Research articles for the 2020-09-15

A Note on New Valuation Measures for Standard & Poor Composite Index Returns
Andrey Sarantsev

Long-run total real returns of the stock market are approximately equal to long-run real earnings growth plus average dividend yield. However, earnings can be distributed to shareholders not only via dividends, but via buybacks and debt retirement. Thus the total returns minus earnings growth can be considered as implied dividend yield. This quantity must be stable in the long run. If this quantity is abnormally high, then the market is overpriced and is more likely to decline. We measure this by (detrended) cumulative sum of differences. We regress the implied dividend yield for the next year upon this current bubble measure. We simulate future returns, starting from current market conditions. In our model the current market is undervalued and is likely to grow faster than historically.

A Review of the Regulatory Impact Analysis of Risk-based Capital Regulations
Hogan, Thomas L.
This paper reviews the cost-benefit analysis, or "regulatory impact analysis" (RIA), in U.S. bank regulators' risk-based capital (RBC) rule proposals. We provide a brief background on RBC rules and review the literature on their costs and benefits. We evaluate 25 proposed RBC rules and related rules on bank liquidity. We find that eight of the 25 rules include RIAs, and none provide quantitative evidence that the benefits exceed the costs. In two proposals, the evidence cited indicates the rules' net benefits may actually be negative.

A Supply-Side Options Pricing Model for Explaining the Moment Risk Premia
Hsieh, PeiLin Billy
This study provides a liquidity-supply side model of options markets for illustrating how options pricing uncertainty affects moment risk premia. The model is based on micro-structure theory such that a representative market maker dynamically replicates options prices, hedges risky positions, and seeks the minimum variance in duplicating errors. Therefore, unlike previous studies extending the pricing kernel of underlying assets to explain moment risk premia, our model focuses on options' hedging uncertainty: i.e., the pricing kernel that distinguishes options from ordinary equities. The risk premia in our model are attributed to options pricing uncertainty. Our model also provides a new perspective on the effects of market friction and the non-normality assumption on moment risk premia and the implied volatility curves.

An Artificial Intelligence Solution for Electricity Procurement in Forward Markets
Thibaut Théate,Sébastien Mathieu,Damien Ernst

Retailers and major consumers of electricity generally purchase an important percentage of their estimated electricity needs years ahead in the forward market. This long-term electricity procurement task consists of determining when to buy electricity so that the resulting energy cost is minimised, and the forecast consumption is covered. In this scientific article, the focus is set on a yearly base load product from the Belgian forward market, named calendar (CAL), which is tradable up to three years ahead of the delivery period. This research paper introduces a novel algorithm providing recommendations to either buy electricity now or wait for a future opportunity based on the history of CAL prices. This algorithm relies on deep learning forecasting techniques and on an indicator quantifying the deviation from a perfectly uniform reference procurement policy. On average, the proposed approach surpasses the benchmark procurement policies considered and achieves a reduction in costs of 1.65\% with respect to the perfectly uniform reference procurement policy achieving the mean electricity price. Moreover, in addition to automating the complex electricity procurement task, this algorithm demonstrates more consistent results throughout the years compared to the benchmark policies. Eventually, the generality of the solution presented makes it well suited for solving other commodity procurement problems.

Are Super Stock Exchange Mergers Motivated by Efficiency or Market Power Gains?
Otchere, Isaac K.,Abukari, Kobana
Stock exchange mergers can lead to increased efficiency; however, increasing levels of concentration can potentially lead to the exercise of market power. We investigate the market power repercussions of stock exchange mergers and find that the industry’s concentration levels have not significantly increased and the concentration levels do not influence exchanges’ profitability in the post-merger period. The profitability of the merging exchanges in the post-merger period is largely influenced by efficiencies in revenue generation and cost management. The absence of evidence that stock exchange mergers lead to the exercise of market power suggests that there does not appear to be an immediate need for regulatory agencies to be overly concerned about mergers among stock exchanges leading to the exploitation of market power to the detriment of consumer welfare.

Asset Prices and Liquidity with Market Power and Non-Gaussian Payoffs
Glebkin, Sergei,Malamud, Semyon,Teguia, Alberto
We consider an economy populated by CARA investors who trade, accounting for their price impact, multiple risky assets with arbitrary distributed payoffs. We propose a constructive solution method: finding the equilibrium reduces to solving a linear ordinary differential equation. With market power and non-Gaussian payoffs: (i) the equilibrium is nonlinear and the model can speak to key stylized facts regarding asymmetry and nonlinearity of price response to order imbalances, (ii) when risk aversion decreases, there are more liquidity providers and/or there is less uncertainty about future asset payoffs, liquidity can decrease, (iii) cross-section of returns is affected by endogenous illiquidity.

Avoiding Taxes: Banks' Use of Internal Debt
Reiter, Franz,Langenmayr, Dominika,Holtmann, Svea
This paper investigates how multinational banks use internal debt to shift profits to low-taxed affiliates. Using regulatory data on multinational banks headquartered in Germany, we show that banks use this tax avoidance channel more aggressively than non-financial multinationals do. We find that a ten percentage points higher corporate tax rate increases the internal net debt ratio by 5.7 percentage points, corresponding to a 20% increase at the mean. Our study also takes into account the existence of conduit entities, which simply pass through financial flows. If conduit entities are systematically located in low-tax countries, previous studies may have underestimated the extent of debt shifting.

Betting Market Efficiency in the Presence of Unfamiliar Shocks: The Case of Ghost Games During the COVID-19 Pandemic
Fischer, Kai,Haucap, Justus
Betting markets have been frequently used as a natural laboratory to test the efficient market hypothesis and to obtain insights especially for financial markets. We add to this literature in analyzing the velocity and accuracy in which market expectations adapt to an exogenous shock: the introduction of soccer ghost games during the COVID-19 pandemic. We find that betting odds do not properly reflect the effect of ghost games regarding changes in home advantage. Furthermore, we present evidence for a slow to non-existing adaption process with respect to new match results, indicating a lack of semi-strong efficiency. Based on these findings, we also identify very simple but highly profitable betting strategies which underline our rejection of the efficient market hypothesis.

