Research articles for the 2021-02-17
RePEC
The size and the leverage of financial market investors and the elasticity of demand of unlevered investors define MinMaSS, the smallest market size that can support a given degree of leverage. The financial system's potential for financial crises can be measured by the stability ratio, the fraction of total market size to MinMaSS. We use that financial stability metric to gauge the buildup of vulnerability in the run-up to the 1998 Long-Term Capital Management crisis and argue that policymakers could have detected the potential for the crisis.
arXiv
Improving road safety and setting targets for reducing traffic-related crashes and deaths are highlighted as part of the United Nation's sustainable development goals and vision zero efforts around the globe. The advent of transportation network companies, such as ridesourcing, expands mobility options in cities and may impact road safety outcomes. In this study, we analyze the effects of ridesourcing use on road crashes, injuries, fatalities, and driving while intoxicated (DWI) offenses in Travis County Texas. Our approach leverages real-time ridesourcing volume to explain variation in road safety outcomes. Spatial panel data models with fixed effects are deployed to examine whether the use of ridesourcing is significantly associated with road crashes and other safety metrics. Our results suggest that for a 10% increase in ridesourcing trips, we expect a 0.12% decrease in road crashes (p<0.05), a 0.25% decrease in road injuries (p<0.001), and a 0.36% decrease in DWI offenses (p<0.0001) in Travis County. Ridesourcing use is not associated with road fatalities at a 0.05 significance level. This study augments existing work because it moves beyond binary indicators of ridesourcing presence or absence and analyzes patterns within an urbanized area rather than metropolitan-level variation. Contributions include developing a data-rich approach for assessing the impacts of ridesourcing use on our transportation system's safety, which may serve as a template for future analyses of other US cities. Our findings provide feedback to policymakers by clarifying associations between ridesourcing use and traffic safety, while helping identify sets of actions to achieve safer and more efficient shared mobility systems.
SSRN
How do credit-constrained communities cope with the financial consequences of environmental crises? Beginning in April 2014, the residents of Flint, Michigan, were exposed to lead-contaminated water resulting from a series of governmental missteps. In this paper, we use the spatial distribution of lead and galvanized pipes in Flint to study the effect of the crisis on households’ financial health, including loan balances, repayment of outstanding debt, and Equifax Risk Scores, as well as on household mobility. We find that relatively more affected households, as measured by exposure to lead pipes, experienced a modest increase in the balance and frequency of past due loans. Equifax Risk Scores declined slightly on average, but more so at the bottom of the Risk Score distribution. In addition, we find that there was no effect on mobility out of the state or county, but that more affected households were more likely to move within the city when the crisis began, away from lead-pipe-dense areas.
arXiv
Market by order (MBO) data - a detailed feed of individual trade instructions for a given stock on an exchange - is arguably one of the most granular sources of microstructure information. While limit order books (LOBs) are implicitly derived from it, MBO data is largely neglected by current academic literature which focuses primarily on LOB modelling. In this paper, we demonstrate the utility of MBO data for forecasting high-frequency price movements, providing an orthogonal source of information to LOB snapshots. We provide the first predictive analysis on MBO data by carefully introducing the data structure and presenting a specific normalisation scheme to consider level information in order books and to allow model training with multiple instruments. Through forecasting experiments using deep neural networks, we show that while MBO-driven and LOB-driven models individually provide similar performance, ensembles of the two can lead to improvements in forecasting accuracy -- indicating that MBO data is additive to LOB-based features.
