Research articles for the 2019-10-30

A Classifiers Voting Model for Exit Prediction of Privately Held Companies
Giuseppe Carlo Calafiore,Marisa Hillary Morales,Vittorio Tiozzo,Serge Marquie

Predicting the exit (e.g. bankrupt, acquisition, etc.) of privately held companies is a current and relevant problem for investment firms. The difficulty of the problem stems from the lack of reliable, quantitative and publicly available data. In this paper, we contribute to this endeavour by constructing an exit predictor model based on qualitative data, which blends the outcomes of three classifiers, namely, a Logistic Regression model, a Random Forest model, and a Support Vector Machine model. The output of the combined model is selected on the basis of the majority of the output classes of the component models. The models are trained using data extracted from the Thomson Reuters Eikon repository of 54697 US and European companies over the 1996-2011 time span. Experiments have been conducted for predicting whether the company eventually either gets acquired or goes public (IPO), against the complementary event that it remains private or goes bankrupt, in the considered time window. Our model achieves a 63\% predictive accuracy, which is quite a valuable figure for Private Equity investors, who typically expect very high returns from successful investments.

Absent Imposition of 'Boundary Rationality Conditions', Can Evidence for Pricing of Behavioral Biases of Economic Agents Be Robust?
Obrimah, Oghenovo A.
This study unambiguously demonstrates that, absent independent establishment of boundary rationality conditions for stock prices, behavioral axioms, such as overconfidence, overreaction, underreaction, and attribution bias cannot be robustly applied to rationalization of market phenomena that are generated by rational economic agents. This general equilibrium outcome is predicated on predictions of a formal theoretical model. In the model, whenever behavioral biases feasibly can influence market returns, qualitatively, such effects cannot be distinguished from presence, in stock markets, of mixtures of 'sophisticated' and 'naive' economic agents. In this respect, the model establishes that any of, rational components of stock prices, information variables that support presence of mixtures of sophisticated and naive agents within stock markets, or behavioral biases, have feasibility of generation of pattern of stock return momentum and reversals. An important side result is the prediction that behavioral axioms, such as overreaction, and underreaction, which of necessity must have opposite effects on prices, cannot subsist contemporaneously within dichotomous groupings of rational economic agents who invest in stock markets.

Bitcoin and Turkey: A Good Match or a Perfect Storm?
Taskinsoy, John
There was only Bitcoin in January 2009, but now over 3,000 altcoins are traded; as of October 25, the combined market cap of 3,047 cryptocurrencies is $248 billion. Bitcoin (at $ 9,285.89) with a market cap of $170 billion still dominates 68.4% of the cryptocurrency market. Both market cap figures are significantly below their peaks on December 17, 2017; $830 billion and $321 billion respectively. The adoption of Bitcoin in Turkey has seen its fastest rise since 2016 on account of the failed coup attempt by a fraction of the Turkish military, the conviction and sentencing of the U.S. Pastor Andrew Brunson of terror-related charges, rising geopolitical risks on account of the strained US-Turkey relations, and Turkey’s ongoing military operations along its borders and into Syria. The confluence of domestic and external factors have been a major catalyst in prompting pockets of people in Turkey to flock to Bitcoin as a safe-haven asset. Cryptocurrencies are known for their extreme volatility, but the Turkish lira’s plunge in August 2018 made even most erratic volatilities of cryptocurrencies seem calmer. Bitcoin investors/enthusiasts must keep in mind that the price of Bitcoin has witnessed a total of 16 price corrections (50% or more) in 7 years, and 8 of these have occurred in the last two years. Another substantial price correction, similar to that of December 2017-18 (i.e. price plummeted from almost $20,000 to $3,236), would result in a severe financial catastrophe for Turkish Bitcoin holders.

