Research articles for the 2021-07-12
arXiv
In many U.S. central cities, property values are relatively low, while rents are closer to those in better-off neighborhoods. This gap can lead to relatively large profits for landlords, and has been referred to as "exploitaton" for renters. While much of this gap might be explained by risk, factors such as income and race might play important roles as well. This study calculates Census tract-level measures of the rent-to-property-value (RPV) ratio for 30 large cities and their surrounding metropolitan areas. After examining the spatial distribution of this ratio and relationships with other socioeconomic variables for Milwaukee and three other cities, Z-scores and quantiles are used to identify "extreme" RPV values nationwide. "Rust Belt" cities such as Detroit, Cleveland, and Milwaukee are shown to have higher median and 95% values than do West Coast cities such as Seattle and San Francisco. A spatial lag regression estimation shows that, controlling for income, property values, and vacancy rates, racial characteristics often have the "opposite" signs from what might be expected and that there is little evidence of purely race-based "exploitation" of renters. A significantly negative coefficient for the percentage of Black residents, for example, might suggest that the RPV ratio is lower in a given tract, all else equal. While this study shows where RPV values are highest within as well as between cities, further investigation might uncover the drivers of these spatial differences more fully.
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I characterize the global solution to the international portfolio problem in full generality, a long-standing open issue in international finance. In this two-country two-good environment, investors have recursive preferences and a bias in consumption towards their local good. The framework highlights the role of the allocation of wealth across international investors for portfolios, asset prices, and risk sharing, an aspect that had received little emphasis in such a setting. The influence of the allocation of wealth grows especially as markets become imperfectly integrated, and as investor heterogeneity rises -- be it through a larger home bias in consumption, the introduction of labor income, or asymmetries in preferences -- to the point where it can match or surpass the impact of fundamentals. The framework lends itself to several applications and extensions. In particular, I show that it can replicate a number of facts about the structure and dynamics of the international financial system, and of asset returns in that context.
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[enter Abstract This paper studies whether board busyness impact stock price crash risk â" the risk of a sudden drop in stock prices when firms accumulate negative news. We empirically test two opposing views (mechanisms) on the relation between board busyness and stock price crash risk: cognitive constraints and positive interlocking. We examine a single large sample of US firms for the period 1999â"2015.
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To align capital flows with the goals of the Paris Agreement, financial institutions must decarbonise primary market transactions, as these continue to provide new capital to the real economy that can create carbon lock-in and the risk of stranded assets. In this paper we define a new metric, Primary Market Carbon Exposure (PMCE), as the proportion of primary market transactions that occur in carbon-intensive sectors. We calculate PMCE for US corporate bond exchange-traded funds (ETFs) and find that these funds systematically partake in carbon-intensive primary market transactions, with a PMCE of 14% from 2015 to 2020, despite tracking indexes that rebalance monthly. High yield ETFs have a higher PMCE than investment grade ETFs and provide more financing to upstream oil & gas. To avoid becoming capital providers of last resort for carbon-intensive sectors, ETF providers need to reduce PMCE in line with Paris Agreement carbon budgets. For policymakers, not only can passive funds contribute to carbon lock-in, but ETFs directly bought by central banks are financing carbon-intensive sectors. We demonstrate this for ETFs bought by the Federal Reserve in 2020.
arXiv
The transition to a low-carbon economy is one of the ambitions of the European Union for 2030. Biobased industries play an essential role in this transition. However, there has been an on-going discussion about the actual benefit of using biomass to produce biobased products, specifically the use of agricultural materials (e.g., corn and sugarcane). This paper presents the environmental impact assessment of 30% and 100% biobased PET (polyethylene terephthalate) production using EU biomass supply chains (e.g., sugar beet, wheat, and Miscanthus). An integral assessment between the life cycle assessment methodology and the global sensitivity assessment is presented as an early-stage support tool to propose and select supply chains that improve the environmental performance of biobased PET production. From the results, Miscanthus is the best option for the production of biobased PET: promoting EU local supply chains, reducing greenhouse gas (GHG) emissions (process and land-use change), and generating lower impacts in midpoint categories related to resource depletion, ecosystem quality, and human health. This tool can help improving the environmental performance of processes that could boost the shift to a low-carbon economy.
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This paper studies the relation between economic policy uncertainty (EPU) and the marginal value of corporate cash holdings. We find that the markets place a lower value on firmsâ cash holdings under high level of EPU. Our quasi-experiment tests relating to the 9/11 terrorist attacks and the gubernatorial elections support the causal relationship between EPU and the value of cash holdings. Further, we show that this relation is more pronounced for firms with less redeployable capital assets, more investment opportunities, poor corporate governance, or positive excess cash. Overall, our evidence suggests that cash is not always king especially in periods of high uncertainty of policies.
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We calibrate to the Brazilian economy a model of means of payment choice, where households have different preferences over anonymity. Financial sector is monopolistically competitive and may break the link between borrowing and lending rates. A sufficiently attractive digital currency reduces holdings of both cash and bank deposits. Since cash use is costly, the digital currency may increase welfare. However, if banks are liquidity constrained, the digital currency may result in less loans and output, and then reduce welfare. The digital currency interest remuneration can be set and be adjusted overtime to optimally balance this trade-off.
arXiv
There is a random variable (X) with a determined outcome (i.e., X = x0), p(x0) = 1. Consider x0 to have a discrete uniform distribution over the integer interval [1, s], where the size of the sample space (s) = 1, in the initial state, such that p(x0) = 1. What is the probability of x0 and the associated information entropy (H), as s increases by means of different functional forms of expansion? Such a process has been characterised in the case of (1) a mono-exponential expansion of the sample space; (2) a power function expansion; (3) double exponential expansion. The double exponential expansion of the sample space with time (from a natural log relationship between t and n) describes a "hyperinflationary" process. Over the period from the middle of 1920 to the end of 1923, the purchasing power of the Weimar Republic paper Mark to purchase one gold Mark became close to zero (1 paper Mark = 10 to the power of -12 gold Mark). From the purchasing power of the paper Mark to purchase one gold Mark, the information entropy of this hyperinflationary process was determined.
arXiv
Blockchains are still perceived chiefly as a new technology. But each blockchain is also a community and a social experiment, built around social consensus. Here I discuss three examples showing how collective intelligence can help, threat or capitalize on blockchain-based ecosystems. They concern the immutability of smart contracts, code transparency and new forms of property. The examples show that more research, new norms and, eventually, laws are needed to manage the interaction between collective behaviour and the blockchain technology. Insights from researchers in collective intelligence can help society rise up to the challenge.
