# Research articles for the 2019-11-04

A Binomial Asset Pricing Model in a Categorical Setting
arXiv

Adachi and Ryu introduced a category Prob of probability spaces whose objects are all probability spaces and whose arrows correspond to measurable functions satisfying an absolutely continuous requirement in [Adachi and Ryu, 2019]. In this paper, we develop a binomial asset pricing model based on Prob. We introduce generalized filtrations with which we can represent situations such as some agents forget information at some specific time. We investigate the valuations of financial claims along this type of non-standard filtrations.

A two-dimensional propensity score matching method for longitudinal quasi-experimental studies: A focus on travel Behavior and the built environment
Haotian Zhong,Wei Li,Marlon G. Boarnet
arXiv

The lack of longitudinal studies of the relationship between the built environment and travel behavior has been widely discussed in the literature. This paper discusses how standard propensity score matching estimators can be extended to enable such studies by pairing observations across two dimensions: longitudinal and cross-sectional. Researchers mimic randomized controlled trials (RCTs) and match observations in both dimensions, to find synthetic control groups that are similar to the treatment group and to match subjects synthetically across before-treatment and after-treatment time periods. We call this a two-dimensional propensity score matching (2DPSM). This method demonstrates superior performance for estimating treatment effects based on Monte Carlo evidence. A near-term opportunity for such matching is identifying the impact of transportation infrastructure on travel behavior.

Accelerated Share Repurchase and other buyback programs: what neural networks can bring
Olivier Guéant,Iuliia Manziuk,Jiang Pu
arXiv

When firms want to buy back their own shares, they have a choice between several alternatives. If they often carry out open market repurchase, they also increasingly rely on banks through complex buyback contracts involving option components, e.g. accelerated share repurchase contracts, VWAP-minus profit-sharing contracts, etc. The entanglement between the execution problem and the option hedging problem makes the management of these contracts a difficult task that should not boil down to simple Greek-based risk hedging, contrary to what happens with classical books of options. In this paper, we propose a machine learning method to optimally manage several types of buyback contract. In particular, we recover strategies similar to those obtained in the literature with partial differential equation and recombinant tree methods and show that our new method, which does not suffer from the curse of dimensionality, enables to address types of contract that could not be addressed with grid or tree methods.

Bitcoin Coin Selection with Leverage
Daniel J. Diroff
arXiv

We present a new Bitcoin coin selection algorithm, "coin selection with leverage", which aims to improve upon cost savings than that of standard knapsack like approaches. Parameters to the new algorithm are available to be tuned at the users discretion to address other goals of coin selection. Our approach naturally fits as a replacement for the standard knapsack ingredient of full coin selection procedures.

Calibration of Local-Stochastic and Path-Dependent Volatility Models to Vanilla and No-Touch Options
Alan Bain,Matthieu Mariapragassam,Christoph Reisinger
arXiv

We propose a generic calibration framework to both vanilla and no-touch options for a large class of continuous semi-martingale models. The method builds upon the forward partial integro-differential equation (PIDE) derived in Hambly et al. (2016), which allows fast computation of up-and-out call prices for the complete set of strikes, barriers and maturities. It also utilises a novel two-states particle method to estimate the Markovian projection of the variance onto the spot and running maximum. We detail a step-by-step procedure for a Heston-type local-stochastic volatility model with local vol-of-vol, as well as two path-dependent volatility models where the local volatility component depends on the running maximum. In numerical tests, we benchmark these new models against standard models for a set of EURUSD market data, all three models are seen to calibrate well within the market no-touch bid--ask.

Climate Change, Operating Flexibility, and Corporate Investment Decisions
Lin, Chen,Schmid, Thomas,Weisbach, Michael S.
SSRN
Extreme temperatures lead to large fluctuations in electricity demand and wholesale prices of electricity, which in turn affects the optimal production process for firms to use. Using a large international sample of planned power plant projects, we measure the way that electric utilitiesâ€™ investment decisions depend on the frequency of extreme temperatures. We find that they invest more in regions with more extreme temperatures. These investments are mostly in flexible gas and oil-fired power plants that can easily adjust their output, to improve their operating flexibility. Our results suggest that climate change is becoming a meaningful factor affecting firmsâ€™ behavior.

Credit Supply: Are There Negative Spillovers from Banksâ€™ Proprietary Trading?
Kurz, Michael,Kleimeier, Stefanie
SSRN
Following the 2008 financial crisis, policy makers considered regulations that restrict banksâ€™ activities which were motivated by concerns that banks use central bank borrowing, government guarantees, or subsidies to fund securities trading instead of lending to the real economy. Using a global sample of 132 major banks from 2003 to 2016, we find that banksâ€™ securities trading is indeed associated with decreased loan supply. Effects are stronger for domestic lending markets, during crisis periods, and in countries with deeper financial markets. However, corporate capital expenditures and employment growth are unaffected, suggesting that policy makersâ€™ concerns are only partly justified.