Cyberterrorism and its Dramatic Impact on Insurance and Security Firms
Afshani, Joshua
Cyberterrorism has come to be one of the most threatening forms of terrorism in 2019. In the face of the negative implications cyberattacks can have on affected firms and consumers, this article focuses on the flip side of the coin: I hypothesize that cyberattacks can produce abnormal positive returns for the stock prices of insurance and security companies. Heretofore practically ignored by most businesses, companies that specialize in insurance and security dealing with cyberterrorism are experiencing increased positive interest and attention. I conducted an event study analysis to investigate how the stock prices of insurance and security companies changed one day and one week after major cyberattacks on large firms. Such cyberattacks investigated range from the 2013 Yahoo attack to the globally destructive Petya Ransomware attack. Using the P-value as a measure of significance, I found that, on average, the companies realized a consistent, positive abnormal return in 11 of the 15 events one day after an attack. This evidence supported my hypothesis as investors understand that increased cyber activity results in increased cyber-awareness. Both insurance and security companies will likely increase premiums and experience higher quarterly revenues. Moreover, it was found that security companies experienced more positive, abnormal returns than insurance companies, as consumers gravitate towards security in hopes of greater protection.

Determinants of Capital Expenditures: Evidence from Borsa Istanbul
Can, Gökberk,Gunay, Samet,Ocak, Murat
[enter Abstract Body]Purpose The aim of this study is to examine the determinants of capital expenditures in terms of ownership structure of firms (Foreign ownership and institutional ownership) and some-firm specific characteristics using Turkish listed companies and is to provide insightful evidence to the corporate governance literature about an emerging market. Design/Methodology/ApproachPanel data estimation procedure was mainly conducted to test the hypotheses. To robust the main estimation results, quantile estimation procedure based on the asset size of each firm/year was employed. Besides, we tested the changing point for listing duration and size by using the squares of the related variables and re-run the model for the crisis years. FindingsThe findings revealed that institutional and foreign ownership do not affect the companies’ capital expenditure behavior in terms of net capital expenditures. Besides, the outcomes showed that companies’ size, cash flows from operating and tobin’s q (i.e. firm value) positively affects the capital expenditure behaviors, however net margin and listing duration decrease the net capital expenditures. On the other hand, when we divided our sample quintiles based on the asset size of each firm, the results show that the companies’ behavioral difference depend on the companies’ size. The results also indicated that the financial crisis affected the capital expenditure behavior of the companies traded in Borsa İstanbul.Originality/ ValueThis paper makes a contribution to the corporate finance by providing an insight on determinants of capital expenditure from an emerging market. This paper is first research to investigate the determinants of capital expenditures of the Turkish listed companies. This paper provides a perspective on the institutional ownership’s impact on the companies’ capital expenditures. The results are also supported by the robustness tests.

Does Geopolitical Risk Affect Mergers and Acquisitions?
Gavriilidis, Konstantinos,Hao, Zhiwei,Prapan, Ahmed Ameya,Vagenas-Nanos, Evangelos
This study examines the relationship between geopolitical risk (GPR) and mergers and acquisitions (M&As). Our findings indicate that GPR negatively affects firm-level acquisitiveness. Supporting our predictions of the real options channel, the negative effects of GPR are strongest for more financially constrained bidders, more complex deals, targets with more irreversible assets and non-multinational bidders. In addition, during periods of high GPR, we find that acquirers, on average, become prudent and perform value-creating deals. Following periods of high GPR, acquirers complete deals more quickly and avoid offering stocks as a payment method. Lastly, deal premiums and target termination fees unanimously confirm the increased negotiation power of targets around high GPR periods.

