Research articles for the 2019-04-25

100 Research Ideas: Extending the Frontiers of Research in Corporate Finance
Ang , James S.
Where can one systematically find research ideas? I propose a scheme to classify and broadly generalize the sources of inspiration for research topics, with emphasis on corporate finance. In the process, I propose 100 new research topics awaiting the brave and persistent.

Born to Be Bad
Clifford, Christopher P.,Ellis, Jesse A.,Gerken, William Christopher
Using a geographic measure of unethical culture developed by Parsons, Sulaeman and Titman (2018) and a novel dataset of financial advisors' childhood residences, we show that advisors who grow up in U.S. counties with less ethical cultures are more likely to commit misconduct as adults. Our identification strategy exploits variation in childhood backgrounds between advisors working together in the same branch office in adulthood, thereby overcoming the reflection problem. Our results are robust to controlling for other factors from the early-life experiences literature such as income, education, ethnicity and religiosity. We find that areas with high concentrations of advisors that hail from less ethical cultures have lower levels of household equity participation. Our findings have important implications for how regional cultural norms regarding misconduct evolve.

CAPM: A Tale of Two Versions
Siddiqi, Hammad
Given that categorization is the core of cognition, we argue that investors do not view firms in isolation. Rather, they view them within a framework of categories that represent prior knowledge. This involves sorting a given firm into a category and using categorization-induced inferences to form earnings and discount-rate expectations. If earnings-aspect is categorization-relevant, then earnings estimates are refined, whereas discount-rates are confounded with the category-exemplar. The opposite happens when discount-rates are categorization relevant. Earnings-focused approach (such as DCF), predominantly used by institutional investors, leads to a version of CAPM in which the relationship between average excess return and stock beta is flat (possibly negative). Value effect and size premium (controlling for quality) arise in this version. Discount-rate focused approach (such as comparables valuation), typically used by individual investors, leads to a second version in which the relationship is strongly positive with growth stocks doing better. The two-version CAPM accounts for several recent empirical findings including fundamentally different intraday vs overnight behavior, as well as behavior on macroeconomic announcement days. Momentum is expected to be an overnight phenomenon, which is consistent with empirical findings. We argue that, perhaps, our best shot at observing classical CAPM in its full glory is a laboratory experiment with subjects who have difficulty categorizing (such as in autism spectrum disorders).

Continuous-Time Mean-Variance Portfolio Optimization via Reinforcement Learning
Haoran Wang,Xun Yu Zhou

We consider continuous-time Mean-variance (MV) portfolio optimization problem in the Reinforcement Learning (RL) setting. The problem falls into the entropy-regularized relaxed stochastic control framework recently introduced in Wang et al. (2019). We derive the feedback exploration policy as the Gaussian distribution, with time-decaying variance. Close connections between the entropy-regularized MV and the classical MV are also discussed, including the solvability equivalence and the convergence as exploration decays. Finally, we prove a policy improvement theorem (PIT) for the continuous-time MV problem under both entropy regularization and control relaxation. The PIT leads to an implementable RL algorithm for the continuous-time MV problem. Our algorithm outperforms an adaptive control based method that estimates the underlying parameters in real-time and a state-of-the-art RL method that uses deep neural networks for continuous control problems by a large margin in nearly all simulations.

Costs of Sovereign Defaults: Restructuring Strategies, Bank Distress and the Capital Inflow-Credit Channel
Asonuma, Tamon,Chamon, Marcos,Erce, Aitor,Sasahara, Akira
Sovereign debt restructurings are associated with declines in GDP, investment, bank credit, and capital flows. The transmission channels and associated output and banking sector costs depend on whether the restructuring takes place preemptively, without missing payments to creditors, or whether it takes place after a default has occurred. Post-default restructurings are associated with larger declines in bank credit, an increase in lending interest rates, and a higher likelihood of triggering a banking crisis than pre-emptive restructurings. Our local projection estimates show large declines in GDP, investment, and credit amplified by severe sudden stops and transmitted through a 'capital inflow-credit channel'.