COVID-19 Impact on Cryptocurrencies: Evidence from a Wavelet-Based Hurst Exponent
Arouxet, M. Belén,Bariviera, Aurelio F.,Pastor, Verónica E.,Vampa, Victoria
Cryptocurrency history begins in 2008 as a means of payment proposal. However, cryptocurrencies evolved into complex, high yield speculative assets. Contrary to traditional financial instruments, they are not (mostly) traded in organized, law-abiding venues, but on online platforms, where anonymity reigns. This paper examines the long term memory in return and volatility, using high frequency time series of eleven important coins. Our study covers the pre-COVID-19 and the subsequent pandemia period. We use a recently developed method, based on the wavelet transform, which provides more robust estimators of the Hurst exponent. We detect that, during the peak of COVID-19 pandemic (around March 2020), the long memory of returns was only mildly affected. However, volatility suffered a temporary impact in its long range correlation structure. Our results could be of interest for both academics and practitioners.

COVID-19 Impact on Global Maritime Mobility
Leonardo M. Millefiori,Paolo Braca,Dimitris Zissis,Giannis Spiliopoulos,Stefano Marano,Peter K. Willett,Sandro Carniel

To prevent the outbreak of the Coronavirus disease (COVID-19), numerous countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data, collected via a global network of Automatic Identification System (AIS) receivers, we analyze the effects that the COVID-19 pandemic and the containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We introduce the notion of a "maritime mobility index," a synthetic composite index, to quantitatively assess ship mobility in a given unit of time. The mobility index calculation used in this study, has a worldwide extent and is based on the computation of cumulative navigated miles (CNM) of all ships reporting their position and navigational status via AIS. We compare 2020 mobility levels to those of previous years assuming that an unchanged growth rate would have been achieved, if not for COVID-19. Following the outbreak, we find an unprecedented drop in maritime mobility, across all categories of commercial shipping. The reduced activity is observable from March to June, when the most severe restrictions were in force, producing a variation of mobility quantified between -5.62% and -13.77% for container ships, between +2.28% and -3.32% for dry bulk, between -0.22% and -9.27% for wet bulk, and between -19.57% and -42.77% for passenger shipping. The presented study is unprecedented for the uniqueness and completeness of the employed AIS dataset, which comprises a trillion AIS messages broadcast worldwide by 50000 ships, a figure that closely parallels the documented size of the world merchant fleet.

Can Volatility Solve the Naive Portfolio Puzzle?
Curran, Michael,Zalla, Ryan
We investigate whether sophisticated volatility estimation improves the out-of-sample performance of mean-variance portfolio strategies relative to the naive 1/N strategy. The portfolio strategies rely solely upon second moments. Using a diverse group of econometric and portfolio models across multiple datasets, most models achieve higher Sharpe ratios and lower portfolio volatility that are statistically and economically significant relative to the naive rule, even after controlling for turnover costs. Our results suggest benefits to employing more sophisticated econometric models than the sample covariance matrix, and that mean-variance strategies often outperform the naive portfolio across multiple datasets and assessment criteria.

Capital Market Financing and Firm Growth
Didier, Tatiana,Levine, Ross Eric,Llovet Montanes, Ruth,Schmukler, Sergio L.
This paper studies whether there is a connection between finance and growth at the firm level. It employs a new dataset of 150,165 equity and bond issuances around the world, matched with income and balance sheet data for 62,653 listed firms in 65 countries over 1990-2016. Three main patterns emerge from the analyses. First, firms that choose to issue in capital markets use the funds raised to grow by enhancing their productive capabilities, increasing their tangible and intangible capital and the number of employees. Growth accelerates as firms raise funds. Second, the faster growth is more pronounced among firms that are more likely to face tighter financing constraints, namely, small, young, and high-R&D firms. Third, capital market issuances are associated with faster growth among firms located in countries with more developed capital markets relative to banks. Capital markets are also comparatively effective at allowing financially constrained firms to raise capital.

Climate Transition Risk in New Zealand Equities
Kennett, Hamish,Diaz-Rainey, Ivan,Biswas, Pallab Kumar,Kuruppuarachchi, Duminda
We examine climate transition risk in New Zealand (NZ) equities given that NZ’s greenhouse gas (GHG) emissions are dominated by agricultural emissions and that carbon pricing has been in place since 2008. Only around half of NZX50 companies disclose emissions and that disclosure is driven by, inter alia, size and profitability. In terms of ‘hypothetical carbon liabilities’, Air New Zealand and Contact Energy are most exposed for Scope 1 and 2 emissions, but when upstream scope 3 GHG emissions are added, Fonterra (multinational dairy firm) is most at-risk. An asset pricing analysis shows that only volatility and extreme price movements in carbon price returns are priced. Overall, the results suggest that despite there being material climate transition risks for NZX50 equities, limited disclosure and low carbon prices mean that these risks are not likely to be fully priced in stock values.

Copula estimation for nonsynchronous financial data
Arnab Chakrabarti,Rituparna Sen

Copula is a powerful tool to model multivariate data. We propose the modelling of intraday financial returns of multiple assets through copula. The problem originates due to the asynchronous nature of intraday financial data. We propose a consistent estimator of the correlation coefficient in case of Elliptical copula and show that the plug-in copula estimator is uniformly convergent. For non-elliptical copulas, we capture the dependence through Kendall's Tau. We demonstrate underestimation of the copula parameter and use a quadratic model to propose an improved estimator. In simulations, the proposed estimator reduces the bias significantly for a general class of copulas. We apply the proposed methods to real data of several stock prices.

