# Research articles for the 2019-08-21

arXiv

The choice of appropriate measures of deprivation, identification and aggregation of poverty has been a challenge for many years. The works of Sen, Atkinson and others have been the cornerstone for most of the literature on poverty measuring. Recent contributions have focused in what we now know as multidimensional poverty measuring. Current aggregation and identification measures for multidimensional poverty make the implicit assumption that dimensions are independent of each other, thus ignoring the natural dependence between them. In this article a variant of the usual method of deprivation measuring is presented. It allows the existence of the forementioned connections by drawing from geometric and networking notions. This new methodology relies on previous identification and aggregation methods, but with small modifications to prevent arbitrary manipulations. It is also proved that this measure still complies with the axiomatic framework of its predecessor. Moreover, the general form of latter can be considered a particular case of this new measure, although this identification is not unique.

SSRN

This article sheds light on the potential cost that could be incurred by Capital One in the wake of the data breach, as part of our efforts to help professionals and the general public fathom the magnitude of the impact. As the investigation continues, further information will be revealed. We hope this article will also offer a framework for those monitoring the investigation to organize and better digest the additional information that will be revealed.

SSRN

Natural disasters are not rare and costless events. Indeed, the evidence indicates there has been an acceleration in the number of disasters and the associated costs over the past century. Such disasters can cause severe property damage in the communities affected. Typically, insurance policies and government disaster relief fail to cover the full amount of damages. In this case, banks can play an important supporting role in providing additional funding for the necessary reconstruction that takes place after disasters. We provide evidence that following natural disasters, banks with branches in the affected areas raise both deposit and loan rates, but the latter more than the former so that net interest margin increases. This, in turn, leads to an increase in return on assets for such banks, but not sufficiently large enough to indicate profiteering. At the same time, banks increase the use of brokered deposits after natural disasters to help fund the increased demand for loans by individuals and firms in affected communities. Thus banks located in the disaster-prone areas contribute to helping communities recover from natural disasters.

SSRN

Current measures of financial literacy focus on knowledge, and the literature on financial literacy has described important findings about the extent and impact of limited financial knowledge across the population. This paper discusses issues associated with broadening the scope of the financial literacy approach to include behaviors related to self-control, budgeting, and heuristics. Although the financial literacy literature models financial literacy as human capital in a neoclassical optimization framework, the discussion suggests that properly modeling this type of human capital cannot be easily accomplished through a neoclassical optimization model in which human capital is treated as a generic stock variable. Rather, budgetary human capital needs to be modeled explicitly as part of psychologically feasible heuristic processes that govern household behavior. In this respect, human capital involves more than fact-based knowledge and computational ability, but also mental processes that underlie action. Human capital pertains to both "knowing'' and "doing.'' Financial literacy in the form of knowledge can only produce better decisions when paired with human capital associated with acting on that knowledge.

SSRN

This study examines whether and when credit rating agencies (CRAs) take negative rating actions against issuers committing accounting fraud before the fraud is publicly revealed and the economic impact of such rating actions. Our findings show that these fraud firms experience a greater number of negative rating actions during the four quarters prior to the public fraud revelation, including lower ratings, more rating downgrades, and more negative credit watch additions, compared to firms with similar economic fundamentals and stock performance. Our findings also show that such negative rating actions are not limited to fraud firms in financial distress, suggesting that our effect reflects CRA responses to accounting fraud per se. In addition, we find CRAs take more timely actions when frauds are more severe, when they involve accounts more often scrutinized by CRAs during their credit analysis, and when short sellers target firms. Last, we find that CRAsÃ¢€™ negative rating actions against fraud firms are informative to the market and are associated with shorter fraud duration. Overall, we conclude that CRAs possess private information about accounting fraud prior to the public revelation of this fraud and that they incorporate this information into negative ratings actions, accelerating fraud discovery.

