Forecasting the Volatility of Stock Index Returns: A Stochastic Neural Network Approach

Slim, Chokri

doi:10.1007/978-3-540-24767-8_98

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…Ao [10] designs a simplified automated system to study the correlation between the US market and the Asian markets by employing the evolutionary computation to simulate the markets interactive dynamics. Slim [11] proposes a stochastic neural network (SNN) to the modelling and forecasting the time varying conditional volatility of the TUNINDEX returns. The empirical analysis shows that out-of-simple volatility forecasts of the SNN are superior to forecasts of traditional linear methods (GARCH) and also better than merely assuming a conditional Gaussian distribution.…”

Section: Introductionmentioning

confidence: 99%

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Xiao¹,

Xiao²,

Lu³

et al. 2014

IJCIS

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Stock e-exchange prices forecasting is an important financial problem that is receiving increasing attention. This study proposes a novel three-stage nonlinear ensemble model. In the proposed model, three different types of neural-network based models, i.e. Elman network, generalized regression neural network (GRNN) and wavelet neural network (WNN) are constructed by three non-overlapping training sets and are further optimized by improved particle swarm optimization (IPSO). Finally, a neural-network-based nonlinear meta-model is generated by learning three neural-network based models through support vector machines (SVM) neural network. The superiority of the proposed approach lies in its flexibility to account for potentially complex nonlinear relationships. Three daily stock indices time series are used for validating the forecasting model. Empirical results suggest the ensemble ANNs-PSO-GA approach can significantly improve the prediction performance over other individual models and linear combination models listed in this study.

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Section: Introductionmentioning

confidence: 99%

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Xiao¹,

Xiao²,

Lu³

et al. 2014

IJCIS

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“…countries: Chile, Brazil y México (Slim, 2004;Tseng et al 2008;Mo & Wang, 2013;Sermpinis et al 2013;Kristjanpoller et al 2014).…”

Section: Forecast Volatilitymentioning

confidence: 99%

Use of Machine Learning in Volatility: A Review Using K-Means

Muñoz

Castañeda²

2023

rev.univ.empresa

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Recently, the use of machine learning (ML) in scientific disciplines has experienced an unprecedented increase. Finance has not been an exception. Several works have been published in recent years using mltechniques. However, one of the topics with the least number of developed papers is volatility in this context. Nevertheless, the data analyzed here suggest changes regarding this issue. Data obtained from the Web of Science database show that between 2001 and 2010 there were 33 published papers associated with this topic. Surprisingly, between 2019 and 2023, 189 manuscripts have been published related to this topic. The purpose of this work is to review the works related to the applications of ml in volatility. For this, a classification of the main proposals on this topic is proposed following a narrative methodology, accompanied by a statistical and bibliometric analysis in which novel techniques such as K-means were used. The results are suggestive. Although most papers focus on volatility prediction through neural networks and support vector machines, there is a lack of studies related to volatility transmission, calibration of volatility surfaces,and corporate finance. Moreover, the obtained results indicate that there is a gap in the production of worksrelated to these topics in finance and economics specialized journals.

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“…This superiority of the neuron model is explained by the fact that this model is a better alternative to traditional linear methods which have many methodological constraints and biases (Slim, 2004).…”

Section: Robustness Testsmentioning

confidence: 99%

Impact of Cultural and Environmental Factors on the Adoption of AAOIFI Accounting Standards in MENA Countries: Logistic Regression and Neural Networks

Aloui¹,

Hrichi²,

Slim³

2022

APMAJ

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The purpose of this study was to investigate the effect of cultural and environmental factors on the decision of adoption of Accounting and Auditing Organization for Islamic Financial Institutions (AAOIFI) in MENA countries. Precisely, we empirically diagnosed the impact of culture, the origin of the legal system, economic growth, the presence of National Shariah Council and the political freedom on the adoption of AAOIFI. Data from 44 countries belonging to the MENA region was used and compared between 26 countries adopting the standards of AAOIFI and 18 not adopting during the period 2007-2017. The paper used a Logit regression analysis to model the binary decision to adopt the AAOIFI or not. Additionally, neural networks were performed to test the robustness of the results of the logistic regression. We found that all macroeconomic factors affected the decision of adoption. Furthermore, the neuronal model showed that the level of education and the political freedom were the most important factors influencing the adoption of AAOIFI. Keywords: AAOIFI standards, culture, legal origins of law, political freedom, economic growth, logistic regression, neuronal networks.

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Forecasting the Volatility of Stock Index Returns: A Stochastic Neural Network Approach

Cited by 8 publications

References 11 publications

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Use of Machine Learning in Volatility: A Review Using K-Means

Impact of Cultural and Environmental Factors on the Adoption of AAOIFI Accounting Standards in MENA Countries: Logistic Regression and Neural Networks

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