# Research articles for the 2020-03-23

SSRN

The COVID-19 pandemic is a major economic shock. With no comparable event having occurred since the 1918 Spanish Flu, and a rapidly changing public health situation, economic policy makers are largely having to improvise their responses. This note assesses the scale of this large shock, suggesting that it is about 10% of global GDP so around five times as large as the credit and liquidity problems that created the global financial crisis of 2007-2008. It then examines the economic policy response, suggesting that to be as effective as possible the response must address two distinct objectives: offset the deflationary economic shock and provide targeted support to those most directly affected. It proposes a surprisingly inexpensive framework â€" â€œretrospective insuranceâ€ at a net additional cost only 2% of GDP â€" that can guide the allocation of public resources to ensure no business goes under as a result of the pandemic and all who merit protection get support.

SSRN

Textual analysis of 14,270 NBER Working Papers published during 1999â€"2016 is done to assess the effects of the 2008 crisis on the economics literature. The volume of crisis-related WPs is counter-cyclical, lagging the financial-instability-index. WPs by the Monetary-Economics, Asset-Pricing, and Corporate-Finance program members, hardly refer to â€œcrisis/crisesâ€ in the pre-crisis period. As the crisis develops, however, their study-efforts of crisis-related issues increase rapidly. In contrast, WPs in macroeconomics-related programs refer quite extensively in the pre-crisis period to â€œcrisis/crisesâ€ and to crises-related topics. Overall, our findings are consistent with the claim that economists were not engaged sufficiently in crises studies before the 2008 crisis. However, counter to the popular image, as soon as the crisis began to unravel, the NBER affiliated economists responded dramatically by switching their focus and efforts to studying and understanding the crisis, its causes and its consequences.

arXiv

While standard estimation assumes that all datapoints are from probability distribution of the same fixed parameters $\theta$, we will focus on maximum likelihood (ML) adaptive estimation for nonstationary time series: separately estimating parameters $\theta_T$ for each time $T$ based on the earlier values $(x_t)_{t<T}$ using (exponential) moving ML estimator $\theta_T=\arg\max_\theta l_T$ for $l_T=\sum_{t<T} \eta^{T-t} \ln(\rho_\theta (x_t))$ and some $\eta\in(0,1]$. Computational cost of such moving estimator is generally much higher as we need to optimize log-likelihood multiple times, however, in many cases it can be made inexpensive thanks to dependencies. We focus on such example: $\rho(x)\propto \exp(-|(x-\mu)/\sigma|^\kappa/\kappa)$ exponential power distribution (EPD) family, which covers wide range of tail behavior like Gaussian ($\kappa=2$) or Laplace ($\kappa=1$) distribution. It is also convenient for such adaptive estimation of scale parameter $\sigma$ as its standard ML estimation is $\sigma^\kappa$ being average $\|x-\mu\|^\kappa$. By just replacing average with exponential moving average: $(\sigma_{T+1})^\kappa=\eta(\sigma_T)^\kappa +(1-\eta)|x_T-\mu|^\kappa$ we can inexpensively make it adaptive. It is tested on daily log-return series for DJIA companies, leading to essentially better log-likelihoods than standard (static) estimation, with optimal $\kappa$ tails types varying between companies. Presented general alternative estimation philosophy provides tools which might be useful for building better models for analysis of nonstationary time-series.

arXiv

In financial automatic feature construction task, genetic programming (GP) is the state-of-the-art technique. It employs reverse polish expression to represent features and then simulate the evolution process. However, with the development of deep learning, more choices to design this algorithm are available. This paper proposes Alpha Discovery Neural Network (ADNN), equipped with different kinds of feature extractors to construct diversified financial technical factors based on prior knowledge. The experiment result shows that both fully-connected network and recurrent network are good at extracting information from financial time series, but convolution network structure can not effectively extract this information. ADNN effectively enrich the current factor pool because in all cases, ADNN can construct more informative and diversified features than GP. Moreover, features constructed by ADNN can always improve original strategy return, Sharpe ratio and max draw-down.

arXiv

This paper investigates a financial market where stock returns depend on a hidden Gaussian mean reverting drift process. Information on the drift is obtained from returns and expert opinions in the form of noisy signals about the current state of the drift arriving at the jump times of a homogeneous Poisson process. Drift estimates are based on Kalman filter techniques and described by the conditional mean and covariance matrix of the drift given the observations. We study the filter asymptotics for increasing arrival intensity of expert opinions and prove that the conditional mean is a consistent drift estimator, it converges in the mean-square sense to the hidden drift. Thus, in the limit as the arrival intensity goes to infinity investors have full information about the drift.

