Investors' risk attitude and risky behavior: a Bayesian approach with imperfect information

In a choice model of risky assets the role of risk aversion is analyzed. The measure of risk preference comes from a direct subjective survey question and it is considered as an imperfect information about the true risk attitude of investors. Misclassification between the true and the observed risk aversion is explicitly taken into account in the empirical model. A Data Augmentation approach, a Bayesian procedure for incomplete-data problems, is applied on data from the 2006 Survey of Household Income and Wealth by the Bank of Italy. Results indicate that when misclassification of investors is taken into account model estimates show the good performance of the subjective question when used as a control in a portfolio choice models. Moreover risk aversion emerges as a strong predictor of the probability to hold risky assets. The analysis also shows that probability of misclassification decreases as latent risk aversion increases, that means that more risk tolerant investors tend to be classified erroneously more often than less risk tolerant investors.Portfolio choice, risk attitude, misclassification error, Bayesian analysis

On the maximum number of rational points on singular curves over finite fields

We give a construction of singular curves with many rational points over finite fields. This construction enables us to prove some results on the maximum number of rational points on an absolutely irreducible projective algebraic curve defined over Fq of geometric genus g and arithmetic genus $\pi$

Tourism market segmentation of Italian families for the summer season

In last decades, the rapid expansion of tourism sector and the major differentiation of the tourism products have stimulated several studies in segmentation of tourism markets; but the applications of that technique has always focused on single consumers, while often the real "buyer" is the family. In this paper, we deal with national leisure tourism of Italian families in summer season; for the analysis, a sample of around 3,500 Italian families from a multi-scope sample survey "Travels and Holidays", collected by the National Institute of Statistics (ISTAT) is used. The major objective of this study is to investigate holiday strategies of Italian families in connection with recent changes in family structure, in order to individuate different profiles and different customs in travel patterns

Travel Profiles Of Family Holidays In Italy

Family represents the most important and emotive connection among humans. In tourism sector, it is the consumer base of the industry; however, the importance of family in travel market is not reflected in tourism research, even if family holiday market has been identified as constituting a major portion of leisure travels around the world. Furthermore, travel choices are clearly influenced by the composition and the characteristics of the families. In this paper, we analyse family holidays in the Italian context; for the purpose of this study, from ISTAT multipurpose survey we use a sample of around 2,000 holidays made in 2013 by almost two components of the same family. The goal is to classify family holidays, and detect their profile

Investment forecasting with business survey data

Business investment is a very important variable for short- and medium-term economic analysis, but it is volatile and difficult to predict. Qualitative business survey data are widely used to provide indicators of economic activity ahead of the publication of official data. Traditional indicators exploit only aggregate survey information, namely the proportions of respondents who report “up” and “down”. As a consequence, neither the heterogeneity of individual responses nor the panel dimension of microdata is used. We illustrate the use of a disaggregate panel-based indicator that exploits all information coming from two yearly industrial surveys carried out on the same sample of Italian manufacturing firms. Using the same sample allows us to match exactly investment plans and investment realisations for each firm and so estimate a panel data model linking individual investment realisations to investment intentions. The model generates a one-year-ahead forecast of investment variation that follows the aggregate dynamics with a limited bias.investment plans, dynamic panel data model, forecasting

Diversity, Coding, and Multiplexing Trade-Off of Network-Coded Cooperative Wireless Networks

In this paper, we study the performance of network-coded cooperative diversity systems with practical communication constraints. More specifically, we investigate the interplay between diversity, coding, and multiplexing gain when the relay nodes do not act as dedicated repeaters, which only forward data packets transmitted by the sources, but they attempt to pursue their own interest by forwarding packets which contain a network-coded version of received and their own data. We provide a very accurate analysis of the Average Bit Error Probability (ABEP) for two network topologies with three and four nodes, when practical communication constraints, i.e., erroneous decoding at the relays and fading over all the wireless links, are taken into account. Furthermore, diversity and coding gain are studied, and advantages and disadvantages of cooperation and binary Network Coding (NC) are highlighted. Our results show that the throughput increase introduced by NC is offset by a loss of diversity and coding gain. It is shown that there is neither a coding nor a diversity gain for the source node when the relays forward a network-coded version of received and their own data. Compared to other results available in the literature, the conclusion is that binary NC seems to be more useful when the relay nodes act only on behalf of the source nodes, and do not mix their own packets to the received ones. Analytical derivation and findings are substantiated through extensive Monte Carlo simulations.Comment: IEEE International Conference on Communications (ICC), 2012. Accepted for publication and oral presentatio

Multi-mode partitioning for text clustering to reduce dimensionality and noises

Co-clustering in text mining has been proposed to partition words and documents simultaneously. Although the main advantage of this approach may improve interpretation of clusters on the data, there are still few proposals on these methods; while one-way partition is even now widely utilized for information retrieval. In contrast to structured information, textual data suffer of high dimensionality and sparse matrices, so it is strictly necessary to pre-process texts for applying clustering techniques. In this paper, we propose a new procedure to reduce high dimensionality of corpora and to remove the noises from the unstructured data. We test two different processes to treat data applying two co-clustering algorithms; based on the results we present the procedure that provides the best interpretation of the data