The speeches stated by influential politicians can have a decisive impact on the future of a country. In particular, the economic content of such speeches affects the economy of countries and their financial markets. For this reason, we examine a novel dataset containing the economic content of 951 speeches stated by 45 US Presidents from George Washington (April 1789) to Donald Trump (February 2017). In doing so, we use an economic glossary carried out by means of text mining techniques. The goal of our study is to examine the structure of significant interconnections within a network obtained from the economic content of presidential speeches. In such a network, nodes are represented by talks and links by values of cosine similarity, the latter computed using the occurrences of the economic terms in the speeches. The resulting network displays a peculiar structure made up of a core (i.e. a set of highly central and densely connected nodes) and a periphery (i.e. a set of non-central and sparsely connected nodes). The presence of different economic dictionaries employed by the Presidents characterize the core-periphery structure. The Presidents' talks belonging to the network's core share the usage of generic (non-technical) economic locutions like "interest" or "trade". While the use of more technical and less frequent terms characterizes the periphery (e.g. "yield"). Furthermore, the speeches close in time share a common economic dictionary. These results together with the economics glossary usages during the US periods of boom and crisis provide unique insights on the economic content relationships among Presidents' speeches.
Here we introduce the idea of using rational expectations, a core concept in economics and finance, as a tool to predict the optimal failure time for a wide class of weighted k-out-of-n * Corresponding author.
This paper merges the statistical analysis of data regularities and decision support systems for investors. Specifically, it discusses the Benford’s law as a decision support device for financial investments. In particular, we illustrate the role of such a property of financial data as risk predictor for financial markets. First of all, we show empirical evidence of accordance between data on market index daily returns and Benford’s law. Then, we highlight that on short time period (1 year) the deviations from Benford’s law are related to low risk and positive trend periods; the p value of the $$\chi ^2$$
χ
2
test against the Benford’s distribution displays some predicting power for the market average return and risk level.
This paper develops a model for predicting the failure time of a wide class of weighted k-out-of-n reliability systems. To this aim, we adopt a rational expectation-type approach by artificially creating an information set based on the observation of a collection of systems of the same class–the catalog. Specifically, we state the connection between a synthetic statistical measure of the survived components’ weights and the failure time of the systems. In detail, we follow the evolution of the systems in the catalog from the starting point to their failure–obtained after the failure of some of their components. Then, we store the couples given by the measure of the survived components and the failure time. Finally, we employ such couples for having a prediction of the failure times of a set of new systems–the in-vivo systems–conditioned on the specific values of the considered statistical measure. We test different statistical measures for predicting the failure time of the in-vivo systems. As a result, we give insights on the statistical measure which is more effective in contributing to providing a reliable estimation of the systems’ failure time. A discussion on the initial distribution of the weights is also carried out.
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