Forecasting Government Bond Spreads with Heuristic Models:Evidence from the Eurozone Periphery

This study investigates the predictability of European long-term government bond spreads through the application of heuristic and metaheuristic support vector regression (SVR) hybrid structures. Genetic, krill herd and sine–cosine algorithms are applied to the parameterization process of the SVR and locally weighted SVR (LSVR) methods. The inputs of the SVR models are selected from a large pool of linear and non-linear individual predictors. The statistical performance of the main models is evaluated against a random walk, an Autoregressive Moving Average, the best individual prediction model and the traditional SVR and LSVR structures. All models are applied to forecast daily and weekly government bond spreads of Greece, Ireland, Italy, Portugal and Spain over the sample period 2000–2017. The results show that the sine–cosine LSVR is outperforming its counterparts in terms of statistical accuracy, while metaheuristic approaches seem to benefit the parameterization process more than the heuristic ones

Reverse adaptive krill herd locally weighted support vector regression for forecasting and trading exchange traded funds

This study introduces a Reverse Adaptive Krill Herd-Locally Weighted Support Vector Regression (RKH-LSVR) model. The Reverse Adaptive Krill Herd (RKH) algorithm is a novel metaheuristic optimization technique inspired by the behavior of krill herds. In RKH-LSVR, the RKH optimizes the locally weighted Support Vector Regression (LSVR) parameters by balancing the search between local and global optima. The proposed model is applied to the task of forecasting and trading six ETF stocks on a daily basis over the period 2010–2015. The RKH-LSVR's efficiency is benchmarked against a set of traditional SVR structures and simple linear and non-linear models. The trading application is designed in order to validate the robustness of the algorithm under study and to provide empirical evidence in favor of or against the Adaptive Market Hypothesis (AMH). In terms of the results, the RKH-LSVR outperforms its counterparts in terms of statistical accuracy and trading efficiency, while the time varying trading performance of the models under study validates the AMH theory

Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the `Rush to Heuristics'

In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods that are based on weak comparisons. This ‘rush to heuristics’ does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems and aims at providing a comprehensive view of the main issues that concern the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems

A survey on financial applications of metaheuristics

Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, and the like. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This article reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. This article also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P, TRA2015-71883-REDT), FEDER, and the Universitat Jaume I mobility program (E-2015-36)

Exploiting Heterogeneous Parallelism on Hybrid Metaheuristics for Vector Autoregression Models

In the last years, the huge amount of data available in many disciplines makes the mathematical modeling, and, more concretely, econometric models, a very important technique to explain those data. One of the most used of those econometric techniques is the Vector Autoregression Models (VAR) which are multi-equation models that linearly describe the interactions and behavior of a group of variables by using their past. Traditionally, Ordinary Least Squares and Maximum likelihood estimators have been used in the estimation of VAR models. These techniques are consistent and asymptotically efficient under ideal conditions of the data and the identification problem. Otherwise, these techniques would yield inconsistent parameter estimations. This paper considers the estimation of a VAR model by minimizing the difference between the dependent variables in a certain time, and the expression of their own past and the exogenous variables of the model (in this case denoted as VARX model). The solution of this optimization problem is approached through hybrid metaheuristics. The high computational cost due to the huge amount of data makes it necessary to exploit High-Performance Computing for the acceleration of methods to obtain the models. The parameterized, parallel implementation of the metaheuristics and the matrix formulation ease the simultaneous exploitation of parallelism for groups of hybrid metaheuristics. Multilevel and heterogeneous parallelism are exploited in multicore CPU plus multiGPU nodes, with the optimum combination of the different parallelism parameters depending on the particular metaheuristic and the problem it is applied to.This work was supported by the Spanish MICINN and AEI, as well as European Commission FEDER funds, under grant RTI2018-098156-B-C53 and grant TIN2016-80565-R

Modelling and trading the Greek stock market with gene expression and genetic programing algorithms

This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter

Forecast Combination

Actualmente existen diversas metodologías de pronóstico, que van desde el conocimiento empírico hasta métodos innovadores, individuales o combinados, que demuestran resultados óptimos. Este documento se deriva de un proceso de investigación y presenta alternativas relacionadas con las combinaciones de pronósticos, utilizando metaheurísticas, por ejemplo, mediante la búsqueda tabú y la programación evolutiva para optimizar el pronóstico. El documento presenta pronósticos combinados basados en la programación evolutiva utilizando mezclas de modelos de regresión bayesiana y modelos de regresión lineal clásico, el modelo de media móvil integrado autorregresivo, el suavizado exponencial y la regresión bayesiana. El documento presenta dos artículos derivados de investigación, la primera compara el algoritmo combinado con los resultados individuales de estos modelos individuales y con la combinación de Bates y Granger utilizando un indicador de error y el valor simétrico de error absoluto medio. Esos modelos y la combinación se aplicaron a la simulación de series temporales y a un caso real de ventas de productos lácteos, generando así pronósticos combinados multiproductos tanto para la simulación como para el caso real. La nueva combinación combinada con la metaheurística evolutiva mostró mejores resultados que los de los otros que se utilizaron. La segunda investigación utiliza series de tiempo simuladas, diseñando dos metaheurísticas basadas en la lista Tabú, que aprenden de los datos con base en el comportamiento estadístico de éstos, como el cluster, así como del mismo valor optimizado del error de ajuste, y se comparan las combinaciones de pronósticos con resultados de modelos individuales a tres tipos de series de tiempo.Currently diverse forecasting methodologies exists, going from the empirical knowledge to the innovative methods, individual or combined, demonstrating optimal results. This document is derived from a research process, and presents alternatives related to forecast combinations, using metaheuristics, for example, by using Tabu search and Evolutive programing to optimize forecasting. One of the designed process consists of creating combination forecasts based on evolutionary programming using, first, a mixture of Bayesian regression models and, second, a mixture of the classical linear regression model, the autoregressive integrated moving average model, exponential smoothing and Bayesian regression. The first research compares the novel combined algorithm with the individual results of these individual models and with the Bates and Granger combination using an error indicator and the symmetrical mean absolute error value. Those models and the novel design were applied to time series simulation and to a real case of dairy products sales, thus generating multiproduct combination forecasts for both the simulation and the real case. The novel combination combined with the evolutionary metaheuristic showed better results than those of the others that were used. The second research uses simulated time series and other metaheuristic that learns from the data an statistical behavior.Tecnológico de Antioquia, Universidad Nacional de Colombia.Doctorad

Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model

This study proposes a novel bioinspired metaheuristic simulating how the coronavirus spreads and infects healthy people. From a primary infected individual (patient zero), the coronavirus rapidly infects new victims, creating large populations of infected people who will either die or spread infection. Relevant terms such as reinfection probability, super-spreading rate, social distancing measures, or traveling rate are introduced into the model to simulate the coronavirus activity as accurately as possible. The infected population initially grows exponentially over time, but taking into consideration social isolation measures, the mortality rate, and number of recoveries, the infected population gradually decreases. The coronavirus optimization algorithm has two major advantages when compared with other similar strategies. First, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Second, the approach has the ability to end after several iterations, without setting this value either. Furthermore, a parallel multivirus version is proposed, where several coronavirus strains evolve over time and explore wider search space areas in less iterations. Finally, the metaheuristic has been combined with deep learning models, to find optimal hyperparameters during the training phase. As application case, the problem of electricity load time series forecasting has been addressed, showing quite remarkable performance.Ministerio de Economía y Competitividad TIN2017-88209-C