Cryptocurrency Economics and the Taxation of Block Rewards
Sutherland, Abraham
SSRN
In this two-part report, Sutherland proposes a single taxation policy for all public cryptocurrencies. Focusing on the mechanics of proof-of-stake networks and the economic incentives underlying their maintenance, Sutherland, in the first installment, begins to make the case that reward tokens should be taxed when they are sold or exchanged, not when theyâ€™re created.

Dark Knights: The Rise in Firm Intervention by CDS Investors
Gamba, Andrea,Danis, Andras
SSRN
We document an increase in cases where credit default swap (CDS) investors intervene in the restructuring of a distressed firm. In our theoretical analysis, we show thatâ€"contrary to popular beliefâ€"intervention by CDS investors is not necessarily reducing firm value. While the equilibrium CDS spread seems excessive for the protection buyer, that cost is offset by the reduced probability of liquidation. Ex ante borrowing costs go down, and investment and firm value both increase. Under certain assumptions, investment reaches first-best. Our results suggest that the empty creditor problem could be at least partially solved by CDS investor intervention.

Decision Making under Uncertainty: An Experimental Study in Market Settings
Federico Echenique,Taisuke Imai,Kota Saito
arXiv

We design and implement a novel experimental test of subjective expected utility theory and its generalizations. Our experiments are implemented in the laboratory with a student population, and pushed out through a large-scale panel to a general sample of the US population. We find that a majority of subjects' choices are consistent with maximization of {\em some} utility function, but not with subjective utility theory. The theory is tested by gauging how subjects respond to price changes. A majority of subjects respond to price changes in the direction predicted by the theory, but not to a degree that makes them fully consistent with subjective expected utility. Surprisingly, maxmin expected utility adds no explanatory power to subjective expected utility. Our findings remain the same regardless of whether we look at laboratory data or the panel survey, even though the two subject populations are very different. The degree of violations of subjective expected utility theory is not affected by age nor cognitive ability, but it is correlated with financial literacy.

Drawdown Measures: Are They All the Same?
Korn, Olaf,MÃ¶ller, Philipp M. ,Schwehm, Christian
SSRN
Over the years, a diverse range of drawdown measures has evolved to guide asset management. We show that almost all of these measures fit into a unified framework. This new framework simplifies the implementation of drawdown measures and improves understanding their similarities and differences. Conceptual differences between drawdown measures translate into different rankings of portfolios, which we document in a simulation study. Our research also shows that all drawdown measures can (to some degree) discriminate between skillful and unskillful portfolio managers, but differ in terms of accuracy. However, the ability to detect skill does not easily improve performance ratios where drawdown measures serve as the denominator. In conclusion, our study shows that the choice of an adequate drawdown measure is vital to the assessment of investments because different measures emphasize different aspects of risk.

ElecSim: Monte-Carlo Open-Source Agent-Based Model to Inform Policy for Long-Term Electricity Planning
Alexander J. M. Kell,Matthew Forshaw,A. Stephen McGough
arXiv

Due to the threat of climate change, a transition from a fossil-fuel based system to one based on zero-carbon is required. However, this is not as simple as instantaneously closing down all fossil fuel energy generation and replacing them with renewable sources -- careful decisions need to be taken to ensure rapid but stable progress. To aid decision makers, we present a new tool, ElecSim, which is an open-sourced agent-based modelling framework used to examine the effect of policy on long-term investment decisions in electricity generation. ElecSim allows non-experts to rapidly prototype new ideas.

Different techniques to model long-term electricity decisions are reviewed and used to motivate why agent-based models will become an important strategic tool for policy. We motivate why an open-source toolkit is required for long-term electricity planning.

