Genetic algorithms and applications to finance

Kingdon, Jason; Feldman, Konrad

doi:10.1080/13504869500000006

Cited by 17 publications

(8 citation statements)

References 32 publications

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“…Davis (1994) muestra una aplicación de algoritmos genéticos en la calificación de créditos bancarios que resultan mejor que otros métodos, como las redes neuronales, debido a la transparencia de los resultados obtenidos. Kingdon y Feldman (1995) usaron algoritmos genéticos para hallar reglas que pronosticaran la bancarrota de las empresas, estableciendo relaciones entre las distintas razones financieras. Bauer (1994) utilizó algoritmos genéticos para desarrollar técnicas de transacción que indicaran la asignación mensual de montos de inversión en dólares y marcos; Pereira (1996) los utilizó para encontrar los valores óptimos de los parámetros usados por tres reglas de transacción distintas para el tipo de cambio de dólar estadounidense/dólar australiano; los parámetros obtenidos mostraron resultados intramuestrales positivos, los cuales disminuyeron al aplicar las reglas fuera de la muestra, aun cuando continuaron siendo rentables.…”

Section: Algoritmos Genéticosunclassified

Predicción de variaciones en el precio del petróleo con el modelo de optimización ARIMA, innovando con fuerza bruta operacional

Parisi-Fernández

Améstica-Rivas

Chileno-Trujillo

2019

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La presente investigación evalúa la eficacia del modelo ARIMA multivariable optimizado con fuerza bruta para el caso del precio del petróleo, con el fin de predecir el comportamiento de las acciones a la semana siguiente de una última fecha analizada. El objetivo es construir un modelo predictivo con un porcentaje de predicción de signo superior al 50% y, por consiguiente, mejorar la toma de decisiones para los inversionistas. Se utilizó la información disponible de la cotización del petróleo y acciones del portal web de finanzas de tres empresas, Exxon Mobil, Gazprom y Rosneft, comprendidos en el periodo del 4 de febrero de 2011 al 4 de febrero de 2016, durante el cual se pudo observar la variación de los precios, y así poder comparar los datos reales con las variaciones pronosticadas a través del modelo. Se utilizaron 12 variables, generando 100.000 iteraciones aleatorias con fuerza bruta, dado que la optimización por simplex o solver limitaba la obtención de algún resultado. Con la técnica de fuerza bruta se pudo establecer una capacidad de predicción superior al 60% para el caso del precio del petróleo y las acciones de empresas petroleras.

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Section: Algoritmos Genéticosunclassified

Predicción de variaciones en el precio del petróleo con el modelo de optimización ARIMA, innovando con fuerza bruta operacional

Parisi-Fernández

Améstica-Rivas

Chileno-Trujillo

2019

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“…Genetic algorithms (GAs) try to imitate the Darwinian logic of evolution through a natural selection while selecting predictors of failure. This approach was first implemented to forecast bankruptcy by Kingdon and Feldman (1995), according to whom GAs can be considered as a class of probabilistic optimisation techniques. More specifically, GAs are similar to the Monte Carlo simulations, drawing a set of inputs from different domains randomly and calculating a result from these inputs.…”

Section: Soft Computing Modelsmentioning

confidence: 99%

“…Researchers have suggested several models to predict bankruptcy, such as discriminant analysis (Beaver, 1966;Altman, 1968), logit and probit models (Ohlson, 1980;Charitou et al, 2004;Jones and Hensher, 2007), artificial neural networks (Wilson and Sharda, 1994;Serrano-Cinca, 1997;Charalambous et al, 2000), genetic algorithms (Kingdon and Feldman, 1995), survival analysis (Luoma and Laitinen, 1991;Shumway, 2001; Gepp and Kumar, 2008) and recursive partitioning algorithm (RPA) (Marais et al, 1984;Frydman et al, 1985).…”

Section: Introductionmentioning

confidence: 99%

Forecasting and preventing bankruptcy: A conceptual review

Bisogno¹,

Restaino²,

Carlo³

2018

Afr. J. Bus. Manage.

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The recent global financial crisis has caused the failure of many firms in several countries, renewing the interest of the literature towards forecasting models for default risk. Although these models have been developed since the 1960s, studies have been published in increased numbers during the last decades, proposing new approaches or comparing different existing models to understand which of them have the best predictive power. However, the recent financial crisis has underlined how important adopting early warning systems are. Although forecasting business failure and implementing warning systems are conceptually different, there is a risk of overlapping these concepts. Accordingly, this study aims to review the literature on these topics by using a conceptual review methodology, at the same time considering the trend to implement approaches aiming to facilitate corporate rescue.

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“…4 Genetic algorithms (Holland, 1975;Kingdon and Feldman, 1995;Varetto, 1998) are processes of optimisation inspired by concepts of natural evolution proposed by Darwin, in which there are two inevitable processes: genetic selection and reproduction. The first identifies which of the components of the population survive and reproduce, and the second one includes the genetic recombination.…”

Section: Innovative Modelsmentioning

confidence: 99%

The ability of the turnaround index to assess going concern assumptions: evidence from its application to Italian listed companies

Provasi

Riva

2016

GBER

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In times of crisis, an entrepreneur often must decide whether to liquidate the company and the whole business or to renew it by activating a turnaround process. A tool increasingly used in international practice is the turnaround index, which can determine whether a company can continue its activities in the future according to the going concern assumption, which is the index measured as a function of the economic and financial performance of the company. This measure can take values between 0.1, for satisfactory performance and therefore unlikely to turn around, and 0.9, for unsatisfactory performance and therefore a high propensity to turnaround. The index determines whether there is a higher or lower risk for a company to enter into crisis. The aim of this paper is to test the effectiveness of the turnaround index in assessing the turnaround urgency by choosing a sample of the Italian companies.

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Genetic algorithms and applications to finance

Cited by 17 publications

References 32 publications

Predicción de variaciones en el precio del petróleo con el modelo de optimización ARIMA, innovando con fuerza bruta operacional

Predicción de variaciones en el precio del petróleo con el modelo de optimización ARIMA, innovando con fuerza bruta operacional

Forecasting and preventing bankruptcy: A conceptual review

The ability of the turnaround index to assess going concern assumptions: evidence from its application to Italian listed companies

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