render it challenging to make a substantial profit using any generalized scheme. This paper intends to discuss our machine learning model, which can make a significant amount of profit in the US stock market by performing live trading in the Quantopian platform while using resources free of cost. Our top approach was to use ensemble learning with four classifiers: Gaussian Naive Bayes, Decision Tree, Logistic Regression with L1 regularization and Stochastic Gradient Descent, to decide whether to go long or short on a particular stock. Our best model performed daily trade between July 2011 and January 2019, generating 54.35% profit. Finally, our work showcased that mixtures of weighted classifiers perform better than any individual predictor about making trading decisions in the stock market.
Corporate Announcements and Market Efficiency: A Case on Indian Capital Market
SSRN
Capital markets being the backbone of the economy, are expected to be functioning efficiently. Efficiently-priced financial markets are considered a catalyst for the economic growth of the nations (Malkiel, 2010). Efficient markets are the reflection of security valuations. In an informationally efficient market, no one can beat the market and make abnormal returns based on the information because the information is instantaneously observed in the stock prices. The current paper analyses the market efficiency of three of the most popular corporate events, i.e., announcement of cash dividends, bonus issues, and stock split in the Indian context. The sample is 2253 pure cash dividend announcements (627 large-caps, 552 mid-caps, and 1074 small-caps), 152 bonus issue announcements (49 large-caps, 33 mid-caps, and 70 small-caps), and 181 stock split announcements (35 large-caps, 34 mid-caps, and 112 small-caps) were used for this study. Event methodology market model used to calculate Average Abnormal Returns (AAR) and Cumulative Average Abnormal Returns (CAAR).The results of the study have few findings which are contradictory to the existing literature on market efficiency. The cash dividend announcements have shown evidence for market efficiency, and results are contrary to Gupta et al. (2012), but the results are similar to Mishra (2005). Bonus issue announcements also have shown evidence for a semi-strong form of efficiency, test results identical to Dhar and Chhaochharia (2008), Kumar and Mittal (2015). Stock split announcements have not shown market efficiency, and the effect is similar to the study of Lakshmi and Roy (2012) and contrary to Chavali and Zahid (2011). Our results also support the premise that the emerging countries depict evidence of market efficiency (Bechev, 2003). Finally, we conclude that market efficiency results differ based on corporate announcements and market capitalization
SSRN
Capital markets being the backbone of the economy, are expected to be functioning efficiently. Efficiently-priced financial markets are considered a catalyst for the economic growth of the nations (Malkiel, 2010). Efficient markets are the reflection of security valuations. In an informationally efficient market, no one can beat the market and make abnormal returns based on the information because the information is instantaneously observed in the stock prices. The current paper analyses the market efficiency of three of the most popular corporate events, i.e., announcement of cash dividends, bonus issues, and stock split in the Indian context. The sample is 2253 pure cash dividend announcements (627 large-caps, 552 mid-caps, and 1074 small-caps), 152 bonus issue announcements (49 large-caps, 33 mid-caps, and 70 small-caps), and 181 stock split announcements (35 large-caps, 34 mid-caps, and 112 small-caps) were used for this study. Event methodology market model used to calculate Average Abnormal Returns (AAR) and Cumulative Average Abnormal Returns (CAAR).The results of the study have few findings which are contradictory to the existing literature on market efficiency. The cash dividend announcements have shown evidence for market efficiency, and results are contrary to Gupta et al. (2012), but the results are similar to Mishra (2005). Bonus issue announcements also have shown evidence for a semi-strong form of efficiency, test results identical to Dhar and Chhaochharia (2008), Kumar and Mittal (2015). Stock split announcements have not shown market efficiency, and the effect is similar to the study of Lakshmi and Roy (2012) and contrary to Chavali and Zahid (2011). Our results also support the premise that the emerging countries depict evidence of market efficiency (Bechev, 2003). Finally, we conclude that market efficiency results differ based on corporate announcements and market capitalization
Credentials Matter, but Only for Men: Evidence from the S&P 500
SSRN
We study gender differences in the value of credentials in managerial labor markets. Exploiting quasi-random variation in S&P 500 index membership, we examine the careers of managers whose firms were "just included" on the index and compare them to the careers of managers of similar firms that were not included. We use within-firm variation in S&P 500 status for marginal firms and managers who joined a marginal firm before its addition to the index to obtain near-random assignment of S&P 500 experience. Men with experience at an S&P 500 firm obtain more subsequent independent directorships and executive roles at other S&P 500 firms, but not at non-S&P 500 firms. The increase is 12-42% relative to the average. Strikingly, we observe no such relationship for women. In fact, a woman with S&P 500 experience obtains fewer future executive positions at S&P 500 firms than a man without it. The incremental S&P 500 positions are only in industries that the manager has previously worked in, and not in other industries. The largest benefits accrue to managers with smaller personal networks. Careers of managers at "just included" and not included firms are not different before the former firms are added to the index. Our results suggest that receiving less credit for similar credentials poses an obstacle for women in the managerial labor market.