Covid-19 Labour Market Shocks and Their Inequality Implications for Financial Wellbeing
Botha, Ferdi,de New, John,de New (née Kassenboehmer), Sonja C.,Ribar, David C.,Salamanca, Nicolas
Using an online survey of Australian residents, we elicit the potential impacts of COVID-19 related labour market shocks on a validated measure of financial wellbeing. Experiencing a reduction in hours and earnings, entering into unemployment or having to file for unemployment benefits during the pandemic are strongly and significantly associated with decreases in financial wellbeing of around 29% or 18 points on the financial wellbeing scale of 0-100, despite various government measures to reduce such effects. Unconditional quantile regression analyses indicate that the negative COVID-19 labour market effects are felt the most by people in the lowest percentiles of the financial wellbeing distribution. Counterfactual distributional analyses and distribution regression indicate a shifting of the financial wellbeing distribution leftwards brought on by those suffering any of the above-mentioned labour market shocks, indicating potential dramatic increases in financial wellbeing disadvantage and inequality.

Data driven value-at-risk forecasting using a SVR-GARCH-KDE hybrid
Marius Lux,Wolfgang Karl Härdle,Stefan Lessmann

Appropriate risk management is crucial to ensure the competitiveness of financial institutions and the stability of the economy. One widely used financial risk measure is Value-at-Risk (VaR). VaR estimates based on linear and parametric models can lead to biased results or even underestimation of risk due to time varying volatility, skewness and leptokurtosis of financial return series. The paper proposes a nonlinear and nonparametric framework to forecast VaR that is motivated by overcoming the disadvantages of parametric models with a purely data driven approach. Mean and volatility are modeled via support vector regression (SVR) where the volatility model is motivated by the standard generalized autoregressive conditional heteroscedasticity (GARCH) formulation. Based on this, VaR is derived by applying kernel density estimation (KDE). This approach allows for flexible tail shapes of the profit and loss distribution, adapts for a wide class of tail events and is able to capture complex structures regarding mean and volatility.

The SVR-GARCH-KDE hybrid is compared to standard, exponential and threshold GARCH models coupled with different error distributions. To examine the performance in different markets, one-day-ahead and ten-days-ahead forecasts are produced for different financial indices. Model evaluation using a likelihood ratio based test framework for interval forecasts and a test for superior predictive ability indicates that the SVR-GARCH-KDE hybrid performs competitive to benchmark models and reduces potential losses especially for ten-days-ahead forecasts significantly. Especially models that are coupled with a normal distribution are systematically outperformed.

Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning
Eric Benhamou,David Saltiel,Jean-Jacques Ohana,Jamal Atif

Deep reinforcement learning (DRL) has reached super human levels in complex tasks like game solving (Go and autonomous driving). However, it remains an open question whether DRL can reach human level in applications to financial problems and in particular in detecting pattern crisis and consequently dis-investing. In this paper, we present an innovative DRL framework consisting in two sub-networks fed respectively with portfolio strategies past performances and standard deviations as well as additional contextual features. The second sub network plays an important role as it captures dependencies with common financial indicators features like risk aversion, economic surprise index and correlations between assets that allows taking into account context based information. We compare different network architectures either using layers of convolutions to reduce network's complexity or LSTM block to capture time dependency and whether previous allocations is important in the modeling. We also use adversarial training to make the final model more robust. Results on test set show this approach substantially over-performs traditional portfolio optimization methods like Markowitz and is able to detect and anticipate crisis like the current Covid one.

Disastrous Defaults
Gourieroux, Christian,Monfort, Alain,Mouabbi, Sarah,Renne, Jean-Paul
We define a disastrous default as the default of a systemic entity. Such an event is expected to have a negative effect on the economy and to be contagious. Bringing macroeconomic structure to a noarbitrage asset-pricing framework, we exploit prices of disaster-exposed assets (credit and equity derivatives) to extract information on the expected (i) influence of a disastrous default on consumption and (ii) probability of a financial meltdown. We find that the returns of disaster-exposed assets are consistent with a systemic default being followed by a 3% decrease in consumption. The recessionary influence of disastrous defaults implies that financial instruments whose payoffs are exposed to such credit events carry substantial risk premiums. We also produce systemic risk indicators based on the probability of observing a certain number of systemic defaults or a sharp drop of consumption.

Does Political Connection Distort Competition and Encourage Corporate Risk Taking? International Evidence.
Otchere, Isaac K.,Senbet, Lemma W.,Zhu, Pengcheng
We investigate the impact of political connection on corporate risk-taking by connected firms, their industry counterparts, as well as non-rival firms from 48 countries. We find that political connection induces higher risk taking by connected firms. By contrast, we do not find evidence that political connection, with the attendant potential competitive distortions in the industry, induces higher risk taking by competitors. We focus on non-financial industries. Our results are consistent with the hypothesis that the inability to avail themselves of political rents compels the non-connected rivals to adopt more conservative strategies. However, large rival firms, generally considered to be too-important-to-fail, exhibit evidence of higher risk taking. The top size quartile industry rivals take as much risk as the politically connected firms. The higher risk exhibited by large rivals of politically connected firms suggests that our baseline regression results of lower risk-taking among rivals of politically connected firms are biased upward by firms that would be considered too-big-to-fail. This finding also suggests that the too-big-to fail phenomenon is not unique to banks. Our results are robust to the use of alternative measures of risk, to the exclusion of privatized and state-owned firms, and to controlling for the effects of financial crisis.