Actual electricity prices are compared with our model and we demonstrate that the use of a Monte-Carlo simulation in the system improves performance by $52.5\%$. Further, using ElecSim we demonstrate the effect of a carbon tax to encourage a low-carbon electricity supply. We show how a {\pounds}40 ($\$50$) per tonne of CO2 emitted would lead to 70% renewable electricity by 2050. European Emission Allowance and the Equity Markets: Evidence from Further Trading Phases Harasheh, Murad,Amaduzzi, Andrea SSRN Purpose - This study aims at investigating the value relevance of the European Emission Allowance (EUA) return and volatility on the equity value of the top listed European Power Generation Firms for the three trading phases of the European Emission Trading Scheme.Design/methodology/approach - We employ the multifactor financial market model over the period 2005-2016 on daily basis for the return relevance relationship, whereas time series models such as ARMA and GARCH are applied on a weighted average portfolio of the sample firms to test serial correlation and volatility of returns. Findings - Our findings are novel in which we show a positive and significant relevance of EUA return on equity return; however, a vanishing effect is seen as we move to further trading phases. Another remarkable finding is that the return relationship remains constant until a certain level in EUA price then inverts. Finally, we show that EUA is considered a systematic factor as firm and country specific features are not statistically significant. Originality/Value - To our knowledge, this study would be the first to offer recent and comprehensive findings on the economic and financial implications of the European Emission Trading Scheme for the three trading phases. Additionally, the research offers time series robustness check besides the standard regression analysis and shows that there is an optimal EUA price that triggers pollutersâ€™ decision on emission and generation. Evolution Equations of Discount Functions and Metrics of Dynamic Inconsistency Chorvat, Terrence R. SSRN This article considers continuous models of time discounting that evolve dynamically. While constant exponential discounting is the paradigmatic model for time discounting, many models which depart from exponential discounting have been proposed to attempt to more closely match the behavior of individuals, firms and markets. This article argues that it is the dynamic inconsistency of behavioral models that gives them their most salient features. The article then develops evolution equations for some of the most prominent continuous discounting models to more clearly consider their dynamic inconsistency. It then proposes metrics for the degree of dynamic inconsistency exhibited by discounting models allowing comparison of dynamic inconsistency both across and within models. Falling Short: Has the SECâ€™s Quest to Control Market Manipulation and Abusive Short Selling Come to an End or Has It Really Just Begun? Ramirez, Richard E. SSRN Despite continued attempts by regulators to curtail abusive short sales and increase transparency, the pattern and practice of fraudulent manipulation continues to proliferate and threaten the capitalization of a wide variety of issuers within the securities market. Identifying a meaningful resolution requires analyzing capital market objectives and addressing the inequities of our current regulatory scheme. Foreign Funded Credit: Funding the Credit Cycle? Duijm, Patty SSRN This study investigates what drives the credit cycle, focusing on the role of foreign funded bank credit (FFC). Considering credit cycles in 41 countries over the period 1985-2015, this study finds that credit booms are associated with an increase in the share of FFC in an economy, both in emerging and developed economies and for business as well as for household credit cycles. The impact of FFC on credit booms is however significantly higher in emerging countries. While FFC increases rapidly during the boom, the period preceding the boom is characterized by an in increase in domestically funded credit relative to FFC. FFC thus accelerates credit during the boom. The increased credit needs during a boom may cause the subsitution of domestically funded credit by FFC, as the growth in FFC is less restricted than domestically funded credit, for example by the domestic deposit base. Fragmentation of Distributed Exchanges Zoican, Marius,Zoican, Sorin SSRN Distributed securities exchanges may become de facto fragmented if they span geographical regions with asymmetric computer infrastructure. First, we build an economic model of a decentralized exchange with two miner clusters, standing in for compact areas of economic activity (e.g., cities). "Local" miners in the area with relatively higher trading activity only join a decentralized exchange if they enjoy a large speed advantage over "long-distance" competitors. This is due to a transfer of economic value across miners, specifically from high- to low-activity clusters. Second, we estimate the speed advantage of "local" over "long-distance" miners in a series of Monte Carlo experiments over a two-cluster, unstructured peer-to-peer network simulated in C. We find that the speed advantage increases in the level of infrastructure asymmetry between clusters. Cross-region DEX blockchains are feasible as long as the asymmetry levels in trading activity and infrastructure availability across regions are positively correlated. How Option Hedging Shapes Market Impact Emilio Said arXiv We present a perturbation theory of the market impact based on an extension of the framework proposed by [Loeper, 2018] -- originally based on [Liu and Yong, 2005] -- in which we consider only local linear market impact. We study the execution process of hedging derivatives and show how these hedging metaorders can explain some stylized facts observed in the empirical market impact literature. As we are interested in the execution process of hedging we will establish that the arbitrage opportunities that exist in the discrete time setting vanish when the trading frequency goes to infinity letting us to derive a pricing equation. Furthermore our approach retrieves several results already established in the option pricing literature such that the spot dynamics modified by the market impact. We also study the relaxation of our hedging metaorders based on the fair pricing hypothesis and establish a relation between the immediate impact and the permanent impact which is in agreement with recent empirical studies on the subject. Labor and Finance: The Role of Financial Reporting Quality Jung, Boochun,Lee, Woo-Jong,Weber, David P.,Yang, Daniel SSRN We examine the role of high quality financial reporting in facilitating corporate employment. We focus on firms with maturing long-term debt, which creates refinancing risk. While refinancing risk is associated with reductions in net hiring, we find that higher quality financial reporting mitigates this effect. This result is stronger when labor market frictions are more significant (e.g., for firms with high labor intensity or unionized labor), and when information and agency problems are otherwise more severe (e.g., for firms with higher likelihoods of default or more heterogeneous debt structures). We also document that higher quality financial reporting improves firmsâ€™ abilities to obtain debt in the face of refinancing risk and reduces the cost of debt among those that obtain it. We show that our results extend beyond firm-level refinancing risk to more general changes in the economy-wide supply of credit. Finally, we supplement our main analyses with a quasi-natural experiment using the implementation of SFAS 131. Overall, our evidence suggests a specific financing channel through which financial reporting quality facilitates corporate employment. Libra and Blockchain Databases - Chances for SMEs (Libra und Blockchain Datenbanken - Potentiale fÃ¼r den Mittelstand) Holste, Bjoern,Horn, Ina SSRN English Abstract: Despite its short history, the blockchain ecosystem offers a plethora of opportunities for SMEs. Identification of technologies which promise monetary Euro value-added is highly complex and requires congruency of corporate strategy and long-term objectives. We evaluate technological idiosyncrasies of single components and provide a classification of the blockchain ecosystem for SMEs. Finally, we propose a decision-making framework regarding selection and implementation of blockchain technology including its newest member, Facebookâ€™s Libra.German Abstract: Obwohl vergleichsweise jung bietet das Blockchain Ecosystem fÃ¼r Anwendungen im Mittelstand eine verwirrende Vielzahl von MÃ¶glichkeiten. Bei der Frage, welche Technologien einen in Euro bezifferbaren Mehrwert erzeugen, ist die Herstellung der Deckungsgleichheit von betrieblicher Strategie und langfristigen Zielen noch von hoher KomplexitÃ¤t geprÃ¤gt. Wir evaluieren die technologischen Eigenschaften der einzelnen Komponenten und ordnen das Blockchain Ecosystem aus Sicht des Mittelstands ein. SchlieÃŸlich schlagen wir ein Rahmenwerk fÃ¼r die Entscheidungsfindung in Bezug auf Nutzung und Einsatz der Blockchain Technologie vor - inklusive ihres neuesten Mitglieds, Facebookâ€™s Libra. Liquidity and Tail-Risk Interdependencies in the Euro Area Sovereign Bond Market Clancy, Daragh,Dunne, Peter G.,Filiani, Pasquale SSRN The likelihood of severe contractions in an asset's liquidity can feed back to the ex ante risks faced by the individual providers of such liquidity. These self-reinforcing effects can spread to other assets through informational externalities and hedging relations. We explore whether such interdependencies play a role in amplifying tensions in European sovereign bond markets and are a source of cross-market spillovers. Using high-frequency data from the inter-dealer market, we find significant own- and cross-market effects that amplify liquidity contractions in the Italian and Spanish bond markets during times of heightened risk. The German Bund's safe-haven status exacerbates these amplification effects. We provide evidence of a post-crisis dampening of cross-market effects following crisis-era changes to euro area policies and institutional architecture. We identify a structural break in Italy's cross-market conditional correlation during rising political tensions in 2018, which significantly reduced liquidity. Overall, our findings demonstrate potential for the provision of liquidity across sovereign markets to be vulnerable to sudden fractures, with possible implications for euro area economic and financial stability. Modal Bank, Tingkat Likuiditas Bank,dan Pertumbuhan Kredit (Bank Capital, Liquidity Level, and Lending Growth) Pratama, Ahmad Aziz Putra SSRN Indonesian Abstract: Tujuan penelitian ini menguji pengaruh modal bank terhadap pertumbuhan kredit dengan moderasi tingkat likuiditas perusahaan perbankan yang terdaftar di Bursa Efek Indonesia. Penelitian ini menggunakan model regresi linear berganda dan Moderated Regression Analysis (MRA). Data diperoleh