arXiv

Given the importance of public support for policy change and implementation, public policymakers and researchers have attempted to understand the factors associated with this support for climate change mitigation policy. In this article, we compare the feasibility of using different supervised learning methods for regression using a novel socio-economic data set which measures public support for potential climate change mitigation policies. Following this model selection, we utilize gradient boosting regression, a well-known technique in the machine learning community, but relatively uncommon in public policy and public opinion research, and seek to understand what factors among the several examined in previous studies are most central to shaping public support for mitigation policies in climate change studies. The use of this method provides novel insights into the most important factors for public support for climate change mitigation policies. Using national survey data, we find that the perceived risks associated with climate change are more decisive for shaping public support for policy options promoting renewable energy and regulating pollutants. However, we observe a very different behavior related to public support for increasing the use of nuclear energy where climate change risk perception is no longer the sole decisive feature. Our findings indicate that public support for renewable energy is inherently different from that for nuclear energy reliance with the risk perception of climate change, dominant for the former, playing a subdued role for the latter.

arXiv

On January 3, 2018 MiFID II regulations came into effect. This paper compares properties of European stocks for 2017 and 2018. The introduced tick size regime impacted the microstructure in accordance with existing literature on tick size changes. Remarkably, the modification of the microstructure also impacted volatility and transacted volume. Furthermore, it is shown that closing auction volumes increased heavily since MiFID II, leading to higher absolute returns in the auctions. Before MiFID II, high closing auction returns reverted overnight, but after MiFID II this reversion disappeared, showing that closing prices became more efficient.

arXiv

What is the best market-neutral implementation of classical Equity Factors? Should one use the specific predictability of the short-leg to build a zero beta Long-Short portfolio, in spite of the specific costs associated to shorting, or is it preferable to ban the shorts and hedge the long-leg with -- say -- an index future? We revisit this question by focusing on the relative predictability of the two legs, the issue of diversification, and various sources of costs. Our conclusion is that, using the same Factors, a Long-Short implementation leads to superior risk-adjusted returns than its Hedged Long-Only counterpart, at least when Assets Under Management are not too large.

SSRN

How does international trade affect the popularity of governments and leaders? The recent backlash against globalization renders this question extremely topical. Yet, most previous work has looked for political effects of aggregate trade flows without decomposing into particular types of products. We provide the first large-scale, global evidence that trade shocks affect political approval and show that what matters is the match between workers' skills and the characteristics of goods traded. Using a unique data set including 118 countries, we show that growth in high skill intensive exports increases approval of incumbents among skilled individuals. Growth in high skill intensive imports has the opposite effect. High skill intensive trade has no discernible effect on the unskilled. To identify exogenous variation, we exploit the time-varying effects of air and sea distances on bilateral trade flows. Our findings help explain responses to trade of economic elites in developing and middle income countries.

SSRN

Everyone is talking about the Coronavirus (a.k.a. â€œCOVID-19â€). What began as a local health situation in China has swiftly become a global epidemic, causing havoc and mayhem all around the world. The damages caused by the virus are overwhelming and will, in all likelihood, have long-term implications for the global economy. Naturally, the traditional financial markets are responding negatively to the news about the virus and share prices are nosediving. However, there are other implications of this crisis, which are perhaps less obvious but equally important. One such implication relates to the cryptocurrency market. In fact, this crisis provides us with the first opportunity to investigate an intriguing question: How does a global crisis, such as the one caused by the Coronavirus, affect cryptocurrencies? On the one hand, cryptocurrencies are supported by a decentralized mechanism, which is independent of governmental functions and available anywhere in the world. Thus, people may respond to the threat of global instability by switching from traditional currencies to cryptocurrencies. On the other hand, cryptocurrencies may be both tightly related to economic activity and, due to lack of sufficient regulation, subject to manipulations by sophisticated investors, so that they cannot escape the fate of traditional markets. Analyzing data on the top 100 cryptocurrencies in the market, we find that the inflow of identified Coronavirus cases is, on average, positively associated with the market cap and trade volume of cryptocurrencies. However, we also find that an opposite trend emerged once Coronavirus cases began to accumulate, eventually leading to a decline in the cryptomarket. Given our findings, we discuss insights on how one can improve the regulation of cryptocurrencies to account also for times of crisis.

