# Research articles for the 2019-12-15

arXiv

Many studies in economics deal with the non-reliability cost to assess insurance fees or investment analyses, but none takes into consideration the mechanical aspect of reliability analysis. Other studies in mechanics give some tools and methods to carry out reliability analyses and fragility study. This study developed a framework where economical and mechanical considerations for infrastructure investment decision-making. The theoretical reasoning is here developed to couple mechanical reliability analyses, which are composed of fragility curves, and economical reliability analyses, which is based on resilience cost functions. This coupling is carried out with some probabilistic considerations, giving the concept of "probable cost of failure". The strength of this framework is that it can be used to analyze all possible critical components in a network with all possible natural hazards or malicious event or other undesired events which it is possible to assess its probability of occurrence. The results of the analysis are indicators of probable cost of failure of an infrastructure, which represents the insurance fee. These indicators can be computed for railway lines, for critical components, for events. This tool enables decision-makers to prioritize safety investments and to guide strategic choices. The next step of this study will be to develop smart data analysis tools, because of this framework needs and produces a lot of data, which must be smartly analyzed and presented.

arXiv

We propose a new approach using the MJ$_1$ semi-metric, from the more general MJ$_p$ class of semi-metrics \cite{James2019}, to detect similarity and anomalies in collections of cryptocurrencies. Since change points are signals of potential risk, we apply this metric to measure distance between change point sets, with respect to returns and variance. Such change point sets can be identified using algorithms such as the Mann-Whitney test, while the distance matrix is analysed using three approaches to detect similarity and identify clusters of similar cryptocurrencies. This aims to avoid constructing portfolios with highly similar behaviours, reducing total portfolio risk.

SSRN

China has experienced large improvements in mortality rates, but there remain substantial variations at the provincial level. This paper develops new models to project mortality at both the national and provincial levels in China. We propose two models in a Bayesian hierarchical framework based on principal components and a random walk process, and compile a new comprehensive database containing mortality data for 31 provinces over the period 1982â€"2010. The baseline two-level model with a nationalâ€"province hierarchy allows for information pooling across provinces, common national factors and consistency conditions. The extended three-level model with a national â€" region â€" province hierarchy pools information in the region and also allows for common factors within the region. Both models provide good estimates and reasonable forecasts for China and its provinces. The baseline two-level model provides good fit and reasonable forecasts with equal width intervals for the provinces. The three-level model has a better fit with a lower deviance information criterion and provides forecast intervals reflecting regional uncertainty. The sensitivity analyses show that the forecasts are robust when changing the trend assumptions and regional groups.

SSRN

We study an endowment economy with heterogeneous agents and two complementary consumption goods, one of which is indivisible. Although agents have standard concave preferences, the indivisibility gives rise to Friedman-Savage convexity in indirect utility. Agents care about relative, not just absolute, wealth, and about the magnitude of wealth differences as well as rankings. While agents dislike small risks, they like large ones. Poorer agents exhibit a preference for lottery-like assets with negative expected returns and also invest a smaller share of wealth than richer agents in assets with positive expected returns. If the difference in wealth is large, poor agents play a lottery among themselves with a single winner. In consequence, richer agents have a higher expected return on wealth and inequality is expected to increase. While initial inequality affects investment choices, it has only a minor effect on ultimate inequality. Therefore, standard prescriptions for reducing inequality may have little effect.

SSRN

Growth-Trend (GT) timing from Philosophical Economics is a brilliant timing strategy which only signals a bear market when both the trend in the unemployment (UE) rate and the SP500 index are bearish. As a result, it captures most market downturns while switching to cash in less than 15% of the time. In this sense, its crash protection is much less drastic than our own â€œcanaryâ€ protection in our DAA strategy (25% in cash) or the breadth protection in our VAA strategy (around 50% in cash). In this paper we apply GT timing to the well-known 60-40 static benchmark (60% SPY - 40% IEF), and search in-sample for variations on 60-40 with GT timing. For these variations, we in particular consider risky portfolios which are also agnostic for inflation and yield, inspired by the various static portfolio like the Permanent Portfolio and its siblings. Our final strategy switches between two static portfolios based on GT timing. This strategy is called the Lethargic Asset Allocation (LAA).