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This paper documents that racial differences in credit distribution during a general mortgage credit expansion can lead to unintended negative consequences on crime. Exploiting a federal mortgage market deregulation, we find a significant increase in mortgage approval to white borrowers, while the approval rate to black borrowers is unchanged. More importantly, the local housing boom induced by this credit expansion leads to an increase in money-related crime rates of black offenders. The results highlight an unintended adverse consequence of credit expansion on the welfare of the minorities.
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With increasing population and rapid industrialization prompted the scientific community to find alternate source of fuel that must be convertible, locally available and environmental friendly. In this context, the role of biodiesel may be found significant in terms of commercially and technically viable. Present study was carried out to evaluate performance and emission characteristics on a 5.2 kW single cylinder water cooled engine using diesel and biodiesel blend at different engine load (0, 20%, 40%, 60%, 80% and 100%). The results revealed that the emission of CO reduced by 10.34% with biodiesel blend in comparison to diesel, while NOx emission in case of biodiesel blend was found to be higher than diesel. The brake thermal efficiency and mechanical efficiency was found to be lower than diesel by 3.6% and 5% respectively.
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Despite extensive evidence of how industry competition affects fund fee and performance outcomes, there is little evidence of how competition affects the incentives of market participants in the mutual fund industry. This paper uses an international sample of active equity mutual funds to examine how product development in mutual fund families is affected by competitive pressure. Fund family product development is defined as improving the quality of existing funds (e.g., level of activity, quality consistency, star funds, and manager changes) or as changes in the fund base (e.g., starting new funds, mergers, and liquidating funds). The results show that greater industry competition motivates fund families to carry out product development through the quality channel rather than the base channel. Furthermore, product quality development increases performance in the family-affiliated funds, and thus benefits the investors. Based on the findings, I argue that competition motivates desired activity in the mutual fund industry and reduces conflicts of interest that stem from the family structure of the industry.
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I estimate that statewide pension funds in the United States incur annual investment expenses averaging 1.3% of asset value. A sample of 24 of them underperformed passive investment during the past decade by an average of 1.4% a year. And yet, those same funds report that they outperformed benchmarks of their own devising by an average of +0.3% a year for the same period. This sharp disconnect raises questions about the usefulness of the fundsâ performance reporting, as well as their heavy reliance on expensive active management.
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It is an important stylized fact that cross-sectional accruals are positively related to earnings and negatively related to operating cash flows. In this paper, we reconsider the question of why these associations occur and how to deal with them in earnings management studies. We propose a simple Bayesian model in which unobservable true profitability is related to cash flows by normal accruals and to reported earnings by discretionary accruals. The overlay of uncertainty concerning true performance and normal and discretionary accruals gives rise to the observed cross-sectional relations, even if earnings are not managed and true performance does not affect accruals. In our model, earnings management appears in the Bayesian estimates of both discretionary and normal accruals. The most efficient way to detect earnings management depends on the sampling of suspect firms. For random sampling, the test should be based on estimated discretionary accruals; for sampling with respect to reported earnings, estimated normal accruals are more efficient; and for sampling with respect to the Bayesian estimate of true profitability, total accruals are most appropriate. Simulations and empirical analyses show that it is crucial but difficult to identify the appropriate performance adjustment approach.
arXiv
We study financial networks where banks are connected by debt contracts. We consider the operation of debt swapping when two creditor banks decide to exchange an incoming payment obligation, thus leading to a locally different network structure. We say that a swap is positive if it is beneficial for both of the banks involved; we can interpret this notion either with respect to the amount of assets received by the banks, or their exposure to different shocks that might hit the system.
We analyze various properties of these swapping operations in financial networks. We first show that there can be no positive swap for any pair of banks in a static financial system, or when a shock hits each bank in the network proportionally. We then study worst-case shock models, when a shock of given size is distributed in the worst possible way for a specific bank. If the goal of banks is to minimize their losses in such a worst-case setting, then a positive swap can indeed exist. We analyze the effects of such a positive swap on other banks of the system, the computational complexity of finding a swap, and special cases where a swap can be found efficiently. Finally, we also present some results for more complex swapping operations when the banks swap multiple contracts, or when more than two banks participate in the swap.
arXiv
Modeling and managing portfolio risk is perhaps the most important step to achieve growing and preserving investment performance. Within the modern portfolio construction framework that built on Markowitz's theory, the covariance matrix of stock returns is required to model the portfolio risk. Traditional approaches to estimate the covariance matrix are based on human designed risk factors, which often requires tremendous time and effort to design better risk factors to improve the covariance estimation. In this work, we formulate the quest of mining risk factors as a learning problem and propose a deep learning solution to effectively "design" risk factors with neural networks. The learning objective is carefully set to ensure the learned risk factors are effective in explaining stock returns as well as have desired orthogonality and stability. Our experiments on the stock market data demonstrate the effectiveness of the proposed method: our method can obtain $1.9\%$ higher explained variance measured by $R^2$ and also reduce the risk of a global minimum variance portfolio. Incremental analysis further supports our design of both the architecture and the learning objective.
arXiv
In this paper, we study analytical properties of the solutions to the generalised delay Ait-Sahalia-type interest rate model with Poisson-driven jump. Since this model does not have explicit solution, we employ several new truncated Euler-Maruyama (EM) techniques to investigate finite time strong convergence theory of the numerical solutions under the local Lipschitz condition plus the Khasminskii-type condition. We justify the strong convergence result for Monte Carlo calibration and valuation of some debt and derivative instruments.
