12 research outputs found
Stock market series analysis using self-organizing maps
In this work a new clustering technique is implemented and tested. The proposed approach is based on the application of a SOM (self-organizing map) neural network and provides means to cluster U-MAT aggregated data. It relies on a flooding algorithm operating on the U-MAT and resorts to the Calinski and Harabask index to assess the depth of flooding, providing an adequate number of clusters. The method is tuned for the analysis of stock market series. Results obtained are promising although limited in scope.Neste trabalho Ă© implementada e testada uma nova tĂ©cnica de agrupamento. A abordagem proposta baseia-se na aplicação de uma rede neuronal SOM (mapa auto-organizado) e permite agrupar dados sobre a matriz de distancias (U-MAT). É utilizado um algoritmo de alagamento ("flooding") sobre a U-MAT e o Ăndice de Calinski e Harabasz avalia a profundidade do alagamento determinando-se, assim, o nĂşmero de grupos mais adequado. O mĂ©todo Ă© desenhado especificamente para a análise de sĂ©ries temporais da bolsa de valores. Os resultados obtidos sĂŁo promissores, embora se registem ainda limitações
Stock market series analysis using self-organizing maps
In this work a new clustering technique is implemented and tested. The proposed approach is based on the application of a SOM (self-organizing map) neural network and provides means to cluster U-MAT aggregated data. It relies on a flooding algorithm operating on the U-MAT and resorts to the Calinski and Harabask index to assess the depth of flooding, providing an adequate number of clusters. The method is tuned for the analysis of stock market series. Results obtained are promising although limited in scope. Neste trabalho Ă© implementada e testada uma nova tĂ©cnica de agrupamento. A abordagem proposta baseia-se na aplicação de uma rede neuronal SOM (mapa autoorganizado) e permite agrupar dados sobre a matriz de distancias (U-MAT). É utilizado um algoritmo de alagamento ("flooding") sobre a U-MAT e o Ăndice de Calinski e Harabasz avalia a profundidade do alagamento determinando-se, assim, o nĂşmero de grupos mais adequado. O mĂ©todo Ă© desenhado especificamente para a análise de sĂ©ries temporais da bolsa de valores. Os resultados obtidos sĂŁo promissores, embora se registem ainda limitações
Hazai vállalkozások csődjének előrejelzése a csődeseményt megelőző egy, két, illetve három évvel korábbi pénzügyi beszámolók adatai alapján
A cikk azt vizsgálja, hogy milyen besorolási pontossággal jelezhető előre a hazai vállalkozások csődje az azt
megelőző egy, két, illetve három évvel korábbi éves beszámolók adatai alapján. A kutatási kérdés megválaszolásához egy, a hazai szakirodalomban még kevésbé elterjedt nem paraméteres módszer: a k legközelebbi
szomszĂ©d eljárást alkalmazza a szerzĹ‘. A tanulmány kĂĽlön figyelmet szentel a legjobb elĹ‘rejelzĹ‘ teljesĂtmĂ©ny
elĂ©rĂ©sĂ©t biztosĂtĂł paramĂ©terek (szomszĂ©dok száma, távolságmĂ©rtĂ©k) optimális megválasztására is.
A számĂtásokat egy hazai vállalkozásokbĂłl állĂł, ezerelemű vĂ©letlen minta adatain vĂ©geztĂ©k el. Nemzetközi
kutatási eredmények szerint nagyobb találati arány érhető el, ha a csődmodellek input változói között nemcsak
a csőd előtti év adatait használják fel, hanem az azt megelőző 2-3 év pénzügyi mutatóit is. E kérdés
vizsgálatát is célul tűzi ki a tanulmány
Metamódszerek alkalmazása a csődelőrejelzésben
A klasszifikációs feladatok megoldására jellemzően egy-egy kiválasztott módszert alkalmaznak a gyakorlatban. Ugyan a legkorszerűbb eljárások kimagasló találati arány elérésére
képesek, a nemzetközi kutatási eredmények azt mutatják, hogy a gyengébb klasszifikációs
teljesĂtmĂ©nyt mutatĂł mĂłdszerek egyĂĽttes alkalmazásával (ensemble) hasonlĂłan magas
találati arány érhető el. A cikk fő célja a csődelőrejelzésben leggyakrabban alkalmazott
kĂ©t metamĂłdszer (AdaBoost, bagging) elĹ‘rejelzĹ‘ kĂ©pessĂ©gĂ©nek összehasonlĂtása egy 976
hazai vállalkozás adataiból álló mintán. A cikk másik célkitűzése annak a vizsgálata, hogy
az egyes pĂ©nzĂĽgyi mutatĂłknak a szakágazati átlagtĂłl vett eltĂ©rĂ©seire Ă©pĂtett csĹ‘dmodellek
találati aránya hogyan viszonyul a nyers pénzügyi mutatókra, illetve az azok dinamikáját
is figyelembe vevĹ‘ változĂłkra Ă©pĂtett modellek találati arányához
A pénzügyi mutatók időbeli tendenciájának figyelembevétele logisztikus regresszióra épülő csődelőrejelző modellekben