SSRN
We study gender differences in the value of credentials in managerial labor markets. Exploiting quasi-random variation in S&P 500 index membership, we examine the careers of managers whose firms were "just included" on the index and compare them to the careers of managers of similar firms that were not included. We use within-firm variation in S&P 500 status for marginal firms and managers who joined a marginal firm before its addition to the index to obtain near-random assignment of S&P 500 experience. Men with experience at an S&P 500 firm obtain more subsequent independent directorships and executive roles at other S&P 500 firms, but not at non-S&P 500 firms. The increase is 12-42% relative to the average. Strikingly, we observe no such relationship for women. In fact, a woman with S&P 500 experience obtains fewer future executive positions at S&P 500 firms than a man without it. The incremental S&P 500 positions are only in industries that the manager has previously worked in, and not in other industries. The largest benefits accrue to managers with smaller personal networks. Careers of managers at "just included" and not included firms are not different before the former firms are added to the index. Our results suggest that receiving less credit for similar credentials poses an obstacle for women in the managerial labor market.
Does Firm Cloud Data Growth Predict Stock Returns?
SSRN
We investigate whether cloud data growth can forecast future stock returns. Using a proprietary data set on firm-level cloud data records during January 2013 to December 2020 from the leading cloud computing platform in China, we find that cloud data growth contains value-relevant information for stock return prediction. A long-short portfolio by buying (selling) stocks with the high (low) monthly growth in cloud data on firms generates a 7.2% risk-adjusted return annually. The return predictability of cloud data growth holds after controlling for firm characteristics and stays persistent in up to two quarters without any reversal. Moreover, cloud data growth positively predicts assets growth, sales growth, ROA, and earnings surprises.
SSRN
We investigate whether cloud data growth can forecast future stock returns. Using a proprietary data set on firm-level cloud data records during January 2013 to December 2020 from the leading cloud computing platform in China, we find that cloud data growth contains value-relevant information for stock return prediction. A long-short portfolio by buying (selling) stocks with the high (low) monthly growth in cloud data on firms generates a 7.2% risk-adjusted return annually. The return predictability of cloud data growth holds after controlling for firm characteristics and stays persistent in up to two quarters without any reversal. Moreover, cloud data growth positively predicts assets growth, sales growth, ROA, and earnings surprises.
Dynamics of Corporate Dividend Policy under Hyperinflation and Dollarization: A Quantile Regression Approach
SSRN
Purpose: Zimbabwe experienced hyperinflation (2000-2008) followed by dollarization from 2009 onwards which had implications on dividend policy. In this context, this study isolates the main determinants and examines their behaviour across the distribution of dividend policy. Design/methodology/approach: The study employs quantile regression analysis and a sample of 30 firms listed on the Zimbabwe Stock Exchange (ZSE), covering the period 2000 to 2016. The fixed effects (FE) analysis is applied as a base model. Finding(s): The most robust determinants are ownership structure, earnings per share (EPS) and taxation. In our context, results are more informative, than those based on FE analysis by showing the change in the impact of each explanatory variable across the distribution. EPS has a positive and significant impact on dividend policy throughout the distribution in both sample periods. Its effect increases in magnitude as firms move from low to high quantiles. The other variables are useful in explaining dividend policy at selected points of the distribution. Thus, there is clear heterogeneity in the determinants of dividend policy. Research limitations/implications: The study shows the importance of developing dividend policy by focusing on the position of the firm on the distribution. Dividend policy should be developed in view of the earnings potential of the firm, ownership concentration and perceived changes in fiscal policy. A well-designed policy should have a differentiated approach to influencing corporate dividends. Originality/value: This study enhances our understanding of dividend policy in unique markets. It confirms the applicability of dividend relevance theories. Furthermore, It shows that quantile analysis provides more reliable estimates than those obtained using standard panel data models.
SSRN
Purpose: Zimbabwe experienced hyperinflation (2000-2008) followed by dollarization from 2009 onwards which had implications on dividend policy. In this context, this study isolates the main determinants and examines their behaviour across the distribution of dividend policy. Design/methodology/approach: The study employs quantile regression analysis and a sample of 30 firms listed on the Zimbabwe Stock Exchange (ZSE), covering the period 2000 to 2016. The fixed effects (FE) analysis is applied as a base model. Finding(s): The most robust determinants are ownership structure, earnings per share (EPS) and taxation. In our context, results are more informative, than those based on FE analysis by showing the change in the impact of each explanatory variable across the distribution. EPS has a positive and significant impact on dividend policy throughout the distribution in both sample periods. Its effect increases in magnitude as firms move from low to high quantiles. The other variables are useful in explaining dividend policy at selected points of the distribution. Thus, there is clear heterogeneity in the determinants of dividend policy. Research limitations/implications: The study shows the importance of developing dividend policy by focusing on the position of the firm on the distribution. Dividend policy should be developed in view of the earnings potential of the firm, ownership concentration and perceived changes in fiscal policy. A well-designed policy should have a differentiated approach to influencing corporate dividends. Originality/value: This study enhances our understanding of dividend policy in unique markets. It confirms the applicability of dividend relevance theories. Furthermore, It shows that quantile analysis provides more reliable estimates than those obtained using standard panel data models.
