Research articles for the 2021-02-25

Bitcoin vs Inflation: Can Bitcoin Be a Macro Hedge? Evidence From a Quantile-on-Quantile Model.
Matkovskyy, Roman,Jalan, Akanksha
In this study we quantify and analyze the dynamic dependence between US, Euro Zone, UK and Japan Bitcoin market returns and realized and unexpected inflation, conditional on different market states and various nuances of inflation. Using a Quantile-on-Quantile regression, we investigate the hedging properties of Bitcoin against inflation, thereby offering a fresh look at the return-inflation puzzle from the point of view of alternative investments. We find that while bullish UK, Euro and Japanese Bitcoin markets facilitate hedging against inflation by offering higher returns, the USD Bitcoin market performs worse with inflation. In general, our results indicate an asymmetric relationship between inflation, both realized and unexpected, and alternative investments such as the Bitcoin market.

Can the variance after-effect distort stock returns?
Berrada, Tony
Variance after-effect is a perceptual bias in the dynamic assessment of variance. Experimental evidence shows that perceived variance is decreased after prolonged exposure to high variance and increased after exposure to low variance. We introduce this effect in an otherwise standard financial model where information about variance is incomplete and updated sequentially. We introduce a variance after- effect adjustment factor in a bayesian learning model and derive the associated predictive variance. We show theoretically how this adjustment factor affects both average and volatility of excess returns. We construct a proxy of the adjustment factor using the sequence of dispersion of analysts earnings forecast. We provide empirical evidence using US stock data over the sample 1982 - 2019, that fluctuations in this measure are significantly and positively related to excess volatility as predicted by the model. Further confirming the model's implications, we also show how stock returns are positively impacted by the adjustment factor and construct long short strategies that generate significant positive alpha with respect to the Fama-French 5 factor model.

Corporate Directors: Who They Are, What They Do, Cyber Risk and Other Challenges
Trautman, Lawrence J.,Butler, Seletha R.,Chang, Frederick,Hooper, Michele,McCray, Ron,Simmons, Ruth J.
Recently, professors Bebchuk, Kastiel, Tallarita, Strine, Rock and others have continued the “fundamental and heated debate about the purpose that corporations should serve, an increasingly influential ‘stakeholderism’ view advocates giving corporate leaders the discretionary power to serve all stakeholders and not just shareholders. Supporters of stakeholderism argue… address[ing] growing concerns about... [the impact] on society and the environment.” Elsewhere, professors Roe, Spamann, Fried, and Wang, critique the 2020 European Commission report, “Study on directors’ duties and sustainable corporate governance.”On November 9, 2020, six experienced corporate governance professionals gathered to discuss the duties and responsibilities of corporate directors and contemporary challenges, including: cyber risk; pandemic; and diversity. Sponsored by the Business Law Association of Prairie View A&M University, our article results from that effort and is augmented with relevant scholarship citations where appropriate to assist the reader who is interested in more information. During 2020 and 2021, global governments, their citizens, and businesses all encountered disruptive economic and political stress. Particularly in such challenging times, effective corporate governance is essential for: business formation; the creation and growth of jobs; and maintenance of the economic engine that powers economies and allows for an environment fostering healthy populations and world peace. During stressful tragedies like the 2020-21 global pandemic, instances of corporate cyberbreach, and other times of crisis, it is the responsibility of corporate directors to provide the governance oversight to business enterprises as they navigate the struggle to preserve jobs and provide for corporate survival. Our article inevitably fails to resolve the ongoing debate regarding the full scope of corporate purpose. However, we believe our comments and observations add to the necessary and important continuing discussion about the efficient functioning of directors as they seek to discharge their duties and responsibilities, particularly with regard to governing cybersecurity risk and issues of board diversity.

Covenant-lite Deals and Financial Reporting Quality
Isin, Adnan Anil,Tharyan, Rajesh
We examine how covlite deals, which are debt contracts that lack systematic covenant compliance requirements, affect financial reporting quality. Examining publicly traded U.S. issuers, we show that firms significantly reduce conservative financial reporting but increase the quality of voluntary non-GAAP earnings information in the post-issue-period. Moreover, covlite issuers tend to reduce accruals-based management incentives, are less likely to receive SEC’s key issue comment letters and are no more likely to have internal control weakness flagged by their auditors in the post-issue-period. We argue that covenant/monitoring quality erosion, often associated with covlite deal structures, does not necessarily result in subordinate financial reporting quality. Instead, we conjecture that firms alter their reporting incentives due to major shifts in their debt market investor profile from traditional banking institutions towards non-bank institutional investors with different financial reporting needs. Our results are robust to alternative data construct and modelling settings that address variable omission and self-selection problems.