Credit Supply and Human Capital: Evidence from Bank Pension Liabilities
Barbosa, Luciana,Bilan, Andrada,Célérier, Claire
We identify the effects of exogenous credit constraints on firm ability to attract and retain skilled workers. To do so, we exploit a shock to the value of the pension obligations of Portuguese banks resulting from a change in accounting norms. Using bank-firm credit exposures that we match with a census of all Portuguese employees, we show that firms in a relationship with affected banks borrow less and reduce employment mostly of high-skilled workers. High-skilled workers are more likely to exit and less likely to join affected firms. Overall, credit market frictions might have long lasting effects on firm productivity and growth through firm accumulation of human capital.

Deep Generative Models for Reject Inference in Credit Scoring
Rogelio A. Mancisidor,Michael Kampffmeyer,Kjersti Aas,Robert Jenssen

Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications. In this research, we use deep generative models to develop two new semi-supervised Bayesian models for reject inference in credit scoring, in which we model the data generating process to be dependent on a Gaussian mixture. The goal is to improve the classification accuracy in credit scoring models by adding reject applications. Our proposed models infer the unknown creditworthiness of the rejected applications by exact enumeration of the two possible outcomes of the loan (default or non-default). The efficient stochastic gradient optimization technique used in deep generative models makes our models suitable for large data sets. Finally, the experiments in this research show that our proposed models perform better than classical and alternative machine learning models for reject inference in credit scoring.

Duty of Disclosure: Comparison of Securities Regulations between U.S. and S. Korea by Cases in Bio-Pharmaceutical Industry
Sohn, Donghoo
This paper introduces the concept of false and misleading statements or omissions regarding companies in the bio-pharmaceutical industry, based on court decisions. Due to essential problem of stock market, “asymmetry of information,” if company’s public statements about material information are not trustworthy, investors who purchased the company’s shares might lose their assets, and the confidence in and the soundness of the stock market would be damaged as a result. This paper attempts to clarify violations of §10(b) of the Securities Exchange Act of 1934 and the corresponding rule of the Securities and Exchange Commission, 17 C.F.R. §240.10b-5 (“Rule 10b-5"), particularly, (1) a material misrepresentation or omission, and (2) scienter through reviewing recent cases of bio-pharmaceutical companies, Aratana case and Chelsea case.In the Republic of Korea, Hanmi Pharm’s caserepresents how the late public announcement or omission would affect investors’ half-baked decision and damage the stock market by insider trading. Pursuant to §391 of the Financial Investment Services and Capital Market Act, Korea Exchange announced a law “Negative Disclosure Regulations on Material Information,” on May 2, 2016. Nevertheless, the regulation does not appear to be effective guideline for investors and stock market. In the proposed scienter tests, this paper introduces ImClone stock trading scandal to represent the relatively easy scienter case in the bio-pharmaceutical industry. Also, this paper illustrates two hypothetical cases, Brandon Therapeutics, Inc. in the United States, and Hoon Biosimilar Co., Ltd. case to suggest the better solution to solve scienter cases in the bio-pharmaceutical industry.In conclusion, this paper compares both American and Korean cases and related regulation, and then proposes a supplement that will promote a sound stock market.

Financial Deepening, Terms of Trade Shocks, and Growth Volatility in Low-Income Countries
Kpodar, Kangni,Le Goff, Maelan,Singh, Raju Jan
This paper contributes to the literature by looking at the possible relevance of the structure of the financial system-whether financial intermediation is performed through banks or markets-for macroeconomic volatility, against the backdrop of increased policy attention on strengthening growth resilience. With low-income countries (LICs) being the most vulnerable to large and frequent terms of trade shocks, the paper focuses on a sample of 38 LICs over the period 1978-2012 and finds that banking sector development acts as a shock-absorber in poor countries, dampening the transmission of terms of trade shocks to growth volatility. Expanding the sample to 121 developing countries confirms this result, although this role of shock-absorber fades away as economies grow richer. Stock market development, by contrast, appears neither to be a shock-absorber nor a shock-amplifier for most economies. These findings are consistent across a range of econometric estimators, including fixed effect, system GMM and local projection estimates.

