Research articles for the 2021-04-30
'I Just Like the Stock' versus 'Fear and Loathing on Main Street' : The Role of Reddit Sentiment in the GameStop Short Squeeze
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This paper investigates the role, if any, played by the social media platform Reddit, in the events around the GameStop short squeeze in early 2021. In particular, we analyse the impact of discussions on the r/WallStreetBets subreddit on the price dynamics of the American online retailer GameStop. We perform textual analysis on 10.8m comments and surface the relationships between the comment sentiments and 1-min GameStop returns. Results indicate that both tone and number of comments influence GME intraday returns. Sentiments extracted from longer threads have a greater influence. Fear is the dominant sentiment in all comments, while comments that express a Sad sentiment show the most significant impact. While investors may just like the stock, it appears that fear and loathing also are important.
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This paper investigates the role, if any, played by the social media platform Reddit, in the events around the GameStop short squeeze in early 2021. In particular, we analyse the impact of discussions on the r/WallStreetBets subreddit on the price dynamics of the American online retailer GameStop. We perform textual analysis on 10.8m comments and surface the relationships between the comment sentiments and 1-min GameStop returns. Results indicate that both tone and number of comments influence GME intraday returns. Sentiments extracted from longer threads have a greater influence. Fear is the dominant sentiment in all comments, while comments that express a Sad sentiment show the most significant impact. While investors may just like the stock, it appears that fear and loathing also are important.
Accounting DVA
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Debt Valuation Adjustment (DVA) is an adjustment to the measurement of derivative liabilities to reflect the own credit risk of the entity. Accounting DVA is a DVA to be recognised for accounting purpose and this paper proposes a methodology to price accounting DVA. In particular the authors utilise the hedging framework of Burgard and Kjaer (2011a, 2011b, 2013 and 2017) to price the accounting DVA by adopting a practical funding strategy. Further the proposed accounting DVA address the widely-acknowledged overlapping between DVA and FBA (Funding Benefit Adjustment). The main contribution of this paper is to bridge the gap between XVA from XVA desk perspective where there is no DVA and XVA from bank perspective that accounting DVA as part of exit price is required by accounting standards.
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Debt Valuation Adjustment (DVA) is an adjustment to the measurement of derivative liabilities to reflect the own credit risk of the entity. Accounting DVA is a DVA to be recognised for accounting purpose and this paper proposes a methodology to price accounting DVA. In particular the authors utilise the hedging framework of Burgard and Kjaer (2011a, 2011b, 2013 and 2017) to price the accounting DVA by adopting a practical funding strategy. Further the proposed accounting DVA address the widely-acknowledged overlapping between DVA and FBA (Funding Benefit Adjustment). The main contribution of this paper is to bridge the gap between XVA from XVA desk perspective where there is no DVA and XVA from bank perspective that accounting DVA as part of exit price is required by accounting standards.
Adaptive Learning for Financial Markets Mixing Model-Based and Model-Free Rl for Volatility Targeting
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Model-Free Reinforcement Learning has achieved meaningful results in stable environments but, to this day, it remains problematic in regime changing environments like financial markets. In contrast, model-based RL is able to capture some fundamental and dynamical concepts of the environment but suffer from cognitive bias. In this work, we propose to combine the best of the two techniques by selecting various model-based approaches thanks to Model-Free Deep Reinforcement Learning. Using not only past performance and volatility, we include additional contextual information such as macro and risk appetite signals to account for implicit regime changes. We also adapt traditional RL methods to real-life situations by considering only past data for the training sets. Hence, we cannot use future information in our training data set as implied by K-fold cross validation. Building on traditional statistical methods, we use the traditional "walk-forward analysis", which is defined by successive training and testing based on expanding periods, to assert the robustness of the resulting agent. Finally, we present the concept of statistical difference's significance based on a two-tailed T-test, to highlight the ways in which our models differ from more traditional ones. Our experimental results show that our approach outperforms traditional financial baseline portfolio models such as the Markowitz model in almost all evaluation metrics commonly used in financial mathematics, namely net performance, Sharpe and Sortino ratios, maximum drawdown, maximum drawdown over volatility.