arXiv

Automated market makers, first popularized by Hanson's Logarithmic Market Scoring Rule for prediction markets, have become important building blocks (often called 'primitives') for decentralized finance. A particularly useful primitive is the ability to measure the price of an asset, a problem often known as the pricing oracle problem. In this paper, we focus on the analysis of a very large class of automated market makers, called constant function market makers, which includes popular market makers such as Uniswap and Balancer. We give sufficient conditions such that, under fairly general assumptions, agents who interact with these constant function market makers are incentivized to correctly report the price of an asset. We also derive several other useful properties including liquidity provider returns in the path independent case.

arXiv

We consider the problem of seeking an optimal set of model points associated to a fixed portfolio of life insurance policies. Such an optimal set is characterized by minimizing a certain risk functional, which gauges the average discrepancy with the fixed portfolio in terms of the fluctuation of the interest rate term structure within a given time horizon. We prove a representation theorem which provides two alternative formulations of the risk functional and which may be understood in connection with the standard approaches for the portfolio immunization based on sensitivity analysis. For this purpose, a general framework concerning some techniques of stochastic integration in Banach space and Malliavin calculus is introduced. A numerical example is discussed when considering a portfolio of whole life policies.

SSRN

This paper explores time series momentum in interest rates across developed and emerging market countries and various interest rate maturities. With a one-year lookback window, almost all countries in our sample of G10 developed countries and 18 emerging market countries strategies have statistically significant positive time series momentum strategy returns. Shorter tenor (2-year) interest rate swaps have greater momentum returns compared to longer tenor (5-year and 10-year) interest rate swaps which arguably is a result of investor underreaction to monetary policy cycles. A significantly greater share of the positive momentum returns across all tenors comes from falling rate environments versus rising rate environments as a result of the secular decline in interest rates in recent decades. This suggests that if low interest rates continue to persist, future interest rate time series momentum strategy returns could be lower.

SSRN

This paper compares current deaths in the United States due to the Corona virus with those caused by seasonal influenza. The study found that seasonal flu has killed 15 to 21 times as many people as the Corona virus in the first 10 weeks of 2020.

arXiv

We study the ex-ante maximization of market efficiency, defined in terms of minimum deviation of market prices from fundamental values, from a centralized planner's perspective. Prices are pressured from exogenous trading actions of leverage targeting banks, which rebalance their portfolios in response to asset shocks. We develop an explicit expression for the matrix of asset holdings which minimizes market inefficiency, and characterize it in terms of two key sufficient statistics: the banks' systemic significances and the statistical moments of asset shocks. Our analysis shows that higher homogeneity in banks' systemic significances requires banks' portfolio holdings to be further away from a full diversification strategy to reduce inefficiencies.

SSRN

This paper provides new empirical evidence for the way in which non-marketability affects asset prices in financial markets. Critically, the results rely on the unique trading friction "T+1" rule in the Chinese A-share market. Consistent with the predictions derived from Longstaff (1995) and option pricing theory, overnight returns of A-share stocks are negative on average, decreasing with asset volatilities, volatility risks, and jump risks. The upper bound of the marketability option is restricted by the average holding period of the asset and is smaller when there are substitutional marketability instruments. Thus, the marketability-option-related variables could explain the negative overnight returns of illiquidity, short-term reversal, and momentum.

arXiv

I study endogenous learning dynamics for people expecting systematic reversals from random sequences - the "gambler's fallacy." Biased agents face an optimal-stopping problem. They are uncertain about the underlying distribution and learn its parameters from predecessors. Agents stop when early draws are "good enough," so predecessors' experience contain negative streaks but not positive streaks. Since biased agents understate the likelihood of consecutive below-average draws, society converges to over-pessimistic beliefs about the distribution's mean and stops too early. Agents uncertain about the distribution's variance overestimate it to an extent that depends on predecessors' stopping thresholds. Subsidizing search partially mitigates long-run belief distortions.

arXiv

This paper investigates optimal consumption, investment, and healthcare spending under Epstein-Zin preferences. Given consumption and healthcare spending plans, Epstein-Zin utilities are defined over an agent's random lifetime, partially controllable by the agent as healthcare reduces Gompertz' natural growth rate of mortality. In a Black-Scholes market, the stochastic optimization problem is solved through the associated Hamilton-Jacobi-Bellman (HJB) equation. Compared with classical Epstein-Zin utility maximization, the additional controlled mortality process complicates the uniqueness of Epstein-Zin utilities and verification arguments. A combination of probabilistic arguments and analysis of the HJB equation are required to resolve the challenges. In contrast to prior work under time-separable utilities, Epstein-Zin preferences largely facilitate calibration. In four different countries we examined, the model-generated mortality closely approximates actual mortality data; moreover, the calibrated efficacy of healthcare is in broad agreement with empirical studies on healthcare across countries.