Fintech in Latin America and the Caribbean: Stocktaking
Berkmen, Pelin,Beaton, Kimberly,Gershenson, Dmitriy,Arze del Granado, F. Javier,Ishi, Kotaro,Kim, Marie,Kopp, Emanuel,Rousset, Marina
In Latin America and the Caribbean (LAC), financial technology has been growing rapidly and is on the agenda of many policy makers. Fintech provides opportunities to deepen financial development, competition, innovation, and inclusion in the region but also creates new and only partially understood risks to consumers and the financial system. This paper documents the evolution of fintech in LAC. In particular, the paper focuses on financial development, fintech landscape for domestic and cross border payments and alternative financing, cybersecurity, financial integrity and stability risks, regulatory responses, and considerations for central bank digital currencies.

Fiscal Policy Multipliers in Small States
Alichi, Ali,Shibata, Ippei,Tanyeri, Kadir
Government debt in many small states has risen beyond sustainable levels and some governments are considering fiscal consolidation. This paper estimates fiscal policy multipliers for small states using two distinct models: an empirical forecast error model with data from 23 small states across the world; and a Dynamic Stochastic General Equilibrium (DSGE) model calibrated to a hypothetical small state's economy. The results suggest that fiscal policy using government current primary spending is ineffective, but using government investment is very potent in small states in affecting the level of their GDP over the medium term. These results are robust to different model specifications and characteristics of small states. Inability to affect GDP using current primary spending could be frustrating for policymakers when an expansionary policy is needed, but encouraging at the current juncture when many governments are considering fiscal consolidation. For the short term, however, multipliers for government current primary spending are larger and affected by imports as share of GDP, level of government debt, and position of the economy in the business cycle, among other factors.

Forecasting in Big Data Environments: an Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)
Ali Habibnia,Esfandiar Maasoumi

This paper considers improved forecasting in possibly nonlinear dynamic settings, with high-dimension predictors ("big data" environments). To overcome the curse of dimensionality and manage data and model complexity, we examine shrinkage estimation of a back-propagation algorithm of a deep neural net with skip-layer connections. We expressly include both linear and nonlinear components. This is a high-dimensional learning approach including both sparsity L1 and smoothness L2 penalties, allowing high-dimensionality and nonlinearity to be accommodated in one step. This approach selects significant predictors as well as the topology of the neural network. We estimate optimal values of shrinkage hyperparameters by incorporating a gradient-based optimization technique resulting in robust predictions with improved reproducibility. The latter has been an issue in some approaches. This is statistically interpretable and unravels some network structure, commonly left to a black box. An additional advantage is that the nonlinear part tends to get pruned if the underlying process is linear. In an application to forecasting equity returns, the proposed approach captures nonlinear dynamics between equities to enhance forecast performance. It offers an appreciable improvement over current univariate and multivariate models by RMSE and actual portfolio performance.

Funding Innovations for Sustainable Growth in Emerging Markets
Neubert, Michael
This study aims to understand the impact of fundraising innovations on sustainable growth in emerging markets. It opted for a multiple-case study research design using different sources of evidence, including nineteen semi-structured interviews. The subject matter experts (SMEs) were selected using a purposive selection method. The theoretical framework of Porter and Kramer is used. The results suggest that crowdinvesting, initial coin offerings, and accelerators might facilitate sustainable growth of private equity and venture capital markets in Africa due to entrepreneurial ecosystems and networks. The findings are relevant for founders and investors. The study contributes to the literature on entrepreneurial finance in emerging markets.

Hiring Disclosure and Shareholder Value: Evidence from Job Postings
Gutierrez, Elizabeth F.,Lourie, Ben,Nekrasov, Alexander,Shevlin, Terry J.
Human capital is a key factor in value creation in the modern corporation, yet the disclosure on investment in human capital is scant. We propose that a company’s job postings are disclosures made outside of the investor relations channel that contain forward-looking information about hiring new employees. We find a positive association between changes in the number of job postings and announcement returns. Changes in the number of job postings are related to positive changes in future employee count, employee-related expenditures, and financial performance. The findings suggest that job postings disclosures contain new information about hiring that investors consider as value increasing. Finally, we find that the market response to hiring news is influenced by firm economic conditions and information environment and is concentrated in recent years when the acquisition and processing cost of job postings is likely to be lower.