arXiv

The present paper provides a study of high-dimensional statistical arbitrage that combines factor models with the tools from stochastic control, obtaining closed-form optimal strategies which are both interpretable and computationally implementable in a high-dimensional setting. Our setup is based on a general statistically-constructed factor model with mean-reverting residuals, in which we show how to construct analytically market-neutral portfolios and we analyze the problem of investing optimally in continuous time and finite horizon under exponential and mean-variance utilities. We also extend our model to incorporate constraints on the investor's portfolio like dollar-neutrality and market frictions in the form of temporary quadratic transaction costs, provide extensive Monte Carlo simulations of the previous strategies with 100 assets, and describe further possible extensions of our work.

arXiv

We consider a fundamental dynamic allocation problem motivated by the problem of $\textit{securities lending}$ in financial markets, the mechanism underlying the short selling of stocks. A lender would like to distribute a finite number of identical copies of some scarce resource to $n$ clients, each of whom has a private demand that is unknown to the lender. The lender would like to maximize the usage of the resource $\mbox{---}$ avoiding allocating more to a client than her true demand $\mbox{---}$ but is constrained to sell the resource at a pre-specified price per unit, and thus cannot use prices to incentivize truthful reporting. We first show that the Bayesian optimal algorithm for the one-shot problem $\mbox{---}$ which maximizes the resource's expected usage according to the posterior expectation of demand, given reports $\mbox{---}$ actually incentivizes truthful reporting as a dominant strategy. Because true demands in the securities lending problem are often sensitive information that the client would like to hide from competitors, we then consider the problem under the additional desideratum of (joint) differential privacy. We give an algorithm, based on simple dynamics for computing market equilibria, that is simultaneously private, approximately optimal, and approximately dominant-strategy truthful. Finally, we leverage this private algorithm to construct an approximately truthful, optimal mechanism for the extensive form multi-round auction where the lender does not have access to the true joint distributions between clients' requests and demands.

SSRN

We investigate how political connections of public firms in China influence the media coverage of corporate violations. We find that corporate violations committed by politically-connected firms receive less negative media coverage than violations committed by non-politically-connected firms. The effect is more pronounced for firms receiving more attention from the market-oriented media. We employ the scandal involving the largest Chinese market-oriented business newspaper as an exogenous shock to establish a causal link. We further find that less negative media coverage of corporate violations is associated with longer duration of the violation being investigated and being enforced. We interpret our findings to suggest that corporate political connections suppress negative media coverage of corporate misconduct, and the suppression serves as a channel through which firms keep receiving benefits from their political ties.

arXiv

We derive an explicit solution for deterministic market impact parameters in the Graewe and Horst (2017) portfolio liquidation model. The model allows to combine various forms of market impact, namely instantaneous, permanent and temporary. We show that the solutions to the two benchmark models of Almgren and Chris (2001) and of Obizhaeva and Wang (2013) are obtained as special cases. We relate the different forms of market impact to the microstructure of limit order book markets and show how the impact parameters can be estimated from public market data. We investigate the numerical performance of the derived optimal trading strategy based on high frequency limit order books of 100 NASDAQ stocks that represent a range of market impact profiles. It shows the strategy achieves significant cost savings compared to the benchmark models of Almgren and Chris (2001) and of Obizhaeva and Wang (2013).

arXiv

In quantitative finance, useful features are constructed by human experts. However, this method is of low efficient. Thus, automatic feature construction algorithms have received more and more attention. The SOTA technic in this field is to use reverse polish expression to represent the features, and then use genetic programming to reconstruct it. In this paper, we propose a new method, alpha discovery neural network, which can automatically construct features by using neural network. In this work, we made several contributions. Firstly, we put forward new object function by using empirical knowledge in financial signal processing, and we also fixed its undifferentiated problem. Secondly, we use model stealing technic to learn from other prior knowledge, which can bring enough diversity into our network. Thirdly, we come up with a method to measure the diversity of different financial features. Experiment shows that ADN can produce more diversified and higher informative features than GP. Besides, if we use GP's output to serve as prior knowledge, its final achievements will be significantly improved by using ADN.

arXiv

In this paper we study both analytic and numerical solutions of option pricing equations using systems of orthogonal polynomials. Using a Galerkin-based method, we solve the parabolic partial diferential equation for the Black-Scholes model using Hermite polynomials and for the Heston model using Hermite and Laguerre polynomials. We compare obtained solutions to existing semi-closed pricing formulas. Special attention is paid to the solution of Heston model at the boundary with vanishing volatility.

SSRN

Profitability which is the ability of the company to generate revenue by using the resources available for the company and it is one of the important aspect for every company in long run. The purpose of the study is to determine the impact of determinants factor that influence the performance of the Padini Holding Berhad. The finding and analysis will shown the company external and internal factor that could influence the companyâ€™s profitability. In addition, this study will also reflect the risk that the company facing and the recommendation given to the company in order to minimize or prevent the risk that the company would face in the future.