SSRN

Our study extends the literature on product innovation by investigating the managerial disclosure of new product innovation in the context of the tension between managerial incentives and disclosure. To test our empirical predictions, we develop a dictionary-based innovation disclosure measure derived from the narratives in new product announcements (NPAs). We find that: 1) investors positively and significantly respond to the extent of innovation disclosed in NPAs, 2) the degree of innovation disclosed in NPAs predicts future financial performance for up to two years (after controlling for R&D capital and patents), and 3) managerial disclosure incentives affect the level of innovation disclosure and its performance predictability. We further document that the effect of managersâ€™ disclosure incentives is not uniform across different earnings components. Overall, our study contributes to the understanding of managersâ€™ strategic behavior in disclosing new product innovation.

arXiv

In this paper the zero vanna implied volatility approximation for the price of freshly minted volatility swaps is generalised to seasoned volatility swaps. We also derive how volatility swaps can be hedged using a strip of vanilla options with weights that are directly related to trading intuition. Additionally, we derive first and second order hedges for volatility swaps using only variance swaps. As dynamically trading variance swaps is in general cheaper and operationally less cumbersome compared to dynamically rebalancing a continuous strip of options, our result makes the hedging of volatility swaps both practically feasible and robust. Within the class of stochastic volatility models our pricing and hedging results are model-independent and can be implemented at almost no computational cost.

arXiv

This letter expands the studies of the informational efficiency in the cryptocurrency market. Most studies have focused on Bitcoin, the foremost known cryptocurrency, and a few more coins. However, this market is more diverse, with cryptocurrencies entering and leaving the market on a weekly basis. This letter fills an important gap in the literature, by studying the informational efficiency using a multi-scaling methodology, which represents a new approach. We compute the generalized Hurst exponent of eighty-four cryptoassets daily returns. The multi-scaling methodology used in this paper find compelling evidence that cryptocurrencies have different degree of long range dependence, and --more importantly -- follow different stochastic processes. Some of them follow traditional monofractal models consistent with fractional Brownian motion, while others exhibit complex multifractal dynamics.

arXiv

Motivation for this paper is to understand the impact of information on asset price bubbles and perceived arbitrage opportunities. This boils down to study optional projections of $\mathbb{G}$-adapted strict local martingales into a smaller filtration $\mathbb{F}$ under equivalent martingale measures. We give some general results as well as analyze in details two specific examples given by the inverse three dimensional Bessel process and a class of stochastic volatility models.

arXiv

We present a parsimonious stochastic model for valuation of options on the fraction of infected individuals during an epidemic. The underlying stochastic dynamical system is a stochastic differential version of the SIR model of mathematical epidemiology.

SSRN

An extensive stream of literature investigates how product market competition, by increasing the proprietary costs of disclosure, influences corporate disclosure policy. However, prior research generally examines disclosure as a binary choice: disclose or withhold. We hypothesize that the extent of proprietary costs incurred is not only determined by whether a disclosure is made, but also by how the disclosure is framed. We predict and find that intensity of competition in the product market is associated with more negative and uncertain earnings calls, both in the management prepared narrative and managersâ€™ responses to analystsâ€™ questions. Our results are robust to common selection bias methods and hold when only examining sentences referencing the future. Additionally, we find consistent results using an alternative measure of linguistic framing (disclosure length) and in an alternative channel of disclosure with an alternative measure of linguistic framing (i.e., disclosure complexity in 10-Ks). Our results demonstrate that managers mitigate proprietary costs by managing disclosure structure and framing.

arXiv

In this paper, we study the financial and economic implications of a zombie epidemic on a major industrialized nation. We begin with a consideration of the epidemiological modeling of the zombie contagion. The emphasis of this work is on the computation of direct and indirect financial consequences of this contagion of the walking dead. A moderate zombie outbreak leaving 1 million people dead in a major industrialized nation could result in GDP losses of 23.44% over the subsequent year and a drop in financial market of 29.30%. We conclude by recommending policy actions necessary to prevent this potential economic collapse.

arXiv

Reinforcement learning algorithms describe how an agent can learn an optimal action policy in a sequential decision process, through repeated experience. In a given environment, the agent policy provides him some running and terminal rewards. As in online learning, the agent learns sequentially. As in multi-armed bandit problems, when an agent picks an action, he can not infer ex-post the rewards induced by other action choices. In reinforcement learning, his actions have consequences: they influence not only rewards, but also future states of the world. The goal of reinforcement learning is to find an optimal policy -- a mapping from the states of the world to the set of actions, in order to maximize cumulative reward, which is a long term strategy. Exploring might be sub-optimal on a short-term horizon but could lead to optimal long-term ones. Many problems of optimal control, popular in economics for more than forty years, can be expressed in the reinforcement learning framework, and recent advances in computational science, provided in particular by deep learning algorithms, can be used by economists in order to solve complex behavioral problems. In this article, we propose a state-of-the-art of reinforcement learning techniques, and present applications in economics, game theory, operation research and finance.