SSRN
Two key factors have reinforced attention to shareholder stewardship as one of the pillars of corporate governance: (1) the rise of a small group of investment (asset) managers with an enormous potential to influence corporate decision-making and (2) the growing voice of activist shareholders. Nevertheless, doubts remain over their ability to be effective monitors. Weak incentives to invest in shareholder oversight and limited resources confine voting and engagement by large investment managers. Shareholder activists, on the other hand, have been repeatedly criticized for giving priority to short-term gains. According to an influential argument, complementary efforts of both offer a solution: activist campaigns can supply institutional investors with company-specific information; activists, in turn, need to align their demands with the long-term preferences of institutional investors to secure their votes. This article advances a thesis that such interaction between large investment managers and activist shareholders is unlikely to happen and offers empirical evidence and illustrative case studies largely supporting this prediction. The theoretical analysis shows that large investment managers and hedge fund activists â€" the most prominent group of activist shareholders â€" have different visions of stewardship which undermine their possible interactions. Several factors come together to shape the stewardship vision of large investment managers where general corporate governance and sustainability themes have priority over the traditional topics of activist demands targeting business and operating matters. Consistent with the different visions of stewardship, voting and activism data from the FTSE 350 companies show that associations between activist demands and the voting behavior of the top investment managers vary based on activist types and demand topics. Activist demands initiated by hedge funds and on business and operating matters receive less support. Importantly, differences between managers of fund groups with predominantly active or passive indexing strategies in the likelihood of opposing corporate managers in activist targeted firms are minimal. These findings offer better understanding of stewardship and have important practical implications for building regulatory frameworks for effective stewardship.
arXiv
With today's technological advancements, mobile phones and wearable devices have become extensions of an increasingly diffused and smart digital infrastructure. In this paper, we examine mobile health (mHealth) platforms and their health and economic impacts on the outcomes of chronic disease patients. We partnered with a major mHealth firm that provides one of the largest mHealth apps in Asia specializing in diabetes care. We designed a randomized field experiment based on detailed patient health activities (e.g., exercises, sleep, food intake) and blood glucose values from 1,070 diabetes patients over several months. We find the adoption of the mHealth app leads to an improvement in health behavior, which leads to both short term metrics (reduction in patients' blood glucose and glycated hemoglobin levels) and longer-term metrics (hospital visits and medical expenses). Patients who adopted the mHealth app undertook more exercise, consumed healthier food, walked more steps and slept for longer times. They also were more likely to substitute offline visits with telehealth. A comparison of mobile vs. PC version of the same app demonstrates that mobile has a stronger effect than PC in helping patients make these behavioral modifications with respect to diet, exercise and lifestyle, which leads to an improvement in their healthcare outcomes. We also compared outcomes when the platform facilitates personalized health reminders to patients vs. generic reminders. Surprisingly, we find personalized mobile messages with patient-specific guidance can have an inadvertent (smaller) effect on patient app engagement and lifestyle changes, leading to a lower health improvement. However, they are more like to encourage a substitution of offline visits by telehealth. Overall, our findings indicate the massive potential of mHealth technologies and platform design in achieving better healthcare outcomes.
arXiv
This paper proposes a framework to model how a country develops its economy by endogenous structural transformation and efficient resource allocation in a market mechanism. To achieve this goal, the paper first summarizes three attributes of economic structures from the literature, namely, structurality, durationality, and transformality, and discuss their implications for methods of economic modeling. Then, with the common knowledge assumption, the paper studies a Ramsey growth model with endogenous structural transformation in which the social planner chooses the optimal industrial structure, recource allocation with the chosen structure, and consumption to maximize the representative household's total utility subject to the resource constraint. The paper next establishes the mathematical underpinning of the static, dynamic, and structural equilibria. The Ramsey growth model and its equilibria are then extended to economies with complicated economic structures consisting of hierarchical production, composite consumption, technology adoption and innovation, infrastructure, and economic and political institutions. The paper concludes with a brief discussion of applications of the proposed methodology to economic development problems in other scenarios.
SSRN
Using new data on S&P 1500 firms’ CEO-to-employee pay ratios disclosed by mandate of Section 953(b) of the Dodd-Frank Act, we examine the effect of within-firm pay inequality on bond yield spreads. We find a significant negative relation between industry-adjusted CEO-to-employee pay ratio and yield spreads while controlling for covariates and endogeneity. This result is the strongest in financially constrained, labor intensive, and small-to-medium sized firms. The evidence supports the incentive-provision explanation of CEO-to-employee pay disparity, reflecting efficient CEO compensation rather than rent extraction. We also document selection bias in self-reported pay ratios, highlighting the efficacy of the Dodd-Frank provisions.
arXiv
The Group Quantization formalism is a scheme for constructing a functional space that is an irreducible infinite dimensional representation of the Lie algebra belonging to a dynamical symmetry group. We apply this formalism to the construction of functional space and operators for quadratic potentials -- gaussian pricing kernels in finance. We describe the Black-Scholes theory, the Ho-Lee interest rate model and the Euclidean repulsive and attractive oscillators. The symmetry group used in this work has the structure of a principal bundle with base (dynamical) group a semi-direct extension of the Heisenberg-Weyl group by SL(2,R), and structure group (fiber) the positive real line.