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Trust in banks is key, especially in turbulent times. Using unique daily payment diary data for a representative panel of Dutch consumers, which has been enriched with questions on trust in banks' payment services, we examine the determinants of trust as well as to what extent the COVID-crisis has affected trust. We have the following main findings. First, narrow-scope trust (trust in consumers' own bank payment services) is in general higher than broad-scope trust (trust in banks' payment services in general). Second, COVID-19 measures have affected trust in banks' payment services. The first lockdown and measures taken by banks - such as increasing contactless payment limits - increased narrow-scope trust and broad-scope trust. The second lockdown decreased both notions of trust. The crisis measures impacted the trust of the elderly the strongest. Third, personal characteristics are significantly related to trust in banks' payment services. We find that both types of trust are increasing with digital literacy and the ease of getting by with income. Also, people who hold an account with a large bank have higher broad-scope trust, while customers of small banks have higher narrow-scope trust. Men have lower broad-scope trust, while there is no difference between men and women for narrow-scope trust. People with high income have higher broad-scope trust, while there is no effect on narrow-scope trust.
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As a new test of models of differences of opinion, we study empirically how the shorting market interacts with the equity volatility. Consistent with theories of differences of opinion, a positive (negative) demand shift in the shorting market predicts higher (lower) future stock volatility: A positive demand shift predicts an increase in the next week's annualized volatility of 4.8 percentage points, which corresponds to a 10.9% increase relative to the average weekly annualized volatility. The effect remains after controlling for other proxies of differences of opinion such as bid-ask spreads and volume, suggesting that the shorting market is an important channel in which investors reveal divergence or convergence in their beliefs about the future trajectory of individual stocks.
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We investigate the effectiveness of CoCo bonds as a credible recapitalization or resolution tool for distressed banks in Europe. Using yields on CoCo and senior bank bonds, we construct a CoCo risk premium to capture bank stress and we analyze whether or not this premium is related to bank systemic risk, captured by the marginal expected shortfall (MES), as well as individual bank risk.We find that increases of the CoCo spread are positively associated with both bank systemic risk and bank default risk. These results suggest that market participants do not consider CoCo bonds as âgoing concernâ capital. Since we also find that senior and subordinated bondholders perceive the probability of a bail-in as higher during times of an elevated CoCo premium, this implies that CoCo bonds are not considered as a credible recovery or resolution tool under the BRRD regime. Nevertheless, the impact of CoCo bonds is limited to bankspecific systemic and credit risk and does not seem to affect the risk profile of other banks. Our results suggest that policy actions are needed to render the European bank bail-in regime more credible.
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We exploit banksâ recently mandated quarterly fair value disclosures to perform the first short-window event study of fair value adjustments excluded from net income. We find these fair value adjustments are positively associated with short-window stock returns and accentuate investorsâ response to GAAP earnings, suggesting that these adjustments are complementary to GAAP earnings. These results concentrate in the period surrounding the financial crisis and in banksâ loan portfolios, where fair values depend on credit quality information not fully available in the public domain. Our results suggest that (i) fair value adjustments that rely on unobservable inputs reflect new information at their disclosure date that investors incorporate into price, (ii) these adjustments help investors interpret GAAP earnings, and (iii) these effects are limited to periods where fair values diverge significantly from book value. Overall, our study suggests that investors benefit from having both GAAP earnings and fair value information.
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The increased equity lending supply (ELS) in the equity loan market, available for short sellers to borrow, exposes a firm to greater short selling threats. Considering short sellersâ strong incentives to uncover firm-specific information and monitor managers, we hypothesize that short selling threats, proxied by ELS, enhance corporate investment efficiency. We find that ELS significantly reduces managerial tendencies to underinvest (overinvest) especially for firms prone to underinvest (overinvest). The effect of ELS on investment efficiency is stronger for firms with higher information asymmetry and weaker corporate governance, confirming short sellersâ role in mitigating information and agency costs. However, short selling risk weakens the effect of ELS. Our evidence is robust to endogeneity checks and suggests that corporate investment can be driven by a particular capital market condition: the amount of lendable shares in the equity loan market.
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Exploiting two novel measures of innovation efficiency and takeover vulnerability, we explore the effect of the takeover market on corporate innovation. Our results reveal that a more active takeover market stifles innovation considerably, consistent with the notion that managers tend to be myopic when more exposed to hostile takeover threats, making investments that produce results in the short run at the expense of long-term projects that lead to more innovation. Additional robustness checks confirm the results, including fixed-effects and random-effects regressions, propensity score matching, GMM dynamic panel data analysis and instrumental-variable analysis. Our results are unlikely driven by endogeneity.
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We provide a general model of due diligence and strategic behavior that can be applied to a wide array of situations with asymmetric information and apply this model to an Analyst's Forecasts about a Studied Firm as a function of Publicly available information and the Studied Firm's unobserved (to the Empiricist) information, when the Studied Firm provides Management Guidance to the market as well. Due diligence requires that the Analyst acquire and analyze Studied Firm-specific information, even potentially information that is not available to the Studied Firm itself, we measure Due-Diligence using the Hausman Specification Test. Under non-strategic behavior, the Analyst provides as Forecast its best estimate of the Studied Firm's earnings, we measure Strategic-Behavior using the Wald Test. We calculate the Z-Score of Due-Diligence and Strategic-Behavior, and each regressand and regressor, which follows standard normal distribution, and report, for ease of empirical interpretation, empirical results corresponding to these Z-Scores. We find that over 1994-2018, Due-Diligence is significantly increasing (at 5% level) in first Analyst Forecast after Management Guidance, log of difference in days between Management Guidance and Analyst Forecast, and log of Analyst's experience, and that Strategic-Behavior is significantly increasing (at 5% level) in first Analyst Forecast after Management Guidance, log of difference in days between Management Guidance and Analyst Forecast, and log of Analyst's experience, and significantly decreasing (at 5% level) in log of mean trading volume of Studied Firm, mean bid-ask spread of Studied Firm's equity, and standard deviation of Studied Firm's daily equity return. Under the additional information assumption that the Actual Earnings (per share) of the Studied Firm are available to the Public at the beginning of the next period as Announced Earnings (per share), ex post Normalized Accuracy -- measured by the negative of the absolute difference between Analyst Forecast (per share) and Announced Earnings (per share), divided by Announced Earnings (per share) if positive -- is positively related to Due-Diligence and Non-Strategic-Behavior.
arXiv
While controversial, e-learning has become an essential tool for all kinds of education: especially the kindergarten-to-twelfth sector. However, pockets of this sector lack access, mainly economically underserved students. This paper explores the options available to underserved and aptly resourced members of the kindergarten-to-twelfth educational sector: a 250-million-person market, with only 9 million students enrolled in online education. The paper also provides a brief overview of the options and challenges of making e-learning available to everyone in the kindergarten-to-twelfth educational sector. To establish whether e-learning is beneficial, it also discusses the results of a survey conducted on students and educators who have experienced e-learning, with the results showing that it is beneficial, with a general trend of teachers showing more comfort with online learning than students. The paper utilizes primary and secondary resources for this purpose, with information both from the internet, and from surveys conducted within people from the system: parents, students, and teachers.