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Model-Free Reinforcement Learning has achieved meaningful results in stable environments but, to this day, it remains problematic in regime changing environments like financial markets. In contrast, model-based RL is able to capture some fundamental and dynamical concepts of the environment but suffer from cognitive bias. In this work, we propose to combine the best of the two techniques by selecting various model-based approaches thanks to Model-Free Deep Reinforcement Learning. Using not only past performance and volatility, we include additional contextual information such as macro and risk appetite signals to account for implicit regime changes. We also adapt traditional RL methods to real-life situations by considering only past data for the training sets. Hence, we cannot use future information in our training data set as implied by K-fold cross validation. Building on traditional statistical methods, we use the traditional "walk-forward analysis", which is defined by successive training and testing based on expanding periods, to assert the robustness of the resulting agent. Finally, we present the concept of statistical difference's significance based on a two-tailed T-test, to highlight the ways in which our models differ from more traditional ones. Our experimental results show that our approach outperforms traditional financial baseline portfolio models such as the Markowitz model in almost all evaluation metrics commonly used in financial mathematics, namely net performance, Sharpe and Sortino ratios, maximum drawdown, maximum drawdown over volatility.
Binging the Billions Back: How Africa and Europe Can End Illicit Capital Flight
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Every year between US$ 850-1000 billion disappears without a trace from developing countries, ending up in tax havens or rich countries3. The main part of this is driven by multinational companies seeking to evade tax where they operate, and has been called “the ugliest chapter in global economic affairs since slavery†4. The sum that leaves developing countries each year as unreported financial outflows, referred to as illicit capital flight, amounts to ten times the annual global aid flows, and twice the debt service developing countries pay each year. For each dollar that goes to the developing world in aid, almost US$10 come back to developed countries through illicit means. This money, if properly registered and taxed in the country of origin, could of course contribute to considerable development and make a major difference in the fight to combat poverty.
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Every year between US$ 850-1000 billion disappears without a trace from developing countries, ending up in tax havens or rich countries3. The main part of this is driven by multinational companies seeking to evade tax where they operate, and has been called “the ugliest chapter in global economic affairs since slavery†4. The sum that leaves developing countries each year as unreported financial outflows, referred to as illicit capital flight, amounts to ten times the annual global aid flows, and twice the debt service developing countries pay each year. For each dollar that goes to the developing world in aid, almost US$10 come back to developed countries through illicit means. This money, if properly registered and taxed in the country of origin, could of course contribute to considerable development and make a major difference in the fight to combat poverty.
COVID-19 Isolation, Managerial Sentiment, and Corporate Policies
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The COVID-19 outbreak has taken an unprecedented toll on the world’s economy and public mental health. Necessary lockdown and social distancing measures cause many individuals to experience isolation and remote working from home, resulting in mental health outcomes such as depression, stress, and pessimism biases. Using the granularity of foot traffic data during the 2020 pandemic year, we show that social isolation when working from home has strong negative effects on managerial sentiment. The evidence is robust to the identification strategy exploiting the staggered implementation of stay-at-home orders across the United States and alternative measures of managerial sentiment. Further analysis suggests that the COVID-19 isolation-induced managerial sentiment plays a role in corporate policies â€" homebound managers tend to adopt conservative corporate policies.
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The COVID-19 outbreak has taken an unprecedented toll on the world’s economy and public mental health. Necessary lockdown and social distancing measures cause many individuals to experience isolation and remote working from home, resulting in mental health outcomes such as depression, stress, and pessimism biases. Using the granularity of foot traffic data during the 2020 pandemic year, we show that social isolation when working from home has strong negative effects on managerial sentiment. The evidence is robust to the identification strategy exploiting the staggered implementation of stay-at-home orders across the United States and alternative measures of managerial sentiment. Further analysis suggests that the COVID-19 isolation-induced managerial sentiment plays a role in corporate policies â€" homebound managers tend to adopt conservative corporate policies.
Diagnosis and Prediction of IIGPS’ Countries Bubble Crashes During BREXIT
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We herein employ an alternative approach to model the financial bubbles prior to crashes and fit a log-periodic power law (LPPL) to IIGPS countries (Italy, Ireland, Greece, Portugal, and Spain) during Brexit. These countries represent the five financially troubled economies of the Eurozone that have suffered the most during the Brexit referendum. It was found that all 77 crashes across the five IIGPS nations from 19 January 2015 until 17 February 2020 strictly fol-lowed a log-periodic power law or other LPPL signature. They all had a speculative bubble phase (following the power law growth) that was then followed by a sudden crash immedi-ately after reaching a critical point. Furthermore, their pattern coefficients were similar as well. This study would surely assist policymakers around the Eurozone to predict future crashes with the help of these parameters.