SSRN

This paper employs a bivariate BEKK-GARCH(1,1) model to examine shock and volatility spillovers between crude oil and stock markets by taking into account the impact of the 2008 global financial crisis. Daily data from crude oil market and the Thai stock market during February 6, 2004 and September 14, 2015 are used in the analyses. The whole sample is divided into the pre- and post- crisis periods. The results show that there are no spillover effects between oil price and stock returns in the pre-crisis period. In the post-crisis period, there are unilateral spillover effects from oil price to some equity sector returns. In the market level, there are unilateral spillovers of shock and volatility from oil price to stock market return. The findings in this paper are crucial for financial market participation to understand shock and volatility transmissions from oil to stock markets such that portfolio management should take into account the presence of oil price risk.

SSRN

Romanian Abstract: AceastÄƒ lucrare abordeazÄƒ evoluÅ£ia randamentelor logaritmice ale indicelui BET-XT, de la Bursa de Valori din BucureÅŸti, Ã®n perioada 17 februarie â€" 16 martie 2020. Seria de timp are un trend descendent ÅŸi o medie aritmeticÄƒ negativÄƒ, care pot fi asociate efectelor recentei pandemii COVID-19. English Abstract: This paper approaches the evolution of BET-XT index logarithmic returns, from the Bucharest Stock Exchange for the period February 17thâ€"March 16th, 2020. This time series has a descendent trend and a negative arithmetic mean, which could be related to the effects of 2019â€"20 coronavirus pandemic.

SSRN

We provide insights into the inputs and valuation models used by valuation specialists. We survey 172 valuation specialists and conduct several follow-up interviews covering various topics, including the valuation inputs, models, and industry information that they use, as well as how they estimate long-term growth and the cost of capital. We find that valuation specialists rely on their professional judgment to select a valuation model but prefer the discounted cash flow (DCF) model. They primarily rely on the firmâ€™s historical performance when forecasting the financial statements, but communication with management is particularly relevant for forecasting future earnings or cash flows. When estimating the cost of capital, they most commonly use the risk-free rate with subjective adjustments. The results of our study provide insights on the information use of valuation specialists that are relevant to other valuation specialists, managers, academic researchers, and regulators.

SSRN

We document significantly lower valuations for government contractors in the United States. While contracting with government agencies reduces firmsâ€™ cost of equity, it significantly lowers their sales growth. These findings are contingent on economic conditions; negative valuations dissipate as operating performance improves during economy - and industry-wide recessions. The overall negative valuation effect of government contracts holds only for government contractors in strategically unimportant industries, as strategically important contractors have higher valuations, driven by better operating performance regardless of economic conditions. This is the first study examining the relationship between government procurement and corporate valuation. It also adds to the growing body of literature on politically connected firms by analyzing government contractors as a relatedâ€'butâ€'separate channel of governmental influence on the corporate world.

SSRN

The longest bull market in US stock market history is over. Uncertainty over the public health and economic impact of the coronavirus pandemic will keep markets extremely volatile, making it likely weâ€™ll touch a wide range of price levels in the months ahead. Amidst such uncertainty, itâ€™s a particularly good time to take stock of long-term return prospects. In doing so, weâ€™ll present an often-overlooked perspective on the marketâ€™s attractiveness which is both intuitive and technically sound. We hope long-term investors will find it useful in deciding how much stock market exposure they want right now, and at other levels the market may visit in the future.

arXiv

This survey develops a dual analysis, consisting, first, in a bibliometric examination and, second, in a close literature review of all the scientific production around cryptocurrencies conducted in economics so far. The aim of this paper is twofold. On the one hand, proposes a methodological hybrid approach to perform comprehensive literature reviews. On the other hand, we provide an updated state of the art in cryptocurrency economic literature. Our methodology emerges as relevant when the topic comprises a large number of papers, that make unrealistic to perform a detailed reading of all the papers. This dual perspective offers a full landscape of cryptocurrency economic research. Firstly, by means of the distant reading provided by machine learning bibliometric techniques, we are able to identify main topics, journals, key authors, and other macro aggregates. Secondly, based on the information provided by the previous stage, the traditional literature review provides a closer look at methodologies, data sources and other details of the papers. In this way, we offer a classification and analysis of the mounting research produced in a relative short time span.