A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Bahrammirzaee, Arash

doi:10.1007/s00521-010-0362-z

Cited by 442 publications

(210 citation statements)

References 224 publications

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“…Beaver (1966) was the first author to introduce financial ratios into bankruptcy prediction. In recent decades there have been a great number of bankruptcy prediction studies based on financial ratios using different statistical and machine-learning techniques, these are reviewed in Altman (1993), Balcaen and Ooghe (2006), Kumar and Ravi (2007), Bahrammirzaee (2010), Verikas et al (2010). Recent papers (e.g.…”

Section: Introductionmentioning

confidence: 99%

Chinese companies distress prediction: an application of data envelopment analysis

Crook

Andreeva

2014

Journal of the Operational Research Society

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Bankruptcy prediction is a key part in corporate credit risk management. Traditional bankruptcy prediction models employ financial ratios or market prices to predict bankruptcy or financial distress prior to its occurrence. We investigate the predictive accuracy of corporate efficiency measures along with standard financial ratios in predicting corporate distress in Chinese companies. Data Envelopment Analysis (DEA) is used to measure corporate efficiency.In contrast to previous applications of DEA in credit risk modelling where it was used to generate a single efficiency -Technical Efficiency, we assume Variable Returns to Scale, and decompose Technical Efficiency into Pure Technical Efficiency and Scale Efficiency. These measures are introduced into Logistic Regression to predict the probability of distress, along with the levels of Returns to Scale. Effects of efficiency variables are allowed to vary across industries through the use of interaction terms, whilst the financial ratios are assumed to have the same effects across all sectors. The results show that the predictive power is improved by this corporate efficiency information.

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Section: Introductionmentioning

confidence: 99%

Chinese companies distress prediction: an application of data envelopment analysis

Crook

Andreeva

2014

Journal of the Operational Research Society

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“…On the other hand, different neuro-evolutionary approaches have been successfully applied to a variety of benchmark problems and real-world classification tasks [25][26][27]32]. Our neuro-evolutionary algorithm, too, has been already tested and applied with success to several real-world problems, showing how such an approach can be useful in different classification problems, like automated trading strategy optimization [3,28], incipient fault diagnosis in electrical drives [29], automated diagnosis of skin diseases [30], etc.…”

Section: Neuro-evolutionary Classifiersmentioning

confidence: 99%

A Neuro-Evolutionary Approach to Electrocardiographic Signal Classification

Azzini

Dragoni

Tettamanzi

2014

Evolution, Complexity and Artificial Life

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Abstract. This chapter presents an evolutionary ANN classifier system as a heartbeat classification algorithm designed according to the rules of the PhysioNet/Computing in Cardiology Challenge 2011 [7], whose aim is to develop an efficient algorithm able to run within a mobile phone, that can provide useful feedback in the process of acquiring a diagnostically useful 12-lead Electrocardiography (ECG) recording. The method used to solve this problem is to apply a very powerful natural computing analysis tool, namely evolutionary neural networks, based on the joint evolution of the topology and the connection weights relying on a novel similarity-based crossover. The chapter focuses on discerning between usable and unusable electrocardiograms tele-medically acquired from mobile embedded devices. A preprocessing algorithm based on the Discrete Fourier Trasform has been applied before the evolutionary approach in order to extract the ECG feature dataset in the frequency domain. Finally, a series of tests has been carried out in order to evaluate the performance and the accuracy of the classifier system for such a challenge.

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“…Among supervised structures, ANN is the most commonly used one and has been successfully adopted for both short-and long-term forecasting of time series where normally a defined error function, which is typically mean square error, is minimized using a gradient descent method [12]. A weight is being assigned to connect every two nodes.…”

Section: B Artificial Neural Networkmentioning

confidence: 99%

Short-term wind speed forecasting by an adaptive network-based fuzzy inference system (ANFIS): an attempt towards an ensemble forecasting method

Yousefi

Hooshyar

Remezani

et al. 2015

Int. J. Adv. Intell. Informatics

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Accurate Wind speed forecasting has a vital role in efficient utilization of wind farms. Wind forecasting could be performed for long or short time horizons. Given the volatile nature of wind and its dependent on many geographical parameters, it is difficult for traditional methods to provide a reliable forecast of wind speed time series. In this study, an attempt is made to establish an efficient adaptive network-based fuzzy interference (ANFIS) for short-term wind speed forecasting. Using the available data sets in the literature, the ANFIS network is constructed, tested and the results are compared with that of a regular neural network, which has been forecasted the same set of dataset in previous studies. To avoid trial-and-error process for selection of the ANFIS input data, the results of autocorrelation factor (ACF) and partial auto correlation factor (PACF) on the historical wind speed data are employed. The available data set is divided into two parts. 50% for training and 50% for testing and validation. The testing part of data set will be merely used for assessing the performance of the neural network which guarantees that only unseen data is used to evaluate the forecasting performance of the network. On the other hand, validation data could be used for parameter-setting of the network if required. The results indicate that ANFIS could not outperform ANN in short-term wind speed forecasting though its results are competitive. The two methods are hybridized, though simply by weightage, and the hybrid methods shows slight improvement comparing to both ANN and ANFIS results. Therefore, the goal of future studies could be implementing ANFIS and ANNs in a more comprehensive ensemble method which could be ultimately more robust and accurate

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A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Cited by 442 publications

References 224 publications

Chinese companies distress prediction: an application of data envelopment analysis

Chinese companies distress prediction: an application of data envelopment analysis

A Neuro-Evolutionary Approach to Electrocardiographic Signal Classification

Short-term wind speed forecasting by an adaptive network-based fuzzy inference system (ANFIS): an attempt towards an ensemble forecasting method

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