The derived category of surface algebras: the case of the torus with one boundary component

In this paper we refine the main result of a previous paper of the author with Grimeland on derived invariants of surface algebras. We restrict to the case where the surface is a torus with one boundary component and give an easily computable derived invariant for such surface algebras. This result permits to give answers to open questions on gentle algebras: it provides examples of gentle algebras with the same AG-invariant (in the sense of Avella-Alaminos and Geiss) that are not derived equivalent and gives a partial positive answer to a conjecture due to Bobi\'nski and Malicki on gentle $2$-cycles algebras.Comment: 22 pages, a mistake concerning the computation of the mapping class group has been fixed, version 3: 25 pages, to appear in Algebras and Representation Theor

Tourism stock prices, systemic risk and tourism growth: a kalman filter with prior update DSGE-VAR model

Dynamic Stochastic General Equilibrium (DSGE) and Vector Autoregressive (VAR) models allow for probabilistic estimations to formulate macroeconomic policies and monitor them. One of the objectives of creating these models is to explain and understand financial fluc tuations through a consistent theoretical framework. In the tourism sector, stock price and sys temic risk are key financial variables in the international transmission of business cycles. Ad vances in Bayesian theory are providing an increasing range of tools that researchers can employ to estimate and evaluate DSGE and VAR models. One area of interest in previous literature has been to design a Bayesian robust filter, that performs well concerning an uncertainty class of possible models compatible with prior knowledge. In this study, we propose to apply the Bayes ian Kalman Filter with Prior Update (BKPU) in a tourism field to increase the robustness of DSGE and VAR models built for small samples and with irregular data. Our results indicate that BKPU improves the estimation of these models in two aspects. Firstly, the accuracy levels of the computing of the Markov Chain Monte Carlo model are increased, and secondly, the cost of the resources used is reduced due to the need for a shorter run time. Our model can play an essential role in the monetary policy process, as central bankers could use it to investigate the relative importance of different macroeconomic shocks and the effects of tourism stock prices and achieve a country´s international competitiveness and trade balance for this sectorUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Data mining for municipal financial distress prediction

Data mining techniques are capable of extracting valuable knowledge from large and variable databases. This work proposes a data mining method for municipal financial distress prediction. Using a new proxy of municipal financial situation and a sample of 128 Spanish municipalities, the empirical experiment obtained satisfactory results, which testifies to the viability and validity of the data mining method proposed for municipal financial distress prediction.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

The market value of SMEs: a comparative study between private and listed firms in alternative stock markets

This study aims to compare the market value of private firms and publicly listed small and medium-sized firms (SMEs) in alternative stock markets through a private discount approach with estimates of value based on discounted cash flow projections and along with a comparable multiples approach. The valuation methodology applied in this study yielded a final sample that included 232 observations between public and private companies in the Spanish market. To calculate the discount, we apply the different approaches of discounted cash flow and multiples, such as valuation, earnings, book value, and revenue. Our results conclude there is no private discount, instead, the outcomes of this article suggest a premium over public firms for some ratios. The negative private company discounts mean a premium and, on the other hand, some multiples suggest a discount according to the method of valuation. This paper proves private discounts resulted does not have any comparable value within the same country although all firms in Spain use the same currency. We value the discounted cash flows of our forecasts using a discount rate based on the Capital Asset Pricing Model (CAPM), so our study can also be viewed as a test sensitivity of CAPM-based approaches to equity risk premium, terminal value, and growth rate. Furthermore, we compare historical transaction multiples of privately held companies with transaction multiples of similar publicly held firmsFunding Open Access funding provided thanks to the CRUE‑CSIC agreement with Springer Nature