Deep Equal Risk Pricing of Financial Derivatives with Multiple Hedging Instruments
Alexandre Carbonneau,Frédéric Godin

This paper studies the equal risk pricing (ERP) framework for the valuation of European financial derivatives. This option pricing approach is consistent with global trading strategies by setting the premium as the value such that the residual hedging risk of the long and short positions in the option are equal under optimal hedging. The ERP setup of Marzban et al. (2020) is considered where residual hedging risk is quantified with convex risk measures. The main objective of this paper is to assess through extensive numerical experiments the impact of including options as hedging instruments within the ERP framework. The reinforcement learning procedure developed in Carbonneau and Godin (2020), which relies on the deep hedging algorithm of Buehler et al. (2019b), is applied to numerically solve the global hedging problems by representing trading policies with neural networks. Among other findings, numerical results indicate that in the presence of jump risk, hedging long-term puts with shorter-term options entails a significant decrease of both equal risk prices and market incompleteness as compared to trading only the stock. Monte Carlo experiments demonstrate the potential of ERP as a fair valuation approach providing prices consistent with observable market prices. Analyses exhibit the ability of ERP to span a large interval of prices through the choice of convex risk measures which is close to encompass the variance-optimal premium.

Deep Stochastic Volatility Model
Xiuqin Xu,Ying Chen

Volatility for financial assets returns can be used to gauge the risk for financial market. We propose a deep stochastic volatility model (DSVM) based on the framework of deep latent variable models. It uses flexible deep learning models to automatically detect the dependence of the future volatility on past returns, past volatilities and the stochastic noise, and thus provides a flexible volatility model without the need to manually select features. We develop a scalable inference and learning algorithm based on variational inference. In real data analysis, the DSVM outperforms several popular alternative volatility models. In addition, the predicted volatility of the DSVM provides a more reliable risk measure that can better reflex the risk in the financial market, reaching more quickly to a higher level when the market becomes more risky and to a lower level when the market is more stable, compared with the commonly used GARCH type model with a huge data set on the U.S. stock market.

Do Accounting Information and Market Environment Matter for Cross-Asset Predictability?
Thakerngkiat, Narongdech,Nguyen, Harvey,Nguyen, Nhut H.,Visaltanachoti, Nuttawat
This paper examines whether the differences in accounting information between stocks affect cross-asset return predictability. We use a comprehensive set of accounting variables and find that abnormal accruals, earnings smoothness, book-to-market, firm age, leverage, abnormal capital investment, and investment growth, among others, explain the variation in return predictability across pairing stocks. Moreover, our results show that cross-asset predictability varies over time and is associated with funding liquidity and market sentiment. A simple trading strategy based on our findings yields a higher mean return, lower standard deviation, and higher Sharpe ratio compared to the buy-and-hold strategy.

Do Gender Quotas Change Attitudes Towards Female Directors?
Naaraayanan, S Lakshmi,Nielsen, Kasper Meisner
We study the long-term effect of gender quotas in India, the largest emerging market to mandate female directors on corporate boards. After the reform, female independent director appointments increase from less than 10% to over 30%. The marginal female director is of similar quality, as measured by education, specialization, and stock price reactions, as the marginal male director. Further, we find that the gender gap in director remuneration decreases from 18% to 3%. Overall, our results suggest that gender quotas can deepen and diversify talent pools in emerging markets where lower corporate governance standards often impede board composition and director quality.

Easy to Parse, Easy to Trade
Dang, Van,Liu, Leo
The delivery of information is as important as the content itself. We present evidence that an announcement's writing literacy is as critical as its information content. We show that the text's readability in firm-specific news affects trading decision, particularly those of algorithmic traders. We find that algorithmic participants trade more aggressively when the news items are more readable. This change in their trade decisions leads to several market impacts. First, we suggest that readability affects algorithmic traders' liquidity provision. Algorithmic traders can capture more rents when the news is more readable while other participants enjoy lower adverse selection costs. The market makers' profit is on average less than the reduced selection costs. Therefore, there is an improvement in overall market welfare in terms of the transaction cost. Finally, price efficiency is improved when the market can trade on highly readable news.

