Research articles for the 2019-04-29

A Multicriteria Decision Making Approach to Study the Barriers to the Adoption of Autonomous Vehicles
Alok Raj,J Ajith Kumar,Prateek Bansal

The automation technology is emerging, but the adoption rate of autonomous vehicles (AV) will largely depend upon how policymakers and the government address various challenges such as public acceptance and infrastructure development. This study proposes a five-step method to understand these barriers to AV adoption. First, based on a literature review followed by discussions with experts, ten barriers are identified. Second, the opinions of eighteen experts from industry and academia regarding inter-relations between these barriers are recorded. Third, a multicriteria decision making (MCDM) technique, the grey-based Decision-making Trial and Evaluation Laboratory (Grey-DEMATEL), is applied to characterize the structure of relationships between the barriers. Fourth, the robustness of the results is tested using sensitivity analysis. Fifth, the key results are depicted in a causal loop diagram (CLD), a systems thinking approach, to comprehend cause-and-effect relationships between the barriers. The results indicate that the lack of customer acceptance (LCA) is the most prominent barrier, the one which should be addressed at the highest priority. The CLD suggests that LCA can be rather mitigated by addressing two other prominent, yet more tangible, barriers -- lack of industry standards and the absence of regulations and certifications. The study's overarching contribution thus lies in bringing to fore multiple barriers to AV adoption and their potential influences on each other, owing to which its insights are relevant to associations related to AVs in prioritizing their endeavors to expedite AV adoption. From the methodological perspective, this is the first study in transportation literature that integrates Grey-DEMATEL with systems thinking.

A Stochastic Model to Evaluate Pricing Distortions in Indemnity Insurance Methods for MTPL Insurance
Fersini, Paola,Forte, Salvatore,Melisi, Giuseppe,Olivieri, Gennaro
Direct compensation or the direct reimbursement scheme is an indemnity insurance method that many European and American countries use to manage motor liability claims in which the driver that suffers an accident is paid by his/her insurance company that possibly later receives a flat-rate reimbursement (known as forfeit). Using non-life actuarial methodologies, this article analyses the distortion effects due to the direct compensation mechanisms and the effects of different forfeit reimbursement systems on policyholder tariffs in the management of motor liability claims involving vehicles in two different sectors, i.e. automobile and motorcycle. We empirically analyse and formalize the distortion effects resulting from the mechanism that different direct reimbursement systems produce, and explore the correlation between increasing tariffs for motorcycle policyholders and decreasing tariffs for other vehicle policyholders. We propose some alternative methods to overcome these distortion effects, evaluating their pricing impact through a stochastic model applied to a case study.

Boosting Quality Education with Inclusive Human Development: Empirical Evidence from Sub-Saharan Africa
Asongu, Simplice,Odhiambo, Nicholas
This study examines the importance of inclusive human development in promoting education quality in a panel of forty-nine Sub-Saharan African countries for the period 2000-2012. The empirical evidence is based on Ordinary Least Squares (OLS), Fixed Effects (FE) and Quantile Regression (QR) estimations. It is apparent from the OLS and FE findings that inclusive human development has a negative effect on the outcome variable. This negative effect implies that inclusive human development improves education quality. This result should be understood in the light of the fact that the adopted education variable is a negative economic signal given that it is computed as the ratio of pupils to teachers. Therefore, a higher ratio reflects diminishing education quality. From QR, with the exception of the highest quantile, the tendency of inclusive human development in reducing poor quality education is consistent throughout the conditional distribution of poor education quality. Policy implications are discussed.

Crowded Stock Coverage
Martineau, Charles,Zoican, Marius
Analyst stock coverage is "crowded:" the most-covered 5% U.S. equities amount to 25% of earnings forecasts. Is information supply optimally distributed in financial markets? We build a model where limited-attention investors endogenously learn about securities. Analysts compete for scarce investor attention, providing forecasts that reduce learning costs. Coverage crowding emerges through strategic complementarity effects. For limited investor attention, analysts prefer to share a crowded space rather than "going against the wind" to cover more opaque assets. However, coverage skewness is excessive from the investors' perspective. The implications echo documented patterns: analysts cluster in large stocks with significant intangible assets.