A vállalatok jövĹ‘beli fizetĹ‘kĂ©pessĂ©gĂ©nek elĹ‘rejelzĂ©sĂ©ben általános gyakorlat a számviteli adatok alapján kalkulált hányados tĂpusĂş pĂ©nzĂĽgyi mutatĂłk használata magyarázĂłváltozĂłkĂ©nt. A hazai Ă©s a nemzetközi szakirodalom általános sajátosságának mondhatĂł az is, hogy e mutatĂłkat csak az utolsĂłkĂ©nt megfigyelt ĂĽzleti Ă©v adatai alapján kalkulálják. Az e változĂłkra Ă©pĂtett modellek azonban statikus jellegűek, melyek nem veszik figyelembe a vállalati gazdálkodás folyamatjellegĂ©t. E hiány pĂłtlására korábban Nyitrai [2014a] tett kĂsĂ©rletet a statikus pĂ©nzĂĽgyi mutatĂłszámok idĹ‘soraibĂłl kĂ©pzett dinamikus pĂ©nzĂĽgyi mutatĂłk segĂtsĂ©gĂ©vel. Az idĂ©zett kutatás azonban csak a döntĂ©si fák vonatkozásában igazolta a dinamikus mutatĂłk hatĂ©konyságát a csĹ‘delĹ‘rejelzĂ©sben. A szerzĹ‘k tanulmányukban bemutatják a dinamikus változĂłk teljesĂtmĂ©nyĂ©t a gyakorlati hitelkockázati modellezĂ©sben általánosan elterjedt logisztikus regressziĂł keretei között, továbbá kĂsĂ©rletet tesznek a dinamikus pĂ©nzĂĽgyi mutatĂłk koncepciĂłjának továbbfejlesztĂ©sĂ©re elĹ‘rejelzĹ‘ kĂ©pessĂ©gĂĽk növelĂ©se Ă©rdekĂ©ben
Synthesis of research studies examining prediction of bankruptcy
The purpose of this study is to synthesize the findings of prior bankruptcy prediction research studies by compiling and classifying the independent variables used as predictor variables in the studies. The objective is to find out the popularity of the different types of the predictor variables by classifying the variables into the categories describing the fincancial function of the variables, and by assessing the popularity of the significant variables in the categories. This work studies elementary theories on firm failure and bankruptcy to discuss and seek justitication for what might be the reasons for using the most popular financial function measures in the bankruptcy prediction.
Bankruptcy prediction research literature covers vast amount of studies in which various different predicton models are developed for predicting bankruptcy. Usually these studies use a prediction model with a set of some financial and/or non-financial variables that are presumed to be relevant proxies for financial distress and eventually business failure and bankrupcty. However, there seems to be no consensus or unified theory on how the variables predicting bankrupcty should be selected, thus the numerous bankruptcy prediction research studies include vast number and various different types of variables that are presumed to be applicable in predicting bankruptcy.
This study includes a systematic literature review where 51 bankruptcy prediction research studies were collected from well-recognized scientific journals. The studies included into the review were such that included a single or multiple bankruptcy prediction models, the detailed description of the independent variables, and the information about the statistical significances of the independent variables. The variables were then classified according to their financial function and a meta-analysis were conducted on those variables which were significant in bankruptcy prediction, to find out the popularity of the different variable categories.
The findings of this study suggest that the most popular predictor variables included into the banktuptcy predicton models are accounting-based financial ratios, particurarly ones measuring liquidity, profitability, and financial leverage, and that there exists also theoretical foundation for using these variables in the bankruptcy prediction
Forecasting Financial Distress With Machine Learning – A Review
Purpose – Evaluate the various academic researches with multiple views on credit risk and artificial intelligence (AI) and their evolution.Theoretical framework – The study is divided as follows: Section 1 introduces the article. Section 2 deals with credit risk and its relationship with computational models and techniques. Section 3 presents the methodology. Section 4 addresses a discussion of the results and challenges on the topic. Finally, section 5 presents the conclusions.Design/methodology/approach – A systematic review of the literature was carried out without defining the time period and using the Web of Science and Scopus database.Findings – The application of computational technology in the scope of credit risk analysis has drawn attention in a unique way. It was found that the demand for identification and introduction of new variables, classifiers and more assertive methods is constant. The effort to improve the interpretation of data and models is intense.Research, Practical & Social implications – It contributes to the verification of the theory, providing information in relation to the most used methods and techniques, it brings a wide analysis to deepen the knowledge of the factors and variables on the theme. It categorizes the lines of research and provides a summary of the literature, which serves as a reference, in addition to suggesting future research.Originality/value – Research in the area of Artificial Intelligence and Machine Learning is recent and requires attention and investigation, thus, this study contributes to the opening of new views in order to deepen the work on this topic
Beta hebbian learning: definition and analysis of a new family of learning rules for exploratory projection pursuit
[EN] This thesis comprises an investigation into the derivation of learning rules in artificial neural networks from probabilistic criteria.
•Beta Hebbian Learning (BHL).