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This study examines the role of board networks in promoting stock liquidity in periods of heightened economic policy uncertainty using a sample of Brazilian firms from 2002-2018. Firstly, the findings show that economic policy uncertainty disproportionately contributes to stock illiquidity. This impact is mainly prominent for high-risk companies, small firms and firms in competitive industries. Secondly, we provide evidence that board networks promote stock liquidity more via the information channel when economic policy uncertainty is very high. Unlike other studies that focus on the adverse effects of economic policy uncertainty on firm outcomes, our novel contribution is that we uncover the role of board networks in mitigating the negative effects of economic policy uncertainty on stock liquidity.
arXiv
Portfolio optimization has been a central problem in finance, often approached with two steps: calibrating the parameters and then solving an optimization problem. Yet, the two-step procedure sometimes encounter the "error maximization" problem where inaccuracy in parameter estimation translates to unwise allocation decisions. In this paper, we combine the prediction and optimization tasks in a single feed-forward neural network and implement an end-to-end approach, where we learn the portfolio allocation directly from the input features. Two end-to-end portfolio constructions are included: a model-free network and a model-based network. The model-free approach is seen as a black-box, whereas in the model-based approach, we learn the optimal risk contribution on the assets and solve the allocation with an implicit optimization layer embedded in the neural network. The model-based end-to-end framework provides robust performance in the out-of-sample (2017-2021) tests when maximizing Sharpe ratio is used as the training objective function, achieving a Sharpe ratio of 1.16 when nominal risk parity yields 0.79 and equal-weight fix-mix yields 0.83. Noticing that risk-based portfolios can be sensitive to the underlying asset universe, we develop an asset selection mechanism embedded in the neural network with stochastic gates, in order to prevent the portfolio being hurt by the low-volatility assets with low returns. The gated end-to-end with filter outperforms the nominal risk-parity benchmarks with naive filtering mechanism, boosting the Sharpe ratio of the out-of-sample period (2017-2021) to 1.24 in the market data.
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This study investigates how policy uncertainty affects the acquisition process during the post-announcement period. Utilizing a sample of Australian mining sector acquisitions over 1998-2017, we find that rising policy uncertainty after initial acquisition announcements is associated with delays in deal completion. In addition, prolonged high policy uncertainty plays a critical role in triggering acquisition abandonment. Further, the stock market reacts less negatively to deal abandonment decisions made amid protracted policy uncertainty. The muted market reactions are also associated with managersâ explanations for deal abandonment. Overall, our findings highlight that policy uncertainty is an important âdeal-breakerâ in acquisitions.
arXiv
Many countries embroiled in non-religious civil conflicts have experienced a dramatic increase in religious competition in recent years. This study examines whether increasing competition between religions affects violence in non-religious or secular conflicts. The study focuses on Colombia, a deeply Catholic country that has suffered one of the world's longest-running internal conflicts and, in the last few decades, has witnessed an intense increase in religious competition between the Catholic Church and new non-Catholic churches. The estimation of a dynamic treatment effect model shows that establishing the first non-Catholic church in a municipality substantially increases the probability of conflict-related violence. The effect is larger for violence by guerrilla groups, and is concentrated on municipalities where the establishment of the first non-Catholic church leads to more intense religious competition. Further analysis suggests that the increase in guerrilla violence is associated with an expectation among guerrilla groups that their membership will decline as a consequence of more intense competition with religious groups for followers.
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This paper examines how CSR disclosure impacts innovation using a sample of Chinese firms. We find that an increase in CSR disclosure leads firms to invest more in R&D investments and generate more patents. Additionally, our findings reveal that firms with high CSR disclosure nurture innovation by engaging less in short-termism activities that crowd resources for innovation. Furthermore, we uncover that CSR disclosure promotes the optimum allocation of R&D expenditure by allocating risky capital toward good investment opportunities and away from poor investment opportunity sets. In addition, we show that CSR disclosure promotes innovation through information and human capital channels.
arXiv
Prescription Drug Monitoring Programs (PDMPs) seek to potentially reduce opioid misuse by restricting the sale of opioids in a state. We examine discontinuities along state borders, where one side may have a PDMP and the other side may not. We find that electronic PDMP implementation, whereby doctors and pharmacists can observe a patient's opioid purchase history, reduces a state's opioid sales but increases opioid sales in neighboring counties on the other side of the state border. We also find systematic differences in opioid sales and mortality between border counties and interior counties. These differences decrease when neighboring states both have ePDMPs, which is consistent with the hypothesis that individuals cross state lines to purchase opioids. Our work highlights the importance of understanding the opioid market as connected across counties or states, as we show that states are affected by the opioid policies of their neighbors.
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This work analyses the impact of social network sites on the life of a university student in terms of intensity of use, personal life satisfaction, social trust, social expression and physical health. Study is based on close end survey design and consist of one hundred and fifty-four (154) respondents of four constituent campuses of Pokhara University, Nepal. The study uses both descriptive and inferential statistical methods to analyze data, draw conclusions and forward recommendations of the study. Chi-square test is also performed on the identified variables under investigation to calculate association among them. The work summarizes both the positive as well as negative aspects of using social network sites on the life university students that helps to build an understanding of role of technology in their life. Further, this study can serve as a base in identifying the socio-psychological aspects of technology of human life. For universities of under developed countries like Nepal, this can be a vital study in understanding technology and its application in academia.
arXiv
Modeling investor behavior is crucial to identifying behavioral coaching opportunities for financial advisors. With the help of natural language processing (NLP) we analyze an unstructured (textual) dataset of financial advisors' summary notes, taken after every investor conversation, to gain first ever insights into advisor-investor interactions. These insights are used to predict investor needs during adverse market conditions; thus allowing advisors to coach investors and help avoid inappropriate financial decision-making. First, we perform topic modeling to gain insight into the emerging topics and trends. Based on this insight, we construct a supervised classification model to predict the probability that an advised investor will require behavioral coaching during volatile market periods. To the best of our knowledge, ours is the first work on exploring the advisor-investor relationship using unstructured data. This work may have far-reaching implications for both traditional and emerging financial advisory service models like robo-advising.