Economic Implications of Scaling Blockchains: Why the Consensus Protocol Matters
John, Kose ,Rivera, Thomas J,Saleh, Fahad
This paper examines the economic implications of scaling blockchains under two different consensus protocols: Proof-of-Work (PoW) and Proof-of-Stake (PoS). We study an economic model whereby agents can store wealth through the blockchain's cryptocurrency but may face a costly delay when liquidating due to the blockchain's finite transaction rate. Agents may expedite processing by paying fees to the blockchain's validators. Within such a model, we study the ability of a malicious agent to compromise the security of the blockchain. We show how improved scaling alleviates congestion, leading to a decrease in equilibrium fees. Under a PoW protocol, this leads validators to earn lower fees and thus spend less on computational power. This reduced computational power then lowers the cost of a successful attack and therefore the security of the PoW blockchain. Scaling has the opposite effect for the PoS protocol as alleviating congestion increases the demand and therefore the market value of the blockchain's cryptocurrency. That increased market value increases the cost of acquiring enough cryptocurrency necessary for a successful attack and thereby improves PoS blockchain security.

Economic Policy Uncertainty and Learning: Theory and Evidence on Mutual Funds
Jiang, Erica Xuewei,Starks, Laura T.,Sun, Sophia
Using the mutual fund industry as a laboratory, we demonstrate theoretically and empirically that economic policy uncertainty an affect investment decisions through an information rather than real options channel. Specifically, we find that fund flow-performance sensitivity decreases in uncertainty and does so more strongly for funds with shorter track records. The evidence supports the predictions of our model, most notably that investor learning about manager ability weakens when uncertainty increases. The results have implications for inefficient capital allocation during periods of higher uncertainty due to the resultant sluggish learning process.

Effects of corporate taxes on private firms’ earnings management: A regression discontinuity analysis
Kong, Gaowen,Kong, Dongmin
We examine the impact of tax incentives on private firms’ earnings management based on a tax reform in China. Firms established after January 2002 face significant tax reduction, thus creating a large and persistent discontinuity in tax rates by establishment date. Using the regression discontinuity design, we show that tax reduction substantially increases private firms’ incentives to manage earnings, and such effect is particularly pronounced when tax collection intensity and government interventions are low. A plausible mechanism is that private firms signal promising outlooks by managing earnings to attain greater financing and improve investment/operation levels when financial constraints are removed.

Implied Cost of Capital and Accounting Conservatism
Karampinis, Nikolaos I.,Lessis, Giannis D.
In this paper, we illuminate the importance of accounting conservatism adjustments when estimating the implied cost of capital (ICC) with the Residual Income Valuation (RIV) and the Abnormal Earnings Growth (AEG) model. Specifically, we adjust for three main limitations in the research of ICC, that is, accounting conservatism, analyst over-optimism, and the degrees of freedom problem (i.e. different forecasting horizons), and compare their effects. We show that, after conservatism adjustments in either model, the correlation and the explanatory power of ICC for realized returns exhibits a substantial improvement. However, we find that the adjustment for analyst bias generates immaterial changes, while the adjustment for the degrees of freedom problem yields mixed results. Contrary to expectations, the adjustments do not align the estimated ICCs from the two models but make them to diverge more. Finally, the ICC from the AEG model outperforms its counterpart from the RIV model either with or without the adjustments.

Incorporating Financial Big Data in Small Portfolio Risk Analysis: Market Risk Management Approach
Donggyu Kim,Seunghyeon Yu

When applying Value at Risk (VaR) procedures to specific positions or portfolios, we often focus on developing procedures only for the specific assets in the portfolio. However, since this small portfolio risk analysis ignores information from assets outside the target portfolio, there may be significant information loss. In this paper, we develop a dynamic process to incorporate the ignored information. We also study how to overcome the curse of dimensionality and discuss where and when benefits occur from a large number of assets, which is called the blessing of dimensionality. We find empirical support for the proposed method.

Information Technology and Gender Economic Inclusion in Sub-Saharan Africa
Asongu, Simplice,Amankwah-Amoah, Joseph,Nting, Rexon Tayong,Afrifa, Godfred
This study investigates how ICT affects gender economic inclusion via gender parity education channels. We examine the issue using data from 49 countries in sub-Saharan Africa for the period 2004-2018 divided into: (i) 42 countries for the period 2004-2014; and (ii) 49 countries for the period 2008-2018. Given the overwhelming evidence of negative net effects in the first sample, an extended analysis is used to establish thresholds of ICT penetration that nullify the established net negative effects. We found that in order to enhance female labor force participation, the following ICT thresholds are worthwhile for the secondary education channel: 165 mobile phone penetration per 100 people, 21.471 internet penetration per 100 people and 3.475 fixed broadband subscriptions per 100 people. For the same outcome of inducing a positive effect on female labor force participation, a 31.966 internet penetration per 100 people threshold, is required for the mechanism of tertiary school education. These computed thresholds have economic meaning and policy relevance because they are within the established ICT policy ranges. In the second sample, a mobile phone penetration threshold of 122.20 per 100 people is needed for the tertiary education channel to positively affect female labor force participation.

