Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection

This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning techniques. We compare the fo recastaccuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a benchmark. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian radial basis function kernel outperforms the rest of the models for the longest forecast horizons. We also find that machine learning methods improve their forecasting accuracy with respect to linear models as forecast horizons increase. This results shows the suitability of SVR for medium and long term forecasting.Peer ReviewedPostprint (published version

Assessment of the effect of the financial crisis on agents’ expectations through symbolic regression

Agents’ perceptions on the state of the economy can be affected during economic crises. Tendency surveys are the main source of agents’ expectations. The main objective of this study is to assess the impact of the 2008 financial crisis on agents’ expectations. With this aim, we evaluate the capacity of survey-based expectations to anticipate economic growth in the United States, Japan, Germany and the United Kingdom. We propose a symbolic regression (SR) via genetic programming approach to derive mathematical functional forms that link survey-based expectations to GDP growth. By combining the main SR-generated indicators, we generate estimates of the evolution of GDP. Finally, we analyse the effect of the crisis on the formation of expectations, and we find an improvement in the capacity of agents’ expectations to anticipate economic growth after the crisis in all countries except Germany.Peer ReviewedPostprint (author's final draft

La acción delictiva a través de la informática en Colombia: el caso particular del lavado de activos y la lucha institucional contra su configuración

Artículo de investigaciónProblemática asociada a la inserción de capitales al circuito económico legal, de dineros provenientes de mafias delictivas. Exponiendo la verificación y uso de las TIC para realizar el delito, y consecuentemente, la manera como las autoridades nacionales actúan a partir de un tipo de legislación, para su prevención, detección y sanción, dejando como reflexión lo débil que resulta el sistema jurídico-institucional colombiano.1. La relación entre el lavado de activos y el uso de las Tecnologías de la Información y la Comunicación 2. Aproximación al concepto de delito informático 3. Legislación colombiana contra los delitos informáticos 4. Legislación colombiana contra el lavado de activos y el financiamiento del terrorismo: penalización del delito 5. Análisis jurisprudencial sobre el delito de lavado de activos 6. Las débiles posibilidades institucionales del Estado colombiano y apuntes para su mejora Conclusiones BibliografíaPregradoAbogad

Small and large strain monitoring of unsaturated soil behavior by means of multiaxial testing and shear wave propagation

The deformation and strength behavior of dry and saturated soils is controlled by the effective stresses as defined by Terzaghi. However, Terzaghi’s definition of the effective stresses fails for unsaturated soils, as capillarity force influence is also important. The effects of capillarity forces in soil are evaluated by the concept of matrix suction. Several techniques are used to evaluate soil suction however their applications involve difficult calibrations and tedious methodology. Furthermore, suction is a microscopic property and it is influenced by interparticle soil attraction, which can change by sampling disturbance. This research program evaluates the effect of suction on stiffness and strength of soils at small strain (at constant fabric) and large strain (with fabric changes) levels. The phenomena are studied using a modified oedometer cell and a multi-axial device with matric suction control that have been equipped with bender elements for shear-wave velocity measurements. The test program consists on testing dry and unsaturated specimens under different boundary conditions: Ko-loading and multi-axial loading. To test the Ko-loading condition, the soil is loaded in the oedometer cell while the bender-elements monitor the changes in state of stresses by evaluating the changes in the velocity of wave propagation. Similarly, triaxial compression and conventional triaxial compression tests, along with monitoring of shear wave velocities, are conducted on 10-cm side cubical specimens of reconstituted soil specimens to study the stress-strain behavior of an unsaturated soil over a range of degrees of suctions and stress paths and the effect they have on the propagation velocity of shear waves. Results show the adequacy of methods and equipment used in this investigation to monitor the behavior of unsaturated soils under the application of a range of suctions and several stress paths. Experimental results are analyzed using simple, yet robust wave propagation models and geo-material behavior. Their interpretation bring a better understanding to low and large strain-stress behavior of near sub-surface soils. Results provide a stronger base for the development of models for the imaging of near-surface geo-materials using elastic wave-based imaging techniques and for better interpretation of geotechnical models of design

Quantification of survey expectations by means of symbolic regression via genetic programming to estimate economic growth in central and eastern european economies

Tendency surveys are the main source of agents' expectations. This study has a twofold aim. First, it proposes a new method to quantify survey-based expectations by means of symbolic regression (SR) via genetic programming. Second, it combines the main SR-generated indicators to estimate the evolution of GDP, obtaining the best results for the Czech Republic and Hungary. Finally, it assesses the impact of the 2008 financial crisis, finding that the capacity of agents' expectations to anticipate economic growth in most Central and Eastern European economies improved after the crisis.Peer ReviewedPostprint (author's final draft

Empirical modelling of survey-based expectations for the design of economic indicators in five European regions