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We herein employ an alternative approach to model the financial bubbles prior to crashes and fit a log-periodic power law (LPPL) to IIGPS countries (Italy, Ireland, Greece, Portugal, and Spain) during Brexit. These countries represent the five financially troubled economies of the Eurozone that have suffered the most during the Brexit referendum. It was found that all 77 crashes across the five IIGPS nations from 19 January 2015 until 17 February 2020 strictly fol-lowed a log-periodic power law or other LPPL signature. They all had a speculative bubble phase (following the power law growth) that was then followed by a sudden crash immedi-ately after reaching a critical point. Furthermore, their pattern coefficients were similar as well. This study would surely assist policymakers around the Eurozone to predict future crashes with the help of these parameters.
Green Bond Issuances and Corporate Cost of Capital
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This study investigates the impact of issuing green bonds for environment protection initiatives on the corporate cost of capital. Accounting for nearly 2 percent of corporate bonds annual issuances during 2016-2020, in China, green bond issuance plays an essential role in sustainable development. By matching green bonds with conventional corporate bonds based on propensity matching scores, we find a 24.9 bps negative green premium on average. We hypothesize that green projects help lower the corporate cost of capital in three channels: (i) reducing information asymmetry, (ii) improving security liquidity, and (â…²) lowering bond issuers’ perceived risk. Our empirical findings are consistent with these expectations. Specifically, we find that the corporate cost of capitalâ€"regardless of whether it is measured by the implied cost of capital or by the weighted average cost of capitalâ€" is significantly lowered after the issuance of green bonds through these three channels. Collectively, the findings suggest a specific venue for environmental protection initiatives that affects s company’s value positively.
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This study investigates the impact of issuing green bonds for environment protection initiatives on the corporate cost of capital. Accounting for nearly 2 percent of corporate bonds annual issuances during 2016-2020, in China, green bond issuance plays an essential role in sustainable development. By matching green bonds with conventional corporate bonds based on propensity matching scores, we find a 24.9 bps negative green premium on average. We hypothesize that green projects help lower the corporate cost of capital in three channels: (i) reducing information asymmetry, (ii) improving security liquidity, and (â…²) lowering bond issuers’ perceived risk. Our empirical findings are consistent with these expectations. Specifically, we find that the corporate cost of capitalâ€"regardless of whether it is measured by the implied cost of capital or by the weighted average cost of capitalâ€" is significantly lowered after the issuance of green bonds through these three channels. Collectively, the findings suggest a specific venue for environmental protection initiatives that affects s company’s value positively.
Have scale effects on cost margins of pension fund investment portfolios disappeared?
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Investment costs of pension funds are crucial for their returns. Consolidation in the pension fund market proceeds continuously, often with cost savings as the main argument. Unused economies of scale in the pension fund investment costs, however, have declined over the years to values close to zero, except for the very small pension funds. This paper investigates investment economies of scale in the Netherlands and pays special attention to the non-linear relationship between investment costs and sizes of pension funds. Furthermore, investment cost margins are disaggregated into three cost types and into six asset categories. Performance fees are in particular paid for complex asset categories held by large pension funds. They reduce the traditional scale economy results for the entire portfolio. Cost savings by consolidation are still possible but are very limited.
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Investment costs of pension funds are crucial for their returns. Consolidation in the pension fund market proceeds continuously, often with cost savings as the main argument. Unused economies of scale in the pension fund investment costs, however, have declined over the years to values close to zero, except for the very small pension funds. This paper investigates investment economies of scale in the Netherlands and pays special attention to the non-linear relationship between investment costs and sizes of pension funds. Furthermore, investment cost margins are disaggregated into three cost types and into six asset categories. Performance fees are in particular paid for complex asset categories held by large pension funds. They reduce the traditional scale economy results for the entire portfolio. Cost savings by consolidation are still possible but are very limited.
Information Leakage in Backtesting
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Testing the performance of statistical models with historical time series requires a careful handling of the data. Even if a dataset is seemingly completely separated in an in-sample and an out-of-sample set information may be leaked. Such leakage can lead to a significant overestimation of the out-of-sample performance of a predictive model. We provide experimental evidence to illustrate how randomised data splits lead to overfitting in the presence of time series structure. The experiment is set up in the framework of option replication, with real-world and simulated data.
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Testing the performance of statistical models with historical time series requires a careful handling of the data. Even if a dataset is seemingly completely separated in an in-sample and an out-of-sample set information may be leaked. Such leakage can lead to a significant overestimation of the out-of-sample performance of a predictive model. We provide experimental evidence to illustrate how randomised data splits lead to overfitting in the presence of time series structure. The experiment is set up in the framework of option replication, with real-world and simulated data.