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Merton (1990, p.44) claims that the market portfolio can be constructed without the knowledge of preferences and the distribution of wealth. However, the analytical equilibrium solution to the CAPM market reveals that it is not true. Investors' preferences and wealth affect the equilibrium price of the market, they do not affect the mathematical form of the CAPM formula, but change the values of the variables in the formula.
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Prior literature shows that the zero earnings discontinuity, which illustrates earnings management to avoid small losses, has disappeared in the United States. We examine whether a similar development has occurred in the European Union and find that this is not the case: the discontinuity in Europe is still pronounced and has remained remarkably stable. We hypothesize that the tendency to avoid small losses of close to zero constitutes only one facet of a more general tendency to reduce reported losses in years with negative cash flows, which would result in a less timely recognition of losses than expected under conditional conservatism as embodied in accounting standards. For the United States, we find that loss avoidance in this broader sense follows a similar developmental pattern over time as that of the zero earnings discontinuity. For the last two decades, losses have been recognized in a timelier manner than gains, which is consistent with conditional conservatism. In Europe, in contrast, the asymmetric timeliness of loss recognition is consistently reversed, which suggests that conditional conservatism is overcompensated by earnings management to reduce reported losses. We conclude that the adoption of International Financial Reporting Standards has not resulted in a decline in loss avoidance behavior in the European Union.
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Inspired by initially proposed IBOR fallback mechanisms, we show how deep learning can be used to quickly and accurately compute the {expected median} of a time series at future inference dates with varying amounts of observed data. While the IBOR fallback spreads were ultimately fixed, the technique outlined here showcases the ability of neural networks to tackle financial problems over seemingly impossibly large domains.
arXiv
Background: The US Food and Drug Administration (FDA) regulates medical devices (MD), which are predicated on a concoction of economic and policy forces (e.g., supply/demand, crises, patents). Assuming that the number of FDA MD (Premarketing Notifications (PMN), Approvals (PMAs), and their sum) Applications behaves similarly to those of other econometrics, this work explores the hypothesis of the existence (and, if so, the length scale(s)) of economic cycles (periodicities). Methods: Beyond summary statistics, the monthly (May, 1976 to December, 2020) number of observed FDA MD Applications are investigated via an assortment of time series techniques (including: Discrete Wavelet Transform, Running Moving Average Filter (RMAF), Complete Ensemble Empirical Mode with Adaptive Noise decomposition (CEEMDAN), and Seasonal Trend Loess (STL) decomposition) to exhaustively search and characterize such periodicities. Results: The data were found to be non-normal, non-stationary (fractional order of integration < 1), non-linear, and strongly persistent (Hurst > 0.5). Importantly, periodicities exist and follow seasonal, 1 year short-term, 5-6 year (Juglar), and a single 24-year medium-term (Kuznets) period (when considering the total number of MD Applications). Economic crises (e.g., COVID-19) do not seem to affect the evolution of the periodicities. Conclusions: This work concludes that (1) PMA and PMN data may be viewed as a proxy measure of the MD industry; (2) periodicities exists in the data with time lengths associated with seasonal/1-year, Juglar and Kuznets affects; (4) these metrics do not seem affected by specific crises (such as COVID-19) (similarly with other econometrics used in periodicity assessments); (5) PMNs and PMAs evolve inversely and suggest a structural industrial transformation; (6) Total MDs are predicted to continue their decline into the mid-2020s prior to recovery.
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This study explores for the first time mutual funds that impose discretionary inflow gates on investors. Using a sample of equity funds in China, we present evidence that funds restrict investor purchases for marketing purposes. Despite not earning a higher future return, funds attract extra flows and a larger retail investor clientele after limiting fund inflows. In addition, funds tilt toward a more aggressive investment strategy when the gate is in place. Overall, we suggest that funds intentionally leave the gate ajar to investors in order to boost fund flows rather than protect investor interests.
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Leveraged investing can aggravate crises when dropping asset prices force sales in falling markets. Leverage control policies introduced to coun- teract this could lead to agents targeting the maximal leverage deemed âprudentâ at all times. Aymanns and Farmer (2015) show how, for a Value at Risk type control policy, this âleverage targetingâ can not only lead to aggravated crises, but can also trigger the crisis to begin with and cause the boom that follows it. In modelling a whole cycle, however, the assumptions that investors base their demand for risky assets only on their leverage target becomes more dubious. In this paper, we allow our agents to repeatedly choose between a leverage targeting and a (leverage constrained) expected utility optimisation strategy. We find that both strategies persist, and in fact are equally prevalent on average. The endogenous cycles that Aymanns and Farmer (2015) found persist, although their amplitude is reduced. Agents will thus regularly choose to invest up to their leverage constraint, without further consideration of the state of the market, even though this destabilises the market. We find that allowing short-selling for agents using the optimisation strategy does stabilise the market.
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Momentum effects, whereby prior stock returns predict future returns, have been documented in a wide variety of contexts ranging from single-stock effects to the performance of industry, sector, and factor indices. However, it remains challenging to reconcile and unify these disparate findings due to differences in methodology, calibration, data universe, and the granularity of tests. Our goal is to attribute stock return predictability to a variety of distinct momentum (and reversal) components within a single coherent framework. We focus on S&P 500 stocks and implement consistent data transformations, nested sets of excess returns, and panel regressions to facilitate this attribution. We find that sector and factor momentum coexist, but they often operate on different horizons, and sector momentum is more prone to crash during volatile markets. Collectively, sector and factor momentum explain away most of the security-specific 12-month momentum effect, with factors explaining the greater share. Traditional 12-month momentum is more prevalent for past âloserâ stocks whereas crashes and reversals are found mostly among past âwinners.â Lastly, we show that in the decade after the 2008-2009 financial crisis compared to the decade prior, sector and industry momentum disappeared at the 12-month horizon but intensified in terms of 1-month reversals.