Crowdfunding, Financing Constraints and Real Effects
Kumar, Praveen,Langberg, Nisan,Zvilichovsky, David
We study the feasibility and optimal design of pre-sale crowdfunding contracts where participating consumers pay a premium above the future expected spot price and financially constrained entrepreneurs balance the potential product-market distortions introduced through pre-sale crowdfunding against the cost of traditional external financing. Our analysis shows how such crowdfunding contracts enable the execution of projects that could not be otherwise undertaken and highlights novel interactions between the cost of capital, demand uncertainty and production. Tighter financing constraints reduce the ability of the monopolist to extract surplus but, contrary to the usual result, may increase production. We evaluate how uncertainty and market size reduce the price-discriminating power of the monopolist and affect the optimal contract regime. Nevertheless, we show how such pre-sale price-discriminating contracts are implementable even when the number of potential consumers is relatively high and their individual demand is stochastic.

Design and Implementation of Federal Credit and Insurance Programs
Park, Sangkyun
The effectiveness and the efficiency of a credit or an insurance program critically depend on the design of the program.In designing a program, the government should carefully consider various factors, including incentives of private parties, possible information advantages of private parties, and competition and liquidity in the target market. Due to failures to reflect these factors in the program design fully, many credit and insurance programs may not be serving their target populations effectively and efficiently.Some partial loan guarantee programs allow private lenders to set the lending rate within a range. In these programs, lenders can increase the profit by lending more to high-risk borrowers who pay a high interest rate. Resulting inefficiencies include underserving of low-risk borrowers, excessive profits for lenders, and excessive costs to the government. Possible solutions include tying the guarantee fee to the lending rate and taking a partial ownership of loans. A main problem with direct loans and full loan guarantees is prepayments by low-risk borrowers. Two main ways to recapture the value of the prepayment option are a prepayment penalty and a high lending rate. The preferable way is a prepayment penalty because a high lending rate may further prompt low-risk borrower to prepay. To finance highly risky activities, such as technology start-ups, the government should take an equity position. Without sharing a few "jackpots," the government cannot recoup its investment. Nevertheless, the government takes a debt position in venture capital investments. The consequence is either that the government suffers large losses or that the government fails to finance truly high-risk, high-return projects. In some insurance programs, the government shares risk with private parties in a way that favors private parties. Private insurers have opportunities to take advantage of mispricing of insurance policies. Policyholders underpay during good times, but they don't necessarily make up the underpayment during bad times. An efficient way of sharing risk is that the government bears all of catastrophic risk and let private insurers bear policy-specific risk. The government implicitly guarantees GSE debts. Since GSEs have incentive to take excessive risk, the government is providing a very expensive guarantee free of charge. An explicit guarantee made available to both GSEs and private entities at a fee should lower the cost to the government, make the market more competitive, and contain financial crises effectively.For proper budget discipline and efficient management, it is important to estimate the costs of credit programs accurately. The cost estimation for credit programs has two main steps: estimating future cashflows and discounting future cashflows to arrive at the net present value of cashflows. Some models estimating future cashflows do not fully consider the effects of economic fluctuations. The consequence can be a systematic underestimation of the cost of a credit program. Some also argue that discounting uncertain cashflows with Treasury rates leads to an underestimation of the cost to taxpayers by ignoring the risk premium. This argument is off the mark. Discounting with Treasury rates is appropriate, considering that budgeting is more about accounting accuracy than economic optimization and that the costs of other government programs do not include the risk premium. The focus should be on estimating cashflows more accurately, fully taking into account the possibilities of extraordinary losses cause by economic downturns.