How Much Would You Pay to Resolve Volatility Risk in Agricultural Commodity Markets?
Yan, Lei,Garcia, Philip
This article investigates the pricing of volatility risk in agricultural commodity markets. We show theoretically that the cost of bearing volatility risk can be measured using returns to delta-neutral straddles. Using a sample of options for five commodities (corn, soybeans, Chicago wheat, live cattle, and lean hogs) for 2002â€"2016, we provide the following findings. First, returns to delta-neutral straddles are negative, ranging between â€"0.87% and â€"0.06% per day, suggesting that investors are willing to pay a cost to avoid volatility risk. Second, volatility risk is priced mainly at short maturities (within 2 months) but is negligible at longer maturities, and this term structure of volatility risk prevails in all markets. Last, volatility risk is more pronounced on the day immediately preceding the U.S. Department of Agriculture (USDA) announcements but is not effectively explained by economic or commodity specific factors.

Inefficiency and Regulation of Private Liquidity
Benigno, Pierpaolo,Robatto, Roberto
We propose a simple model to study the efficiency of liquidity creation by financial intermediaries, which can take the form of either safe or risky debt. Liquidity crises arise when risky debt is defaulted on and stops providing liquidity services. Owing to a novel externality related to liquidity premia and the cost of issuing safe debt, the laissez-faire equilibrium is inefficient, characterized by an excessive supply of risky debt. However, the optimal policy requires the regulation of safe debt as well. Capital requirements targeting risky debt alone have unintended welfare-reducing consequences.

Learning the population dynamics of technical trading strategies
Nicholas Murphy,Tim Gebbie

We use an adversarial expert based online learning algorithm to learn the optimal parameters required to maximise wealth trading zero-cost portfolio strategies. The learning algorithm is used to determine the relative population dynamics of technical trading strategies that can survive historical back-testing as well as form an overall aggregated portfolio trading strategy from the set of underlying trading strategies implemented on daily and intraday Johannesburg Stock Exchange data. The resulting population time-series are investigated using unsupervised learning for dimensionality reduction and visualisation. A key contribution is that the overall aggregated trading strategies are tested for statistical arbitrage using a novel hypothesis test proposed by Jarrow et al. on both daily sampled and intraday time-scales. The (low frequency) daily sampled strategies fail the arbitrage tests after costs, while the (high frequency) intraday sampled strategies are not falsified as statistical arbitrages after costs. The estimates of trading strategy success, cost of trading and slippage are considered along with an offline benchmark portfolio algorithm for performance comparison. In addition, the algorithms generalisation error is analysed by recovering a probability of back-test overfitting estimate using a nonparametric procedure introduced by Bailey et al.. The work aims to explore and better understand the interplay between different technical trading strategies from a data-informed perspective.

Loss-based risk statistics with scenario analysis
Fei Sun

Since the investors and regulators pay more attention to losses rather than gains, we will study a new class of risk statistics, named loss-based risk statistics in this paper. This new class of risk statistics can be considered as a kind of risk extension of risk statistics introduced by Kou, Peng and Heyde (2013), and also data-based versions of loss-based risk measures introduced by Cont et al. (2013) and Sun et al. (2018).

Mapping Algorithms, Agricultural Futures, and the Relationship Between Commodity Investment Flows and Crude Oil Futures Prices
Yan, Lei,Irwin, Scott H.,Sanders, Dwight R.
Several studies employ mapping algorithms to infer index positions in WTI crude oil futures from positions in agricultural futures and report an economically large and statistically significant impact of index positions on crude oil futures prices. In this article, we provide direct evidence that the apparent impact of index investment based on mapping algorithms is spurious. Specifically, an idiosyncratic spike in agricultural index positions during 2007â€"08, coupled with the spike in oil prices, causes the spurious impact of index investment on crude oil futures prices found in these earlier studies.

Mean-Variance Portfolio Optimization Based on Ordinal Information
Eranda, Cela,Hafner, Stephan,Mestel, Roland,Pferschy, Ulrich
We propose a new approach that allows for incorporating qualitative views, such as ordering information, into estimates of future asset returns within the Black-Litterman model. We develop a mathematical framework and numerical computation methods for this setting. We find importance sampling to be the most appropriate numerical approach in terms of accuracy and computation time. Using empirical stock market data, we find our extended Black-Litterman model to process ordering information on future asset returns better than two previously suggested approaches. Our new estimator is successfully evaluated in the context of mean-variance portfolio optimization.