Un modelo global de predicción de quiebra con redes neuronales

El capítulo 3 está dedicado al proceso de obtención de las muestras, a las variables utilizadas y a los criterios tenidos en cuenta para la selección de las mismas. Por su parte, en el capítulo 4 se presentan los resultados del análisis empírico, dejando constancia de los modelos de predicción de la quiebra desarrollados y de la robustez de los mismos. Finalmente, el trabajo concluye con una discusión sobre los resultados alcanzados, con la exposición de las principales conclusiones obtenidas y con el detalle de la bibliografía consultada. Fecha de lectura de Tesis Doctoral: 29 de enero 2019.El presente trabajo trata de responder a la cuestión de investigación de si es posible mejorar la precisión de los modelos globales de predicción de quiebra existentes en la literatura previa. Para responder a esta cuestión se ha tenido en cuenta los excelentes resultados de clasificación que proporcionan los métodos computacionales tales como las redes neuronales artificiales, y se han construidos tanto modelos regionales para Asia, Europa y Norte América, como modelos globales. En concreto, se ha utilizado el denominado Perceptrón Multicapa y los resultados obtenidos han permitido constatar una mayor precisión de los métodos computacionales frente a las técnicas estadísticas tradicionales. La estructura del presente trabajo de investigación es la siguiente. En el capítulo 1 se lleva a cabo un análisis de la literatura previa sobre predicción de quiebra. De este análisis se han obtenido conclusiones sobre los métodos aplicados y su perfeccionamiento, sobre las variables empleadas, y sobre la evolución de los resultados obtenidos por los distintos modelos. Además, y atendiendo al enfoque de estudio adoptado, se ha analizado la literatura diferenciando entre modelos globales y modelos regionales. Este primer capítulo concluye aportando una clasificación de los estudios previos en la que se pone de manifiesto los principales argumentos utilizados y la brecha existente acerca de la superioridad de los modelos globales frente a los modelos regionales. El capítulo 2 aborda los fundamentos del método de naturaleza computacional utilizado en el presente trabajo. Además, se presentan la técnica de validación cruzada y los principales criterios de selección de modelos, que han sido adicionalmente utilizados para el contraste de los resultados

Modelos de predicción de crisis financieras internacionales con técnicas de aprendizaje automático: aplicaciones a la reputación país

El presente estudio ha escogido tres de los tipos más importantes de crisis tratadas en las finanzas internacionales: la crisis de deuda soberana, la crisis de divisas y la crisis sistémica bancaria. Por tanto, se trata de responder a la cuestión de investigación de si es posible mejorar la precisión de los modelos globales de predicción de crisis existentes en la literatura previa. Para responder a esta cuestión se ha tenido en cuenta no solo técnicas estadísticas, sino también técnicas computacionales que han arrojado excelentes resultados de clasificación en las últimas décadas en cuestiones de predicción económica. Para ofrecer una mayor diversidad explicativa y comparativa, se han utilizado tanto modelos globales como modelos regionales para África y Oriente Próximo, Asia, América Latina y Europa. Los resultados obtenidos han permitido constatar una mayor precisión de los métodos computacionales frente a las técnicas estadísticas tradicionales. Incluso técnicas computacionales muy novedosas han mostrado un potencial interesante en la precisión de estos eventos de crisis. Estos modelos de predicción de crisis de deuda soberana, divisas y sistema bancario pueden ser de utilidad para valorar de una manera más precisa la reputación de un país frente al mundo. La reputación país explica cómo las características más importantes de un país, por ejemplo, factores sociales y económicos, influye en la imagen o marca en la que el país se proyecta al mundo. El concepto de reputación país es análogo al de reputación corporativa, y de hecho, algunos autores han demostrado la influencia que tiene la reputación país en las empresas y en la forma en la que comercializan sus productos. Por este motivo, la presente tesis estudia el factor de estabilidad financiera en la reputación país a través de los modelos de predicción de crisis de deuda soberana, divisas y sistema bancario

The market value of SMEs: a comparative study between private and listed firms in alternative stock markets

This study aims to compare the market value of private firms and publicly listed small and medium-sized firms (SMEs) in alternative stock markets through a private discount approach with estimates of value based on discounted cash flow projections and along with a comparable multiples approach. The valuation methodology applied in this study yielded a final sample that included 232 observations between public and private companies in the Spanish market. To calculate the discount, we apply the different approaches of discounted cash flow and multiples, such as valuation, earnings, book value, and revenue. Our results conclude there is no private discount, instead, the outcomes of this article suggest a premium over public firms for some ratios (...