Giving Priority to Social Good and Public Benefit with Meaningful Accountability Thereto: 'Differentiated Social Good' and the Social Primacy Company
Tyler, John E.
There is a heightened lack of clarity and understanding about the new U.S. business forms both in terms of theory and practice, especially with regard to relative priorities of social good (or the absence thereof), decision-making, and meaningful accountability thereto (or the lack thereof). As a likely result, investors, entrepreneurs, and their respective advisors are not using the forms, or they too often are using the forms for the wrong purposes. Moreover, policymakers risk incentivizing something other than what they intend while punishing those they do not intend to punish. In addition, capital is not flowing to businesses that pursue and prioritize social good as had been hoped. Contributing to the complexity and consequences are efforts to re-characterize businesses as “social” and investing as “impact” any or all businesses or investments that have even a smidge of social good. As a result, too often, too many people feel good about what they believe they are doing without realizing that they are not doing what they believe. An intentional framework for decision-making and evaluation can help inhibit these results. This article presents a framework grounded in understanding commitment to social good, desired connections between efforts/resources and social results, and approaches to social good that are distinct from those of traditional approaches â€" i.e., “differentiated social good.” It also juxtaposes the proposed “social primacy company” as an example of what a form that actually does what too many mistakenly believe traditional and the still new hybrid forms purportedly do. Investors, entrepreneurs, their legal and other advisors, policymakers, journalists, academics, researchers, and others would benefit from such clear understandings, framework, and considerations of differentiated social good.

How Effective are the Measures taken by Banks to Protect the Interests of the Stakeholders Online from the Threat of Cybercrime?
Sridhar, Kedaar
Cyber-crime is a worldwide phenomenon that is growing in sophistication, despite advances in cyber-security. It is emerging as a serious threat to privacy and personal data. The world of technology today has become a parallel form of life and cyber-criminals have taken advantage of society’s over-dependence on technology. This paper focuses on online cyber-crime, and more importantly the systems that banks use to prevent this and protect their customers online. The financial sector is facing an increase in cyber-threats and it is imperative that security is improved to protect the interests of banks and its stakeholders. One of the main responsibilities of banks is to protect the accounts and identities of their customers. Banks must deal with the social and ethical issues of security and privacy. It is their duty to use security measures, ensuring that the social impacts on stakeholders are positive. This paper will not only discuss the methods that banks use to fight cyber-crime but also their effectiveness and impact on stakeholders. I will support my secondary research with primary data obtained from a survey and interview with financial experts. This leads to the research question being explored: How effective are the measures taken by banks to protect the interests of the stakeholders online from the threat of cyber-crime?

How to De-identify Personal Data in South Korea: An Evolutionary Tale
Ko, Haksoo
In early 2020, South Korea’s legislature made amendments to major laws in the area of data protection in order to, among others, promote the utilization of pseudonymised personal data. With these amendments, pseudonymised personal data can be processed, without consent from data subjects, for archiving purposes, scientific research purposes, or statistical purposes. Arguably, these amendments are largely inspired by the relevant provisions contained in the EU GDPR, although details differ between GDPR and South Korea’s amended statutes. One unique aspect of South Korea’s amended statutes is that they introduce a scheme under which designated agencies carry out the task of combining pseudonymised data that different entities possess.

Information Asymmetry, Time until Deal Completion and Post-M&A Performance
Thompson, Ephraim Kwashie,Kim, Changki
We show that information asymmetry plays a vital role in the post-M&A performance-time until deal completion nexus. We find that the due diligence hypothesis and the overdue hypothesis proposed and tested in Thompson and Kim (2020) is influenced by the information asymmetry of the target during the negotiation process. Thus, mergers that involve more opaque targets which take a shorter time to close perform better, while those that take too long to close experience poor post-M&A performance. Conversely, there is no such effect when the mergers involve targets that are transparent and not plagued with large information asymmetry problems. These results hold for the short term supporting the evidence that information asymmetry problems are severe before the merger is consummated and become attenuated post-merger.

Initial Public Offerings Chinese Style
Qian, Yiming,Ritter, Jay R.,Shao, Xinjian
We examine various aspects of the IPO market in China â€" the policy history, IPO pricing, bids and allocation, and aftermarket trading under two lenses: IPO theories and the unique regulatory environment in China. We show that heavy-handed regulations cause inefficient IPO offer prices and high initial returns, resulting in a high cost of going public. Investors treat IPOs as lotteries with extreme short-term returns, with little incentive for long-term investment. The auction selling method, however, works in the way it is supposed to. Mutual funds bid more smartly than other investors, and their advantages are unlikely to be due to underwriters’ preferential treatment. We also discuss the direction of future regulation reforms (including the latest science and technology board, or STAR market).

International Comparison of the Efficiency of Electricity Futures
Bevin-McCrimmon, Fergus,Diaz-Rainey, Ivan,Gehricke, Sebastian,Sise, Greg
The paper makes an original contribution to the electricity market literature by using an international sample to explore the efficiency of electricity futures across different markets. We focus on the efficiency of five futures markets (Australia, Germany, New Zealand, Nord Pool and the PJM Interconnection) between 2002 and 2016. Our analysis has three components; first we explore the validity of the unbiasedness hypothesis in this context; second, we test the forecast power of futures prices; and; third we explore market characteristics as determinants of efficiency. Our analysis of the unbiasedness hypothesis finds that the German and New Zealand markets are the only efficient markets for the entire sample, even though they are at the two extremes in terms of liquidity and market maturity. When testing the forecast power, all futures markets outperform naïve forecasts and the predictability seems to be improving as markets mature, except for the U.S. futures which show a decline. The generating mix does not seem to affect forecastability. We find that market efficiency is time varying, mostly improving with market maturity and is related to risk and liquidity factors.

Life-Cycle Asset Allocation in the Presence of Housing and Tax Deferred Investing
Marekwica, Marcel,Schaefer, Alexander,Sebastian, Steffen P.
We study the dynamic consumption-portfolio problem over the life cycle, with respect to tax-deferred investing for investors who acquire housing services by either renting or owning a home. The joint existence of these two investment vehicles creates potential for tax arbitrage. Specifically, investors can deduct mortgage interest payments from taxable income, while simultaneously earning interest in tax-deferred accounts tax-free. Matching empirical evidence, our model predicts that investors with higher retirement savings choose higher loan-to-value ratios to exploit this tax arbitrage opportunity. However, many households could benefit from more effectively taking advantage of tax arbitrage.