SSRN
We document the heterogeneous effects of turnover on mutual fund performance, which help explain the weak cross-sectional turnover-performance relations reported in existing studies. For funds skilled in exploiting short-term investment opportunities, there is a positive empirical relation between turnover and performance. For unskilled funds, the relation turns negative. As a result, performance persistence is stronger among funds with higher turnover. Further, we find that the heterogeneous effects of turnover on performance are not driven by liquidity premium or trade execution skills, but rather due to substantial dispersion in short-term stock selection information.
SSRN
Online businesses have been surging worldwide during the past decade, especially during the recent COVID-19 epidemic. However, the market share of online real estate transactions is still limited, mainly due to the information-asymmetry problem. In this study, we manually collect data on online judicial housing auctions in China, which is currently the largest online real estate market globally, and investigate how online information disclosure helps facilitate online real estate transactions. The empirical results suggest a higher quality of online information disclosure can significantly help attract potential buyers. In particular, providing more comprehensive information such as professional appraisal reports or videos can help convert buyers’ initial interests to actual transaction participation, leading to higher success probabilities and transaction prices. Such an information effect is particularly strong when combined with offline services, or in a more mature online market, or for lower-value properties. We also provide a preliminary analysis of factors affecting online-information-disclosure quality. The findings provide evidence on the feasibility of online real estate transactions and deliver suggestions for the future development of the emerging online real estate market.
SSRN
We utilize the exogenous intertemporal variation in employment protection across countries and study the impact of employment protection on international equity markets. We find robust evidence that firms located in countries with weak labor protection regulation exhibit a low level of one-year-ahead stock price crash risk relative to those in countries with strong labor protection regulation. It is consistent with the view that, when employees face material unemployment risk, they are more incentivized to engage in information search and analysis, thereby curtailing the effectiveness of managerial bad-news-hoarding activities (i.e., the protection heightening risk hypothesis). Our finding is robust to a battery of sensitivity tests. Further evidence shows that the impact of employment protection on crash risk is more pronounced for firms with a higher proclivity to suppress bad news and for firms in countries with stronger legal enforcement. Our findings have important implications for policymakers concerning equity extreme risk and labor protection around the globe.
SSRN
We investigate the causal relationship between the efficiency of country’s judicial system and the quality of bank lending, using the contracts enforcement reforms that have been implemented in four European countries as a quasi-natural experiment. We find that strengthening of contract enforcement determines large, significant, and persistent reductions of banks’ non-performing-loans (NPLs). Our results have important policy implications: they point at judicial efficiency as a key determinant of the stability of the banking sector and its resilience to negative shocks such as the recent COVID-19 pandemic.
arXiv
This work aims to analyse the predictability of price movements of cryptocurrencies on both hourly and daily data observed from January 2017 to January 2021, using deep learning algorithms. For our experiments, we used three sets of features: technical, trading and social media indicators, considering a restricted model of only technical indicators and an unrestricted model with technical, trading and social media indicators. We verified whether the consideration of trading and social media indicators, along with the classic technical variables (such as price's returns), leads to a significative improvement in the prediction of cryptocurrencies price's changes. We conducted the study on the two highest cryptocurrencies in volume and value (at the time of the study): Bitcoin and Ethereum. We implemented four different machine learning algorithms typically used in time-series classification problems: Multi Layers Perceptron (MLP), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) neural network and Attention Long Short Term Memory (ALSTM). We devised the experiments using the advanced bootstrap technique to consider the variance problem on test samples, which allowed us to evaluate a more reliable estimate of the model's performance. Furthermore, the Grid Search technique was used to find the best hyperparameters values for each implemented algorithm. The study shows that, based on the hourly frequency results, the unrestricted model outperforms the restricted one. The addition of the trading indicators to the classic technical indicators improves the accuracy of Bitcoin and Ethereum price's changes prediction, with an increase of accuracy from a range of 51-55% for the restricted model, to 67-84% for the unrestricted model.