SSRN

We study the impact of sovereign bond auctions on secondary markets and their feedback to the sovereignsÃ¢€™ debt cost. This linkage is established through the actions of primary dealers, participating in the auctions, and also acting as market-makers. We model financially-constrained primary dealers who buy newly issued bonds and manage their inventory, while maintaining market liquidity. We find empirical support for our model and its resulting hypotheses: primary dealers tend to liquidate more liquid and more risky bonds from their inventory in order to be able to participate in sovereign bond auctions and minimize the impact of their portfolios.

arXiv

The hidden action model captures a fundamental problem of principal-agent theory and provides an optimal sharing rule when only the outcome but not the effort can be observed. However, the hidden action model builds on various explicit and also implicit assumptions about the information of the contracting parties. This paper relaxes key assumptions regarding the availability of information included the hidden action model in order to study whether and, if so, how fast the optimal sharing rule is achieved and how this is affected by the various types of information employed in the principal-agent relation. Our analysis particularly focuses on information about the environment and feasible actions for the agent to carry out the task. For this, we follow an approach to transfer closed-form mathematical models into agent-based computational models. The results show that the extent of information about feasible options to carry out a task only has an impact on performance, if decision-makers are well informed about the environment, and that the decision whether to perform exploration or exploitation when searching for new feasible options only affects performance in specific situations. Having good information about the environment, in contrary, appears to be crucial in almost all situations.

arXiv

We consider a classical risk process with arrival of claims following a non-stationary Hawkes process. We study the asymptotic regime when the premium rate and the baseline intensity of the claims arrival process are large, and claim size is small. The main goal of the article is to establish a diffusion approximation by verifying a functional central limit theorem and to compute the ruin probability in finite-time horizon. Numerical results will also be given.

arXiv

Many researchers both in academia and industry have long been interested in the stock market. Numerous approaches were developed to accurately predict future trends in stock prices. Recently, there has been a growing interest in utilizing graph-structured data in computer science research communities. Methods that use relational data for stock market prediction have been recently proposed, but they are still in their infancy. First, the quality of collected information from different types of relations can vary considerably. No existing work has focused on the effect of using different types of relations on stock market prediction or finding an effective way to selectively aggregate information on different relation types. Furthermore, existing works have focused on only individual stock prediction which is similar to the node classification task. To address this, we propose a hierarchical attention network for stock prediction (HATS) which uses relational data for stock market prediction. Our HATS method selectively aggregates information on different relation types and adds the information to the representations of each company. Specifically, node representations are initialized with features extracted from a feature extraction module. HATS is used as a relational modeling module with initialized node representations. Then, node representations with the added information are fed into a task-specific layer. Our method is used for predicting not only individual stock prices but also market index movements, which is similar to the graph classification task. The experimental results show that performance can change depending on the relational data used. HATS which can automatically select information outperformed all the existing methods.

SSRN

Donald Trump has served as President of the United States for more than two years. His performance in office has provided many valuable lessons about leadership, especially about what not to do. This paper offers a partial list of leadership principles one can deduce from observing his leadership style. In addition, actual Trump quotations are provided.

SSRN

In this paper, I investigate risk premium of long run and short run volatility component of exchange rate returns in currency market. I find that high interest rate currencies of carry trade strategy load negatively on long run volatility innovation, while low interest rate currencies load positively. Risk price of long run volatility innovation is negative which implies that high carry trade returns are considered as compensation for time varying long run volatility risk. In contrast, risk price of short run volatility innovation is positive. Low interest currencies deliver low returns and high interest rate currencies yield high returns under times of high short run volatility. In terms of momentum strategy, risk price of short run volatility innovation is negative and statistically significant, while risk price of long run component is insignificant. Therefore, long run volatility does not provide any explanation for high returns of currency momentum strategy. High momentum returns, on the other hand are reward for investors to bear short run volatility risk.

SSRN

This paper examines the foreign exchange rate exposures of US companies and how they are linked to foreign macroeconomic determinants. I use US trade-weighted macroeconomic indices of foreign countries to explain the variation in foreign exchange rate exposures, measured as the sensitivities of stock returns to exchange rate returns of US non-financial companies over the period 1995 to 2017. I find strong evidence that the after-hedging exposures of potential exporters are affected by their expectations of foreign market gross domestic products, current account balances, consumer price indices, term spreads, unit labor costs as well as government expenditures.

arXiv

Index tracking is a popular form of asset management. Typically, a quadratic function is used to define the tracking error of a portfolio and the look back approach is applied to solve the index tracking problem. We argue that a forward looking approach is more suitable, whereby the tracking error is expressed as expectation of a function of the difference between the returns of the index and of the portfolio. We also assume that there is an uncertainty in the distribution of the assets, hence a robust version of the optimization problem needs to be adopted. We use Bregman divergence in describing the deviation between the nominal and actual distribution of the components of the index. In this scenario, we derive the optimal robust index tracking strategy in a semi-analytical form as a solution of a system of nonlinear equations. Several numerical results are presented that allow us to compare the performance of this robust strategy with the optimal non-robust strategy. We show that, especially during market downturns, the robust strategy can be very advantageous.