Evidence on Usage Behavior and Future Adoption Intention of Fintechs and Digital Finance Solutions
Gerlach, Johannes M.,Lutz, Julia K. T.
Financial Technology Companies are gaining popularity and becoming more relevant within financial services industries worldwide. This growth can be encouraged by the EY FinTech Adoption Index, which indicates a global average FinTech Adoption of 33.0% in 2017. With regard to Financial Technology Companies and Digital Finance Solutions, this figure emphasizes the importance of this study’s objective to identify potential determinants of current use behavior and future usage intention. To both theoretically and empirically address this research question, we conducted a questionnaire-based survey with 381 participants from three German universities. Because our study bases on both the theory of reasoned action and the unified theory of acceptance and usage of technology 2, we contribute not only to the general understanding of Financial Technology Companies and Digital Finance Solutions but also to the existing literature on behavioral intention and technology acceptance. Thus, we contribute to several strands of literature. However, based on this study’s results, we defined certain fields of interest and derived corresponding strategic and managerial implications from the viewpoint of traditional financial institutions. Moreover, we contribute to the practical solution of the current challenges faced by traditional financial services providers. Finally, based on our analyses, we identify future research opportunities regarding these important issues.

Financial Literacy and Suboptimal Financial Decisions at Older Ages
Fong, Joelle H.,Koh, Benedict S.,Mitchell, Olivia S.,Rohwedder, Susann
Over the life-cycle, wealth holdings tend to be highest in the early part of retirement. The quality of financial decisions among older adults is therefore an important determinant of their financial security during the asset drawdown phase. This paper assesses how financial literacy shapes financial decision-making at older ages. We devised a special module in the Singapore Life Panel survey to measure financial literacy to study its relationship with three aspects of household financial and investment behaviors: credit card debt repayment, stock market participation, and adherence to age-based investment glide paths. We found that the majority of respondents age 50+ has some grasp of concepts such as interest compounding and inflation, but fewer know about risk diversification. We provide evidence of a statistically significant positive association between financial literacy and each of the three aspects of suboptimal financial decision-making, controlling for many other factors, including education. A one-unit increase in the financial literacy score was associated with an 8.3 percentage point greater propensity to hold stocks, and a 1.7 percentage point higher likelihood of following an age-appropriate investment glide path. The financial literacy score is only weakly positively linked with timely credit card balance repayment, both in terms of statistical significance and estimate size.

Find what you are looking for: A data-driven covariance matrix estimation
Sven Husmann,Antoniya Shivarova,Rick Steinert

The global minimum-variance portfolio is a typical choice for investors because of its simplicity and broad applicability. Although it requires only one input, namely the covariance matrix of asset returns, estimating the optimal solution remains a challenge. In the presence of high-dimensionality in the data, the sample estimator becomes ill-conditioned, which negates the positive effect of diversification in an out-of-sample setting. To address this issue, we review recent covariance matrix estimators and extend the literature by suggesting a multi-fold cross-validation technique. In detail, conducting an extensive empirical analysis with four datasets based on the S\&P 500, we evaluate how the data-driven choice of specific tuning parameters within the proposed cross-validation approach affects the out-of-sample performance of the global minimum-variance portfolio. In particular, for cases in which the efficiency of a covariance estimator is strongly influenced by the choice of a tuning parameter, we detect a clear relationship between the optimality criterion for its selection within the cross-validation and the evaluated performance measure. Finally, we show that using cross-validation can improve the performance of highly efficient estimators even when the data-driven covariance parameter deviates from its theoretically optimal value.

From microscopic price dynamics to multidimensional rough volatility models
Mehdi Tomas,Mathieu Rosenbaum

Rough volatility is a well-established statistical stylised fact of financial assets. This property has lead to the design and analysis of various new rough stochastic volatility models. However, most of these developments have been carried out in the mono-asset case. In this work, we show that some specific multivariate rough volatility models arise naturally from microstructural properties of the joint dynamics of asset prices. To do so, we use Hawkes processes to build microscopic models that reproduce accurately high frequency cross-asset interactions and investigate their long term scaling limits. We emphasize the relevance of our approach by providing insights on the role of microscopic features such as momentum and mean-reversion on the multidimensional price formation process. We in particular recover classical properties of high-dimensional stock correlation matrices.

