Research articles for the 2020-08-25

A Data Envelopment Analysis Approach to Benchmark the Performance of Mutual Funds in India
Adit Chopra

As the Indian economy grows digitally and becomes more financially inclusive, more and more investors have started to invest in the Indian capital markets. The number of retail and institutional folios with Indian mutual fund schemes have continued to rise for the 74th consecutive month. This study considers 139 mutual fund schemes (98 equity schemes) and aims to ascertain the various metrics and parameters, retail and institutional investors continue to rely on to make investment recommendations. We compare these with the results from a data envelopment analysis model that generates an efficiency frontier based on an optimal risk, cost, and return trade-off. We further put forth an iteration of the DEA model, not only considering risk, cost, and return characteristics but also incorporating metrics such as the information ratio which hold significance for retail and institutional investors. We compare these results with traditional metrics and fund rankings published by established industry rating agencies.

A big data based method for pass rates optimization in mathematics university lower division courses
Fernando A Morales,Cristian C Chica,Carlos A Osorio,Daniel Cabarcas J

In this paper an algorithm designed for large databases is introduced for the enhancement of pass rates in mathematical university lower division courses with several sections. Using integer programming techniques, the algorithm finds the optimal pairing of students and lecturers in order to maximize the success chances of the students' body. The students-lecturer success probability is computed according to their corresponding profiles stored in the data bases.

An energy-driven macroeconomic model validated by global historical series since 1820
Herve Bercegol,Henri Benisty

Global historical series spanning the last two centuries became recently available for primary energy consumption (PEC) and Gross Domestic Product (GDP). Here through a thorough analysis of the data, we propose a new, simple macroeconomic model whereby physical power fuels economic power. From 1820 to 1920, the linearity between global PEC and world GDP justifies basic equations where, originally, PEC incorporates unskilled human labor that consumes and converts food energy. In a consistent model, both physical capital and human capital are fed by PEC and store energy. In the following century, 1920-2016, GDP grows quicker than PEC, displaying periods of linearity of the two variables, separated by distinct jumps interpreted as radical technology shifts. The GDP to PEC ratio accumulates game-changing innovation, at an average growth rate proportional to PEC. These results seed alternative strategies for modelling and political management of the climate crisis and energy transition.

Business Continuity in Times of Distress: Debt Restructuring Agreements and Compositions with Creditors in Italy
Danovi, Alessandro,Donati, Iacopo,Ilaria, Forestieri,Orlando, Tommaso,Zorzi, Andrea
The Italian insolvency framework makes several restructuring tools available to firms and their creditors, so that distress does not necessarily lead to liquidation. This paper analyses two such instruments: debt restructuring agreements (DRAs) and compositions with creditors (CCs), both commonly used to reorganize distressed firms and preserve their continuity. These procedures typically involve large firms, particularly in the case of DRAs where judicial control over negotiations is milder. Firms using DRAs are in less critical economic conditions when they file for restructuring, but they do so after longer periods of distress. Despite their declared aim, the effectiveness of these instruments in terms of business continuity is limited: many firms that use them end up exiting the market, in particular in DRAs. Firms that survive display only partial recovery, which is relatively more intense in CCs. However, the apparently superior performance of CCs is overshadowed by the long duration of restructuring, which may prevent us from observing definitive outcomes.

Conflict externalization and the quest for peace: theory and case evidence from Colombia
Hector Galindo-Silva

I study the relationship between the likelihood of a violent domestic conflict and the risk that such a conflict "externalizes" (i.e. spreads to another country by creating an international dispute). I consider a situation in which a domestic conflict between a government and a rebel group has the potential to externalize. I show that the risk of externalization increases the likelihood of a peaceful outcome, but only if the government is sufficiently powerful relative to the rebels, the risk of externalization is sufficiently high, and the foreign actor who can intervene in the domestic conflict is sufficiently uninterested in material costs and benefits. I show how this model helps to understand the recent and successful peace process between the Colombian government and the country's most powerful rebel group, the Revolutionary Armed Forces of Colombia (FARC).

Costs and information leakages on asset-borrowing markets
Pankratov, Andrey
I study a financial market with asymmetric information. In particular, I study markets with the possibility to sell short, and focus on mechanisms behind short-selling.To establish a short position, agents have to borrow assets from institutions. This creates a disincentive to short-sell coming from two sources. First, the lender charges a commission. Second, the lender can infer private information and front-run the informed trader. An informed short-seller, has an incentive to hide this information from the lender by diminshing the size of the short position.I analyze a Nash equilibrium in a game among informed agent, a lender, and a market maker. The implications of this model will include profit sharing between the agents, market effiency, and volatility.