Machine Learning Sentiment Analysis, Covid-19 News and Stock Market Reactions
Costola, Michele,Nofer, Michael,Hinz, Oliver,Pelizzon, Loriana
The possibility to investigate the impact of news on stock prices has observed a strong evolution thanks to the recent use of natural language processing (NLP) in finance and economics. In this paper, we investigate COVID-19 news, elaborated with the ”Natural Language Toolkit” that uses machine learning models to extract the news’ sentiment. We consider the period from January till June 2020 and analyze 203,886 online articles that deal with the pandemic and that were published on three platforms:, and Our findings show that there is a significant and positive relationship between sentiment score and market returns. This result indicates that an increase (decrease) in the sentiment score implies a rise in positive (negative) news and corresponds to positive (negative) market returns. We also find that the variance of the sentiments and the volume of the news sources for Reuters and MarketWatch, respectively, are negatively associated to market returns indicating that an increase of the uncertainty of the sentiment and an increase in the arrival of news have an adverse impact on the stock market.

Month-End Reporting, Cash-Flow News, and Asset Pricing
Hong, Claire Yurong ,Yu, Jialin
We show that the stock market regularly and systematically receives information about company fundamentals through month-end reporting, even before the quarterly earnings announcement. Such cash-flow news concentrates at the beginning of a month and affects company announcements, analyst revisions, and stock returns. Using this time variation in cash-flow news, we show evidence supporting cash-flow news being more persistent than discount-rate news. Individual stock returns exhibit a post-monthly-announcement drift. Time series market momentum exists only when conditioning on past first-half month return, and is stronger when the past market-wide earnings surprise is bigger.

Network Structures of Collective Intelligence: The Contingent Benefits of Group Discussion
Joshua Becker,Abdullah Almaatouq,Agnes Horvat

Research on belief formation has produced contradictory findings on whether and when communication between group members will improve the accuracy of numeric estimates such as economic forecasts, medical diagnoses, and job candidate assessments. While some evidence suggests that carefully mediated processes such as the "Delphi method" produce more accurate beliefs than unstructured discussion, others argue that unstructured discussion outperforms mediated processes. Still others argue that independent individuals produce the most accurate beliefs. This paper shows how network theories of belief formation can resolve these inconsistencies, even when groups lack apparent structure as in informal conversation. Emergent network structures of influence interact with the pre-discussion belief distribution to moderate the effect of communication on belief formation. As a result, communication sometimes increases and sometimes decreases the accuracy of the average belief in a group. The effects differ for mediated processes and unstructured communication, such that the relative benefit of each communication format depends on both group dynamics as well as the statistical properties of pre-interaction beliefs. These results resolve contradictions in previous research and offer practical recommendations for teams and organizations.

New Evidence on Commodity Stocks
Daskalaki, Charoula
This paper builds on the latter literature on the commodity futures markets and undertakes, for the first time, an in-depth analysis on the investment properties of commodity-related equities (commodity stocks). The empirical evidence suggests that there have been changes in the diversification potential of commodity futures and commodity stocks that coincide with the financialization of commodity futures markets. Over the pre-financialization period, investors are better off when they incorporate commodity futures in their portfolios, whereas over the post-financialization period, commodity stock investing is more profitable. Further analysis reveals that price-based signals from commodity futures markets, such as the futures basis and prior returns, can be used as indicators of high commodity stock returns.

On Heterogeneous Memory in Hidden-Action Setups: An Agent-Based Approach
Patrick Reinwald,Stephan Leitner,Friederike Wall

We follow the agentization approach and transform the standard-hidden action model introduced by Holmstr\"om into an agent-based model. Doing so allows us to relax some of the incorporated rather "heroic" assumptions related to the (i) availability of information about the environment and the (ii) principal's and agent's cognitive capabilities (with a particular focus on their memory). In contrast to the standard hidden-action model, the principal and the agent are modeled to learn about the environment over time with varying capabilities to process the learned pieces of information. Moreover, we consider different characteristics of the environment. Our analysis focuses on how close and how fast the incentive scheme, which endogenously emerges from the agent-based model, converges to the second-best solution proposed by the standard hidden-action model. Also, we investigate whether a stable solution can emerge from the agent-based model variant. The results show that in stable environments the emergent result can nearly reach the solution proposed by the standard hidden-action model. Surprisingly, the results indicate that turbulence in the environment leads to stability in earlier time periods.

Optimal Bidding Strategy for Maker Auctions
Michael Darlin,Nikolaos Papadis,Leandros Tassiulas

The Maker Protocol is a decentralized finance application that enables collateralized lending. The application uses open-bid, second-price auctions to complete its loan liquidation process. In this paper, we develop a bidding function for these auctions, focusing on the costs incurred to participate in the auctions. We then optimize these costs using parameters from historical auction data, and compare our optimal bidding prices to the historical auction prices. We find that the majority of auctions end at higher prices than our recommended optimal prices, and we propose several theories for these results.

Optimal liquidation trajectories for the Almgren-Chriss model with Levy processes
Arne Lokka,Junwei Xu

We consider an optimal liquidation problem with infinite horizon in the Almgren-Chriss framework, where the unaffected asset price follows a Levy process. The temporary price impact is described by a general function which satisfies some reasonable conditions. We consider an investor with constant absolute risk aversion, who wants to maximise the expected utility of the cash received from the sale of his assets, and show that this problem can be reduced to a deterministic optimisation problem which we are able to solve explicitly. In order to compare our results with exponential Levy models, which provides a very good statistical fit with observed asset price data for short time horizons, we derive the (linear) Levy process approximation of such models. In particular we derive expressions for the Levy process approximation of the exponential Variance-Gamma Levy process, and study properties of the corresponding optimal liquidation strategy. We then provide a comparison of the liquidation trajectories for reasonable parameters between the Levy process model and the classical Almgren-Chriss model. In particular, we obtain an explicit expression for the connection between the temporary impact function for the Levy model and the temporary impact function for the Brownian motion model (the classical Almgren-Chriss model), for which the optimal liquidation trajectories for the two models coincide.

