L'effet des variables macroéconomiques sur la volatilité des marchés boursiers

Nous étudions l'effet des variables macroéconomiques sur la volatilité du marché des actions en utilisant des données américaines pour la période allant de janvier 1959 à décembre 2004. Nous essayons de mieux comprendre la volatilité des rendements réalisés et de déterminer comment cette volatilité réagit aux changements de la politique monétaire et de l'économie passés et futurs. Nous adoptons la méthodologie de Schwert (1989) pour examiner la relation entre la volatilité et le niveau de l'économie. Cependant, nous utilisons des données quotidiennes pour le calcul des estimés de la volatilité, lesquels sont moins biaisés. En plus, nous corrigeons notre modèle pour tenir compte du problème d'endogénéité et de l'autocorrélation des résidus qui sont évidents dans Schwert (1989). Enfin, non seulement nous étudions l'effet des variables macro économiques sur la volatilité du S&P ; mais aussi sur celles du NYSE et du NASDAQ. Nos résultats confirment les conclusions de Schwert (1989) que les chocs de l'offre de monnaie et de la production industrielle expliquent les variations dans la volatilité. Cependant, nos résultats ne confirment pas les conclusions de Schwert (1989) que la volatilité n'est pas reliée aux variations de l'inflation. Nos résultats suggèrent aussi que l'hypothèse d'efficience des marchés financiers est rejetée. Les taux de discount, de l'offre de monnaie, de l'inflation et d'utilisation de la capacité industrielle anticipés expliquent la volatilité future des marchés des actions. Cette dernière conclusion est soumise à l'appréciation du lecteur. ______________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : Volatilité, Agrégats macroéconomiques, Politique monétaire, Endogénéité, Autocorrélation, Hypothèse des marchés efficients

Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images

A new methodology for automatic feature extraction from biomedical images and subsequent classification is presented. The approach exploits the spatial orientation of high-frequency textural features of the processed image as determined by a two-step process. First, the two-dimensional discrete wavelet transform(DWT) is applied to obtain the HH high-frequency subband image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor-filtered image whose entropy and uniformity are computed. Finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier. The approach was validated on mammograms, retina, and brain magnetic resonance (MR) images.The obtained classification accuracies show better performance in comparison to common approaches that use only the DWT or Gabor filter banks for feature extraction

A comparative study of back-propagation algorithms in financial prediction

ABSTRAC

Detection of pathologies in retina digital images an empirical mode decomposition approach

Accurate automatic detection of pathologies in retina digital images offers a promising approach in clinicalapplications. This thesis employs the discrete wavelet transform (DWT) and empirical mode decomposition (EMD) to extract six statistical textural features from retina digital images. The statistical features are the mean, standard deviation, smoothness, third moment, uniformity, and entropy. The purpose is to classify normal and abnormal images. Five different pathologies are considered. They are Artery sheath (Coat’s disease), blot hemorrhage, retinal degeneration (circinates), age-related macular degeneration (drusens), and diabetic retinopathy (microaneurysms and exudates). Four classifiers are employed; including support vector machines (SVM), quadratic discriminant analysis (QDA), k-nearest neighbor algorithm (k-NN), and probabilistic neural networks (PNN). For each experiment, ten random folds are generated to perform cross-validation tests. In order to assess the performance of the classifiers, the average and standard deviation of the correct recognition rate, sensitivity and specificity are computed for each simulation. The experimental results highlight two main conclusions. First, they show the outstanding performance of EMD over DWT with all classifiers. Second, they demonstrate the superiority of the SVM classifier over QDA, k-NN, and PNN. Finally, principal component analysis (PCA) was employed to reduce the number of features in hope to improve the accuracy of classifiers. We find that there is no general and significant improvement of the performance, however. In sum, the EMD-SVM system provides a promising approach for the detection of pathologies in digital retina

Neural networks for cryptocurrency evaluation and price fluctuation forecasting

International audienceToday, there is a growing number of digital assets, often built on questionable technical foundations. We design and implement supervized learning models in order to explore different aspects of a cryptocurrency affecting its performance, its stability as well as its daily price fluctuation. One characteristic feature of our approach is that we aim at a holistic view that would integrate all available information: First, financial information, including market capitalization and historical daily prices. Second, features related to the underlying blockchain from blockchain explorers like network activity: blockchains handle the supply and demand of a cryptocurrency. Lastly, we integrate software development metrics based on GitHub activity by the supporting team. We set two goals. First, to classify a given cryptocurrency by its performance, where stability and price increase are the positive features. Second, to forecast daily price tendency through regression; this is of course a well-studied problem. A related third goal is to determine the most relevant features for such analysis. We compare various neural networks using most of the widely traded digital currencies (e.g. Bitcoin, Ethereum and Litecoin) in both classification and regression settings. Simple Feedforward neural networks are considered, as well as Recurrent neural networks (RNN) along with their improvements, namely Long Short-Term Memory and Gated Recurrent Units. The results of our comparative analysis indicate that RNNs provide the most promising results