Is Public Equity Deadly? Evidence from Workplace Safety and Productivity Tradeoffs in the Coal Industry
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We study how ownership structure, in particular public listing status, affects workplace safety and productivity tradeoffs. Theory offers competing hypotheses on how listing related frictions affect these tradeoffs. We exploit detailed asset-level data in the U.S. coal industry and find that workplace safety deteriorates dramatically under public firm ownership, primarily in mines that experience the largest productivity increases. We find evidence consistent with information asymmetry between managers and shareholders of public firms, and ties of private firm ownership with local communities being first-order drivers of workplace safety and productivity tradeoffs.
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We study how ownership structure, in particular public listing status, affects workplace safety and productivity tradeoffs. Theory offers competing hypotheses on how listing related frictions affect these tradeoffs. We exploit detailed asset-level data in the U.S. coal industry and find that workplace safety deteriorates dramatically under public firm ownership, primarily in mines that experience the largest productivity increases. We find evidence consistent with information asymmetry between managers and shareholders of public firms, and ties of private firm ownership with local communities being first-order drivers of workplace safety and productivity tradeoffs.
Lottery Stocks and Stop-loss Rules
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We show that stop-loss rules increase the returns to investment in stocks with lottery features. These stocks, which are popular with individual investors, typically have sporadic big gains and frequent small losses. However, stop-loss rules can reduce losses and allow investors to receive the gains from large price increases. We also highlight the sell signals of popular technical rules are like stop-loss rules and are effective at increasing lottery stock risk-adjusted returns. These rules could help investors avoid instances of major historical drawdowns, are particularly beneficial in declining markets, and are robust to the inclusion of transaction costs.
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We show that stop-loss rules increase the returns to investment in stocks with lottery features. These stocks, which are popular with individual investors, typically have sporadic big gains and frequent small losses. However, stop-loss rules can reduce losses and allow investors to receive the gains from large price increases. We also highlight the sell signals of popular technical rules are like stop-loss rules and are effective at increasing lottery stock risk-adjusted returns. These rules could help investors avoid instances of major historical drawdowns, are particularly beneficial in declining markets, and are robust to the inclusion of transaction costs.
Optimal Execution with Quadratic Variation Inventories
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The first half of the paper is devoted to description and implementation of statistical tests arguing for the presence of a Brownian component in the inventories and wealth processes of individual traders. We use intra-day data from the Toronto Stock Exchange to provide empirical evidence of this claim. We work with regularly spaced time intervals, as well as with asynchronously observed data. The tests reveal with high significance the presence of a non-zero Brownian motion component. The second half of the paper is concerned with the analysis of trader behaviors throughout the day. We extend the theoretical analysis of an existing optimal execution model to accommodate the presence of It\^o inventory processes, and we compare empirically the optimal behavior of traders in such fitted models, to their actual behavior as inferred from the data.
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The first half of the paper is devoted to description and implementation of statistical tests arguing for the presence of a Brownian component in the inventories and wealth processes of individual traders. We use intra-day data from the Toronto Stock Exchange to provide empirical evidence of this claim. We work with regularly spaced time intervals, as well as with asynchronously observed data. The tests reveal with high significance the presence of a non-zero Brownian motion component. The second half of the paper is concerned with the analysis of trader behaviors throughout the day. We extend the theoretical analysis of an existing optimal execution model to accommodate the presence of It\^o inventory processes, and we compare empirically the optimal behavior of traders in such fitted models, to their actual behavior as inferred from the data.
Real Options Valuation with Multiple Uncertainties Using K-Dimensional Models
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The binomial model presents a set of properties that make it a suitable approach in order to value the real options, throughout an easy and practical application. This is possible by the adaptation of the valuation principle for non-arbitrage, own of the options pricing theory. However, their adoption may be limited for those options that have multiple sources of uncertainty, given that their interaction should be incorporated into the valuation process. In response, financial theory has proposed valuation approaches that allow different sources of uncertainty to be represented by a consolidated estimate of volatility, such as the Marketed Asset Disclaimer (mad) approach developed by Copeland and Antikarov (2001). As an alternative, a treatment that incorporates the dynamics of each uncertainty can be given. In this context, there are different proposals that extend the Binomial model to k-dimensional or multi-dimensional context. To achieve the application, it is necessary an approximation of the k-dimensional stochastic process, as well as its correlations. This paper presents a concise review of the different methods proposed in this context, as well as their benefits, limitations and, some alternative approaches.