Optimal periodic replenishment policies for spectrally positive L\'evy demand processes
José-Luis Pérez,Kazutoshi Yamazaki,Alain Bensoussan

We consider a version of the stochastic inventory control problem for a spectrally positive L\'evy demand process, in which the inventory can only be replenished at independent exponential times. We show the optimality of a periodic barrier replenishment policy that restocks any shortage below a certain threshold at each replenishment opportunity. The optimal policies and value functions are concisely written in terms of the scale functions. Numerical results are also provided.

Quantum Annealing Algorithm for Expected Shortfall based Dynamic Asset Allocation
Samudra Dasgupta,Arnab Banerjee

The 2008 mortgage crisis is an example of an extreme event. Extreme value theory tries to estimate such tail risks. Modern finance practitioners prefer Expected Shortfall based risk metrics (which capture tail risk) over traditional approaches like volatility or even Value-at-Risk. This paper provides a quantum annealing algorithm in QUBO form for a dynamic asset allocation problem using expected shortfall constraint. It was motivated by the need to refine the current quantum algorithms for Markowitz type problems which are academically interesting but not useful for practitioners. The algorithm is dynamic and the risk target emerges naturally from the market volatility. Moreover, it avoids complicated statistics like generalized pareto distribution. It translates the problem into qubit form suitable for implementation by a quantum annealer like D-Wave. Such QUBO algorithms are expected to be solved faster using quantum annealing systems than any classical algorithm using classical computer (but yet to be demonstrated at scale).

Recent scaling properties of Bitcoin price returns
Tetsuya Takaishi

While relevant stylized facts are observed for Bitcoin markets, we find a distinct property for the scaling behavior of the cumulative return distribution. For various assets, the tail index $\mu$ of the cumulative return distribution exhibits $\mu \approx 3$, which is referred to as "the inverse cubic law." On the other hand, that of the Bitcoin return is claimed to be $\mu \approx 2$, which is known as "the inverse square law." We investigate the scaling properties using recent Bitcoin data and find that the tail index changes to $\mu \approx 3$, which is consistent with the inverse cubic law. This suggests that some properties of the Bitcoin market could vary over time. We also investigate the autocorrelation of absolute returns and find that it is described by a power-law with two scaling exponents. By analyzing the absolute returns standardized by the realized volatility, we verify that the Bitcoin return time series is consistent with normal random variables with time-varying volatility.

Reconciling Mean-Variance Portfolio Theory with Non-gaussian Returns
Lassance, Nathan
Mean-variance portfolio theory remains frequently used as investment rationale because of its simplicity, its closed-form solution, and the availability of many well-performing robust estimators. At the same time, it is also frequently rejected on the grounds that it ignores the higher moments of non-Gaussian returns. However, higher-moment portfolios are associated with many different objective functions, are numerically more complex, and exacerbate estimation risk. In this paper, we reconcile mean-variance portfolio theory with non-Gaussian returns by identifying, among all portfolios on the mean-variance efficient frontier, the one that optimizes a chosen higher-moment criterion. Via numerical simulations and an empirical analysis, we find that, for three higher-moment objective functions and adjusting for transaction costs, the resulting portfolios outperform the minimum-variance and fully optimized portfolios out of sample both in terms of Sharpe ratio and higher moments, thus striking a favorable tradeoff between specification and estimation error.

Regulatory Measures to Dismantle Pyramidal Business Groups: Evidence the United States, Japan, Korea and Israel
Hamdani, Assaf,Kosenko, Konstantin,Yafeh, Yishay
Large business enterprises, from the railroad barons of nineteenth century America to Amazon and Google today, are often perceived as important for economic performance and, at the same time, as potential abusers of their political and economic power. In this study, we compare the experiences of four countries that implemented policies to curb the influence of one type of large corporate entities â€" pyramidal business groups: The US in the 1930s; Japan during the American occupation (1945-1952); Korea following the Asian crisis (late 1990s); and Israel in the last decade (2010-2018). Novel regulatory measures, applied consistently in the US and Japan, where the extreme political circumstances were very favorable to economic reform, led to the demise of pyramidal business groups in these countries. Israel, where the reforms did not follow a severe crisis, also used specifically-designed regulatory tools over a decade-long period, resulting in a significant decline in the number and size of business groups. Korea, after experimenting with variety of regulatory measures, chose to rely primarily on corporate governance-focused reforms to curb the influence of the chaebol, but with limited effects; groups continue to dominate the Korean economy. Our findings point to the importance of specifically-designed regulatory tools, applied consistently over time, against the backdrop of a pro-reform political climate.

Risk-Sensitive Reinforcement Learning: a Martingale Approach to Reward Uncertainty
Nelson Vadori,Sumitra Ganesh,Prashant Reddy,Manuela Veloso

We introduce a novel framework to account for sensitivity to rewards uncertainty in sequential decision-making problems. While risk-sensitive formulations for Markov decision processes studied so far focus on the distribution of the cumulative reward as a whole, we aim at learning policies sensitive to the uncertain/stochastic nature of the rewards, which has the advantage of being conceptually more meaningful in some cases. To this end, we present a new decomposition of the randomness contained in the cumulative reward based on the Doob decomposition of a stochastic process, and introduce a new conceptual tool - the \textit{chaotic variation} - which can rigorously be interpreted as the risk measure of the martingale component associated to the cumulative reward process. We innovate on the reinforcement learning side by incorporating this new risk-sensitive approach into model-free algorithms, both policy gradient and value function based, and illustrate its relevance on grid world and portfolio optimization problems.

