A variable impact neural network analysis of dividend policies and share prices of transportation and related companies

Abdou, Hussein A.; Pointon, John; El‐Masry, Ahmed; Olugbode, Moji; Lister, Roger J.

doi:10.1016/j.intfin.2012.04.008

Cited by 15 publications

(3 citation statements)

References 29 publications

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“…Both Networks are theoretically like k-Nearest Neighbour known as k-NN but with completely different applications. Furthermore, Abdou et al, (2012Abdou et al, ( , 2021 explained that GRNN does not require various stationarity tests that regression family models would require. Figure 5 presents an example of a GRNN architecture.…”

Section: Generalised Regression Neural Networkmentioning

confidence: 99%

The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning

2022

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This study aims to explore the role of cryptocurrencies and the US dollar in predicting oil prices pre and during COVID-19 pandemic. The study uses three neural network models (i.e., Support vector machines, Multilayer Perceptron Neural Networks and Generalized regression neural networks (GRNN)) over the period from January 1, 2018, to July 5, 2021. Our results are threefold. First, our results indicate Bitcoin is the most influential in predicting oil prices during the bear and bull oil market before COVID-19 and during the downtrend during COVID-19. Second, COVID-19 variables became the most influential during the uptrend, especially the number of death cases. Third, our results also suggest that the most accurate model to predict the price of oil under the conditions of uncertainty that prevailed in the world during the bear and bull prices in the wake of COVID-19 is GRNN. Though the best prediction model under normal conditions before COVID-19 during an uptrend is SVM and during a downtrend is GRNN. Our results provide crucial evidence for investors, academics and policymakers, especially during global uncertainties.

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Section: Generalised Regression Neural Networkmentioning

confidence: 99%

The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning

2022

Self Cite

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“…We found that neural networks, namely GRNN, have been neglected in extant research. Following previous literature on other finance disciplines such as dividend policy (Abdou, Pointon, El-Masry, Olugbode, & Lister, 2012), which has found that nonlinear neural networks outperformed conventional regressions and capital structure (Abdou, Kuzmic, Pointon, & Lister, 2012;Pao, 2008), and that neural networks accomplish better model-fitting and predictions, we expect that our GRNN can enhance the quality of conventional regressions and provide results that are more robust. The following hypothesis is therefore proposed:…”

Section: Corporate Governance-earnings Management Nexus Statisticalmentioning

confidence: 74%

Corporate Governance and Earnings Management Nexus: Evidence from the UK and Egypt Using Neural Networks

Abdou

Ellelly²,

Elamer

et al. 2020

SSRN Journal

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Using conventional regressions and generalized regression neural networks (GRNNs), we examine the relationship between corporate governance (CG) and earnings management (EM). We also examine whether governance quality moderates the association between EM and CG for a sample of British and Egyptian companies. Our findings show that: (a) UK firms are likely to have lower levels of EM if they: have smaller boards, are dominated by independent outside directors, and have a low percentage of female directors; (b) Egyptian firms are likely to have lower levels of EM if they: have larger boards, are dominated by independent outside directors, and have a low percentage of female directors; (c) The governance quality (control of corruption) has a significant hidden effect on EM. Since our results provide empirical evidence that the board of directors plays a vital role in mitigating EM, these findings might lead to an improvement in the credibility of financial statements for investors in both the UK and Egypt. As policy implications, our findings inform regulators and policy-makers that corruption has a very strong hidden effect on EM and that they can deter EM by controlling the corruption level in their countries.

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“…Their results documented that SVM outperforms other techniques to forecast dividend policy. Abdou et al ( 2012 ) predicted the share price and dividend yield performance of transportation globally from 2005 to 2012. They revealed that the generalized regression neural network performs well in minimizing errors and is better than the conventional regressions.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting cash holdings using supervised machine learning algorithms

Özlem

Tan

2022

Financ Innov

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This study predicts the cash holdings policy of Turkish firms, given the 20 selected features with machine learning algorithm methods. 211 listed firms in the Borsa Istanbul are analyzed over the period between 2006 and 2019. Multiple linear regression (MLR), k-nearest neighbors (KNN), support vector regression (SVR), decision trees (DT), extreme gradient boosting algorithm (XGBoost) and multi-layer neural networks (MLNN) are used for prediction. Results reveal that MLR, KNN, and SVR provide high root mean square error (RMSE) and low R2 values. Meanwhile, more complex algorithms, such as DT and especially XGBoost, derive higher accuracy with a 0.73 R2 value. Therefore, using advanced machine learning algorithms, we may predict cash holdings considerably.

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A variable impact neural network analysis of dividend policies and share prices of transportation and related companies

Cited by 15 publications

References 29 publications

The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning

The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning

Corporate Governance and Earnings Management Nexus: Evidence from the UK and Egypt Using Neural Networks

Predicting cash holdings using supervised machine learning algorithms

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