This is a post-peer-review, pre-copyedit version of an article published in Empirica. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10663-017-9395-1”.In this study we use agents’ expectations about the state of the economy to generate indicators of economic activity in twenty-six European countries grouped in five regions (Western, Eastern, and Southern Europe, and Baltic and Scandinavian countries). We apply a data-driven procedure based on evolutionary computation to transform survey variables in economic growth rates. In a first step, we design five independent experiments to derive a formula using survey variables that best replicates the evolution of economic growth in each region by means of genetic programming, limiting the integration schemes to the main mathematical operations. We then rank survey variables according to their performance in tracking economic activity, finding that agents’ ‘‘perception about the overall economy compared to last year’’ is the survey variable with the highest predictive power. In a second step, we assess the out-of-sample forecast accuracy of the evolved indicators. Although we obtain different results across regions, Austria, Slovakia, Portugal, Lithuania and Sweden are the economies of each region that show the best forecast results. We also find evidence that the forecasting performance of the survey-based indicators improves during periods of higher growth.This is a post-peer-review, pre-copyedit version of an article published in Empirica. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10663-017-9395-1”.Peer ReviewedPostprint (author's final draft

Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model

This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism demand to all seventeen regions of Spain, the performance of the proposed model is assessed in a multiple-step-ahead forecasting comparison. The results of the experiment in a multivariate setting show that the Gaussian process regression model significantly improves the forecasting accuracy of a multi-layer perceptron neural network used as a benchmark. The results reveal that incorporating the connections between different markets in the modelling process may prove very useful to refine predictions at a regional level.Peer ReviewedPostprint (author's final draft

Data pre-processing for neural network-based forecasting: does it really matter?

This study aims to analyze the effects of data pre-processing on the forecasting performance of neural network models. We use three different Artificial Neural Networks techniques to predict tourist demand: multi-layer perceptron, radial basis function and the Elman neural networks. The structure of the networks is based on a multiple-input multiple-output (MIMO) approach. We use official statistical data of inbound international tourism demand to Catalonia (Spain) and compare the forecasting accuracy of four processing methods for the input vector of the networks: levels, growth rates, seasonally adjusted levels and seasonally adjusted growth rates. When comparing the forecasting accuracy of the different inputs for each visitor market and for different forecasting horizons, we obtain significantly better forecasts with levels than with growth rates. We also find that seasonally adjusted series significantly improve the forecasting performance of the networks, which hints at the significance of deseasonalizing the time series when using neural networks with forecasting purposes. These results reveal that, when using seasonal data, neural networks performance can be significantly improved by working directly with seasonally adjusted levels.Peer ReviewedPostprint (author's final draft

Propuesta de una herramienta de valoración por los métodos de descuento de flujos de efectivo y valoración relativa. Caso Banco Bac San José, S.A.

Proyecto de Graduación (Maestría en Administración de Empresas con énfasis Finanzas) Instituto Tecnológico de Costa Rica. Escuela de Administración de Empresas, 2017.The dynamism seen in the Costa Rican baking sector in terms of mergers and acquisitions (M&A) deals in the last decade coupled with the increased presence of global banks in Costa Rica, which could lead to more deals, motivated this research project. The research is about a proposal of a valuation model involving the valuation methods of discounted cash flows and relative valuation, which was validated with the case of Banco Bac José, S.A. (Bac San José). This model is intended to be used in the event of a M&A deal. It is based on Bac San José´s audited financial statements as well as data on reference interest rates. The data was analyzed through vertical and horizontal analysis aiming to identify historical associations and trends that facilitate the forecast of Bac San José´s future cash flows and profits which are required for the valuation methods. The forecasts also reflect the analysis of Bac San José´s current and future strategy as well as the analysis of expectations of future interest rates in colones and dollars- The valuation methods suggested different values for Bac San José although similar. The discounted cash flows suggested a value of nearly USD1.550 million while the relative valuation suggested a value of around USD1.480 million, with a difference close to USD70 million. Nevertheless, the results of both methods should be considered and therefore in the event of a real M&A deal the price of Bac San José should be somewhere between USD1.480 million and USD1.550 million

Evolutionary computation for macroeconomic forecasting

The final publication is available at Springer via http://dx.doi.org/10.1007/s10614-017-9767-4The main objective of this study is twofold. First, we propose an empirical modelling approach based on genetic programming to forecast economic growth by means of survey data on expectations. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, deriving mathematical functional forms that approximate the target variable. The set of empirically-generated proxies of economic growth are used as building blocks to forecast the evolution of GDP. Second, we use these estimates of GDP to assess the impact of the 2008 financial crisis on the accuracy of agents’ expectations about the evolution of the economic activity in four Scandinavian economies. While we find an improvement in the capacity of agents’ to anticipate economic growth after the crisis, predictive accuracy worsens in relation to the period prior to the crisis. The most accurate GDP forecasts are obtained for Sweden.Peer ReviewedPostprint (author's final draft