E-commerce Livestream Flows Growth and Cross-sectional Stock Returns
SSRN
We study the role of E-commerce livestream flows growth in the stock price discovery process. Using a proprietary data set on firm-level E-commerce livestream flows records during July 2016 to December 2020 from the leading E-commerce livestream platform in China, we find that E-commerce livestream contains value-relevant information for stock return prediction. A long-short portfolio by buying (selling) stocks with the high (low) weekly growth in E-commerce livestream flows on firms generates a 6.864% risk- adjusted return annually. The return predictability of E-commerce livestream flows growth holds after controlling for firm characteristics and stays persistent in up to one quarter without return reversal. Moreover, livestream flows growth positively predicts firm profits growth, sales growth, ROA, and earnings surprises.
SSRN
We study the role of E-commerce livestream flows growth in the stock price discovery process. Using a proprietary data set on firm-level E-commerce livestream flows records during July 2016 to December 2020 from the leading E-commerce livestream platform in China, we find that E-commerce livestream contains value-relevant information for stock return prediction. A long-short portfolio by buying (selling) stocks with the high (low) weekly growth in E-commerce livestream flows on firms generates a 6.864% risk- adjusted return annually. The return predictability of E-commerce livestream flows growth holds after controlling for firm characteristics and stays persistent in up to one quarter without return reversal. Moreover, livestream flows growth positively predicts firm profits growth, sales growth, ROA, and earnings surprises.
Effect of Risk Management, Intellectual Capital on Performance of State Corporations in Kenya: Moderating Approach
SSRN
Purpose: The main aim of the study was to determine the moderating effect of intellectual capital on the relationship between ERM risk structure practices and organizational performance of state corporations in Kenya. This study was guided by dynamic capabilities theory which attempts to explain the perspective of how intellectual capital and ERM practices affect organizational performance. Design/methodology/approach: The study used explanatory cross sectional survey design. Primary data on ERM risk structure practices, intellectual capital and organizational performance was collected from structured questionnaires. A survey was carried out on 218 state corporations in Kenya. The research hypothesis was tested using hierarchical regression analysis. Finding: The study found that intellectual capital had an enhancing and significant moderation effect on the relationship between ERM structure practice and organizational performance (β =.314, Ï< .05). Originality/value: This study contributes to theory by centering intellectual capital on the empirical testing of theory as well as the influence of intellectual assets on the relationship between enterprise risk management practices and organizational performance. In addition, the study supports the theory of Enterprise Risk Management (ERM) that emphasizes on holistic and company-wide approach of managing risks. The study further suggests that future research could build on this study by examining enterprise risk management practices in different sectors and industries using both financial and non-financial measures of performance.
SSRN
Purpose: The main aim of the study was to determine the moderating effect of intellectual capital on the relationship between ERM risk structure practices and organizational performance of state corporations in Kenya. This study was guided by dynamic capabilities theory which attempts to explain the perspective of how intellectual capital and ERM practices affect organizational performance. Design/methodology/approach: The study used explanatory cross sectional survey design. Primary data on ERM risk structure practices, intellectual capital and organizational performance was collected from structured questionnaires. A survey was carried out on 218 state corporations in Kenya. The research hypothesis was tested using hierarchical regression analysis. Finding: The study found that intellectual capital had an enhancing and significant moderation effect on the relationship between ERM structure practice and organizational performance (β =.314, Ï< .05). Originality/value: This study contributes to theory by centering intellectual capital on the empirical testing of theory as well as the influence of intellectual assets on the relationship between enterprise risk management practices and organizational performance. In addition, the study supports the theory of Enterprise Risk Management (ERM) that emphasizes on holistic and company-wide approach of managing risks. The study further suggests that future research could build on this study by examining enterprise risk management practices in different sectors and industries using both financial and non-financial measures of performance.
Financial intermediation and risk in decentralized lending protocols
SSRN
We provide an overview of decentralized protocols like Compound and Aave that provide collateralized loans for cryptoasset investors. Compound and Aave are two of the most important application in the decentralized finance ecosystem (DeFi). We obtain publicly available information on rates, supply and borrow activity, and accounts to analyze different elements of the protocols. In particular, we estimate ex-post margins that give a comprehensive account of the cost of financial intermediation. We find that ex-post margins considering all markets are 1% and lower for stablecoin markets. In addition, we estimate quarterly indicators regarding solvency, asset quality, earnings and market risk similar to the ones used in traditional banking. This provides a first look at the use of these metrics and a comparison between the similarities and challenges to our understanding of financial intermediation in these protocols based on tools used for traditional banking.
SSRN
We provide an overview of decentralized protocols like Compound and Aave that provide collateralized loans for cryptoasset investors. Compound and Aave are two of the most important application in the decentralized finance ecosystem (DeFi). We obtain publicly available information on rates, supply and borrow activity, and accounts to analyze different elements of the protocols. In particular, we estimate ex-post margins that give a comprehensive account of the cost of financial intermediation. We find that ex-post margins considering all markets are 1% and lower for stablecoin markets. In addition, we estimate quarterly indicators regarding solvency, asset quality, earnings and market risk similar to the ones used in traditional banking. This provides a first look at the use of these metrics and a comparison between the similarities and challenges to our understanding of financial intermediation in these protocols based on tools used for traditional banking.