Going Abroad, Friends on Board: Cross-Border Venture Capital and Syndication
Qiu, Zhiyi,Chen, Rong,Yang, Ye
Cross-border venture capitals (CBVCs) are increasingly prevailing in recent decades, especially in emerging markets like China. With foreignness growing, it turns from liability into advantage in the context of CBVCs. We investigate the VC firm’s syndication when faced with foreignness caused by culture differences. We find an inverse U-shape relationship between foreignness and syndication, with the firm’s reputation as moderator. Apart from syndication, foreign firms establish a local subsidiary when faced with foreignness.

How Did Order-Flow Impact Bond Prices During the European Sovereign Debt Crisis?
Lin, Zhongguo,Li, Youwei,Hamill, Philip Anthony,Sun, Zhuowei,Waterworth, James
The impact of trades on price dynamics in the European sovereign debt markets is of significant importance to policy makers and market participants. This paper uses high-frequency quote and transaction data from the MTS European sovereign bond inter-dealer platform to investigate price-order-flow dynamics from July 2005 until December 2011 for Germany, France, Portugal, Italy, Ireland, Spain and Greece. We find that order-flow had a larger impact on quote revision in a relatively low-intensity trading environment than in a relatively high-intensity trading environment implying that informed traders should only execute in low-intensity trading environments when they value immediacy over discretion. This analysis is consistent with the limited prior literature for European debt markets. Our analysis indicates that this relationship persists during turbulent market conditions. Also, we find that the impact of order-flow on subsequent trades was larger during periods of high-trading intensity implying that market participants use order splitting trading strategies.

Industry Distress and Default Recovery Rates: The Unconditional Quantile Regression Approach
Chuang, Hui-Ching,Chen, Jau‐er
In this study, we estimate the effect of industry distress on recovery rates by using the unconditional quantile regression (UQR) proposed in Firpo, Fortin, and Lemieux (2009). The UQR provides better interpretative and thus policy-relevant information on the marginal effect of the covariates than the conditional quantile regression (CQR, Koenker and Bassett, 1978). To deal with a broad set of macroeconomic and industry variables, we use the LASSO-based double selection to identify the effects of industry distress and select variables.Our sample consists of 5,334 debt and loan instruments in Moody's Default and Recovery Database from 1990 to 2017. The results show that industry distress decreases recovery rates from 15.80% to 2.94% for the 15th to 55th percentile range and slightly increases the recovery rates in the lower and the upper tails. In contrast to the CQR, the UQR provide quantitative measurements to the loss given default during a downturn that the Basel Capital Accord requires.

Inequality and Gender Inclusion: Minimum ICT Policy Thresholds for Promoting Female Employment in Sub-Saharan Africa
Asongu, Simplice,Odhiambo, Nicholas
The study assesses how ICT modulates the effect of inequality on female economic participation in a panel of 42 countries in sub-Saharan Africa over the period 2004-2014. Three inequality indicators are used, namely: the Gini coefficient, the Atkinson index and the Palma ratio. The adopted ICT indicators are mobile phone penetration, internet penetration and fixed broadband subscriptions. Three gender economic inclusion indicators are also used for the analysis, namely: female labour force participation, female unemployment and female employment. The Generalised Method of Moments is employed as empirical strategy. The findings show that enhancing ICT beyond certain thresholds is necessary for ICT to mitigate inequality in order to enhance gender economic participation. First, for female labour force participation, a minimum threshold of 165.714 mobile phone penetration per 100 people is required for the Palma ratio. Second, minimum ICT thresholds for the reduction of female unemployment are: 87.783, 107.486 and 152.500 mobile phone penetration per 100 people for respectively, the Gini coefficient, the Atkinson index and the Palma ratio; 39.618 internet penetration per 100 people for the Atkinson index and 4.500 fixed broadband subscriptions for the Palma ratio. Third, the corresponding ICT thresholds for the promotion of female employment are: 120.369 and 85.533 mobile phone penetration per 100 people for respectively, the Gini coefficient and the Atkinson index and 30.005 internet penetration per 100 people for the Gini coefficient. The established thresholds make economic sense and can be feasibly implemented by policy makers in order to induce favourable effects on gender economic inclusion dynamics.