dari laporan keuangan perusahaan yang dipublikasikan pada periode 2010-2016. Variabel dependen dalam penelitian ini adalah pertumbuhan kredit yang diproksikan dengan Net Loans Growth. Variabel independen yang digunakan adalah modal bank yang diproksikan dengan Capital Adequacy Ratio (CAR). Variabel moderasi pada penelitian ini menggunakan tingkat likuiditas yang diproksikan dengan rasio likuiditas. Selain itu, variabel kontrol dalam penelitian ini adalah ukuran perusahaan yang diproksikan dengan logaritma dari total aset dan kualitas kredit yang diproksikan dengan Non Performing Loan (NPL). Hasil penelitian menunjukkan bahwa modal bank memiliki pengaruh positif signifikan terhadap pertumbuhan kredit dan tingkat likuiditas memperkuat pengaruh positif modal bank terhadap pertumbuhan kredit. Variabel kontrol ukuran perusahaan memiliki pengaruh positif signifikan terhadap pertumbuhan kredit dan variabel NPL berpengaruh tidak signifikan terhadap pertumbuhan kredit. English Abstract: The purpose of this research is to examine the effect of bank capital on lending growth with moderation of liquidity level of banking companies listed in Indonesian Stock Exchange. This study used multiple linear regression model and Moderated Regression Analysis (MRA). Data obtained from the company's financial report published in 2010-2016 period. Dependent variable in this research is lending growth proxied with Net Loans Growth. Independent variable used bank capital proxied with Capital Adequacy Ratio (CAR). Moderating variable in this research used liquidity level proxied with liquidity ratio. In addition, controlling variables in this study are firm size proxied with logarithm of total assets and credit quality proxied with Non Performing Loan (NPL). The results showed that bank capital has significant positive effect on lending growth, while the liquidity ratio strengthens positive effect of bank capital on lending growth. Size control variable has significant positive effect on lending growth while NPL variable has no significant effect on lending growth. Modeling National Latent Socioeconomic Health and Examination of Policy Effects via Causal Inference F. Swen Kuh,Grace S. Chiu,Anton H. Westveld arXiv This research develops a socioeconomic health index for nations through a model-based approach which incorporates spatial dependence and examines the impact of a policy through a causal modeling framework. As the gross domestic product (GDP) has been regarded as a dated measure and tool for benchmarking a nation's economic performance, there has been a growing consensus for an alternative measure---such as a composite wellbeing' index---to holistically capture a country's socioeconomic health performance. Many conventional ways of constructing wellbeing/health indices involve combining different observable metrics, such as life expectancy and education level, to form an index. However, health is inherently latent with metrics actually being observable indicators of health. In contrast to the GDP or other conventional health indices, our approach provides a holistic quantification of the overall health' of a nation. We build upon the latent health factor index (LHFI) approach that has been used to assess the unobservable ecological/ecosystem health. This framework integratively models the relationship between metrics, the latent health, and the covariates that drive the notion of health. In this paper, the LHFI structure is integrated with spatial modeling and statistical causal modeling, so as to evaluate the impact of a policy variable (mandatory maternity leave days) on a nation's socioeconomic health, while formally accounting for spatial dependency among the nations. We apply our model to countries around the world using data on various metrics and potential covariates pertaining to different aspects of societal health. The approach is structured in a Bayesian hierarchical framework and results are obtained by Markov chain Monte Carlo techniques. Online Appendix for 'Network Motivated Lending Decisions: A Rationale for Forbearance Lending' Ogura, Yoshiaki,Okui, Ryo,Saito, Yukiko SSRN This online appendix includes supplemental materials for "Network-motivated Lending Decisions: A Rationale for Forbearance Lending'' by Ogura, Okui, and Saito. Online Appendix 1 contains the proofs of the propositions. Online Appendix 2 presents a numerical example for the theoretical model. Online Appendix 3 derives the bias property of the OLS estimator applied to the spatial autoregressive model. Online Appendix 4 presents the formula for standard errors for the OLS estimator, which are adjusted to address the problem that the demand influence coefficients are estimated regressors. Online Appendix 5 presents additional empirical results to examine the robustness of the empirical results in the main text. Personalized Robo-Advising: Enhancing Investment through Client Interaction Agostino Capponi,Sveinn Olafsson,Thaleia Zariphopoulou arXiv Automated investment managers, or robo-advisors, have emerged as an alternative to traditional financial advisors. Their viability crucially depends on timely communication of information from the clients they serve. We introduce and develop a novel human-machine interaction framework, in which the robo-advisor solves an adaptive mean-variance control problem, with the risk-return tradeoff dynamically updated based on the risk profile communicated by the client. Our model predicts that clients who value a personalized portfolio are more suitable for robo-advising. Clients who place higher emphasis on delegation and clients with a risk profile that changes frequently benefit less from robo-advising. Pursuing Financial Stability under the Guise of Decision Usefulness? An Analysis of the IFRS 9 Expected Credit Loss Model in Light of the Public Interest Orthaus, Selina,Rugilo, Daniel SSRN In response to the financial crisis, the IASBâ€™s mission statement of 2015 clarifies that the IASB serves the public interest by developing standards which produce decision useful information for capital providers. Thereby, IFRS are assumed to contribute in the long term to financial stability which the IASB clearly deems as the objective of prudential regulation. This paper takes the introduction of the expected credit loss model in IFRS 9 as a case to study the validity of the causalities set out in the mission statement in more depth. Our analysis comprises the IASBâ€™s documentation, including all comment letters, and public discourses surrounding the drafting process of IFRS 9. We argue that the ex-ante-recognition of day-1-losses, as required by IFRS 9, exceeds the realm of providing decision useful information for investors, since it prescribes a temporary retention of equity for all IFRS preparers â€" similar to prudential capital buffers. The relationship of decision usefulness and financial stability considerations, as claimed in the IASBâ€™s mission statement, is therefore reversed. Our analysis of the drafting process of IFRS 9 shows that the incorporation of day-1-losses resembles a compromise stemming from concessions to constituents, others than investors, including the FASB, preparers and the political-regulatory sphere. Along this line, our paper sheds light on the practical limitations of theoretical â€˜guidanceâ€™-documents such as the mission statement, which sketch ideal states of affairs, and questions whether the investor-only focus brought forward in the mission statement adequately reflects how the IASB actually works in the public interest. Response to 'Order Flows and Financial Investor Impacts in Commodity Futures Markets' Henderson, Brian J.,Pearson, Neil D.,Wang, Li SSRN Financial institutions that issue commodity-linked notes hedge their liabilities by buying commodity futures. Henderson, Pearson and Wang (2015) show that these futures trades impact commodity futures prices and interpret this as evidence that uninformed financial flows into the commodity markets impact commodity prices. Ready and Ready (2019) criticize the analysis and conclusions in Henderson, Pearson and Wang (2015) and instead conclude that there is no evidence that the uninformed financial flows of CLN hedging trades impact commodity prices. This note explains why the analysis, criticisms, and conclusions in Ready and Ready (2019) are incorrect. Shareholder Value Bites the Hand That Feeds It Swanson, Brad SSRN Shareholder primacy has many victims, including employees, the environment and the communities where corporations operate. Surprisingly, it harms its intended beneficiaries, shareholders, as well, according to a number of studies.By contrast, studies show that sustainable management -- which explicitly takes environment, social and governance (ESG) factors into account -- benefits shareholders, as well as other stakeholders, by improving companiesâ€™ profitability and stock market price.In other words, doing the right thing is not only good for the world but also good for the wallets of corporate owners and investors. Sind Krypto-WÃ¤hrungsmÃ¤rkte Fair? (Are Crypto-Currency Markets Fair?) Holste, Bjoern,Gallus, Christoph SSRN German Abstract: KryptowÃ¤hrungen als alternatives Investment ziehen nach wie vor groÃŸe Aufmerksamkeit auf sich. Getrieben vom Anlagedruck durch ein nachhaltig niedriges Zinsumfeld und rechnerisch hohen Renditechancen in KryptowÃ¤hrungen beginnen zunehmend auch institutionelle Anleger mit der Evaluation dieser neuen Anlageklasse. Die Autoren untersuchen Preisdifferenzen an verschiedenen HandelsplÃ¤tzen und die Existenz von ArbitragemÃ¶glichkeiten. Arbitragefreiheit ist ein wesentliches Merkmal fairer MÃ¤rkte und eine notwendige Voraussetzung fÃ¼r Investoren, um sich auf den Marktmechanismus verlassen zu kÃ¶nnen.English Abstract: Cryptocurrencies continue to attract attention as an alternative investment opportunity. Given the continued low interest rate environment and the theoretical possibility of achieving high price returns in cryptocurrencies, institutional investors are tentatively exploring this investment opportunity. However, arbitrage-free markets are an important pre-requisite both on a theoretical level as well as for the practical execution of purchases at fair market level. In this article price differences between different trading platforms for cryptocurrencies are analysed and the possibility of arbitrage transactions is discussed. Stochastic Orderings of Multivariate Elliptical Distributions Chuancun Yin arXiv Let${\bf X}$and${\bf X}$be two$n$-dimensional elliptical random vectors, we establish an identity for$E[f({\bf Y})]-E[f({\bf X})]$, where$f: \Bbb{R}^n \rightarrow \Bbb{R}\$ fulfilling some regularity conditions. Using this identity we provide a unified derivation of sufficient and necessary conditions for classifying multivariate elliptical random vectors according to several main integral stochastic orders. As a consequence we obtain new inequalities by applying it to multivariate elliptical distributions. The results generalize the corresponding ones for multivariate normal random vectors in the literature.