Measuring China's Stock Market Sentiment
Li, Jia,Chen, Yun,Shen, Yan,Wang, Jingyi,Huang, Zhuo
This paper develops textual sentiment measures for China's stock market by extracting the textual tone of 60 million messages posted on a major online investor forum in China from 2008 to 2018. We conduct sentiment extraction by using both conventional dictionary methods based on customized word lists and supervised machine-learning methods (support vector machine and convolutional neural network). The market-level textual sentiment index is constructed as the average of message-level sentiment scores, and the textual disagreement index is constructed as their dispersion. These textual measures allow us to test a range of predictions of classical behavioral asset-pricing models within a unified empirical setting. We find that textual sentiment can significantly predict market return, exhibiting a salient underreaction-overreaction pattern on a time scale of several months. This effect is more pronounced for small and growth stocks, and is stronger under higher investor attention and during more volatile periods. We also find that textual sentiment exerts a significant and asymmetric impact on future volatility. Finally, we show that trading volume will be higher when textual sentiment is unusually high or low and when there are more differences of opinion, as measured by our textual disagreement. Based on a massive textual dataset, our analysis provides support for the noise-trading theory and the limits-to-arbitrage argument, as well as predictions from limited-attention and disagreement models.

Multivariate Garch with dynamic beta
Matthias Raddant,Friedrich Wagner

We present a solution for the problems related to the application of multivariate Garch models to markets with a large number of stocks by restricting the form of the covariance matrix. It contains one component describing the market and a second simple component to account for the remaining contribution to the volatility. This allows the analytical calculation of the inverse covariance matrix. We compare our model with the results of other Garch models for the daily returns from the S&P500 market. The description of the covariance matrix turns out to be similar to the DCC model but has fewer free parameters and requires less computing time. The model also has the advantage that it contains the calculation of dynamic beta values. As applications we use the daily values of $\beta$ coefficients available from the market component to confirm a transition of the market in 2006. Further we discuss the relationship of our model with the leverage effect.

On Fairness of Systemic Risk Measures
Francesca Biagini,Jean-Pierre Fouque,Marco Frittelli,Thilo Meyer-Brandis

In our previous paper, "A Unified Approach to Systemic Risk Measures via Acceptance Set" (\textit{Mathematical Finance, 2018}), we have introduced a general class of systemic risk measures that allow for random allocations to individual banks before aggregation of their risks. In the present paper, we prove the dual representation of a particular subclass of such systemic risk measures and the existence and uniqueness of the optimal allocation related to them. We also introduce an associated utility maximization problem which has the same optimal solution as the systemic risk measure. In addition, the optimizer in the dual formulation provides a \textit{risk allocation} which is fair from the point of view of the individual financial institutions. The case with exponential utilities which allows for explicit computation is treated in details.

Perspective of Arbitrase Institutions to the Shut of Sharia Business (Ash-Shulh Wa Tahkim)
Munawar Albadri, Abdul Aziz
The Qur'an is the main basis of Shari'ah. It states the principles while the sunnah of the Prophet provides the details of their application. For example, the Qur'an says: establish Salah, observe sawm, pay zakah, take decisions by consultation, do not earn or spend in wrong ways but it does not describe how to do these things. It is the sunnah of the Prophet which gives us the details. The Qur'an is the main book of guidance and the Prophet taught how to follow it. The Prophet not only told us how to follow the guidance, but he also practiced it himself. The Qur’an has rules for every aspect of life. It is complete and perfect and guarantees us success, welfare, and peace in this life on earth and in the life after death. For example, arbitrate and et all.