How Green are Green Debt Issuers?
SSRN
Green debt markets are rapidly growing while product design and standards are evolving. Many policymakers and investors view green debt as an important component in the policy mix to achieve the transition to a low carbon economy and ensure the pricing of climate risks. Our analysis contributes to the nascent literature on the environmental impact of green debt by documenting the CO2 emission intensity of corporate green debt issuers. We find lower emission intensities for green bond issuers relative to other firms, but no difference for green loan and sustainability-linked loan borrowers. Green bond, green loan, and sustainability-linked loan borrowers lower their emission intensity over time at a faster rate than other firms.
SSRN
Green debt markets are rapidly growing while product design and standards are evolving. Many policymakers and investors view green debt as an important component in the policy mix to achieve the transition to a low carbon economy and ensure the pricing of climate risks. Our analysis contributes to the nascent literature on the environmental impact of green debt by documenting the CO2 emission intensity of corporate green debt issuers. We find lower emission intensities for green bond issuers relative to other firms, but no difference for green loan and sustainability-linked loan borrowers. Green bond, green loan, and sustainability-linked loan borrowers lower their emission intensity over time at a faster rate than other firms.
Inverse Options in a Black-Scholes World
SSRN
Most trading in cryptocurrency options is on inverse products, so called because the contract size is denominated in US dollars and they are margined and settled in crypto, typically bitcoin or ether. Their popularity stems from allowing professional traders in bitcoin or ether options to avoid transferring fiat currency to and from the exchanges. We derive new analytic pricing and hedging formulae for inverse options under the assumption that the underlying follows a geometric Brownian motion. The boundary conditions and hedge ratios exhibit relatively complex but very important new features which warrant further analysis and explanation. We also illustrate some inconsistencies, exhibited in time series of Deribit bitcoin option implied volatilities, which indicate that traders may be applying direct option hedging and valuation methods erroneously. This could be because they are unaware of the correct, inverse option characteristics which are derived in this paper.
SSRN
Most trading in cryptocurrency options is on inverse products, so called because the contract size is denominated in US dollars and they are margined and settled in crypto, typically bitcoin or ether. Their popularity stems from allowing professional traders in bitcoin or ether options to avoid transferring fiat currency to and from the exchanges. We derive new analytic pricing and hedging formulae for inverse options under the assumption that the underlying follows a geometric Brownian motion. The boundary conditions and hedge ratios exhibit relatively complex but very important new features which warrant further analysis and explanation. We also illustrate some inconsistencies, exhibited in time series of Deribit bitcoin option implied volatilities, which indicate that traders may be applying direct option hedging and valuation methods erroneously. This could be because they are unaware of the correct, inverse option characteristics which are derived in this paper.
Liquidity Provision with Adverse Selection and Inventory Costs
SSRN
We study one-shot Nash competition between an arbitrary number of identical dealers that compete for the order flow of a client. The client trades either because of proprietary information, exposure to idiosyncratic risk, or a mix of both trading motives. When quoting their price schedules, the dealers do not know the client's type but only its distribution, and in turn choose their price quotes to mitigate between adverse selection and inventory costs. Under essentially minimal conditions, we show that a unique symmetric Nash equilibrium exists and can be characterized by the solution of a nonlinear ODE.
SSRN
We study one-shot Nash competition between an arbitrary number of identical dealers that compete for the order flow of a client. The client trades either because of proprietary information, exposure to idiosyncratic risk, or a mix of both trading motives. When quoting their price schedules, the dealers do not know the client's type but only its distribution, and in turn choose their price quotes to mitigate between adverse selection and inventory costs. Under essentially minimal conditions, we show that a unique symmetric Nash equilibrium exists and can be characterized by the solution of a nonlinear ODE.
Literature Review of Experimental Asset Markets with Insiders
SSRN
Many researchers have conducted experiments to study different aspects of insider trading. Experimental laboratory asset markets allow the researcher to control parameters that are impossible to control or even measure in empirical data (e.g., fundamental value of the asset, quality and quantity of information traders receive). This paper provides an exhaustive overview of the results from experimental economics on asset markets with asymmetrically informed participants.
SSRN
Many researchers have conducted experiments to study different aspects of insider trading. Experimental laboratory asset markets allow the researcher to control parameters that are impossible to control or even measure in empirical data (e.g., fundamental value of the asset, quality and quantity of information traders receive). This paper provides an exhaustive overview of the results from experimental economics on asset markets with asymmetrically informed participants.
Mean-variance insurance design with counterparty risk and incentive compatibility
SSRN
This paper studies the optimal insurance design from the perspective of an insured when there is possibility for the insurer to default on its promised indemnity. Default of the insurer leads to limited liability, and the promised indemnity is only partially recovered in case of a default. To alleviate the potential ex post moral hazard, an incentive compatibility condition is added to restrict the permissible indemnity function. Under the actuarial premium principle and mean-variance preferences of the insured, we derive the explicit structure of the optimal indemnity function through the marginal indemnity function formulation of the problem. It is shown that the optimal indemnity function depends on the first and second order conditional expectations of the random recovery rate. The methodology and results in this paper complement the literature regarding the optimal insurance subject to the default risk and provide new insights on problems of similar types. Moreover, we also apply the techniques in this paper to the case of additive background risk, which yields an alternative proof of the main result of Chi and Tan (2021).