Testimony of Jesse M. Fried on Stock Buybacks Before the U.S. House of Representatives Subcommittee on Investor Protection, Entrepreneurship, and Capital Markets
Fried, Jesse M.
SSRN
This statement presents my views on buybacks and my general reactions to provisions in four pieces of legislation relating to stock buybacks. Part I describes the role of stock buybacks in the economy and offers some â€œinvestor-benignâ€ explanations for firmsâ€™ use of repurchases rather than dividends to distribute cash to investors. Part I then explains that the overall level of shareholder payouts (that is, the total amount of dividends and repurchases) does not appear to be too high; in fact, it may well be too low.Part II describes the current regulation of buybacks, which I believe is too lax and enables their abuse by corporate executives. In particular, I will explain how current regulation can enable executives to use buybacks to enrich themselves at the expense of public investors, through (1) indirect insider trading, (2) the manipulation of the stock price and EPS metrics in compensation arrangements, and (3) â€œfalse signaling:â€ announcing repurchases that executives do not intend to carry out, solely to boost the stock price before executives unload shares.Part III suggests a disclosure rule that would reduce executivesâ€™ ability to engage in the above-mentioned abuses, and therefore, better protect public investors: requiring public firms (like their insiders) to disclose trades in firm stock within two business days. I also describe additional measures that could be taken if this disclosure rule turns out be insufficient.Part IV offers initial reactions to key provisions in these four pieces of legislation.