Empirical facts characterizing banking crises: an analysis via binary time series
Paolo Di Caro,Giuseppe Pernagallo,Antonino Damiano Rossello,Benedetto Torrisi

Various works have already showed that common shocks and cross-country financial linkages caused the banking systems of several countries to be highly interconnected with the result that during bad times, banking crises may arise simultaneously in different countries. Our aim is to provide further evidence on the topic using a dataset made by dichotomous banking crises time series for 66 countries from 1800 to 2014. Via the use of heatmap matrices we show that several countries exhibit pairwise correlation, which means that banking crises tend to occur in the same year. Clustering analysis suggests that developed countries (for the most European ones) are highly similar in terms of the path of events. An analysis of the events that followed the Great Depression and the Great Recession shows that after the crisis of 2008, banking crises tend to characterize countries tied by financial links whereas before 2008 contagion seems to affect countries in the same geographical area. Clustering analysis shows also that after financial liberalization, crises affected countries with similar economic structures and growth. Further researches should enlighten the origin of these linkages investigating how the process of contagion eventually happens.

Flow-Induced Trades and Asset Pricing Factors
Huang, Shiyang,Song, Yang,Xiang, Hong
We show that mutual funds’ flow-induced trades significantly influence returns and co-movement among 50 well-known asset pricing factors (anomalies). Mutual fund investors are ignorant about both systematic and idiosyncratic risks when allocating capital among funds. We measure the non-fundamental demand shocks to each factor by aggregating mutual funds’ flow-induced trading of individual stocks underlying the factor. We show that flow-induced demand shifts largely determine factor return dynamics and that the expected (co)variance of flow-induced trades of factors strongly forecasts factor return (co)variance. Our results indicate that these factors are heavily exposed to flow-driven “noise trader” risk, which we further show is significantly priced. The flow-driven effects on factor return dynamics can partially explain factor momentum and underperformance of large-sized mutual funds relative to small funds.

Gated deep neural networks for implied volatility surfaces
Yu Zheng,Yongxin Yang,Bowei Chen

In this paper, we propose a gated deep neural network model to predict implied volatility surfaces. Conventional financial conditions and empirical evidence related to the implied volatility are incorporated into the neural network architecture design and calibration including no static arbitrage, boundaries, asymptotic slope and volatility smile. They are also satisfied empirically by the option data on the S&P 500 over a ten years period. Our proposed model outperforms the widely used surface stochastic volatility inspired model on the mean average percentage error in both in-sample and out-of-sample datasets. The research of this study has a fundamental methodological contribution to the emerging trend of applying the state-of-the-art information technology into business studies as our model provides a framework of integrating data-driven machine learning algorithms with financial theories and this framework can be easily extended and applied to solve other problems in finance or other business fields.

How Algorithmic Trading Alters the Information Environment? Evidence from Insider Trades
Huang, Yuyun,Chang, Millicent,Treepongkaruna, Sirimon,Yang, Joey (Wenling)
In this paper, we explore how algorithmic trading (AT) intensity affects the information environment by studying its effect on insider trading behaviour. We first examine how changes in AT affects insiders’, decision to trade, timing of and profitability of their trades. Our results indicate lower profits for insider sales only as AT increases, and the reduction in profit is for routine traders. Insiders are also found to have fewer incentives to trade when AT intensity is high. Further analysis reveals that, around earnings announcements, increased AT is associated with smaller amounts of information acquisition before announcement. This suggests that, although AT improves price efficiency by reducing insider profitability; the informed traders’ reduced incentives to trade deters information acquisition prior to earnings announcements, and this causes a large price correction at the announcement.

Identification of Key Companies for International Profit Shifting in the Global Ownership Network
Tembo Nakamoto,Abhijit Chakraborty,Yuichi Ikeda

In the global economy, the intermediate companies owned by multinational corporations are becoming an important policy issue as they are likely to cause international profit shifting and diversion of foreign direct investments. The purpose of this analysis is to call the intermediate companies with high risk of international profit shifting as key firms and to identifying and clarify them. For this aim, we propose a model that focuses on each affiliate's position on the ownership structure of each multinational corporation. Based on the information contained in the Orbis database, we constructed the Global Ownership Network, reflecting the relationship that can give significant influence to a firm, and analyzed for large multinational corporations listed in Fortune Global 500. In this analysis, first, we confirmed the validity of this model by identifying affiliates playing an important role in international tax avoidance at a certain degree. Secondly, intermediate companies are mainly found in the Netherlands and the United Kingdom, etc., and tended to be located in jurisdictions favorable to treaty shopping. And it was found that such key firms are concentrated on the IN component of the bow-tie structure that the giant weakly connected component of the Global Ownership Network consist of. Therefore, it clarifies that the key firms are geographically located in specific jurisdictions, and concentrates on specific components in the Global Ownership Network. The location of key firms are related with the ease of treaty shopping, and there is a difference in the jurisdiction where key firms are located depending on the location of the multinational corporations.