SSRN
This paper studies the optimal insurance design from the perspective of an insured when there is possibility for the insurer to default on its promised indemnity. Default of the insurer leads to limited liability, and the promised indemnity is only partially recovered in case of a default. To alleviate the potential ex post moral hazard, an incentive compatibility condition is added to restrict the permissible indemnity function. Under the actuarial premium principle and mean-variance preferences of the insured, we derive the explicit structure of the optimal indemnity function through the marginal indemnity function formulation of the problem. It is shown that the optimal indemnity function depends on the first and second order conditional expectations of the random recovery rate. The methodology and results in this paper complement the literature regarding the optimal insurance subject to the default risk and provide new insights on problems of similar types. Moreover, we also apply the techniques in this paper to the case of additive background risk, which yields an alternative proof of the main result of Chi and Tan (2021).
MobilityCoins -- A new currency for the multimodal urban transportation system
arXiv
arXiv
The MobilityCoin is a new, all-encompassing currency for the management of the multimodal urban transportation system. MobilityCoins includes and replaces various existing transport policy instruments while also incentivizing a shift to more sustainable modes as well as empowering the public to vote for infrastructure measures.
Numerical approximation of hybrid Poisson-jump Ait-Sahalia-type interest rate model with delay
arXiv
arXiv
While the original Ait-Sahalia interest rate model has been found considerable use as a model for describing time series evolution of interest rates, it may not possess adequate specifications to explain responses of interest rates to empirical phenomena such as volatility 'skews' and 'smiles', jump behaviour, market regulatory lapses, economic crisis, financial clashes, political instability, among others collectively. The aim of this paper is to propose a modified version of this model by incorporating additional features to collectively describe these empirical phenomena adequately. Moreover, due to lack of a closed-form solution to the proposed model, we employ several new truncated EM techniques to examine this model and justify the scheme within Monte Carlo framework to compute expected payoffs of some financial quantities such as a bond and a barrier option.
Paid Family Leave, Inventor Mobility, and Firm Innovation
SSRN
We exploit the adoption of US state-level Paid Family Leave (PFL) laws to test whether family-friendly policies affect firm innovation. We find that PFL policies increase the innovation outputs of firms whose employees are more exposed to these laws. The stronger attraction and the higher retention of female inventors contribute to the output gains. Tests at the state level suggest that younger inventors move into states after PFL adoption, and the move-in inventors are generally more productive than the move-out inventors at the personal level. Further tests at inventor level show that females are less likely to drop out from the inventor career after PFL adoption. Overall, the behavior change in inventor career choices is the main channel through which PFLs affect firm innovation.
SSRN
We exploit the adoption of US state-level Paid Family Leave (PFL) laws to test whether family-friendly policies affect firm innovation. We find that PFL policies increase the innovation outputs of firms whose employees are more exposed to these laws. The stronger attraction and the higher retention of female inventors contribute to the output gains. Tests at the state level suggest that younger inventors move into states after PFL adoption, and the move-in inventors are generally more productive than the move-out inventors at the personal level. Further tests at inventor level show that females are less likely to drop out from the inventor career after PFL adoption. Overall, the behavior change in inventor career choices is the main channel through which PFLs affect firm innovation.
Portfolio Selection: A Target-Distribution Approach
SSRN
We introduce a general framework to the portfolio-selection problem in which investors aim at targeting a distribution of returns, which can accommodate a wide range of preferences. The resulting optimal portfolio has a return density that is as close as possible to the target-return density. We study the theoretical properties of this approach for two classes of target distribution that allow for different first four moments. Three results that stand out are, first, that the fit to higher moments is controlled by the entropy of standardized portfolio returns when targeting a Gaussian distribution. Second, when targeting a specific Dirac-delta distribution, no norm-constrained portfolio can stochastically dominate the proposed optimal portfolio. Third, if the target-return mean and variance are located on or above the efficient frontier, the optimal portfolio is mean-variance efficient when asset returns are Gaussian. For non-Gaussian returns, the optimal portfolio may move away from the frontier to better fit the higher moments of the target distribution. The empirical analysis illustrates that the proposed framework helps the investor obtain portfolio returns in line with her preferences.
SSRN
We introduce a general framework to the portfolio-selection problem in which investors aim at targeting a distribution of returns, which can accommodate a wide range of preferences. The resulting optimal portfolio has a return density that is as close as possible to the target-return density. We study the theoretical properties of this approach for two classes of target distribution that allow for different first four moments. Three results that stand out are, first, that the fit to higher moments is controlled by the entropy of standardized portfolio returns when targeting a Gaussian distribution. Second, when targeting a specific Dirac-delta distribution, no norm-constrained portfolio can stochastically dominate the proposed optimal portfolio. Third, if the target-return mean and variance are located on or above the efficient frontier, the optimal portfolio is mean-variance efficient when asset returns are Gaussian. For non-Gaussian returns, the optimal portfolio may move away from the frontier to better fit the higher moments of the target distribution. The empirical analysis illustrates that the proposed framework helps the investor obtain portfolio returns in line with her preferences.
