Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model

The study of climatic variables that govern the Indian summer monsoon has been widely explored. In this work, we use a non-linear deep learning-based feature reduction scheme for the discovery of skilful predictors for monsoon rainfall with climatic variables from various regions of the globe. We use a stacked autoencoder network along with two advanced machine learning techniques to forecast the Indian summer monsoon. We show that the predictors such as the sea surface temperature and zonal wind can predict the Indian summer monsoon one month ahead, whereas the sea level pressure can predict ten months before the season. Further, we also show that the predictors derived from a combination of climatic variables can outperform the predictors derived from an individual variable. The stacked autoencoder model with combined predictors of sea surface temperature and sea level pressure can predict the monsoon (June-September) two months ahead with a 2.8% error. The accuracy of the identified predictors is found to be superior to the state-of-the-art predictions of the Indian monsoon.     

A graduates ’ satisfaction index for the evaluation of the university overall quality

In the higher educational setting, students provide a relevant contribution to the quality of educational services. In such a context, the measurement of the perceived quality and related satisfaction for the university experience are of primary interest to evaluate the efficiency and efficacy of the learning processes. In this contribution, we aim at assessing the overall quality of the graduates’ university experience in terms of internal and external efficacy by applying the ECSI (European Customer Satisfaction Index) methodology, based on structural equation models and primarily developed in the context of customer satisfaction. For this aim, we propose a modified ECSI model tailored for the higher educational setting, explicitly taking into account the differences among groups of degree program. The study is carried out on data collected by the AlmaLaurea surveys at the University of Florence (Italy) in the period 2014–2017 and concerns a sample of more than 2,000 graduates. We find out eight latent variables that contribute to define the overall quality of university experience. These variables are differently affected by the type of degree program, with the highest levels of external efficacy observed for degree programs belonging to the educational, health, and engineering groups. It also turns out that interventions on the internal efficacy (i.e., quality of hardware and quality of humanware) have a direct positive effect on the university (i.e., loyalty) and an indirect positive effect on the society (i.e., external efficacy).     

Who are better monitors? Comparing styles of supervisory and independent directors

This paper compares the unobservable style effect between independent directors and supervisory directors on firm performance. Utilizing the unique Chinese board system with both independent and supervisory directors and a large panel data of 2,240 public firms from 2003 to 2017, this paper finds that both supervisory and independent style is crucial in determining variations of firm performance. The effects of both independent and supervisory director style are less significant when the “type II” agency problem is more severe in a firm. Moreover, outside independent director style is more effective when information costs are low or when firms need more outside resources, while inside supervisor director style is more effective when there are no major internal changes. Finally, the results also suggest that increasing qualified supervisor representations are beneficial to firms.     

Network based evidence of the financial impact of Covid-19 pandemic

How much the largest worldwide companies, belonging to different sectors of the economy, are suffering from the pandemic? Are economic relations among them changing? In this paper, we address such issues by analyzing the top 50 S&P companies by means of market and textual data. Our work proposes a network analysis model that combines such two types of information to highlight the connections among companies with the purpose of investigating the relationships before and during the pandemic crisis. In doing so, we leverage a large amount of textual data through the employment of a sentiment score which is coupled with standard market data. Our results show that the COVID-19 pandemic has largely affected the US productive system, however differently sector by sector and with more impact during the second wave compared to the first.     

Potential user factors driving adoption of IPTV. What are customers expecting from IPTV?

Internet Protocol Television (IPTV), the convergence services of television and Internet, is being rapidly developed around the world. The advent of digital technologies has changed the convergence market dramatically with the wide diffusion of the convergent services. Using the Technology Acceptance Model as a conceptual framework and method of logistic regression, this research analyzes the demand for IPTV by drawing data from 452 consumers. Individuals' responses to questions about whether they accept IPTV are collected and combined with observations of their socio-economic status and intrinsic/extrinsic factors modified from the Technology Acceptance Model. Results of logistic regression show two variables (intrinsic and extrinsic factors) that seem to explain what influences consumer behavior towards adopting IPTV. Overall, the logistic regression model explains over 50% of the variance in the IPTV adoption. The variances shed light on the multi-open platform environment that IPTV will forge.     

公司治理思想的历史演变及其经验启示

现代企业之间的竞争,也是治理结构的竞争.在此,以文献梳理的形式,对公司治理的内涵及理论渊源进行阐述,并评析了治理模式、形成原因及趋同表现.在我国治理模式选择的问题上,提出应汲取西方治理经验并结合我国国情,在基于利益相关者的原则下,着力构建内部治理与外部治理相结合,以内部治理为主的中国特色公司治理结构.     

保险公司偿付能力恶化预测研究

文章在分析公司财务预测研究方法应用前提的基础上,运用MDA模型和Logistic线性回归模型预测方法对我国保险公司偿付能力恶化进行预测研究。结果表明两个主流模型均能在保险公司偿付能力恶化前1~2年较好地进行预测,但是由于两类错估率的不同,MDA模型要优于Logistic模型。     

Asymmetric ACD models: Introducing price information in ACD models

This paper proposes an asymmetric autoregressive conditional duration (ACD) model, which extends the ACD model of Engle and Russell (1998). The asymmetry consists of letting the duration process depend on the state of the price process. If the price has increased, the parameters of the ACD model can differ from what they are if the price has decreased. The model is applied to the bid-ask quotes of two stocks traded on the NYSE and the evidence in favour of asymmetry is strong. Information effects (Easley and O'Hara 1992) are also empirically relevant. As the model is a transition model for the price process, it delivers `market forecasts' of where prices are heading. A trading strategy based on the model is implemented using tick-by-tick data.While remaining responsible for any error in this paper, the authors would like to thank R. Anderson, G. Le Fol, C. Gouriéroux, J. Jasiak, W. Pohlmeier, A. Roell, O. Scaillet, S. Wei and three anonymous referees for useful remarks and suggestions on previous versions. The authors would also like to thank A. Ruttiens from KBC-CBC for useful discussions on practical issues related to trading. Support of the European Commission Human Capital and Mobility Program through the network `Econometric inference using simulation methods' is gratefully acknowledged. This paper presents research results of the Belgian Program on Interuniversity Poles of Attraction initiated by the Belgian State, Prime Minister's Office, Science Policy Programming. The scientific responsibility is assumed by the authors.     

The equilibrium set of two-player games with complementarities is a sublattice

Summary. I prove that the equilibrium set in a two-player game with complementarities, and totally ordered strategy spaces, is a sublattice of the joint strategy space. Received: May 31, 2001; revised version: October 4, 2002     

A bioeconomic model for estimating the optimal level of deer and tag sales

This paper presents a dynamic model that determines the optimal number of deer hunting permit sales, subject to the objective of maximizing the discounted economic benefits stream from both the consumptive and nonconsumptive uses of deer. This bioeconomic model integrates economic benefits estimated using the dichotomous choice contingent valuation method with biological growth constraints on deer. Using a hunting zone in California as a case study, the model found that the optimal levels of buck hunting permits sold should change on a rotational basis over time and that the current practice of not selling doe hunting permits for that zone is non optimal.