Investment Analysis under Equity Default Swaps with Asymmetric Information
Liu, Xiang,Yang, Zhaojun
We assume an entrepreneur (borrower) must borrow money from a lender (bank) to start a project in a single-period model. The debt is secured by an insurer who takes the project and pays the lender all the outstanding principal and interest in case of default. The borrower grants the insurer a fraction of the money borrowed, or of the project's payoff, or of a call option underlying on the project payoff. The corresponding three parties' agreement is called the fee-for-guarantee swap (FGS), equity-for-guarantee swap (EGS), and option-for-guarantee swap (OGS) respectively. We assume the project payoff follows a lognormal distribution. The variance of its logarithm is common knowledge but the mean is only known to the borrower and follows a two-point distribution to the insurer, i.e. there exists asymmetric information. We show that asymmetric information benefits low-profitability borrowers or insurers at the expense of high-profitability ones. The benefit and expense under OGS are the most while those under FGS are the least. In a pooling equilibrium, a high-profitability borrower must transfer a fraction of its profit to the low-profitability one. There is no social welfare loss only if the net investment value of the low-profitability project is positive. In a separating equilibrium, the high-profitability borrower might transfer a fraction of its profit to the insurer and surprisingly, the net investment value of a high-profitability borrower increases with investment cost.

Investor Mix and Mutual Fund Performance: A Flow Based Measure of Relative Smartness
Zhou, You,Li, Peng,Cai, Charlie X.,Keasey, Kevin
We study the information content of the mutual-fund investor mix at the fund level. Building on the fund-flow determinant literature, we develop a method to attribute the proportion of fund flow explained by a fund’s fundamental characteristics and past performance as smart and dumb money respectively. The fund-level Smart Dumb Ratio (SDR) positively predicts future cross-sectional fund return. A series of tests shows that SDR captures investor sophistication and positively correlates with other skill measures. Our findings confirm that investor composition of the fund can be a useful source of information to estimate the fund-level smart-money effect.

Lender Effects on Gains from Mergers and Acquisitions
Massoud, Nadia,Song, Keke,Tran, Nam
This paper employs a new approach to identify merger and acquisition (M&A) transactions financed by syndicated loans and provides evidence that acquirer announcement returns are higher in loan-financed M&A deals than in other deals. Utilizing an instrumental variable approach and a quasi-natural experiment, we provide evidence that lenders contribute to the higher acquirer announcement returns in loan-financed M&A deals. Lenders’ performance in M&A financing is persistent over time. Lenders’ participation in the M&A market can resolve uncertainty about the M&A deal quality, improve corporate governance by preventing value-destroying M&A transactions, and provide long-term monitoring benefits to acquirer shareholders.

Michael Milken: The Junk Dealer
Ravi Kashyap

We take a closer look at the life and legacy of Micheal Milken. We discuss why Michael Milken, also know as the Junk Bond King, was not just any other King or run-of-the-mill Junk Dealer, but "The Junk Dealer". We find parallels between the three parts to any magic act and what Micheal Milken did, showing that his accomplishments were nothing short of a miracle. His compensation at that time captures to a certain extent the magnitude of the changes he brought about, the eco-system he created for businesses to flourish, the impact he had on the wider economy and also on the future growth and development of American Industry. We emphasize two of his contributions to the financial industry that have grown in importance over the years. One was the impetus given to the Private Equity industry and the use of LBOs. The second was the realization that thorough research was the key to success, financial and otherwise. Perhaps an unintended consequence of the growth in junk bonds and tailored financing was the growth of Silicon valley and technology powerhouses in the California bay area. Investors witnessed that there was a possibility for significant returns and that financial success could be had due to the risk mitigation that Milken demonstrated by investing in portfolios of so called high risk and low profile companies. We point out the current trend in many regions of the world, which is the birth of financial and technology firms and we suggest that finding innovative ways of financing could be the key to the sustained growth of these eco-systems.