arXiv

The problem of portfolio allocation in the context of stocks evolving in random environments, that is with volatility and returns depending on random factors, has attracted a lot of attention. The problem of maximizing a power utility at a terminal time with only one random factor can be linearized thanks to a classical distortion transformation. In the present paper, we address the problem with several factors using a perturbation technique around the case where these factors are perfectly correlated reducing the problem to the case with a single factor. We illustrate our result with a particular model for which we have explicit formulas. A rigorous accuracy result is also derived using sub- and super-solutions of the HJB equation involved. In order to keep the notations as explicit as possible, we treat the case with one stock and two factors and we describe an extension to the case with two stocks and two factors.

arXiv

A dilated causal one-dimensional convolutional neural network architecture is proposed for quantile regression. The model can forecast any arbitrary quantile, and it can be trained jointly on multiple similar time series. An application to Value at Risk forecasting shows that QCNN outperforms linear quantile regression and constant quantile estimates.

SSRN

Andrikogiannopoulou and Papakonstantinou (AP) call into question the applicability of the False Discovery Rate (FDR) for evaluating mutual fund performance. They argue that this approach produces estimators of the proportions of zero and non-zero alpha funds that are largely biased. In this reply, we explain that the bias reported by AP is overestimated because their simulation analysis suffers from three shortcomings: (i) their assumed level of fund volatility, (ii) their assumed relations between the fund parameters, and (iii) their estimation procedure. When we address these issues, the probability of misclassifying a fund with a 2% annual alpha is not 65%, as AP claim, but only 29%. Given these results and the multiple advantages of the FDR approach, we remain convinced that it is useful in mutual fund performance evaluation and in other research areas in finance and economics involving large-scale multiple hypothesis tests.

arXiv

In recent years, the evaluation of the minimal investment risk of the quenched disordered system of a portfolio optimization problem and the investment concentration of the optimal portfolio has been actively investigated using the analysis methods of statistical mechanical informatics. However, the work to date has not sufficiently compared the optimal portfolios of different portfolio optimization problems. Therefore, in this paper, we use the Lagrange undetermined multiplier method and replica analysis to examine the relationship between the optimal portfolios of the expected return maximization problem and the expected return minimization problem with constraints of budget and investment risk. In particular, we derive the mean square error and the correlation coefficient of the optimal portfolios of these maximization and minimization problems as functions of a variable (the degree of risk tolerance) that can characterize the feasible subspace defined by the two constraints.

SSRN

Stock-based compensation (SBC) reduces the value of shareholder equity, ceteris paribus, and is a significant and growing expense for many firms. Despite its valuation implications and its growing importance, anecdotal evidence suggests that market participants ignore SBC in valuation. We first find that firms with higher SBC exhibit both higher valuation ratios and lower returns, suggesting overvaluation. In particular, such pattern becomes stronger for firms with larger analyst coverage, implying that the sell-side optimism is probably the primary driver of the overvaluation. We then examine how financial analysts treat SBC in their valuation models. We find that analysts exclude SBC in their street earnings forecasts, and consequently provide more optimistically-biased target prices for firms with higher SBC. A hand-collected sample of analyst reports indicates that analysts who ignore SBC in valuation derive optimistically-biased price targets, whereas analysts who treat SBC as an expense are unbiased on average. Together, our evidence indicates that market participantsÃ¢€™ failure to account for stock-based compensation as an expense leads to the overvaluation of equity.

SSRN

Autoregressive models such as the heterogeneous autoregressive (HAR) model capture the linear footprint inherent in realized volatility. We cast the problem of estimating structural breaks in the autoregressive volatility dynamics as a model selection problem. Interestingly, we find the number of breaks to be heavily influenced by Box-Cox transformations applied to realized volatility series of eight stock market indices: For example, while we find breaks in the original series, no breaks are found in log-realized volatility, a measure often used in applied research. These transformations change the autoregressive dynamics of the series and thus affect the detection of structural breaks.

SSRN

This paper examines the cash management practices of firms during the period of increased FDIC insurance on noninterest-bearing accounts. While the Transaction Account Guarantee Program and Dodd-Frank Act were intended to help banks by preventing deposit withdrawals, they also seem to have contributed to a change in cash management practices given that cash increased at public firms during this time as well. In addition, the increase seems to be driven by financially unconstrained firms, firms not at risk of default, firms with an investment grade bond rating, and firms with low cash flow variation. An analysis of aggregate data shows that a similar increase was not observed for private firms.