SSRN
The binomial model presents a set of properties that make it a suitable approach in order to value the real options, throughout an easy and practical application. This is possible by the adaptation of the valuation principle for non-arbitrage, own of the options pricing theory. However, their adoption may be limited for those options that have multiple sources of uncertainty, given that their interaction should be incorporated into the valuation process. In response, financial theory has proposed valuation approaches that allow different sources of uncertainty to be represented by a consolidated estimate of volatility, such as the Marketed Asset Disclaimer (mad) approach developed by Copeland and Antikarov (2001). As an alternative, a treatment that incorporates the dynamics of each uncertainty can be given. In this context, there are different proposals that extend the Binomial model to k-dimensional or multi-dimensional context. To achieve the application, it is necessary an approximation of the k-dimensional stochastic process, as well as its correlations. This paper presents a concise review of the different methods proposed in this context, as well as their benefits, limitations and, some alternative approaches.
Real(istic) Time-Varying Probability of Consumption Disasters
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We model the time-varying probability of consumption disasters with international risk interactions and estimate the model using national accounts data of 42 countries back to 1833. The estimated world and country-specific disaster probabilities accord well with historical macroeconomic disasters. A match of equity premium requires a relative risk aversion coefficient around 5, substantively smaller than the previous estimates. Also, the model delivers a significantly better match for the equity volatility than alternative rare disaster models. Finally, the disaster probability index estimated from the model can predict equity returns in the very long term---up to 50 years.
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We model the time-varying probability of consumption disasters with international risk interactions and estimate the model using national accounts data of 42 countries back to 1833. The estimated world and country-specific disaster probabilities accord well with historical macroeconomic disasters. A match of equity premium requires a relative risk aversion coefficient around 5, substantively smaller than the previous estimates. Also, the model delivers a significantly better match for the equity volatility than alternative rare disaster models. Finally, the disaster probability index estimated from the model can predict equity returns in the very long term---up to 50 years.
Shareholder Litigation Rights and Stock Price Crash Risk
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We study the impact of shareholder-initiated litigation risk on a firm's stock price crash risk. Our empirical analysis takes advantage of the staggered adoption of universal demand laws, which led to an exogenous decline in derivative litigation risk. We find that a decline in the threat of derivative litigation reduces crash risk and that information hoarding associated with earnings management is a channel through which litigation risk affects crash risk. The relationship is also moderated by how exposed firms are to the other primary form of shareholder litigation, namely securities class-action lawsuits.
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We study the impact of shareholder-initiated litigation risk on a firm's stock price crash risk. Our empirical analysis takes advantage of the staggered adoption of universal demand laws, which led to an exogenous decline in derivative litigation risk. We find that a decline in the threat of derivative litigation reduces crash risk and that information hoarding associated with earnings management is a channel through which litigation risk affects crash risk. The relationship is also moderated by how exposed firms are to the other primary form of shareholder litigation, namely securities class-action lawsuits.
Sports Event Outcomes and Stock Market Behaviour: Evidence from Bangladesh
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Stock market movements are the results of changes in investor sentiment (INSEN) which can even be induced by non-economic events. We consider international cricket events to empirically investigate the notions. Implementing portfolio approach, we conduct the event study along with OLS regression model to quantify empirically the effects of sports event outcomes (SEOs) on investor sentiment stimulation and stock market movements. Our investigation finds abnormal returns in the event windows [-2; +2]. Additionally, constructing a market-based sentiment index, we explore whether the market abnormalities are the true effects of SEOs. The results elucidate the significant stimulus of INSEN as a mediator to push the market returns up when BCT teams won the matches, but report the mixed phenomena in case of lost games. These findings not only unveil new aspects to the regulatory authorities to control the illegal market making in small markets but also contribute the literature with extensions of learning investor reactions to the major events.
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Stock market movements are the results of changes in investor sentiment (INSEN) which can even be induced by non-economic events. We consider international cricket events to empirically investigate the notions. Implementing portfolio approach, we conduct the event study along with OLS regression model to quantify empirically the effects of sports event outcomes (SEOs) on investor sentiment stimulation and stock market movements. Our investigation finds abnormal returns in the event windows [-2; +2]. Additionally, constructing a market-based sentiment index, we explore whether the market abnormalities are the true effects of SEOs. The results elucidate the significant stimulus of INSEN as a mediator to push the market returns up when BCT teams won the matches, but report the mixed phenomena in case of lost games. These findings not only unveil new aspects to the regulatory authorities to control the illegal market making in small markets but also contribute the literature with extensions of learning investor reactions to the major events.