Enkephalon - technological platform to support the diagnosis of alzheimer’s disease through the analysis of resonance images using data mining techniques

Dementia can be considered as a decrease in the cognitive function of the person. The main diseases that appear are Alzheimer and vascular dementia. Today, 47 million people live with dementia around the world. The estimated total cost of dementia worldwide is US $ 818 billion, and it will become a trilliondollar disease by 2019 The vast majority of people with dementia not received a diagnosis, so they are unable to access care and treatment. In Colombia, two out of every five people presented a mental disorder at some point in their lives and 90% of these have not accessed a health service. Here it´s proposed a technological platform so early detection of Alzheimer. This tool complements and validates the diagnosis made by the health professional, based on the application of Machine Learning techniques for the analysis of a dataset, constructed from magnetic resonance imaging, neuropsychological test and the result of a radiological test. A comparative analysis of quality metrics was made, evaluating the performance of different classifier methods: Random subspace, Decorate, BFTree, LMT, Ordinal class classifier, ADTree and Random forest. This allowed us to identify the technique with the highest prediction rate, that was implemented in ENKEPHALON platform

Idiopathic encapsulating peritonitis revealed by an acute bowel occlusion in a young patient

La péritonite encapsulante est une péritonite chronique aboutissant à une membrane fibreuse épaisse, blanc nacré. C’est une affection rare dont la physiopathologie reste mal expliquée et le diagnostic est souvent porté en peropératoire ; elle peut être la cause d’une urgence chirurgicale, le caractère idiopathique est exceptionnel, retrouvé chez l’adolescent provenant des régions tropicales et subtropicales, jamais dans le Maghreb. Nous rapportons l’observation d’une jeune patiente marocaine de 18 ans, opérée pour une occlusion intestinale, chez qui le diagnostic d’une péritonite encapsulante a été posé en peropératoire.Encapsulating peritonitis is a chronic peritonitis leading to the constitution of a thick pearly-white fibrosis membrane. It is a rare affection, which physiopathology is poorly elucidated. Diagnosis is usually assessed during surgery; the idiopathic character is exceptional, occurring in teenagers coming from the tropical and subtropical countries, never in Maghreb. We report an unpublished case of an 18-year-old patient, admitted for bowel obstruction; diagnosis was made during surgery revealing an encapsulating peritonitis

Perception-based fuzzy partitions for visual texture modelling

Visual textures in images are usually described by humans using linguistic terms related to their perceptual properties, like “very coarse”, “low directional”, or “high contrasted”. Computational models with the ability of providing a perceptual texture characterization on the basis of these terms can be very useful in tasks like semantic description of images, content-based image retrieval using linguistic queries, or expert systems design based on low level visual features. In this paper, we address the problem of simulating the human perception of texture, obtaining linguistic labels to describe it in natural language. For this modeling, fuzzy partitions defined on the domain of some of the most representative measures of each property are employed. In order to define the fuzzy partitions, the number of linguistic labels and the parameters of the membership functions are calculated taking into account the relationship between the computational values given by the measures and the human perception of the corresponding property. The performance of each fuzzy partition is analyzed and tested using the human assessments, and a ranking of measures is obtained according to their ability to represent the perception of the property, allowing to identify the most suitable measure

Application of ARIMA Model in Financial Time Series in Stocks

In order to study the development of stock exchange between China and the United States during the Sino-U.S. trade war, the stock trends of the two countries were compared and analyzed, combined with the time series prediction, and displayed with the visual result chart. Judging the data’s stability from its original time sequence diagram, autocorrelation diagram and p-value, make difference for non-stationary data, then determine the appropriate parameters P and Q in ARIMA model according to autocorrelation diagram and partial autocorrelation diagram, confirm the model for model test, select the model with the lowest AIC, BIC and hqlc values to predict 10% of the total data and visualize. From the visual results, the prediction effect is not very good, there are relatively large errors, and the trend of stock closing price is not consistent. ARIMA model is not very good in the application of stock market, which needs to be improved