SSRN
We explore the economics and optimal design of “permissioned†distributed ledger technology (DLT) in a credit economy. Designated validators verify transactions and update the ledger at a cost that is derived from a supermajority voting rule, thus giving rise to a public good provision game. Without giving proper incentives to validators, however, their records cannot be trusted because they cannot commit to verifying trades and they can accept bribes to incorrectly validate histories. Both frictions challenge the integrity of the ledger on which credit transactions rely. In this context, we examine the conditions under which the process of permissioned validation supports decentralized exchange as an equilibrium, and analyze the optimal design of the trade and validation mechanisms. We solve for the optimal fees, number of validators, supermajority threshold and transaction size. A stronger consensus mechanism requires higher rents be paid to validators. Our results suggest that a centralized ledger is likely to be superior, unless weaknesses in the rule of law and contract enforcement necessitate a decentralized ledger.
arXiv
We consider a stochastic game between a trader and a central bank in a target zone market with a lower currency peg. This currency peg is maintained by the central bank through the generation of permanent price impact, thereby aggregating an ever increasing risky position in foreign reserves. We describe this situation mathematically by means of two coupled singular control problems, where the common singularity arises from a local time along a random curve. Our first result identifies a certain local time as that central bank strategy for which this risk position is minimized. We then consider the worst-case situation the central bank may face by identifying that strategy of the strategic investor that maximizes the expected inventory of the central bank under a cost criterion, thus establishing a Stackelberg equilibrium in our model.
SSRN
Survey data are ubiquitous in the economics literature, ranging from unemployment rate and CPI to surveys of professional forecasts and consumer finances. However, their potential biases are rarely discussed. By randomizing the order of responses to the questions in an economic survey, we document a pervasive response order bias: respondents tend to select answers at or near the top of the lists, leading to a systematic bias in survey results. This bias is smaller when respondents are more certain about their answers and disappears for “objective†questions (e.g., questions on demographics and recent experiences). Our evidence directly shows the bias in the levels of many survey-based variables and indirectly implies a bias in their changes, the latter likely correlated with the uncertainty in the economy. To assess bias magnitude, we examine two salient features at the time of our survey: the COVID pandemic and the approaching 2020 presidential election. We find that respondents’ expectations are shaped by their political leanings and personal experience during the pandemic: when forecasting stock returns, GDP growth, or COVID vaccine development, respondents are more pessimistic if they lean Democratic or personally know someone with COVID, but are more optimistic if they expect their preferred presidential candidate to win. The magnitude of these effects is comparable to the size of the response order bias. The implications of our evidence for financial services, public health policy, and political elections are discussed.
SSRN
Extant literature finds insignificant abnormal returns around shareholder meetings. We verify those findings but show that option implied volatility gradually declines by about 1.04 percent between record and meeting dates and then by about 0.30 percent right after annual meetings. These declines occur even if meetings do not have shareholder proposals or close votes. The post-meeting decline is more pronounced for meetings with close-call shareholder proposals. Our evidence indicates that investors anticipate meeting outcomes to affect stock prices and that shareholder proposals are consequential but have heterogeneous value implications.
SSRN
We evaluate the role of foreign short-sale restrictions in muting the full return-response following negative earnings surprises for stocks cross-listed in unbanned markets. We update the global timeline of short-sale restrictions until the COVID-19 crisis period. Instead of regulatory price arbitrage, we surprisingly observe cross-border reach of bans manifested in a delayed price response, accompanied by a reduction in short interest and failures to deliver. Nonetheless, large profit opportunities result in price arbitrage and full return-response. Analysis of earnings management practices and CEO compensation structure reinforces the role of the profit opportunity in moderating the effects of short-sale restrictions.
