Efficient Prediction of Stock Market Indices Using Adaptive Neural Network

Bebarta, Dwiti Krishna; Rout, Ajit Kumar; Biswal, Birendra; Dash, P.K.

doi:10.1007/978-81-322-0491-6_28

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…The complexity of financial series has been widely acknowledged, with Bebarta et al (2012) and S. P. Das and Padhy (2018) both noting the challenge it presents. Cao and Tay (2001) developed a seminal method for applying computational methods with nonlinear characteristics to predict future contracts' returns, outlining the advantages and disadvantages of the models used.…”

Section: Related Literaturementioning

confidence: 99%

“…With the increasing participation of individuals in the financial market, new techniques, perspectives, and scientific research are continually emerging, providing renewed inspiration for both innovators and traditional stakeholders (investors, analysts, banks, managers, regulatory agencies, etc.). It is widely acknowledged that stocks are a high‐risk (Vui et al, 2013) and relatively appealing asset class to predict (Bebarta et al, 2012; S. P. Das & Padhy 2018), given their varying expressions of the return‐liquidity‐volatility triad.…”

Section: Introductionmentioning

confidence: 99%

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A review of artificial intelligence quality in forecasting asset prices

Barboza

Silva

Fiorucci

2023

Journal of Forecasting

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Researchers and practitioners globally, from a range of perspectives, acknowledge the difficulty in determining the value of a financial asset. This subject is of utmost importance due to the numerous participants involved and its impact on enhancing market structure, function, and efficiency. This paper conducts a comprehensive review of the academic literature to provide insights into the reasoning behind certain conventions adopted in financial value estimation, including the implementation of preprocessing techniques, the selection of relevant inputs, and the assessment of the performance of computational models in predicting asset prices over time. Our analysis, based on 109 studies sourced from 10 databases, reveals that daily forecasts have achieved average error rates of less than 1.5%, while monthly data only attain this level in optimal circumstances. We also discuss the utilization of tools and the integration of hybrid models. Finally, we highlight compelling gaps in the literature that provide avenues for further research.

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Section: Related Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A review of artificial intelligence quality in forecasting asset prices

Barboza

Silva

Fiorucci

2023

Journal of Forecasting

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“…where a j represents the output of the previous layer neuron, W ij is the weight between the ith an d jth neuron, and W io is the input bias of this neuron. In this work, the MLP network is trained using the back propagation method, and the detailed explanation is presented in [26,27].…”

Section: Bayes and Naive Bayes Classifiersmentioning

confidence: 99%

Fault Detection and Classification of Shunt Compensated Transmission Line Using Discrete Wavelet Transform and Naive Bayes Classifier

et al. 2020

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This paper presents the methodology to detect and identify the type of fault that occurs in the shunt compensated static synchronous compensator (STATCOM) transmission line using a combination of Discrete Wavelet Transform (DWT) and Naive Bayes (NB) classifiers. To study this, the network model is designed using Matlab/Simulink. Different types of faults, such as Line to Ground (LG), Line to Line (LL), Double Line to Ground (LLG) and the three-phase (LLLG) fault, are applied at disparate zones of the system, with and without STATCOM, considering the effect of varying fault resistance. The three-phase fault current waveforms obtained are decomposed into several levels using Daubechies (db) mother wavelet of db4 to extract the features, such as the standard deviation (SD) and energy values. Then, the extracted features are used to train the classifiers, such as Multi-Layer Perceptron Neural Network (MLP), Bayes and the Naive Bayes (NB) classifier to classify the type of fault that occurs in the system. The results obtained reveal that the proposed NB classifier outperforms in terms of accuracy rate, misclassification rate, kappa statistics, mean absolute error (MAE), root mean square error (RMSE), percentage relative absolute error (% RAE) and percentage root relative square error (% RRSE) than both MLP and the Bayes classifier.

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