Counting the costs of COVID-19: a critique of Miles et al
Adrian Kent

Miles et al. [1] recently produced an analysis of the costs and benefits of lockdown policies in the face of COVID-19, focussing on the case of the U.K. They argue that the March-June UK lockdown was more costly than the benefit of lives saved, if the latter are evaluated using the NICE threshold of {\pounds}30000 for a quality-adjusted life year (QALY). Looking forwards, they argue that the costs of a lockdown for 13 weeks from mid-June would be vastly greater than the benefits under any plausible QALY costing, even in a scenario in which easing lockdown led to a second infection wave that caused more than 7000 deaths a week by mid-September. I note here two key problems that certainly significantly affect their estimates and cast doubt on their conclusions. Firstly, they cut off their calculations arbitrarily after 13 weeks, without costing the epidemic state at the end of the period. That is, they assume that we should be indifferent between mid-September states of 13 deaths a week and 7500 deaths a week, and corresponding infection rates. This seems indefensible unless one assumes that (a) there will be no future vaccine and no future improvements in treatment or in non-medical interventions, (b) that COVID-19 will inevitably continue to propagate until herd immunity is reached. Even under these assumptions it is very questionable. Secondly, they ignore the costs of serious illness, possible long-term lowering of life quality, and possible lowering of life expectancy for COVID-19 survivors. These are uncertain, but clearly not negligible, and plausibly comparable to or larger than the costs in lives lost.

Deep xVA solver -- A neural network based counterparty credit risk management framework
Alessandro Gnoatto,Athena Picarelli,Christoph Reisinger

In this paper, we present a novel computational framework for portfolio-wide risk management problems, where the presence of a potentially large number of risk factors makes traditional numerical techniques ineffective. The new method utilises a coupled system of BSDEs for the valuation adjustments (xVA) and solves these by a recursive application of a neural network based BSDE solver. This not only makes the computation of xVA for high-dimensional problems feasible, but also produces hedge ratios and dynamic risk measures for xVA, and allows simulations of the collateral account.

Dilution and True Economic Gain from Cryptocurrency Block Rewards
Landoni, Mattia,Sutherland, Abraham
Dilution is the loss experienced by incumbent owners upon the creation of new ownership units (such as shares or tokens). Although a number of ad hoc patches to the U.S. tax code typically provide incumbents with some form of tax allowance for their loss, there appears to be no unified theory of accounting for dilution â€" for tax or any other purposes. When additions to one’s balance from newly created units are viewed as an income realization event, whereas dilution is not, net income is systematically overstated. The resulting over-taxation could be a serious hurdle to the adoption of proof-of-stake cryptocurrencies, which rely on token creation by incumbent owners as an integral part of network maintenance. In this short article we quantify the potential for over-taxation â€" defined herein as the excess of taxable income under a strict realization approach over true economic income â€" for a real-world taxpayer holding cryptocurrency tokens. Our example taxpayer is a Tezos staker â€" a token holder who acquires new Tezos cryptocurrency tokens by participating in the maintenance of the Tezos network. We present the pros and cons of different methods of accounting for dilution when the cryptocurrency’s aggregate network value, the taxpayer’s ownership balance, and the rate at which dilution happens are all time-varying. We conclude that the acquisition of those tokens should not be an income realization event, although any of the methods we propose would be preferable to an approach of strict realization that ignores dilution entirely. Tax policy aside, the methods we develop to quantify the economic value lost to dilution are independently interesting to investors and other finance and accounting practitioners.

Drivers learn city-scale dynamic equilibrium
Ruda Zhang,Roger Ghanem

Understanding collective human behavior and dynamics at urban-scale has drawn broad interest in physics, engineering, and social sciences. Social physics often adopts a statistical perspective and treats individuals as interactive elementary units,while the economics perspective sees individuals as strategic decision makers. Here we provide a microscopic mechanism of city-scale dynamics,interpret the collective outcome in a thermodynamic framework,and verify its various implications empirically. We capture the decisions of taxi drivers in a game-theoretic model,prove the existence, uniqueness, and global asymptotic stability of Nash equilibrium. We offer a macroscopic view of this equilibrium with laws of thermodynamics. With 870 million trips of over 50k drivers in New York City,we verify this equilibrium in space and time,estimate an empirical constitutive relation,and examine the learning process at individual and collective levels. Connecting two perspectives,our work shows a promising approach to understand collective behavior of subpopulations.

Economic Uncertainty Exposure and Cross-Sectional Return: Mispricing and Risk Premium
Cai, Charlie X.,Kerestecioglu, Semih,Fu, Xi
Previous studies on the pricing effect of economic uncertainty exposure (EUE) cannot differentiate the mis-pricing from risk premium effects. Examining its effect conditioning on a common mis-pricing index, we find that EUE induces disagreement which amplifies traditional mis-pricing. The highest EUE group produces an annualized FF 6-factor mis-pricing alpha of 8.16%, more than double the unconditional mis-pricing effect. A risk premium of 3.84% alpha is documented in the “non-mis-pricing” portfolio. This risk premium effect becomes more clear when mis-pricing is accounted for by the latest risk models such as mis-pricing and q5 models. These findings are robust to a series of alternative measures and complement existing explanations.