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 numerically study this model and justify the scheme within Monte Carlo framework to compute some financial quantities such as a bond and a barrier option.
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In a system where expectations and realisations of a price feed back to each other, it has been found that the sign and strength of this feedback is an important predictor of the market stability. In this paper we contribute to the generalisation of this result to a two dimensional system, where the expectations and realisations of two prices affect each other. We conduct a Learning to Forecast Experiment and show that eigenvalues can be used as predictors of stability in such a higher dimensional framework. We investigate eigenvalues of positive real part, and found that complex eigenvalues with a polar angle of 45⦠lead to more stable dynamics than real eigenvalues with the same absolute value. For the real eigenvalues we find a change from stable to unstable dynamics inside the unit circle, which is in line with the findings from the one dimensional case. In order to reproduce the decisions being made in individual time steps, simple models like an adaptive rule are often sufficient. In order to reproduce long run dynamics, we develop a two dimensional Heuristic Switching Model. We use this model to predict the stability of systems with eigenvalues which we did not test experimentally.
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Objective - Singkawang City, which is located in the Province of West Kalimantan-Indonesia, is a city with a significant level of ethnic heterogeneity, making it very vulnerable to various conflicts. However, in 2018, Singkawang City was named the most tolerant city in Indonesia through an assessment from the Setara Institute. For this reason, this study was conducted to analyze the political form of harmony and social capital, E-government as a Tolerant City in Singkawang City.Methodology â" The method used in this research is descriptive qualitative with literature study as a data collection method. Data analysis was carried out in stages, namely collecting data, summarizing data, and making conclusions. This study finds that the form of political harmony is the intense collaboration be-tween state actors, the Religious Harmony Forum, and the community.Findings â" Interaction and commu-nicative relationships complement and strengthen each other. As the main actor, the people of Singkawang City have also seen and understood that they have diverse perspectives to avoid discrimination and intolerance. In addition, there are also forms of social capital created from the relationship between ethnic communities in Singkawang City, namely in the form of general norms and group characteristics.Therefore, it is concluded that the success of the Singkawang City government in making its area the most tolerant city in Indonesia from the Setara Institute in 2018 cannot be separated from the social capital owned by each tribe to live side by side in harmony with high values. spirit of tolerance.Novelty â" In addition, e-government and knowledge management are also important points in the formation of a tolerant society in Singkawang City which has people from various backgrounds.Type of Paper - Review
arXiv
Financial markets tend to switch between various market regimes over time, making stationarity-based models unsustainable. We construct a regime-switching model independent of asset classes for risk-adjusted return predictions based on hidden Markov models. This framework can distinguish between market regimes in a wide range of financial markets such as the commodity, currency, stock, and fixed income market. The proposed method employs sticky features that directly affect the regime stickiness and thereby changing turnover levels. An investigation of our metric for risk-adjusted return predictions is conducted by analyzing daily financial market changes for almost twenty years. Empirical demonstrations of out-of-sample observations obtain an accurate detection of bull, bear, and high volatility periods, improving risk-adjusted returns while keeping a preferable turnover level.
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Options paying the product of put and or call option payouts at different strikes on two underlying assets are observed to synthesize joint densities and replicate differentiable functions of two underlying asset prices. The pricing of such options is undertaken from three perspectives. The first uses a geometric two dimensional Brownian motion model. The second inverts two dimensional characteristic functions. The third uses a bootstrapped physical measure to propose a risk charge minimizing hedge using options on the two underlying assets. The options are priced at the cost of the hedge plus the risk charge.
arXiv
We consider a generalization of the recursive utility model by adding a new component that represents utility of investment gains and losses. We also study the utility process in this generalized model with constant elasticity of intertemporal substitution and relative risk aversion degree, and with infinite time horizon. In a specific, finite-state Markovian setting, we prove that the utility process uniquely exists when the agent derives nonnegative gain-loss utility, and that it can be non-existent or non-unique otherwise. Moreover, we prove that the utility process, when it uniquely exists, can be computed by starting from any initial guess and applying the recursive equation that defines the utility process repeatedly. We then consider a portfolio selection problem with gain-loss utility and solve it by proving that the corresponding dynamic programming equation has a unique solution. Finally, we extend certain previous results to the case in which the state space is infinite.
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Separate account investors outperform category-matched mutual fund investors yearly by 14â"24 bps gross and 50â"75 bps net. That outperformance is entirely explained by differences in scale/size between these investors and by structural and reporting differences between the vehicles. Controlling for the average outperformance of separate accounts over their mutual fund twins, separate account investments perform at the same level as mutual fund investments, gross of fees, and slightly below them, net. Separate account investors choose products that, once adjusted, are more expensive than those chosen by mutual fund investors. Their fixed income picks do better whereas their equity picks do worse.
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Prior research posits that traders with short-lived information favor lit exchanges over dark pools due to execution certainty. This paper focuses on the relation between informed trading based on firm fundamentals and dark pool volume because the preferred venue for traders with longer-lived information is less certain. The proportion of trading volume executed in dark pools is positively correlated with short interest. This result is stronger for stocks that suffer from greater uncertainty and stocks targeted by transient institutional investors. Short sellers profit substantially from their information as subsequent returns are lower for heavily shorted stocks with greater dark pool volume.
arXiv
Unabated coal power in India must be phased out by mid-century to achieve global climate targets under the Paris Agreement. Here we estimate the costs of hybrid power plants - lithium-ion battery storage with wind and solar PV - to replace coal generation. We design least cost mixes of these technologies to supply baseload and load-following generation profiles in three Indian states - Karnataka, Gujarat, and Tamil Nadu. Our analysis shows that availability of low cost capital, solar PV installation costs of $<$\$300/kW, and battery storage capacity costs of $<$\$75/kWh will be required to phase out existing coal power plants. Phaseout by 2040 requires a 5% annual decline in the cost of hybrid systems over the next two decades. Solar PV is more suited to pairing with short duration storage than wind power. Our results describe the challenging technological and policy advances needed to achieve the goals of the Paris Agreement.
