财务预警模型综述

财务预警模型是指借助企业财务指标和非财务指标体系来判别企业财务状况的模型。它通常包括以下六类 :一元判定模型 (Univariate)、多元判定模型 (MDA)、多元逻辑 (Logit)回归模型、多元概率比 (Probit)回归模型、人工网络 (ANN)模型和联合预测模型。通过对财务预警模型进行回顾和评述 ,可以拓展财务预警研究的视野 ,便于我们在借鉴前人思路和方法的基础上进行更深入的研究。     

资本约束对信贷扩张及经济增长的影响:分析框架和典型案例

监管当局的最低资本要求改变了传统货币政策传导机制。本文从银行信贷渠道和银行资本渠道两方面并借助简化的商业银行行为决策模型考察资本约束的信贷扩张效应,利用CC-LM模型讨论资本约束对经济增长的影响。本文认为,在资本短缺情况下,严格资本监管将导致贷款供给下降,进而对经济增长产生影响;1990年代美国、日本出现的信贷紧缩部分应归因于1988年资本协议的实施,监管当局和货币当局必须重视资本充足率监管的宏观经济效应。     

香奈儿品牌形象对消费者购买意愿的影响分析

中国消费者为现阶段全球最大的奢侈品消费群体。自香奈儿进入中国市场以来,因其高贵、优雅、简约的设计风格受到中产阶级以上女性消费群体的欢迎和喜爱。在奢侈品行业中,品牌形象作为企业重要的无形资产,成为了品牌竞争的另一领域。基于上述背景,论文以“香奈儿品牌形象对消费者购买意愿的影响分析”为选题,以贝尔模型为研究基础,使用问卷调查法获取数据,运用SPSS 19.0统计软件对数据进行处理分析,并结合消费者行为学相关理论,研究品牌形象的不同变量对于消费者购买行为产生的影响。     

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).     

Evaluation of the social and economic impact of carbon dioxide (CO2) emissions on sustainable mobility using cumulative ordinal models: trend odds model

In the context of environmental sustainability evaluation, grouped under climate change, health and ecosystem themes, an impact that could be investigated is the Global Warming Potential (GWP), whose sources are a multitude. In urban areas the evaluation of real vehicles emissions is an essential activity in order to suggest possible solution to local administrators. They still express the need to improve and maintain the characteristics of the breathing air at the best possible quality level. Moreover, these solutions, such as planning measures or traffic control management in respect of pollution, would be apply at different geographical levels, i.e. national, regional or urban scale. Another factor to be investigated is the effect of technologies and emission control systems to comply more stringent limits (Euro 4/5/6) and improve air quality to a lower environmental impact. GWP, indicator of climate change, is measured in terms of CO₂ equivalency emission values variable. To perform this activity an experimental campaign was carried out with several vehicles from different manufacturers and with a wide variety in terms of mass, power, engine displacement and type approval technology. The experimental plan includes some repetitions of the urban section in Naples city centre.The purpose of this paper is twofold: first, to provide a strategy on the choice of a logistic model with ordinal data and with trend, and, second, to evaluate the usefulness of such models for environmental sustainability (CO₂ and other vehicular pollutant emissions), with particular emphasis on model formulation, the interpretation of model coefficients, and the implications of such models.     

Playing the synthesizer with Canadian data: Adding polls to a structural forecasting model

Election forecasting has become a fixture of election campaigns in a number of democracies. Structural modeling, the major approach to forecasting election results, relies on ‘fundamental’ economic and political variables to predict the incumbent’s vote share usually a few months in advance. Some political scientists contend that adding vote intention polls to these models—i.e., synthesizing ‘fundamental’ variables and polling information—can lead to important accuracy gains. In this paper, we look at the efficiency of different model specifications in predicting the Canadian federal elections from 1953 to 2015. We find that vote intention polls only allow modest accuracy gains late in the campaign. With this backdrop in mind, we then use different model specifications to make ex ante forecasts of the 2019 federal election. Our findings have a number of important implications for the forecasting discipline in Canada as they address the benefits of combining polls and ‘fundamental’ variables to predict election results; the efficiency of varying lag structures; and the issue of translating votes into seats.     

How does news sentiment affect the states of Japanese stock return volatility?

This paper examines the impact of public news sentiment on the volatility states of firm-level returns on the Japanese Stock market. We firstly adopt a novel Markov Regime Switching Long Memory GARCH (MRS-LMGARCH), which is employed to estimate the latent volatility states of intraday stock return. By using the RavenPack Dow Jones News Analytics database, we fit discrete choice models to investigate the impact of news sentiment on changes of volatility states of the constituent stocks in the TOPIX Core 30 Index. Our findings suggest that news occurrence and sentiment, especially those of macro-economic news, are a key factor that significantly drives the volatility state of Japanese stock returns. This provides essential information for traders of the Japanese stock market to optimize their trading strategies and risk management plans to combat volatility.     

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.