Stock Market Value and Deal Value in Appraisal Proceedings
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This essay considers the relationship between two methods of valuing public companies in appraisal proceedings: the unaffected market price, the reliability of which depends on the applicability of the efficient capital markets hypothesis, and the deal price (less synergies, as applicable), the reliability of which depends on the robustness of the target’s sales process. Following the landmark cases DFC and Dell, the Delaware courts have favored market-based methods of valuation in appraisal cases, and so they have commonly looked to both the market price and the deal price in valuing companies. When the preconditions for the reliability of one method have not been satisfied, courts have sensibly looked to the other method. There are many cases, however, where the preconditions of both methods are satisfied, and in such cases there should be some principled basis for using one method rather than the other. The deal price is routinely much greater than the market price, and any principled basis for using one price rather than the other will depend on how we account for this phenomenon. This essay argues that the explanations implicit in the caselaw (synergies, material non-public information) are manifestly inadequate, and the correct explanation is that shares of the company’s stock have downwardly sloping demand curves arising from the fact that, even when sophisticated market participants have all the same information about the company, they will interpret it differently and so come to different judgments about the company’s value. Adopting this heterogeneous expectations assumption, the essay argues that, when the preconditions of both methods of valuation are satisfied, courts should generally look to the deal price (less synergies, as applicable) rather than the unaffected market price to value the company in appraisal.
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This essay considers the relationship between two methods of valuing public companies in appraisal proceedings: the unaffected market price, the reliability of which depends on the applicability of the efficient capital markets hypothesis, and the deal price (less synergies, as applicable), the reliability of which depends on the robustness of the target’s sales process. Following the landmark cases DFC and Dell, the Delaware courts have favored market-based methods of valuation in appraisal cases, and so they have commonly looked to both the market price and the deal price in valuing companies. When the preconditions for the reliability of one method have not been satisfied, courts have sensibly looked to the other method. There are many cases, however, where the preconditions of both methods are satisfied, and in such cases there should be some principled basis for using one method rather than the other. The deal price is routinely much greater than the market price, and any principled basis for using one price rather than the other will depend on how we account for this phenomenon. This essay argues that the explanations implicit in the caselaw (synergies, material non-public information) are manifestly inadequate, and the correct explanation is that shares of the company’s stock have downwardly sloping demand curves arising from the fact that, even when sophisticated market participants have all the same information about the company, they will interpret it differently and so come to different judgments about the company’s value. Adopting this heterogeneous expectations assumption, the essay argues that, when the preconditions of both methods of valuation are satisfied, courts should generally look to the deal price (less synergies, as applicable) rather than the unaffected market price to value the company in appraisal.
Supplementary Paper Series for the "Assessment" (1): The Effects of the Bank of Japan's ETF Purchases on Risk Premia in the Stock Markets
RePEC
This paper provides an empirical investigation of the effects of the Bank of Japan's exchange traded funds (ETF) purchases on risk premia in the stock markets. The analysis examines the following two indicators of risk premia: equity risk premium implied by Nikkei 225 option prices, and yield spreads of individual stocks. The former indicator is analyzed at daily frequency, and the latter is analyzed at weekly frequency. The analysis also examines how the effects of ETF purchases vary depending on market conditions and the size of ETF purchases. The results show that the Bank of Japan's ETF purchases have lowering effects on risk premia. The results also suggest that the lowering effects are larger (1) the lower the stock price index relative to its moving average trend, (2) the higher the volatility in the stock market when the stock price index is below its trend, (3) the larger the percentage decline in the stock price index immediately before the purchases, and (4) the larger the size of the purchases.
RePEC
This paper provides an empirical investigation of the effects of the Bank of Japan's exchange traded funds (ETF) purchases on risk premia in the stock markets. The analysis examines the following two indicators of risk premia: equity risk premium implied by Nikkei 225 option prices, and yield spreads of individual stocks. The former indicator is analyzed at daily frequency, and the latter is analyzed at weekly frequency. The analysis also examines how the effects of ETF purchases vary depending on market conditions and the size of ETF purchases. The results show that the Bank of Japan's ETF purchases have lowering effects on risk premia. The results also suggest that the lowering effects are larger (1) the lower the stock price index relative to its moving average trend, (2) the higher the volatility in the stock market when the stock price index is below its trend, (3) the larger the percentage decline in the stock price index immediately before the purchases, and (4) the larger the size of the purchases.
The Effects of Media Co-coverage on Investors’ Perceived Relatedness between Two Firms: Evidence from Information Transfers
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This study examines the effects of media co-coverageâ€"a phenomenon where multiple firms are simultaneously mentioned in the same news articleâ€"on investors’ perceived relatedness between the co-covered firms. Using the setting of information transfers during two co-covered firms’ earnings announcements, I find evidence consistent with co-coverage increasing the firms’ perceived relatedness. Specifically, the announcement return of an early-announcing co-covered peer negatively predicts the announcement return of the subsequently-announcing focal firm, suggesting that focal firm investors overreact to the peer’s earnings news. The negative return predictability is stronger when investors are more likely to pay attention to the article where the two firms were co-covered and when the peer’s earnings are more relevant to the focal firm’s upcoming earnings announcement. These findings shed light on an unintended consequence of journalists’ co-coverage practice on the efficiency with which equity investors use peer information.