SSRN
While incorporating the firm’s parent company in a tax haven offers tax savings, it also imposes risks. We predict and find a higher cost of equity capital in firms with parent companies that are incorporated in tax havens. We find that the observed cost of equity premium is enhanced by tax risk, firm-level information risk, and country-level legal risk. We also employ corporate inversions in a difference-in-differences test and again find a positive relation between tax haven parent incorporation and the cost of capital. Our study contributes to the literatures on valuation of tax haven use, tax and nontax costs of corporate strategies, corporate inversions, and the relation between taxes and the cost of capital.
SSRN
This paper studies the sensitivity of investment in apartment building maintenance to building debt levels. I use a novel data set combining housing code violations from 45 US cities with apartment financing information to show that highly leveraged buildings tend to incur more code violations. I then exploit a natural experiment that effectively increased building leverage for some New York City rent stabilized buildings, but not others. Following the shock, violations increased for affected buildings relative to unaffected buildings. This change in violations was concentrated among more highly leveraged buildings. The results are consistent with a theory that debt reduces investment in maintenance.
SSRN
Financial sophistication does not uniformly impact home ownership decisions. Sophisticated households are less likely to pay too high a mortgage rate and more likely to refinance when financially advantageous to do so but more likely to over pay for a house and less likely to default when underwater. We argue purchasing a home or defaulting are emotional decisions while deciding on mortgage terms or to refinance are analytical decisions amenable to the analyses of sophisticated households. Consistent with this, households learn over time to make better mortgage rate and refinancing decisions but not better house purchase price decisions.
arXiv
We study to what extent the Bitcoin blockchain security permanently depends on the underlying distribution of cryptocurrency market outcomes. We use daily blockchain and Bitcoin data for 2014-2019 and employ the ARDL approach. We test three equilibrium hypotheses: (i) sensitivity of the Bitcoin blockchain to mining reward; (ii) security outcomes of the Bitcoin blockchain and the proof-of-work cost; and (iii) the speed of adjustment of the Bitcoin blockchain security to deviations from the equilibrium path. Our results suggest that the Bitcoin price and mining rewards are intrinsically linked to Bitcoin security outcomes. The Bitcoin blockchain security's dependency on mining costs is geographically differenced - it is more significant for the global mining leader China than for other world regions. After input or output price shocks, the Bitcoin blockchain security reverts to its equilibrium security level.
SSRN
Cryptomining, the clearing of cryptocurrency transactions, uses large quantities of electricity. We document that cryptominers' use of local electricity implies higher prices for existing small businesses and households. Studying the electricity market in Upstate NY and using the Bitcoin price as an exogenous shifter of the supply curve faced by the community, we estimate that small businesses and households have a negative elasticity to the instrumented price of electricity, with elasticities of -0.17 and -0.07 respectively. Using our estimations, we calculate counterfactual electricity bills, finding that small businesses and households paid $79 million and $165 million extra annually in Upstate NY (or $1B nationally) because of cryptomining demand-for-electricity effects. Using data on China, where prices are fixed, we find that rationing of electricity in cities with cryptomining entrants deteriorate wages and investments, consistent with an electricity crowding out of the local economy. Local governments in both Upstate NY and China, however, realize more business taxes, but only offsetting a small portion of the higher community electricity bill costs. Our results point to a yet-unstudied negative spillover from technology processing to local communities, which would need to be considered against welfare benefits.
SSRN
This paper estimates mutual funds’ preferences for governance structures, using data on proxy vote records. I elicit funds' revealed preferences by studying the differences in their votes on the same issue across their portfolio firms’ shareholder proposals, and develop funds' preference rankings by implementing the Metropolis-Hastings Markov chain Monte Carlo algorithm. Funds prefer firms with low board independence, high insider ownership, and high abnormal compensation to adopt certain governance provisions that increase shareholder rights. Contrary to the view that the net benefits of takeover defenses are higher for young and small firms, funds are not enthusiastic about large and mature firms increasing shareholder rights. Large and mature firms are disproportionately targeted by shareholder proposals, suggesting the possibility that investors vote down worthless proposals submitted to such firms. I find a mixed relation between fund preferences and firm performance. Active and passive funds have similar preferences. Fund preferences are moderately correlated with overall vote support on relevant shareholder proposals.