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We examine how domestic and foreign monetary policy affect the supply of bank credit when bank lending is denominated in domestic and foreign currencies, and how domestic macroprudential regulation shapes the transmission of foreign monetary spillovers. We use a country-level and a bank-level dataset from several European emerging economies on the lending activities of local banks broken down by currency denomination. We merge this information with indicators of monetary and macroprudential policy actions, and with bilateral trade linkages between countries. We document three main results. First, there exists a domestic currency composition channel of monetary policy: domestic monetary changes affect the share of lending in foreign currency in the domestic banking sector. Second, monetary shocks transmit across countries through international trade networks giving rise to an international bank lending channel in its currency dimension. Third, macroprudential policies enacted in home lending banking systems partly offset the spillover effects of monetary policies initiated abroad, suggesting an active role for macroprudential regulation in shielding the home economy from foreign shocks.
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The zero-earnings discontinuity in the United States disappeared around the time when the Sarbanes-Oxley Act (SOX) became effective, suggesting that SOX might have had a real and lasting impact on earnings management. In this research note, we examine a potential confounding effect of the dotcom boom at the turn of the millennium. Many firms that went public in this period had no sales revenues and therefore invariably incurred losses. When the stock market valuation was high, the losses scaled by the market value of equity often fell into the smallest loss interval, reducing the discontinuity in the overall sample. However, these observations do not indicate a decline in earnings management. We find that the dotcom effect is nonnegligible. When filtering out the firms without sales, our results no longer suggest a sharp decline in the zero-earnings discontinuity after SOX. Rather, our findings are consistent with a gradual decline in earnings management over time.
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Objective - Loan loss provision is an accrual for the banking industry, and therefore has a significant effect on bank accounting earnings and capital requirements. Previous studies showed inconsistent results for the relationship between earnings management, signaling, and loan loss provision. The difference in the results is thought to be caused by bank capitalization. Therefore, this study aims to investigate the role of bank capitalization on the effect of earnings management and signaling on loan loss provision.Methodology â" The sample consists of 86 conventional banks in Indonesia for the period of 2015-2019. Furthermore, this study used panel data analysis of multiple regression.Findings â" The results showed earnings management has no effect on loan loss provision. In contrast, signaling has a positive and significant effect. Although bank capitalization is not proven to weaken the effect of earnings management on loan loss provision, it strengthens the positive effect of signaling on loan loss provision.Novelty â" This study proves that bank capitalization has an important role in moderating signaling impact on loan loss provision but not for the effect of earnings management. This is due to the potential for earnings management in banks is relatively low because banks are highly regulated entities and with regulated governance mechanisms limit the managers' discretionary accounting decisions.Type of Paper - Empirical
arXiv
We compare three populations commonly used in experiments by economists and other social scientists: undergraduate students at a physical location (lab), Amazon's Mechanical Turk (MTurk), and Prolific. The comparison is made along three dimensions: the noise in the data due to inattention, the cost per observation, and the elasticity of response. We draw samples from each population, examining decisions in four one-shot games with varying tensions between the individual and socially efficient choices. When there is no tension, where individual and pro-social incentives coincide, noisy behavior accounts for 60% of the observations on MTurk, 19% on Prolific, and 14% for the lab. Taking costs into account, if noisy data is the only concern Prolific dominates from an inferential power point of view, combining relatively low noise with a cost per observation one fifth of the lab's. However, because the lab population is more sensitive to treatment, across our main PD game comparison the lab still outperforms both Prolific and MTurk.
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Using U.S. state-level labor protection law as an exogenous shock, we find that the adoption ofthe law alleviates the wages pressure on highly leveraged firms, primarily due to employeesâimproved job security offered by the law. Particularly, our finding is more pronounced forfinancially constrained/distressed firms, and for firms whose employees have strong bargainingpower or face high unemployment risk. Overall, our work offers a new perspective towardsunderstanding the relation between leverage and wages, showing a positive side of the laborprotection law in lowering labor costs for highly leveraged firms.
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In the presence of strong minority shareholder protection mechanisms, shareholders may become placid and even inattentive with regards to their firmsâ nonoperating activities in the hope that those mechanisms serve to assure agentsâ incentive compatibility. Relying on a cross-country panel database, the paper evidences that a stronger minority shareholder protection at the country level is associated with larger firm-level long-term nonoperating investments, higher share of nonoperating investment incomes in the firmsâ bottom line, and relatively larger acquisitions. At the company level, the implementation of stronger minority shareholder protection mechanisms, like internal policies favoring shareholder engagement and equality of voting rights, is documented to result in higher reliance on nonoperating incomes, larger acquisitions, higher cash reserves and lower dividend payouts. At the same time, the atomization of shareholder base appears to reduce the effectiveness of nonoperating activities as the firms with a larger shareholder base tend to hold more long-term investments while concomitantly recording lower investment incomes. In contrast, higher shareholder diversification is shown to be associated with lower cash holdings and higher dividend payouts. Overall, while being attentive to cash management and dividend policies, minority shareholders seem to be excessively forbearing with regards to the use of the bulk of nonoperating assets.
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The study covers several aspects of FDI in the country, ranging from FDI patterns and FDIdrivers to FDI relations, growth and exports, taking into account several factors such as theformation of raw equities, macroeconomic stability, institutional capital and human capital.In recent years, the FDI has increased so greatly that it has surpassed all other metrics ofeconomic transactions. Countries are bidding for the highest levels of FDI, as they are thecheapest foreign funding. The FDI rate has increased to the developed countries in the lasttwo decades, compared to the previous trend. There may have been a surprising rise of Asiaas big FDI recipients. In the 2014 industry review, the highest FDI for the service sector wasfound. In the fields of training, accounting, infrastructure and telecoms, most of the FDIinflows are generated. The self-employed industries authorize government investments inchemical, metallurgical, automobile, Pharmaceutical and tourism sectors. The main recipientis FDI, but FDI flows are subject to policy constraints. Despite the lack of restrictions onFDI inflows in metallurgical, chemical, automotive, pharmaceutical and tourism industries,FDI growth in those sectors was much lower than in the FDI markets for utilities andtelecoms.The study focuses on the impact of Indian Economic Factors on Indian foreigndirect investment.
