Machine learning applied to the oxygen-18 isotopic composition, salinity and temperature/potential temperature in the Mediterranean sea

This study proposed different techniques to estimate the isotope composition (δ18O), salinity and temperature/potential temperature in the Mediterranean Sea using five different variables: (i–ii) geographic coordinates (Longitude, Latitude), (iii) year, (iv) month and (v) depth. Three kinds of models based on artificial neural network (ANN), random forest (RF) and support vector machine (SVM) were developed. According to the results, the random forest models presents the best prediction accuracy for the querying phase and can be used to predict the isotope composition (mean absolute percentage error (MAPE) around 4.98%), salinity (MAPE below 0.20%) and temperature (MAPE around 2.44%). These models could be useful for research works that require the use of past data for these variables.Universidade de Vigo | Ref. 0000 131H TAL 641Xunta de Galicia | Ref. ED431C 2018/42Xunta de Galicia | Ref. POS-B / 2016/00

Destek Vektör Makineleri ile Borsa Endekslerinin Tahmini

Destek Vektör Makineleri (DVM) en popüler makine öğrenme algoritmalarından birisidir. Bu çalışmada BIST100 endeksi ile birlikte dünyanın önde gelen borsa endekslerinden S&P 500, DAX ve NIKKEI 225 endekslerinin getiri yönlerinin sınıflandırılmasında bir makine öğrenme tekniği olan DVM’lerin kullanılması ve bu tekniklerin tahmin (sınıflandırma) performanslarının ortaya konulması amaçlanmıştır. Bu amaçla DVM’ler, borsa endekslerinin “yükseliş” ve “düşüş” trendlerinin modellenmesinde kullanılmıştır. Ayrıca çalışmada, makroekonomik değişkenlerin borsa endekslerine olan etkileri analiz edilmiştir. Çalışmanın veri seti, bağımlı ve bağımsız değişkenlerin 01.01.2013 – 30.11.2019 dönemleri arasındaki aylık olarak 82 adet gözlem değerini içermektedir. Bu gözlem değerlerinin 70 adedi (%85’i) algoritmanın modellenmesi (eğitim) için, 12 adedi (%15’i) ise sınıflandırma (test) için kullanılmıştır. Çalışma sonucunda modelin yükseliş yönlü tahminlerde sınıflandırma başarısının iyi olduğu, ancak düşüş yönlü tahminlerinde aynı başarıyı göstermediği ortaya çıkmıştır

Modelos de alerta temprana para pronosticar crisis bancarias en Ecuador

PDFEn el Ecuador no se han publicado trabajos de EWS para el sistema bancario, a junio del 2017. Por lo que se propuso encontrar el mejor método de clasificación de bancos en riesgo de quiebre en el sistema bancario ecuatoriano. Se usó el algoritmo Vecino Más Cercano “K-NN” para los datos faltantes y para la clasificación: Mínimos Cuadrados Parciales Discriminantes “PLSD”, Support Vector Machine “SVM” y Árbol de Clasificación (CART). El método PLSD alcanzó una clasificación global del 94.99% y una clasificación de bancos quebrados (18 meses antes de su cierre) del 18.89%. Los bancos que no quiebran tienen altos ratios en eficiencia financiera, margen de intermediación, resultados de ejercicios y cobertura de 100 mayores depositantes. Mientras los bancos que quiebran, tienen altos niveles de morosidad, vulnerabilidad del patrimonio, cartera improductiva y gastos operacionales. El Árbol de clasificación CART arrojó un buen modelo de clasificación, dando un 99.72% de clasificación global y 92.59% de bancos quebrados. Además, nos da información de las variables que mejor discriminan, bajo qué condiciones y nos indica el porcentaje de la población que se encuentra en dicho nodo. Con el SVM se obtuvo la mejor clasificación, un 99.14% de clasificación global. Clasificando el 100% de bancos quebrados y el 99.12% de bancos no quebrados. Esta investigación ayuda a difundir herramientas no tradicionales en el área financiera de América Latina y que sirve de base para impulsar dichas herramientas a otras problemáticas de clasificaciónIn Ecuador, no EWS work has been published for the banking system, in June 2017. Therefore, it was proposed to find the best method for classifying banks at risk of bankruptcy in the Ecuadorian banking system. The nearest neighbor algorithm "K-NN" was used for the missing data and for the classification: Discriminant Partial Least Squares "PLSD", Support Vector Machine "SVM" and Classification Tree (CART). The PLSD method achieved a global rating of 94.99% and a classification of bankruptcy (18 months before closing) of 18.89%. Banks that do not break down have high ratios in financial efficiency, net interest income, exercise results and coverage of 100 largest depositors. While banks that fail, have high levels of delinquency, vulnerability of assets, unproductive portfolio and operating expenses. The CART Classification Tree gave a good classification model, giving a 99.72% overall rating and 92.59% of broken banks. In addition, it gives us information about the variables that best discriminate, under what conditions and indicates the percentage of the population that is in that node. With the SVM the best classification was obtained, a 99.14% overall rating. Classifying 100% of broken banks and 99.12% of banks not broken. This research helps to disseminate nontraditional tools in the financial area of ​​Latin America and serves as a basis to promote these tools to other issues of classificatio

Artificial Intelligence in Asset Management

Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways

Empirical Analysis of Natural Gas Markets

Recent developments in the natural gas industry warrant new analysis of related issues. Environmental, social, and governance (ESG) investments have accelerated the shift away from coal as the dominant source of electricity. Its low environmental impact, reduced volume, and broad availability make liquefied natural gas (LNG) a popular alternative, during this time of transition between traditional fuels and newer options. In the United States, the shale gas revolution has made natural gas a game changer. In this book, we focus on empirical analyses of the natural gas market and its growing relevance worldwide