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Predicting the returns of the US real estate investment trust market : evidence from the group method of data handling neural network
Zhang, Wendi; Li, Bin; Liew, Alan Wee-Chung; Roca, Eduardo - In: Financial innovation : FIN 9 (2023) 1, pp. 1-33
with GARCH, GMDH's accuracy is much higher, indicating that the machine learning approach can provide a highly accurate … train. Even with small training samples, GMDH can outperform GARCH in prediction accuracy. Research limitations …Purpose: The Group Method of Data Handling (GMDH) neural network has demonstrated good performance in data mining …
Persistent link: https://www.econbiz.de/10014363989
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Modelling EPS of an Indian auto major
Chaudhuri, Ranjan - In: International journal of strategic business alliances 3 (2013/2014) 4, pp. 250-262
Persistent link: https://www.econbiz.de/10011456004
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A genetic-based hybrid approach to corporate failure prediction
Lin, Ping-Chen; Chen, Jiah-Shing - In: International Journal of Electronic Finance 2 (2008) 2, pp. 241-255
This paper proposes a genetic-based hybrid approach to predict the possibility of corporate failure. We use Genetic Algorithm (GA) to select the critical variables set and optimise the weight of each classifier for integrating the best features of several classification approaches (such as...
Persistent link: https://www.econbiz.de/10008539365
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A genetic-based hybrid approach to corporate failure prediction
Lin, Ping-Chen; Chen, Jiah-Shing - In: International Journal of Electronic Finance 2 (2008) 2, pp. 241-255
This paper proposes a genetic-based hybrid approach to predict the possibility of corporate failure. We use Genetic Algorithm (GA) to select the critical variables set and optimise the weight of each classifier for integrating the best features of several classification approaches (such as...
Persistent link: https://www.econbiz.de/10005753702
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Paradigms of Intelligent Systems
ANDRISESCU, Dana Ramona - In: Informatica Economica XI (2007) 2, pp. 90-94
This paper approaches the subject of paradigms for the categories of intelligent systems. First we can look at the term paradigm in its scientific meaning and then we make acquaintance with the main categories of intelligent systems (expert systems, intelligent systems based on genetic...
Persistent link: https://www.econbiz.de/10009416318
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A Conexionist Intelligent System for Accounting
APARASCHIVEI, Florin - In: Informatica Economica XII (2008) 1, pp. 71-76
Neural networks are a computing paradigm developed from artificial intelligence and brain modelling’s fields, which lately has become very popular in business. Many researchers are seeing neural networks systems as solutions to business problems like modelling and forecasting, but...
Persistent link: https://www.econbiz.de/10009416312
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The modeling and comparison of GMDH and RBF artificial neural networks in forecasting consumption of petroleum products in the agricultural sector
Abbasian, Mojtaba; Shahraki, Ali Sardar; Shahraki, Javad - In: Iranian journal of economic studies : IJES 8 (2019) 1, pp. 91-105
Persistent link: https://www.econbiz.de/10012055557
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Spillover effects between US and major European stock markets
Al-Zeaud, Hussein Ali - In: American journal of finance and accounting 3 (2013/14) 2/4, pp. 172-184
Persistent link: https://www.econbiz.de/10010403630
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Volatility analysis of REITs : empirical evidence for the EU peripheral countries
Begiazi, Kyriaki; Asteriou, Dimitrios - In: International journal of banking, accounting and finance 6 (2015) 2, pp. 87-98
Persistent link: https://www.econbiz.de/10011607417
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The price–volume relationship in Gulf Cooperation Council stock markets
Abdalla, Abdelgader M.A.; Al-Khouri, Ritab S. - In: International Journal of Economics and Business Research 3 (2011) 1, pp. 15-28
This paper examines the empirical relationship among stock return, trading volume and volatility for the seven stock markets that comprise the Gulf Cooperation Council. The dataset includes seven national stock markets for the period spanning from 1 July 2004 to 3 September 2008. Granger...
Persistent link: https://www.econbiz.de/10009352448