Effect of pop-up bike lanes on cycling in European cities
Sebastian Kraus,Nicolas Koch

The bicycle is a low-cost means of transport linked to low risk of COVID-19 transmission. Governments have incentivized cycling by redistributing street space as part of their post-lockdown strategies. Here, we evaluate the impact of provisional bicycle infrastructure on cycling traffic in European cities. We scrape daily bicycle counts spanning over a decade from 736 bicycle counters in 106 European cities. We combine this with data on announced and completed pop-up bike lane road work projects. On average 11.5 kilometers of provisional pop-up bike lanes have been built per city. Each kilometer has increased cycling in a city by 0.6\%. We calculate that the new infrastructure will generate \$3 billion in health benefits per year, if cycling habits are sticky.

Effects of MiFID II on stock price formation
Mike Derksen,Bas Kleijn,Robin de Vilder

This paper examines effects of MiFID II on European stock markets. We study the effects of the new tick size regime, both intraday and in the closing auction. An increase (decrease) in tick size is associated with a decrease (increase) in intraday liquidity, but a more (less) stable market. In the closing auction an increase in tick size has a positive effect on liquidity. Moreover, we report a positive relationship between tick size and transacted volume, in particular in the closing auction. Finally, closing auction volumes increased heavily since MiFID II and price formation in closing auctions became more efficient.

Expert Advice: Industry Expertise of M&A Advisors and Acquirer Shareholder Returns
Wang, Cong,Xie, Fei,Zhang, Kuo
We find that acquirers create higher shareholder returns when advised by investment banks with more experience in the target industry. This finding is stronger when acquirers face more difficulties understanding and evaluating the targets. Further analyses show that these banks help acquirers avoid overpaying for targets and thus capture more of deal synergy rather than making deals generating higher synergy. Our results are robust to controlling for an exhaustive set of determinants of acquirer returns and an identification strategy that exploits exogenous shocks to the supply of investment banks with target industry experience.

Explicit description of all deflators for market models under random horizon with applications to NFLVR
Tahir Choulli,Sina Yansori

This paper considers an initial market model, specified by its underlying assets $S$ and its flow of information $\mathbb F$, and an arbitrary random time $\tau$ which might not be an $\mathbb F$-stopping time. As the death time and the default time (that $\tau$ might represent) can be seen when they occur only, the progressive enlargement of $\mathbb F$ with $\tau$ sounds tailor-fit for modelling the new flow of information $\mathbb G$ that incorporates both $\mathbb F$ and $\tau$. In this setting of informational market, the first principal goal resides in describing as explicit as possible the set of all deflators for $(S^{\tau}, \mathbb G)$, while the second principal goal lies in addressing the No-Free-Lunch-with-Vanishing-Risk concept (NFLVR hereafter) for $(S^{\tau}, \mathbb G)$. Besides this direct application to NFLVR, the set of all deflators constitutes the dual set of all "admissible" wealth processes for the stopped model $(S^{\tau},\mathbb G)$, and hence it is vital in many hedging and pricing related optimization problems. Thanks to the deep results of Choulli et al. [7], on martingales classification and representation for progressive enlarged filtration, our two main goals are fully achieved in different versions, when the survival probability never vanishes. The results are illustrated on the two particular cases when $(S,\mathbb F)$ follows the jump-diffusion model and the discrete-time model.

Fast Agent-Based Simulation Framework of Limit Order Books with Applications to Pro-Rata Markets and the Study of Latency Effects
Peter Belcak,Jan-Peter Calliess,Stefan Zohren

We introduce a new software toolbox, called Multi-Agent eXchange Environment (MAXE), for agent-based simulation of limit order books. Offering both efficient C++ implementations and Python APIs, it allows the user to simulate large-scale agent-based market models while providing user-friendliness for rapid prototyping. Furthermore, it benefits from a versatile message-driven architecture that offers the flexibility to simulate a range of different (easily customisable) market rules and to study the effect of auxiliary factors, such as delays, on the market dynamics.

Showcasing its utility for research, we employ our simulator to investigate the influence the choice of the matching algorithm has on the behaviour of artificial trader agents in a zero-intelligence model. In addition, we investigate the role of the order processing delay in normal trading on an exchange and in the scenario of a significant price change. Our results include the findings that (i) the variance of the bid-ask spread exhibits a behavior similar to resonance of a damped harmonic oscillator with respect to the processing delay and that (ii) the delay markedly affects the impact a large trade has on the limit order book.

