Predicting bloc support in Irish general elections 1951–2020: A political history model

Election forecasting is a growing enterprise. Structural models relying on “fundamental” political and economic variables, principally to predict government performance, are popular in political science. Conventional wisdom though is these standard structural models fall short in predicting individual blocs’ performance and their applicability to multiparty systems is restricted. We challenge this by providing a structural forecast of bloc performance in Ireland, a case primarily overlooked in the election forecasting literature. Our model spurns the economic and performance variables conventionally associated with structural forecasting enterprises and instead concentrates on Ireland’s historical party and governance dynamics in the vein of testing whether these patterns alone offer solid predictions of election outcomes. Using Seemingly Unrelated Regression (SUR), our approach, comprising measures of incumbency, short-term party support, and political and economic shocks, offers reasonable predictions of the vote share performance of four blocs: Ireland's two major parties, Fianna Fáil and Fine Gael, Independents, and the Left bloc combined across 20 elections spanning 60 years

Оцінка інвестиційної привабливості промислових підприємств

The article is devoted to methodological issues of assessing the investment attractiveness of industrial enterprises, based on the nature of investments and the goals of their initiators. Various methods for assessing the economic efficiency of investments in newly created, existing and reconstructed industrial enterprises are examined, and the conditions for their application are determined. The necessity of calculating the economic efficiency of direct investment as part of changing the entire production and economic system of the investor is justified. A structural and logical scheme of interaction between the potential investor and the investment object directly for the implementation of investment projects was proposed, taking into account the number of investments, type of investor and term of the investment. The authors also proposed a methodology that can be used by an investor to plan for increasing the investment attractiveness of an enterprise and a formula for calculating the coefficient of investment attractiveness of an enterprise.Стаття присвячена методологічним питанням оцінки інвестиційної привабливості промислових підприємств, виходячи з характеру інвестицій та цілей їх ініціаторів. Розглядаються різні методи оцінки економічної ефективності інвестицій у новостворені, існуючі та реконструйовані промислові підприємства та визначаються умови їх застосування. Обґрунтовано необхідність розрахунку економічної ефективності прямих інвестицій як частини зміни всієї виробничої та економічної системи інвестора. Запропонована структурно-логічна схема взаємодії між потенційним інвестором та об'єктом інвестування безпосередньо для реалізації інвестиційних проектів з урахуванням кількості інвестицій, типу інвестора та строку інвестування. Автори також запропонували методологію, за допомогою якої інвестор може планувати підвищення інвестиційної привабливості підприємства, та формулу для розрахунку коефіцієнта інвестиційної привабливості підприємства

A DECISION SUPPORT SYSTEM FOR COMMODITY MARKETING MANAGEMENT

Marketing,

An Evaluation of Effectiveness of Fuzzy Logic Model in Predicting the Business Bankruptcy

In front of the current global financial crisis, the future existence of the firms is uncertain. The characteristics and the dynamics of the current world and the interdependences between the financial and economic markets around it demand a continuous research for new methods of bankruptcy prediction. The purpose of this article is to present a fuzzy logic-based system that predicts bankruptcy for one, two and three years before the possible failure of companies. The proposed fuzzy model uses as inputs financial ratios, that is dynamics of the financial ratios. In order to design and to implement the model, authors have used financial statements of 132 stock equity companies (25 bankrupt and 107 nonbankrupt). The paper presents also the testing and validation of the created fuzzy logic models.bankruptcy, crisis, prediction, fuzzy logic, ratings

An Empirical Study of Operational Performance Parity Following Enterprise System Deployment

This paper presents an empirical investigation into whether the implementation of packaged Enterprise Systems (ES) leads to parity in operational performance. Performance change and parity in operational performance are investigated in three geographically defined operating regions of a single firm. Order lead time, the elapsed time between receipt of an order and shipment to a customer, is used as a measure of operational performance. A single ES installation was deployed across all regions of the subject firm\u27s operations.Findings illustrate parity as an immediate consequence of ES deployment. However, differences in rates of performance improvement following deployment eventually result in significant (albeit smaller than pre-deployment) performance differences. An additional consequence of deployment seems to be an increased synchronization of performance across the formerly independent regions

Do sentiment indicators help to assess and predict actual developments of the Chinese economy?

This paper evaluates the usefulness of business sentiment indicators for forecasting developments in the Chinese real economy. We use data on diffusion indices collected by the People’s Bank of China for forecasting industrial production, retail sales and exports. Our bivariate vector autoregressive models, each composed of one diffusion index and one real sector variable, generally outperform univariate AR models in forecasting one to four quarters ahead. Similarly, principal components analysis, combining information from various diffusion indices, leads to enhanced forecasting performance. Our results indicate that Chinese business sentiment indicators convey useful information about current and future developments in the real economy. They also suggest that the official data provide a fairly accurate picture of the Chinese economy.forecasting; diffusion index; VAR; China

Finding kernel function for stock market prediction with support vector regression

Stock market prediction is one of the fascinating issues of stock market research. Accurate stock prediction becomes the biggest challenge in investment industry because the distribution of stock data is changing over the time. Time series forcasting, Neural Network (NN) and Support Vector Machine (SVM) are once commonly used for prediction on stock price. In this study, the data mining operation called time series forecasting is implemented. The large amount of stock data collected from Kuala Lumpur Stock Exchange is used for the experiment to test the validity of SVMs regression. SVM is a new machine learning technique with principle of structural minimization risk, which have greater generalization ability and proved success in time series prediction. Two kernel functions namely Radial Basis Function and polynomial are compared for finding the accurate prediction values. Besides that, backpropagation neural network are also used to compare the predictions performance. Several experiments are conducted and some analyses on the experimental results are done. The results show that SVM with polynomial kernels provide a promising alternative tool in KLSE stock market prediction