Short-Selling Bans in Europe: Evidence from the COVID-19 Pandemic
Della Corte, Pasquale ,Kosowski, Robert,Rapanos, Nikolaos
A number of European countries - Austria, Belgium, France, Greece, Italy, and Spain - responded to the market disruption caused by the COVID-19 pandemic by introducing temporary bans on short-selling activity. These restrictions were imposed on all stocks and remained in place between March 18 and May 18 across all six countries. Other European countries, unlike the 2007-09 global financial crisis, abstained from introducing any form of short-selling constraints. We exploit this cross-country variation in short-sale regimes to identify their effects on liquidity, price discovery, and stock prices. We find that bans were detrimental for liquidity and failed to support prices, in line with the early work of Beber and Pagano (2013).

Subsidizing Failing Firms: Evidence from Chinese Restaurants
Deng, Yinglu,Lu, Fangzhou,Yu, Jiaheng,Zheng, Hao
Can government stimulus or private-sector subsidization save small businesses in the COVID-19 crisis? Leveraging a large dataset on the chained restaurants in China, we find that rent reduction programs significantly increase survival rates. Rent reduction by the equivalent of a restaurant employee's annual salary can save at least one job. Rent reductions increase the financial strength of restaurants, and lead to strategic reactions of the restaurants to lean against the crisis, including increasing order discounts and promoting take-out and delivery services. Moreover, the effect of rent reduction is heterogeneous, increasing the monthly revenue by 69% for franchise-based restaurants, 51% for small company-owned restaurants, and 28% for large company-owned restaurants. Our results suggest that the organizational structure and the size of a firm can have large impact on the effectiveness and pass-through of financial stimulus programs, such as the Paycheck Protection Program.

The Contribution of Loans to Economic Activity
Cafiso, Gianluca
We study the contribution of loans, granted to different borrower groups, to economic activity in the USA over the period 1971q1-2018q4. Significant economic recessions occurred along the period considered, we center our discussion around the recent Global Financial Crisis. Results are delivered through a historical decomposition analysis based on the estimation of a large VAR through Bayesian techniques. Loans to households emerge as the most important driver of economic activity when compared to other groups, mortgages contribute the most with respect to other typologies. The analysis shows that loan shocks have truly undermined economic activity during the Global Financial Crisis.

The Effects of COVID-19 on U.S. Small Businesses: Evidence from Owners, Managers, and Employees
Alekseev, Georgij,Amer, Safaa,Gopal, Manasa,Kuchler, Theresa,SCHNEIDER, JW,Stroebel, Johannes,Wernerfelt, Nils
We analyze a large-scale survey of owners, managers, and employees of small businesses in the United States to understand the effects of the early stages of the COVID-19 pandemic on those businesses. The survey was fielded in late April 2020 among Facebook business page administrators, frequent sellers on Facebook’s e-commerce platform Marketplace, and the general Facebook user population. We observe more than 66,000 responses covering most sectors of the economy, including many businesses that had stopped operating due to the pandemic. The survey asks 136 questions covering topics such as changes in business operations and employment, changes in financing patterns, and the interaction of household and business responsibilities. We characterize the adjustments implemented to survive the pandemic and explore the key challenges to continue operating or to re-open. We show how these patterns differ across industry, firm size, owner gender, and other firm characteristics.

The Irony of Health Care’s Public Option
Hoffman, Allison K.
The idea of a public health insurance option is at least a half century old, but has not yet had its day in the limelight. This chapter explains why if that moment ever comes, health care’s public option will fall short of expectations that it will provide a differentiated, meaningful alternative to private health insurance and will spur health insurance competition. Health care’s public option bubbled up in its best-known form in California in the early 2000s and got increasing mainstream attention in the lead up to the 2010 health reform, the Patient Protection and Affordable Care Act (ACA). The idea has reemerged with vigor once again as a core tenet of Democratic presidential nominee Joe Biden’s plans to build on the ACA. When people talk about health care’s public option, they mean a public health insurance plan, typically based on Medicare that someone could select in the individual, or nongroup, health insurance market instead of a private health insurance offering. Proponents have argued that a public health insurance option could deliver better cost-control than private insurance, while also being able to offer a broad choice of providers and quality control. Health care’s public option died in the 2010 ACA legislative process, but had it been enacted, it would have faced serious obstacles to produce these results its architects hoped. The assumption that people will select the public option if it is better than other options is belied by a mounting body of empirical literature showing how we struggle, and do not do all that well, when choosing among health insurance options. Even more, political thorniness would almost certainly have prevented the public option from being a clear best alternative, which would have further impeded its ability to stand out in a crowd of options. If consumers fail to gravitate overwhelmingly to a public option, it cannot catalyze the market pressure necessary to produce lower prices or higher quality. For a public health insurance option to have transformative potentialâ€"to promote greater health equity and freedomâ€"it needs to be more than an option among many, what Sitaraman and Alstott call a competitive public option. It must be designed in a way that does not rely on people weighing it against other options and selecting it when it is the best. This chapter examines possibilities for health care’s public option in three parts. It first explains the theory behind health care’s competitive public option, the form envisioned in the ACA and similar to early proposals by the Biden Campaign during the Democratic primaries. It then considers the challenges this competitive public option would have faced had it become policy reality. Finally, it examines more effective ways that public health insurance might be integrated into a public/private hybrid system to achieve greater health equity.

The impact of social influence in Australian real-estate: market forecasting with a spatial agent-based model
Benjamin Patrick Evans,Kirill Glavatskiy,Michael S. Harré,Mikhail Prokopenko

Housing markets are inherently spatial, yet many existing models fail to capture this spatial dimension. Here we introduce a new graph-based approach for incorporating a spatial component in a large-scale urban housing agent-based model (ABM). The model explicitly captures several social and economic factors that influence the agents' decision-making behaviour (such as fear of missing out, their trend following aptitude, and the strength of their submarket outreach), and interprets these factors in spatial terms. The proposed model is calibrated and validated with the housing market data for the Greater Sydney region. The ABM simulation results not only include predictions for the overall market, but also produce area-specific forecasting at the level of local government areas within Sydney. In addition, the simulation results elucidate movement patterns across submarkets, in both spatial and homeownership terms, including renters, first-time home buyers, as well as local and overseas investors.