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Technological advancement in the maritime industry is progressing in a rapid pace and needs continuous development of seafarerâs education to cope with this development. In the area of automation, unmanned shipping is expected to offer more effective way of moving freight in a way that reduce costs and accidents caused by human errors ,moreover the autonomous operations supports the transition towards sustainable and eco- friendly transportation MO is reviewing the existing IMO adopted conventions to perceive how they may apply to ships with differing levels of automation , to establish a regulatory framework for Maritime Autonomous Surface Ships (MASS).The current version of STCW 1978 (as amended) has 19 competence themes consisting of 66 Knowledge, Understanding & Proficiency items (KUPs), which specifies the minimum standard of competence for officers in charge of a navigational watch on ships of 500 gross tonnage or more ,The curriculums in maritime education and training (MET) university education must react and adjust to the upcoming changes within the maritime industry which demand raising the awareness among all industry stakeholders on the essential Competencies for Autonomous Maritime Operation. This paper will review the readiness of the existing STCW framework for the implementation of the different degrees of the Autonomous vessels, further to investigate the seafarer training needs for Operating Autonomous Ships, in particular degree 1 and degree 2 Autonomous Ships.
arXiv
Consider a financial market with nonnegative semimartingales which does not need to have a num\'{e}raire. We are interested in the absence of arbitrage in the sense that no self-financing portfolio gives rise to arbitrage opportunities, where we are allowed to add a savings account to the market. We will prove that in this sense the market is free of arbitrage if and only if there exists an equivalent local martingale deflator which is a multiplicative special semimartingale. In this case, the additional savings account relates to the finite variation part of the multiplicative decomposition of the deflator.
arXiv
The Chinese stock market experienced an abrupt crash in 2015 and over one-third of its market value evaporated. Given its associations with fear and fine resolutions in frequency, the illiquidity of stocks may offer a promising perspective for understanding and even signaling a market crash. In this study, by connecting stocks with illiquidity comovements, an illiquidity network is established to model the market. Compared to noncrash days, on crash days the market is more densely connected due to heavier but more homogeneous illiquidity dependencies that facilitate abrupt collapses. Critical stocks in the illiquidity network, particularly those in the finance sector, are targeted for inspection because of their crucial roles in taking over and passing on the losses of illiquidity. The cascading failures of stocks in market crashes are profiled as disseminating from small degrees to high degrees that are usually located in the core of the illiquidity network and then back to the periphery. By counting the days with random failures in the previous five days, an early signal is implemented to successfully predict more than half of the crash days, especially consecutive days in the early phase. Additional evidence from both the Granger causality network and the random network further testifies to the robustness of the signal. Our results would help market practitioners like regulators detect and prevent the risk of crashes in advance.
arXiv
Optimal transport has become part of the standard quantitative economics toolbox. It is the framework of choice to describe models of matching with transfers, but beyond that, it allows to: extend quantile regression; identify discrete choice models; provide new algorithms for computing the random coefficient logit model; and generalize the gravity model in trade. This paper offer a brief review of the basics of the theory, its applications to economics, and some extensions.
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The volatility of betting-against-beta (BAB) and -idiosyncratic volatility (BAV) factors negatively forecasts their respective Sharpe ratios and abnormal returns. This predictability causes signicant performance gains from volatility timing these factors and provides new time-series evidence on leading theories of the low-risk anomaly. Consistent with the limits-to-arbitrage theory, we show that the abnormal returns of the volatility-managed BAV strategy are concentrated in overpriced stocks. However, controlling for mispricing, arbitrage risk, lottery demand, and multiple risk factors has no effect on the timing benefits of BAB. We further show that the leverage constraints model predicts a counterfactual positive relation between volatility and subsequent BAB Sharpe ratios, and highly active institutions shift from high- to low-beta stocks as volatility increases, suggesting their demand contributes to the abnormal returns of BAB. Overall, the predictive power of volatility challengesour current understanding of the low-risk anomaly.
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The Global Financial Crisis creates liquidity shocks, banks failures and a global economic downturn while led to significant supervisory and regulatory reforms affecting banks efficiency performance. We offer new insights investigating overall inefficiency and by decomposing into transient and persistent components. Using a panel data on 5698 US banks during the period 2010-2016, this study incorporates the measure of both transient and persistent production and cost inefficiency. We find that persistent efficiency seems to be more important for both specifications, as it accounts for 32\% (for the production) and 39\% (for the cost function) of the inefficiency comparing with the transient part. We argue that most of banks faced increasing returns to scale in production and cost, independently from ownership and size dimension.
arXiv
This paper formulates and solves the optimal stopping problem for a loan made to one's self from a tax-advantaged retirement account such as a 401(k), 403(b), or 457(b) plan. If the plan participant has access to an external asset with a higher expected rate of return than the investment funds and indices that are available within the retirement account, then he must decide how long to wait before exercising the loan option. On the one hand, taking the loan quickly will result in many years of exponential capital growth at the higher (external) rate; on the other hand, if we wait to accumulate more funds in the 457(b), then we can make a larger deposit into the external asset (albeit for a shorter period of time). I derive a variety of cutoff rules for optimal loan control; in general, the investor must wait until he accumulates a certain amount of money (measured in contribution-years) that depends on the disparate yields, the loan parameters, and the date certain at which he will liquidate the retirement account. Letting the horizon tend to infinity, the optimal (horizon-free) policy gains in elegance, simplicity, and practical robustness to different life outcomes. When asset prices and returns are stochastic, the (continuous time) cutoff rule turns into a "wait region," whereby the mean of terminal wealth is rising and the variance of terminal wealth is falling. After his sojourn through the wait region is over, the participant finds himself on the mean-variance frontier, at which point his subsequent behavior is a matter of personal risk preference.
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I estimate welfare benefits of eliminating idiosyncratic consumption shocks unrelated to the business cycle as 47.3% of household utility and benefits of eliminating idiosyncratic shocks related to the business cycle as 3.4% of utility. Estimates of the former substantially exceed earlier ones because I distinguish between idiosyncratic shocks related/unrelated to the business cycle, estimate the negative skewness of shocks, target moments of idiosyncratic shocks from household-level CEX data, and target market moments. Benefits of eliminating aggregate shocks are 7.7% of utility. Policy should focus on insuring idiosyncratic shocks unrelated to the business cycle, such as the death of a householdâs prime wage earner and job layoffs not necessarily related to recessions.