Microscopic Derivation of Mean Field Game Models
Martin Frank,Michael Herty,Torsten Trimborn

Mean field game theory studies the behavior of a large number of interacting individuals in a game theoretic setting and has received a lot of attention in the past decade (Lasry and Lions, Japanese journal of mathematics, 2007). In this work, we derive mean field game partial differential equation systems from deterministic microscopic agent dynamics. The dynamics are given by a particular class of ordinary differential equations, for which an optimal strategy can be computed (Bressan, Milan Journal of Mathematics, 2011). We use the concept of Nash equilibria and apply the dynamic programming principle to derive the mean field limit equations and we study the scaling behavior of the system as the number of agents tends to infinity and find several mean field game limits. Especially we avoid in our derivation the notion of measure derivatives. Novel scales are motivated by an example of an agent-based financial market model.

Non-compliance in randomized control trials without exclusion restrictions
Masayuki Sawada

This study presents a method to identify treatment effects without exclusion restrictions for randomized experiments with non-compliance. It exploits a baseline survey that is commonly available in randomized control trials. I show the identification of the average treatment effect on the treated (ATT) and the local average treatment effect (LATE), assuming that a baseline variable maintains similar rank orders to the control outcome. I apply this strategy to a microcredit experiment with one-sided non-compliance to identify the ATT. I find that the instrumental variable (IV) estimate of log revenue is 2.2 times larger than my preferred estimate of log revenue.

Noncooperative dynamics in election interference
David Rushing Dewhurst,Christopher M. Danforth,Peter Sheridan Dodds

Foreign power interference in domestic elections is an age-old, existential threat to societies. Manifested through myriad methods from war to words, such interference is a timely example of strategic interaction between economic and political agents. We model this interaction between rational game players as a continuous-time differential game, constructing an analytical model of this competition with a variety of payoff structures. Structures corresponding to all-or-nothing attitudes regarding the effect of the interference operations by only one player lead to an arms race in which both countries spend increasing amounts on interference and counter-interference operations. We then confront our model with data pertaining to the Russian interference in the 2016 United States presidential election contest, introducing and estimating a Bayesian structural time series model of election polls and social media posts by Russian internet trolls. We show that our analytical model, while purposefully abstract and simple, adequately captures many temporal characteristics of the election and social media activity.

Portfolio Optimization with Expectile and Omega Functions
Alexander Wagner,Stan Uryasev

This paper proves equivalences of portfolio optimization problems with negative expectile and omega ratio. We derive subgradients for the negative expectile as a function of the portfolio from a known dual representation of expectile and general theory about subgradients of risk measures. We also give an elementary derivation of the gradient of negative expectile under some assumptions and provide an example where negative expectile is demonstrably not differentiable. We conducted a case study and solved portfolio optimization problems with negative expectile objective and constraint.

Predicting Consumer Default: A Deep Learning Approach
Albanesi, Stefania,Vamossy, Domonkos F.
We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model is interpretable and is able to provide a score to a larger class of borrowers relative to standard credit scoring models while accurately tracking variations in systemic risk. We argue that these properties can provide valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders, as well as macroprudential regulation.

Price Discovery and Market Segmentation in China’s Credit Market
Geng, Zhe,Pan, Jun
We study the extent of price discovery in the onshore Chinese corporate bond market, focusing in particular on the information content of credit spreads in China. Using Merton’s model of default, we construct credit measures of publicly listed firms, using information from their financial statements and stock valuation. We find that, only after the first default in 2014, do credit spreads in China become informative. Compared with the findings in the US credit market, the magnitude of price discovery in the Chinese market is rather limited. We also find that the presence of outside government support for state-owned enterprises (SOEs) in China results in a market segmentation between SOE and non-SOE issuers that is harmful to price efficiency and market stability. Since 2018, the non-SOE issuers have suffered from explosive credit spreads, unprecedented defaults, and shrinking new issuance, while the SOE issuers have remained largely intact. Meanwhile, our default measures show that the non-SOE issuers are in fact stronger in credit quality than their SOE counterparts of the same rating category. Examining the impact of this segmentation on price discovery, we find that non-SOE credit spreads become significantly more informative since 2018, as concerns over credit become front and center for the non-SOE issuers. By contrast, as investors seek safety in SOE bonds, there is no improvement in the information content of SOE credit spreads.