Managing extreme cryptocurrency volatility in algorithmic trading: EGARCH via genetic algorithms and neural networks.

Política de acceso abierto tomada de: https://www.aimspress.com/index/news/solo-detail/openaccesspolicyThe blockchain ecosystem has seen a huge growth since 2009, with the introduction of Bitcoin, driven by conceptual and algorithmic innovations, along with the emergence of numerous new cryptocurrencies. While significant attention has been devoted to established cryptocurrencies like Bitcoin and Ethereum, the continuous introduction of new tokens requires a nuanced examination. In this article, we contribute a comparative analysis encompassing deep learning and quantum methods within neural networks and genetic algorithms, incorporating the innovative integration of EGARCH (Exponential Generalized Autoregressive Conditional Heteroscedasticity) into these methodologies. In this study, we evaluated how well Neural Networks and Genetic Algorithms predict “buy” or “sell” decisions for different cryptocurrencies, using F1 score, Precision, and Recall as key metrics. Our findings underscored the Adaptive Genetic Algorithm with Fuzzy Logic as the most accurate and precise within genetic algorithms. Furthermore, neural network methods, particularly the Quantum Neural Network, demonstrated noteworthy accuracy. Importantly, the X2Y2 cryptocurrency consistently attained the highest accuracy levels in both methodologies, emphasizing its predictive strength. Beyond aiding in the selection of optimal trading methodologies, we introduced the potential of EGARCH integration to enhance predictive capabilities, offering valuable insights for reducing risks associated with investing in nascent cryptocurrencies amidst limited historical market data. This research provides insights for investors, regulators, and developers in the cryptocurrency market. Investors can utilize accurate predictions to optimize investment decisions, regulators may consider implementing guidelines to ensure fairness, and developers play a pivotal role in refining neural network models for enhanced analysis.This research was funded by the Universitat de Barcelona, under the grant UB-AE-AS017634

Neural networks for estimating Macro Asset Pricing model in football clubs

The recent crisis caused by COVID-19 directly affected consumption habits and thestability sof financial markets. In particular, the football industry has been hit hard bythis pandemic and therefore has more volatile stock prices. Given this new scenario,further research is needed to accurately estimate the value of the shares of footballclubs. In this paper, we estimate an asset pricing model in football clubs with differentcompositions of risk nature using non-linear techniques of artificial neural networks.Usually, asset pricing models have been estimated with linear methods such as ordi-nary least squares (...

Quantum Monte Carlo simulations for estimating FOREX markets: a speculative attacks experience.

The foreign exchange markets, renowned as the largest financial markets globally, also stand out as one of the most intricate due to their substantial volatility, nonlinearity, and irregular nature. Owing to these challenging attributes, various research endeavors have been undertaken to effectively forecast future currency prices in foreign exchange with precision. The studies performed have built models utilizing statistical methods, being the Monte Carlo algorithm the most popular. In this study, we propose to apply Auxiliary-Field Quantum Monte Carlo to increase the precision of the FOREX markets models from different sample sizes to test simulations in different stress contexts. Our findings reveal that the imple- mentation of Auxiliary-Field Quantum Monte Carlo significantly enhances the accuracy of these models, as evidenced by the minimal error and consistent estimations achieved in the FOREX market. This research holds valuable implications for both the general public and financial institutions, empowering them to effectively anticipate significant volatility in exchange rate trends and the associated risks. These insights provide crucial guidance for future decision-making processes.This research was funded by the Universidad de Málaga. We would also like to thank the Universitat de Barcelona, UB-AE-AS017634, for funding this research