Layoffs, Inequity and COVID-19: A Longitudinal Study of the Journalism Jobs Crisis in Australia from 2012 to 2020
Nik Dawson,Sacha Molitorisz,Marian-Andrei Rizoiu,Peter Fray

In Australia and beyond, journalism is reportedly an industry in crisis, a crisis exacerbated by COVID-19. However, the evidence revealing the crisis is often anecdotal or limited in scope. In this unprecedented longitudinal research, we draw on data from the Australian journalism jobs market from January 2012 until March 2020. Using Data Science and Machine Learning techniques, we analyse two distinct data sets: job advertisements (ads) data comprising 3,698 journalist job ads from a corpus of over 8 million Australian job ads; and official employment data from the Australian Bureau of Statistics. Having matched and analysed both sources, we address both the demand for and supply of journalists in Australia over this critical period. The data show that the crisis is real, but there are also surprises. Counter-intuitively, the number of journalism job ads in Australia rose from 2012 until 2016, before falling into decline. Less surprisingly, for the entire period studied the figures reveal extreme volatility, characterised by large and erratic fluctuations. The data also clearly show that COVID-19 has significantly worsened the crisis. We then tease out more granular findings, including: that there are now more women than men journalists in Australia, but that gender inequity is worsening, with women journalists getting younger and worse-paid just as men journalists are, on average, getting older and better-paid; that, despite the crisis besetting the industry, the demand for journalism skills has increased; and that, perhaps concerningly, the skills sought by journalism job ads increasingly include social media and generalist communications.

Matching with Trade-offs: Revealed Preferences over Competing Characteristics
Alfred Galichon,Bernard Salanié

We investigate in this paper the theory and econometrics of optimal matchings with competing criteria. The surplus from a marriage match, for instance, may depend both on the incomes and on the educations of the partners, as well as on characteristics that the analyst does not observe. Even if the surplus is complementary in incomes, and complementary in educations, imperfect correlation between income and education at the individual level implies that the social optimum must trade off matching on incomes and matching on educations. Given a flexible specification of the surplus function, we characterize under mild assumptions the properties of the set of feasible matchings and of the socially optimal matching. Then we show how data on the covariation of the types of the partners in observed matches can be used to test that the observed matches are socially optimal for this specification, and to estimate the parameters that define social preferences over matches.

Optimal Dynamic Futures Portfolios Under a Multiscale Central Tendency Ornstein-Uhlenbeck Model
Tim Leung,Yang Zhou

We study the problem of dynamically trading multiple futures whose underlying asset price follows a multiscale central tendency Ornstein-Uhlenbeck (MCTOU) model. Under this model, we derive the closed-form no-arbitrage prices for the futures contracts. Applying a utility maximization approach, we solve for the optimal trading strategies under different portfolio configurations by examining the associated system of Hamilton-Jacobi-Bellman (HJB) equations. The optimal strategies depend on not only the parameters of the underlying asset price process but also the risk premia embedded in the futures prices. Numerical examples are provided to illustrate the investor's optimal positions and optimal wealth over time.

Option-Implied Network Measures of Tail Contagion and Stock Return Predictability
Pedio, Manuela
The Great Financial Crisis of 2008 â€" 2009 has raised the attention of policy-makers and researchers about the interconnectedness among the volatility of the returns of financial assets as a potential source of risk that extends beyond the usual changes in correlations and include transmission channels that operate through the higher order co-moments of returns. In this paper, we investigate whether a newly developed, forward-looking measure of volatility spillover risk based on option implied volatilities shows any predictive power for stock returns. We also compare the predictive performance of this measure with that of the volatility spillover index proposed by Diebold and Yilmaz (2008, 2012), which is based on realized, backward-looking volatilities instead. While both measures show evidence of in-sample predictive power, only the option-implied measure is able to produce out-of-sample forecasts that outperform a simple historical mean benchmark.