Insider Trading Laws as a Defeat Device
Baur, Dirk G.,Dimpfl, Thomas
Insider trading laws are designed to ensure a level-playing eld and trust in financial markets at the expense of less efficient markets. This paper argues that insider trading laws fail to ensure a level-playing field and trust and instead facilitate fraud and manipulation. We use a model to show that insider trading laws result in delayed price movements, lower liquidity, increased volatility and crash risk. An analysis of the Volkswagen Diesel Scandal as a prominent empirical example supports the predictions of the theoretical model.

Mean-variance portfolio selection under Volterra Heston model
Bingyan Han,Hoi Ying Wong

Motivated by empirical evidence for rough volatility models, this paper investigates the continuous-time mean-variance (MV) portfolio selection under Volterra Heston model. Due to the non-Markovian and non-semimartingale nature of the model, classic stochastic optimal control frameworks are not directly applicable to the associated optimization problem. By constructing an auxiliary stochastic process, we obtain the optimal investment strategy that depends on the solution to a Riccati-Volterra equation. The MV efficient frontier is shown to maintain a quadratic curve. Numerical studies show that both roughness and volatility of volatility affect the optimal strategy materially.

Mentorship Processes within Startup Accelerators
Avnimelech, Gil,Rechter, Eyal
The last decade has seen the development of a new support infrastructure in the entrepreneurial ecosystem: the startup accelerator. Accelerators offer a structured developmental process that includes an educational component, a bundle of networks and intensive mentorship. Despite the central place of mentoring in accelerators, current evaluation studies do not explicitly include its contribution to programs’ impact. Moreover, there are no comprehensive descriptions of mentor types, roles and practices within accelerators, and how these should be integrated in their operation. This paper aims to fill this gap in the literature by presenting a comprehensive description and classification of mentors in accelerators and their roles, goals, characteristics and practices. Building on these characteristics, we suggest best practices in terms of mentoring style and mentor-mentee fit. Our review and suggestions are based on 64 in-depth interviews with accelerator managers, mentors, entrepreneurs and key agents that participated in accelerator programs in Israel during the years 2011-2018. It is also based on the open sections of structured interviews we conducted with over 300 entrepreneurs and 150 mentors that participated in accelerators in Israel. We believe this study has important implications for practitioners, researchers in the field and policy makers.

Pricing and hedging of VIX options for Barndorff-Nielsen and Shephard models
Takuji Arai

The VIX call options for the Barndorff-Nielsen and Shephard models will be discussed. Derivatives written on the VIX, which is the most popular volatility measurement, have been traded actively very much. In this paper, we give representations of the VIX call option price for the Barndorff-Nielsen and Shephard models: non-Gaussian Ornstein--Uhlenbeck type stochastic volatility models. Moreover, we provide representations of the locally risk-minimizing strategy constructed by a combination of the underlying riskless and risky assets. Remark that the representations obtained in this paper are efficient to develop a numerical method using the fast Fourier transform. Thus, numerical experiments will be implemented in the last section of this paper.

Regulating AI: do we need new tools?
Otello Ardovino,Jacopo Arpetti,Marco Delmastro

The Artificial Intelligence paradigm (hereinafter referred to as "AI") builds on the analysis of data able, among other things, to snap pictures of the individuals' behaviors and preferences. Such data represent the most valuable currency in the digital ecosystem, where their value derives from their being a fundamental asset in order to train machines with a view to developing AI applications. In this environment, online providers attract users by offering them services for free and getting in exchange data generated right through the usage of such services. This swap, characterized by an implicit nature, constitutes the focus of the present paper, in the light of the disequilibria, as well as market failures, that it may bring about. We use mobile apps and the related permission system as an ideal environment to explore, via econometric tools, those issues. The results, stemming from a dataset of over one million observations, show that both buyers and sellers are aware that access to digital services implicitly implies an exchange of data, although this does not have a considerable impact neither on the level of downloads (demand), nor on the level of the prices (supply). In other words, the implicit nature of this exchange does not allow market indicators to work efficiently. We conclude that current policies (e.g. transparency rules) may be inherently biased and we put forward suggestions for a new approach.