Financial Returns to Household Inventory Management
R. Baker, Scott,Johnson, Stephanie,Kueng, Lorenz
Households tend to hold substantial amounts of non-financial assets in the form of inventory. Households can obtain significant financial returns from strategic shopping and optimally managing these inventories of consumer goods. In addition, they choose to maintain liquid savings â€" household working capital â€" not just for precautionary motives but also to support this inventory management. We demonstrate that households earn high returns from inventory management at low levels of inventory, though returns decline rapidly as inventory levels increase. We provide evidence using scanner and survey data that supports this conclusion. High returns from inventory management that are declining in wealth offer a new rationale for poorer households not to participate in risky financial markets, while wealthier households invest in both financial assets and working capital.

Financial Spillovers to Emerging Economies: the Role of Exchange Rates and Domestic Fundamentals
Ciarlone, Alessio,Marconi, Daniela
Financial integration of emerging economies is on the rise and so are financial and monetary spillovers, especially those originating from US economic policy decisions and the (related) evolution of the US dollar. We revisit the “trilemma” vs. “dilemma” hypothesis and assess whether, and to what extent, exchange rate regimes and other relevant country fundamentals affect the sensitivity of domestic financial conditions to global risk aversion and US financial conditions. Results for a sample of 17 emerging economies over the period 1990-2018 suggest that the trilemma hypothesis appears to be still valid, as more flexible exchange rate regimes help in mitigating spillovers to stock market returns, sovereign spreads and real credit growth. However, other country fundamentals such as the current account, trade integration and US dollar debt exposure are also important factors.

Governing Global Digital Finance
Mitha, Aiaze,Zadek, Simon,Arner, Douglas W.
Digitalization is reshaping finance, opening new market and development opportunities, and bringing with it new risks. Digital finance, or ‘fintech’, makes a difference by providing access to more, better and cheaper data, removing unnecessary intermediation, enhancing efficiency to reduce barriers and catalyzing innovation. The current crisis has transformed digital finance from a convenience into an existential lifeline.The UN Secretary General’s Task Force on Digital Financing for the Sustainable Development Goals (SDGs) highlights the potential of fintech, for example, to crowd in SDG-related risk and impact data into financing decisions, reduce financing costs, open opportunities for business innovations particularly those targeting the poor, and enable citizens to have a greater say over their money, as savers, lenders, investors, and tax payers. It also has identified risks that need to be mitigated if it is to be an enabler of financing aligned to the SDGs.One feature of digitalization, and a source of risk as well as opportunity, is that it enables ever-increasing returns to scale, increasing market concentration: ‘network effects’. A small number of digital finance platforms, often arising from ‘BigTech’ particularly e-commerce, social media, and indeed governments, have grown rapidly, a direction of travel likely to accelerate as a result of the crisis. These ‘global digital finance’ platforms, of ‘BigFintech’, will be increasingly impactful across the world, particularly in developing countries with smaller, weaker or under-developed financial systems, economies, and policy frameworks but where also the opportunities for transformation are greatest. With such extensive footprints, there is need, and yet challenges to securing the most appropriate policies, regulations, and broadly governance. Governance considerations of BigFintech are often narrowly focused, setting aside many SDG aspects, risks and opportunities. Moreover, they are likewise often not inclusive, notably of the voices of countries most likely to be directly impacted, particularly outside of the major economies. The Task Force’s ‘Dialogue on Global Digital Finance’ has been established as a complementary and supportive initiative aiming to enhance and rebalance governance debate, innovation and developments with these two factors in mind.

Hedge Fund Performance under Misspecified Models
Ardia, David,Barras, Laurent,Gagliardini, Patrick,Scaillet , O.
We develop a new approach for evaluating performance across hedge funds. Our approach allows for performance comparisons between models that are misspecified â€" a common feature given the numerous factors that drive hedge fund returns. The empirical results show that the standard models used in previous work omit similar factors because they (i) perform exactly like the CAPM, and (ii) produce large and positive alphas. In contrast, we observe a large and statistically significant decrease in performance with a new model formed with alternative factors that capture variance, correlation, liquidity, betting-against-beta, carry, and time-series momentum strategies. Overall, the results suggest that the average returns of hedge funds are largely explained by mechanical trading strategies.

High-frequency Estimation of the L\'evy-driven Graph Ornstein-Uhlenbeck process
Valentin Courgeau,Almut E.D. Veraart

We consider the Graph Ornstein-Uhlenbeck (GrOU) process observed on a non-uniform discrete time grid and introduce discretised maximum likelihood estimators with parameters specific to the whole graph or specific to each component, or node. Under a high-frequency sampling scheme, we study the asymptotic behaviour of those estimators as the mesh size of the observation grid goes to zero. We prove two stable central limit theorems to the same distribution as in the continuously-observed case under both finite and infinite jump activity for the L\'evy driving noise. When a graph structure is not explicitly available, the stable convergence allows to consider purpose-specific sparse inference procedures, i.e. pruning, on the edges themselves in parallel to the GrOU inference and preserve its asymptotic properties. We apply the new estimators to wind capacity factor measurements, i.e. the ratio between the wind power produced locally compared to its rated peak power, across fifty locations in Northern Spain and Portugal. We show the superiority of those estimators compared to the standard least squares estimator through a simulation study extending known univariate results across graph configurations, noise types and amplitudes.