The Multiplicity of International Corporate Social Responsibility Standards: Implications for Global Value Chain Governance
Fransen, Luc,Kolk, Ans,Rivera-Santos, Miguel
SSRN
Purpose: This paper examines the multiplicity of Corporate Social Responsibility (CSR) standards, explaining its nature, dynamics, and implications for Multinational Enterprises (MNEs) and International Business (IB), especially in the context of CSR and global value chain (GVC) governance.Design/methodology/approach: This paper leverages insights from the literature in political science, policy, regulation, governance, and IB; from our own earlier work; and from an inventory of CSR standards across a range of sectors and products.Findings: This analysisâ€™ more nuanced approach to CSR standard multiplicity helps distinguish the different categories of standards; uncovers the existence of different types of standard multiplicity; and highlights complex trends in their evolution over time, discussing implications for the various firms targeted by, or involved in, these initiatives, and for CSR and GVC governance research.Research limitations/implications: This paper opens many avenues for future research on CSR multiplicity and its consequences; on lead firms governing GVCs from an IB perspective; and on institutional and market complexity.Practical/Societal implications: By providing overviews and classifications, this paper helps clarify CSR standards as â€œnew regulatorsâ€ and â€œinstrumentsâ€ for actors in business, society and government. Originality/value: This paper contributes by filling gaps in different existing literatures concerning standard multiplicity. It also specifically adds a new perspective to the IB literature, which thus far has not fully incorporated the complexity and dynamics of CSR standard multiplicity in examining GVCs and MNE strategy and policy.

