Predicting mutual fund performance using artificial neural networks

Indro, Daniel C.; Jiang, Christine X.; Patuwo, B. Eddy; Zhang, G. P.

doi:10.1016/s0305-0483(98)00048-6

Cited by 81 publications

(31 citation statements)

References 29 publications

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“…The nonlinear structures of neural networks have been very useful in forecasting (Wasserman 1989;Indro et al 1999). Muzaffer (2004) also indicated that neural network had numerous advantages, such as the capability of being adaptive and the capability to learn to identify patterns between dependent and independent variables in a data set.…”

Section: Neural Networkmentioning

confidence: 98%

Modeling and forecasting tourism demand: the case of flows from Mainland China to Taiwan

Moutinho

Huarng

et al. 2008

Serv Bus

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Section: Neural Networkmentioning

confidence: 98%

Modeling and forecasting tourism demand: the case of flows from Mainland China to Taiwan

Moutinho

Huarng

et al. 2008

Serv Bus

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“…Stated differently, in a linear regression run over time between the realized excess returns and the expected excess returns, the y-intercept (alpha) is expected to yield a nil value. A positive alpha indicates good performance (Indro, Jiang, Patuwo & Zhang, 1999), whereas a negative alpha signals inadequate performance. A significant positive alpha indicates consistent actual returns above the returns required according to the systematic risk of the share and therefore represents superior performance.…”

Section: Jensen's Alphamentioning

confidence: 99%

Indicators of corporate financial success: Similar studies in South Africa and the USA, different results

Wet

Erasmus

2011

Acta Commercii

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Purpose: The aim of this study was to test whether findings by Johnson and Soenen (2003) regarding indicators of successful companies in the USA also apply to South African JSE-listed companies.Problem investigated: To date, no South African study has tried to determine the indicators of the financial success of local companies specifically along the lines of Johnson and Soenen's (2003) study. Determining whether the indicators found to be most highly significant in the US study also apply in South Africa would constitute valuable information in the South African context. Approach:The study tested the significance of the linear relationships between possible indicators of financial success and three measures of financial success for South African companies and compared them to the results of the US study. Findings:The findings revealed that the relationships are far less significant for South African companies. Value of research:The study highlighted the fact that indicators of financial success for US companies are not necessarily contributors to the success of South African listed companies and that models developed in different environments should therefore be used with caution when applied in South Africa. Conclusion:Further studies need to be undertaken in order to identify the most significant South African indicators of corporate financial success.

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“…From this point, artificial neural network models are broadly used to perform forecasting in short, medium, and long term predictions. (Jhee & Lee, 1993;Chiang, Urban, & Baldridge, 1996;Stern, 1996;Hill, O'Connor, & Remus, 1996;Kohzadi, Boyd, Kermanshahi, & Kaastra, 1996;Indro, Jiang, Patuwo, & Zhang, 1999;Nayak, Sudheer, Rangan, & Ramasastri, 2004;Karunasinghe & Liong, 2006;Oliveira & Meira, 2006;Gareta, Romeo, & Gil, 2006. For example, Peng et al (1992 utilized minimumdistance-based identification for the appropriate load data and degree of temperature used for training of the ANN.…”

Section: Forecasting the Electricity Demand And Strategic Planningmentioning

confidence: 99%

Forecasting the Monthly Electricity Demand of Georgia using Competitive Models and Advices for the Strategic Planning

Demir

Özsoy

2014

IJAREMS

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Electricity is a kind of energy that consumption increases rapidly. However, Electricity is used from the residences to the industry everywhere. This non-storable energy needs to be predicted precisely and supplied sufficiently. For this reason in this research electricity demand of Georgia is predicted using comparative models as Box Jenkins and Neural Network. By this way, an accurate model was developed to perform a strategic plan. For the calculation of significance level, MAPE is utilized to see error. It was seen that artificial neural network multilayer perceptron model provided better predictions rather than SARIMA model. Finally, some suggestions are expressed to the government of Georgia for future plans.

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Predicting mutual fund performance using artificial neural networks

Cited by 81 publications

References 29 publications

Modeling and forecasting tourism demand: the case of flows from Mainland China to Taiwan

Modeling and forecasting tourism demand: the case of flows from Mainland China to Taiwan

Indicators of corporate financial success: Similar studies in South Africa and the USA, different results

Forecasting the Monthly Electricity Demand of Georgia using Competitive Models and Advices for the Strategic Planning

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