Renewable Energy Targets and Unintended Storage Cycling: Implications for Energy Modeling
arXiv
arXiv
To decarbonize the economy, many governments have set targets for the use of renewable energy sources. These are often formulated as relative shares of electricity demand or supply. Implementing respective constraints in energy models is a surprisingly delicate issue. They may cause a modeling artifact of excessive electricity storage use. We introduce this phenomenon as 'unintended storage cycling', which can be detected in case of simultaneous storage charging and discharging. In this paper, we provide an analytical representation of different approaches for implementing minimum renewable share constraints in models, and show how these may lead to unintended storage cycling. Using a parsimonious optimization model, we quantify related distortions of optimal dispatch and investment decisions as well as market prices, and identify important drivers of the phenomenon. Finally, we provide recommendations on how to avoid the distorting effects of unintended storage cycling in energy modeling.
Retail Lending In January-April 2021: Growth of Debt Burden Indicator
SSRN
The growth rate of lending to individuals increased significantly in 2021 contributing to the expansion of consumer demand. Certain concern is caused by the growth of lending that is largely due to loans to high-debt borrowers. In this regard, the Bank of Russia decided to return macroprudential premiums on unsecured loans to the level preceding the pandemic.
SSRN
The growth rate of lending to individuals increased significantly in 2021 contributing to the expansion of consumer demand. Certain concern is caused by the growth of lending that is largely due to loans to high-debt borrowers. In this regard, the Bank of Russia decided to return macroprudential premiums on unsecured loans to the level preceding the pandemic.
Serving the Underserved: Microcredit as a Pathway to Commercial Banks
SSRN
A large-scale microcredit expansion program---together with a credit bureau accessible to all lenders---can enable unbanked borrowers to build a credit history, facilitating their transition to commercial banks. Loan-level data from Rwanda show the program improved access to credit and reduced poverty. A sizable share of first-time borrowers switched to commercial banks, which cream-skim less risky borrowers and grant them larger, cheaper, and longer-maturity loans. Switchers have lower default risk than non-switchers and are not riskier than other bank borrowers. Switchers also obtain better loan terms from banks compared with first-time bank borrowers without a credit history.
SSRN
A large-scale microcredit expansion program---together with a credit bureau accessible to all lenders---can enable unbanked borrowers to build a credit history, facilitating their transition to commercial banks. Loan-level data from Rwanda show the program improved access to credit and reduced poverty. A sizable share of first-time borrowers switched to commercial banks, which cream-skim less risky borrowers and grant them larger, cheaper, and longer-maturity loans. Switchers have lower default risk than non-switchers and are not riskier than other bank borrowers. Switchers also obtain better loan terms from banks compared with first-time bank borrowers without a credit history.
The Effect of Investor Credit Supply on Housing Prices
SSRN
What is the effect of investor credit supply on housing prices? We provide evidence on this question using quasi-experimental variation in credit supply to investors caused by two macroprudential policies implemented in Australia. The first policy placed a bank-level cap on mortgage credit growth to investors while the second policy placed a bank-level cap on the share of interest-only mortgage lending. We show that the first policy caused a sharp and large drop in credit growth to investors relative to owner-occupiers, while the second policy caused a modest relative decline in credit growth for investors, who disproportionately use interest-only loans. We use variation in the investor ownership share across regions and dwelling types to identify the effect of investor credit supply on housing prices, rents and transaction volumes. We find no significant effect on the growth rate of housing prices caused by the first policy. However, there was a relative rise in the price of investor housing following the second policy, which is consistent with the use of interest-only lending being a more binding constraint for owner-occupiers than investors. There is evidence that the lending restrictions lowered transaction volumes but rents were unaffected. Our findings are consistent with models assuming a largely unconstrained housing rental sector.
SSRN
What is the effect of investor credit supply on housing prices? We provide evidence on this question using quasi-experimental variation in credit supply to investors caused by two macroprudential policies implemented in Australia. The first policy placed a bank-level cap on mortgage credit growth to investors while the second policy placed a bank-level cap on the share of interest-only mortgage lending. We show that the first policy caused a sharp and large drop in credit growth to investors relative to owner-occupiers, while the second policy caused a modest relative decline in credit growth for investors, who disproportionately use interest-only loans. We use variation in the investor ownership share across regions and dwelling types to identify the effect of investor credit supply on housing prices, rents and transaction volumes. We find no significant effect on the growth rate of housing prices caused by the first policy. However, there was a relative rise in the price of investor housing following the second policy, which is consistent with the use of interest-only lending being a more binding constraint for owner-occupiers than investors. There is evidence that the lending restrictions lowered transaction volumes but rents were unaffected. Our findings are consistent with models assuming a largely unconstrained housing rental sector.