Housing Market and Entrepreneurship: Micro Evidence from China
Han, Bing,Han, Lu,Zhou, Zhengyi
Using a unique survey data of Chinese households, we study the impact of house price growth and house price risk on entrepreneurship. House price risk, measured as the sensitivity of house price growth to local GDP growth, negatively impacts the entrepreneurship of homeowners relative to renters. This finding is concentrated only among sophisticated households and is consistent with the portfolio effect when housing and occupational choices are integral parts of the household portfolio. Moreover, a high past house price growth reduces the entrepreneurship of homeowners relative to renters. This holds for both sophisticated and unsophisticated households. We propose a new economic channel based on extrapolative belief and provide further supportive evidence.

How Changing Firm Characteristics Can Shape Behaviour: Has Increased Executive Diversity Led to a Decrease in Corporate Aggression?
Cordeiro, Peter
This paper explores whether executives within a firm that have certain attributes tend to lead their corporation to become less tax aggressive. Specifically, we use previous literature regarding tax compliance of individuals (Hasseldine and Hite 2003; Murphy 2004) to see if these would extend to firms. We tried to find if there was evidence to suggest that an increase in age and gender diversity would lead less aggressive tax policies instituted by the firm. Our findings could not show that this was the case and suggest that more work will need to be done to provide support or overturn this theory, especially across other demographic dimensions.

Information Frictions in the Market for Startup Acquisitions
Conti, Annamaria,Guzman, Jorge,Rabi, Ron
We document and quantify the importance of information frictions in the market for startup acquisitions. Examining a sample of 5,729 Israeli venture-backed startups, we implement machine learning algorithms to generate dyads of technologically similar companies. Difference-in-differences and instrumental variable models show that the acquisition of a startup by a foreign company increases the chances that its technologically-related pair is also acquired by a foreign company by approximately 56\%. This effect is largest for later-stage startups, as they are relatively close to exit, and for acquisitions that are prominent in the news. Consistent with information frictions being more severe for distant acquirers, acquisitions of Israeli startups by foreign companies minimally affect the pairs' likelihood of being acquired domestically. Foreign companies are insensitive to acquisitions of Israeli startups by domestic incumbents, indicating that the information value of an acquisition is greatest when the acquirer is foreign. Investors do not increase their investments in startups whose pairs have been acquired by a foreign company as improved information leads them to offload their portfolios. Accordingly, these startups are not acquired at a higher price.

Interacting Regional Policies in Containing a Disease
Arun G. Chandrasekhar,Paul Goldsmith-Pinkham,Matthew O. Jackson,Samuel Thau

Regional quarantine policies, in which a portion of a population surrounding infections are locked down, are an important tool to contain disease. However, jurisdictional governments - such as cities, counties, states, and countries - act with minimal coordination across borders. We show that a regional quarantine policy's effectiveness depends upon whether (i) the network of interactions satisfies a balanced-growth condition, (ii) infections have a short delay in detection, and (iii) the government has control over and knowledge of the necessary parts of the network (no leakage of behaviors). As these conditions generally fail to be satisfied, especially when interactions cross borders, we show that substantial improvements are possible if governments are proactive: triggering quarantines in reaction to neighbors' infection rates, in some cases even before infections are detected internally. We also show that even a few lax governments - those that wait for nontrivial internal infection rates before quarantining - impose substantial costs on the whole system. Our results illustrate the importance of understanding contagion across policy borders and offer a starting point in designing proactive policies for decentralized jurisdictions.

News Sentiment and Financial Assets Returns during COVID-19
Maghyereh, Aktham Issa,Abdoh, Hussein
This study is the first to document the impact of news sentiment on different classes of assets’ returns (stocks, bonds, oil, natural gas, gold, commodities, and foreign exchange rate) during the COVID-19 pandemic. By using time-varying causality test, we find that the causality running from sentiment to asset returns increases remarkably during the pandemic, mainly during March and April, 2020. The causality effect of sentiment is the highest on S&P 500 index. When examining the dynamic connectedness between sentiment and asset returns, we find that the connectedness is higher during the pandemic for all assets and the increase in connectedness is prolonged during the pandemic for certain assets such as natural gas, bond, commodity and S&P 500. Overall, the pandemic caused greater sentiment predictability on asset returns.