Predicting cell phone adoption metrics using satellite imagery
Edward J. Oughton,Jatin Mathur

Approximately half of the global population does not have access to the internet, even though digital connectivity can reduce poverty by revolutionizing economic development opportunities. Due to a lack of data, Mobile Network Operators and governments struggle to effectively determine if infrastructure investments are viable, especially in greenfield areas where demand is unknown. This leads to a lack of investment in network infrastructure, resulting in a phenomenon commonly referred to as the `digital divide`. In this paper we present a machine learning method that uses publicly available satellite imagery to predict telecoms demand metrics, including cell phone adoption and spending on mobile services, and apply the method to Malawi and Ethiopia. Our predictive machine learning approach consistently outperforms baseline models which use population density or nightlight luminosity, with an improvement in data variance prediction of at least 40%. The method is a starting point for developing more sophisticated predictive models of infrastructure demand using machine learning and publicly available satellite imagery. The evidence produced can help to better inform infrastructure investment and policy decisions.

Social Welfare Maximization and Conformism via Information Design in Linear-Quadratic-Gaussian Games
Furkan Sezer,Hossein Khazaei,Ceyhun Eksin

We consider linear-quadratic Gaussian (LQG) games in which players have quadratic payoffs that depend on the players' actions and an unknown payoff-relevant state, and signals on the state that follow a Gaussian distribution conditional on the state realization. An information designer decides the fidelity of information revealed to the players in order to maximize the social welfare of the players or reduce the disagreement among players' actions. Leveraging the semi-definiteness of the information design problem, we derive analytical solutions for these objectives under specific LQG games. We show that full information disclosure maximizes social welfare when there is a common payoff-relevant state, when there is strategic substitutability in the actions of players, or when the signals are public. Numerical results show that as strategic substitution increases, the value of the information disclosure increases. When the objective is to induce conformity among players' actions, hiding information is optimal. Lastly, we consider the information design objective that is a weighted combination of social welfare and cohesiveness of players' actions. We obtain an interval for the weights where full information disclosure is optimal under public signals for games with strategic substitutability. Numerical solutions show that the actual interval where full information disclosure is optimal gets close to the analytical interval obtained as substitution increases.

Tax Policy Beliefs and Investment: Evidence from the 2016 U.S. Election and the Tax Cuts and Jobs Act
Gallemore, John,Hollander, Stephan,Jacob, Martin,Zheng, Xiang
We examine how beliefs about tax policy affect firms’ investment decisions. Exploiting the periods around the surprise election of Donald Trump, who campaigned heavily on tax reform, and the Tax Cuts and Jobs Act (TCJA), we find that expectations regarding tax policy have both first-moment (sentiment) and second-moment (uncertainty) effects on investment, which vary heterogeneously across events and firms. In particular, we document that tax policy sentiment (uncertainty) enhances (dampens) the change in investment around the passage of the TCJA, suggesting that these beliefs affect the ability of tax reforms to spur economic growth.

The Good, the Bad and the Complex: Product Design with Imperfect Information
Asriyan, Vladimir,Foarta, Dana,Vanasco, Victoria
We study the joint determination of product quality and complexity in a rational setting. We introduce a novel notion of complexity, which affects how costly it is for an agent to acquire information about product quality. In our model, an agent can accept or reject a product proposed by a designer, who can affect the quality and the complexity of the product. Examples include banks that design financial products that they offer to retail investors, or policymakers who propose policies for approval by voters. We find that complexity is not necessarily a feature of low quality products. While an increase in alignment between the agent and the designer leads to more complex but better quality products, higher product demand or lower competition among designers leads to more complex and lower quality products. Our findings produce novel empirical implications on the relationship between quality and complexity, which we relate to evidence within the context of financial products and regulatory policies.

The Stochastic Balance Equation for the American Option Value Function and its Gradient
Malkhaz Shashiashvili

In the paper we consider the problem of valuation and hedging of American options written on dividend-paying assets whose price dynamics follow the multidimensional diffusion model. We derive a stochastic balance equation for the American option value function and its gradient. We prove that the latter pair is the unique solution of the stochastic balance equation as a result of the uniqueness in the related adapted future-supremum problem.

Vector quantile regression and optimal transport, from theory to numerics
Guillaume Carlier,Victor Chernozhukov,Gwendoline De Bie,Alfred Galichon

In this paper, we first revisit the Koenker and Bassett variational approach to (univariate) quantile regression, emphasizing its link with latent factor representations and correlation maximization problems. We then review the multivariate extension due to Carlier et al. (2016, 2017) which relates vector quantile regression to an optimal transport problem with mean independence constraints. We introduce an entropic regularization of this problem, implement a gradient descent numerical method and illustrate its feasibility on univariate and bivariate examples.