Politique de dividende: les enjeux (Dividend Policy: The Issues)
Taleb, Lotfi
French Abstract: Le présent ouvrage étudie la problématique de distribution de profit aussi bien par voie de dividende que par voie de rachat d'actions. En dehors des hypothèses de perfections des marchés financiers, de neutralité de la fiscalité et de la maximisation de la richesse des actionnaires, d'autres courants de recherches sont venus enrichir le débat sur la problématique de distribution de profit. Ces courants de recherches se basent d'une part sur le caractère asymétrique de l'information caractérisant les marchés financiers pour donner lieu à la théorie de signalisation et d'autre part, sur le caractère asymétrique du pouvoir et de la relation conflictuelle caractérisant les différents partenaires de la firme permettant de donner lieu à la théorie d'agence. Cet ouvrage constitue une synthèse des principaux travaux théoriques et empiriques traitant la problématique de distribution de profit sous ces deux hypothèses.English Abstract: This book examines the issue of the payout policy both through dividends and stock repurchase. Apart from the assumptions of financial market perfections, tax neutrality and the maximization of shareholders' wealth, other lines of research have enriched the debate on the issue of profit distribution. These currents of research are essentially based on the information asymmetry characterizing the financial markets to give rise to the signaling theory and also, shareholder conflict and other agency problems characterizing the different partners of the firm, to give rise to the agency theory. This book is a synthesis of the main theoretical and empirical works dealing with the issue of profit distribution under these two hypotheses.

Regulation, Ownership and Bank Performance in the MENA Region: Evidence From Islamic and Conventional Banks
Mateev, Miroslav,Bachvarov, Petko
This paper investigates the impact of regulation and ownership on the performance of banks in 19 countries in the Middle East and North Africa (MENA) region. We test the hypothesis that the effect of regulation on bank profitability depends on the type of ownership structure. The public and private views of bank regulation are also tested along with the interaction of bank regulation and ownership. We find regulation measures to have a strong influence on bank profitability, whereas ownership structure seems to play a limited role in explaining bank profitability in the MENA region. The results support the private view of bank regulation and suggest that capital requirements and private monitoring when interacted with ownership concentration exert a strong influence on bank profitability. When the analysis is done separately for conventional and Islamic banks, we find that the impact of bank regulations although strongly significant, does not depend on the type of ownership structure prevailing in conventional banks. In contrast, regulatory effects seem to be important drivers of profitability of Islamic banks. This effect is more pronounced for government banks and banks with high level of foreign ownership. Therefore, it is very important for policy makers in these countries not to treat the two types of banks identically when setting up and implementing bank regulations.

Rise and Fall of Calendar Anomalies over a Century
Plastun, Oleksiy,Sibande, Xolani,Gupta, Rangan,Wohar, Mark E.
In this paper, we conduct a comprehensive investigation of calendar anomaly evolution in the US stock market (given by the Dow Jones Industrial Average) for the 1900 to 2018 period. We employ various statistical techniques (average analysis, Student’s t-test, ANOVA, the Kruskal-Wallis and Mann-Whitney tests) and the trading simulation approach to analyse the evolution of the following calendar anomalies: day of the week effect, turn of the month effect, turn of the year effect, and the holiday effect. The results revealed that ‘golden age’ of calendar anomalies was in the middle of the 20th century. However, since the 1980s all calendar anomalies disappeared. This is consistent with the Efficient Market Hypothesis.

Risk-neutral pricing for APT
Laurence Carassus,Miklos Rasonyi

We consider the problem of super-replication (hedging without risk) for the Arbitrage Pricing Theory. The dual characterization of super-replication cost is provided. It is shown that the reservation prices of investors converge to this cost as their respective risk-aversion tends to infinity.

Shared factory: a new production node for social manufacturing in the context of sharing economy
Pingyu Jiang,Pulin Li

Manufacturing industry is heading towards socialization, interconnection, and platformization. Motivated by the infiltration of sharing economy usage in manufacturing, this paper addresses a new factory model -- shared factory -- and provides a theoretical architecture and some actual cases for manufacturing sharing. Concepts related to three kinds of shared factories which deal respectively with sharing production-orders, manufacturing-resources and manufacturing-capabilities, are defined accordingly. These three kinds of shared factory modes can be used for building correspondent sharing manufacturing ecosystems. On the basis of sharing economic analysis, we identify feasible key enabled technologies for configuring and running a shared factory. At the same time, opportunities and challenges of enabling the shared factory are also analyzed in detail. In fact, shared factory, as a new production node, enhances the sharing nature of social manufacturing paradigm, fits the needs of light assets and gives us a new chance to use socialized manufacturing resources. It can be drawn that implementing a shared factory would reach a win-win way through production value-added transformation and social innovation.