The Politics of News Personalization
Lin Hu,Anqi Li,Ilya Segal
arXiv

We study how news personalization affects policy polarization. In a two-candidate electoral competition model, an attention-maximizing infomediary aggregates information about candidate valence into news, whereas voters decide whether to consume news, trading off the expected utility gain from improved expressive voting against the attention cost. Broadcast news attracts a broad audience by offering a symmetric signal. Personalized news serves extreme voters with skewed signals featuring own-party bias and occasional big surprise. Rational news aggregation yields policy polarization even if candidates are office-motivated. Personalization makes extreme voters the disciplining entity for equilibrium polarization and increases polarization through occasional big surprise.

The Reactive Beta Model
Sebastien Valeyre,Denis S. Grebenkov,Sofiane Aboura
arXiv

We present a reactive beta model that includes the leverage effect to allow hedge fund managers to target a near-zero beta for market neutral strategies. For this purpose, we derive a metric of correlation with leverage effect to identify the relation between the market beta and volatility changes. An empirical test based on the most popular market neutral strategies is run from 2000 to 2015 with exhaustive data sets including 600 US stocks and 600 European stocks. Our findings confirm the ability of the reactive beta model to withdraw an important part of the bias from the beta estimation and from most popular market neutral strategies.

The Role of Underwriter-Affiliated Institutional Investors in the IPO Aftermarket
Pratobevera, Giuseppe
SSRN
Using institutional trading data in a sample of U.S. IPOs, I show that investment managers provide costly price support in the aftermarket of IPOs in which their parent banks are non-lead syndicate members. The costly support is concentrated in cold IPOs and IPOs net sold by independent institutions. Divestitures of parent banks' subsidiaries corroborate a causal interpretation of the results. I provide evidence consistent with non-lead syndicate banks using their affiliated investors to build a relationship with IPO lead underwriters and boost their underwriting business. Affiliated institutions are rewarded with more allocations in IPOs led by their parent banks.