SSRN

The health of a population is affected by social, environmental, and economic factors. Pension providers and consultants, insurance companies, government agencies and individuals in the developed world have a vested interest in understanding how the economic growth will impact on the life expectancy of their population. Therefore, changes in death rates may occur due to climate and economic changes. In this study, we extend a previous study into excess deaths as a result of climate change to also provide a comprehensive investigation of the impact of economic changes using annual female and male data for 5 developed OECD countries. We find that there is strong negative relationship between mortality index, and climate and economic proxies. This model shows to provide better fitting and forecasting results both for females and males, and for all countries studied.

SSRN

The purpose of this study is to investigate the relationship between the financial performance and the capital structure of the company. The performance of the company will be measured by analysing the trend of various financial ratios obtained from the companyâ€™s annual report from 2014 to 2018. The relationship between the profitability and the internal and external factors are also analysed with the companyâ€™s performance. The performance of Maxis Berhad is represented by the return on asset over 5 yearsâ€™ period. The capital structure is measured by collecting data from the balance sheet of the annual reports. As for the financial performance, the data is collected from the income statements and balance sheets. Ratio analysis is then measured from the obtained data. As for the internal factors, these are the relevant variables to be measured; current ratio, quick ratio, average-collection period, debt-to-income, operational ratio, and operating margin. The additional measurement is the corporate index score, asset size, net profit margin, GDP growth rate, inflation, interest rate, and exchange rate. The data was carefully analysed by applying regression and correlation. The Model Summary table also shows that debt-to-income has the most reliable variable as the R Square is equals to 79.9%. There is a strong relationship between ROA and debt-to-income with -0.894 and sig. 0.020. There are three variables that significant to the ROA, namely, debt-to-income, operating margin and, the interest rate as the sig. value is below 0.1.

arXiv

Urban waste heat recovery, in which low temperature heat from urban sources is recovered for use in a district heat network, has a great deal of potential in helping to achieve 2050 climate goals. For example, heat from data centres, metro systems, public sector buildings and waste water treatment plants could be used to supply ten percent of Europe's heat demand. Despite this, at present, urban waste heat recovery is not widespread and is an immature technology. To help achieve greater uptake, three policy recommendations are made. First, policy raising awareness of waste heat recovery and creating a legal framework is suggested. Second, it is recommended that pilot projects are promoted to help demonstrate technical and economic feasibility. Finally, a pilot credit facility is proposed aimed at bridging the gap between potential investors and heat recovery projects.

SSRN

EVA (Economic Value Added or economic profit) has a long history of intermittent use in corporate performance measurement. It was widely used as a funding formula for corporate incentive plans in the first half of the 20th century, it enjoyed considerable popularity in the 1990s under the leadership of the consulting firm Stern Stewart & Co and it has recently been embraced by the proxy advisor ISS. In all of these periods of popularity, the focus has been on current EVA (or âˆ†EVA) as a comprehensive measure of performance, ignoring the critical insight of the EVA math that investorsâ€™ dollar excess return has two components in addition to the capitalized future value of âˆ†EVA: the capitalized future value of expected âˆ†EVA and, most importantly, the unexpected change in future growth value (FGV). FGV is the capitalized present value of expected future âˆ†EVA. In a simple world with constant ROIC and capital growth rates, current âˆ†EVA is a perfect proxy for âˆ†FGV because both are determined by the current EVA spread and dollar capital growth. In the real world, where ROIC and capital growth rates are volatile and capital has delayed productivity, current âˆ†EVA is a very poor proxy for âˆ†FGV. In and out of turnaround situations, âˆ†EVA and âˆ†FGV move in opposite directions. For growing companies, better estimates of âˆ†FGV come from using expected, not actual, capital growth and measures of profitability, such as [EBITDA + R&D] ROIC, that are calculated before delayed productivity expenses such as capital charge, depreciation and R&D. In this paper, I show that operating performance measurement can be greatly improved by using regression models to estimate the âˆ†FGV associated with changes in expected profit from capital growth as well as the FGV fade associated with âˆ†EVA- and âˆ†EVA+. I show that operating excess return, using a model of âˆ†FGV, explains 65% of the variance in market excess returns for the median industry vs. only 38% for âˆ†EVA. The critical take-away for performance measurement is that we canâ€™t accurately assess current EVA performance without estimating the change in future growth value.