First of all, it is derived a new family of learning rules which are based on maximising the likelihood of the residual from a negative feedback network when such residual is deemed to come from the Beta Distribution, obtaining an algorithm called Beta Hebbian Learning, which outperforms current neural algorithms in Exploratory Projection Pursuit.
• Beta-Scale Invariant Map (Beta-SIM).
Secondly, Beta Hebbian Learning is applied to a well-known Topology Preserving Map algorithm called Scale Invariant Map (SIM) to design a new of its version called Beta-Scale Invariant Map (Beta-SIM). It is developed to facilitate the clustering and visualization of the internal structure of high dimensional complex datasets effectively and efficiently, specially those characterized by having internal radial distribution. The Beta-SIM behaviour is thoroughly analysed comparing its results, in terms performance quality measures with other well-known topology preserving models.
• Weighted Voting Superposition Beta-Scale Invariant Map (WeVoS-Beta-SIM).
Finally, the use of ensembles such as the Weighted Voting Superposition (WeVoS) is tested over the previous novel Beta-SIM algorithm, in order to improve its stability and to generate accurate topology maps when using complex datasets. Therefore, the WeVoS-Beta-Scale Invariant Map (WeVoS-Beta-SIM), is presented, analysed and compared with other well-known topology preserving models.
All algorithms have been successfully tested using different artificial datasets to corroborate their properties and also with high-complex real datasets.[ES] Esta tesis abarca la investigaciĂłn sobre la derivaciĂłn de reglas de aprendizaje en redes neuronales
artificiales a partir de criterios probabilĂsticos.
• Beta Hebbian Learning (BHL).
En primer lugar, se deriva una nueva familia de reglas de aprendizaje basadas en maximizar la
probabilidad del residuo de una red con retroalimentaciĂłn negativa cuando se considera que
dicho residuo proviene de la DistribuciĂłn Beta, obteniendo un algoritmo llamado Beta Hebbian
Learning, que mejora a algoritmos neuronales actuales de bĂşsqueda de proyecciones
exploratorias.
• Beta-Scale Invariant Map (Beta-SIM).
En Segundo lugar, Beta Hebbian Learning se aplica a un conocido algoritmo de Mapa de
PreservaciĂłn de la TopologĂa llamado Scale Invariant Map (SIM) para diseñar una nueva versiĂłn
llamada Beta-Scale Invariant Map (Beta-SIM). Este nuevo algoritmo ha sido desarrollado para
facilitar el agrupamiento y visualizaciĂłn de la estructura interna de conjuntos de datos complejos
de alta dimensionalidad de manera eficaz y eficiente, especialmente aquellos caracterizados por
tener una distribuciĂłn radial interna. El comportamiento de Beta-SIM es analizado en
profundidad comparando sus resultados, en términos de medidas de calidad de rendimiento con
otros modelos bien conocidos de preservaciĂłn de topologĂa.
• Weighted Voting Superposition Beta-Scale Invariant Map (WeVoS-Beta-SIM).
Finalmente, el uso de ensembles como el Weighted Voting Superposition (WeVoS) sobre el
algoritmo Beta-SIM es probado, con objeto de mejorar su estabilidad y generar mapas
topolĂłgicos precisos cuando se utilizan conjuntos de datos complejos. Por lo tanto, se presenta,
analiza y compara el WeVoS-Beta-Scale Invariant Map (WeVoS-Beta-SIM) con otros modelos
bien conocidos de preservaciĂłn de topologĂa.
Todos los algoritmos han sido probados con Ă©xito sobre conjuntos de datos artificiales para corroborar
sus propiedades, asĂ como con conjuntos de datos reales de gran complejidad
Three essays on the use of neural networks for financial prediction
The number of studies trying to explain the causes and consequences of the economic and financial crises usually rises considerably after a banking crisis occurs. The dramatic effects of the most recent financial crisis on the real economy around the world call for a better comprehension of previous crises as a way to anticipate future crisis episodes. It is precisely this objective, preventing future crises, the main motivation of this PhD dissertation.
We identify two important mechanisms that have failed during the latest years and that are closely related to the onset of the financial crisis: The assessment of the solvency of banks along with the systemic risk over the time, and the detection of the macroeconomic imbalances in some countries, especially in Europe, which made the financial crisis evolve through a sovereign crisis. Our dissertation is made up of three different essays, trying to go a step ahead in the knowledge of these mechanisms.Departamento de EconomĂa Financiera y ContabilidadDoctorado en EconomĂa de la Empres
Nonsmooth optimization models and algorithms for data clustering and visualization
Cluster analysis deals with the problem of organization of a collection of patterns into clusters based on a similarity measure. Various distance functions can be used to define this measure. Clustering problems with the similarity measure defined by the squared Euclidean distance have been studied extensively over the last five decades. However, problems with other Minkowski norms have attracted significantly less attention. The use of different similarity measures may help to identify different cluster structures of a data set. This in turn may help to significantly improve the decision making process. High dimensional data visualization is another important task in the field of data mining and pattern recognition. To date, the principal component analysis and the self-organizing maps techniques have been used to solve such problems. In this thesis we develop algorithms for solving clustering problems in large data sets using various similarity measures. Such similarity measures are based on the squared LDoctor of Philosoph