Note on simulation pricing of $\pi$-options
Zbigniew Palmowski,Tomasz Serafin

In this work, we adapt a Monte Carlo algorithm introduced by Broadie and Glasserman (1997) to price a $\pi$-option. This method is based on the simulated price tree that comes from discretization and replication of possible trajectories of the underlying asset's price. As a result this algorithm produces the lower and the upper bounds that converge to the true price with the increasing depth of the tree. Under specific parametrization, this $\pi$-option is related to relative maximum drawdown and can be used in the real-market environment to protect a portfolio against volatile and unexpected price drops. We also provide some numerical analysis.

Quantifying the impact of Covid-19 on stock market: An analysis from multi-source information
Asim Kumer Dey,Toufiqul Haq,Kumer Das,Yulia R. Gel

We investigate the impact of Covid-19 cases and deaths, local spread spreads of Covid-19, and Google search activities on the US stock market. We develop a temporal complex network to quantify US county level spread dynamics of Covid-19. We conduct the analysis by using the following sequence of methods: Spearman's rank correlation, Granger causality, Random Forest (RF) model, and EGARCH (1,1) model. The results suggest that Covid-19 cases and deaths, its local spread spreads, and Google searches have impacts on the abnormal stock price between January 2020 to May 2020. However, although a few of Covid-19 variables, e.g., US total deaths and US new cases exhibit causal relationship on price volatility, EGARCH model suggests that Covid-19 cases and deaths, local spread spreads of Covid-19, and Google search activities do not have impacts on price volatility.

Seeking Analyst Coverage: Steering User-Generated Content Using Monetary Incentives
Claussen, Jörg,Litterscheidt, Rouven,Streich, David
We study how monetary incentive structures affect the selection of stocks covered by non-professional analysts (NPA), as well as the quality of the published research articles. Specifically, we use two exogenous incentive structure changes on a peer-to-peer financial analysis platform as a natural experiment with professional analysts (PA) as the control group. Our results suggest that monetary incentive structures are an effective tool to increase and steer NPA research support. The incentive structure changes increased coverage in the targeted market capitalization segments and primarily affected contributors who joined the platform more recently. We further show that NPA coverage affects market liquidity to a similar extent as PA coverage. The incentive structure changes did not deteriorate the quality of NPA research support as measured by its impact on liquidity. In summary, our findings suggest that NPA coverage may be a suitable complement to, if not substitute for, PA coverage.

Should VCs' Principals Specify Minimum Portfolio Returns to be Delivered by Venture Capitalists (VCs)?
Obrimah, Oghenovo A.
This study finds specification of minimum portfolio returns to be delivered by VCs in contracts that subsist between VCs and their principals is not a necessary condition for incentivization of optimal portfolio performance. Within populations of VCs who are characterized by risk aversion, formal theoretical predictions show competition only serves for revelation of true ability of VCs, as such, in entirety is self serving. Within said population, efficiency of financial inter-mediation is endogenously induced by structure of investment opportunity set risk. Absent demonstrations of ability by risk seeking VCs, the continuum of investment opportunity set risk that is occupied by said VCs would be lacking in economic viability. We have then that within said continuum, demonstrations of ability on part of risk seeking VCs induce each of viability and efficiency of financial inter-mediation. While there exists some overlap region in interior of the continuum that is occupied by each of risk averse and risk seeking VCs, we arrive at clustering of risk averse or risk seeking VCs to the right, and to the left, respectively of the overlap region. Overlap is feasible, because the overlap region is, for each of risk averse or risk seeking VCs, the `lowest portfolio return segment', and because VCs' principals assess each of risk averse or risk seeking VCs on basis of First-order Stochastic Dominance and skewness of project returns. In aggregate, the continuum of opportunity set risk consists of a `native risk-return' segment populated by risk averse VCs, an `ability-induced risk-return' segment populated by risk seeking VCs, and an overlap region in interior of the continuum. Absent any assumptions to the effect, the formal theoretical model generates heterogeneity of necessity of pay-performance sensitivities within venture capital markets. In presence of heterogeneity of pay-performance sensitivities, and feasibility of negotiation of higher `carry' by reputable VCs, VCs which achieve superior performance rationally are able to transition to smaller fund sizes. Formal theoretical support for rationality of transitions to smaller fund sizes by superior performers within venture capital markets had hitherto been lacking in literature of finance.