The Odious Haitian Independence Debt
RePEC
This article introduces the Haitian Independence Debt of 1825 to the odious debt and sovereign debt literatures. We argue that the legal doctrine of odious debt is surprisingly and perhaps indefensibly narrow possibly because of historical contingency rather than any underlying logic or principle. The story of the Haitian Independence Debt of 1825 serves as an illustrative case study. In the context of telling that story, we provide estimates of the evolution of Haiti’s external debt-to-GDP ratio over 1825-2020, and discuss the implications of the independence debt for the economy of Haiti. We conclude by discussing the implications of Haiti’s Independence Debt for the doctrine of odious debt and the possibilities for Haiti to recover compensation.
RePEC
This article introduces the Haitian Independence Debt of 1825 to the odious debt and sovereign debt literatures. We argue that the legal doctrine of odious debt is surprisingly and perhaps indefensibly narrow possibly because of historical contingency rather than any underlying logic or principle. The story of the Haitian Independence Debt of 1825 serves as an illustrative case study. In the context of telling that story, we provide estimates of the evolution of Haiti’s external debt-to-GDP ratio over 1825-2020, and discuss the implications of the independence debt for the economy of Haiti. We conclude by discussing the implications of Haiti’s Independence Debt for the doctrine of odious debt and the possibilities for Haiti to recover compensation.
Time Varying Risk in U.S. Housing Sector and Real Estate Investment Trusts Equity Return
SSRN
This study examines how housing sector volatilities affect real estate investment trust (REIT) equity return in the United States. I argue that unexpected changes in housing variables can be a source of aggregate housing risk, and the first principal component extracted from the volatilities of U.S. housing variables can predict the expected REIT equity returns. I propose and construct a factor-based housing risk index as an additional factor in asset price models that uses the time-varying conditional volatility of housing variables within the U.S. housing sector. The findings show that the proposed housing risk index is economically and theoretically consistent with the risk-return relationship of the conditional Intertemporal Capital Asset Pricing Model (ICAPM) of Merton (1973), which predicts an average maximum of 5.6 percent of risk premium in REIT equity return. In subsample analyses, the positive relationship is not affected by sample periods' choice but shows higher housing risk beta values for the 2009-18 sample period. The relationship remains significant after controlling for VIX, Fama-French three factors, and a broad set of macroeconomic and financial variables. Moreover, the proposed housing beta also accurately forecasts U.S. macroeconomic and financial conditions.
SSRN
This study examines how housing sector volatilities affect real estate investment trust (REIT) equity return in the United States. I argue that unexpected changes in housing variables can be a source of aggregate housing risk, and the first principal component extracted from the volatilities of U.S. housing variables can predict the expected REIT equity returns. I propose and construct a factor-based housing risk index as an additional factor in asset price models that uses the time-varying conditional volatility of housing variables within the U.S. housing sector. The findings show that the proposed housing risk index is economically and theoretically consistent with the risk-return relationship of the conditional Intertemporal Capital Asset Pricing Model (ICAPM) of Merton (1973), which predicts an average maximum of 5.6 percent of risk premium in REIT equity return. In subsample analyses, the positive relationship is not affected by sample periods' choice but shows higher housing risk beta values for the 2009-18 sample period. The relationship remains significant after controlling for VIX, Fama-French three factors, and a broad set of macroeconomic and financial variables. Moreover, the proposed housing beta also accurately forecasts U.S. macroeconomic and financial conditions.
Towards an enabling environment for social accountability in Bangladesh
arXiv
arXiv
Social accountability refers to promoting good governance by making ruling elites more responsive. In Bangladesh, where bureaucracy and legislature operate with little effective accountability or checks and balances, traditional horizontal or vertical accountability proved to be very blunt and weak. In the presence of such faulty mechanisms, ordinary citizens access to information is frequently denied, and their voices are kept mute. It impasses the formation of an enabling environment, where activists and civil society institutions representing the ordinary peoples interest are actively discouraged. They become vulnerable to retribution. Social accountability, on the other hand, provides an enabling environment for activists and civil society institutions to operate freely. Thus, leaders and administration become more accountable to people. An enabling environment means providing legal protection, enhancing the availability of information and increasing citizen voice, strengthening institutional and public service capacities and directing incentives that foster accountability. Donors allocate significant shares of resources to encouraging civil society to partner with elites rather than holding them accountable. This paper advocate for a stronger legal environment to protect critical civil society and whistle-blowers, and for independent grant-makers tasked with building strong, self-regulating social accountability institutions.