Spatial risk measures and rate of spatial diversification
Erwan Koch

An accurate assessment of the risk of extreme environmental events is of great importance for populations, authorities and the banking/insurance/reinsurance industry. Koch (2017) introduced a notion of spatial risk measure and a corresponding set of axioms which are well suited to analyze the risk due to events having a spatial extent, precisely such as environmental phenomena. The axiom of asymptotic spatial homogeneity is of particular interest since it allows one to quantify the rate of spatial diversification when the region under consideration becomes large. In this paper, we first investigate the general concepts of spatial risk measures and corresponding axioms further and thoroughly explain the usefulness of this theory for both actuarial science and practice. Second, in the case of a general cost field, we give sufficient conditions such that spatial risk measures associated with expectation, variance, Value-at-Risk as well as expected shortfall and induced by this cost field satisfy the axioms of asymptotic spatial homogeneity of order $0$, $-2$, $-1$ and $-1$, respectively. Last but not least, in the case where the cost field is a function of a max-stable random field, we provide conditions on both the function and the max-stable field ensuring the latter properties. Max-stable random fields are relevant when assessing the risk of extreme events since they appear as a natural extension of multivariate extreme-value theory to the level of random fields. Overall, this paper improves our understanding of spatial risk measures as well as of their properties with respect to the space variable and generalizes many results obtained in Koch (2017).

Style Consistency and Mutual Fund Returns: The Case of Russia
Bayarmaa, Adiya,Caporale, Guglielmo Maria
This paper carries out style analysis for Russian mutual funds using monthly data from the National Managers' Association over the period January 2008-December 2017; specifically, it applies the RSBA method developed by Sharpe (1992) for evaluating the impact of style on returns, and uses the Style Drift Score (SDS) introduced by Idzorek (2004) as a measure of a fund's style drifting activity. The main findings can be summarised as follows. In the Russian case there is a significant positive relationship between style consistency and profitability of funds. Further, Russian funds are characterised by a high level of style drift, namely deviations from the investment strategy declared at the time of registration as required by Russian law.

Subjective Bond Risk Premia and Belief Aggregation
Buraschi, Andrea,Piatti, Ilaria,Whelan, Paul
This paper documents large heterogeneity and forecasting skill in the cross section of survey based bond risk premia. We propose a novel approach to aggregate individual expectations to proxy for the belief of the marginal agent. Our measure is motivated by economic theory, is available in real-time and generates forecast errors that are not easily corrected. Comparing our measure with risk premium factors based on structural models we find support for variation in bond risk premia via the quantity of risk channel.A previous version of this paper can be found at:

Takeovers, Shareholder Litigation, and the Free-Riding Problem
Broere, Mark,Christmann, Robin
When shareholders of a target firm expect a value improving takeover to be successful, they are individually better off not tendering their shares to the buyer and the takeover potentially fails. Squeeze-out procedures can overcome this free-riding dilemma by allowing a buyer to enforce a payout of minority shareholders and seize complete control of the target firm. However, it is often argued that shareholder protection laws and litigation restore or intensify the free-riding dilemma. Applying a game theoretic setting, we demonstrate that it is not shareholder litigation that brings back the free-riding dilemma, but rather the strategic gambling of buyers for lower prices and flaws in the design and application of squeeze-out laws. We find, for example, that lawmakers should refrain from setting separate legal thresholds for corporate control and squeeze-outs. We also analyze a favorable change in jurisdiction of the German Federal Court and provide implications for legal policy.

The Fair Reward Problem: The Illusion of Success and How to Solve It
Sornette, Didier ,Wheatley, Spencer,Cauwels, Peter
Humanity has been fascinated by the pursuit of fortune since time immemorial, and many successful outcomes benefit from strokes of luck. But success is subject to complexity, uncertainty, and change â€" and at times becoming increasingly unequally distributed. This leads to tension and confusion over to what extent people actually get what they deserve (i.e., fairness/meritocracy). Moreover, in many fields, humans are over-confident and pervasively confuse luck for skill (I win, it’s skill; I lose, it’s bad luck). In some fields, there is too much risk-taking; in others, not enough. Where success derives in large part from luck â€" and especially where bailouts skew the incentives (heads, I win; tails, you lose) â€" it follows that luck is rewarded too much. This incentivizes a culture of gambling, while downplaying the importance of productive effort. And, short term success is often rewarded, irrespective, and potentially at the detriment, of the long-term system fitness. However, much success is truly meritocratic, and the problem is to discern and reward based on merit. We call this the fair reward problem. To address this, we propose three different measures to assess merit: (i) raw outcome; (ii) risk-adjusted outcome, and (iii) prospective. We emphasize the need, in many cases, for the deductive prospective approach, which considers the potential of a system to adapt and mutate in novel futures. This is formalized within an evolutionary system, comprised of five processes, inter alia handling the exploration-exploitation trade-off. Several human endeavors â€" including finance, politics, and science â€" are analyzed through these lenses, and concrete solutions are proposed to support a prosperous and meritocratic society.