The survival of start-ups in time of crisis. A machine learning approach to measure innovation
Marco Guerzoni,Consuelo R. Nava,Massimiliano Nuccio
arXiv

This paper shows how data science can contribute to improving empirical research in economics by leveraging on large datasets and extracting information otherwise unsuitable for a traditional econometric approach. As a test-bed for our framework, machine learning algorithms allow us to create a new holistic measure of innovation built on a 2012 Italian Law aimed at boosting new high-tech firms. We adopt this measure to analyse the impact of innovativeness on a large population of Italian firms which entered the market at the beginning of the 2008 global crisis. The methodological contribution is organised in different steps. First, we train seven supervised learning algorithms to recognise innovative firms on 2013 firmographics data and select a combination of those with best predicting power. Second, we apply the former on the 2008 dataset and predict which firms would have been labelled as innovative according to the definition of the law. Finally, we adopt this new indicator as regressor in a survival model to explain firms' ability to remain in the market after 2008. Results suggest that the group of innovative firms are more likely to survive than the rest of the sample, but the survival premium is likely to depend on location.

The transport-based meshfree method (TMM) and its applications in finance: a review
Philippe G. LeFloch,Jean-Marc Mercier
arXiv

We review a numerical technique, referred to as the Transport-based Meshfree Method (TMM), and we discuss its applications to mathematical finance. We recently introduced this method from a numerical standpoint and investigated the accuracy of integration formulas based on the Monte-Carlo methodology: quantitative error bounds were discussed and, in this short note, we outline the main ideas of our approach. The techniques of transportation and reproducing kernels lead us to a very efficient methodology for numerical simulations in many practical applications, and provide some light on the methods used by the artificial intelligence community. For applications in the finance industry, our method allows us to compute many types of risk measures with an accurate and fast algorithm. We propose theoretical arguments as well as extensive numerical tests in order to justify sharp convergence rates, leading to rather optimal computational times. Cases of direct interest in finance support our claims and the importance of the problem of the curse of dimensionality in finance applications is briefly discussed.

Wasserstein Index Generation Model: Automatic Generation of Time-series Index with Application to Economic Policy Uncertainty
Fangzhou Xie
arXiv

I propose a novel method, the Wasserstein Index Generation model (WIG), to generate a public sentiment index automatically. To test the model`s effectiveness, an application to generate Economic Policy Uncertainty (EPU) index is showcased.

Wealth Effects of Relative Firm Value in M&A Deals: Reallocation of Physical vs Intangible Assets
Bhattacharya, Debarati,Li, Wei-Hsien
SSRN
We distinguish between value creation through redistribution of physical assets and that from intangible assets. We decompose the M/B ratio into fundamental value and unexplained components and find that mergers create wealth when high-value firms primarily acquire physical assets from low-value firms. In contrast, deals motivated by transfer of investment opportunities generate wealth when growth-constrained low-value firms acquire substantial intangible assets from high-value targets. By separating the two motives for mergers, we provide empirical evidence of two diametrically opposed effects of relative firm value on wealth gains to shareholders, which reconcile conflicting evidence of the â€˜high-buys-lowâ€™ effect from earlier studies. Concomitantly, the findings also explain the patterns of firm pairings in merger data that run contrary to conventional wisdom. Our empirical framework considers the effects of mispricing, governance, and size of assets reallocated and addresses concerns of selection bias. Additionally, we find evidence of post-merger wealth generation through acquisition of growth opportunities in the form of intangible asset transfer from a high-value target to a low-value acquirer.

Who Benefits from Innovations in Financial Technology?
Mihet, Roxana
SSRN
Financial technology affects both efficiency and equity in the stock market. The impact is non-trivial because several key technological developments have altered multiple dimensions of investors' opportunity sets at the same time. For example, better and faster computing has made it cheaper for retail investors to participate and to find funds that meet their needs. However, it has also made it cheaper for sophisticated investors to learn about asset returns. Some experts believe these innovations will increase financial inclusion. Others worry about possible anti-competitive effects that can lead to more unequal rent distribution. To address this debate, I first build a theoretical model of intermediated trading under asymmetric information that allows me to differentiate between the effects of each innovation. Second, I interpret US macro data from the last 40 years through the lens of my model and find that, although the gains from financial technology were accruing to low-wealth investors throughout the 1990s, they have been accruing to high-wealth investors since the early 2000s. The key is that, even if investors have access to the equity premium through cheap funds, improvements in financial technology disproportionately benefit informed, sophisticated traders. This reduces the participation rate of low-wealth investors, improves price informativeness, enlarges (but, at the same time, consolidates) the sophisticated asset management industry, and amplifies capital income inequality. Further advances in modern computing, big data, and artificial intelligence in asset management, in the absence of any gains redistribution, may accelerate the rate of change.

Why Capitalâ€™s Effect Differs in Bank Size?