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This study examines the effects of media co-coverageâ€"a phenomenon where multiple firms are simultaneously mentioned in the same news articleâ€"on investors’ perceived relatedness between the co-covered firms. Using the setting of information transfers during two co-covered firms’ earnings announcements, I find evidence consistent with co-coverage increasing the firms’ perceived relatedness. Specifically, the announcement return of an early-announcing co-covered peer negatively predicts the announcement return of the subsequently-announcing focal firm, suggesting that focal firm investors overreact to the peer’s earnings news. The negative return predictability is stronger when investors are more likely to pay attention to the article where the two firms were co-covered and when the peer’s earnings are more relevant to the focal firm’s upcoming earnings announcement. These findings shed light on an unintended consequence of journalists’ co-coverage practice on the efficiency with which equity investors use peer information.
The Persistence of Share Repurchases, Financing, and Firm Maturity
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Share repurchases have become persistent. Firms use cash flow as the primary source of capital to finance repeated share repurchases. This internal financing increases (decreases) retained earnings (paid-in-capital) in the capital structure and weakens the sensitivity of investment to cash flow. Our findings suggest that traditional explanations for share repurchasesâ€"to distribute temporary cash flows, signal undervaluation, or increase the leverage ratioâ€"have lost power, and that share repurchases have become associated with a firm’s financial maturity as captured by retained earnings-to-assets, with mature firms displaying a long-term increase (decrease) in the sensitivity of share repurchases (investment) to cash flows.
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Share repurchases have become persistent. Firms use cash flow as the primary source of capital to finance repeated share repurchases. This internal financing increases (decreases) retained earnings (paid-in-capital) in the capital structure and weakens the sensitivity of investment to cash flow. Our findings suggest that traditional explanations for share repurchasesâ€"to distribute temporary cash flows, signal undervaluation, or increase the leverage ratioâ€"have lost power, and that share repurchases have become associated with a firm’s financial maturity as captured by retained earnings-to-assets, with mature firms displaying a long-term increase (decrease) in the sensitivity of share repurchases (investment) to cash flows.
The Time Has Come for Disaggregated Sovereign Bankruptcy
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This essay argues that the international community should use the energy generated by the COVID-19 pandemic to move toward what might be called ‘disaggregated sovereign bankruptcy,’ in part by establishing institutions to more effectively address future crises. It first notes the country financial difficulties generated by the current situation and the ways in which national responses may have long-term financial impacts that make states more vulnerable to debt distress, particularly in the developing world. It also delineates how any restructuring efforts that might result from such distress would have to contend with longstanding problems in the global architecture relevant to sovereign debt. It then mentions several proposals that have been put forward to address the pandemic-related financial crisis, but contends thatâ€"in addition to short-term, emergency-focused proposalsâ€"the need for a more rational global debt restructuring platform remains. Ultimately, the essay argues that the time has come for ‘disaggregated sovereign bankruptcy’â€"which can be understood as a framework by which multiple processes at varying levels simultaneously support or instantiate a shared set of sovereign debt resolution principles and commitments. Such an approach moves beyond overly simplistic and binary framings of market-based versus statutory options, and instead conceives of improvements in the contractual realm, in the multilateral arena, and at the level of domestic legislation as complementary rather than competitive. The essay also clarifies that the explicit embrace of a more disaggregated framework for implementing debt resolution principles need not be disorganized. It argues in favor of establishing an international body purpose-built to recommend, coordinate, and facilitate steady, incremental progress in the architecture for dealing with sovereign debt across multiple vectors. Advocates of more rational debt restructuring should take steps now to adopt an infrastructure that would make future debt crises less severe and perhaps less likelyâ€"even when the spotlights are directed elsewhere.