Sunsetting as an Adaptive Strategy
Romano, Roberta,Levin, Simon
Major financial legislation is invariably enacted in the wake of a financial crisis. Yet legislating following a crisis is hazardous, because information is scarce regarding causes of the crisis, let alone what would be an appropriate response. Compounding the lack of information, crisis-driven legislation is sticky, but financial markets are dynamically innovative, which can undermine the efficacy of regulation. As a result, it is entirely foreseeable that such legislation will contain at least some provisions that are inapt or inadequate or, more often, have consequences that are not well understood or even knowable. This paper advocates the use of sunsetting as a mechanism for mitigating the potentially adverse consequences of crisis-driven financial legislation. With sunsetting, after a fixed time span, legislation and its implementing regulation must be reenacted to remain in force. Such a requirement would compel a timely revisiting of crisis-driven legislation when far more information is available than at the time of enactment. This approach has parallels in evolutionary biology, in which a central issue is the ability to adapt to changing environments. Sunsetting does not mean simply discarding (or reenacting) existing regulations, but revisiting them and improving them, much as mutation and recombination do in the evolutionary process. In evolutionary modification, mutational changes would leave most of the genome alone, and selectively replace some; recombination would use existing segments, but recombine them in ways that produce higher fitness. Similarly, in modifying existing legislation, one can keep some provisions, modify others, and “recombine” by adding provisions from other regulators. The latter is more akin to the process of horizontal transfer in evolution, as for example through the (possibly cross-species) exchange by bacteria of extrachromosomal DNA that might confer antibiotic resistance.

Supervised Machine Learning for Eliciting Individual Demand
John A. Clithero,Jae Joon Lee,Joshua Tasoff

Direct elicitation, guided by theory, is the standard method for eliciting latent preferences. The canonical direct-elicitation approach for measuring individuals' valuations for goods is the Becker-DeGroot-Marschak procedure, which generates willingness-to-pay (WTP) values that are imprecise and systematically biased by understating valuations. We show that enhancing elicited WTP values with supervised machine learning (SML) can substantially improve estimates of peoples' out-of-sample purchase behavior. Furthermore, swapping WTP data with choice data generated from a simple task, two-alternative forced choice, leads to comparable performance. Combining all the data with the best-performing SML methods yields large improvements in predicting out-of-sample purchases. We quantify the benefit of using various SML methods in conjunction with using different types of data. Our results suggest that prices set by SML would increase revenue by 28% over using the stated WTP, with the same data.

Tempered Stable Processes with Time Varying Exponential Tails
Young Shin Kim,Kum-Hwan Roh,Raphael Douady

In this paper, we introduce a new time series model having a stochastic exponential tail. This model is constructed based on the Normal Tempered Stable distribution with a time-varying parameter. The model captures the stochastic exponential tail, which generates the volatility smile effect and volatility term structure in option pricing. Moreover, the model describes the time-varying volatility of volatility. We empirically show the stochastic skewness and stochastic kurtosis by applying the model to analyze S&P 500 index return data. We present the Monte-Carlo simulation technique for the parameter calibration of the model for the S&P 500 option prices. We can see that the stochastic exponential tail makes the model better to analyze the market option prices by the calibration.

The Collateral Rule: Theory for the Credit Default Swap Market
Du, Chuan,Capponi, Agostino,Giglio, Stefano
We develop a model of endogenous collateral requirements in the credit default swap (CDS) market. Our model provides an interpretation for the empirical findings of Capponi et al. (2020), according to which extreme tail risk measures have a higher explanatory power for observed collateral requirements than standard value at risk rules. The model predicts that this conservativeness of collateral levels can be explained through disagreement of market participants about the extreme states of the world, in which CDSs pay out and counter-parties default.

The Impact of Sodomy Law Repeals on Crime
Riccardo Ciacci,Dario Sansone

We exploit variation in the timing of decriminalization of same-sex sexual intercourse across U.S. states to estimate the impact of these law changes on crime through difference-in-difference and event-study models. We provide the first evidence that sodomy law repeals led to a decline in the number of arrests for disorderly conduct, prostitution, and other sex offenses. Furthermore, we show that these repeals led to a reduction in arrests for drug and alcohol consumption.

The Impact of the IRB Approach on the Relationship Between the Cost of Credit for Public Companies and Financial Market Conditions
Gallo, Raffaele
This paper examines whether the regulatory approach adopted by banks to calculate capital requirements has a different impact on the loan rates for public and private companies when financial market conditions change. Using Italian data for the period 2008-18, the analysis documents that the adoption of the internal ratings-based (IRB) approach has led to a significantly greater sensitivity of the loan rates applied to public companies to financial market conditions, proxied by the VSTOXX index. For credit granted by IRB banks, being public is associated with a significant loan cost advantage when the level of financial instability is low. However, when VSTOXX rises, public companies experience a greater increase in loan rates than private firms; the effect is determined mostly by less capitalized IRB banks. In contrast, for credit granted by banks that adopt the standardized approach (SA), public borrowers do not benefit from a significant loan cost advantage compared with private ones, and a change in financial market conditions has a similar impact on loan rates for both types of companies.