Key Words: Accountability, Legal Protection, Efficiency, Civil Society, Responsiveness
Une année de crise COVID : impact sur la dynamique de l’activité des entreprises en France. Une évaluation sur données individuelles (Corporate Activity in France Amid the Covid-19 Crisis. A Granular Data Analysis)
SSRN
French Abstract: Nous étudions l’impact de la crise sanitaire sur l’activité de plus de 645 000 entreprises, à partir de données individuelles permettant d’estimer leur chiffre d’affaires à une fréquence mensuelle. Notre approche, fondée sur un modèle de micro-simulation, est innovante à triple titre. Premièrement, nous quantifions la perte d'activité par rapport à une situation contrefactuelle dans laquelle la crise n'aurait pas eu lieu. Deuxièmement, nous estimons ce choc au niveau individuel, permettant une analyse détaillée de l'hétérogénéité des chocs d’activité. Nous mettons en lumière la dispersion du choc à la fois entre secteurs et au sein des secteurs. Nous montrons que le secteur de l'entreprise explique jusqu'à 48% de la variance des chocs d'activité mensuels pondérés par l'emploi en 2020, soit une part beaucoup plus importante que lors d'une année normale. Enfin, nous identifions quatre profils de trajectoires, caractéristiques de l’évolution de l'activité en 2020. Le secteur est le principal déterminant d'appartenance à un profil donné. Conditionnellement au secteur, le profil de trajectoire est également corrélé à la capacité d'adaptation organisationnelle des entreprises.English Abstract: Taking advantage of detailed firm-level data on VAT returns, we estimate the monthly impact of the Covid-19 crisis on the turnover of more than 645,000 French firms. Our approach, based on a micro-simulation model, is innovative in a triple way. Firstly, we quantify the activity loss with respect to a counterfactual situation in which the crisis would not have hit. Secondly, we estimate this shock at the firm level, enabling a thorough analysis of activity loss heterogeneity throughout the crisis. In particular, we shade light on the dispersion of the shock both within and between industries. We show that the industry the firm operates in explains up to 48% of the monthly activity shocks’ variance weighted by employment, a much larger share than in a normal year. Finally, we leverage our monthly firm-level data on sales to show how corporate activity has evolved along four distinct trajectories throughout 2020. The main determinant of belonging to a given profile of activity is the firm industry â€" defined at a very granular level. Conditional on industry, the activity trajectory is also correlated with the ability to adapt some firms have demonstrated during the crisis in terms of organization and production.
SSRN
French Abstract: Nous étudions l’impact de la crise sanitaire sur l’activité de plus de 645 000 entreprises, à partir de données individuelles permettant d’estimer leur chiffre d’affaires à une fréquence mensuelle. Notre approche, fondée sur un modèle de micro-simulation, est innovante à triple titre. Premièrement, nous quantifions la perte d'activité par rapport à une situation contrefactuelle dans laquelle la crise n'aurait pas eu lieu. Deuxièmement, nous estimons ce choc au niveau individuel, permettant une analyse détaillée de l'hétérogénéité des chocs d’activité. Nous mettons en lumière la dispersion du choc à la fois entre secteurs et au sein des secteurs. Nous montrons que le secteur de l'entreprise explique jusqu'à 48% de la variance des chocs d'activité mensuels pondérés par l'emploi en 2020, soit une part beaucoup plus importante que lors d'une année normale. Enfin, nous identifions quatre profils de trajectoires, caractéristiques de l’évolution de l'activité en 2020. Le secteur est le principal déterminant d'appartenance à un profil donné. Conditionnellement au secteur, le profil de trajectoire est également corrélé à la capacité d'adaptation organisationnelle des entreprises.English Abstract: Taking advantage of detailed firm-level data on VAT returns, we estimate the monthly impact of the Covid-19 crisis on the turnover of more than 645,000 French firms. Our approach, based on a micro-simulation model, is innovative in a triple way. Firstly, we quantify the activity loss with respect to a counterfactual situation in which the crisis would not have hit. Secondly, we estimate this shock at the firm level, enabling a thorough analysis of activity loss heterogeneity throughout the crisis. In particular, we shade light on the dispersion of the shock both within and between industries. We show that the industry the firm operates in explains up to 48% of the monthly activity shocks’ variance weighted by employment, a much larger share than in a normal year. Finally, we leverage our monthly firm-level data on sales to show how corporate activity has evolved along four distinct trajectories throughout 2020. The main determinant of belonging to a given profile of activity is the firm industry â€" defined at a very granular level. Conditional on industry, the activity trajectory is also correlated with the ability to adapt some firms have demonstrated during the crisis in terms of organization and production.
Where do DeFi stablecoins go? A closer look at what DeFi composability really means.
SSRN
One of the benefits of decentralized finance (DeFi) â€" an alternative financial system built on blockchain â€" is composability, which means the system’s building blocks (tokens) can freely interact with one another to form new services. One example is stablecoin, a token with fixed exchange rate, which is backed by token collaterals. While stablecoins can be used to facilitate payments and exchanges, in DeFi they can be used to earn returns (“yield farmingâ€), potentially multiplicatively. We use transaction-level blockchain data to analyze a stablecoin’s flows between protocols and provide suggestive evidence of DeFi yield-chasing behavior. We shed light on what DeFi total value locked might really measure and highlight the complexity in DeFi analysis and market surveillance.
SSRN
One of the benefits of decentralized finance (DeFi) â€" an alternative financial system built on blockchain â€" is composability, which means the system’s building blocks (tokens) can freely interact with one another to form new services. One example is stablecoin, a token with fixed exchange rate, which is backed by token collaterals. While stablecoins can be used to facilitate payments and exchanges, in DeFi they can be used to earn returns (“yield farmingâ€), potentially multiplicatively. We use transaction-level blockchain data to analyze a stablecoin’s flows between protocols and provide suggestive evidence of DeFi yield-chasing behavior. We shed light on what DeFi total value locked might really measure and highlight the complexity in DeFi analysis and market surveillance.