The impact of supply-chain networks on interactions between the anti-COVID-19 lockdowns in different regions
Hiroyasu Inoue,Yohsuke Murase,Yasuyuki Todo

To prevent the spread of COVID-19, many cities, states, and countries have `locked down', restricting economic activities in non-essential sectors. Such lockdowns have substantially shrunk production in most countries. This study examines how the economic effects of lockdowns in different regions interact through supply chains, a network of firms for production, simulating an agent-based model of production on supply-chain data for 1.6 million firms in Japan. We further investigate how the complex network structure affects the interactions of lockdowns, emphasising the role of upstreamness and loops by decomposing supply-chain flows into potential and circular flow components. We find that a region's upstreamness, intensity of loops, and supplier substitutability in supply chains with other regions largely determine the economic effect of the lockdown in the region. In particular, when a region lifts its lockdown, its economic recovery substantially varies depending on whether it lifts lockdown alone or together with another region closely linked through supply chains. These results propose the need for inter-region policy coordination to reduce the economic loss from lockdowns.

Transparency in Fragmented Markets: Experimental Evidence
Hendershott, Terrence,Wee, Marvin,Wen, Yuanji
This paper examines the effects of pre-trade opacity on market liquidity in the presence of market fragmentation. In the laboratory, we create a fragmented market by allowing trading on two venues (i.e., limit order books). By varying the features on one of the venues, we study the treatment effects of two forms of pre-trade opacity using a within-subjects design. We find that order strategies are affected by: (1) whether traders can trade in a dark venue (i.e., dark market), or (2) whether traders can submit non-displayed orders (i.e., hidden market). Compared to the benchmark setting where both venues are lit, liquidity is higher and market quality is improved in the dark market. However, transaction costs are not lower on the dark market as traders are unable to observe and hence to route their orders to the venue with the lower transaction cost. In contrast to the dark market, we find the usage of non-displayed orders is low in the hidden market, suggesting the costs of using such orders outweigh the benefits. Consequently, we find no difference in the market quality of the hidden market compared to the benchmark. Our findings show that the two forms of pre-trade opacity yield different market outcomes.

Unmasking Mutual Fund Derivative Use During the COVID-19 Crisis
Kaniel, Ron,Wang, Pingle
Utilizing newly available data from the SEC on derivative performance and detailed derivative holdings, this paper studies how derivatives impact mutual fund performance, with an emphasis on the COVID-19 pandemic period. In contrast to previous research concluding derivatives are used for hedging, we find that most active equity funds use derivatives to amplify market exposure. Despite the seemingly small weight, derivatives have a significant impact on funds' leverage and contribute largely to fund returns. In response to the initial outbreak of COVID-19, funds trade more heavily on short derivative positions. This behavior is more prevalent among managers residing in states with early state-level Stay-at-home orders, where the risk of recession is likely more salient. Funds that used derivatives for hedging purposes before the crisis significantly outperform nonusers by over 9% during the initial outbreak, as their distribution of derivative returns shifts to the right. By the end of June, they still outperform by 1.6%. On the contrary, funds that used derivatives to amplify market exposure underperform, and their distribution of derivative returns shifts to the left. While they do shift strategies, they are slow to open short positions and remain mostly amplifying funds. Consequently, by the time they shift, the market has already started to recover, so that they lose on their short positions. The shifts in derivative return distributions during the COVID-19 crisis are mostly driven by swap contracts, which have been ignored by prior studies.

What factors have caused Japanese prefectures to attract a larger population influx?
Keisuke Kokubun

Regional promotion and centralized correction in Tokyo have long been the goals of the Government of Japan. Furthermore, in the wake of the recent new coronavirus (COVID-19) epidemic, the momentum for rural migration is increasing, to prevent the risk of infection with the help of penetration of remote work. However, there is not enough debate about what kind of land will attract the population. Therefore, in this paper, we will consider this problem by performing correlation analysis and multiple regression analysis with the inflow rate and the excess inflow rate of the population as the dependent variables, using recent government statistics for each prefecture. As a result of the analysis, in addition to economic factor variables, variables of climatic, amenity, and human factors correlated with the inflow rate, and it was shown that the model has the greatest explanatory power when multiple factors were used in addition to specific factors. Therefore, local prefectures are required to take regional promotion measures focusing on not only economic factors but also multifaceted factors to attract the outside population.

Which Trading Agent is Best? Using a Threaded Parallel Simulation of a Financial Market Changes the Pecking-Order
Michael Rollins,Dave Cliff

This paper presents novel results generated from a new simulation model of a contemporary financial market, that cast serious doubt on the previously widely accepted view of the relative performance of various well-known public-domain automated-trading algorithms. Various public-domain trading algorithms have been proposed over the past 25 years in a kind of arms-race, where each new trading algorithm was compared to the previous best, thereby establishing a "pecking order", i.e. a partially-ordered dominance hierarchy from best to worst of the various trading algorithms. Many of these algorithms were developed and tested using simple minimal simulations of financial markets that only weakly approximated the fact that real markets involve many different trading systems operating asynchronously and in parallel. In this paper we use BSE, a public-domain market simulator, to run a set of experiments generating benchmark results from several well-known trading algorithms. BSE incorporates a very simple time-sliced approach to simulating parallelism, which has obvious known weaknesses. We then alter and extend BSE to make it threaded, so that different trader algorithms operate asynchronously and in parallel: we call this simulator Threaded-BSE (TBSE). We then re-run the trader experiments on TBSE and compare the TBSE results to our earlier benchmark results from BSE. Our comparison shows that the dominance hierarchy in our more realistic experiments is different from the one given by the original simple simulator. We conclude that simulated parallelism matters a lot, and that earlier results from simple simulations comparing different trader algorithms are no longer to be entirely trusted.