The Liquidity Premium Across Asset Classes
Jansen, Kristy A.E.,Werker, Bas J. M.
Empirical studies show mixed evidence of first-order liquidity premiums for several asset classes. In this study, we solve a flexible model that captures both transactions costs and the infrequencies of trading opportunities for illiquid assets to achieve better guidance as to which asset classes have first-order liquidity premiums. For the asset classes of private equity, direct real estate, corporate bonds, and stocks, we model heterogeneous agents to derive the liquidity premiums. Our model shows an average annual liquidity premium of 5-15 basis points for private equity, 15-35 basis points for direct real estate, 30-50 basis points for corporate bonds, and 20-45 basis points for stocks. The source of illiquidity and the heterogeneity in the share of investors that demand first-order liquidity premiums across asset classes drive these findings.

To Be or Not to Be? The Questionable Benefits of Mutual Clearing Agreements for Derivatives
Tywoniuk, Magdalena
Recently, for standard asset classes, the first mutual clearing agreements between Central Coun- terparties (CCPs) have come into existence. There are already global concerns over the unique threats and benefits which arise from these situations, and further concern for an extension of agree- ments to derivatives CCPs. This paper applies the current mutual agreement framework to credit default swaps (derivatives) CCPs and compares this to clearing without any such agreement. Key results concern: The magnitude of price dispersion between multiple CCPs (as trading moves asset prices away from fundamental value), the magnitude of default contagion, the price impact of pre- dation, and the disciplinary mechanism inherent in the mutual cross-margin fund (between CCPs). Current regulatory debate, concerning the safety of permitting use of the default fund to meet inter-CCP shortfalls, is settled. Finally, a large-scale dynamic simulation models the price process â€" through variation margin exchange â€" and provides real-world policy/regulatory implications for a variety of market liquidity states.

Trade Shocks and Credit Reallocation
Federico, Stefano,Hassan, Fadi,Rappoport, Veronica
This paper shows that there are endogenous financial constraints arising from trade liberalization. We find that banks with a high share of loans to firms exposed to competition from China experience an increase in non-performing loans and a reduction in their credit capacity. The drop in credit supply affects both firms directly exposed to import-competition from China and firms expected to expand upon trade liberalization, with economically relevant implications in terms of employment, investment, and output. This financial spillover between losers and winners from trade holds back the reallocation of factors of production between firms and sectors, which is crucial to the welfare implication of trade liberalization.

Trends, Reversion, and Critical Phenomena in Financial Markets
Christof Schmidhuber

Financial markets across all asset classes are known to exhibit trends. These trends have been exploited by traders for decades. Here, we empirically measure when trends revert, based on 30 years of daily futures prices for equity indices, interest rates, currencies and commodities. We find that trends tend to revert once they reach a critical level of statistical significance. Based on polynomial regression, we carefully measure this critical level. We find that it is universal across asset classes and has a universal scaling behavior, as the trend's time horizon runs from a few days to several years. The corresponding regression coefficients are small, but statistically highly significant, as confirmed by bootstrapping and out-of-sample testing. Our results signal to investors when to exit a trend. They also reveal how markets have become more efficient over the decades. Moreover, they point towards a potential deep analogy between financial markets and critical phenomena: our analysis supports the conjecture that financial markets can be modeled as statistical mechanical ensembles of Buy/Sell orders near critical points. In this analogy, the trend strength plays the role of an order parameter, whose dynamcis is described by a Langevin equation.

True Cost of Immediacy
Hendershott, Terrence,Li, Dan,Livdan, Dmitry,Schürhoff, Norman
Traditional liquidity measures can provide a false impression of the liquidity and stability of financial market trading. Using data on auctions (bids wanted in competition; BWICs) from the collateralized loan obligation (CLO) market, we show that a standard measure of liquidity, the effective bid-ask spread, dramatically underestimates the true cost of immediacy because it does not account for failed attempts to trade. The true cost of immediacy is substantially higher than the observed costs for successful BWICs. This cost gap is higher in lower-rated CLOs and stressful market conditions when failure rates exceed 50%. Across our 2012-2020 sample period for trades in senior CLOs, the observed cost is four basis points (bps) while the true cost of immediacy is 13bps. In stressful periods, such as the COVID-19 pandemic, for junior tranches the observed cost of trading increases from an average of 12bps to 25bps while the true cost of immediacy increases from less than 3% to almost 15%.

Trustworthiness in the Financial Industry
Gill, Andrej,Schumacher, Heiner,Heinz, Matthias,Sutter, Matthias
The financial industry has been struggling with widespread misconduct and public mistrust. Here we argue that the lack of trust into the financial industry may stem from the selection of subjects with little, if any, trustworthiness into the financial industry. We identify the social preferences of business and economics students, and follow up on their first job placements. We find that during college, students who want to start their career in the financial industry are substantially less trustworthy. Most importantly, actual job placements several years later confirm this association. The job market in the financial industry does not screen out less trustworthy subjects. If anything the opposite seems to be the case: Even among students who are highly motivated to work in finance after graduation, those who actually start their career in finance are significantly less trustworthy than those who work elsewhere.