Rough volatility of Bitcoin
Tetsuya Takaishi

Recent studies have found that the log-volatility of asset returns exhibit roughness. This study investigates roughness or the anti-persistence of Bitcoin volatility. Using the multifractal detrended fluctuation analysis, we obtain the generalized Hurst exponent of the log-volatility increments and find that the generalized Hurst exponent is less than $1/2$, which indicates log-volatility increments that are rough. Furthermore, we find that the generalized Hurst exponent is not constant. This observation indicates that the log-volatility has multifractal property. Using shuffled time series of the log-volatility increments, we infer that the source of multifractality partly comes from the distributional property.

Selection mechanisms affect volatility in evolving markets
David Rushing Dewhurst,Michael Vincent Arnold,Colin Michael Van Oort

Financial asset markets are sociotechnical systems whose constituent agents are subject to evolutionary pressure as unprofitable agents exit the marketplace and more profitable agents continue to trade assets. Using a population of evolving zero-intelligence agents and a frequent batch auction price-discovery mechanism as substrate, we analyze the role played by evolutionary selection mechanisms in determining macro-observable market statistics. In particular, we show that selection mechanisms incorporating a local fitness-proportionate component are associated with high correlation between a micro, risk-aversion parameter and a commonly-used macro-volatility statistic, while a purely quantile-based selection mechanism shows significantly less correlation.

Tail models and the statistical limit of accuracy in risk assessment
Ingo Hoffmann,Christoph J. Börner

In risk management, tail risks are of crucial importance. The assessment of risks should be carried out in accordance with the regulatory authority's requirement at high quantiles. In general, the underlying distribution function is unknown, the database is sparse, and therefore special tail models are used. Very often, the generalized Pareto distribution is employed as a basic model, and its parameters are determined with data from the tail area. With the determined tail model, statisticians then calculate the required high quantiles. In this context, we consider the possible accuracy of the calculation of the quantiles and determine the finite sample distribution function of the quantile estimator, depending on the confidence level and the parameters of the tail model, and then calculate the finite sample bias and the finite sample variance of the quantile estimator. Finally, we present an impact analysis on the quantiles of an unknown distribution function.

The Causal Mechanisms of Horizontal Shareholding
Elhauge, Einer
Although empirical studies show that common shareholding affects corporate conduct and that common horizontal shareholding lessens competition, critics have argued that the law should not take any action until we have clearer proof on the causal mechanisms. I show that we actually have ample proof on causal mechanisms, but that antitrust enforcement should focus on anticompetitive market structures, rather than on causal mechanisms. I debunk claims that every type of causal mechanism that might produce anticompetitive effects is either empirically untested or implausible. I also show that critics are wrong in claiming that common shareholders lack incentives to influence corporations to increase portfolio value by lessening competition. Finally, I show that preventing anticompetitive horizontal shareholding need not restrict diversification or discourage desirable institutional investor influence on corporate conduct.

Understanding Cryptocurrencies
Härdle, Wolfgang K.,Harvey, Campbell R.,Reule, Raphael C. G.
Cryptocurrency refers to a type of digital asset that uses distributed ledger, or blockchain, technology to enable a secure transaction. Although the technology is widely misunderstood, many central banks are considering launching their own national cryptocurrency. In contrast to most data in financial economics, detailed data on the history of every transaction in the cryptocurrency complex are freely available. Furthermore, empirically-oriented research is only now beginning, presenting an extraordinary research opportunity for academia. We provide some insights into the mechanics of cryptocurrencies, describing summary statistics and focusing on potential future research avenues in financial economics.