The Introduction of Formal Insurance and its Effect on Redistribution
Anderberg, Dan,Smarzynska Javorcik, Beata
Transfers motivated by altruism, norms of giving, and guilt play an important role in supporting individuals who suffer losses due to risk. We present empirical evidence from an artefactual field experiment in Ethiopia in which we introduce formal insurance in a setting where donors make redistributive transfers to anonymously paired recipients. We find that donors reduce their transfers to recipients who don't take-up insurance, and that this effect is larger for donors who hold the ex ante belief that the recipient is more likely to take-up insurance. The findings are consistent with a model of a norm of giving where donors feel guilty for deviating from the norm. The feelings of guilt decline with the expected social distance, that is revealed by the recipients' observable insurance uptake decisions. The model highlights how the introduction of formal insurance may erode norms of giving and lead vulnerable groups to face more volatile consumption.

Trade Credit and Markups
Garcia-Marin, Alvaro,Justel, Santiago,Schmidt-Eisenlohr, Tim
Trade credit is the most important form of short-term finance for U.S. firms. In 2017, non-financial firms had about $3 trillion in trade credit outstanding equaling 20 percent of U.S. GDP. Why do sellers lend to their buyers in the presence of a well-developed financial sector? This paper proposes an explanation for the puzzling dominance of trade credit: When sellers charge markups over production costs and financial intermediation is costly, then buyer-seller pairs can save on their overall financing costs by utilizing trade credit. We derive a model of trade credit and markups that captures this mechanism. In the model, the larger is the markup and the larger is the difference between the borrowing and the deposit rate, the more attractive is trade credit. The model also implies that trade credit use increases with repeated interactions and that this effect is stronger for complex products. Using Chilean data at the firm-level to estimate markups and at the trade-transaction level to analyze payment choices, we find strong support for the model.

Transaction-Tax Evasion in the Housing Market
Montalvo, Jose G.,Piolatto, Amedeo,Raya, Josep
We model the behaviour of a buyer trying to evade the real estate transfer tax. We identify over-appraisal as a key, easily-observable element that is inversely related with tax evasion. We conclude that the tax authority could focus auditing efforts on low-appraisal transactions. We include ‘behavioural’ components (shame and stigma) allowing to introduce buyers' (education) and societal (social capital) characteristics that explain individual and idiosyncratic variations. Our empirical analysis confirms the predictions using a unique database, where we directly observe: real payment, value declared to the authority, appraisal, buyers' educational level and local levels of corruption and trust.

What Flows Around Comes Around: Mean Reversion and Portfolio Flows
Mair, Florian,Thoma, Alexander
This paper investigates mean reversion properties of real effective exchange rates (REERs) using a semi-parametric quantile autoregression approach. This method accounts for non-normality and captures asymmetric and dynamic adjustments towards the REER's long run equilibrium, conditional on the size of the shock to the REER. Due to our tests' nonstandard limiting distribution, we apply a resampling procedure for robust inference. Using a sample of 29 countries over the period 1980-2017, we indeed show that the REER features non-linear mean-reverting tendencies following large shocks. The REER adjusts dynamically and asymmetrically towards its long run equilibrium, conditional on the size of the shock. We find half lives of less than one year in some cases for the most extreme quantiles. Additionally, panel regressions indicate that this behavior can be explained by portfolio flows. Large deviations in the REER from its long run mean are followed by debt portfolio flows from international investors. These flows are associated with an appreciation in the REER, conditional on the level of deviation and the shocks incurred, leading to faster mean reversion in REERs. In the most extreme quantile, the flows move the REER back towards its mean by 1.78% per month.