THE ENTROPIC ANALYSIS OF ELECTORAL RESULTS: THE CASE OF EUROPEAN COUNTRIES

Entropy is a measure of information and uncertainty which has been used recently in different areas, besides of its original utilization in physics. Finance, microeconomics, macroeconomics, utility functions or even psychology are approached areas, using analogies between the areas physics and nature, creating a new research area: Econophysics (see, for example, Ausloos et al, 1999 or Bouchaud, 2002). This paper intends to explore the utilization of entropy through politics and election results, an area just slightly explored (Gill, 2005). It generalizes interpretation of entropy, considering it a measure of dissatisfaction and disillusion of populations in relation to politics. Some phenomena like the increase of abstention in a country, consequence of the dissatisfaction of population and of their alienation in relation to politics could be detected and analysed. This discontentment could result, for example, in the appearance of new political parties, with more division of votes and increasing entropy (result of the discontentment and uncertainty by electors). Absolute majorities, while imply less dispersion of votes, are synonym of more confidence in a given party, making a reduction of entropy. Electoral results could also be influenced by particular phenomena, like those terrorist attacks made in vespers of the two last elections in Spain, with consequences on the affluence to the polls by electors, and influencing levels of entropy. Elections' dates could also influence results: for example, elections on summer season suffer from more abstention. Elections' results could also be connected with aspects like safety feeling of citizens, with unpopular socio-economic policies taken by government or even with the economic performance of a country. One of the purposes of this paper is to find these types of phenomena and try to relate them with the concept of entropy. Another objective is to analyse the reality in different European countries.Entropy, electoral results, satisfaction and dissatisfaction of population

On the globalization of stock markets: An application of Vector Error Correction Model, Mutual Information and Singular Spectrum Analysis to the G7 countries

This paper analyzes stock market relationships among the G7 countries between 1973 and 2009 using three different approaches: (i) a linear approach based on cointegration, Vector Error Correction (VECM) and Granger Causality; (ii) a nonlinear approach based on Mutual Information and the Global Correlation Coefficient; and (iii) a nonlinear approach based on Singular Spectrum Analysis (SSA). While the cointegration tests are based on regression models and capture linearities in the data, Mutual Information and Singular Spectrum Analysis capture nonlinear relationships in a non-parametric way. The framework of this paper is based on the notion of market integration and uses stock market correlations and linkages both in price levels and returns. The main results show that significant co-movements occur among most of the G7 countries over the period analyzed and that Mutual Information and the Global Correlation Coefficient actually seem to provide more information about the market relationships than the Vector Error Correction Model and Granger Causality. However, unlike the latter, the direction of causality is difficult to distinguish in Mutual Information and the Global Correlation Coefficient. In this respect, the nonlinear Singular Spectrum Analysis technique displays several advantages, since it enabled us to capture nonlinear causality in both directions, while Granger Causality only captures causality in a linear way. The results also show that stock markets are closely linked both in terms of price levels and returns (as well as lagged returns) over the 36 years analyzed

An econophysics approach to analyse uncertainty in financial markets: an application to the Portuguese stock market

In recent years there has been a closer interrelationship between several scientific areas trying to obtain a more realistic and rich explanation of the natural and social phenomena. Among these it should be emphasized the increasing interrelationship between physics and financial theory. In this field the analysis of uncertainty, which is crucial in financial analysis, can be made using measures of physics statistics and information theory, namely the Shannon entropy. One advantage of this approach is that the entropy is a more general measure than the variance, since it accounts for higher order moments of a probability distribution function. An empirical application was made using data collected from the Portuguese Stock Market.Comment: 8 pages, 2 figures, presented in the conference Next Sigma-Phi 200

GME versus OLS - Which is the best to estimate utility functions?

This paper estimates von Neumann andMorgenstern utility functions comparing the generalized maximum entropy (GME) with OLS, using data obtained by utility elicitation methods. Thus, it provides a comparison of the performance of the two estimators in a real data small sample setup. The results confirm the ones obtained for small samples through Monte Carlo simulations. The difference between the two estimators is small and it decreases as the width of the parameter support vector increases. Moreover the GME estimator is more precise than the OLS one. Overall the results suggest that GME is an interesting alternative to OLS in the estimation of utility functions when data is generated by utility elicitation methods.Generalized maximum entropy; Maximum entropy principle; von Neumann and Morgenstern utility; Utility elicitation.

Mutual information: a dependence measure for nonlinear time series

This paper investigates the possibility to analyse the structure of unconditional or conditional (and possibly nonlinear) dependence in financial returns without requiring the specification of mean-variance models or a theoretical probability distribution. The main goal of the paper is to show how mutual information can be used as a measure of dependence in financial time series. One major advantage of this approach resides precisely in its ability to account for nonlinear dependencies with no need to specify a theoretical probability distribution or use of a mean-variance model.Mutual information, nonlinear dependence, market efficiency

Non-linear dependencies in African stock markets: Was subprime crisis an important factor?

The historical dependence in stock markets it is a very explored issue, especially in developed markets. In this paper we try to address the question of global dependency in African stock markets, and for that purpose we use a global approach able to capture the long-term dependencies being linear or non-linear ones. Are there significant differences in terms of results compared to the major international markets? Results point to an affirmative answer. The Hurst exponent shows that long-term dependence is probably linked not only to size or liquidity

The potentialities of Chinese airline market for Lisbon international airport: the empirical modelling analysis

In this paper we propose a comparative analysis between an empirical gravity model and a dynamic regression model with the objective to explain the potentialities of the Chinese airline market for Lisbon International Airport (air passengers demand). We confirm the viability to create some direct flights from Lisbon International Airport to Chinese airline market, with the strategy to attract the transfer passengers flow with origin on Latin America. Panel dada is used to determine the influence of the explanatory variables, on average number of passengers (air passengers demand). The results are creating by the Stata final outputs. We also demonstrate that the dynamic regression model used in this paper is more robust and better than the empirical gravity model, often considered as a reference method in the field of aviation. The most relevant variables on the dynamic regression model are PPP (gross national income (GNI) converted to international dollars using purchasing power parity rates), Business and Trade Factor, and Tourism and Cultural Factor. Furthermore, we find some possible explanations for the results

Searching for a New Balance for the Eurozone Governance in the Aftermath of the Coronavirus Crisis

The economic crisis triggered by the coronavirus exposed the gaps in the European Monetary Union (EMU) governance, creating fears to repeat the sovereign debt crisis. In view of the magnitude of the crisis and the financial interdependence inside that union, strong policy coordination and central measures are crucial. The discussion on debt mutualisation continued to be based on clashing political positions. This chapter identifies the instability recorded in sovereign spreads at the beginning of the crisis and reviews the structural factors that caused it. We discuss the limited role of the ECB answer. So, the issue of Eurobonds is revisited, bringing new arguments since the EMU faces an exogenous shock. We sustain that the approval of the European Commission’s original Next Generation EU programme on the European Council in July 21, could anchor a new governance model for the EMU, based on the principle of subsidiarity and the refusal of moral hazard arguments

Linear and nonlinear models for the analysis of the relationship between stock market prices and macroeconomic and financial factors

The main objective of this paper is to assess how mutual information as a measure of global dependence between stock markets and macroeconomic factors can overcome some of the weaknesses of the traditional linear approaches commonly used in this context. One of the advantages of mutual information is that it does not require any prior assumption regarding the specification of a theoretical probability distribution or the specification of the dependence model. This study focuses on the Portuguese stock market where we evaluate the relevance of the macroeconomic and financial variables as determinants of the stock prices behaviour.nonlinear dependence, stock market, financial and macroeconomic factors