SSRN
This essay argues that the international community should use the energy generated by the COVID-19 pandemic to move toward what might be called ‘disaggregated sovereign bankruptcy,’ in part by establishing institutions to more effectively address future crises. It first notes the country financial difficulties generated by the current situation and the ways in which national responses may have long-term financial impacts that make states more vulnerable to debt distress, particularly in the developing world. It also delineates how any restructuring efforts that might result from such distress would have to contend with longstanding problems in the global architecture relevant to sovereign debt. It then mentions several proposals that have been put forward to address the pandemic-related financial crisis, but contends thatâ€"in addition to short-term, emergency-focused proposalsâ€"the need for a more rational global debt restructuring platform remains. Ultimately, the essay argues that the time has come for ‘disaggregated sovereign bankruptcy’â€"which can be understood as a framework by which multiple processes at varying levels simultaneously support or instantiate a shared set of sovereign debt resolution principles and commitments. Such an approach moves beyond overly simplistic and binary framings of market-based versus statutory options, and instead conceives of improvements in the contractual realm, in the multilateral arena, and at the level of domestic legislation as complementary rather than competitive. The essay also clarifies that the explicit embrace of a more disaggregated framework for implementing debt resolution principles need not be disorganized. It argues in favor of establishing an international body purpose-built to recommend, coordinate, and facilitate steady, incremental progress in the architecture for dealing with sovereign debt across multiple vectors. Advocates of more rational debt restructuring should take steps now to adopt an infrastructure that would make future debt crises less severe and perhaps less likelyâ€"even when the spotlights are directed elsewhere.
The Vasicek credit risk model: A Machine Learning approach
SSRN
This paper explores the ability of the Machine Learning (ML) techniques to calibrate models that replicate the outputs of the Vasicek credit risk model. This model measures the loss distribution of a portfolio made up of loans that can be exposed to multiple systemic factors and it is widely used in the financial sector and by regulators. Under some assumptions this model provides a closed-form expression of the loss distribution but, in the general case, it requires computationally demanding Monte Carlo simulations to estimate this distribution. The ML approach only requires an initial calibration process and our results show that, for different portfolios, we can replicate outputs with a high degree of accuracy. Using just two variables, the confidence level and a Gaussian copula based loss distribution estimate, the tree based models provide quick and accurate estimates of the real loss distribution. This is the case for granular and also for concentrated portfolios.
SSRN
This paper explores the ability of the Machine Learning (ML) techniques to calibrate models that replicate the outputs of the Vasicek credit risk model. This model measures the loss distribution of a portfolio made up of loans that can be exposed to multiple systemic factors and it is widely used in the financial sector and by regulators. Under some assumptions this model provides a closed-form expression of the loss distribution but, in the general case, it requires computationally demanding Monte Carlo simulations to estimate this distribution. The ML approach only requires an initial calibration process and our results show that, for different portfolios, we can replicate outputs with a high degree of accuracy. Using just two variables, the confidence level and a Gaussian copula based loss distribution estimate, the tree based models provide quick and accurate estimates of the real loss distribution. This is the case for granular and also for concentrated portfolios.
What triggers consumer adoption of CBDC?
SSRN
Central banks around the world are examining the possibility of introducing Central Bank Digital Currency (CBDC). The public’s preferences concerning the usage of CBDC for paying and saving are important determinants of the success of CBDC. Using data from a representative panel of Dutch consumers we find that roughly half of the public would open a CBDC current account. The same holds for a CDBC savings account. Thus, we find clear potential for CBDC in the Netherlands. This suggests that consumers perceive CBDC as distinct from current and savings accounts offered by traditional banks. Intended adoption is positively related to respondents’ knowledge of CBDC and trust in banks and in the central bank. Price incentives matter as well. The amount respondents want to deposit in the CBDC savings account depends on the interest rate offered. Furthermore, intended usage of the CBDC current account is highest among people who find privacy and security important and among consumers with low trust in banks in general. These results suggest that central banks can steer consumers’ adoption of CBDC via the interest rate, by a design of CBDC that takes into account the public’s need for security and privacy, and by clear communication about what CBDC entails.
SSRN
Central banks around the world are examining the possibility of introducing Central Bank Digital Currency (CBDC). The public’s preferences concerning the usage of CBDC for paying and saving are important determinants of the success of CBDC. Using data from a representative panel of Dutch consumers we find that roughly half of the public would open a CBDC current account. The same holds for a CDBC savings account. Thus, we find clear potential for CBDC in the Netherlands. This suggests that consumers perceive CBDC as distinct from current and savings accounts offered by traditional banks. Intended adoption is positively related to respondents’ knowledge of CBDC and trust in banks and in the central bank. Price incentives matter as well. The amount respondents want to deposit in the CBDC savings account depends on the interest rate offered. Furthermore, intended usage of the CBDC current account is highest among people who find privacy and security important and among consumers with low trust in banks in general. These results suggest that central banks can steer consumers’ adoption of CBDC via the interest rate, by a design of CBDC that takes into account the public’s need for security and privacy, and by clear communication about what CBDC entails.