Random concave functions
Peter Baxendale,Ting-Kam Leonard Wong

Spaces of convex and concave functions appear naturally in theory and applications. For example, convex regression and log-concave density estimation are important topics in nonparametric statistics. In stochastic portfolio theory, concave functions on the unit simplex measure the concentration of capital, and their gradient maps define novel investment strategies. The gradient maps may also be regarded as optimal transport maps on the simplex. In this paper we construct and study probability measures supported on spaces of concave functions. These measures may serve as prior distributions in Bayesian statistics and Cover's universal portfolio, and induce distribution-valued random variables via optimal transport. The random concave functions are constructed on the unit simplex by taking a suitably scaled (mollified, or soft) minimum of random hyperplanes. Depending on the regime of the parameters, we show that as the number of hyperplanes tends to infinity there are several possible limiting behaviors. In particular, there is a transition from a deterministic almost sure limit to a non-trivial limiting distribution that can be characterized using convex duality and Poisson point processes.

Rank-size law, financial inequality indices and gain concentrations by cyclist teams. The case of a multiple stage bicycle race, like Tour de France
Marcel Ausloos

This note examines financial distributions to competing teams at the end of the most famous multiple stage professional (male) bicyclist race, TOUR DE FRANCE. A rank-size law (RSL) is calculated for the team financial gains. The RSL is found to be hyperbolic with a surprisingly simple decay exponent (about equal to -1). Yet, the financial gain distributions unexpectedly do not obey Pareto principle of factor sparsity. Next, several (8) inequality indices are considered : the Entropy, the Hirschman-Herfindahl, Theil, Pietra-Hoover, Gini, Rosenbluth indices, the Coefficient of Variation and the Concentration Index are calculated for outlining diversity measures. The connection between such indices and their concentration aspects meanings are presented as support of the RSL findings. The results emphasize that the sum of skills and team strategies are effectively contributing to the financial gains distributions. From theoretical and practical points of view, the findings suggest that one should investigate other "long multiple stage races" and rewarding rules. Indeed, money prize rules coupling to stage difficulty might influence and maybe enhance (or deteriorate) purely sportive aspects in group competitions. Due to the delay in the peer review process, the 2019 results can be examined. They are discussed in an Appendix; the value of the exponent (-1.2) is pointed out to mainly originating from the so called "king effect"; the tail of the RSL rather looks like an exponential.

The Competitive Threat from TechFins and BigTech in Financial Services
King, Michael R.
The Chinese TechFins Ant Financial and Tencent's WePay are harnessing technology to redefine financial services and increase financial inclusion. TechFins purport to use technology to create a world where customers have access to financial services just like tap water â€" you open the tap and water just flows out. From being a minority view several years ago, the new consensus among bank insiders and industry commentators is that TechFins, not FinTechs, represent a greater threat over the next decade to financial incumbents. The other threat comes from a diverse collection of North American technology companies known collectively as “BigTech”. The main competitive strength of BigTech companies such as Amazon, Apple, Facebook and Google comes from massive datasets on customer transactions and behaviour in their platform ecosystems. This forthcoming chapter reviews the history and strategies of two companies that have moved farthest into financial services â€" Ant Financial and Amazon. It examines their key competitive strengths to gauge the threat they pose to financial incumbents over the coming decade.

