Modeling investor optimism with fuzzy connectives

Optimism or pessimism of investors is one of the important characteristics that determine the investment behavior in financial markets. In this paper, we propose a model of investor optimism based on a fuzzy connective. The advantage of the proposed approach is that the influence of different levels of optimism can be studied by varying a single parameter. We implement our model in an artificial financial market based on the LLS model. We find that more optimistic investors create more pronounced booms and crashes in the market, when compared to the unbiased efficient market believers of the original model. In the case of extreme optimism, the optimistic investors end up dominating the market, while in the case of extreme pessimism, the market reduces to the benchmark model of rational informed investors

A new approach to dealing with missing values in data-driven fuzzy modeling

Real word data sets often contain many missing elements. Most algorithms that automatically develop a rule-based model are not well suited to deal with incomplete data. The usual technique is to disregard the missing values or substitute them by a best guess estimate, which can bias the results. In this paper we propose a new method for estimating the parameters of a Takagi-Sugeno fuzzy model in the presence of incomplete data. We also propose an inference mechanism that can deal with the incomplete data. The presented method has the added advantage that it does not require imputation or iterative guess-estimate of the missing values. This methodology is applied to fuzzy modeling of a classification and regression problem. The performance of the obtained models are comparable with the results obtained when using a complete data set

Prediction of the MSCI EURO index based on fuzzy grammar fragments extracted from European central bank statements

We focus on predicting the movement of the MSCI EURO index based on European Central Bank (ECB) statements. For this purpose we learn and extract fuzzy grammars from the text of the ECB statements. Based on a set of selected General Inquirer (GI) categories, the extracted fuzzy grammars are grouped around individual content categories. The frequency at which these fuzzy grammars are encountered in the text constitute input to a Fuzzy Inference System (FIS). The FIS maps these frequencies to the levels of the MSCI EURO index. Ultimately, the goal is to predict whether the MSCI EURO index will exhibit upward or downward movement based on the content of ECB statements, as quantified through the use of fuzzy grammars and GI content categories

Computational content analysis of European Central Bank statements

In this paper we present a framework for the computational content analysis of European Central Bank (ECB) statements. Based on this framework, we provide two approaches that can be used in a practical context. Both approaches use the content of ECB statements to predict upward and downward movement in the MSCI EURO index. General Inquirer (GI) is used for the quantification of the content of the statements. In the first approach, we rely on the frequency of adjectives in the text of the ECB statements in relation to the content categories they represent. The second approach uses fuzzy grammar fragments composed of economic terms and content categories. Our results indicate that the two proposed approaches perform better than a random classifier for predicting upward or downward movement of the MSCI EURO index

Hippocampal Insulin Signaling And Neuroprotection Mediated By Physical Exercise In Alzheimeŕs Disease

Epidemiological studies indicate continuous increases in the prevalence of Alzheimer's Disease (AD) in the next few decades. The key feature of this disease is hippocampal neurodegeneration. This structure has an important role in learning and memory. Intense research efforts have sought to elucidate neuroprotective mechanisms responsible for hippocampal integrity. Insulin signaling seems to be a very promising pathway for the prevention and treatment of AD. This hormone has been described as a powerful activator of neuronal survival. Recent research showed that reduced insulin sensitivity leads to low-grade inflammation, and both phenomena are closely related to AD genesis. Concomitantly, exercise has been shown to exert anti-inflammatory effects and to promote improvement in insulin signaling in the hippocampus, which supports neuronal survival and constitutes an interesting non-pharmacological alternative for the prevention and treatment of AD. This review examines recent advances in understanding the molecular mechanisms involved in hippocampal neuroprotection mediated by exercise.2

Thermodynamic Comparison and the Ideal Glass Transition of A Monatomic Systems Modeled as an Antiferromagnetic Ising Model on Husimi and Cubic Recursive Lattices of the Same Coordination Number

Two kinds of recursive lattices with the same coordination number but different unit cells (2-D square and 3-D cube) are constructed and the antiferromagnetic Ising model is solved exactly on them to study the stable and metastable states. The Ising model with multi-particle interactions is designed to represent a monatomic system or an alloy. Two solutions of the model exhibit the crystallization of liquid, and the ideal glass transition of supercooled liquid respectively. Based on the solutions, the thermodynamics on both lattices was examined. In particular, the free energy, energy, and entropy of the ideal glass, supercooled liquid, crystal, and liquid state of the model on each lattice were calculated and compared with each other. Interactions between particles farther away than the nearest neighbor distance are taken into consideration. The two lattices show comparable properties on the transition temperatures and the thermodynamic behaviors, which proves that both of them are practical to describe the regular 3-D case, while the different effects of the unit types are still obvious.Comment: 27 pages, 13 figure

Function Approximation Using Probabilistic Fuzzy Systems

We consider function approximation by fuzzy systems. Fuzzy systems are typically used for approximating deterministic functions, in which the stochastic uncertainty is ignored. We propose probabilistic fuzzy systems i

Conditional Density Models Integrating Fuzzy and Probabilistic Representations of Uncertainty

__Abstract__ Conditional density estimation is an important problem in a variety of areas such as system identification, machine learning, artificial intelligence, empirical economics, macroeconomic analysis, quantitative finance and risk management. This work considers the general problem of conditional density estimation, i.e., estimating and predicting the density of a response variable as a function of covariates. The semi-parametric models proposed and developed in this work combine fuzzy and probabilistic representations of uncertainty, while making very few assumptions regarding the functional form of the response variable's density or changes of the functional form across the space of covariates. These models possess sufficient generalization power to approximate a non-standard density and the ability to describe the underlying process using simple linguistic descriptors despite the complexity and possible non-linearity of this process. These novel models are applied to real world quantitative finance and risk management problems by analyzing financial time-series data containing non-trivial statistical properties, such as fat tails, asymmetric distributions and changing variation over time

Estimation of flexible fuzzy GARCH models for conditional density estimation

In this work we introduce a new flexible fuzzy GARCH model for conditional density estimation. The model combines two different types of uncertainty, namely fuzziness or linguistic vagueness, and probabilistic uncertainty. The probabilistic uncertainty is modeled through a GARCH model while the fuzziness or linguistic vagueness is present in the antecedent and combination of the rule base system. The fuzzy GARCH model under study allows for a linguistic interpretation of the gradual changes in the output density, providing a simple understanding of the process. Such a system can capture different properties of data, such as fat tails, skewness and multimodality in one single model. This type of models can be useful in many fields such as macroeconomic analysis, quantitative finance and risk management. The relation to existing similar models is discussed, while the properties, interpretation and estimation of the proposed model are provided. The model performance is illustrated in simulated time series data exhibiting complex behavior and a real data application of volatility forecasting for the S&P 500 daily returns series