Econophysics review: I. Empirical facts

This article and the companion paper aim at reviewing recent empirical and theoretical developments usually grouped under the term Econophysics. Since the name was coined in 1995 by merging the words ‘Economics’ and ‘Physics’, this new interdisciplinary field has grown in various directions: theoretical macroeconomics (wealth distribution), microstructure of financial markets (order book modeling), econometrics of financial bubbles and crashes, etc. We discuss the interactions between Physics, Mathematics, Economics and Finance that led to the emergence of Econophysics. We then present empirical studies revealing the statistical properties of financial time series. We begin the presentation with the widely acknowledged ‘stylized facts’, which describe the returns of financial assets—fat tails, volatility clustering, autocorrelation, etc.—and recall that some of these properties are directly linked to the way ‘time’ is taken into account. We continue with the statistical properties observed on order books in financial markets. For the sake of illustrating this review, (nearly) all the stated facts are reproduced using our own high-frequency financial database. Finally, contributions to the study of correlations of assets such as random matrix theory and graph theory are presented. The companion paper will review models in Econophysics from the point of view of agent-based modeling.     

The Money Trail: Ranking Donor Transparency in Foreign Aid

Transparency of aid activity is being recognized to be one of the key areas whereby aid effectiveness can be improved. In this paper, we propose an index to measure and rank donors on the transparency of their aid activities. The Transparency Index rates 31 bilateral and multilateral donor agencies on six measures of transparency. We find that being a member of the IATI is a powerful signal of a donor being more transparent across most other dimensions as well. We find no relationship between transparency and donor aid volumes. Overall IDA and Australia are identified as the most transparent donors, while Korea and IDB Special Fund are the least transparent.     

Externalities, potential, value and consistency

We provide new characterization results for the value of games in partition function form. In particular, we use the potential of a game to define the value. We also provide a characterization of the class of values which satisfies one form of reduced game consistency.     

Best constants in Chebyshev inequalities with various applications

In this article we describe some ways to significantly improve the Markov-Gauss-Camp-Meidell inequalities and provide specific applications. We also describe how the improved bounds are extendable to the multivariate case. Applications include explicit finite sample construction of confidence intervals for a population mean, upper bounds on a tail probability P(X>k) by using the density at k, approximation of P-values, simple bounds on the Riemann Zeta function, on the series , improvement of Minkowski moment inequalities, and construction of simple bounds on the tail probabilities of asymptotically Poisson random variables. We also describe how a game theoretic argument shows that our improved bounds always approximate tail probabilities to any specified degree of accuracy. Received: April 1999     

On the Value Relevance of Retailer Advertising Spending and Same-store Sales Growth

In response to recent calls to study factors that determine a retailer's stock price, this study draws on signaling theory to examine the impact of two key marketing metrics that are widely disclosed by retailers to investors, advertising spending and growth in same-store sales (COMPS), and highlights the moderating role of various firm- and sector-specific factors. Using a stock-response model estimated on a sample of 1,646 observations for 257 retailers, the authors find that the value relevance of advertising spending and COMPS depends on the financial condition of, and the competitive pressures faced by, the retailer. In addition, the positive effect of COMPS on stock returns is found to be stronger in the presence of decreases in advertising spending.     

Nowcasting sales growth of manufacturing companies in India

The performance of private corporate sector is used as an important demand indicator for monetary policy making. As these data are received with a lag, assessing and monitoring of corporate sales on a real-time basis poses a significant challenge to policy makers in India. In this context, this article attempts to nowcast quarterly sales growth of Indian manufacturing companies and GDP growth of India using dynamic factor modelling framework. A multiple-level framework through turning point analysis and elastic net structure is used to overcome the overfitting problem during variable selection. Empirical results show improvement in forecast accuracy for one quarter ahead nowcast using 3-factor and 4-factor models over the benchmark model. However, absolute dominance of 3-factor models over 4-factor models was not established. As such, the article has proposed a forecast combination technique to nowcast sales growth of manufacturing companies in India.     

Does competition under Medicare Prospective Payment selectively reduce expenditures on high-cost patients?

Competition and prospective payment have been widely used to control health care costs but may together provide incentives to selectively reduce expenditures on high-cost relative to low-cost patients. We use patient discharge and hospital financial data from California to examine the effects of competition on costs for high- and low-cost admissions in the 12 largest Diagnosis-Related Groups before and after the Medicare Prospective Payment System (PPS). We find that competition increased costs before PPS, but that this effect decreased afterward, especially inpatients with the highest costs.We conclude that competition and PPS selectively reduced spending among the most expensive patients and that careful assessment of these patients' outcomes is important.     

QaaS (quality as a service) model for web services using big data technologies

Quality of service (QoS) determines the service usability and utility and both of which influence the service selection process. The QoS varies from one service provider to other. Each web service has its own methodology for evaluating QoS. The lack of transparent QoS evaluation model makes the service selection challenging. Moreover, most QoS evaluation processes do not consider their historical data which not only helps in getting more accurate QoS but also helps for future prediction, recommendation and knowledge discovery. QoS driven service selection demands a model where QoS can be provided as a service to end users. This paper proposes a layered QaaS (quality as a service) model in the same line as PaaS and software as a service, where users can provide QoS attributes as inputs and the model returns services satisfying the user’s QoS expectation. This paper covers all the key aspects in this context, like selection of data sources, its transformation, evaluation, classification and storage of QoS. The paper uses server log as the source for evaluating QoS values, common methodology for its evaluation and big data technologies for its transformation and analysis. This paper also establishes the fact that Spark outperforms the Pig with respect to evaluation of QoS from logs.