The Singular Impact of High Conviction Overweight Positions for Active Managers
Panchekha, Alexey
Is active management’s decade‐long losing streak to passive management due to high fees, a lack of manager skill, or something else? What’s required to answer this question is not rampant speculation but a fact‐based assessment of manager decision making, and the facts need to come from analysis of daily stock holdings of mutual funds. As the saying goes, “You cannot manage what you cannot measure.”Key findings:- The only source of stock selection alpha comes from managers’ High Conviction Overweight positions.- The average active fund manager reduced their sole source of excess return (High ConvictionOverweight) by half, severely diluting their ability to outperform after fees.Shorter version of this paper published on CFA website.

Time-consistent conditional expectation under probability distortion
Jin Ma,Ting-Kam Leonard Wong,Jianfeng Zhang

We introduce a new notion of conditional nonlinear expectation under probability distortion. Such a distorted nonlinear expectation is not sub-additive in general, so is beyond the scope of Peng's framework of nonlinear expectations. A more fundamental problem when extending the distorted expectation to a dynamic setting is time-inconsistency, that is, the usual "tower property" fails. By localizing the probability distortion and restricting to a smaller class of random variables, we introduce a so-called distorted probability and construct a conditional expectation in such a way that it coincides with the original nonlinear expectation at time zero, but has a time-consistent dynamics in the sense that the tower property remains valid. Furthermore, we show that in the continuous time model this conditional expectation corresponds to a parabolic differential equation whose coefficient involves the law of the underlying diffusion. This work is the first step towards a new understanding of nonlinear expectations under probability distortion, and will potentially be a helpful tool for solving time-inconsistent stochastic optimization problems.

Time-dependent lead-lag relationships between the VIX and VIX futures markets
Yan-Hong Yang,Ying-Hui Shao

We utilize the symmetric thermal optimal path (TOPS) method to examine the dynamic interaction patterns between the VIX and VIX futures markets. We document that the VIX dominates the VIX futures more in the first few years, especially before the introduction of VIX options. We further observe that the TOPS paths show an alternate lead-lag relationship instead of a dominance between the VIX and VIX futures in most of the time periods. Meanwhile, we find that the VIX futures have been increasingly more important in the price discovery since the launch of several VIX ETPs.

Two-Side Cvars and the Cross-Sectional Expected Stock Returns: Evidences from Chinese Stock Market
Ling, Aifan,Cao, Zizi
Tail risks attract recently lots of attentions to predict the cross-sectional expected returns of stocks in literature. Using a modified conditional value at risk (CVaR), the extreme loss and gain of stocks can be measured by the left tail CVaR- and the right tail CVaR+, respectively. The left and right tail CVaRs are unified as two-side CVaRs which correspond to the two-side tails of returns. We empirically examine the relations between two-side CVaRs and the cross-sectional expected returns of stocks and find, economically and statistically, that both have significantly negative relations with the cross-sectional expected returns. The empirical results are robust when we control the firm size, idiosyncratic volatility, liquidity risk, downside beta and the maximum daily return in previous month (MAX). The pricing powers of two-side CVaRs are strongly significant and can not be explained by the Fama-French three- and five-factor models.

What Determines Climate Policy Preferences If Reducing Greenhouse-Gas Emissions Is A Global Public Good?
Bechtel, Michael M.,Scheve, Kenneth,van Lieshout, Elisabeth
A wide variety of international policy problems, including climate change, have been characterized as global public goods. This paper adopts this theoretical framework to identify the baseline determinants of public opinion about reducing greenhouse-gas emissions. We show formally that this model implies that support for climate action will be increasing in future benefits, their timing, and the probability that a given country's contribution will be decisive while decreasing in expected costs. Utilizing novel data from original surveys in France, Germany, the United Kingdom, and the United States, we provide experimental and observational evidence that expected benefits, costs, and the probability of successful provision are critical for explaining variation in support for climate action. Surprisingly, we find no evidence that the temporality of policy benefits shapes support for climate action. These results suggest effective strategies for building public support for climate action and designing institutions that facilitate global public goods provision.